6,971 Matching Annotations
  1. Mar 2024
    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility, and clarity (Required)):

      1. In this manuscript, Imoto et al. analyze the specific role of the Dynamin1 splice variant Dyn1xA in so-called ultrafast endocytosis, an important mechanism of synaptic vesicle recycling at synapses. In a previous publication (Imoto et al. Neuron 2022), some of the authors had shown that Dyn1xA, and not the other splice variant Dyn1xB, is essential for ultrafast endocytosis. Moreover, Dyn1xA forms clusters around the active zone for exocytosis and interacts with Syndapin 1 in a phosphorylation dependent manner. However, it was unclear which molecular interactions underlie the specific role of Dyn1xA. Here, the authors provide convincing evidence with pull down assays and CSP that Dyn1xA PRR interacts with EndophilinA1/2 with two binding sites. The first binding site lies in the part common to xA and xB, was previously characterized. The second site was previously uncharacterized, is specific for Dyn1xA, and is regulated by phosphorylation (phosphobox 2). The location of these splice variants and mutated forms at presynaptic sites correlate with the prediction made by the biochemical assays. Finally, the authors perform rescue experiments ('flash and freeze' and VGLUT1-pHluorin imaging experiments) to show that Dyn1xA-EndophilinA1/2 binding is important for ultrafast endocytosis. I find the results interesting, providing an important step in the understanding of the interplay between dynamin and the endocytic proteins interacting with it (endophilin, syndapin, amphiphysin) in the context of synaptic vesicle recycling. The manuscript is clearly written and for the most part the data supports the authors' conclusions (see specific comments below). However, there are some issues which need to be clarified before this manuscript is fully suitable for publication.

      We thank the reviewer for noting the importance of our study. Indeed, our previous study has raised the question as to why only the Dyn1xA splice variant mediates ultrafast endocytosis, and our current manuscript now resolves this issue.

      Introduction: the dynx1B Calcineurin binding motif is written PxIxIT consensus but actual sequence is PRITISDP. Is this a typo?

      The sequence is correct. One thing we failed to mention is that the last amino acid in this motif can be either threonine or serine for calcineurin binding, as we demonstrated previously [Jing, et al., 2011 JBC; PMC3162388]. We have amended the text as follows.

      1. calcineurin-binding motif (PxIxI[T/S]) 19.

      Figure 1: the difference between the constructs used in panels C and D is not clear. In D, is it a truncation without residues 796 and 845? If so, it should be labelled clearly in the Western blots. In Panel E, Dyn1xA 746-798 should be labeled Dyn1x 746-798 because it is common to both splice variants.

      We thank the reviewer for pointing this out. Both C and D used the full-length PRRs of Dyn1xA-746 to 864 and xB-746 to 851. To make the labeling clear, we changed Dyn1xA PRR to “Dyn1xA PRR (746-864)” and Dyn1xB PRR to “Dyn1xB PRR 746-851” in Figure 1. In the main text, we made the following changes.

      1. 4: “To identify the potential isoform-selective binding partners, the full-length PRRs of Dyn1xA746-864 and xB746-851 (hereafter, Dyn1xA-PRR and Dyn1xB-PRR, respectively).”

      Figure 1: For amphiphysin binding the authors write that "No difference in binding to Amphiphysin 1 was observed among these peptides (Figure1D-F)." They should write that Dyn1x 746-798 does not bind Amphiphysin1 SH3 domain, confirming the specificity of binding to the 833-838 motif.

      We edited the sentence as suggested.

      1. “Dyn1x 746-798 does not bind Amphiphysin1 SH3 domain (Figure 1G), confirming the specificity of binding to the 833-838 motif as reported in previous studies 29,30. (Figure 1D-F).”

      Figure S2. The panels are way too small to see the shifts and the labelling. Please provide bigger panels

      As suggested, we have now provided bigger panels in Figure S2, and amended the text and Figure legend accordingly.

      We also removed Figure S2B as it was not referred to in the text in any way. (It was the reverse experiment – HSQCs of 15N-labelled SH3 titrated with unlabelled dynamin).l

      Figure 2 panel B. There is a typo in the connecting line between the sequence and the CSP peaks. It is 846 instead of 864 (after 839).

      Corrected.

      Figure 3 panel E. In the text, the authors write that "Western blotting of the bound proteins from the R838A pull-down experiment showed that R838A almost abolished both Endophilin and Amphiphysin binding in xA806-864 (Figure 3D), and reduced Endophilin binding to xA-PRR (Figure 3E)." I think they should write "only slightly reduced Endophilin binding..." it is more faithful to the result and consistent with the conclusion that Endophilin A1 has two binding sites on Dyn1xA PRR.

      We have now provided quantitative data for R838A and R846A (Fig. 3F and G). Endophilin binding is significantly reduced with R846A.

      It is unclear why the R846A mutant affects binding of Dyn1xA 806-864 but not Dyn1xA-PRR-.

      The reviewer asks why the R846A mutant affects binding of Dyn1xA 806-864, but not so much of Dyn1xA-PRR. The explanation is simply that there are two endophilin binding sites in Dyn1xA-PRR. The first is not present in the xA806-864 peptide, while both are present in Dyn1xA-PRR (the full length tail). When doing pull-down experiments, the binding tends to saturate – even when the second site is blocked by R846A. The first site is still able to bind, and the binding appears as normal. The same applies to the R838A mutant.

      Moreover, it affects binding to endophilin as well as amphiphysin, and therefore it is not specific. It is thus not correct to write that "R846 is the only residue found to specifically regulate the Dyn1 interaction with Endophilin as a part of an SDE". In the Discussion (page 11), the authors refer to the R846A mutation as specifically affecting Endophilin binding. This should be toned down, as it also affects Amphiphysin binding. For this important point, the data on quantification of Endophilin binding should be presented.

      The reviewer’s concern is about our claims of specificity of Endophilin A binding in Dyn1xA R846 mutation experiments. The reviewer is correct, and we have now defined specific parameters for those claims. Specifically, we have added new quantitative data from the Western blots in Fig 3E (full-length Dyn1aX-PRR) as Fig 3F-G. We used full-length Dyn1aX-PRR rather than the xA806-864 peptide because the subsequent transfection experiments use full length Dyn1xA. In the new figures 3F and 3G, we quantified Endophilin A, Amphiphysin and Syndapin1 amounts from the multiple Western blots such as Figure 3E (now n=14, 6 experiments, each in with 2-4 replicates for Dyn1xA PRR). R846A mutated in Dyn1xA-PRR significantly reduces the binding to Endophilin A, but it does not significantly affect the binding to Amphiphysin 1and Syndapin1 (Fig 3G). Therefore, this particular Dyn1xA-PRR mutation specifically affects Endophilin A binding, in the context of the full-length tail Dyn1aX-PRR. To make these results clear, we modified the text as below.

      P7. “R838A and R846A caused smaller reductions in Endophilin binding compared to wild-type Dyn1xA-PRR, (Figure 3E, 3F, R838A, median 68.5 ; Figure 3G, R846A, median 59.3 % : R838A reduced the Dyn1/Amphiphysin interaction (Figure 3E, 3F, median 14.2 % binding compared to wild-type Dyn1xA-PRR). By contrast, R846A did not affect Amphiphysin and Syndapin binding to Dyn1xA-PRR (Figure 3E, 3G). Therefore, R846, being part of an SDE, is the only residue we found to specifically regulate the Dyn1 interaction with Endophilin in the context of the full length tail (DynxA-PRR)”.

      Additionally, the reviewer notes that “the authors refer to the R846A mutation as specifically affecting Endophilin binding. This should be toned down, as it also affects Amphiphysin binding.” In the light of the above data and new quantitative analysis (Fig 3F-G), we have clarified the conclusion. However, to be clear that this statement is only correct in the context of the full-length DynxA-PRR, we amended texts as follows:

      P7. “By contrast, R846A did not affect Amphiphysin and Syndapin binding to Dyn1xA-PRR (Figure 3E, 3G). Therefore, R846, being part of an SDE, is the only residue we found to specifically regulate the Dyn1 interaction with Endophilin in the context of the full length tail (DynxA-PRR)”.

      New legends for Figure 3F and G have now been added as follows.

      “(F) The binding of Endophilin A, and Amphiphysin 1 and Syndapin1 to Dyn1xA-PRR (wild type) or R838A mutant quantified from Western blots in (E). n=14 (6 experiments with 2-4 replicates in each). Median and 95% confidential intervals are shown. Kruskal-Wallis with Dunn’s multiple comparisons test (**p (G) The binding of Endophilin A, and Amphiphysin 1 and Syndapin1 to Dyn1xA-PRR (wild type) or R846A mutant quantified from Western blots in (E). n=14 (6 experiments with 2-4 replicates in each). Median and 95% confidential intervals are shown. Kruskal-Wallis with Dunn’s multiple comparisons test was applied (*p

      Figure 3F-G (which are now 3H and 3I in the revised text): what do the star symbols represent in the graphs? I guess the abscissa represents retention time. Please write it clearly instead of a second ordinate for molecular mass, which does not make much sense if this reflects the estimate for the 3 conditions.

      The “stars” are crosses (x) and represent individual data points. The figure legends have been updated for clarity. The reviewer is correct that the X-axis is retention time (min). The second Y-axis is needed to define the points in the curve marked with crosses (x’s). The legends for Figure 3H and I are now changed as follows.

      “(H) SEC-MALS profiles for Dyn1xA alone (in green), Endophilin A SH3 alone (in red) and the complex of the two (in black) are plotted. The x-axis shows retention time. The left axis is the corresponding UV absorbance (280 nm) signals in solid lines, and the right axis shows the molar mass of each peak in crosses. The molecular weight of the complex was determined and tabulated in comparison with the predicted molecular weight. x represent individual data points.

      (I) SEC-MALS profiles for a high concentration of Dyn1xA-PRR/Endophilin A SH3 complex (0.5 mg) (in dark blue) and a low concentration of Dyn1xA-PRR/endophilin A SH3 complex (0.167 mg) (in blue). The x-axis shows retention time. The left axis is the corresponding UV absorbance (280 nm) signals in solid lines, and the right axis shows the molar mass of each peak in crosses. The molecular weight of the complex was determined and tabulated in the table. x represent individual data points.”

      Figure 4: The statement that "By contrast [to Dyn1xA], Endophilin A1 or A2 formed multiple clusters (1-5 clusters)" is not at all clear on the presented pictures. The authors should provide views of portions of axons with several varicosities, for the reader to appreciate the cases where there are more EndoA clusters than Dyn1 clusters.

      In the revised Figure S4, we added additional STED images for a region of axons with more EndoA1/2 clusters than Dyn1xA clusters. The locations of Dyn1xA and EndoA1/2 clusters are annotated in each image based on the local maximum of intensity, which is determined using our custom Matlab analysis scripts (Imoto, et al., Neuron 2022; for the description of the methods, please refer to the Point #14 below). We also added Figure S3 to describe our analysis pipelines. In the Dyn1xA channel, outer contour indicates 50% of local maxima (boundary of Dyn1xA cluster) while inner contour indicates 70% of local maxima of the clusters. In the EndoA1/2 channel, local maxima of the clusters are indicated as points. To reflect these changes, we modified text as below.

      P 9. “By contrast, Endophilin A1 or A2 formed multiple clusters (1-5 clusters) (Figure S4)”

      The legends for Figure S4 are now as follows.

      “Figure S4. Additional STED images for Figure 4.

      (A) The top image shows an axon containing multiple boutons. Signals show overexpression of GFP-tagged Dyn1xA (Dyn1xA) and mCherry-tagged Endophilin A1 (EndoA1). The bottom images show magnifications of four boutons in the top image. Red hot look-up table (LUT) images on the right side of Dyn1xA and EndoA1 images are enhanced contrast images. Outer and inner contours represent 50% and 70% of local maxima of the Dyn1xA, respectively. Black circles represent local maxima of Endophilin A1. In these boutons, multiple EndophilinA1 puncta are present.

      (B) The top image shows an axon congaing multiple boutons. Signals show overexpression of mCherry-tagged Dyn1xA (Dyn1xA) and GFP-tagged Endophilin A1 (EndoA1). The bottom images show magnifications of four boutons in the top image. Red hot LUT images on the right side of Dyn1xA and EndoA2 images are enhanced contrast images. Outer and inner contours represent 50% and 70% of local maxima of the Dyn1xA, respectively. Black circles represent local maxima of Endophilin A2. In these boutons, multiple EndophilinA2 puncta are present.

      (C) STED micrographs of the same synapses as in Figure 4E with an active zone marker Bassoon (magenta) visualized by antibody staining. GFP-tagged Dyn1xA, Dyn1xA S851D/857D or Dyn1xA R846A (green) are additionally stained with GFP-antibodies. Local maxima of Dyn1xA, Dyn1xA S851D/857D or Dyn1xA R846A signals and minimum distance to the active zone boundary are indicated by dark blue lines.”

      Moreover, overexpression of EndophilinA1/2-mCherry is not sufficient to assess its localization. Please consider either immunofluorescence or genome editing (e.g. Orange or TKIT techniques).

      We agree with the reviewer that overexpression obscures the endogenous localization of proteins. To address this point in our previous publication, we titrated the amount of plasmids for Dyn1xA-GFP and transfected neurons just for 20 hours – this protocol allowed us to uncover the endogenous localization of Dyn1xA despite the fact that it was overexpressed in wild-type neurons (Imoto, et al., 2022). We also confirmed this localization by ORANGE-based CRISPR knock-in of GFP-tag in the endogenous locus of Dyn1 just after the exon 23 and confirm the true endogenous localization of Dyn1xA (Imoto, et al., 2022). Similar approaches were taken by the Chapman lab to localize Synaptotagmin-1 and Synaptobrevin 2 in axons (Watson et al, 2023, eLife, PMID: 36729040). We did not emphasize this in the first submission, but we took the same approach for the EndoA1/2 localization. This does not mean that they also unmask the endogenous localization, and the reviewer is correct that additional evidence would strengthen the data here. Thus, as suggested, we have looked at the endogenous EndophilinA1 localization by antibody staining. As the reviewer is likely aware, EndophilinA1 also localizes to other places including dendrites and postsynaptic terminals, making it difficult to analyze the data. However, we observe colocalization of Dyn1xA with endogenous EndoA1. Thus, we believe that our major conclusion here drawn based on EndoA1/2-mCherry overexpression is valid (Reviewer’s Figure 1). Since the Endophilin signals in neighboring processes obscures its localization in synapses-of-interest, repeating this localization experiments with ORANGE-based knock-in would be ideal. However, with the lead author starting his own group and many validations needed to confirm the knock-in results, this experiment would require us at least 4-6 months, and thus, it is beyond the scope of our current study. We will follow up on this localization in the near future, but given that endophilin is required for ultrafast endocytosis (Watanabe, et al., Neuron 2018, PMID: 29953872) and these proteins need to be in condensates at the endocytic sites for accelerating the kinetics of endocytosis (Imoto, et al., Neuron 2022, PMID: 35809574), we are confident that endogenous

      EndoA1/2 are localized with Dyn1xA.

      The analysis of the confocal microscopy data is not explained. How is the number of clusters determined? How far apart are they? Confocal microscopy may not have the resolution to distinguish clusters within a synapse.

      We apologize for the insufficient description of the method. We had provided a more thorough description of the methods in our previous publication (Imoto, et al., Neuron 2022, PMID: 35809574). To make this more automated, we improved our custom Matlab scripts. Please note that all the analysis for the cluster location is performed on STED images, not on normal confocal images. To determine the cluster, first, presynaptic regions (based on Bassoon signals or Dyn1xA signals within boutons) in each STED image are cropped with 900 by 900 nm (regions-of-interest) ROIs. Then, our Matlab scripts calculate the local maxima of fluorescence intensity within the ROIs. To determine the distance between the active zone and the Dyn1xA or EndoA1/2 clusters, the Matlab scripts perform the same local maxima calculations in both channels and make contours at 50% intensity of the local maxima. The minimum distance reflects the shortest distance between the active zone and Dyn1xA/EndoA1/2 contours. To make these points clearer, we modified the main text and the Methods section. In addition, we have added workflow of these analysis as Figure S3.

      P9. Main. “Signals of these proteins are acquired by STED microscopy and analyzed by custom MATLAB scripts, similarly to our previous work23.”

      P20. Methods. “All the cluster distance measurements are performed on STED images. For the measurements, a custom MATLAB code package23 was modified using GPT-4 (OpenAI) to perform semi-automated image segmentation and analysis of the endocytic protein distribution relative to the active zone marked by Bassoon or relative to Dyn1xA cluster in STED images. First, the STED images were blurred with a Gaussian filter with radius of 1.2 pixels to reduce the Poisson noise and then deconvoluted twice using the built-in deconvblind function: the initial point spread function (PSF) input is measured from the unspecific antibodies in the STED images. The second PSF (enhanced PSF) input is chosen as the returned PSF from the initial run of blind deconvolution62. The enhanced PSF was used to deconvolute the STED images to be analyzed. Each time, 10 iterations were performed. All presynaptic boutons in each deconvoluted image were selected within 3030-pixel (0.81 mm2) ROIs based on the varicosity shape and bassoon or Dyn1xA signals. The boundary of active zone or Dyn1xA puncta was identified as the contour that represents half of the intensity of each local maxima in the Bassoon channel. The Dyn1xA clusters and Endophilin A clusters were picked by calculating pixels of local maxima. The distances between the Dyn1xA cluster and active zone boundary or Endophilin A clusters were automatically calculated correspondingly. For the distance measurement, MATLAB distance2curve function (John D'Errico 2024, MATLAB Central File Exchange) first calculated the distance between the local maxima pixel and all the points on the contour of the active zone or Dyn1xA cluster boundary. Next, the shortest distance was selected as the minimum distance. Signals over crossing the ROIs and the Bassoon signals outside of the transfected neurons were excluded from the analysis. The MATLAB scripts are available by request.”

      In the legend of Figure S3,

      “Protein localization in presynapses is determined by semi-automated MATLAB scripts (see Methods).

      (A) Series of deconvoluted STED images are segmented to obtain 50-100 presynapse ROIs in each condition.

      (B) Two representations of the MATLAB analysis interface are shown. The first channel (ch1, green) is processed to identify the pixels of local maxima within this channel. The second channel (ch2, magenta) is normally an active zone protein, Bassoon. Active zone boundary is determined by the contour generated at 50% intensity of the local maxima of ch2. The contours outside of the transfected neurons are manually selected on the interface and excluded from the analysis. Minimum distances from each pixel of the local maxima in ch1 to the contour in ch2 are calculated and shown in the composite image. The plot “Distance distribution” shows all the minimum distance identified in this presynapses ROI (unit of the y axis is nanometer). The plot “Accumulated distance distribution” shows the accumulated distance distribution from the initial to the current presynapses ROI. The plot “Histogram of total intensity” shows the intensity counts around individual local maxima pixels in ch1.”

      For the STED microscopy, a representation of the processed image (after deconvolution) and the localization of the peaks would be important to assess the measurement of distances. If Dyn1xA S851/857D is more diffuse, are there still peaks to measure for every synapse?

      We thank the reviewer for bringing up this important question. In Figure S4C, we have added the position of the local maxima of wild-type and mutant Dyn1xA shown in the main Figure 4E. As the reviewer pointed out, when a protein is more diffuse, it is difficult to find the peak intensity by STED. However, since these proteins are still found at a higher density within a very confined space of a presynapse and synapses are packed with organelles like synaptic vesicles and macromolecules, signals from even diffuse proteins can be detected as clusters, and local maxima can be detected in these images.

      To illustrate this point better, we added Reviewer’s Figure 2 below. In this experiment, we transfected neurons with a typical amount of plasmids (2.0 µg/well) or ~10x lower amount (0.25 µg/well). When the density of cytosolic proteins is high (Reviewer’s Figure 2A), the depletion laser has to be strong enough to induce sufficient stimulated emission and resolve protein localization. Insufficient power would produce low resolution images, leading to inappropriate detection of the local maxima (Reviewer’s Figure 1A). Thus, we set our excitation and depletion laser powers to resolve the protein localization to ~40-80 nm at presynapses. Furthermore, to avoid mislocalization of proteins due to the overexpression, we use 0.25-0.5 ug/well (in 12-well plate) of plasmid DNA for transfection, which is around 10 times lower than the amount used in the typical lipofectamine neuronal transfection protocol (Imoto, et al., Neuron 2022). We also change the medium around 20 hours after the transfection instead of the typical 48 hours (Imoto, et al., Neuron 2022). With these modifications and settings, we can obtain the location of the local maxima of the diffuse signals (Reviewer’s Figure 1B and Figure 4E and Figure S4). We modified the Method section to make these points clearer.

      P 17, “Briefly, plasmids were mixed well with 2 µl Lipofectamine in 100 µl Neurobasal media and incubated for 20 min. For Dyn1xA and Endophilin A expressions, 0.5 µg of constructs were used to reduce the overexpression artifacts23. The plasmid mixture was added to each well with 1 ml of fresh Neurobasal media supplemented with 2 mM GlutaMax and 2% B27. After 4 hours, the medium was replaced with the pre-warmed conditioned media. To prevent too much expression of proteins, neurons were transfected for less than 20 hours and fixed for imaging.”

      P 20, “Quality of the STED images are examined by comparing the confocal and STED images and measuring the size of signals at synapses and PSF (non-specific signals from antibodies).”

      Legends for Figure S4C,

      “(C) STED micrographs of the synapses shown in Figure 4F with an active zone marker Bassoon (magenta). GFP-tagged Dyn1xA, Dyn1xA S851D/857D or Dyn1xA R846A are visualized by antibody staining of GFP (green). Local maxima of Dyn1xA, Dyn1xA S851D/857D or Dyn1xA R846A signals and minimum distance to the active zone boundary are overlaid.”

      Figures 5 and 6: No specific comment. The data and its analysis are very nice and elegant. The comment on the lack of rescue of Dyn1xA on endosome maturation may be a bit overstated, because many "controls" (shRNA control Figure S5 or Dyn3 KO in Imoto et al. 2022) have a significant number of endosomes 10 s after stimulation.

      We thank the reviewer for noting the strength of our data and pointing out this issue on endosomal resolution. In particular, the reviewer is concerned about our interpretation of the ferritin positive endosomes present at 10 s in time-resolved electron microscopy experiments. Indeed, the number of ferritin positive endosomes in Dyn1 KO, Dyn1xA OEx neurons (0.1/profile) is similar to the control conditions: scramble shRNA control (0.1/profile, Figure S5) and Dyn3KO neurons (0.2/profile) in our previous study (Imoto et al. 2022). Although we do not consider Dyn3 KO as a control, given the presence of abnormal endosomal structures, we agree with the reviewer that scramble shRNA control in Figure S5 does indicate that some ferritin-positive endosomes even at 10 s after stimulation. We would like to note that this result is in stark contrast to our previous studies where we observed the number of ferritin positive endosomes returning to the basal level in both wild-type neurons and many scramble shRNA controls (Watanabe et al. 2014, 2018, Imoto et al 2022). Thus, the majority of the data we have indicate that the number of ferritin positive endosomes returns to basal level by 10 s, suggesting that endosomes are typically resolved into synaptic vesicles by this time. However, given that we do not know the nature of the inconsistency here and we cannot exclude the possibility of overexpression artifact of Dyn1xA as an alternative, we changed the following lines.

      P. 10, “Interestingly, the number of ferritin-positive endosomes did not return to the baseline (Figure 5E, F) as in previous studies3,35,36, suggesting that Dyn1xA may not fully rescue the knockout phenotypes or that overexpression of Dyn1xA causes abnormal endosomal morphology.”

      By the way, why did the authors use Dyn1 KO in this study, and not Dyn1,3 DKO as in Imoto et al. 2022?

      This is simply because Dyn3KO displayed an endosomal defect in our previous study (Imoto et al 2022), and we wanted to focus on endocytic phenotypes of Dyn1 KO and mutant rescues in this study.

      In the Discussion, the authors present the binding sites (for endophilin and amphiphysin SH3 domains) as independent. However, these proteins form dimers or even multimers as they cluster around the neck of a forming vesicle. Even though they provide evidence in vitro (Figure 3) that in these conditions of high concentration one dyn1xA-PRR binds one SH3 domain, in cells multiple binding sites on the PRR to these proteins may involve avidity effects, as discussed for example in Rosendale et al. 2019 doi 10.1038/s41467-019-12434-9. For example, the high affinity binding of Dyn1-PRR to amphiphysin cannot be explained only by the sequence 830-838.

      The reviewer suggests “In the Discussion, the authors present the binding sites (for endophilin and amphiphysin SH3 domains) as independent.” However, we do not claim these interactions are functionally independent, except in the context of in vitro experiments where they are sequence-independent.

      They also suggest “However, these proteins form dimers or even multimers as they cluster around the neck of a forming vesicle”. However we do not agree with this in the context of our Discussion, because the evidence of multimers and clustering is convincing but is entirely in vitro data.

      Thirdly they comment that “For example, the high affinity binding of Dyn1-PRR to amphiphysin cannot be explained only by the sequence 830-838.” We fully agree with the statement and felt we had addressed this in the manuscript. To explain, it’s important to point out our relatively new concept here and previously reported by us (Lin Luo et al 2016, PMID: 26893375) of the existence and importance of SDE and LDE for SH3 domains (Endophilin here, syndapin in our previous report). These elements act at a distance from the so-called core PxxP motifs and they provide much higher affinity and specificity than the core region alone. We had further mentioned this in the p11 discussion “Although this is a previously characterized binding site for Amphiphysin and is also present in Dyn1xB-PRR, the extended C-terminal tail of Dyn1xA contains short and long distance elements (SDE and LDE) essential for Endophilin binding, making it higher affinity for Endophilin.” Because the NMR identified F862 as a chemical shift for dynamin, we performed a pulldown with this mutant in the xA746-798 construct (which only contains the higher affinity site) and found that indeed “.F862A reduced Endophilin binding 29% (pOverall, the reviewer correctly points out that “multiple binding sites on the PRR to these proteins may involve avidity effects*” could play a role in vivo. We agree that avidity is an additional possibility, not examined in our study. Therefore, as suggested, we added the following sentence to the discussion on the SDE and LDE impacts.

      P. 11. “Our pull-down results showed that R846A abolished endophilin binding to xA806-864 (which contains only the second and higher affinity binding site and the associated SDE (A839) and LDE (F862)) and reduced about 40% of endophilin binding to the Dyn1xA-PRR (which contains both binding sites) without affecting its interaction with Amphiphysin, providing important partner specificity, although we cannot exclude the possibility that avidity effects may additionally come in play in vivo 42

      Reviewer #1 (Significance (Required)):

      This study provides a significant advance on the mechanisms of dynamin recruitment to endocytic zones in presynaptic terminals. The work adds a significant step by experienced labs (Robinson, Watanabe) who have provided important insight in the mechanisms by many publications in the last years.

      We thank the reviewer for the careful read of our manuscript and positive outlook of our work.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      1. This is a compelling study that reports a key discovery to understand the molecular mechanism of ultrafast endocytosis. The authors demonstrate that the Dynamin splice version 1xA (Dynamin 1xA) uniquely binds Endophilin A, in contrast to Dynamin splice version 1xB (Dynamin 1xB) that does not bind Endophilin A and it is not required for ultrafast endocytosis. In addition, the Endophilin A binding occurs in a dephosphorylation-regulated manner. The study is carefully carried out and it is based on high quality data obtained by means of advanced biochemical methodologies, state-of-art flash-freezing electron microscopy analysis, superresolution microscopy and dynamic imaging of exo-and endocytosis in neuronal cultures. The results convincingly support the conclusions.

      We thank the reviewer for supporting the conclusions of our study.

      1. Although additional experiments are not essential to support the claims of the paper there is room, however, for improvement within the pHluorin experiments. These experiments, that are clearly informative and consistent with the rest of experimental data, do not apply the useful approach to separate endo- from exocytosis. The use of bafilomycin or folimycin to block the vesicular proton pump allows the unmasking the endocytosis that is occurring during the stimulus, that should correspond to ultrafast endocytosis. It would be very elegant to demonstrate that such a component, as expected according to the electron microscopy data, requires the binding of Endophilin A to Dynamin 1xA. If the authors have the pHluorin experiments running, the suggested experiments are very much doable because the reagents and the methodology is already in place and the new data could be generated in around six weeks.

      We thank the reviewer for the suggestion. The reviewer is concerned that vGlut1 pHluorin experiment in Figure 6 may not correspond to ultrafast endocytosis. We agree that bafilomycin/folimycin treatment will reveal the amount of endocytosis that takes place while neurons are stimulated. However, we are not certain that endocytosis during this phase would fully correspond to ultrafast endocytosis because reacidification of endocytosed vesicles typically takes 3-4 s (Atluri and Ryan, 2006, PMID: 16495458; although see https://elifesciences.org/articles/36097) and thus, the nature of endocytosis cannot be fully determined by this assay. To claim that endocytosis measured by pHluorin assay during stimulation all correspond to ultrafast endocytosis, we would need to perform very careful work to track single pHluorin molecules at the ultrastructural level and corelate their internalization to pHluorin signals. Perhaps, a rapid acid quench technique used by the Haucke group would also be appropriate to estimate the amount of ultrafast endocytosis (Soykan et al. 2017 PMID: 28231467), but we are not set up to perform such experiments here. Also, our lead author, Yuuta Imoto, is leaving the lab to start up his own group, and it will take us months rather than weeks to get the requested experiments done. Since the point of this experiment was to test whether the interaction of Dyn1xA and EndoA is essential for protein retrieval regardless of the actual mechanisms and the reviewer acknowledges that this point is sufficiently supported by the experiments, we will set this experiment as the priority for the next paper.

      Instead of the bafilomycin or rapid acid quenching experiments, we have now added data from vglut1-pHluorin experiment with a single action potential. With a single action potential, all synaptic vesicle recycling is mediated by ultrafast endocytosis in these neurons (Watanabe et al, 2013 PMID: 24305055; Watanabe et al. 2014, PMID: 25296249). Our electron microscopy experiments in Figure 5 is also performed with a single action potential. As with 10 action potentials, 20 Hz experiments, re-acidification of vglut1-pHluorin is blocked when Dyn1 and EndophilinA1 interaction is disrupted (Figure 6 F-I). We added a description of this result as below.

      P 11. “Similar defects were observed when the experiments were repeated with a single action potential – synaptic vesicle recycling is mediated by ultrafast endocytosis with this stimulation paradigm25 (S851/857 recovery is 73.3% above the baseline; R846A, recovery is 30.0% above the baseline) (Figure S9 A-D). Together, these results suggest that the 20 amino acid extension of Dyn1xA is important for recycling of synaptic vesicle proteins mediated by specific phosphorylation and Endophilin binding sites within the extension.”

      The methods are carefully explained. Some of the experiments are only replicated in two cultures and the authors should justify the reasons to convince the audience that the approaches used have enough low variability for not increasing the n number. The pHluorin experiments, however, are performed only in a single culture; they should replicate these experiments in at least 3 different cultures (three different mice).

      The reviewer is correct. The variability is very low in our ultrastructural studies and STED imaging, and thus, in all our previous publications, two independent cultures are used. We do agree that in the ideal case, we would like to have three independent cultures, but given the nature of ultrastructural studies (control, mutants, and multiple time points), triplicating the data would add another year to our work. We are currently developing AI-based segmentation analysis, and once this pipeline is established, we will be able to increase N. However, please note that for these experiments, we examine around 200 synapses from each condition in electron microscopy studies (Table S2)– these numbers are far more than the gold standard in the field. Likewise, 50-100 synapses are examined for STED experiments (Table S2). To examine variability of our analysis results, we compared a significance between the dataset using cumulative curves and Kolmogorov–Smirnov test (Figure S11). As shown in the summarized data and p value in each condition, there are no significant difference between the datasets.

      For pHluorin analysis, the reviewer is correct. We repeated the experiments twice to increase the N after the initial submission. The data are consistent, and the conclusions are not changed by the additional experiments (Figure 6 and Figure S9). We also changed the Statistical analysis section in Methods as below.

      P. 19. “All electron microscopy data are pooled from multiple experiments after examined on a per-experiment basis (with all freezing on the same day); none of the pooled data show significant deviation from each replicate (Table S2).”

      p 19, “All fluorescence microscopy data were first examined on a per-experiment basis. For Figure 4, the data were pooled; none of the pooled data show significant deviation from each replicate (Figure S11 and Table S2). Sample sizes were 2 independent cultures, at least 50-100 synapses from 4 different neurons in each condition..”

      Legends for Figure S11

      Figure S11. Data variability in Figure 4.

      Cumulative curves are made from each dataset of (A) distance of Endophilin A1 puncta from the edge of Dyn1xA puncta, (B) distance of Endophilin A2 puncta from the edge of Dyn1xA puncta, distance distribution of Dyn1xA from active zone edge in (C) neurons expressing wild-type Dyn1xA-GFP, (D) Dyn1xA-S851/857-GFP and (E) Dyn1xA-R846-GFP. n > 4 coverslips from 2 independent cultures. Kolmogorov–Smirnov (KS) test, p values are indicated in each plot.

      Minor comments: 4. Prior studies referenced appropriately and the text and figures are clear and accurate.

      We thank the reviewer for the careful read of our manuscript.

      The authors should discuss about the mediators (enzymes) responsible for dephosphorylation of phosphor-box 2 that is key for the Dynamin 1xa-Endophilin A interaction.

      We thank the reviewer for the suggestion. We added a discussion on a potential mediator, Dyrk1, as below.

      P. 12. ”What are the kinases that regulate Dyn1? The phosphorylation of phosphobox-1 is mediated by Glycogen synthase kinase-3 beta (GSK3ß) and Cyclin-dependent kinase 5 (CDK5)17, while phosphobox-2 is likely phosphorylated by Trisomy 21-linked dual-specificity tyrosine phosphorylation-regulated kinase 1A (Mnb/Dyrk1)44,45 since Ser851 in phosphobox-2 is shown to be phosphorylated by Mnb/Dyrk1 in vitro32. Furthermore, overexpression of Mnb/Dyrk1 in cultured hippocampal neurons causes slowing down the retrieval of a synaptic vesicle protein vGlut146. Consistently, our data showed that phosphomimetic mutations in phosphobox-2 results disruption of Dyn1xA localization, perturbation of ultrafast endocytosis, and slower kinetics of vGlut1 retrieval. However, how these kinases interplay to regulate the interaction of Dyn1xA, Syndapin1 and Endophilin A1 for ultrafast endocytosis is unknown.”

      It would be very helpful to include a final cartoon depicting the key protein-protein interactions regulated by dephosphorylation (activity) and the sequence of molecular events that leads to ultrafast endocytosis

      As suggested, we made a model figure, (new Figure 7) showing how Dyn1xA and its interaction with EndoA and Syndapin1 increases the kinetics of endocytosis at synapses. Regarding the sequence of molecular events, we think that there are already dephosphorylated fraction of Dyn1xA molecules sitting on the endocytic zone at the resting state and they mediate ultrafast endocytosis. However, it is equally possible that activity-dependent dephosphorylation of Dyn1xA also may play a role (Jing et al. 2011, PMID: 21730063). However, we have no evidence about the sequence of activity dependent modulation of Dyn1xA and its binding partners during ultrafast endocytosis yet. This is much beyond what we have reported in this work and therefore, excluded from the model figure. We added the following to the end of the discussion:

      p13, “Nonetheless, these results suggest that Dyn1xA long C-terminal extension allows multivalent interaction with endocytic proteins and that the high affinity interaction with Endophilin A1 permits phospho-regulation of their interaction and defines its function at synapses (Figure S7)”.

      Figure legend Figure 7,

      “Figure 7. Schematics depicting how specific isoforms Dyn1xA and Endophilin A mediate ultrafast endocytosis.

      A splice variant of dynamin 1, Dyn1xA, but not other isoforms/variants can mediate ultrafast endocytosis. (A) Dyn1xA has 20 amino acid extension which introduces a new high affinity Endophilin A1 binding site. Three amino acids, R846 at the splice site boundary, S851 and S857, act as long-distance element which can enhance affinity of proline rich motifs (PRM) to SH3 motif from outside of the PRM core sequence PxxP. (B) At a resting state, Dyn1xA accumulates at endocytic zone with SH3 containing BAR protein Syndapin 123 and Endophilin A1/2. When phosphobox-1 (Syndapin1 binding) and phosphobox-2 (Endophilin A1/2 binding, around S851/S857) within Dyn1xA PRD are phosphorylated, these proteins are diffuse within the cytoplasm. A dephosphorylated fraction of Dyn1xA molecules can interact with these BAR domain proteins. Loss of interactions including Dyn1xA-R846A or -S851/857D mutations, disrupts endocytic zone pre-accumulations. Consequently, ultrafast endocytosis fails.”

      Reviewer #2 (Significance (Required)):

      This is a remarkable and important advance in the field of endocytosis. The study reports a key discovery to understand the molecular mechanism of ultrafast endocytosis. Scientist interested in synaptic function and the general audience of cell biologist interested in membrane trafficking will very much value this study. The mechanism reported will potentially be included in textbooks in the near future.

      My field of expertise includes molecular mechanisms of presynaptic function and membrane trafficking.

      I have not enough experience to evaluate the quality of the NMR experiments, however, I do not have any problem at all with, in my opinion, elegant results reported.

      We thank the reviewer for the positive outlook of our manuscript.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Please find below a point-by-point reply to the reviewers, with our comments in plain text, and reviewer comments in italics. Direct quotations of MS revisions in the below point-by-point reply are in quotation marks.


      *Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      **The manuscript "Circadian regulation of protein turnover and proteome renewal" investigates the role of protein degradation in the circadian control of proteostasis. The researchers suggest that the relatively static levels of protein levels in a cell are incongruent with the known oscillation in protein synthesis. They therefore hypothesize that there should be a compensatory mechanism to counteract rhythmic protein synthesis, rhythmic protein degradation. To investigate this, they employ bulk pulse chase labeling to study the process of degradation. They identify a synchronization between the creation and turnover of proteins in a cell, implying the clock helps to maintain homeostasis through a novel mechanism. They note that these phases align with energy availability, granting a plausible reasoning behind the biological implementation of this regulation. In summary, this is a sound manuscript that adds to the research field. The experiments in this manuscript are well thought out, organized, and explained. In general, the authors do not go further in their conclusions than I think is warranted given the data that they have, though I think that there are some key items that should be addressed before the publication of this manuscript. *

      Thank you for reading and appreciating our work

      Major notes: 1) In figure 1, a clearer idea of what the ** means would be appreciated. What was the standard of significance for this measure?

      Thank you, this was already reported in the methods section but is now reported in the figure legend also.

      * 2) In Figure 1b, it is important to note clearly in the text that the this is not a direct measure of protein degradation, but a subtractive proxy. Though I don't think that necessarily makes the authors conclusions incorrect, the same result could also be obtained if an extra 15% of the proteins were moved into the insoluble fraction. This is the same for Figure 1E and F. *

      Considering only the pulse shown in the left-hand graph of 1B, the reviewer is correct that this could arise by rhythmic partitioning of nascently synthesised proteins between digitonin-soluble and insoluble fractions. This could not readily explain the variation in the % of nascently synthesised digitonin-soluble protein that is degraded however (right hand graph), hence the need for pulse-chase rather than pulse alone. As such, we do not exclude circadian-regulated solubility of nascently synthesised protein or that there is a rhythm of protein synthesis in the soluble fraction, both are likely true. Rather Figure 1B indicates the relative proportion of nascently-synthesised protein in the soluble fraction that is degraded within 1h of synthesis is not constant over time. This is consistent with current understanding of the regulated increase in activity of protein quality control mechanisms (including proteasome-mediated degradation) that are required to maintain protein homeostasis upon an increase in bulk translation (Gandin and Topisirovic, Translation, 2014).

      In contrast, the lysates probed in Fig 1F were extracted in denaturing urea/thiourea buffer and so cannot be explained by variation in protein solubility.

      Considering 1E, to explain this result entirely through solubility changes would require that puromycinylated polypeptides to become more soluble, at discrete phases of the circadian cycle, but only when the proteasome is inhibited. Whilst we cannot formerly exclude this possibility, we are not aware of evidence to support it, whereas there is prior evidence supporting circadian regulation of protein synthesis and proteasome activity.

      To communicate all of this more clearly we have made the following revisions to the text:

      Page 6: ".The experiment was performed over a 24h time series followed by soluble protein extraction using digitonin, which preferentially permeabilises the plasma membrane over organelle membrane."

      Page 6: " Importantly, the proportion of degraded protein varied over time, being highest at around the same time as increased protein synthesis (Fig 1B), indicating time-of-day variation in digitonin-soluble protein turnover which cannot be solely attributed to previously reported circadian regulation of protein solubility (Stangherlin et al, 2021b). Rather, it suggests that global rates of protein degradation may be co-ordinated with protein synthesis rates, and may vary over the circadian cycle."

      Fig 1a legend: "...with digitonin buffer"

      Fig 1e legend: "...in digitonin buffer"

      Fig1f legend: "... and extracted with urea/thiourea buffer"

      * 3) In figure 1c, is the noted oscillation in protease activity due to the oscillation of these proteins? What are the predicted mechanisms behind this? I don't think that this is necessarily within the scope of this paper but should be addressed in the discussion. Also, the peak degradation rate from Figure 1B is 4 hours before the peak enzyme activities. How can this observation be reconciled? *

      Besides this study, our two previous proteomic investigations of the fibroblast circadian proteome detected no biologically significant or consistent rhythm in proteasome subunit abundance (Wong et al., EMBO J, 2021; Hoyle et al., Science Translational Medicine, 2017). Moreover, proteasomes are long-lived stable complexes whose activity is determined by a combination of substrate-level, allosteric and post-translational regulatory mechanisms that includes their reversible sequestration into storage granules (Albert et al., PNAS, 2020; Fu et al., PNAS, 2021; Yasuda et al., Nature, 2020). It is therefore very likely that the observed rhythm in trypsin- and chymotrypsin-like activity occurs post-translationally. Proteasome subunit composition is also known to change, which might be another reason for differences between the protease activities (Marshall and Vierstra, Front Mol Biosci, 2019; Zheng et al., J Neurochem, 2012).

      Due to the nature of the experiment, the degradation rate inferred from Figure 1B does not reflect proteasome activity, exclusively. Rather it reflects the combined sum of processes that remove nascently produced proteins from the cell's digitonin-soluble fraction, which includes proteasomal degradation, but also autophagy, protein secretion and sequestration into other compartments. Therefore, the peak degradation in Fig 1B would not necessarily be expected to coincide with the peak of proteasome activity in Fig 1C. Figure 1A/B is intended as an exemplar for the investigation's rationale and was the first to be performed chronologically.

      To communicate this succinctly, we have revised the relevant text as follows:

      Page 7: "Previous proteomics studies under similar conditions have revealed minimal circadian variation in proteasome subunit abundance (Wong et al, 2022), suggesting that proteasome activity rhythmicity, and therefore rhythms in UPS-mediated protein degradation, are regulated post-translationally (Marshall & Vierstra, 2019; Hansen et al, 2021)"

      * 4) For the pSILAC analysis, the incorporation scheme has a six-hour window between the comparison of the light and heavy peptides. This makes it somewhat difficult to assess whether you are looking a clock effect from T1 or T1+6. This does not negate the findings, but it does question when the synthesis is occurring and what is being compared, which I think should be more clearly discussed in the manuscript. This is discussed later in the manuscript but should be mentioned in this section. *

      Thank you for this suggestion. To communicate this more clearly, we have rearranged the labels at the top of schematic graphs in figures 2b and 3b in order to clearly distinguish the pulse-labelling window from the time of sample collection. The following text has been added to the methods section:

      Page 9: "To enable sufficient heavy labelling for detection, a 6h time window was employed, thus measuring synthesis and abundance within each quarter of the circadian cycle "

      * 5) There are no error bars on figure 2C. What the pSILAC just done in a singlet? If so, the rhythms estimation is likely a large overestimate and should be noted. *

      This first pSILAC experiment was performed in singlet with respect to external time for the RAIN analysis, but is duplicate for the two-way ANOVA that is also reported, by treating each cycle as a separate replicate. In fact, the 6.2% of proteins that were significantly rhythmically abundant by RAIN actually agree well with two previous experiments we performed using mouse fibroblasts under identical conditions: the first with 3h resolution over 3 cycles in singlet (7% rhythmic), the second with 4 biological independent replicates over one cycle (8% rhythmic) (Wong et al., EMBO J, 2021). The curve fits shown in 2C are the standard damped sine wave fits, with p-values from RAIN reported in the figure legend.­­

      Most importantly however, and as noted in the text, the absolute % of rhythmically abundant proteins is rather irrelevant and indeed the absolute numbers of 'rhythmic' proteins can vary wildly, dependent on the analysis method and stringency. The only important point to be gleaned from the estimates shown in Figure 2e is that by either statistical test, most rhythmically abundant proteins are not rhythmically synthesised, and vice versa; however, the % of proteins that are both rhythmically synthesised and rhythmically abundant is 6 to 11--fold higher than would be expected by chance (taking proteins rhythmic by RAIN and ANOVA, respectively; in both cases the overlap between the two sets is highly significant) . This serves as a positive control, i.e., a minority of proteins show correlated rhythms of synthesis and abundance that are consistent with the canonical activity of 'clock-controlled genes' which cannot be explained by overestimation of rhythmicity.

      Odds Ratio comparison synthesis vs total

      Synthesis rhythmic by RAIN - listA size=148, e.g. A8Y5H7, B2RUR8, E9Q4N7

      Total rhythmic by RAIN - listB size=149, e.g. A1A5B6, A2A6T1, A2AI08

      Intersection size=34, e.g. A8Y5H7, O08795, O54910

      Union size=263, e.g. A8Y5H7, B2RUR8, E9Q4N7

      Genome size=2528

      Contingency Table:

      notA inA

      notB 2265 114

      inB 115 34

      Overlapping p-value=5.4e-13

      Odds ratio=5.9

      Overlap tested using Fisher's exact test (alternative=greater)

      Jaccard Index=0.1

      Synthesis rhythmic by ANOVA - listA size=66, e.g. A8Y5H7, O35639, O55143

      Total rhythmic by ANOVA - listB size=83, e.g. A8Y5H7, B2RQC6, E9Q6J5

      Intersection size=16, e.g. A8Y5H7, P22561-2, Q3TB82

      Union size=133, e.g. A8Y5H7, O35639, O55143

      Genome size=2528

      Contingency Table:

      notA inA

      notB 2395 50

      inB 67 16

      Overlapping p-value=9.7e-11

      Odds ratio=11.4

      Overlap tested using Fisher's exact test (alternative=greater)

      Jaccard Index=0.1

      Nevertheless, we agree with the reviewer's general point and have revised the text as follows:

      Page 9: "... and may be susceptible to overestimation of rhythmicity."

      Page 9: "Consistent with similar previous studies, Page 9: "The proportion of such proteins was more than expected by chance (pMethods, Page 21: "...(n=1 per timepoint)"

      * 6) Why were the genes selected in 2C? these are not discussed anywhere else in the manuscript.*

      These are simply illustrative examples so that the reader can better understand what we mean, i.e., two proteins in different phases and one that did not change, all within a similar range of abundance. The selected proteins were not discussed because we do not expect the reader to attach any specific meaning to them. We have revised the figure to include in 2C examples of each rhythmicity category shown in 2E. To make this clear, we now state the following:

      Figure 2 legend: "No specific meaning is inferred from the protein identities”.

      • 7) The authors note that for Figure 2 "These observations are consistent with widespread rhythmic regulation of protein degradation." However, only 5-10% of the proteome is oscillating at any level and less with a discrepancy between synthesis and abundance, so "widespread" is an exaggeration and this statement should be limited to the degradation in the rhythmic proteome. *

      We take the reviewer's point, but the term rhythmic proteome is also inaccurate since half the proteins with rhythmic degradation did not show an abundance rhythm in both mass spec experiments. We therefore revised this sentence as follows:

      Page 10: "These observations are consistent with widespread temporal organisation of protein degradation within the circadian-regulated proteome."

      * 8) The authors note that their more developed strategy in figure 3 would allow for the detection of less abundant proteins. However, they do not discuss that they in fact found less proteins overall, or if they were able to detect proteins of lower abundance. This is of some concern in determining if this is indeed the better method that they predict. How can the authors reconcile this issue? How can they rationalize this explains their increase in oscillating elements? *

      Thank you for raising this point, we did not explain ourselves sufficiently clearly. As stated in the revised text, once we had analysed the first iteration of pSILAC (Fig 2), we realised that detection of heavy-labelled proteins was "inevitably limited and biased the proteome coverage towards abundant proteins with higher synthesis rates". In other words, in order to be considered in our analysis both unlabelled and heavy-labelled peptides needed to be detected in every sample at every time point. In fact, if we do not consider heavy-labelling, the overall coverage in the Fig 3 experiment (6577 proteins) was better than the Figure 2 experiment (6264 proteins), as expected, due to technical improvements in the methods used (by the time of the experiment in Fig. 3, we were able to perform the analysis using mass spectrometry techniques with better fractionation and detection, namely FAIMS and MS3). When the analysis criteria are applied however, this falls to 2302 and 2528 proteins, respectively. Because of the way that mass spectrometry works, many proteins needed to be excluded from analysis because the heavy label wasn't detected in one or more samples. In these cases, we cannot infer that no heavy-labelled protein was present in that sample or even that it was present at lower levels than other samples - it simply wasn't detected and therefore we cannot make any quantitative comparisons. Non-detection of any given heavy peptide may occur for several reasons, the most likely being that it co-elutes from the chromatography column at the same time as other much more abundant (light) peptides and simply escapes detection. This is an unavoidable limitation of the technique, we hope the reviewer can understand our need to restrict the analysis to those proteins whose nascent synthesis, and total abundance in the MMC fraction, can be confidently quantified.

      As the experiments in Fig 2 and Fig 3 were performed independently, with separate TMT sets and different instrumentation, we are also unable to compare absolute abundances of the proteins between the two.

      To communicate this more clearly we have amended Figures 2e and 3e to state the total coverage in the legends, as well as clearly stating the coverage of heavy-labelled proteins in the figure itself. We have also added the following explanation to the text:

      Page 11:

      “Despite enriching for only one cellular compartment, the overall coverage in this experiment was similar to the previous one (6577 and 6264 proteins, respectively), due to the altered and more targeted approach; with heavy peptides detected for 2302 proteins."

      *9) In the comparison of complex turnover rates, the authors need to provide a metric that backs their statement that "the majority of component subunits not only showed similar average heavy to total protein ratios but also a similar change in synthesis over the daily cycle" for figure 3F. *

      Our apologies for this oversight, this is now presented in new Fig S3D.

      * 10) In reference to the AHA incorporation, why is the hypothesis not that, like the puramycin, you would not see oscillation unless you add BTZ? Shouldn't the active degradation regulate the incorporation of AHA such that there is no visible rhythm unless you suppress degradation? *

      AHA is a methionine analogue that is sparsely incorporated into polypeptide chains with minimal effect on protein function/structure (Dietrich et al., PNAS, 2006). Unlike puromycin, therefore, AHA does not lead to chain termination or protein misfolding/degradation (Dermit et al., Mol Biosyst, 2017) and so pulsed application at different phases of the circadian cycle is sufficient to reveal protein synthesis rhythms. The novelty in Fig 3H is the combination of AHA labelling with native PAGE that allows us to validate rhythmic production of high molecular weight protein complexes. This would not be possible with puromycin because prematurely-terminated polypeptide chains are not able to assemble into native complexes unless chain termination happens to occur at the extreme C-terminus and the C-terminus does not partake in any intermolecular interactions within the assembled complex.

      * 11) The authors claim that there is enrichment of the actin cytoskeleton, but where this data can be found should be explained. The only thing that is shown is a few selected graphs of proteins in this pathway. *

      We previously reported circadian regulation of the actin cytoskeleton in Hoyle et al. (Sci Trans Med, 2017). The extremely high relative amplitude of Beta-actin (the structural component of microfilaments) in the MMC fraction is, in and of itself, entirely sufficient to demonstrate a circadian rhythm in the relative ratio of globular to filamentous actin that was originally identified by Ueli Schibler's lab (Gerber et al., Cell, 2013) and then shown to have a cell-autonomous basis in fibroblasts in Hoyle et al (2017). We have included further examples of an actin-binding protein (Corinin1b) and a motor protein (Myosin 6) to further illustrate this, but do not feel further discussion is warranted because it was comprehensively addressed in our previous work. The enrichment for actin was determined by GO analysis, which is now shown in the Fig 4A and referred to in the text.

      The important point in Fig 4C is the difference in phase with the examples shown in Fig 4B and summarised in Figure 4A, i.e., there are a small number of proteins whose presence in the MMC fraction is highest in advance of the majority of rhythmically abundant proteins, but this earlier group doesn't show any significant synthesis rhythm. Actin is one of the most abundant cellular proteins, and by mass it accounts for 67% of the circadian variation of rhythmically abundant proteins that peak in this fraction at the same phase. All these data and analyses are available for scrutiny in Supplementary Table 2.

      To communicate this more clearly we have expanded on this point as follows:

      Page 13: " These proteins were enriched by 9-fold for actin and associated regulators of the actin cytoskeleton (q* 12) The authors note an oscillation in the total levels of p-eif2, commenting that these do not arise from the rhythms in total eif2a but temperature and feeding rhythms. However, unless I misunderstood, this work was done in fibroblast cell culture, so in this case, where would these temperature and feeding rhythms come from? *

      We were insufficiently clear. Daily rhythms of p-eIF2 have been observed under physiological conditions in mouse, in vivo. We do not observe similar rhythms in cultured fibroblasts under constant conditions unless the cells are challenged by stress. By inference therefore, it seems likely that daily rhythms of p-eIF2 in vivo arise from the interaction between cell-autonomous mechanisms and daily systemic cues such as, insulin/IGF-1 signalling and body temperature that are in turn driven by daily rhythms in CNS control, daily feed/fast rhythms and daily rest/activity rhythms, respectively. We have amended the text as follows:

      Page 15: "...and so suggest that daily p-eIF2α rhythms in mouse tissues likely arise through the interaction between cell-autonomous mechanisms and daily cycles of systemic cues, e.g., insulin/IGF-1 signalling and body temperature rhythms driven by daily feed/fast and rest/activity cycles, respectively."

      * 13) In Figure 5d, the treatment impeding degradation is causing cell death while the inhibition of translation does not. However, wouldn't too much, or not enough, translation, without compensatory regulation from degradation cause a problem in the same way that degradation does? *

      It is well-established that acute treatment with high concentrations of proteasomal inhibitors rapidly leads to proteotoxic stress that will trigger apoptosis unless resolved (Dantuma and Lindsten, Cardiovasc Res, 2010). Treatment with CHX is certainly stressful to cells, but in a different way, and cells die through mechanisms generally regarded to be necrotic and certainly do not involve the canonical proteotoxic stress responses that are activated by MG132 and similar drugs. Our findings show that, by whatever mechanisms cells die with CHX treatment, it does not change over the circadian cycle whereas death via proteotoxic stress does, consistent with our prediction. We hope the reviewer agrees it is beyond the scope of our study to explain why CHX-mediated cell death does not show a circadian rhythm in mouse fibroblasts.

      *Reviewer #1 (Significance (Required)):

      *The information that stems from this work is relevant and of interest to circadian clock field as how the regulation of the output of the circadian clock is implemented is still a major question in the field. This manuscript suggests a novel and plausible method for how, at least in part, this regulation occurs. However, the manuscript uses methods that do not measure degradation directly, which is a minor limitation. In addition, the mechanisms by which this regulation is imparted are not addressed in any meaningful way, even in the discussion.

      We are sorry that we did not adequately discuss the extensive previous work that has already addressed regulatory mechanisms. We would like to stress that this manuscript concerns protein turnover and proteome renewal, of which degradation is obviously an important part but not the sole focus.

      To communicate this more clearly, we have amended the title to:

      "Circadian regulation of macromolecular complex turnover and proteome renewal"

      ... which we previously explicitly predicted in the discussion of previous papers (Feeney et al., Nature, 2016; O'Neill et al., Nat Comms, 2020; Wong et al., EMBO J, 2022) and our recent review (Stangherlin et al., Curr Opin Syst Biol, 2021).

      With respect to measurement of degradation - Physiologically, cellular rates of proteasomal degradation are so intimately coupled with protein synthesis that, over circadian timescales, the former cannot meaningfully be studied in isolation. It is possible that the reviewer is alluding to historical methods that measure change over time in the presence of translational or proteasomal inhibitors, but these have long been known to introduce artifacts - because translational inhibition rapidly leads to reduced proteasome activity, whereas proteasomal inhibition rapidly reduces protein synthesis rates through the integrated stress response. We would be interested to hear of any more direct method for measuring protein degradation proteome-wide than the pulsed SILAC method we developed, as we are not aware of any. Even proteasomal proximity labelling coupled with MG132 treatment, recently developed by the Ori lab, does not directly measure degradation (bioarxiv https://www.biorxiv.org/content/10.1101/2022.08.09.503299v1). By definition, degradation can only be measured through the disappearance of something that was previously present, usually by comparing its rate of production with the change in steady state concentration (if any), which we have done using multiple methods.

      With respect to regulation of degradation - We speculated on the mechanisms regulating rhythms in protein turnover in our several previous papers (Feeney et al., Nature, 2016; O'Neill et al., Nat Comms, 2020; Wong et al., EMBO J, 2021; Stangherlin et al, Nat Comms, 2021), whereas outside the circadian field these mechanisms have been addressed extensively. This was also discussed in detail in our recent review on the topic (see Stangherlin et al., COISB, 2021). In this review, we lay out the evidence for a model whereby most aspects of circadian cellular physiology might be explained by daily rhythms in the activity of mammalian target-of-rapamycin complexes (mTORC). This model makes multiple predictions and informs the central hypothesis which is tested in the current manuscript: that circadian rhythms in complex turnover and proteome renewal should be prevalent over abundance rhythms. An enormous body of work over the last two decades has already clearly established mTORC1 as the master regulator of bulk protein synthesis and degradation, and a substantial number of independent observations have demonstrated circadian regulation of mTORC1 activity in vivo and in cultured cells. The mechanisms that drive cell-autonomous mTORC1 signalling are only partially understood (e.g. Feeney et al., Nature, 2016; Wu et al., Cell Metab, 2019), and we continue to explore this experimentally but they certainly lie well beyond the scope of this investigation.

      Therefore, to address the reviewer's concern about inadequate discussion of mechanism, we have expanded on mTORC in the introduction and discussion, as follows:

      Page 3: "Daily rhythms of PERIOD and mTORC activity facilitate daily rhythms of gene expression and protein synthesis. In particular, mTORC1 is a master regulator of bulk 5'-cap-dependent protein synthesis, degradation and ribosome biogenesis (Valvezan & Manning, 2019) whose activity is circadian-regulated in tissues and in cultured cells (Ramanathan et al, 2018; Feeney et al, 2016a; Stangherlin et al, 2021b; Mauvoisin et al, 2014; Jouffe et al, 2013; Sinturel et al, 2017; Cao, 2018). It is plausible that daily rhythms of mTORC activity underlie many aspects of daily physiology (Crosby et al, 2019; Stangherlin et al, 2021a; Beale et al, 2023b)."

      Page 17: "The mechanistic underpinnings for cell-autonomous circadian regulation of the translation and degradation machineries remain to be fully explored, but are likely to be driven by daily rhythms in the activity of mTORC: a key regulator of protein synthesis and degradation as well as macromolecular crowding and sequestration (Stangherlin et al, 2021b, 2021a; Cao, 2018; Adegoke et al, 2019; Ben-Sahra & Manning, 2017; Delarue et al, 2018). In particular, global protein synthesis rates are greatest when mTORC1 activity is highest, in tissues and cultured cells, whereas pharmacological treatments that inhibit mTORC1 activity reduce daily variation in crowding and protein synthesis rates (Feeney et al, 2016a; Lipton et al, 2015; Stangherlin et al, 2021b). Given our focus on proteomic flux and translation-associated protein quality control, autophagy was not directly within the scope of this study but is also mTORC-regulated and subject to daily regulation (Ma et al, 2011; Ryzhikov et al, 2019). In vivo, daily regulation of mTORC activity arises primarily through growth factor signalling associated with daily feed/fast cycles (Crosby et al, 2019; Byles et al, 2021). The mechanisms facilitating cell-autonomous circadian mTORC activity rhythms are incompletely understood but may include Mg.ATP availability (Feeney et al, 2016a) and its direct regulation by PERIOD2 (Wu et al, 2019). This will be an important area for future work."

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: This is a very interesting and well written paper that addresses key questions in the circadian organization of proteostasis. The paper investigates origins of cellular circadian rhythms, invoking a premise early that there is a poor correlation between rhythmic gene expression - regulated by the canonical TTFL - and rhythms of the proteome, which are rather meager. Specifically, they ask how a relatively stable proteome is possible if cells engage in rhythms of cellular protein synthesis? Their hypothesis is that protein degradation must rhythmically compensate for rhythms of synthesis and much of the manuscript is focused on defining the relationship between rhythmic global synthesis and rhythmic degradation. They employ a series of detailed proteomic investigations and biochemical assessments of protein synthesis coupled with various circadian reporters to assess proteosome function. The proteomic experiments reveal a limited number of proteins with oscillations in either synthesis or abundance or both and no discernible pathway organization however, a followup and more refined study that utilized fractionated samples and boosted heavy SILAC identified strikingly, that many proteins in relatively heavy fractions are rhythmic and that these fall into possible complexes including ribosome and chaperonins. Finally, they perform in vivo experiments testing whether the timing of proteotoxic stimuli regulates the degree of the integrated stress response measured as pEif2a. Overall, I think that this is a fascinating paper that addresses and important question but falls short on mechanistically unifying them and completely contextualizing the findings in light of the canonical modes of circadian timekeeping leaving us with an important, but mostly descriptive set of findings. In addition, there are a number of important questions about data interpretation, some issues with data quality that should be addressed outlined below. With revision and further explication, this study will be an excellent addition to the growing field of circadian organization of the cellular proteome. *

      Thank you for reading and appreciating our work

      *Major and minor Comments. Figure 1. Fig 1a. The difference in Pulse and Chase at ZT24 does not appear to reflect the quantified data in 1b. This should be reconciled to make the figure convincing. *

      When working with radioactive cell lysates it is not possible to equalise the level of protein loaded on each gel beforehand as would happen with a western blot, for example. For this reason, the radioactive signal was normalised to the protein level subsequently measured by coomassie staining, as is standard practise for this type of assay, with all 4 replicates being shown in supplementary Fig.1A. An overnight phosphor screen image is presented in the main Fig.1A for illustrative purposes, but we take the point that this might not be immediately obvious. In revised Fig 1A we therefore now also show the relevant coomassie as well as labelling to make clear that the radioactive signal was normalised to protein levels.

      * How was the timing of the chase collection determined? *

      For these proof-of-principle experiments, we empirically determined the minimum duration of pulse and chase necessary to detect a quantifiable signal.

      *Fig 1d-e. What is the evidence that puro labeling results in 'rapid' turnover. *

      Apologies, this has been established for some time. Some additional papers are now cited in this section of the text (Liu et al, PNAS, 2012; Lacsina et al., PLoS One, 2011; Szeto et al., Autophagy, 2006)

      *Fig 1e seems to be missing the data from the treated and untreated conditions? How are the lines produced (e.g. linear versus rhythmic? Are these drawn lines or actual regressions?). *

      Fig 1e depicts the result of the experiment schematically explained in 1d. The only conditions were +Puro or +Puro+BTZ. There was no completely untreated condition, as puromycin incorporation is the basis of the assay (Lacsina et al., PLoS One, 2012; Szeto et al., Autophagy, 2006) and puromycin does not occur naturally in cells. We realise the figure could potentially be confusing without the associated raw data (anti-puromycin blots) - these are shown in supplementary Fig. 2A.

      To explain the method more clearly, the following has been added to the results section where this experiment is described:

      " As determined by anti-puromycin western blots, over two days under constant conditions, puromycin incorporation in the presence of BTZ showed significant circadian variation. In contrast, cells that were treated with puromycin alone showed no such variation, and nor did total cellular protein levels (Fig 1E, Fig S2A).”

      The fit lines are produced by statistical comparison of fits, i.e., our hypothesis (damped cosine fit) vs null hypothesis (no or constant change over time, linear fit, y = mx+c), using sum-of-squares F test. The statistically preferred fit is plotted and p-value displayed on the graph, i.e., the regression line of the preferred fit and parameters are plotted. These details are reported in the figure legends.

      * Why was 30 minutes chosen as labeling time? It seems hard to understand here how protein degradation kinetics can be measured by puromycin labeling if the authors' claim that puromycin labeling potentially changes degradation rates as a function - primary or secondary - of the labeling itself. It seems they are measuring the potential to degrade proteins. *

      Puromycin labelling is a 20 year-old widely-used technique that can be employed in a range of applications. It was first used in a circadian context by Lipton et al (Cell, 2015) whose work we quickly followed (Feeney et al, Nature, 2016). Briefly, puromycin mimics tyrosyl-tRNA to block translation by labelling and releasing elongating polypeptide chains from translating ribosomes. When used at low concentrations (1 ug/mL in this case) puromycin is sparsely and sporadically incorporated into a small minority of elongating polypeptide chains. Those prematurely terminated chains have puromycin at the C-terminus, which can be detected by western blotting. We chose 30 minutes after optimisation experiments, as it was the shortest incubation time where a robust signal could be observed in these cells with this concentration of puromycin. The puromycinylated peptides are preferentially degraded by the ubiquitin-proteasome system because they are efficiently recognised as misfolded/aberrant proteins by chaperones within tens of minutes of being translated. Unless used at much higher concentrations, or over much longer timescales, there is no reason to believe that puromycin affects the degradation machinery itself, but the degradation of puromycinylated peptides depends on the proteasome. Therefore, puromycin+a proteasome inhibitor provides a reliable proxy for translation rate in the preceding 30 minutes, whereas puromycin alone tells us the steady state concentration under normal conditions, i.e., where proteasomes remain active. By subtracting the latter from the former we can infer the level of degradation of puromycinylated peptides that must have occurred in the previous 30 minutes. It is not a perfect technique, but its results agree with other findings in this manuscript: that protein turnover varies more than steady state protein abundance. With respect to the potential to degrade proteins, this is measured in Fig 1C.

      * How do they determine that they are measuring degradation of functionally relevant proteins as opposed to a host of premature truncations? *

      We do not. This is measured by stable isotope labelling in Figures 2-4. Figure 1 provides the rationale for what follows in subsequent figures, i.e., proof-principle experiments suggesting that turnover is not constant over the circadian cycle. No single experiment in Figure 1 is expected to convince the reader that of circadian turnover. Rather, several independent methods suggest that bulk protein synthesis and degradation (turnover) are not constant over time, and deviate from the null hypothesis with variation that appears to change over the 24h circadian cycle.

      * Fig 1e bottom - again is this a true regression line? *

      It is not a regression line, otherwise a p-value of fit would be shown. Fig1e bottom shows the bioluminescence measured at each timepoint from parallel control cultures (average of triplicates, error bars shown as dotted lines). Due to very high temporal resolution (every 30 min) and robustness of the cell line, it appears as a virtually perfect damped (co)sine wave. We apologise that this was not explained more clearly in the figure legend, now amended as follows:

      "Parallel PER2::LUC bioluminescence recording from replicate cell cultures (mean +/- SEM, every 30 min) is shown below, acting as phase marker."

      *Perhaps two time points should be examined here - similar to the pulse chase performed with 35S labeling? *

      We are sorry we were not fully clear with our method here. The puromycin (+/- BTZ) labelling was performed over two days every 4h (so 12 timepoints in total), which can be inferred from the data points in the top two graphs in Fig. 1E, and x-axis - but is now also clearly stated in the figure legend. The bottom right graph was a continuous bioluminescence recording, integrated every 30 min from the set of parallel culture dishes. The bioluminescence data serves as a circadian phase marker, so that we can infer at which biological times synthesis and inferred turnover was higher vs lower.

      We’ve adjusted the text to explain our method more clearly:

      “Acute (30 min) puromycin treatment of cells in culture, with or without proteasomal inhibition (by bortezomib, BTZ), allowed us to measure both total nascent polypeptide production (+BTZ) and the amount of nascent polypeptides remaining when the UPS remained active (-BTZ). This allowed inference of the level of UPS-mediated degradation of puromycylated peptides within each time window, as a proxy for nascent protein turnover (Fig. 1D).”

      * Fig 1f. It appears that Puro labeling results in a rhythm between ZT1 and ZT13 but no statistic is provided and appears that the 'ns' is the results of variance in the data as opposed to difference in means? - would this not contradict the cellular result? What accounts for the rhythm reversal in the presence/absence of BTZ. *

      To be clear, we measured the level of puromycin incorporation in mouse liver in vivo following a similar method employed by Lipton et al, Cell, 2015 (Figure 2). The prediction was that, exactly as in cells (Fig 1E), treatment with a proteasome inhibitor would lead to a much greater increase in puromycinylated peptides at ZT13 than ZT1, because this is when protein synthesis is known to be higher and thus (we predict) protein degradation should also be higher. The experiment was not designed or powered to detect a time effect, it was designed to detect an interaction between time-of-puromycin treatment and BTZ, with the specific prediction being that BTZ would have a greater effect during the active phase. This is what we observed.

      * While the authors have previously demonstrated an increase in rhythmicity of the proteome in Cry1/Cry2 double knockout cells, it would have been welcome here to test a global loss of circadian transcription in the degradation assay. One might expect that these rhythms would also be even higher. What I am really asking is: what is the mechanism for rhythmic degradation and is it dependent on the canonical clock? *

      To address the reviewer's curiosity, we used the proteasome-Glo assay (also used in Fig 1C) to assess whether there was an interaction between genotype (WT vs CKO) and time at opposite phases of the circadian cycle over 2 days. We found a significant interaction by two-way ANOVA, indicating that components of the 'canonical clock' regulate the temporal organisation of proteasomal activity (see revised Figure S1). Circadian regulation of mammalian cellular functions, such as protein turnover, is a complex and dynamic process, whereas gene deletion affects the steady state and may be epistatic to phenotype rather than revealing gene function. We are therefore reluctant to speculate what this result means in the present manuscript, which is focused entirely on testing the hypothesis that global protein turnover and complex biogenesis have cell-intrinsic circadian rhythms in non-stressed, wild type cells.

      To communicate this, the text has been revised as follows:

      "Moreover, we detected a significant interaction between genotype and biological time when comparing trypsin-lik proteasome activity between wild type and Cryptochrome1/2-deficient cells, that lack canonical circadian transcriptional feedback repression (Fig S1B-E). "

      * **Fig 2. How was the 'fixed window' timeframe determined? *

      A trial experiment was performed with labelling windows of various length, and 6h was determined to be the shortest window where enough heavy label incorporation was detected to be able to assess circadian changes. This was the case with our first methodology, which was subsequently improved (Figure 3), and therefore labelling window reduced to 1.5h.

      * *Fig 3h. While admittedly difficult, the native PAGE is not of great quality and kind of unconvincing. Also not really sure why the AHA labeling is used here an nowhere else in the paper.

      AHA is a methionine analogue that is sparsely incorporated into polypeptide chains with minimal effect on protein function/structure (Dietrich et al., PNAS, 2006). Unlike puromycin, therefore, AHA does not lead to chain termination or protein misfolding/degradation (Dermit et al., Mol Biosyst, 2017). In Figure 1, the aim was to validate previous reports of rhythmic protein synthesis assess whether there was any evidence for rhythmic turnover. To this end, we employed two independent methods (35S-labelling and puromycin-incorporation). We did not want to rely on AHA for measuring turnover: although it has been validated and used for this purpose in some studies (McShane et al., Cell, 2016), AHA is not fully equivalent to methionine, and cellular aminoacyl-tRNA synthetases have much higher affinity to methionine than they do to AHA (Ma and Yates, Expert Rev Proteomics, 2018). It is thus impossible to perform AHA labelling without methionine-free medium, and in turn methionine starvation and media changes are known to have an effect on cell signalling and cell metabolism, which would be particularly pronounced in circadian context (over days rather than over hours).

      By contrast, in Fig 3H, we use AHA with native PAGE to specifically validate one inference from the mass spectrometry analyses: circadian production of high molecular weight protein complexes. This would not be possible with puromycin because prematurely terminated polypeptide chains are not able to assemble into native complexes unless chain termination happens to occur at the extreme C-terminus and the C-terminus does not partake in any intermolecular interactions within the assembled complex.

      The raw data (full gels, all replicates) are presented in Figure S2e, which of course was used for quantification. We have now picked a different example for the main figure, which hopefully allows for clearer representation.

      The text in the results section describing the AHA experiment is now amended as follows:

      " To validate these observations by an orthogonal method, we pulse-labelled cells with methionine analogue L-azidohomoalanine (Dieterich et al, 2006). AHA is an exogenous substrate, that cells have lower affinity to than methionine, and it could potentially impact on stability of the labelled proteins (Ma & Yates, 2018) – therefore, we only used AHA to assess nascent complex synthesis, rather than turnover. We analysed the incorporation of the newly synthesised, AHA labelled proteins into highest molecular weight protein species detected under native-PAGE conditions (Fig 3H, S3F). We observed a high amplitude daily rhythm of AHA labelling, indicating the rhythmic translation and assembly of nascent protein complexes. Taken together, these results show that daily rhythms in synthesis and degradation may be particularly pertinent for subunits of macromolecular protein complexes"

      Fig 4. I was a little disappointed here that the authors did not directly assess macromolecular assembly of at least one of their "hits" and demonstrate functional relevance and most of the analysis is maintained at a very superficial, systemic level. STRING assemblies are not terribly helpful without clear k-means clustering or some other clearly visualizable metric for stratifying and organizing the putative PPI data - this figure (S3) could be markedly improved.

      We agree that validation is important. The ribosome is by far the most abundant macromolecular complex in the cell, and was one of the major complexes to show clear evidence for circadian regulation of turnover, but not abundance, by our pSILAC proteomics. To validate this result, we took advantage of two important observations: (1) that all fully assembled ribosomes incorporate ribosomal RNA (rRNA) which can readily be separated from other cellular RNA by density gradient centrifugation; (2) pulse-labelling with heavy uridine-15N2 allows nascent RNA to be distinguished from pre-existing RNA. Thus, combining stable isotope labelling with ribosome purification, we can distinguish nascently assembled ribosomes from total when the RNA is extracted, digested with RNAse, and the % heavy/total UMP quantified by mass spectrometry. These data are presented in new figure 5, and are consistent with findings in Figures 3/4 that circadian regulation of ribosome turnover is prevalent over abundance, and that the phase of highest ribosome turnover coincides with the phases of high translation and turnover overall. We hope by addressing the reviewer's question by an entirely orthogonal method, they can share more confidence in our conclusions.

      The statistical metric for STRING, specifically the p-value for enrichment in physical protein-protein interactions, is presented in the main Fig. 3G. It is now also reported in the legend for new Figure S4 itself.

      * Is it possible that some macromolecular complexes have rhythms because their constituent proteins have differential half-lives when in one complex compared with another in circadian time? This possibility was not discussed. *

      To our knowledge, there is no evidence that any major macromolecular complex in the cell has a functionally significant rhythm in abundance on a cell-autonomous basis. The reviewer’s suggestion is an intriguing possibility, but we can think of no way that it could be measured, even in principle. The simplest interpretation of our data from the independent techniques we employ (pSILAC with fractionation, native PAGE + AHA incorporation) is a rhythm in synthesis.

      *Fig. 5. Why is the first histogram in 3c not at unity? *

      This measures the average fold-induction in aggregation when cells are treated with MG132 for 4h at the indicated timepoints. Unity would indicate no induction at all, so the presented quantifications show that MG132 always elicited an increase in aggregation, with an effect size that varied with circadian phase.

      * Do ZT24 and ZT48 differ, similarly do ZT36 and ZT60?*

      No, neither difference is statistically significant (adjusted p-values of p=0.9 and p=0.07, respectively). This is now specified in the figure legend. Tendency to aggregate is also likely to change as a function of time in culture, which is why we think there is a slight increase overall in the second day of the experiment.

      * Fig S4f is not of good quality with missing eIF2a total and therefore no loading controls. *

      Thank you for prompting us to double-check this. We found that the levels of eIF2a were quite variable between the animals, and therefore we performed this experiment with 6 biological replicates. We have double-checked the quantification, and have now excluded 3 unreliable samples (the ones with undetectable levels of total eIF2a – ZT18 +BTZ replicate 1 & ZT18 -BTZ replicate 2, as well as ZT6 +BTZ replicate 4, where a smear does not allow for a reliable quantification of phospho-eIF2a) instead of 2 that were excluded originally. This still leaves at least 5 biological replicates in each group. In fact, the difference between BTZ and control in ZT6 is now deemed to be even more significant, going down to adjusted p=0.0007.

      *S4e? true regression lines? *

      The same method was used as in Figure 1. The fit lines are produced by statistical comparison of fits, i.e. our hypothesis (damped cosine fit) vs null hypothesis (no change over time, linear fit), using sum-of-squares F test. The statistically preferred fit is plotted and p-value displayed on the graph. These details are reported in the figure legends and methods section.

      While I thought these experiments were effective, they did not tie back well to the rest of the paper. What are the consequences of a temporally sensitive ISR? Which pathways does it effect in circadian time? Here, the main holes in this study are somewhat exposed; namely, a lack of mechanistic depth in explaining the very fascinating, albeit mostly descriptive, findings. The implicit assumption made here is that aggregation is 'bad' but could the opposite be just as true? Taking these considerations in account would further strengthen the discussion.

      The purpose of (former) Fig 5 was entirely to test the functional consequences and potential translational relevance of a daily rhythm in protein turnover. The mechanisms upstream and downstream of the ISR, and link with many diseases, are already quite well understood but we apologise that we did not draw more heavily on the prior literature to provide sufficient context for this experiment. Protein aggregation has long been associated with proteotoxic stress, and we do not assume it is good or bad, we simply use it as an additional validation of a temporally sensitive ISR. To correct this omission we have added the following to the results section before these experiments are introduced:

      "Disruption of proteostasis and sensitivity to proteotoxic stress are strongly linked with a wide range of diseases (Wolff et al, 2014; Harper & Bennett, 2016; Labbadia & Morimoto, 2015; Hipp et al, 2019). Evidently, global protein translation, degradation and complex assembly are crucial processes for cellular proteostasis in general, so cyclic variation in these processes would be expected to have (patho)physiological consequences....

      ...Informed by our observations, we predicted that circadian rhythms of global protein turnover would have functional consequences for maintenance of proteostasis. Specifically, we expected that cells would be differentially sensitive to perturbation of proteostasis induced by proteasomal inhibition using small molecules such as MG132 and BTZ, depending on time-of-day."

      Reviewer #2 (Significance (Required)):

      This is a fascinating paper that addresses key questions in the circadian organization of the proteome. The paper's main findings are that rhythms of protein synthesis and degradation are temporally coordinated to maintain overall stability of the proteome in mouse fibroblasts. Furthermore, the authors present evidence that this temporal organization may be important for assembly of macromolecular complexes. While very interesting, the main limitations are a lack of biochemical and mechanistic explanation and evidence that verifies these, mostly descriptive, findings.

      The fundamental biochemical mechanisms of protein synthesis, degradation, protein quality control and stress response have been studied for decades and are increasingly well understood, at least in cultured cancer cells. What is not understood is the extent to which all of these essential cellular systems are subject to physiological variation over the circadian cycle in quiescent cells. This is the fundamental knowledge gap our study attempts to fill by testing the discrete hypotheses that (1) circadian regulation of macromolecular complex turnover is more prevalent than abundance and that (2) proteome renewal is more prevalent than compositional variation. We suggest that establishing these essential principles of circadian cellular physiology is an essential prerequisite for performing the type perturbational experiments we presume the reviewer would prefer. We would like to reassure the reviewer that such studies have been and are being performed, but we are concerned that the inclusion of a very extensive additional body of work within this manuscript would detract from the clear communication of our major finding that complex turnover and proteome renewal has a cell-autonomous basis.

      *There are some relatively minor statistical and data quality issues that are probably addressable relatively quickly.

      **Upon revision the study would be a welcome addition to investigators interested in proteostasis, circadian biology, cell biology and proteomics.

      **I am a physician-scientist with expertise in circadian rhythms, cell biology, protein synthesis, and biochemistry.

      **Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      **Seinkmane et al investigate circadian regulation of protein synthesis and degradation in cultured cells and in mice. Their main new finding is that protein synthesis and degradation are in many cases rhythmic but coordinated such that the proteome is rhythmically renewed without an apparent rhythm in total protein abundance. Particularly the pool of large protein complexes is rhythmically renewed in this fashion.

      Using pulsed SILAC in combination with mass spectrometry, the authors are able to distinguish between total and newly synthesized protein levels in mouse lung fibroblasts. Analysis of these data shows that the synthesis of a large number of proteins is rhythmic although the total amount is constant, or that proteins are synthesized at a constant rate but the total amount is rhythmic, suggesting that degradation is rhythmic. By analyzing macromolecular complexes, defined as a high-speed pellet, they also present evidence that the rhythmic components of large complexes oscillate in the same phase and have a similar protein turnover rate. The authors conclude that complexes assemble rhythmically. **The authors also present evidence that the activity of the proteasome oscillates in a circadian manner. Based on this observation, they show (in fibroblasts and in mice) that the response to proteotoxic stress (monitored by eIF2alpha phosphorylation levels, protein aggregation, and apoptosis) is higher at circadian times of high proteasome activity.

      **I am an expert in the circadian field, and the hypothesis and concept behind the work presented here are potentially very interesting, and the experimental design is in principle suitable to answer these questions. However, after reading the paper several times, I cannot find the set of experiments that would convincingly support the authors' conclusions.

      **Major questions/points:

      *The major limitation of the manuscript is that the conclusions rely heavily on statistical analysis and massive processing of data from a bewilderingly large number of very different experiments. In looking at the figures, I have often wondered if the presence or absence of a rhythm is real or a product of the heavily processed data. The fact that a cosine wave fits through data points better than a straight line does not necessarily mean that a circadian rhythm is present.

      We agree that comparison of fits alone does not provide sufficiently reliable evidence. However, the fact that many independent methods (cosinor, RAIN, ANOVA) yield similar overall findings lends more confidence to our findings. We would also argue that the large number of different experiments is a positive aspect of the paper and lends weight to the general conclusions. We instead ask the reviewer to consider an alternative question - we and many other labs have found no evidence for any change in total cellular protein content, and yet there is extensive evidence from independent labs for a 'translational rush hour' whilst (excepting some low abundance transcription factors) very few cellular proteins change by more than 10% over the circadian cycle (see Stangherlin et al, COISB, 2022 for extended discussion of this). We hypothesised a parsimonious explanation for this clear contradiction, and designed experiments whose data were analysed by widely used methods that yielded results that were consistent with prediction. Perhaps the reviewer will at least concede that, if the presented findings do not refute the hypothesis, it should not be rejected until a superior one is proposed?

      * I think that in particular, the SILAC experiment(s) should be repeated and also performed with an arrhythmic control (such as CRY1/2 KO). *

      Whilst we agree that CRY1/2 KO cells show no circadian regulation of transcription and much more variable rhythms in PER2::LUC activity than wild type controls (Putker et al., EMBO J, 2021), in our hands circadian rhythms in proteome composition and protein phosphorylation in CRY1/2 KO are at least as prevalent as in wild type cells (see Wong et al., EMBO J, 2022). Indeed, when we performed a proteasome activity assay in CRY1/2 KO fibroblasts, we observed there was an apparent circadian variation, similar to WT but with a different phase. These data are now presented in revised Figure S1. Similarly, Lipton et al (Cell, 2015) showed circadian translational rhythms in cultured Bmal1 KO cells (see final figure), therefore it is not clear what would constitute an appropriate 'arrhythmic' control.

      In this study, for proteomics experiments, we used a combination of SILAC and TMT, as each technique alone would not be sufficient to answer our specific questions. These two techniques are very resource-intensive on their own, and even more so in combination. We therefore had to prioritise and for the second SILAC-TMT experiment decided to focus on cellular fractionation and questions pertaining macromolecular complexes, which were directly relevant to our hypothesis. While it would undoubtedly also be interesting to study how canonical clock genes, such as Cry1/2, impact turnover on a proteome-wide scale, the focus of our study is physiological regulation of proteome composition, rather than the function of Cryptochrome genes which we already explored in previous work (Putker et al., EMBO J, 2021; Wong et al., EMBO J, 2022).

      Comparability between the whole cell and MMC SILAC experiments is also limited due to the different experimental conditions (6h vs. 1.5h pulse, +booster).

      We do not make any direct comparisons, other than to report that broadly comparable numbers of proteins were detected. Implicitly this means there must be greater coverage of protein complexes in the second pSILAC experiment, which our data bears out. If we were not to report the first experiment, the reader would not understand why we refined the method used in the second. In reporting the results of the 6h pulse, we make the limitations of this experiment very clear i.e. biased towards highly abundant, highly turnover proteins, irrespective of cellular compartment. We should add that even in this experiment there was a clear trend towards rhythmic turnover of ribosomal proteins, but this did not quite achieve significance (p = 0.07) and so we did not want to make claims beyond the data.

      *The essential and new message of the paper is that (at least some) macromolecular complexes undergo circadian renewal (degradation and synthesis). Rather than just analysing an operationally defined pellet fraction by mass spectrometry, this could be shown in more detail and directly for one or two specific macromolecular complexes. Ribosomes, for example, seem particularly suitable, because there would also be the very simple approach of measuring the synthesis of ribosomal RNA by pulse labelling. To me, such an analysis would be perfectly sufficient as a proof of principle. I would then omit aspects such as rhythmic stress response, since many additional experiments are needed to demonstrate this convincingly. *

      Thank you for the excellent suggestion, we agree that validation is important. The ribosome is by far the most abundant macromolecular complex in the cell and was one of the major complexes to show clear evidence for circadian regulation of turnover, but not abundance, by our pSILAC proteomics. To validate this result, we took advantage of two important observations: (1) that all fully assembled ribosomes incorporate ribosomal RNA (rRNA) which can readily be separated from other cellular RNA by density gradient centrifugation; (2) pulse-labelling with heavy uridine-15N2 allows nascent RNA to be distinguished from pre-existing RNA. Thus, combining stable isotope labelling with ribosome purification, we can distinguish nascently assembled ribosomes from total ribosomes when the RNA is extracted, digested with RNAse, and the ratio of light to heavy UMP quantified by mass spectrometry. These data are presented in new figure 5, and are consistent with findings in Figures 3/4 that circadian regulation of ribosome turnover is prevalent over abundance, and that the phase of highest ribosome turnover coincides with the phases of high translation and turnover overall. We hope by addressing the reviewer's question by an entirely orthogonal method, he/she can share more confidence in our conclusions.

      The final figure is included because it tests predictions that were informed by the preceding experiments. It is not intended to be comprehensive exploration of how the integrated stress response changes with the circadian cycle, nor have we claimed this.

      * Specific points: The reader is strongly influenced by the cosine wave or straight lines in the graphs (e.g. 1c, e, 3h, 5b, etc) produced by the analysis of rhythmicity, which basically only gives a yes or no answer. But it is not really that simple. If the algorithm detects a rhythm what is its period? Is it the same as the period of the luciferase reporter? If the period lengths correlate, do the phases as well (e.g. see differences in phases 1c and e)? These questions are not addressed. *

      The temporal resolution of the time course data is much lower than the luciferase reporter and so the error of the fit is greater (usually 1-2h). For the cosine wave curve fit and the associated extra sum-of-squares F test, the period of the oscillation was fixed at either 24h or 25h, as determined from a parallel PER2::LUC control recording. This is now explicitly stated in the methods section

      In terms of phase, the general trend across all experiments is that bulk protein turnover, synthesis and degradation is higher during the 6-8h following the peak of PER2::LUC than at any other point in the circadian cycle. This is also consistent with our previous findings in mouse and human cells (Feeney et al, Nature, 2016; Stangherlin et al., Nat Comms, 2021) as well as findings from many different labs in vivo (e.g. Janich et al., Genome Res, 2016; Atger et al., 2015, PNAS; Sinturel et al., 2017, Cell). We are cautious about trying to be any more specific than this because each assay is measuring something different, and (as can be seen across the figures) there is also some modest variation in the phase of PER2::LUC between experiments, with respect the prior entraining temperature cycle (this will be reported in our forthcoming publication, Rzechorzek et al, in prep). To address the reviewer's point therefore, we have added the following to the discussion:

      "Across all experiments in this study, we find that protein synthesis, degradation and turnover is highest during the 6-8h that follow maximal production of the clock protein PER2. This is coincident with increased glycolytic flux and respiration (Putker et al, 2018), increased macromolecular crowding in the cytoplasm, decreased intracellular K+ concentration and increased mTORC activity (Feeney et al, 2016a; Stangherlin et al, 2021b; Wong et al, 2022)."

      * **The algorithm in Fig 1c predicts a rhythm for the chymotrypsin-like and the trypsin-like but not for the caspase-like activity. The peptide assay measures core proteasome activity independent of ubiquitylation and should therefore be dependent on proteasome concentration in the sample. How can then only two of the three proteasomal activities be rhythmic? Please elaborate and repeat with arrhythmic cells (e.g. CRY1/2 KO). The period length does not seem to correlate with the one of the reporter. Why is that? *

      The arrhythmic controls idea is partially addressed in the response above. We did perform a proteasome activity assay in CRY1/2 KO fibroblasts, and observed daily variation similar to WT, albeit with a different apparent phase. These data are now shown in Figure S1, and referred to in the main text as follows:

      "Moreover, we detected a significant interaction between genotype and biological time when comparing trypsin-like proteasome activity between wild type and Cryptochrome1/2-deficient cells, that lack canonical circadian transcriptional feedback repression (Fig S1B-E)".

      Besides this study, our two previous proteomic investigations of the fibroblast circadian proteome detected no biologically significant or consistent rhythm in proteasome subunit abundance (Wong et al., EMBO J, 2021; Hoyle et al., Science Translational Medicine, 2017). Moreover, proteasomes are long-lived stable complexes whose activity is determined by a combination of substrate-level, allosteric and post-translational regulatory mechanisms that includes their reversible sequestration into storage granules (Albert et al., PNAS, 2020; , Fu et al., PNAS, 2021; Yasuda et al., Nature, 2020). It is therefore very likely that the observed rhythm in trypsin- and chymotrypsin-like activity occurs post-translationally. Proteasome subunit composition is also known to change, which might be another reason for differences between the protease activities (Marshall and Vierstra, Front Mol Biosci, 2019; Zheng et al., J Neurochem, 2012).

      To communicate this succinctly, we have revised the relevant text as follows:

      Page 7: "Moreover, we detected a significant interaction between genotype and biological time when comparing trypsin-like proteasome activity between wild type and Cryptochrome1/2-deficient cells, that lack canonical circadian transcriptional feedback repression (Fig S1B, (Wong et al, 2022)). Previous proteomics studies under similar conditions have revealed minimal circadian variation in proteasome subunit abundance (Wong et al, 2022), suggesting that proteasome activity rhythmicity, and therefore rhythms in UPS-mediated protein degradation, are regulated post-translationally (Marshall & Vierstra, 2019; Hansen et al, 2021)."

      Regarding period length, we apologise for an oversight in Fig 1c: unlike all other experiments presented here, these fits were originally done with a flexible period length (between 20h and 36h). This has now been re-fitted in a similar manner to the other experiments (fixed period of 24h, same as the parallel PER2::LUC controls), and the updated data are presented. This has not influenced the results of the statistical tests (only changed the p-values slightly, but the significance levels remain the same).

      Fig. 1a,b suggest that there is a rhythm in global protein synthesis with a significant peak at 40h. Yet, Fig. 1e suggests otherwise. How can that be? Also, the degradation graph (lower panel 1c) has to be plotted with the ratios calculated from the data points and not the heavily processed fitted graphs. This can be very misleading.

      Fig1a,b was performed under quite different conditions to 1e. As described in the methods section, 35S-labelling experiments require a medium change during both pulse and chase (to replace normal Met with radioactive Met, and vice versa). To avoid growth factor/mTORC1-mediated stimulation of protein synthesis & turnover, these acute media changes must occur in the absence of serum; otherwise media changes would introduce artifacts. In contrast, puromycin labelling (Fig 1e) is performed without any media changes (as puromycin can be added directly to culture cell media), and therefore was performed in normal culture conditions of 10% serum. Thus, due to its well-established effect of growth factor/mTORC1 signalling on bulk translation rate, it is very likely that differences in the phase of translational rhythms between Fig1a,b and 1e are attributable to differing serum concentrations – this phenomenon of serum-dependency of phase is also described in Beale et al, 2023, bioRxiv https://doi.org/10.1101/2023.06.22.546020. The only important point, is that neither of these proof-of-principle experiments support the null hypothesis: that translation rate and turnover remains constant over the circadian cycle. Thus, the hypothesis being tested in Figure 1 is not rejected, and provides the rationale for the subsequent proteome-wide analyses.

      With respect to 1E, given the variance of measurement, the curve fits to Puro and Puro+BTZ already serve to test whether there is any significant ~24h component, a ratio of the respective data points would simply compound the error of measurement. The degradation plot is provided purely for illustrative purposes to help the reader i.e. if these fits were true, what would be expected? We have revised the figure to more clearly communicate that the degradation plot is presented purely as a visual aid, labelled “inferred”, and now show ratio plots in revised Figure 1.

      * **It also strikes me as odd that the amplitude of degradation increases (peak at 28h lower than at 30h) while the amplitude of the core clock oscillation dampens over time (peak at 54h higher than at 53h due to desynchronisation. Only two data values around 54h are responsible for the detected rhythm (2nd peak). Furthermore, phase and period do not agree with the rhythm of proteolytic activities shown in 1c. How can this be explained? *

      Due to the nature of the experiment, the degradation rate inferred from Figure 1B & 1E does not reflect proteasome activity exclusively. Rather it reflects the combined sum of processes that remove nascently produced proteins from the cell's digitonin-soluble fraction, which includes proteasomal degradation, but also autophagy, protein secretion and sequestration into other compartments. Therefore, the peak degradation in Fig 1B & E would not necessarily be expected to coincide with the peak of proteasome activity in Fig 1C. Again, these experiments in Figure 1 simply serve to test the hypothesis (change over circadian cycle) vs the null hypothesis (no change over the circadian cycle).

      To the question of amplitude increase, we speculate that this is due to metabolic changes in cultures over the course of three days – as serum and nutrients from the last medium change at T0 are depleted, cells need to increase degradation to promote turnover and recycling. As we suggest that the rhythms in turnover help cellular bioenergetic efficiency, it is quite plausible that amplitude increases as nutrient-concentrations fall. We are in process of further investigation into how exactly these rhythms vary with nutrient and serum status.i

      * Regarding the MS data shown in Figure 2, is it possible to show a positive / quality control? Best would be MS data of Luciferase (or PER2,3, RevErb/alpha, DBP) to show oscillation of protein levels with the same phase and period as the reporter. *

      Unfortunately, none of these low abundance transcription factors were detected in our MS runs. This is not surprising, given that their copy numbers are estimated at * In Fig. 2c examples of the 4 groups of proteins presented in 2e should be shown (both synthesis and total abundance arrhythmic, either one rhythmic or both rhythmic) and not just what appears to be random examples of rhythmic and arrhythmic proteins. *

      As also requested by another reviewer, we have revised the figure to include examples of each of the rhythmicity categories. No specific meaning is inferred from the chosen protein identities.

      Is it possible at all to distinguish between synthesis/turnover and assembly/disassembly of macromolecular complexes in the MMC SILAC experiment? If so, how?

      We followed the established protocol originally developed in our collaborator Kathryn Lilley's lab, where it has previously been shown that most proteins in the MMC fraction are in macromolecular assemblies (Geladaki et al, Nat Commun, 2019). Proteins that are rhythmically abundant in this fraction, but without an accompanying synthesis rhythm (e.g. Beta-actin, see Hoyle et al., Sci Trans Medicine, 2017) can be reliably assumed to arise solely from rhythmic assembly/disassembly i.e. they are captured in this fraction when assembled, but lost, and therefore not detected, in this fraction when disassembled. However, in the case of rhythmic synthesis and abundance, it is not possible with this technique to directly infer that rhythmic synthesis of a given protein is responsible for its rhythmic assembly in a complex, though they do correlate.

      Therefore, our new figure 5 (with thanks again for this suggestion) approaches this by an orthogonal method, relying on the important observations that a) ribosomes incorporate ribosomal RNA (rRNA) b) this can be readily separated from most other cellular RNA by density gradient centrifugation and c) pulse-labelling with heavy uridine-15N2 allows nascent RNA to be distinguished from pre-existing RNA. Using this technique, we validate a rhythm in production and assembly of mature ribosomes, with its peak consistent with the highest turnover time as measured in Figs 1 and 3, and MMC fraction proteomics (Supplemental table 3), at the descending phase of PER2::LUC.

      * **Looking at Fig. 4b,c, what is the fraction of rhythmic proteins from the MMC experiment that also oscillate in either synthesis, total abundance or both in the whole cell? Is there a general correlation at all? Please show. *

      There were no correlations greater than would be expected by chance (the sets of proteins rhythmic in either synthesis or degradation did not overlap significantly between whole-cell and MMC fractions, as determined by an odds ratio test).

      To communicate this we have added the following text:

      "It is also worth noting that although there were small sets of proteins that were rhythmic in both whole-cell (Figure 2) and MMC fractions (Figure 3), in both synthesis and total abundance, none of these four overlaps were higher than would have been expected by chance."

      * **Why is the phase of the oscillating proteins different in the two experiments (compare Figs. 2f,g and 4a) and does either of them match with the phase of the PER2::LUC reporter, which should be the peak synthesis phase of the clock? *

      This was a labelling error on our part, our apologies and thanks for drawing it to our attention. We had attempted to harmonise all these phase values so that they were mutually comparable between the two mass spec experiments, but omitted to update all the figures. They have now all been updated to be inter-consistent. From our experiments, the peak of PER2::LUC consistently precedes the timing of maximum bulk translation. This phase difference is, at least in part, attributable to the inactivation kinetics of firefly luciferase (see Feeney et al., J Biol Rhythms, 2016), i.e., under conditions of saturating luciferin substrate, PER2 protein abundance peaks several hours later than PER2::LUC activity when measured in longitudinal live cell assays.

      * Regarding the sensitivity to MG132 in Fig. 5b it doesn't make sense that, while eIF2alpha phosphorylation is arrhythmic in untreated cells and the levels of eIF2alpha phosphorylation are (apparently) not exhibiting a rhythmic change by administration of MG132 at different circadian timepoints, the ratio of P-eIF2alpha with and without MG132 suddenly is. Please show in Fig. S4b quantifications of the individual experiments with and without MG132. What is presented in 5b is after all the ratio of ratios of quantifications of Western blots, each of which individually does not display any appreciable rhythm. For me this is two much of processing of data. In my opinion, the MG132 4h acute treatment must show a detectable rhythm.*

      We apologise for being unclear in this panel and description. Our hypothesis concerned the fold-induction of the p-eIF2alpha:eIF2alpha ratio changing as a function of MG132 and time. Our reasoning being that the ratio may be more biologically-relevant as it is the relative change that cells sense and respond to, and not the absolute abundance of p-eIF2alpha. We applied a quantitative, two-channel fluorescent antibody technique to enable detection and quantification of p-eIF2alpha and eIF2alpha from each replicate at each time point from the same band of the same blot. We agree that no p-eIF2alpha rhythm is evident from a cursory inspection of any of the blots. This is due to the innate variance between dishes in extracted protein concentration, as well as the levels of basal eIF2alpha and its phosphorylation, and is the reason that we took great pains to be as quantitative as possible using the two-channel immuno-detection (LICOR). Due to the natural and stochastic variation in eIF2alpha levels and extraction between replicates and over time, it is difficult to get identical eIF2alpha loading to reveal the overlying rhythm in p-eIF2alpha, and furthermore, identical loading would give a misleading impression of the level of temporal variation of eIF2alpha levels. Quantification reveals temporal variation in the MG132 treated samples but not in the untreated controls (Supp Fig 5A) – suggesting that there may be circadian regulation of the cellular response to MG132 challenge, rather than a cell-autonomous p-eIF2alpha rhythm under basal conditions. We quantified fold-induction from MG132 vs untreated to present in Figure 6A. We have presented all the raw data in supplementary figure 5 for readers to validate through their own analysis.

      *Minor:

      In Fig. 1f please show dot blot with error bars as well as the individual experiments in the supplementals. Please check the graph legend (N>=3?) *

      Thank you for pointing out these omissions. The dot blot with error bars is now shown in Fig. 1F, and the full gels are now included as Fig. S2B. The main figure legend for 1f has also had the following added (explaining the N numbers):s

      "Four mice were used per condition, but in some cases one of the four injections were not successful i.e. no puromycin labelling was observed and so no quantification could be performed (full data in Fig. S2B)."

      * Please explain the mechanism of the "booster" used in the second SILAC experiment. *

      The following has been revised in the text:

      " Namely, we added a so-called booster channel: an additional fully heavy-labelled cell sample within a TMT mixture (Klann et al, 2020). When the mixture is analysed by MS, heavy peptides from the booster channel increase the overall signal of all identical heavy peptides at MS1 level; at MS2 and MS3 this results in improved detection of heavy proteins in the other TMT channels of interest, and is particularly advantageous for the proteins with lower turnover that would fall below the MS1 detection limit without the booster."

      *

      **p10 3rd paragraph: S2e not S3e *

      Thank you, this has been fixed.

      p12 last paragraph please add reference to Figs. 5f,g

      Thank you, this has been added.

      *Reviewer #3 (Significance (Required)): *

      xxxxx

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This manuscript represents a cleanly designed experiment for assessing biological motion processing in children (mean age = 9) with and without ADHD. The group differences concerning accuracy in global and local motion processing abilities are solid, but the analyses suggesting dissociable relationships between global and local processing and social skills, age, and IQ need further interrogation. The results are useful in terms of understanding ADHD and the ontogenesis of different components of the processing of biological motion.

      We thank the editors for the positive assessment of our manuscript. We have carefully considered the reviewers’ constructive and helpful comments and revised our manuscript accordingly. To address the question about the dissociable relationships between global and local BM processing, we have provided more evidence and additional analyses in this revised version.

      Reviewer #1 (Public Review):

      Summary:

      The paper presents a nice study investigating differences in biological motion perception in participants with ADHD in comparison with controls. Motivated by the idea that there is a relationship between biological motion perception and social capabilities, the authors investigated local and global (holistic) biological motion perception, the group, and several additional behavioral variables that are affected in ADHS (IQ, social responsiveness, and attention/impulsivity). As well as local global biological motion perception is reduced in ADHD participants. In addition, the study demonstrates a significant correlation between local biological motion perception skills and the social responsiveness score in the ADHD group, but not the controls. A path analysis in the ADHD data suggests that general performance in biological motion perception is influenced mainly by global biological motion perception performance and attentional and perceptual reasoning skills.

      Strengths:

      It is true that there exists not much work on biological motion perception and ADHD. Therefore, the presented study contributes an interesting new result to the biological motion literature and adds potentially also new behavioral markers for this clinical condition. The design of the study is straightforward and technically sound, and the drawn conclusions are supported by the presented results.

      Thank you for your positive assessment of our work.

      Weaknesses:

      Some of the claims about the relationship between genetic factors and ADHD and the components of biological motion processing have to remain speculative at this point because genetic influences were not explicitly tested in this paper.

      We agree that the relationship between genetic factors and BM processing in ADHD needs more investigation, We have modified our statement in Discussion section as following:

      “Using the classical twin method, Wang et al. found that the distinction between local and global BM processing may stem from the dissociated genetic bases. The former, to a great degree, seems to be acquired phylogenetically20,21,59,60, while the latter is primarily obtained through individual development19.” (lines 421 - 425),

      Reviewer #2 (Public Review):

      Summary:

      Tian et al. aimed to assess differences in biological motion (BM) perception between children with and without ADHD, as well as relationships to indices of social functioning and possible predictors of BM perception (including demographics, reasoning ability and inattention). In their study, children with ADHD showed poorer performance relative to typically developing children in three tasks measuring local, global, and general BM perception. The authors further observed that across the whole sample, performance in all three BM tasks was negatively correlated with scores on the social responsiveness scale (SRS), whereas within groups a significant relationship to SRS scores was only observed in the ADHD group and for the local BM task. Local and global BM perception showed a dissociation in that global BM processing was predicted by age, while local BM perception was not. Finally, general (local & global combined) BM processing was predicted by age and global BM processing, while reasoning ability mediated the effect of inattention on BM processing.

      Strengths:

      Overall, the manuscript is presented in a relatively clear fashion and methods and materials are presented with sufficient detail so the study could be reproduced by independent researchers. The study uses an innovative, albeit not novel, paradigm to investigate two independent processes underlying BM perception. The results are novel and have the potential to have wide-reaching impact on multiple fields.

      We appreciate your positive assessment of our work.

      Weaknesses:

      Except for the main analysis, it is unclear what the authors' specific predictions are regarding the three different tasks they employ. The three BM tasks are used to probe different processes underlying BM perception, but it is difficult to gather from the introduction why these three specific tasks were chosen and what predictions the authors have about the performance of the ADHD group in these tasks. Relatedly, the authors do not report whether (and if so, how) they corrected for multiple comparisons in their analyses. As the number of tests one should control for depends on the theoretical predictions (http://daniellakens.blogspot.com/2016/02/why-you-dont-need-to-adjust-you-alpha.html), both are necessary for the reader to assess the statistical validity of the results and any inferences drawn from them. The same is the case for the secondary analyses exploring relationships between the 3 individual BM tasks and social function measured by the social responsivity scale (SRS).

      We appreciate these constructive suggestions. In response, we have included a detailed description in the Introduction section explaining why we employed three different tasks and our predictions about the performance in ADHD:

      “Despite initial indications, a comprehensive investigation into BM perception in ADHD is warranted. We proposed that it is essential to deconstruct BM processing into its multiple components and motion features, since treating them as a single entity may lead to misleading or inconsistent findings31. To address this issue, we employed a carefully designed behavioral paradigm used in our previous study19, making slight adjustments to adapt for children. This paradigm comprises three tasks. Task 1 (BM-local) aimed to assess the ability to process local BM cues. Scrambled BM sequences were displayed and participants could use local BM cues to judge the facing direction of the scrambled walker. Task 2 (BM-global) tested the ability to process the global configuration cues of the BM walker. Local cues were uninformative, and participants used global BM cues to determine the presence of an intact walker. Task 3 (BM-general) tested the ability to process general BM cues (local + global cues). The stimulus sequences consisted of an intact walker and a mask containing similar target local cues, so participants could use general BM cues (local + global cues) to judge the facing direction of the walker.” (lines 116 - 130)

      “In Experiment 1, we examined three specific BM perception abilities in children with ADHD. As mentioned earlier, children with ADHD also show impaired social interaction, which implies atypical social cognition. Therefore, we speculated that children with ADHD performed worse in the three tasks compared to TD children.” (lines 131 - 134)

      Additionally, we have reported the p values corrected for multiple comparisons (false discovery rate, FDR) in the revised manuscript wherever it was necessary to adjust the alpha (lines 310 - 316; Table 2). The pattern of the results remained unchanged.

      In relation to my prior point, the authors could provide more clarity on how the conclusions drawn from the results relate to their predictions. For example, it is unclear what specific conclusions the authors draw based on their findings that ADHD show performance differences in all three BM perception tasks, but only local BM is related to social function within this group. Here, the claim is made that their results support a specific hypothesis, but it is unclear to me what hypothesis they are actually referring to (see line 343 & following). This lack of clarity is aggravated by the fact that throughout the rest of the discussion, in particular when discussing other findings to support their own conclusions, the authors often make no distinction between the two processes of interest. Lastly, some of the authors' conclusions related to their findings on local vs global BM processing are not logically following from the evidence: For instance, the authors conclude that their data supports the idea that social atypicalities are likely to reduce with age in ADHD individuals. However, according to their own account, local BM perception - the only measure that was related to social function in their study - is understood to be age invariant (and was indeed not predicted by age in the present study).

      Thank you for pointing out this issue. We have carefully revised the Discussion section about our findings to clarify these points:

      “Our study contributes several promising findings concerning atypical biological motion perception in ADHD. Specifically, we observe the atypical local and global BM perception in children with ADHD. Notably, a potential dissociation between the processing of local and global BM information is identified. The ability to process local BM cues appears to be linked to the traits of social interaction among children with ADHD. In contrast, global BM processing exhibits an age-related development. Additionally, general BM perception may be affected by factors including attention.” (lines 387 - 393)

      We have provided a detailed discussion on the two processes of interest to clarify their potential differences and the possible reasons behind the difference of the divergent developmental trajectories between local and global BM processing:

      “BM perception is considered a multi-level phenomenon56-58. At least in part, processing information of local BM and global BM appears to involve different genetic and neural mechanisms16,19. Using the classical twin method, Wang et al. found that the distinction between local and global BM processing may stem from the dissociated genetic bases. The former, to a great degree, seems to be acquired phylogenetically20,21,59,60, while the latter is primarily obtained through individual development19. The sensitivity to local rather than global BM cues seems to emerge early in life. Visually inexperienced chicks exhibit a spontaneous preference for the BM stimuli of hen, even when the configuration was scrambled20. The same finding was reported in newborns. On the contrary, the ability to process global BM cues rather than local BM cues may be influenced by attention28,29 and shaped by experience24,56.” (lines 419 - 430)

      “We found that the ability to process global and general BM cues improved significantly with age in both TD and ADHD groups, which imply the processing module for global BM cues tends to be mature with development. In the ADHD group, the improvement in processing general and global BM cues is greater than that in processing local BM cues, while no difference was found in TD group. This may be due to the relatively higher baseline abilities of BM perception in TD children, resulting in a relatively milder improvement. These findings also suggest a dissociation between the development of local and global BM processing. There seems to be an acquisition of ability to process global BM cues, akin to the potential age-related improvements observed in certain aspects of social cognition deficits among individuals with ADHD5, whereas local BM may be considered an intrinsic trait19.” (lines 438 -449)

      In addition, we have rephased some inaccurate statements in revised manuscript. Another part of social dysfunction might be stable and due to the atypical local BM perception in ADHD individuals, although some studies found a part of social dysfunction would reduce with age in ADHD individuals. One reason is that some factors related to social dysfunction would improve with age, like the symptom of hyperactivity.

      Results reported are incomplete, making it hard for the reader to comprehensively interpret the findings and assess whether the conclusions drawn are valid. Whenever the authors report negative results (p-values > 0.05), the relevant statistics are not reported, and the data not plotted. In addition, summary statistics (group means) are missing for the main analysis.

      Thanks for your comments. We have provided the complete statistical results in the revised manuscript (lines 309 - 316) and supplementary material, which encompass relevant statistics and plots of negative results (Figure 4, Figure S2 and S3), in accordance with our research questions. And we have also included summary statistics in the Results section (lines 287 - 293).

      Some of the conclusions/statements in the article are too strong and should be rephrased to indicate hypotheses and speculations rather than facts. For example, in lines 97-99 the authors state that the finding of poor BM performance in TD children in a prior study 'indicated inferior applicability' or 'inapplicable experimental design'. While this is one possibility, a perhaps more plausible interpretation could be that TD children show 'poor' performance due to outstanding maturation of the underlying (global) BM processes (as the authors suggest themselves that BM perception can improve with age). There are several other examples where statements are too strong or misleading, which need attention.

      We thank you for pointing out the issue. We have toned down and rephrased the strong statements and made the necessary revisions.

      “Another study found that children with ADHD performed worse in BM detection with moderate ratios of noise34. This may be due to the fact that BM stimuli with noise dots will increase the difficulty of identification, which highlights the difference in processing BM between the two groups33,35.” (lines 111 - 115)

      Reviewer #3 (Public Review):

      Summary:

      The authors presented point light displays of human walkers to children (mean = 9 years) with and without ADHD to compare their biological motion perception abilities and relate them to IQ, social responsiveness scale (SRS) scores and age. They report that children with ADHD were worse at all three biological motion tasks, but that those loading more heavily on local processing related to social interaction skills and global processing to age. The important and solid findings are informative for understanding this complex condition, as well as biological motion processing mechanisms in general. However, I am unsure that these differences between local and global skills are truly supported by the data and suggest some further analyses.

      Strengths:

      The authors present clear differences between the ADHD and TD children in biological motion processing, and this question has not received as much attention as equivalent processing capabilities in autism. They use a task that appears well controlled. They raise some interesting mechanistic possibilities for differences in local and global motion processing, which are distinctions worth exploring. The group differences will therefore be of interest to those studying ADHD, as well as other developmental conditions, and those examining biological motion processing mechanisms in general.

      We appreciate your positive feedback. In revised manuscript, we have added more analyses to support the differences between local and global motion processing. Please refer to our response to the point #3 you mentioned below.

      Weaknesses:

      I am unsure that the data are strong enough to support claims about differences between global and local processing wrt social communication skills and age. The mechanistic possibilities for why these abilities may dissociate in such a way are interesting, but do not seem so plausible to me. I am also concerned about gender, and possible autism, confounds when examining the effect of ADHD. Specifics:

      Gender confound. There are proportionally more boys in the ADHD than TD group. The authors appear to attempt to overcome this issue by including gender as a covariate. I am unsure if this addresses the problem. The vast majority of participants in the ADHD group are male, and gender is categorically, not continuously, defined. I'm pretty sure this violates the assumptions of ANCOVA.

      We appreciate your comments. We concur with you that although we observed a clear difference between local and global BM processing in ADHD, the evidence is to some extent preliminary. The mechanistic possibilities for why these abilities may dissociate have been discussed in revised manuscript. Please refer to the response to reviewer 2’s point #2. To further examine if gender played a role in the observed results, we used a statistical matching technique to obtain a sub-dataset. The pattern of results remained with the more balanced dataset (see Supplementary Information part 1). According to your suggestion, we have also presented the results without using gender as a covariate in main text and also separated the data of boys and girls on the plots (see Figure 1 and Figure S1). There were indeed no signs of a gender effect.

      Autism. Autism and ADHD are highly comorbid. The authors state that the TD children did not have an autism or ADHD diagnosis, but they do not state that the ADHD children did not have an autism diagnosis. Given the nature of the claims, this seems crucial information for the reader.

      Thanks for your suggestion. We have confirmed that all children with ADHD in our study were not diagnosed with autism. We used a semi-structured interview instrument (K-SADSPL-C) to confirm every recruited child with ADHD but not with ASD. The exclusion criteria for both groups were mentioned in the Materials and methods section:

      “Exclusion criteria for both groups were: (a) neurological diseases; (b) other neurodevelopmental disorders (e.g., ASD, Mental retardation, and tic disorders), affective disorders and schizophrenia…” (lines 158 - 162)

      Conclusions. The authors state frequently that it was the local BM task that related to social communication skills (SRS) and not the global tasks. However, the results section shows a correlation between SRS and all three tasks. The only difference is that when looking specifically within the ADHD group, the correlation is only significant for the local task. I think that if the authors wish to make strong claims here they must show inferential stats supporting (1) a difference between ADHD and TD SRS-Task 1 correlations, and (2) a difference in those differences for Task 2 and 3 relative to Task 1. I think they should also show a scatterplot of this correlation, with separate lines of best fit for the two groups, for Tasks 2 and 3 as well. I.e. Figure 4 should have 3 panels. I would recommend the same type of approach for age. Currently, they have small samples for correlations, and are reading much of theoretical significance between some correlations passing significance threshold and others not. It would be incredibly interesting if the social skills (as measured by SRS) only relate to local BM abilities, and age only to global, but I think the data are not so clear with the current information. I would be surprised if all BM abilities did not improve with age. Even if there is some genetic starter kit (and that this differs according to particular BM component), most abilities improve with learning/experience/age.

      Thank you for this recommendation. We have added more statistics to test differences between the correlations (a difference between ADHD and TD in SRS-Task 1 correlations (see the first paragraph of Supplementary Information part 2), a difference in SRS-response accuracy correlations for Task 2 and 3 relative to Task 1(see the second paragraph of Supplementary Information part 2), and a difference in age-response accuracy correlations for Task 2 and 3 relative to Task 1 in ADHD group (see Supplementary Information part 3)). Additionally, we have included scatterplots for SRS-Task1, SRS-Task2, SRS-Task3 (with separate lines of best fit for the two groups in each, see Figure 4), SRS-ADHD, SRS-TD, age-ADHD and age-TD (with separate lines of best fit for the three tasks in each, see Figure S2 and S3) to make a clear demonstration. Detailed results have been presented in the revised manuscript and Supplementary Information. We expect these further analyses would strengthen our conclusions.

      Theoretical assumptions. The authors make some sweeping statements about local vs global biological motion processing that need to be toned down. They assume that local processing is specifically genetically whereas global processing is a product of experience. The fact their global, but not local, task performance improves with age would tend to suggest there could be some difference here, but the existing literature does not allow for this certainty. The chick studies showing a neonatal preference are controversial and confounded - I cannot remember the specifics but I think there an upper vs lower visual field complexity difference here.

      Thank you for pointing out this issue. We have toned down rephrased our claims that the difference between local and global BM processing according to your suggestion:

      “These findings suggest that local and global mechanisms might play different roles in BM perception, though the exact mechanisms underlying the distinction remain unclear. Exploring the two components of BM perception will enhance our understanding of the difference between local and global BM processing, shedding light on the psychological processes involved in atypical BM perception.” (lines 87 - 92)

      Reviewer #1 (Recommendations For The Authors):

      I have only a number of minor points that should be addressed prior to publication:

      L. 95ff: What is meant by 'inapplicability of experimental designs' ? This paragraph is somewhat unclear.

      In revised manuscript, we have clarified this point (lines 111 - 115).

      L. 146: The groups were not perfectly balanced for sex. Would results change fundamentally in a more balanced design, or can arguments be given that gender does not play a role, like it seems to be the case for some functions in biological motion perception (e.g. Pavlova et al. 2015; Tsang et al 2018). One could provide a justification that this disbalance does not matter or test for subsampled balanced data sets maybe.

      This point is similar to the point #1 from reviewer 3, and we have addressed this issue in our response above.

      L. 216 f.: In this paragraph it does not become very clear that the mask for the global task consisted of scrambles generated from walkers walking in the same direction. The mask for the local task then should consist of a balanced mask that contains the same amount of local motion cues indicating right and leftwards motion. Was this the case? (Not so clear from this paragraph.)

      Regarding the local task, the introduction of mask would make the task too difficult for children. Therefore, in the local task, we only displayed a scrambled walker without a mask, which was more suitable for children to complete the task. We have made clear this point in the corresponding paragraph (lines 232 - 241).

      L. 224 ff.: Here it would be helpful to see the 5 different 'facing' directions of the walkers. What does this exactly mean? Do they move on oblique paths that are not exactly orthogonal to the viewing directions, and how much did these facing directions differ?

      Out of the five walkers we used, two faced straight left or right, orthogonal to the viewing directions. Two walked with their bodies oriented 45 degrees from the observer, to the left or right. The last one walked towards the observer. We have included a video (Video 4) to demonstrate the 5 facing directions.

      L. 232: How was the number of 5 practicing trials determined/justified?

      As mentioned in main text, global BM processing is susceptible to learning. Therefore, too many practicing trials would increase BM visual experience and influence the results. We determined the number of training trials to be 5 based on the results of the pilot experiment. During this phase, we observed that nearly all children were able to understand the task requirements well after completing 5 practicing trials.

      L 239: Apparently no non-parametric statistics was applied. Maybe it would be good to mention in the Statistics section briefly why this was justified.

      We appreciate your suggestion and have cited two references in the Statistics section (Fagerland et al. 2012, Rochon et al. 2012). Fagerland et al., mentioned that when the sample size increases, the t-test is more robust. According to the central limit theorem, when the sample size is greater than 30, the sampling distribution of the mean can be safely assumed to be normal.

      (http://www2.psychology.uiowa.edu/faculty/mordkoff/GradStats/part%201/I.07%20normal.p df). In fact, we also ran non-parametric statistics for our data and found the results to be robust.

      L 290: 'FIQ' this abbreviation should be defined.

      Regarding the abbreviation ’FIQ’, it stands for the abbreviation of the full-scale intellectual quotient, which was mentioned in Materials and methods section:

      “Scores of the four broad areas constitute the full-scale intellectual quotient (FIQ).”

      L. 290 ff.: These model 'BM-local = age + gender etc ' is a pretty sloppy notation. I think what is meant that a GLM was used that uses the predictors gender etc. time appropriate beta_i values. This formula should be corrected or one just says that a GLM was run with the predictors gender ....

      The same criticism applies to these other models that follow.

      We thank you for pointing this out. We have modified all formulas accordingly in the revised manuscript (see part3 of the Results section).

      All these models assume linearity of the combination of the predictors.was this assumption verified?

      We referred to the previous study of BM perception in children. They found main predictor variables, including IQ (Rutherford et al., 2012; Jones et al., 2011) and age (Annaz et al., 2010; van et al., 2016), have a linear relation with the ability of BM processing.

      L. 296ff.: For model (b) it looks like general BM performance is strongly driven by the predictor global BM performance in the group of patients. Does the same observation also apply to the normals?

      The same phenomenon was not observed in TD children. We have briefly discussed this point in the Discussion section of the revised manuscript (lines 449 - 459).

      Reviewer #2 (Recommendations For The Authors):

      (1) Please add public access to the data repository so data availability can be assessed.

      The data of the study will be available at https://osf.io/37p5s/.

      (2) Although overall, the language was clear and understandable, there are a few parts where language might confuse a reader and lead to misconceptions. For instance, line 52: Did the authors mean to refer to 'emotions and intentions' instead of 'emotions and purposes'? See also examples where rephrasing may help to reflect a statement is speculation rather than fact.

      Thanks for the comments. We have carefully checked the full text and rephrased the confused statements.

      (3) Line 83/84: Autism is not a 'mental disorder' - please change to something like 'developmental disability'. Authors are encouraged to adapt their language according to terms preferred by the community (e.g., see Fig. 5 in this article:

      https://onlinelibrary.wiley.com/doi/10.1002/aur.2864)

      Suggestion well taken. We have changed the wording accordingly:

      “In recent years, BM perception has received significant attention in studies of mental disorders (e.g., schizophrenia30) and developmental disabilities, particularly in ASD, characterized by deficits in social communication and social interaction31,32.” (lines 93 - 95)

      (4) Please report how the sample size for the study was determined.

      In the Materials and methods section (lines 168 - 173), we explained how the sample size was determined.

      Line 94: It would be helpful to have a brief description of what neurophysiological differences have been observed upon BM perception in children with ADHD.

      Thanks for the comment. We have added a brief description of neurophysiological findings in children with ADHD (lines 108 - 111).

      (6) Line 106/107 and 108/109: please add references.

      We have revised this part, and the relevant findings and references are in line with the revised manuscript (lines 77, 132 - 133).

      (7) Line 292: Please add what order the factors were entered into each regression model.

      Regarding this issue, we used SPSS 26 for the main analysis. SPSS utilizes the Type III sum of squares (default) to evaluate models. Regardless of the order in the GLM, we will obtain the same result. For more information, please refer to the documentation of SPSS 26 (https://www.ibm.com/docs/en/spss-statistics/26.0.0?topic=features-glm-univariate-analysis).

      Reviewer #3 (Recommendations For The Authors)

      (1) Task specifics. It is key to understanding the findings, as well as the dissociation between tasks, that the precise nature of the stimuli is clear. I think there is room for improvement in description here. Task 1 is described as involving relocating dots within the range of the intact walker. Of course, PLWs are created by presenting dots at the joints, so relocation can involve either moving to another place on the body, or random movement within the 2D spatial array (which likely involves moving it off the body). Which was done? It is said that Ps must indicate the motion direction, but what was the display of the walker? Sagittal? Task 2 requires detecting whether there is an intact walker amongst scrambled walkers. Were all walkers completely overlaid? Task 3 requires detecting the left v right facing of an intact walker at different orientations, presented amongst noise. So Task 3 requires determining facing direction and Task 1 walking direction. Are these tasks the same but described differently? Or can walkers ever walk backwards? Wrt this point, I also think it would help the reader if example videos were uploaded.

      We appreciate you for bringing this to our attention. With regards to Task 1, it appears that your second speculation is correct. We scrambled the original dots and randomly presented them within the 2D spatial array (which likely involved moving them off the body). As a result, the global configuration of the 13 dots was completed disrupted while preserving the motion trajectory of each individual dot. This led to the display of scrambled dots on the monitor (which does not resemble a human). In practice, these local BM cues contain information about motion direction. In Task 2, the target walkers completely overlaid by a mask that is approximately 1.44 times the size of the intact walker. The task requirements of Task 1 and Task3 are same, which is judging the motion (walking) direction. The difference is that Task 1 displayed a scrambled walker while Task 3 displayed an intact walker within a mask. We have clarified these points and improved our descriptions in Procedure section and created example videos for each task, which we believe will be helpful for the readers to understand each task.

      (2) Gender confound (see above). I think that the authors should present the results without gender as a covariate. Can they separate boys and girls on the plots with different coloured individual datapoints, such that readers can see whether it's actually a gender effect driving the supposed ADHD effect? And show that there are no signs of a gender effect in their TD group?

      This point is similar to the point #1 you mentioned. Please refer to our response to that point above.

      (3) Autism possible confound (see above). I think the authors must report whether any of the ADHD group had an autism diagnosis.

      Please refer to the response for the point #2 your mentioned.

      (4) Conclusions concerning differences between the local and global tasks wrt SRS and age (see above). I believe the authors should add stats demonstrating differences between the correlations to support such claims, as well as demonstrating appropriate scatterplots for SRS-Task 1, SRS-Task 2, SRS-Task 3 and age-Task 1, age-Task2 and age-Task 3 (with separate lines of best fit for the two groups in each).

      Please refer to the response for the point #3 your mentioned.

      (5) Theoretical assumptions (see above). I would suggest rephrasing all claims here to outline that these discussed mechanistic differences between local and global BM processing are only possibilities and not known on the basis of existing data.

      Please refer to the response for the point #4 your mentioned.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      I only have a few minor suggestions:

      Abstract: I really liked the conclusion (that IM and VWM are two temporal extremes of the same process) as articulated in lines 557--563. (It is always satisfying when the distinction between two things that seem fundamentally different vanishes). If something like this but shorter could be included in the Abstract, it would highlight the novel aspects of the results a little more, I think.

      Thank you for this comment. We have added the following to the abstract:

      “A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.”

      L 216: There's an orphan parenthesis in "(justifying the use".

      Fixed.

      L 273: "One surprising result was the observed set size effect in the 0 ms delay condition". In this paragraph, it might be a good idea to remind the reader of the difference between the simultaneous and zero-delay conditions. If I got it right, the results differ between these conditions because it takes some amount of processing time to interpret the cue and free the resources associated with the irrelevant stimuli. Recalling that fact would make this paragraph easier to digest.

      That is correct. However, at this point in the text, we have not yet fitted the DyNR model to the data. Therefore, we believe that introducing cue processing and resource reallocation as concepts that differentiate between those two conditions would disrupt the flow of this paragraph. We address these points soon after, in a paragraph starting on line 341.

      Figures 3, 5: The labels at the bottom of each column in A would be more clear if placed at the top of each column instead. That way, the x-axis for the plots in A could be labeled appropriately, as "Error in orientation estimate" or something to that effect.

      We edited both figures, now Figure 4 and Figure 6, as suggested.

      L 379: It should be "(see Eq 6)", I believe.

      That is correct, line 379 (currently line 391) should read ‘Eq 6’. Fixed.

      L 379--385: I was a bit mystified as to why the scaled diffusion rate produced a worse fit than a constant rate. I imagine the scaled version was set to something like

      sigma^2_diff_scaled = sigma^2_base + K*(N-1)

      where N is the set size and sigma^2_base and K are parameters. If this model produced a similar fit as with a constant diffusion rate, the AIC would penalize it because of the extra parameter. But why would the fit be worse (i.e., not match the pattern of variability)? Shouldn't the fitter just find that the K=0 solution is the best? Not a big deal; the Nelder-Mead solutions can wobble when that many parameters are involved, but if there's a simple explanation it might be worth commenting on.

      The scaled diffusion was implemented by extending Eq 6 in the following way:

      σ(t)2 = (t-toffset) * σ̇ 2diff * N

      where N is set size. Therefore, the scaling was not associated with a free parameter that could become 0 if set size did not affect diffusion rate, but variability rather mandatory increased with set size. We now clarify this in the text:

      “The second variant was identical to the proposed model, except that we replaced the constant diffusion rate with a set size scaled diffusion rate by multiplying the right side of Eq 6 by N.“

      Figure 4 is not mentioned in the main text. Maybe the end of L 398 would be a good place to point to it. The paragraph at L 443-455 would also benefit from a couple of references to it.

      Thank you for this suggestion. Figure 4 (now Figure 5) was previously mentioned on line 449 (previously line 437), but now we have included it on line 410 (previously line 398), within the paragraph spanning lines 455-467 (previously 443-455), and also on line 136 where we first discuss masking effects.

      L 500: Figure S7 is mentioned before Figures S5 and S6. Quite trivial, I know....

      Thank you for this comment. There was no specific reason for Figure S7 to appear after S5 & S6, so we simply swapped their order to be consistent with how they are referred to in the manuscript (i.e., S7 became S5, S5 became S6, and S6 became S7).

      Reviewer #2 (Recommendations For The Authors):

      (1) One potential weakness is that the model assumes sensory information is veridical. However, this isn't likely the case. Acknowledging noise in sensory representations could affect the model interpretation in a couple of different ways. First, neurophysiological recordings have shown normalization affects sensory representations, even when a stimulus is still present on the screen. The DyNR model partially addresses this concern because reports are drawn from working memory, which is normalized. However, if sensory representations were also normalized, then it may improve the model variant where subjects draw directly from sensory representations (an alternative model that is currently described but discarded).

      Thank you for this suggestion. We can consider two potential mechanisms through which divisive normalization might be incorporated into sensory processing within the DyNR model.

      The first possibility involves assuming that normalization is pre-attentive. In this scenario, the sensory activity of each object would be rescaled at the lowest level of sensory processing, occurring before the allocation of attentional or VWM resources. One strong prediction of such an implementation is that recall error in the simultaneous cue condition (Experiment 1) should vary with set size. However, this prediction is inconsistent with the observed data, which failed to show a significant difference between set sizes, and is more closely aligned with the hypothesis of no-difference (F(2,18) = 1.26, p = .3, η2 = .04, BF10 = 0.47). On that basis, we anticipate that introducing normalization as a pre-attentive mechanism would impair the model fit.

      An alternative scenario is to consider normalization as post-attentive. In the simultaneous cueing condition, only one item is attended (i.e., the cued one), regardless of the displayed set size. Here, we would expect normalized activity for a single item, regardless of the number of presented objects, which would then be integrated into VWM. This expanded DyNR model with post-attentive normalization would make exactly the same predictions as the proposed DyNR for recall fidelity, so distinguishing between these models would not be possible based on working memory experiments.

      To acknowledge the possibility that sensory signals could undergo divisive normalization and to motivate future research, we have added the following to our manuscript:

      “As well as being implicated in higher cognitive processes including VWM (Buschman et al, 2011; Sprague et al., 2014), divisive normalization has been shown to be widespread in basic sensory processing (Bonin et al., 2005; Busse et al., 2009; Ni et al., 2017). The DyNR model presently incorporates the former but not the latter type of normalization. While the data observed in our experiments do not provide evidence for normalization of sensory signals (note comparable recall errors across set size in the simultaneous cue condition of Experiment 1), this may be because sensory suppressive effects are localized and our stimuli were relatively widely separated in the visual field: future research could explore the consequences of sensory normalization for recall from VWM using, e.g., centre-surround stimuli (Bloem et al., 2018).”

      Bloem, I. M., Watanabe, Y. L., Kibbe, M. M., & Ling, S. (2018). Visual Memories Bypass Normalization. Psychological Science, 29(5), 845–856. https://doi.org/10.1177/0956797617747091

      Bonin, V., Mante, V., & Carandini, M. (2005). The Suppressive Field of Neurons in Lateral Geniculate Nucleus. The Journal of Neuroscience, 25(47), 10844–10856. https://doi.org/10.1523/JNEUROSCI.3562-05.2005

      Buschman, T. J., Siegel, M., Roy, J. E., & Miller, E. K. (2011). Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences, 108(27), 11252–11255. https://doi.org/10.1073/pnas.1104666108

      Busse, L., Wade, A. R., & Carandini, M. (2009). Representation of Concurrent Stimuli by Population Activity in Visual Cortex. Neuron, 64(6), 931–942. https://doi.org/10.1016/j.neuron.2009.11.004

      Ni, A. M., & Maunsell, J. H. R. (2017). Spatially tuned normalization explains attention modulation variance within neurons. Journal of Neurophysiology, 118(3), 1903–1913. https://doi.org/10.1152/jn.00218.2017

      Sprague, T. C., Ester, E. F., & Serences, J. T. (2014). Reconstructions of Information in Visual Spatial Working Memory Degrade with Memory Load. Current Biology, 24(18), 2174–2180. https://doi.org/10.1016/j.cub.2014.07.066

      Second, visual adaptation predicts sensory information should decrease over time. This would predict that for long stimulus presentation times, the error would increase. Indeed, this seems to be reflected in Figure 5B. This effect is not captured by the DyNR model.

      Indeed, neural responses in the visual cortex have been observed to quickly adapt during stimulus presentation, showing reduced responses to prolonged stimuli after an initial transient (Groen et al., 2022; Sawamura et al., 2006; Zhou et al., 2019). This adaptation typically manifests as 1) reduced activity towards the end of stimulus presentation and 2) a faster decay towards baseline activity after stimulus offset.

      In the DyNR model, we use an idealized solution in which we convolve the presented visual signal with a response function (i.e., temporal filter). At the longest presentation durations, in DyNR, the sensory signal plateaus and remains stable until stimulus offset. Because our psychophysical data does not allow us to identify the exact neural coding scheme that underlies the sensory signal, we tend to favour this simple implementation, which is broadly consistent with some previous attempts to model temporal dynamics in sensory responses (e.g., Carandini and Heeger, 1994). However, we agree with the reviewer that some adaptation of the sensory signal with prolonged presentation would also be consistent with our data.

      We have added the following to the manuscript:

      “In Experiment 2, the longest presentation duration shows an upward trend in error at set sizes 4 and 10. While this falls within the range of measurement error, it is also possible that this is a meaningful pattern arising from visual adaptation of the sensory signal, whereby neural populations reduce their activity after prolonged stimulation. This would mean less residual sensory signal would be available after the cue to supplement VWM activity, predicting a decline in fidelity at higher set sizes. Visual adaptation has previously been successfully accounted for by a type of delayed normalization model in which the sensory signal undergoes a series of linear and nonlinear transformations (Zhou et al., 2019). Such a model could in future be incorporated into DyNR and validated against psychophysical and neural data.”

      Carandini, M., & Heeger, D. J. (1994). Summation and division by neurons in primate visual cortex. Science, 264(5163), 1333–1336. https://doi.org/10.1126/science.8191289

      Groen, I. I. A., Piantoni, G., Montenegro, S., Flinker, A., Devore, S., Devinsky, O., Doyle, W., Dugan, P., Friedman, D., Ramsey, N. F., Petridou, N., & Winawer, J. (2022). Temporal Dynamics of Neural Responses in Human Visual Cortex. The Journal of Neuroscience, 42(40), 7562–7580. https://doi.org/10.1523/JNEUROSCI.1812-21.2022

      Sawamura, H., Orban, G. A., & Vogels, R. (2006). Selectivity of Neuronal Adaptation Does Not Match Response Selectivity: A Single-Cell Study of the fMRI Adaptation Paradigm. Neuron, 49(2), 307–318. https://doi.org/10.1016/j.neuron.2005.11.028

      Zhou, J., Benson, N. C., Kay, K., & Winawer, J. (2019). Predicting neuronal dynamics with a delayed gain control model. PLOS Computational Biology, 15(11), e1007484. https://doi.org/10.1371/journal.pcbi.1007484

      (2) A second potential weakness is that, in Experiment 1, the authors briefly change the sensory stimulus at the end of the delay (a 'phase shift', Fig. 6A). I believe this is intended to act as a mask. However, I would expect that, in the DyNR model, this should be modeled as a new sensory input (in Experiment 2, 50 ms is plenty of time for the subjects to process the stimuli). One might expect this change to disrupt sensory and memory representations in a very characteristic manner. This seems to make a strong testable hypothesis. Did the authors find evidence for interference from the phase shift?

      The phase shift was implemented with the intention of reducing retinal after-effects, essentially acting as a mask for retinal information only; crucially the orientation of the stimulus is unchanged by the phase shift, so from the perspective of the DyNR model, it transmits the same orientation information to working memory as the original stimulus.

      If our objective were to model sensory input at the level of individual neurons and their receptive fields, we would indeed need to treat this phase shift as a novel input. Nevertheless, for DyNR, conceived as an idealization of a biological system for encoding orientation information, we can safely assume that visual areas in biological organisms have a sufficient number of phase-sensitive simple cells and phase-indifferent complex cells to maintain the continuity of input to VWM.

      When comparing conditions with and without the phase shift of stimuli (Fig S1B), we found performance to be comparable in the perceptual condition (simultaneous presentation) and with the longest delay (1 second), suggesting that the phase shift did not change the visibility or encoding of information into VWM. In contrast, we found strong evidence that observers had access to an additional source of information over intermediate delays when the phase shift was not used. This was evident through enhanced recall performance from 0 ms to 400 ms delay. Based on this, we concluded that the additional source of information available in the absence of a phase shift was accessible immediately following stimulus offset and had a brief duration, aligning with the theoretical concept of retinal afterimages.

      (3) It seems odd that the mask does not interrupt sensory processing in Experiment 2. Isn't this the intended purpose of the mask? Should readers interpret this as all masks not being effective in disrupting sensory processing/iconic memory? Or is this specific to the mask used in the experiment?

      Visual masks are often described as instantly and completely halting the visual processing of information that preceded the mask. We also anticipated the mask would entirely terminate sensory processing, but our data indicate the effect was not complete (as indicated by model variants in Experiment 2). Nevertheless, we believe we achieved our intended goal with this experiment – we observed a clear modulation of response errors with changing stimulus duration, indicating that the post-stimulus information that survived masking did not compromise the manipulation of stimulus duration. Moreover, the DyNR model successfully accounted for the portion of signal that survived the mask.

      We can identify two possible reasons why masking was incomplete. First, it is possible that the continuous report measure used in our experiments is more sensitive than the discrete measures (e.g., forced-choice methods) commonly employed in experiments that found masks to be 100% effective. Second, despite using a flickering white noise mask at full contrast, it is possible that it may not have been the most effective mask; for instance, a mask consisting of many randomly oriented Gabor patches matched in spatial frequency to the stimuli could prove more effective. We decided against such a mask because we were concerned that it could potentially act as a new input to orientation-sensitive neurons, rather than just wiping out any residual sensory activity.

      (4) I apologize if I missed it, but the authors did not compare the DyNR model to a model without decaying sensory information for Experiment 1.

      We tested two DyNR variants in which the diffusion process was solely responsible for memory fidelity dynamics. These models assumed that the sensory signal terminates abruptly with stimuli offset, and the VWM signal encoding the stimuli was equal to the limit imposed by normalization, independent of the delay duration.

      As variants of this model failed to account for the observed response errors both quantitatively (see 'Fixed neural signal' under Model variants) and qualitatively (Figure S3), we decided not to test any more restrictive variants, such as the one without sensory decay and diffusion.

      (5) In the current model, selection is considered to be absolute (all or none). However, this need not be the case (previous work argues for graded selection). Could a model where memories are only partially selected, in a manner that is mediated by load, explain the load effects seen in behavior?

      Thank you for this point. If attentional selection was partial, it would affect the observers’ efficiency in discarding uncued objects to release allocated resources and encode additional information about the cued item. We and others have previously examined whether humans can efficiently update their VWM when previous items become obsolete. For example, Taylor et al. (2023) showed that observers could efficiently remove uncued items from VWM and reallocate the released resources to new visual information. These findings align with results from other studies (e.g., Ecker, Oberauer, & Lewandowsky, 2014; Kessler & Meiran, 2006; Williams et al., 2013).

      Based on these findings, we feel justified in assuming that observers in our current task were capable of fully removing all uncued objects, allowing them to continue the encoding process for the cued orientation that was already partially stored in VWM, such that the attainable limit on representational precision for the cued item equals the maximum precision of VWM.

      Partial removal could in principle be modelled in the DyNR model by introducing an additional plateau parameter specifying a maximum attainable precision after the cue. Our concern would be that such a plateau parameter would trade off with the parameter associated with Hick’s law (i.e., cue interpretation time). The former would control the amount of information that can be encoded into VWM, while the latter regulates the amount of sensory information available for encoding. We are wary of adding additional parameters, and hence flexibility, to the model where we do not have the data to sufficiently constrain them.

      Ecker, U. K. H., Oberauer, K., & Lewandowsky, S. (2014b). Working memory updating involves item-specific removal. Journal of Memory and Language, 74, 1–15. https://doi.org/10.1016/j.jml. 2014.03.006

      Kessler, Y., & Meiran, N. (2006). All updateable objects in working memory are updated whenever any of them are modified: Evidence from the memory updating paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 570–585. https://doi.org/10.1037/0278-7393.32.3.570

      Taylor, R., Tomić, I., Aagten-Murphy, D., & Bays, P. M. (2023). Working memory is updated by reallocation of resources from obsolete to new items. Attention, Perception, & Psychophysics, 85(5), 1437–1451. https://doi.org/10.3758/s13414-022-02584-2

      Williams, M., & Woodman, G. F. (2012). Directed forgetting and directed remembering in visual working memory. Journal of Experimental Psychology. Learning, Memory, and Cognition, 38(5), 1206–1220. https://doi.org/10.1037/a0027389

      (6) Previous work, both from the authors and others, has shown that memories are biased as if they are acted on by attractive/repulsive forces. For example, the memory of an oriented bar is biased away from horizontal and vertical and biased towards diagonals. This is not accounted for in the current model. In particular, this could be one mechanism to generate a non-uniform drift rate over time. As noted in the paper, a non-uniform drift rate could capture many of the behavioral effects reported.

      The reviewer is correct that the model does not currently include stimulus-specific effects, although our work on that topic provides a clear template for incorporating them in future (e.g. Taylor & Bays, 2018). Specifically on the question of generating a non-uniform drift, we have another project that currently looks at this exact question (cited in our manuscript as Tomic, Girones, Lengyel, and Bays; in prep.). By examining various datasets with varying memory delays, including the Additional Dataset 1 reported in the Supplementary Information, we found that stimulus-specific effects on orientation recall remain constant with retention time. Specifically, although there is a clear increase in overall error over time, estimation biases remain constant in direction and amplitude, indicating that the bias does not manifest in drift rates (see also Rademaker et al., 2018; Figure S1).

      Taylor, R., & Bays, P. M. (2018). Efficient coding in visual working memory accounts for stimulus-specific variations in recall. The Journal of Neuroscience, 1018–18. https://doi.org/10.1523/JNEUROSCI.1018-18.2018

      Rademaker, R. L., Park, Y. E., Sack, A. T., & Tong, F. (2018). Evidence of gradual loss of precision for simple features and complex objects in visual working memory. Journal of Experimental Psychology: Human Perception and Performance. https://doi.org/10.1037/xhp0000491

      (7) Finally, the authors use AIC to compare many different model variants to the DyNR model. The delta-AICs are high (>10), indicating a strong preference for the DyNR model over the variants. However, the overall quality of fit to the data is not clear. What proportion of the variance in data was the model able to explain? In particular, I think it would be helpful for the reader if the authors reported the variance explained on withheld data (trials, conditions, or subjects).

      Thank you for this comment.

      Below we report the estimates of r2, representing the goodness of fit between observed data (i.e., RMSE) and the DyNR model predictions.

      In Experiment 1, the r2 values between observations and predictions were computed across delays for each set size, yielding the following estimates: r2ss1 = 0.60; r2ss4 = 0.87; r2ss10 = 0.95. Note that lower explained variance for set size 1 arises from both data and model predictions having near-constant precision.

      In Experiment 2, we calculated r2 between observations and predictions across presentation durations, separately for each set size, resulting in the following estimates: r2ss1 = 0.88; r2ss4 = 0.71; r2ss10 = 0.70. Note that in this case the decreasing percentage of explained variance with set size is a consequence of having less variability in both data and model predictions with larger set sizes.

      While these estimates suggest that the DyNR model effectively fits the psychophysical data, a more rigorous validation approach would involve cross-validation checks across all conditions with a withheld portion of trials. Regrettably, due to the large number of conditions in each experiment, we could only collect 50 trials per condition. We are sceptical that fitting the model to even fewer trials, as necessary for cross-validation, would provide a reliable assessment of model performance.

      Minor: It isn't clear to me why the behavioral tasks are shown in Figure 6. They are important for understanding the results and are discussed earlier in the manuscript (before Figure 3). This just required flipping back and forth to understand the task before I could interpret the results.

      Thank you for this comment. We have now moved the behavioural task figure to appear early in the manuscript (as Figure 3).

      Reviewer #3 (Recommendations For The Authors):

      (1) Dynamics of sensory signals during perception

      I believe that the modeled sensory signal is a reasonable simplification and different ways to model the decay function are discussed. I would like to ask the authors to discuss the implications of slightly more complex initial sensory transients such as the ones shown in Teeuwen (2021). Specifically for short exposure times, this might be particularly relevant for the model fits as some of the alternative models diverge from the data for short exposures. In addition, the role of feedforward (initial transient?) and feedback signaling (subsequent "plateau" activity) could be discussed. The first one might relate more strongly to sensory signals whereas the latter relates more to top-down attention/recurrent processing/VWM.

      Particularly, this latter response might also be sensitive to the number of items present on the screen which leads to a related question pertaining to the limitations of attention during perception. Some work suggests that perception is similarly limited in the amount of information that can be represented concurrently (Tsubomi, 2013). Could the authors discuss the implications of this hypothesis? What happens if maximum sensory amplitude is set as a free parameter in the model?

      Tsubomi, H., Fukuda, K., Watanabe, K., & Vogel, E. K. (2013). Neural limits to representing objects still within view. Journal of Neuroscience, 33(19), 8257-8263.

      Thank you for this question. Below, we unpack it and answer it point by point.

      While we agree our model of the sensory response is justified as an idealization of the biological reality, we also recognise that recent electrophysiological recordings have illuminated intricacies of neuronal responses within the striate cortex, a critical neural region associated with sensory memory (Teeuwen et al, 2021). Notably, these recordings reveal a more nuanced pattern where neurons exhibit an initial burst of activity succeeded by a lower plateau in firing rate, and stimulus offset elicits a second small burst in the response of some neurons, followed by a gradual decrease in activity after the stimulus disappears (Teeuwen et al, 2021).

      In general, asynchronous bursts of activity in individual neurons will tend to average out in the population making little difference to predictions of the DyNR model. Synchronized bursts at stimulus onset could affect predictions for the shortest presentations in Exp 2, however the model appears to capture the data very well without including them. We would be wary of incorporating these phenomena into the model without more clarity on their universality (e.g., how stimulus-dependent they are), their significance at the population level (as opposed to individual neurons), and most importantly, their prominence in visual areas outside striate cortex. Specifically, while Teeuwen et al. (2021) described activity in V1, our model does not make strong assumptions about which visual areas are the source of the sensory input to WM. Based on these uncertainties we believe the idealized sensory response is justified for use in our model.

      Next, thank you for the comment on feedforward and feedback signals. We have added the following to our manuscript:

      “Following onset of a stimulus, the visual signal ascends through visual areas via a cascade of feedforward connections. This feedforward sweep conveys sensory information that persists during stimulus presentation and briefly after it disappears (Lamme et al., 1998). Simultaneously, reciprocal feedback connections carry higher-order information back towards antecedent cortical areas (Lamme and Roelfsema, 2000). In our psychophysical task, feedback connections likely play a critical role in orienting attention towards the cued item, facilitating the extraction of persisting sensory signals, and potentially signalling continuous information on the available resources for VWM encoding. While our computational study does not address the nature of these feedforward and feedback signals, a challenge for future research is to describe the relative contributions of these signals in mediating transmission of information between sensory and working memory (Semedo et al., 2022).”

      Lamme, V. A., Supèr, H., & Spekreijse, H. (1998). Feedforward, horizontal, and feedback processing in the visual cortex. Current Opinion in Neurobiology, 8(4), 529–535. https://doi.org/10.1016/S0959-4388(98)80042-1

      Lamme, V. A. F., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11), 571–579. https://doi.org/10.1016/S0166-2236(00)01657-X

      Semedo, J. D., Jasper, A. I., Zandvakili, A., Krishna, A., Aschner, A., Machens, C. K., Kohn, A., & Yu, B. M. (2022). Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nature Communications, 13(1), 1099. https://doi.org/10.1038/s41467-022-28552-w

      Finally, both you and Reviewer 2 raised a similar interesting question regarding capacity limitations of attention during perception Such a limitation could be modelled by freely estimating sensory amplitude and implementing divisive normalization to that signal, similar to how VWM is constrained. We can consider two potential mechanisms through which divisive normalization might be incorporated into sensory processing within the DyNR model.

      The first possibility involves assuming that normalization is pre-attentive. In this scenario, the sensory activity of each object would be rescaled at the lowest level of sensory processing, occurring before the allocation of attentional or VWM resources. One strong prediction of such an implementation is that recall error in the simultaneous cue condition (Experiment 1) should vary with set size. However, this prediction is inconsistent with the observed data, which failed to show a significant difference between set sizes, and is more closely aligned with the hypothesis of no-difference (F(2,18) = 1.26, p = .3, η2 = .04, BF10 = 0.47). On that basis, we anticipate that introducing normalization as a pre-attentive mechanism would impair the model fit.

      An alternative scenario is to consider normalization as post-attentive. In the simultaneous cueing condition, only one item is attended (i.e., the cued one), regardless of the displayed set size. Here, we would expect normalized activity for a single item, regardless of the number of presented objects, which would then be integrated into VWM. This expanded DyNR model with post-attentive normalization would make exactly the same predictions as the proposed DyNR for recall fidelity, so distinguishing between these models would not be possible based on working memory experiments.

      To acknowledge the possibility that sensory signals could undergo divisive normalization and to motivate future research, we have added the following to our manuscript:

      “As well as being implicated in higher cognitive processes including VWM (Buschman et al, 2011; Sprague et al., 2014), divisive normalization has been shown to be widespread in basic sensory processing (Bonin et al., 2005; Busse et al., 2009; Ni et al., 2017). The DyNR model presently incorporates the former but not the latter type of normalization. While the data observed in our experiments do not provide evidence for normalization of sensory signals (note comparable recall errors across set size in the simultaneous cue condition of Experiment 1), this may be because sensory suppressive effects are localized and our stimuli were relatively widely separated in the visual field: future research could explore the consequences of sensory normalization for recall from VWM using, e.g., centre-surround stimuli (Bloem et al., 2018).”

      Bloem, I. M., Watanabe, Y. L., Kibbe, M. M., & Ling, S. (2018). Visual Memories Bypass Normalization. Psychological Science, 29(5), 845–856. https://doi.org/10.1177/0956797617747091

      Bonin, V., Mante, V., & Carandini, M. (2005). The Suppressive Field of Neurons in Lateral Geniculate Nucleus. The Journal of Neuroscience, 25(47), 10844–10856. https://doi.org/10.1523/JNEUROSCI.3562-05.2005

      Buschman, T. J., Siegel, M., Roy, J. E., & Miller, E. K. (2011). Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences, 108(27), 11252–11255. https://doi.org/10.1073/pnas.1104666108

      Busse, L., Wade, A. R., & Carandini, M. (2009). Representation of Concurrent Stimuli by Population Activity in Visual Cortex. Neuron, 64(6), 931–942. https://doi.org/10.1016/j.neuron.2009.11.004

      Ni, A. M., & Maunsell, J. H. R. (2017). Spatially tuned normalization explains attention modulation variance within neurons. Journal of Neurophysiology, 118(3), 1903–1913. https://doi.org/10.1152/jn.00218.2017

      Sprague, T. C., Ester, E. F., & Serences, J. T. (2014). Reconstructions of Information in Visual Spatial Working Memory Degrade with Memory Load. Current Biology, 24(18), 2174–2180. https://doi.org/10.1016/j.cub.2014.07.066

      (2) Effectivity of retro-cues at long delays

      Can the authors discuss how cues presented at long delays (>1000 ms) can still lead to increased memory fidelity when sensory signals are likely to have decayed? A list of experimental work demonstrating this can be found in Souza & Oberauer (2016).

      Souza, A. S., & Oberauer, K. (2016). In search of the focus of attention in working memory: 13 years of the retro-cue effect. Attention, Perception, & Psychophysics, 78, 1839-1860.

      The increased memory fidelity observed with longer delays between memory array offset and cue does not result from integrating available sensory signals into VWM because the sensory signal would have completely decayed by that time. Instead, research so far has indicated several alternative mechanisms that could lead to higher recall precision for cued items, and we can briefly summarize some of them, which are also reviewed in more detail in Souza and Oberauer (2016).

      One possibility is that, after a highly predictive retro-cue indicates the to-be-tested item, uncued items can simply be removed from VWM. This could result in decreased interference for the cued item, and consequently higher recall precision. Secondly, the retro-cue could also indicate which item can be selectively attended to, and thereby differentially strengthening it in memory. Furthermore, the retro-cue could allow evidence to accumulate for the target item ahead of decision-making, and this could increase the probability that the correct information will be selected for response. Finally, the retro-cued stimulus could be insulated from interference by subsequent visual input, while the uncued stimuli may remain prone to such interference.

      A neural account of this retro-cue effect based on the original neural resource model has been proposed in Bays & Taylor, Cog Psych, 2018. However, as we did not use a retro-cue design in the present experiments, we have decided not to elaborate on this in the manuscript.

      (3) Swap errors

      I am somewhat surprised by the empirically observed and predicted pattern of swap errors displayed in Figure S2. For set size 10, swap probability does not consistently increase with the duration of the retention interval, although this was predicted by the author's model. At long intervals, swap probability is significantly higher for large compared to small set sizes, which also seems to contrast with the idea of shared, limited VWM resources. Can the authors provide some insight into why the model fails to reproduce part of the behavioral pattern for swap errors? The sentence in line 602 might also need some reconsideration in this regard.

      Determining the ground truth for swap errors poses a challenge. The prevailing approach has been to employ a simpler model that estimates swap errors, such as a three-component mixture model, and use those estimates as a proxy for ground truth. However, this method is not without its shortcomings. For example, the variability of swap frequency estimates tends to increase with variability in the report feature dimension (here, orientation). This is due to the increasing overlap of response probability distributions for swap and non-swap responses. Consequently, the discrepancy between any two methods of swap estimation is most noticeable when there is substantial variability in orientation reports (e.g., 10 items and long delay or short exposure).

      When modelling swap frequency in the DyNR model, our aim was to provide a parsimonious account of swap errors while implementing similar dynamics in the spatial (cue) feature as in the orientation (report) feature. This parametric description captured the overall pattern of swap frequency with set size and retention and encoding time, but is still only an approximation of the predictions if we fully modelled memory for the conjunction of cue and report features (as in e.g. Schneegans & Bays, 2017; McMaster et al, 2020).

      We expanded the existing text in the section ‘Representational dynamics of cue-dimension features’ of our manuscript:

      “… Although we did not explicitly model the neural signals representing location, the modelled dynamics in the probability of swap errors were consistent with those of the primary memory feature. We provided a more detailed neural account of swap errors in our earlier works that is theoretically compatible with the DyNR model (McMaster et al., 2020; Schneegans & Bays, 2017).

      The DyNR model successfully captured the observed pattern of swap frequencies (intrusion errors). The only notable discrepancy between DyNR and the three-component mixture model (Fig. S2) arises with the largest set size and longest delay, although with considerable interindividual variability. As the variability in report-dimension increases, the estimates of swap frequency become more variable due to the growing overlap between the probability distributions of swap and non-swap responses. This may explain apparent deviations from the modelled swap frequencies with the highest set size and longest delay where orientation response variability was greatest. “

      McMaster, J. M. V., Tomić, I., Schneegans, S., & Bays, P. M. (2022). Swap errors in visual working memory are fully explained by cue-feature variability. Cognitive Psychology, 137, 101493. https://doi.org/10.1016/j.cogpsych.2022.101493

      Schneegans, S., & Bays, P. M. (2017). Neural Architecture for Feature Binding in Visual Working Memory. The Journal of Neuroscience, 37(14), 3913–3925. https://doi.org/10.1523/JNEUROSCI.3493-16.2017

      (4) Direct sensory readout

      The model assumes that readout from sensory memory and from VWM happens with identical efficiency. Currently, we don't know if these two systems are highly overlapping or are fundamentally different in terms of architecture and computation. In the case of the latter, it might be less reasonable to assume that information readout would happen at similar efficiencies, as it is currently assumed in the manuscript. Perhaps the authors could briefly discuss this possibility.

      In the direct sensory read-out model, we did not explicitly model the efficiency of readout from either sensory or VWM store. However, the distinctive prediction of this model is that the precision of recall changes exponentially with delay at every set size, including one item. This prediction does not depend on the relative efficiency of readout from sensory and working memory, but only on the principle that direct readout from sensory memory bypasses the capacity limit on working memory. This prediction is inconsistent with the pattern of results observed in Experiment 1, where early cues did not show a beneficial effect on recall error for set size 1. While the proposal raised by the reviewer is intriguing, even if we were to model the process of readout from both the sensory and VWM stores with different efficiencies, the direct read-out model could not account for the near-constant recall error with delay for set size one.

      (5) Encoding of distractors

      One of the model assumptions is that, for simultaneous presentations of memory array and cue only the cued feature will be encoded. Previous work has suggested that participants often accidentally encode distractors even when they are cued before memory array onset (Vogel 2005). Given these findings, how reasonable is this assumption in the authors' model?

      Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438(7067), 500-503.

      Although previous research suggested that observers can misinterpret the pre-cue and encode one of the uncued items, our results argue against this being the case in the current experiment. Such encoding failures would manifest in overall recall error, resulting in a gradient of error with set size, owing to the presence of more adjacent distractors in larger set sizes. However, when we compared recall errors between set sizes in the simultaneous cue condition, we did not find a significant difference between set sizes, and moreover, our results were more likely under the hypothesis of no-difference (F(2,18) = 1.26, p = .3, η2 = .04, BF10 = 0.47). If observers occasionally encoded and reported one of the uncued items in the simultaneous cue condition, those errors were extremely infrequent and did not affect the overall error distributions.

    1. Author Response

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Zeng and Staley provide a valuable analysis of the molecular requirements for the export of a reporter mRNA that contains a lariat structure at its 5' end in the budding yeast S. cerevisiae. The authors provide evidence that this is regulated by the main mRNA export machinery (Yra1, Mex67, Nab2, Npl3, Tom1, and Mlp1). Of note, Mlp1 has been mainly implicated in the nuclear retention of unspliced pre-mRNA (i.e. quality control), and relatively little has been done to investigate its role in mRNA export in budding yeast.

      Strengths:

      There is relatively little information in the current literature about the nuclear export of splicing intermediates. This paper provides one of the first analyses of this process and dissects the molecular components that promote this form of RNA export. Overall, the strength of the data presented in the manuscript is solid. The paper is well written and the message is clear and of general interest to the mRNA community.

      We thank the reviewer for highlighting these strengths.

      Weaknesses:

      There are three problems with the paper, although these are not major and likely would not affect the final model as most aspects of the molecular details are confirmed by multiple complementary assays.

      (1) The brG reporter produces both unspliced pre-mRNA and a lariat-containing intermediate RNA. Based on the primer extension assay the authors claim that only 33% of the final product is in pre-mRNA form and that this "is insufficient to account for the magnitude of the cytoplasmic signal from the brG reporter (83%)". Nevertheless, it is possible that primer extension is incomplete or that the lariat-containing RNA is inaccessible for smFISH. The authors could easily perform a dual smFISH experiment (similar to Adivarahan et l., Molecular Cell 2018) where exon 1 is labelled with probes of one color, and the region that overlaps the lariat-containing intermediate is labelled with probes of a second color. If the authors are correct, then one-third of the smFISH foci should have both labels and the rest would have only the second label. This would also confirm that the latter (i.e. the lariat-containing RNAs) are exported to the cytoplasm. Using this approach, the authors could then show that MLP1-depletion (or depletion of any of the other factors) affect(s) one pool of RNAs (i.e. those that are lariat-containing) but not the other (i.e. pre-mRNA). Including these experiments would make the evidence for their model more convincing.

      We appreciate the reviewer’s comments and suggestions. Concerning the primer extension analysis, we are considering alternative assays to quantitate the pre-mRNA and lariat intermediate levels. Concerning the accessibility of the lariat intermediate in smRNA-FISH, in a dbr1∆ strain the only major species from the UAc reporter that is detected by primer extension is the lariat intermediate (Fig. S3), and this reporter is readily detected by smRNA-FISH, indicate that the lariat intermediate is accessible to smRNA-FISH. Concerning discriminating between pre-mRNA and lariat intermediate by smRNA-FISH, we agree with the reviewer that a dual smFISH experiment would directly distinguish between the signals of these species. The brG reporter we used in most smRNA-FISH experiments has a 5’ exon that is too short for smRNA-FISH probes, as is typical of most budding yeast 5’ exons. We have tried to replace the 5’ exon with a longer sequence (GFP) to allow for smRNA-FISH; however, this substitution inhibited splicing. Therefore, to distinguish signals from pre-mRNA versus lariat intermediate, we used additional reporters: G1c and brC reporters, which accumulate pre-mRNA essentially exclusively (Fig. S2A-C), and the UAc reporter, which accumulates lariat intermediate exclusively, in a dbr1∆ strain (Fig. S3). Whereas the mlp1 deletion did not change beta-galactosidase activities of the G1c and brC pre-mRNA-accumulating reporters (Fig. S2E), the mlp1 deletion in a dbr1∆ background did reduce the beta-galactosidase activities of the UAc lariat intermediate-accumulating reporter (Fig. 3D) and did increase smRNA-FISH signal of this reporter in the nucleus (Fig. 3E). These observations corroborate our interpretation based on the brG reporter that Mlp1p is required for efficient export of lariat intermediates but not pre-mRNAs.

      (2) In some cases, the number of smFISH foci appears to change drastically depending on the genetic background. This could either be due to the stochastic nature of mRNA expression between cells or reflect real differences between the genetic backgrounds that could alter the interpretation of the other observations.

      We thank the reviewer for raising this point. We will review our data to distinguish between these possibilities.

      (3) The authors state in the discussion that "the general mRNA export pathway transports discarded lariat intermediates into the cytoplasm". Although this appears to be the case for the reporters that are investigated in this paper, I don't think that the authors should make such a broad sweeping claim. It may be that some discarded lariat intermediates are exported to the cytoplasm while others are targeted for nuclear retention and/or decay.

      The reviewer’s point is well-taken. We will revise the wording accordingly.

      Reviewer #2 (Public Review):

      In this report, Zeng and Staley have used an elegant combination of RNA imaging approaches (single molecule FISH), RNA co-immunoprecipitations, and translation reporters to characterize the factors and pathways involved in the nuclear export of splicing intermediates in budding yeast. Their study notably involves the use of specific reporter genes, which lead to the accumulation of pre-mRNA and lariat species, in a battery of mutants impacting mRNA export and quality control.

      The authors convincingly demonstrate that mRNA species expressed from such reporters are exported to the cytoplasm in a manner depending on the canonical mRNA export machinery (Mex67 and its adaptors) and the nuclear pore complex (NPC) basket (Mlp1). Interestingly, they provide evidence that the export of splicing intermediates requires docking and subsequent undocking at the nuclear basket, a step possibly more critical than for regular mRNAs.

      We thank the reviewer for this overall positive assessment.

      However, their assays do not always allow us to define whether the impacted mRNA species correspond to lariats and/or pre-mRNAs. This is all the more critical since their findings apparently contradict previous reports that supported a role for the nuclear basket in pre-mRNA quality control. These earlier studies, which were similarly based on the use of dedicated yet distinct reporters, had found that the nuclear basket subunit Mlp1, together with different cofactors, prevents the export of unspliced mRNA species. It would be important to clarify experimentally and discuss the possible reasons for these discrepancies.

      It is true that we did not assess export of all reporters in all mutant strains by smFISH; however, we did validate the key conclusion that the export of lariat intermediates requires the nuclear basket gene MLP1: the export of both the brG reporter (mostly lariat intermediate) and the UAc reporter (exclusively lariat intermediate) showed a dependence on MLP1 (Fig. 3). Further, by beta-galactosidase activity, we tested in total five separate reporters – three that accumulated lariat intermediate and two that accumulated exclusively pre-mRNA; only the three reporters accumulating lariat intermediate showed a dependence of export on MLP1 (Fig. 4B,D; Fig S2D); the reporters accumulating pre-mRNA did not show a dependence on MLP1 (Fig. S2E), further validating our main conclusion. We are considering additional experiments to validate this key conclusion even further. Also, see response to comment 1 from reviewer 1.

      We agree that the main conclusion from this manuscript differs from earlier studies. A key difference is that prior studies monitored exclusively pre-mRNA. In our study, we monitored pre-mRNA and lariat intermediate species and in doing so revealed a role for MLP1 in the export of lariat intermediates. This study, our previous study, as well as the previous studies of others have all provided evidence for efficient export of pre-mRNA; all of these studies are in conflict with the studies purporting a general role for the nuclear basked in retaining immature mRNA. Still, these past apparently conflicting studies can be re-interpreted in the context of our model that the export of such species requires docking at the nuclear basket, followed by undocking. In a revised manuscript, we will discuss the possibility that pre-mRNA apparently “retained” by the nuclear basket are stalled in export at the undocking stage.

      Reviewer #3 (Public Review):

      Summary:

      Zeng and Stanley show that in yeast, intron-lariat intermediates that accumulated due to defects in pre-mRNA splicing, are transported to the cytoplasm using the canonical mRNA export pathway. Moreover, they demonstrate that export requires the nuclear basket, a sub-structure of the nuclear pore complex previously implicated with the retention of immature mRNAs. These observations are important as they put into question a longstanding model that the main role of the nuclear basket is to ensure nuclear retention of immature or faulty mRNAs.

      Strengths:

      The authors elegantly combine genetic, biochemical, and single-molecule resolution microscopy approaches to identify the cellular pathway that mediates the cytoplasmic accumulation of lariat intermediates. Cytoplasmic accumulation of such splicing intermediates had been observed in various previous studies but how these RNAs reach the cytoplasm had not yet been investigated. By using smFISH, the authors present compelling, and, for the first time, direct evidence that these intermediates accumulate in the cytoplasm and that this requires the canonical mRNA export pathway, including the RNA export receptor Mex67 as well as various RNA-binding proteins including Yra1, Npl3 and Nab2. Moreover, they show that the export of lariat intermediates, but not mRNAs, requires the nuclear basket (Mlp1) and basket-associated proteins previously linked to the mRNP rearrangements at the nuclear pore. This is a surprising and important observation with respect to a possible function of the nuclear basket in mRNA export and quality control, as it challenges a longstanding model that the role of the basket in mRNA export is primarily to act as a gatekeeper to ensure that immature mRNAs are not exported. As discussed by the authors, their finding suggests a role for the basket in promoting the export of certain types of RNAs rather than retention, a model also supported by more recent studies in mammalian cells. Moreover, their findings also collaborate with a recent paper showing that in yeast, not all nuclear pores contain a basket (PMID: 36220102), an observation that also questioned the gatekeeper model of the basket, as it is difficult to imagine how the basket can serve as a gatekeeper if not all nuclear pore contain such a structure.

      We thank the reviewer for highlighting the importance and surprising nature of our findings.

      Weaknesses:

      One weakness of this study is that all their experiments rely on using synthetic splicing reporter containing a lacZ gene that produces a relatively long transcript compared to the average yeast mRNA.

      We are considering repeating some of our experiments to monitor export of RNAs with more average lengths.

      The rationale for using a reporter containing the brG (G branch point) resulting in more stable lariat intermediates due to them being inefficient substrates for the debranching enzyme Dbr1 could be described earlier in the manuscript, as this otherwise only becomes clear towards the end, what is confusing.

      We thank the reviewer for this comment. We will revise the text to explain sooner the rationale for using the brG reporter to assess the export of lariat intermediates.

      Discussion of their observation in the context that, in yeast, not all pores contain a basket would be useful.

      Thanks for this suggestion. We will raise this point that a nuclear basket is not present on all nuclear pores and discuss the implications.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Weaknesses:

      The weaknesses are the brevity of the simulations, the concomitant lack of scope of the simulations, the lack of depth in the analysis, and the incomplete relation to other relevant work.

      A 1 µs simulation of CCh (Video 1, part 2) shows that m3 (ACHA) is stable, throughout. The DG comparisons, in silico versus in vitro, indicate that 200 ns simulations are sufficient to identify LA versus HA conformational populations. Figure 6-table supplement 1 shows distances. New citations have been added.

      Reviewer #2 (Public Review):

      Weaknesses:

      After carrying out all-atom molecular dynamics, the authors revert to a model of binding using continuum Poisson-Boltzmann, surface area, and vibrational entropy. The motivations for and limitations associated with this approximate model for the thermodynamics of binding, rather than using modern atomistic MD free energy methods (that would fully incorporate configurational sampling of the protein, ligand, and solvent) could be provided. Despite this, the authors report a correlation between their free energy estimates and those inferred from the experiment. This did, however, reveal shortcomings for two of the agonists. The authors mention their trouble getting correlation to experiment for Ebt and Ebx and refer to up to 130% errors in free energy. But this is far worse than a simple proportional error, because -24 Vs -10 kcal/mol is a massive overestimation of free energy, as would be evident if the authors were to instead express results in terms of KD values (which would have an error exceeding a billion fold). The MD analysis could be improved with better measures of convergence, as well as a more careful discussion of free energy maps as a function of identified principal components, as described below. Overall, however, the study has provided useful observations and interpretations of agonist binding that will help understand pentameric ligand-gated ion channel activation.

      The objective of the calculations was to identify structural populations, not to estimate binding free energies. We knew the actual LA and HA energies (for all 4 agonists) from real-world electrophysiology experiments. We conclude that the simple PBSA method worked as a tool for identification because the calculated efficiencies match those from experiments (Figure 4B, Figure 4-Source Data 1). We discuss the mismatches in absolute G in the Results and Discussion. Methods for estimating experimental binding free energies are described in a cited, eLife companion paper. The G ratio relates to agonist efficiency.

      Main points:

      Regarding the choice of model, some further justification of the reduced 2 subunit ECD-only model could be given. On page 5 the authors argue that, because binding free energies are independent of energy changes outside the binding pocket, they could remove the TMD and study only an ECD subunit dimer. While the assumption of distant interactions being small seems somewhat reasonable, provided conformational changes are limited and localised, how do we know the packing of TMD onto the ECD does not alter the ability of the alpha-delta interface to rearrange during weak or strong binding? They further write that "fluctuations observed at the base of the ECD were anticipated because the TMD that offers stability here was absent.". As the TMD-ECD interface is the "gating interface" that is reshaped by agonist binding, surely the TMD-ECD interface structure must affect binding. It seems a little dangerous to completely separate the agonist binding and gating infrastructure, based on some assumption of independence. Given the model was only the alpha and delta subunits and not the pentamer with TMD, I am surprised such a model was stable without some heavy restraints. The authors state that "as a further control we carried out MD simulation of a pentamer docked with ACh and found similar structural changes at the binding pocket compared to the dimer." Is this sufficient proof of the accuracy of the simplified model? How similar was the model itself with and without agonist in terms of overall RMSD and RMSD for the subunit interface and the agonist binding site, as well as the free energy of binding to each model to compare?

      The statement that distant interactions are small is not an "assumption", but rather a conclusion based on data. Mutant cycle analysis of 83 pairs shows (with a few exceptions) non-additivity of free energy change prevails only with separations <~15 A (Fig.3 in Gupta et al 2017). Regardless, the adequacy of dimers and convergence by 200 ns are supported by the calculated and experimental agonist efficiencies match (Figure 4B) and the 1 ms simulation (Video 1 part 2). Apo 200ns simulation of the ECD dimer is now added (Figure 2-figure supplement 2) and the dimer interface seems to be adequate (stable).

      Although the authors repeatedly state that they have good convergence with their MD, I believe the analysis could be improved to convince us. On page 8 the authors write that the RMSD of the system converged in under 200 ns of MD. However, I note that the graph is of the entire ECD dimer, not a measure for the local binding site region. An additional RMSD of local binding site would be much more telling. You could have a structural isomerisation in the site and not even notice it in the existing graph. On page 9 the authors write that the RMSF in Figure S2 showed instability mainly in loops C and F around the pocket. Given this flexibility at the alpha-delta interface, this is why collecting those regions into one group for the calculation of RMSD convergence analysis would have been useful. They then state "the final MD configuration (with CCh) was well-aligned with the CCh-bound cryo-EM desensitized structure (7QL6)... further demonstrating that the simulation had converged." That may suggest a change occurred that is in common with the global minimum seen in cryo EM, which is good, but does not prove the MD has "converged". I would also rename Figure S3 accordingly.

      The description is now changed to “aligns well” with desensitized structure (7QL6.PDB)”. RMSD of not just the binding pocket but the whole ECD dimer is well aligned with first apo (m1) and with desensitized state (m3).

      The authors draw conclusions about the dominant states and pathways from their PCA component free energy projections that need clarification. It is important first to show data to demonstrate that the two PCA components chosen were dominant and accounted for most of the variance. Then when mapping free energy as a function of those two PCA components, to prove that those maps have sufficient convergence to be able to interpret them. Moreover, if the free energies themselves cannot be used to measure state stability (as seems to be the case), that the limitations are carefully explained. First, was PCA done on all MD trajectories combined to find a common PC1 & PC2, or were they done separately on each simulation? If so, how similar are they? The authors write "the first two principal components (PC-1 and PC-2) that capture the most pronounced C. displacements". How much of the total variance did these two components capture? The authors write the changes mostly concern loop C and loop F, but which data proves this? e.g. A plot of PC1 and PC2 over residue number might help.

      The PCA analyses have been enriched. Figure 3-Source Data 1. shows the dominance of PC1 and PC2. Because the binding energy match was sufficient to identify affinity states, we did not explore additional PCs. Residue-wise PC1 and PC2 analysis and comparison with RMSF are in Figure 2-figure supplement 2. PC1 and PC2 both correlate with fluctuations in loops C and F. Overlap analysis in different runs is shown in Figure 3-figure supplement 1. Lower variance in a particular region of the PCA landscape indicates that the system frequently visits these states, suggesting stability (a preference for these conformations).

      The authors map the -kTln rho as a free energy for each simulation as a function of PC1 & PC2. It is important to reveal how well that PC1-2 space was sampled, and how those maps converged over time. The shapes of the maps and the relative depths of the wells look very different for each agonist. If the maps were sampled well and converged, the free energies themselves would tell us the stabilities of each state. Instead, the authors do not even mention this and instead talk about "variance" being the indicator of stability, stating that m3 is most stable in all cases. While I can believe 200ns could not converge a PC1-2 map and that meaningful delta G values might not be obtained from them, the issue of lack of sampling must be dealt with. On page 12 they write "Although the bottom of the well for 3 energy minima from PCA represent the most stable overall conformation of the protein, they do not convey direct information regarding agonist stability or orientation". The reasons why not must be explained; as they should do just that if the two order parameters PC1 and PC2 captured the slowest degrees of freedom for binding and sampling was sufficient. The authors write that "For all agonists and trajectories, m3 had the least variance (was most stable), again supporting convergence by 200 ns." Again the issue of actual free energy values in the maps needs to be dealt with. The probabilities expressed as -kTln rho in kcal/mol might suggest that m2 is the most stable. Instead, the authors base stability only on variance (I guess breadth of the well?), where m3 may be more localised in the chosen PC space, despite apparently having less preference during the MD (not the lowest free energy in the maps).

      The motivations and justifications for the use of approximate PBSA energetics instead of atomistic MD free energies should be dealt with in the manuscript, with limitations more clearly discussed. Rather than using modern all-atom MD free energy methods for relative or absolute binding free energies, the author selects clusters from their identified states and does Poisson-Boltzmann estimates (electrostatic, vdW, surface area, vibrational entropy). I do believe the following sentence does not begin to deal with the limitations of that method: "there are limitations with regard to MM-PBSA accurately predicting absolute binding free energies (Genheden & Ryde, 2015; Hou et al., 2011) that depends on the parameterization of the ligand (Oostenbrink et al., 2004)." What are the assumptions and limitations in taking continuum electrostatics (presumably with parameters for dielectric constants and their assignments to regions after discarding solvent), surface area (with its assumptions and limitations), and of course assuming vibration of a normal mode can capture entropy. On page 30, regarding their vibrational entropy estimate, they write that the "entropy term provides insights into the disorder within the system, as well as how this disorder changes during the binding process". It is important that the extent of disorder captured by the vibrational estimate be discussed, as it is not obvious that it has captured entropy involving multiple minima on the system's true 3N-dimensional energy surface, and especially the contribution from solvent disorder in bound Vs dissociated states.

      As discussed above, errors in the free energy estimates need to be more faithfully represented, as fractional errors are not meaningful. On page 21 the authors write "The match improved when free energy ratios rather than absolute values were compared." But a ratio of free energies is not a typical or expected measure of error in delta G. They also write "For ACh and CCh, there is good agreement between.Gm1 and GLA and between.Gm3 and GHA. For these agonists, in silico values overestimated experimental ones only by ~8% and ~25%. The agreement was not as good for the other 2 agonists, as calculated values overestimated experimental ones by ~45%(Ebt) and ~130% (Ebt). However, the fractional overestimation was approximately the same for GLA and GHA." See the above comment on how this may misrepresent the error. On page 21 they write, in relation to their large fractional errors, that they "do not know the origin of this factor but speculate that it could be caused by errors in ligand parameterization". However the estimates from the PBSA approach are, by design, only approximate. Both errors in parameterisation (and their likely origin) and the approximate model used, need discussion.

      Again, the goal of calculating binding free energy was to identify structural correspondence to LA and HA and not to obtain absolute binding free energy values. Along with the least variance (distribution) for the principle component for m3, it also had the highest binding free energy. An association of m1 to LA and m3 to HA was done after comparing them to experimental values (efficiencies). This comparison not only validates our approach but also underscores the utility of PBSA in supplementing MD and PCA analyses with broader energetics perspectives.

      Reviewer #3 (Public Review):

      Weaknesses:

      Although the match in simulated vs experimental energies for two ligands was very good, the calculated energies for two other ligands were significantly different than the experiment. It is unclear to what extent the choice of method for the energy calculations influenced the results. See above.

      A control simulation, such as for an apo site, is lacking. Figure 2-figure supplement 2. shows the results of 200 ns MD simulations of the apo structure (n=2).

      Reviewer #4 (Public Review):

      Weaknesses:

      Timescales (200 ns) do not capture global rearrangements of the extracellular domain, let alone gating transitions of the channel pore, though this work may provide a launching point for more extended simulations. A more general concern is the reproducibility of the simulations, and how representative states are defined. It is not clear whether replicates were included in principal component analysis or subsequent binding energy calculations, nor how simulation intervals were associated with specific states.

      We are interested eventually in using MD to study the full isomerization, but these investigations are for the future and likely will involve full length pentamers and longer timescales. However, in response to this query we have in the Discussion raised this issue and offer speculations. See above, PCA has be compared between replicates (Figure 3-figure supplement 1).

      Structural analysis largely focuses on snapshots, with limited direct evidence of consistency across replicates or clusters. Figure legends and tables could be clarified.

      Snapshots and distance measurements (Figure 6-table supplement 1) were extracted from m1, m2 and m3 plateau regions of trajectories. Incorporated in the legend.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      This study gives interesting insights into the possible dynamics of ligand binding in ACh receptors and establishes some prerequisites for necessary and urgent further work. The broad interest in this receptor class means this work will have some reach.

      Suggestions:

      (1) I found the citation of relevant literature to be rather limited. In the following paper, the agonist glutamate was shown to bind in two different orientations, and also to convert. These are much longer simulations than what is presented here (nearly 50 µs), which allowed a richer view of conformational changes and ligand binding dynamics in the AMPA Receptor. Albert Lau has published similar work on NMDA, delta, and kainate receptors, including some of it in eLife. Perhaps the authors could draw some helpful comparisons with this work.

      Yu A et al. (2018) Neurotransmitter Funneling Optimizes Glutamate Receptor Kinetics. Neuron

      Likewise, the comparison to a similar piece of work on glycine receptors (not cited, https://pubs.acs.org/doi/10.1021/bi500815f) could be instructive. Several similar computational techniques were used, and interactions observed (in the simulations) between the agonist and the receptor were tested in the context of wet experiments. In the absence of an equivalent process in this paper (no findings were tested using an orthogonal approach, only compared against known results, from perhaps a narrow spectrum of papers), we have to view the major findings of the paper (docking in cis that leads to a ligand somersault) with some hesitancy.

      The Gharpure 2019 paper is cited in the context of the delta subunit but this paper was about a3b4 neuronal nicotinic receptors. This could be tidied up. Also, the simulations from that paper could be used as an index of the stability of the HA state (if ligand orientation is being cited as transferrable, other observations could be too).

      New citations have been added. It is difficult to generalize from Yu A and Yu R eta al, because in neither study was the ligand orientation associated with LA versus HA binding energy.

      (2) "To start, we associated the agonist orientation in the hold end states as cis in AC-LA versus trans in AC-HA."

      I think this a valid start, but one is left with the feeling that this is all we have and the validity of the starting state is not tested. What was really shown here? Is the docking reliable? What evidence can the authors summon for the ligand orientation that they use as a starting structure? In addition to docking energies, the match between PBSA and electrophysiology Gs and temporal sequence (m1-m2-m3) support the assignment.

      Given that these simulations cover a circumscribed part of the binding process, I think the limitations should be acknowledged. Indeed the authors do mention a number of remaining open questions.

      Paragraphs regarding 'catch' have been added to the Discussion.

      (3) Results around line 90. Hypothetical structures and states that were determined from Markov analyses are discussed as if they are well understood and identified. Plausible though these are, I think the text should underline at least the source of such information. In these simulations, a further intermediate has been identified.

      The model in Figure 1B was first published in 2012 and has been used and extended over the intervening years. In our lab, catch-and-hold is standard. We have published many papers (in top journals), plus reviews, regarding this scheme. We made presentations that are on Youtube. Here, at the end of the Introduction we now cite a new review article (Biophysical Journal, 2024). I am not sure what more we can do to raise awareness regarding catch and hold.

      (4) The figures are dense and could be better organised. Figure 2 is key but has a muddled organization. The placement of the panel label (C) makes it look like the top row (0 ns) is part of (A). Panel B- what is shown in the oval inset (not labeled or in legend). Why not show more than one view, perhaps a sequence of time points? It is confusing to change the colour of the loops in (C). Please show the individual values in D.

      Figure 2 has been redone.

      (5) A lot is made of the aK145 salt bridge with aD200 and the distances - but I didn't see any measurements, or time course. This part is vague to the point of having no meaning ("bridge tightening").

      We present a Table of distance measurements in the SI (Figure 6-table supplement 1).

      Reviewer #2 (Recommendations For The Authors):

      All main comments have been given in the above review. There are a few other minor comments below.

      The 4 agonists examined were acetylcholine (ACh), carbamylcholine (CCh), epibatidine (Ebt), and epiboxidine (Ebx). Could the choices be motivated for the reader?

      New in Methods: the agonists are about the same size yet represent different efficiency classes (citation to companion eLife paper). One of our (unmet) objectives was to understand the structural correlates of agonist efficiency.

      The authors write that state structures generated in the MD simulation were identified by aligning free energy values with those from experiments. It would be good to explain to the reader, in the introduction, how LA and HA free energies were extracted from experiments, rather than relying on them to read older papers.

      In the Introduction, we say that to get G, just measure an equilibrium constant and take the log. We think it is excessive to explain in detail in this paper how to measure the equilibrium binding constants (several methods suffice). However, we have added in Methods our basic approach: measure KLA and L2 by using electrophysiology, and compute KHA from the thermodynamic cycle using L0. We think this paper is best understood in the context of its companion, also in eLife.

      In all equilibrium equations of the type A to B (e.g. on page 5), rather than using "=" signs it would be much better to use equilibrium reversible arrow symbols.

      It is incorporated.

      Reviewer #3 (Recommendations For The Authors):

      (1) Although the match in simulated vs experimental energies for two ligands was very good, the calculated energies for Ebt and Ebx were significantly different than the experiment. Are there any alternative methods for calculating binding energies from the MD simulations that could be readily compared to?

      See above. We did not use more sophisticated energy calculations because we already knew the answers. Our objective was to identify states, not to calculate energies.

      (2) It would be nice to see control simulations of an apo site to ensure that the conformational changes during the MD are due to the ligands and not an artifact of the way the system is set up. I am primarily asking about this as the simulation of the isolated ECDs for the binding site interface seems like it may be unhappy without the neighboring domains that would normally surround it. On that note, was the protein constrained in any way during the MD?

      Apo simulation results are presented in Figure 2-figure supplement 2. The dimer interface seems to be adequate (stable).

      (3) Figure 4A-B: Should the colors for m1 and m3 be reversed?

      Colors have been changed and a bar chart has been added.

      Reviewer #4 (Recommendations For The Authors):

      (1) Although simulations are commendably run in triplicate, it is difficult in some places to discern their consistency.

      (1a) Table S1 provides important quantification of deviations in different replicates and with different agonists. Please confirm that the reported values are accurate. All values reported for the epibatidine system are identical to those reported for carbamylcholine, which seems statistically improbable. Similarly, runs 1 and 3 with epiboxidine seem identical to one another, and runs 1 and 2 with acetylcholine are nearly the same.

      Figure 2-Source Data 1 has been corrected.

      (1b) In reference to Figure S3, the authors comment that the simulated system (one replicate with carbamylcholine) converges within 0.5 Å RMSD of a desensitized experimental structure. This seems amazing; please specify over what atoms this deviation was calculated and with reference to what alignment. It would be interesting to know the reproducibility of this remarkable convergence in additional replicates or with other ligands; for example, Figure 5 indicates that loop C transitions to a lesser extent in the context of epibatidine than other agonists.

      The comparison was for the entire dimer ECD; 0.5 Å is the result. It may be worthwhile to pursue this remarkable convergence, but not in this paper. Here, we are concerned with identifying ACLA and ACHA. Similarity between ACHA and AD structures is for a different study.

      (1c) For principal-component and subsequent analyses, it appears that only one trajectory was considered for each system. Please clarify whether this is the case; if so, a rationale for the selection would be helpful, and some indication of how reproducible other replicates are expected to be.

      We have added new PCA results (Results, Figure 3-figure supplement 1) that show comparable principal components in other replicates.

      (2) Figure 3 shows free energy landscapes defined by principal components of fluctuation in Cα positions.

      (2a) Do experimental structures (e.g. PDB IDs 6UWZ, 7QL6u) project onto any of these landscapes in informative ways?

      6UWZ.pdb matches well with the apo (7QKO.pdb), comparable to m1, and 7QL6.pdb with the m3.

      (2b) Please indicate the meaning of colored regions in the righthand panels.

      The color panels in the top left panel indicate the colored regions in the righthand panel also, which is indicative of direction and magnitude of changes with PC1 and PC2.

      (2c) Please also check the legend; do the porcupine plots really "indicate the direction and magnitude of changes between PC1 and PC2," or rather between negative and positive values of each principal component?

      It indicates the direction and magnitude of changes with PC1 and PC2.

      (3) It would be helpful to clarify how trajectory segments were assigned to specific minima, particularly m2 and m3.

      (3a) Please verify the timeframes associated with the m2 minima, reported as "20-50 ns [with acetylcholine], 50-60 ns [with carbamylcholine], 60-100 ns [with epibatidine, and] 100-120 ns [with epiboxidine]." It seems improbable that these intervals would interleave so precisely in independent systems. Furthermore, the intervals associated with acetylcholine and epiboxidine do not appear to correspond to the m2 regions indicated in Figure S8.

      Times are given in Figure 4-Source Data 1 and Figure 3-figure supplement 2. The m2 classification is based on loop displacement as well as agonist orientation. For all agonists, the selection was strictly from PCA and cluster analysis.

      (3b) The text (and legend to Figure 3) indicate that 180+ ns of each trajectory was assigned to m3, which seems surprisingly consistent. However, Figure S5 indicates this minimum is more variable, appearing at 160 ns with acetylcholine but at 186 ns with carbamylcholine. Please clarify.

      see above: the selection was from PCA and cluster analysis. Times are in Figure 3-figure supplement 2 and also in Figure 4-Source Data 1 (none in Fig. 3 legend).

      (3c) Figures 5, 6, S6, and S7 illustrate structural features of free-energy minima in each ligand system. Please clarify what is shown, e.g. a representative snapshot, centroid, or average structure from a particular prominent cluster associated with a given minimum.

      They are all representative snapshots (now in Methods). Snapshots and distance measurements (Figure 6-table supplement 1) were extracted from m1, m2 and m3 plateau regions of trajectories.

      (4) Figure S4 helpfully shows the behavior of a pentameric control system; however, some elements are unclear.

      (4a) The 2.5-6.5 Å jump in RMSD at ~40 ns seems abrupt; can it be clarified whether this corresponds to a transition to either m2 or m3 poses, or to another feature of e.g. alignment?

      Figure 2-figure supplement 4 left bottom is just the ligand. The jump is the flip, m1 to m2.

      (4b) It seems difficult to reconcile the apparently bimodal distribution of states with the proposed 3-state model. Into which RMSD peak would the m2 intermediate fall?

      The simulations are only to 100 ns, where we found a complete flip of the agonist represented in the histograms. This confirmed that dimer showed similar pattern as the pentamer. In depth analysis was only done only on dimers.

      (4c) The top panel is labeled "Com" with a graphical legend indicating "ACh." Does this indicate the ligand or, as described in the text legend, "the pentamer" (i.e. the receptor)? For both panels, please verify whether they are calculated on the basis of center-of-mass, heavy atoms, Cα, etc.

      "Com" (for complex) has been changed to system (protein+ligand).

      (5) Minor concerns:

      (5a) In Figures 1 and S3, correct the PDB references (6UWX and 7QL7 are not nAChRs).

      They are now corrected.

      (5b) In Figure 4, do all panels represent mean {plus minus} standard deviation calculated across all cluster-frames reported in Table 1?

      Yes.

      Also check the graphical legend in panel A: presumably the red bars correspond to m1/LA, and the blue to m3/HA?

      Corrected

      (5c) In the legend to Figure S1, please clarify that panel B is reproduced from Indurthi & Auerbach 2023.

      This figure has been deleted.

      (5d) As indicated in Figure S2, it seems surprising that the RMSF is so apparently low at the periphery, where the subunits should contact neighbors in the extracellular domain; how might the authors account for this? Specify whether these results apply to all replicates of each system.

      The redness in the periphery for all four systems indicates the magnitude of fluctuation. As we focus on the orthosteric site, we highlight the loops around the agonist binding pocket and kept other regions 75% transparent. We now include Apo simulations and the dimer appears to be stable even without an agonist present.

      (5e) Within each minimum in Figure S5, three "prominent" clusters appear to be colored (by heteroatom) with carbons in cyan, pink, and yellow respectively. If this is correct, note these colors in the text legend.

      Colors have been added to the legend.

      (5f) In Figure S6, note in the legend that key receptor sidechains are shown as spheres, with the ligand as balls-and-sticks, and that ligand conformations in both low- and high-affinity complexes are shown in both receptor states for comparison.

      This is now added in the legend.

      (5g) The legend to Figure S6 also notes "The agonists are as in Fig S4," but that figure contains a single replicate of a different system; please check this reference.

      This has been updated to Figure 5.

      (5h) In Figure S8, the colors in the epibatidine system appear different from the others.

      The colors are the same for m1, m2 and m3 in all systems including epibatidine.

      (5i) In Table 1, does "n clusters" indicate the number of simulation frames included in the three prominent clusters chosen for MM-PBSA analysis? Perhaps "n frames" would be more clear.

      It was a good suggestion. It has now been changed to ‘n frames’

      (5j) Pg 24-ln 453 presumably should read "...that separate it from m1 and m3..."

      This sentence is now changed in the discussion.

    1. Thus the worth of any object to be acquired by our action is always conditional. Beings the exis­tence of which rests not on our will but on nature, if they are beings with­out reason, still have only a relative worth, as means, and are therefore called things, whereas rational beings are called persons because their nature already marks them out as an end in itself, that is, as something that may not be used merely as a means, and hence so far limits all choice (and is an

      I think he is saying that we as humans are not be to used as means to end. To do so would be ethically wrong.

  2. small-tech.org small-tech.org
    1. Personal Small Technology are everyday tools for everyday people. They are not tools for startups or enterprises. Easy to use Personal technology are everyday things that people use to improve the quality of their lives. As such, in addition to being functional, secure, and reliable, they must be convenient, easy to use, and inclusive. If possible, we should aim to make them delightful. Related aspects: inclusive Non-colonial Small technology is made by humans for humans. They are not built by designers and developers for users. They are not built by Western companies for people in African countries. If our tools specifically target a certain demographic, we must ensure that our development teams reflect that demographic. If not, we must ensure people from a different demographic can take what we make and specialise it for their needs. Related aspects: share alike, non-commercial, interoperable Private by default A tool respects your privacy only if it is private by default. Privacy is not an option. You do not opt into it. Privacy is the right to choose what you keep to yourself and what you share with others. “Private” (i.e., for you alone) is the default state of small technologies. From there, you can always choose who else you want to share things with. Related aspects: zero knowledge, peer to peer Zero knowledge Zero-knowledge tools have no knowledge of your data. They may store your data, but the people who make or host the tools cannot access your data if they wanted to. Examples of zero-knowledge designs are end-to-end encrypted systems where only you hold the secret key, and peer-to-peer systems where the data never touches the devices of the app maker or service provider (including combinations of end-to-end encrypted and peer-to-peer systems). Related aspects: private by default, peer to peer Peer to peer Peer-to-peer systems enable people to connect directly with one and another without a person (or more likely a corporation or a government) in the middle. They are the opposite of client/server systems, which are centralised (the servers are the centres). On peer to peer systems, your data – and the algorithms used to analyze and make use of your data – stay in spaces that you own and control. You do not have to beg some corporation to not abuse your data because they don’t have it to begin with. Related aspects: zero knowledge, private by default Share alike Most people’s eyes cloud over when technology licenses are mentioned but they’re crucial to protecting your freedom. Small Technology is licensed under Copyleft licenses. Copyleft licenses stipulate that if you benefit from technology that has been put into the commons, you must share back (“share alike”) any improvements, changes, or additions you make. If you think about it, it’s only fair: if you take from the commons, you should give back to the commons. That’s how we cultivate a healthy commons. Related aspects: interoperable, non-colonial, non-commercial Interoperable Interoperable systems can talk to one another using well-established protocols. They’re the opposite of silos. Interoperability ensures that different groups can take a technology and evolve it in ways that fit their needs while still staying compatible with other tools that implement the same protocols. Interoperability, coupled with share alike licensing, helps us to distribute power more equally as rich corporations cannot “embrace and extend” commons technology, thereby creating new silos. Interoperability also means we don’t have to resort to colonialism in design: we can design for ourselves and support other groups who design for themselves while allowing all of us to communicate with each other within the same global network. Related aspects: share alike, non-colonial Non-commercial The primary purpose for Small Technology is not to make a profit but to increase human welfare. As such, they are built by not-for-profit organisations. Eventually, we hope that small technologies will be recognised for their contribution to the common good and therefore supported from the commons (e.g., from our taxes). In the interim, some methods for monetising Small Technology include: Charging for hosting and maintenance services Sales on App Stores (for native apps) Donations and patronage Grants and awards Equity-based / Venture Capital investment is incompatible with Small Technology as the success criterion is the sale of the organisation (either to a larger organisation or to the public at large via an IPO). Small Technology is not about startups (temporary companies designed to either fail fast or grow exponentially and get sold), it’s about stayups (sustainable organisations that contribute to the common good). Related aspects: non-colonial, share alike, interoperable Inclusive Being inclusive in technology is ensuring people have equal rights and access to the tools we build and the communities who build them, with a particular focus on including people from traditionally marginalised groups. Accessibility is the degree to which technology is usable by as many people as possible, especially disabled people Small Technology is inclusive and accessible. With inclusive design, we must be careful not to assume we know what’s best for others, despite us having differing needs. Doing so often results in colonial design, creating patronising and incorrect solutions.

      Small Technology Small Technology are everyday tools for everyday people designed to increase human welfare, not corporate profits.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary Maintenance of the histone H3 variant CENP-A at centromeres is necessary for proper kinetochore assembly and correct chromosome segregation. The Mis18 complex recruits the CENP-A chaperone HJURP to centromeres to facilitate CENP-A replenishment. Here the authors characterise the Mis18 complex using hybrid structural biology, and determine complex interface separation-of-function mutants.

      Major Comments The SAXS and EM data on the full-length Mis18 components must be included in the main Figures, either as an additional figure or by merging/rearranging the existing figures. The authors discuss these results in three whole paragraphs, which are a very important part of the paper.

      We thank the reviewer for this constructive suggestion. We have now included an additional figure (new Fig. 2, attached below), that highlights the fit of the integrative model against the SAXS and EM data.

      Could the authors also compare the theoretical SAXS scattering curves generated by their final model(s) with the experimental SAXS curves? This would provide some additional evidence for the overall shape of their complex model beyond the consistency with the Dmax/Rg.

      We acknowledge the importance of this suggestion. We have now compared the theoretical SAXS scattering curve of the Mis18a/b core complex (named Mis18a/b DN), which lacks the flexible elements (disordered regions and the helical region flexibility connected to the Yippee domains). The theoretically calculated SAXS scattering curve of the model matches nicely with the experimental data with c2 value of 1.36. This data is now included in new Fig. 2 (Fig. 2f) and is referenced on page 9 line 21.

      Minor Comments

      While the introduction is clearly written, an additional cartoon schematic, representing the system/question would be helpful to a non-specialist reader to interpret the context of the study.

      We have now included a cartoon in the revised Fig. 1 to support the introduction on centromere maintenance and the central role of the Mis18a/b/BP1 complex in this process. Please find the new Fig. 1 below.

      No doubt the authors had a reason for choosing their figure allocation, but I wonder if more material couldn't be brought from the supplementary into the main figures?

      As addressed in our response to one of the major comments, we have now moved key CLMS, SAXS and EM data from the supplemental figure into the main figure, new Fig. 2.

      Page 6 "Mis18-alpha possesses an additional alpha-helical domain" - please make it clear in addition to what (I assume it's in addition to Mis18-beta).

      Apologies for the lack of clarity. We have now rephrased this sentence to highlight that this difference is in comparison with Mis18b on page 6 line 15.

      Page 7 - Report the RMSD of the Pombe vs. Human Mis18-alpha yipee structures?

      The S. pombe Mis18 Yippee structure superposes on to the Human Mis18a Yippee domain with an RMSD of 0.92 angstroms with is now mentioned on page 7 line 9.

      Page 7 - "We generated high-confidence structural models...." is there a metric for the confidence as reported by RaptorX? Perhaps includinging the PAE plots in the supplementary for the AlphaFold generated models would be useful?

      We thank the reviewer for the valid suggestion. We have now included the PAE plot corresponding to the AlphaFold model in the supplementary Fig. S1d and reference on page 7 line 18. RaptorX ranks models based on estimated error. We have now included this information in the new figure legend for Supplementary Fig. S1.

      Figure 1 - Perhaps label figure 1b as being experimentally determined, with the R values (as for Figure 1d), and 1c being a predicted model.

      We have included Rfree and Rwork values for the Mis18a Yippee homo dimer structure and labelled Mis18a/b Yippee hetero-dimer as the predicted model in Fig. 1c and 1d.

      Page 8 "This observation is consistent with the theoretically calculated pI of the Mis18alpha helix" This is a circular argument, of course this region has a low pI due to the amino acid composition. Please remove this statement.

      We have now removed this statement as suggested.

      Page 8 "...reveals tight hydrophobic interactions" these are presumably shown in Figure 1d rather than in the referenced 1e.

      We apologise for the oversight. We have now referred to the correct figure (Fig. 1f in the revised Fig. 1).

      Page 8 - The authors should briefly somewhere discuss why there is a difference between their results and those in Pan et al 2009. As I understand it, the Pan et al paper was based in part on modelling with CLMS data as restraints.

      We thank the reviewer for this suggestion. According to Pan et al., 2009, the model shown by them was generated using CCBuilder, and their CLMS data could not differentiate the two models with the 2nd Mis18a C-terminal helix in either parallel or anti-parallel orientation. We now briefly discuss this on page 8 and line 22 as follows: "Although the Pan et al., 2019 model presented the 2nd Mis18a in a parallel orientation, they did not rule out the possibility of this assembling in an anti-parallel orientation within the Mis18a/b C-terminal helical assembly (Pan et al., 2019)."

      Figure 1 - The labelling of the residues for Mis18-alpha in Figure 1d is problematic, they are black on dark purple (might be my printer/screen/eyes) suggest amending.

      We have now rearranged the label positions to overcome this issue. For clarity, the labels that could not be moved appropriately are shown in white.

      Figure S3a - Do the authors have some data to show the mass of the cross-linked complex that was loaded onto grids is consistent with what is expected?

      Unfortunately, the amount of material that we recover after performing GraFix is not sufficient enough to determine the molecular weight of the crosslinked sample by techniques such as SEC-MALS. However, GraFix fractions were analysed by SDS PAGE, and fractions that ran around the expected molecular weight were selected for EM analysis. We have now included the corresponding SDS-PAGE showing the migration of the crosslinked sample analysed by EM (Supplementary Fig. S3a).

      Figure S3b - scale bar

      Revised Fig. 2d now includes the scale bar shown.

      Figure S3c - Could the authors show or explain the differences between these different 3D reconstructions?

      The models mainly differ in the relative orientations of the bulkier structural features that are referred to as 'ear' and 'mouth' pieces of a telephone handset. This has been mentioned in the text, but we note that the figure is not referenced right next to this statement. We have now amended this (Page 9 line 19), and to make it clear, we have also highlighted the difference using an arrowhead in Fig. 2e and S3b. The different orientations are also stated in the corresponding figure legends.

      Page 9 - The use of "AFM" for AlphaFoldMultimer" is a little confusing since AFM is the established acronym for Atomic Force Microscopy. Perhaps AF2M?

      We have now replaced AFM with AF2M on page 9 to avoid confusion.

      Figure S4a - Control missing for Mis18-alpha wild-type

      Apology for the confusion, this control is present in Fig. 4a. We have now stated this in the figure legend of S4a for clarity.

      Figure S4 d and e - The contrast between the bands and the background is very bad (at least in my copy).

      We have now adjusted the contrast of the blots in Fig. S4d and S4e response to this comment.

      Page 13 "Our structural analysis suggests that two Mis18BP1 fragments.....". How did you arrive at this conclusion? Is this based on the AlphaFold/RaptorX model? What additional evidence do you have that the positioning of the Mis18BP1 is correct? Does the CLMS data support this?

      We confirm that this statement is based on AlphaFold model. We have now explicitly highlighted this on page 14, line 5. As noted in the same paragraph (page 14, line 19), this model agrees with the contacts suggested by the cross-linking mass spectrometry data presented here.

      Figure 4a - Would the authors like to consider using a different colour for Mis18BP1? The contrast is not great, especially in the electrostatic surface inset.

      In response to this suggestion, the Mis18BP1 helix is now shown in grey in the inset of Fig. 5a.

      Reviewer #1 (Significance (Required)):

      General Assessment The paper is extremely clearly written. Likewise the figures are beautifully presented and the data extremely clean and fully supportive of the authors conclusions. Indeed it is seldom that one sees the depth of the structural approaches (X-ray, CLMS, EM, SAXS) in one paper which is a huge strength of the manuscript. In addition the translation of this data into very clean cell biological experiments, makes the paper truly outstanding.

      Advance The authors provide the first model of the Mis18 complex, with extensive evidence to back up this model. The authors provide additional evidence as to how the deposition/renewal of CENP-A might be mediated by the Mis18 complex. The advance comes from both the level of clarity, detail, and scope achieved in this paper.

      Audience This will likely be of great interest to anyone with an interest in chromosome biology, plus be of interest to structural biologists as an outstanding example of hybrid structural biology.

      Expertise I am a biochemist with a background in structural biology with some familiarity with centromere biology

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The manuscript "structural basis for Mis18 complex assembly: implications for centromere maintenance" by Thamkachy and colleagues describes a study that uses structural analysis to test essential candidate residues in Mis18 complex components in CENP-A loading. For chromosomes to faithfully segregate during cell division, CENP-A levels must be maintained at the centromere. How CENP-A levels are maintained is therefore important to understand at the mechanistic level. The Mis18 complex has been found to be important, but how exactly the various Mis18 complex components interact and how they regulate new CENP-A loading remains not fully understood. This study set out to characterize the critical residues using X-ray crystallography, negative staining EM, SEC analysis, molecular modeling (Raptorx, AlphaFold2, and AlphaFold-multimer) to identify the residues of Mis18a and Mis18b that are critical for the formation of the Mis18a/b hetero-hexamer and which residues are important for Mis18a and Mis18BP1 interactions. A complex beta-sheet interface dictates the Mis18a and Mis18b interactions. Mutating the Mis18a residues that are important for the Mis18a/b interactions resulted in impaired pull-down of Mis18b and reduced centromeric levels of mutated Mis18a. The functional consequences of mutating residues that impair Mis18a/b interactions is that with reduced centomeric levels of Mis18a, also impaired new CENP-A loading. Interestingly, mutated Mis18b did not impact centromeric Mis18a levels and only modestly impaired new CENP-A loading. These data were interpreted that Mis18a is critical for new CENP-A loading, whereas Mis18b might be involved in finetuning how much new CENP-A is loaded. Overall, it is a very well described and well written study with exciting data.

      Major comments:

      • Overall, the structural data and the IF data support the importance of Mis18a residues 103-105 are critical for centromeric localization and new CENP-A loading, whereas Mis18b residues L199 and I203 are critical for centromeric localization, but only very modestly impair centromeric Mis18a localization and new CENP-A loading. In the discussion the authors argue that the N-terminal helical region of Mis18a mediate HJURP binding. This latter is postulated based on published work, but not tested in this work. This should be clarified as such.

      We thank the reviewer for this comment. Our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, serendipitously has now validated this suggestion and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a, which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al. bioRxiv, 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/b interaction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitroand efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      • Overall, the authors clearly describe their data and methodology and use adequate statistical analyses. The structural data of the Mis18a/b complex being a hetero-hexamer is convincing, but the validation in vivo is missing. As structural experiment are not performed under physiological conditions, it is important to establish the stoichiometry in vivo to further support the totality of the findings of the structural experiments and modeling. The data for the hierarchical assembly of Mis18a and Mis18b at the centromere and its importance in new CENP-A loading is convincing. An additional open question is whether "old" centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a to the centromere and whether the identified residues have a role in Mis18a centromeric localization. These data would provide a solid link between the Mis18 complex and how it is directly linked to new CENP-A loading.

      We agree that establishing the stoichiometry of Mis18 subunits of the Mis18 complex in vivo would be insightful. However, considering that the Mis18 complex assembles in a specific window of the cell cycle (late Mitosis and early G1), we think characterising the stoichiometry in cells is extremely difficult and technically challenging. However, consistent with our structural model, several lines of independent evidence (Pan et al., 2017 and Spiller et al., 2017) using different biophysical methods (Analytical Ultra Centrifugation (Pan et al., 2017), SEC-MALS (Spiller et al., 2017)) showed that recombinantly purified Mis18 complex (irrespective of the expression host, from both E. Coli or insect cells) is a hetero-octamer made of a hetero-hexameric Mis18a/b (4 Mis18a and 2 Mis18 b) complex bound to two copies of Mis18BP1. These observations suggested that hetero-hexamerisation of the Mis18a/b complex may be needed to bind and dimerise Mis18BP1 in cells. Previously published cellular studies support the in vivo requirement of the hetero-octameric Mis18 assembly as: (i) Perturbing the hetero-hexamerisation of the Mis18a/b complex (by introducing mutations at the Mis18a/b Yippee dimerisation interface, which while did not disrupt Mis18a/b complex formation, perturbed its hetero-hexamerisation and resulted in a hetero-trimeric Mis18a/b complex made of 2 Mis18aand 1 Mis18b) abolished Mis18BP1 binding in vitro and in cells, consequently abolished CENP-A deposition (Spiller et al., 2017) and (ii) artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 highlighting the requirement of Mis18a/b hexameric assembly mediated dimerization of Mis18BP1 in cells (Pan et al., 2017). While these studies highlighted the importance of maintaining the right stoichiometry (hetero-octamer of 4 Mis18a, 2 Mis18b and 2 Mis18BP1), lack of structural information on how this essential biological assembly is established remained a major knowledge gap. Our work presented here fills this critical knowledge gap by showing that a segment of Mis18BP1 (aa 20-51) also binds at the Yippee dimerisation interface. To highlight this, we have included the following statements in the introduction on page 5 and 20 "Perturbing the Yippee domain-mediated hexameric assembly of Mis18a/b (that resulted in a Mis18a/b hetero-trimer, 2 Mis18a and 1 Mis18b) abolished its ability to bind Mis18BP1 in vitro and in cells (Spiller et al., 2017), emphasising the requirement of maintaining correct stoichiometry of Mis18a/b subunits. Consistent with this, artificial dimerisation of Mis18BP1, by expressing Mis18BP1 as a GST-tagged protein, enhanced the centromere localisation of Mis18BP1 (Pan et al., 2017)." and in the Results section on page 14 line 12: "Mis18BP120-51 contains two short b strands that interact at Mis18a/b Yippee interface extending the six-stranded-b sheets of both Mis18a and Mis18b Yippee domains. This provides the structural rationale for why Yippee domains-mediated Mis18a/b hetero-hexamerisation is crucial for Mis18BP1 binding (Spiller et al., 2017)."

      Regarding the question "whether 'old' centromeric CENP-A or HJURP:new CENP-A complex is needed to recruit Mis18a centromere localisation and whether identified residues have a role in Mis18a centromere localisation": According to the published literature, the Mis18 complex associates with centromeres through interaction with CCAN components CENP-C and CENP-I (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011, Hoffmann et al., 2020). Considering CCAN assembles on CENP-A nucleosomes, and HJURP:new CENP-A centromere recruitment depends on the Mis18 complex, it will be reasonable to argue that the 'old' centromeric CENP-A contributes to the centromere localisation of the Mis18 complex. Amongst the components of the Mis18 complex, Mis18BP1 and Mis18bhave previously been suggested to interact with CENP-C. Within the Mis18 complex, we (Spiller et al., 2017) and others (Pan et al., 2017) have shown that Mis18a can directly interact with Mis18BP1, but it does so more efficiently when Mis18a hetero-oligomerises with Mis18b via their Yippee domains. Here, our structural analysis mapped the interaction interfaces and showed that Mis18a residues E103, D104 and T105 contribute to Mis18BP1 binding, as mutating these residues abolishes centromere localisation of Mis18a (Fig. 5c and 5d). To accentuate our findings, we have now included the following paragraph in the discussion section (page 17 line 26): "One of the key outstanding questions in the field is how does the Mis18 complex associate with the centromere. Previous studies identified CCAN subunits CENP-C and CENP-I as major players mediating the centromere localisation of the Mis18 complex mainly via Mis18BP1 (Shono et al., 2015, Dambacher et al., 2012, Moree et al., 2011), although Mis18b subunit has also been suggested to interact with CENP-C (Stellfox et al., 2016). Within the Mis18 complex, we and others have shown that the Mis18a/b Yippee hetero-dimers can directly interact with Mis18BP1. Here our structural analysis allowed us to map the interaction interface mediating Mis18a/b-Mis18BP1 binding. Perturbing this interface on Mis18a completely abolished Mis18a centromere localisation and reduced Mis18BP1 centromere levels. These observations show that Mis18a associates with the centromere mainly via Mis18BP1, and assembly of the Mis18 complex itself is crucial for its efficient centromere association, as previously suggested. Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments:

      • The bar graphs shown ideally also show the individual data points for the authros to appreciate the spread of the data. These figures can be replicated in the Supplemental to avoid making the main figures look too busy.

      We thank the reviewers for this suggestion. Reviewer #3 made a similar comment and suggested we use Superplot, which allows visualisation of individual data points of independent experiments. We have now revised all bar graphs using Superplot to address both reviewers' suggestions.

      Reviewer #2 (Significance (Required)):

      • This study uses a broad range of structural techniques, including molecular modeling which were subsequently validated by in vitro pull-down assays, co-IP, and IF. This combination of these techniques is important because many structural techniques cannot be performed under physiological conditions. Validating the main findings of the structural results by IF and co-IP is therefore critical.
      • This work greatly advances our structural understanding how Mis18a, Mis18b, and Mis18BP1 form the Mis18 complex and how the critical residues in especially Mis18a help the Mis18 complex localize to the centromere and influence new CENP-A loading. This study also provides the first strong evidence in hierarchical assembly of the Mis18 complex.
      • How centromere identity is maintained is a critical question in chromosome biology and genome integrity. The Mis18 complex has been identified as an important complex in the process. Several structural and mutational studies (all adequately cited in this manuscript) have tried to address which residues guide the assembly and functional regions of the Mis18 complex. This work builds and expands our understanding how especially Mis18a holds a pivotal role in both Mis18 complex formation and its impact on maintaining centromeric CENP-A levels.
      • This work will be of interest to the chromosome field in general and anyone studying the mechanism of cell division.
      • Chromatin, centromere, CENP-A, cell division. This reviewer has limited expertise in structural biology.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Centromere identity is defined by CENP-A loading to specific sites on genomic DNA. CENP-A loading is known to rely on the Mis18 complex, and several regulators are known; yet how the Mis18 complex achieves this complex process has remained puzzle. By elucidating the structural basis of Mis18 complex assembly using integrative structural approaches the authors show that multiple homo and heterodimeric interfaces of Mis18alpha, beta and Mis18BP1 are involved in centromere maintenance. The authors show that Mis18alpha can associate with centromeres and deposit CENP-A independent of Mis18 β. Mis18α functions in CENP-A deposition at centromeres independent of Mis18β. Mis18β is required for maintaining a specific level of CENP-A occupancy at centromeres. Thus, using structure-guided and separation-of-function mutants the study reveals how Mis18 complex ensures centromere maintenance. Major comments: This is an excellent study on centromere inheritance, combining structural and cell biology techniques. The comments here primarily refer to Cell biology aspect of the work.

      Figures show that new CENP-A deposits in Mis18βL199D/I203D mutants, but the level was reduced moderately. Based on this observation, the authors make a strong conclusion that Mis18β licenses the optimal levels of CENP-A at centromeres. Mis18α may be essential for both CENP-A incorporation and depositing a specific amount of CENP-A, as Mis18α and CENP-A levels are both reduced in Mis18βL199D/I203D mutants which failed to form the triple helical assembly with Mis18α as shown in Figure 3B and 3C. The authors may want to qualify some of these claims as preliminary or speculative.

      We thank the reviewer for this suggestion. We agree that although the reduction in CENP-A levels upon replacing WT Mis18b with Mis18b L199D/I203D is more prominent than the reduction in centromere localised Mis18a, one cannot completely rule out the contribution of reduced Mis18a on CENP-A loading. This also raises an interesting possibility where Mis18b ensures the correct amount of CENP-A deposition by facilitating the optimal level of Mis18a at centromeres. We now explicitly discuss this in the discussion as follows (page 16 line 26): "Whilst proteins involved in CENP-A loading have been well established, the mechanism by which the correct levels of CENP-A are controlled is yet to be thoroughly explored and characterised. The data presented here suggest that Mis18b mainly contributes to the quantitative control of centromere maintenance - by ensuring the right amounts of CENP-A deposition at centromeres - and maybe one of several proteins that control CENP-A levels. We also note that the Mis18b mutant, which cannot interact with Mis18a, moderately reduced Mis18a levels at centromeres, and hence, it is possible that Mis18b ensures the correct level of CENP-A deposition by facilitating optimal Mis18a centromere recruitment. Future studies will focus on dissecting the mechanisms underlying the Mis18b-mediated control of CENP-A loading amounts along with any other mechanisms involved."

      This work and others show that phosphorylation of Mis18BP1 by CDK1 can interfere with complex function (Spiller et al., 2017, Pan et al., 2017). Does the structure provide any insight into PLK1-mediated phosphorylation surfaces for activation of the complex? If yes, a brief discussion would help to link CDK1 and PLK1 mediated opposing actions will strengthen the work.

      As described in our response to the first major comment of Reviewer 2, our very recent study aimed at understanding the licencing role of Plk1, independent of the work reported here, identified and evaluated the functional contribution of Plk1 phosphorylation on the subunits of the Mis18 complex (Parashara et al., bioRxiv 2024). Serendipitously, this recent work has now validated our hypothesis proposed based on the structural characterisation reported here and demonstrates that a Plk1-mediated phosphorylation cascade activates the Mis18a/b complex via a conformational switch of the N-terminal helical region of Mis18a which facilitates a robust HJURP-Mis18a/b interaction (Parashara et al. bioRxiv 2024). An independent study from the Musacchio lab (Conti et al., bioRxiv 2024) also reports similar findings, mutually strengthening our independent conclusions. Overall, these studies highlight the importance of the critical structural insights into the Mis18 complex this study reports. We now explicitly discuss the validation of our original hypothesis by citing our recent work along with that of the Musacchio lab. The corresponding section of the last paragraph now reads as follows (page 17 line 10): "Previously published work identified amino acid sequence similarity between the N-terminal region of Mis18a and R1 and R2 repeats of the HJURP that mediates Mis18a/binteraction (Pan et al., 2019). Deletion of the Mis18a N-terminal region enhanced HJURP interaction with the Mis18 complex (Pan et al., 2019). Here, we show that the N-terminal helical region of Mis18a makes extensive contact with the C-terminal helices of Mis18a and Mis18b, which had previously been shown to mediate HJURP binding by Pan et al., 2019. Collectively these observations suggest that the N-terminal region of Mis18a might directly interfere with HJURP - Mis18 complex interaction. Two independent recent studies (Parashara et al., 2024, Conti et al., 2024) reveal that this is indeed the case and a Plk1-mediated phosphorylation cascade involving several phosphorylation and binding events of the Mis18 complex subunits relieve the intramolecular interactions between the Mis18a N-terminal helical region and the HJURP binding surface of the Mis18a/b C-terminal helical bundle. This facilitates robust HJURP-Mis18a/b interaction in vitro and efficient HJURP centromere recruitment and CENP-A loading in cells. Overall, these studies also highlight the importance of the critical structural insights into the Mis18 complex we report here."

      I am happy with the way cell biology data and the methods are presented so that they can be reproduced. The experiments are adequately replicated and the statistical analysis adequate. It will help to include sample size of cells or centromeres used for building the graphs.

      We have now included this information in figure legends of Fig. 3a, 3c, 4b, 4c, 5b, 5c and 5d.

      This is a strong interdisciplinary study using a variety of in vitro and in vivo techniques. Can the authors discuss if they expect chromatin associated Mis18 complex to host a similar structure as the soluble one? In other words, are they able to comment on any key differences between chromatin and non-chromatin associated Mis18 complexes.

      We thank the reviewer for the suggestion. We agree that one cannot rule out the possibility of the Mis18 complex undergoing compositional and/or conformational variations during the processes of CENP-A loading at centromeres. We now explicitly discuss this possibility in the last paragraph of the discussion section (page 18 line 10): "Future work aimed at characterising the intermolecular contact points between the subunits of the Mis18 complex, centromeric chromatin and CCAN components and understanding if the Mis18 complex undergoes any conformational and/or compositional variations upon centromere association and/or during CENP-A deposition process, will be crucial to delineate the mechanisms underpinning the centromere maintenance."

      Minor comments: -

      In cell biology experiments, fluorescence intensities could be presented as a superplot for added value across cells and repeats (instead of bar graphs). More on superplot:https://doi.org/10.1083/jcb.202001064.

      We thank the reviewers for this kind suggestion. We have now included graphs made using 'superplot' as suggested.

      In general, ACA levels do not appear to change significantly between WT and mutant expressing cells although new CENP-A loading is significantly absent in the presence of a few mutants - please comment if ACA used here can recognise CENP-A. Would this mean that old CENP-A remains normally?

      We thank the reviewer for this comment. While new CENP-A incorporated at centromeres is selectively labelled using the SNAP-tag, the ACA antibody used in these experiments can recognise CENP-A, CENP-B and CENP-C, with CENP-B being the primary target (Kallenberg, Clinical Rheumatology,1990). We would also like to note that ACA has commonly been used to locate the centromere in CENP-A loading assays where new CENP-A levels are assessed via selective labelling (e.g. McKinley 2014).

      It is unclear whether any of the mutant acted in a dominant negative fashion in the presence of endogenous Mis18 proteins. It would have been useful to test this particularly in the context of mis18alpha mutants that seem to fully abolish new CENP-A recruitment.

      As Mis18 subunits oligomerise (homo and hetero), we thought expressing these mutants in the presence of endogenous proteins might interfere with endogenous protein in a heterogenous manner and might make the interpretation difficult. Hence, we did not test this. Instead, as described in the manuscript we have tested these mutants in siRNA rescue experiments (Fig. 3, 4 and 5).

      In figure 3a, GFP panel (input lane, 1) is shown to mark a band corresponding to GFP. Is this expected? Please comment.

      Yes, as a control, an empty vector was transfected to express just GFP along with Mis18a-mCherry. These were used to show that there was no unspecific interaction between the beads used for IP or Mis18a-mCherry and GFP tag, and that any interaction seen was due to Mis18b. A similar control was used in S4b, where mCherry was expressed along with Mis18b-GFP. We have now clarified this in the corresponding legends of Fig. 4a and S4b.

      Would be useful to have the scale for the cropped images presented as insets. Figure 4B should read YFP and not YPF.

      We apologise for this typographical error. We have now corrected this.

      The authors may want to explain whether the tag differences matter for their study (Case in point: His-SUMO-Mis18a191-233 WT and mutant His-MBP-Mis18b188-229 proteins).

      The MBP tag was chosen to perform amylose pull-down assays, whereas the SUMO tag was chosen to increase the protein size. This is crucial as the C-terminal fragments of Mis18a and Mis18b are less than 50 amino acids long and are not easy to visualise by the band intensity in the Coomassie-stained SDS PAGE gels.

      Reviewer #3 (Significance (Required)):

      This work elucidates the structural basis of Mis18 complex assembly and the intermolecular interfaces essential for Mis18 functions. This is a significant advance in the field as it helps researchers in the field better understand CENP-A deposition and mechanism underpinning the maintenance of centromere identity. This is a broad area of research benefitting those studying cell division, genome stability, centromere identity and epigenetics might all be interested in and influenced by these findings. Novelty and strength lies in combining structural and cell biology work. Strengths of the work are structural details of the Mis18 complex. Minor weakness is the link between Mis18 structure and Centromere inheritance is limited to one immunostaining assay (I have mentioned this as a minor comment because addressing this may not be within the scope of this manuscript and is likely to require a repeat of a vast majority of the work with additional reagents which may not directly add value to the current manuscript).

    1. Author Response

      Reviewer #1 (Public Review):

      Weaknesses:

      The manuscript needs proper editing and is not complete. Some wordings lack precision and make it difficult to follow (e.g. line 98 "we assembled a chromosome-scale genome of ..." should read instead "we assembled a chromsome-scla genome sequence of ...". Also, panel Figure 2E is missing.

      We will make the suggested change of adding “sequence”. Concerning additional changes, we have carefully edited our manuscript and looked for any incomplete sections. Unfortunately, it is difficult to see what other issues are being raised here without any further information. And the example given is not helpful to ascertain what other changes may be necessary, since we cannot see any problem with the sentence “we assembled a chromosome-scale genome of” as this phrase is widely used in many similar publications.

      As for panel E of figure 2, it is not missing. The panel located to the right, just below “Target Cells”.

      The shortcomings of the manuscripts are not limited to the writing style, and important technical and technological information is missing or not clear enough, thereby preventing a proper evaluation of the resolution of the genomic resources provided:

      • Several RNASeq libraries from different tissues have been built to help annotate the genome and identify transcribed regions. This is fine. But all along the manuscript, gene expression changes are summarized into a single panel where it is not clear at all which tissue this comes from (whole embryo or a specific tissue ?), or whether it is a cumulative expression level computed across several tissues (and how it was computed) etc. This is essential information needed for data interpretation.

      No fertilised eggs or embryos have been sequenced, individual tissues derived from juvenile fish were used for the genome annotation and whole larval fish for the developmental analysis. We will specify in the figures and text that the results shown are from whole larvae, and add more detail to the material and methods section about which type of sample was analysed in which way.

      • The bioinformatic processing, especially of the assemble and annotation, is very poorly described. This is also a sensitive topic, as illustrated by the numerous "assemblathon" and "annotathon" initiatives to evaluate tools and workflows. Importantly, providing configuration files and in-depth description of workflows and parameter settings is highly recommended. This can be made available through data store services and documents even benefit from DOIs. This provides others with more information to evaluate the resolution of this work. No doubt that it is well done,but especially in the field of genome assembly and annotation, high resolution is VERY cost and time-intensive. Not surprisingly, most projects are conditioned by trade-offs between cost, time, and labor. The authors should provide others with the information needed to evaluate this.

      We will upload the code used to assemble and annotate this genome to a public repository or add it to the supplementary material.

      The genome assembly did not use a specific workflow (e.g., nextflow), but was done with a simple command and standard parameters in IPA. Scaffolding was carried out by Phase Genomics using their standardised proprietary workflow, of which a detailed description provided by Phase Genomics can be found in the supplementary material. The annotation workflow has been described in a previous publication already, but an in-depth description can also be found in the Material and methods section, including parameters used for specific steps. The RNA-seq mapping and analysis part has also been described in the Material and Methods section, including parameters and models for DESEq2.

      • Quantifications of T3 and T4 levels look fairly low and not so convincing. The work would clearly benefit from a discussion about why the signal is so low and what are the current technological limitations of these quantifications. This would really help (general) readers.

      We will add a comment on this in the manuscript as suggested. Basically, the T3/T4 levels are consistent with other published work in fish. In the present manuscript for grouper we have a peak level of 1.2 ng/g (1,200 pg/g) of T4 and 0.06 ng/g (60 pg/g) of T3. This is a higher level of T4 and comparable level of T3 to what was found in convict tang (Holzer et al. 2017; Figure 2) with 30 pg/g of T4 and 100 pg/g of T3. Of course, there are also examples with higher levels, such as clownfish (Roux et al. 2023; Figure 1), with 10 ng/g (10,000 pg/g) of T4 and 2 ng/g (2,000 pg/g) of T3.

      The differences could be due to different structure of fish tissues and therefore different hormone extraction efficiency, different hormone measurement protocols, different fish physiology, different fish size (e.g., the weighting of tiny grouper larvae is difficult and less precise than in convict tang). What is important is not the absolute level but the relative level, which shows the change within different larval stages of a species with identical extraction and measurement protocols. Which means our data is internally consistent and coherent with what the grouper literature says.

      Holzer, Guillaume, et al. "Fish larval recruitment to reefs is a thyroid hormone-mediated metamorphosis sensitive to the pesticide chlorpyrifos." Elife 6 (2017): e27595.

      Roux, Natacha, et al. "The multi-level regulation of clownfish metamorphosis by thyroid hormones." Cell Reports 42.7 (2023).

      • Differential analysis highlights up to ~ 15,000 differentially expressed genes (DEG), out of a predicted 26k genes. This corresponds to more than half of all genes. ANOVA-based differential analysis relies on the simple fact that only a minority of genes are DEG. Having >50% DEG is well beyond the validity of the method. This should be addressed, or at least discussed.

      As the reviewer notes, there are a large number of differentially expressed genes due to the fact that this is coming from a larval developmental transcriptome going from one day old larva to fully metamorphosed juveniles at around day 60.

      While DESeq2 indeed works on an assumption that most genes are not differentially expressed, this affects normalization but not hypothesis testing (Wald-test, LRT tests or ANOVA). Normalisation in DESeq2 is fairly robust to this assumption. According to the author of DESeq2, Micheal Love, DESeq2 is using the median ratio for normalisation, and as long as the number of up and down regulated genes is relatively even, DESeq2 will be able to handle the data. As part of our general quality control for this project we consulted the MA plots, which do not show any overrepresented up or down expression patterns. Additionally see Michael Love comment on comparing different tissues, which is also applicable here when comparing vastly different larval stages (https://support.bioconductor.org/p/63630/): “For experiments where all genes increase in expression across conditions, the median ratio method will not be able to capture this difference, but this is typically not the case for a tissue comparison, as there are many "housekeeping" genes with relatively similar expression pattern across tissues.”

      Reviewer #3 (Public Review):

      Weaknesses:

      However, the authors make substantial considerations that are not proven by experimental or functional data. In fact, this is a descriptive study that does not provide any functional evidence to support the claims made.

      We agree with the reviewer that our paper lacks functional experiments but despite that, the transcriptomic data clearly show the activation of TH and corticoid pathways during two distinct periods; an early activation between D1 and D10, and a second one between D32 and juvenile stage. These data are interesting as they call for further examination of 1) the possible interaction of corticoids and TH during metamorphosis, a question that is certainly not settled yet in teleost fishes, and 2) the existence of an early larval developmental step also involving TH and corticosteroids.

      Especially 2) is of interest and importance, since this early activation (unique to our knowledge in any teleost fish studied so far) raises a lot of new questions and once again will certainly be scrutinised by other groups in the years to come, therefore ensuring a good citation impact of our study. We hope that the reviewer, while disagreeing with some our statements, will recognize that our study will be stimulating at that level and that this is what scientific studies should do.

      The consideration that cortisol is involved in metamorphosis in teleosts has never been shown, and the only example cited by the authors (REF 20) clearly states that cortisol alone does not induce flatfish metamorphosis. In that work, the authors clearly state that in vivo cortisol treatment had no synergistic effect with TH in inducing metamorphosis. Moreover, in Senegalensis, the sole pre-otic CRH neuron number decreases during metamorphosis, further arguing that, at least in flatfish, cortisol is not involved in flatfish metamorphosis (PMID: 25575457).

      We will do our best to improve the clarity of the revised manuscript to avoid any misunderstanding about our claims. However, we would like to point out the semantic shift in the reviewer first sentence: Indeed “being involved” is not the same as “cortisol alone does not induce”. In ref 20 the authors explicitly wrote that “Cortisol further enhanced the effects of both T4 and T3, but was ineffective in the absence of thyroid hormones” and in our view this indeed corresponds to ”being involved in metamorphosis”.

      We are not claiming that cortisol alone is involved in metamorphosis as the reviewer suggests, but simply that there is a possible involvement of cortisol together with TH in metamorphosis. We stand on this claim as we indeed observed an activation of corticoid pathway genes around D32, which is sufficient to say it is involved. We do agree that functional experiments will be needed to properly demonstrate the involvement of corticoids in grouper metamorphosis, but this was not possible in the current study as it would imply to set up a full grouper life cycle in lab conditions which is impossible for the scope of this manuscript.

      We also mentioned in the discussion that the role of corticoids in fish larval development is still debated, and we agree that this remain a contentious issue.

      We wrote that “there is contrasting evidence of communication between these two pathways [TH and corticosteroids] in teleost fish with some data suggesting a synergic and other an antagonistic relationship. In terms of synergy, an increase in cortisol level concomitantly with an increase in TH levels has been observed in flatfish (ref 19), golden sea bream (ref 100) and silver sea bream (ref 101). Cortisol was also shown to enhance in vitro the action of TH on fin ray resorption (phenomenon occurring during flatfish metamorphosis) in flounder (ref 20). TH exposure increases MR and GR genes expression in zebrafish embryo (ref 55). It has also been shown that cortisol regulates local T3 bioavailability in the juvenile sole via regulation of deiodinase 2 in an organ-specific manner (ref 56) On the antagonistic side, it has been shown that experimentally induced hyperthyroidism in common carp, decreasing cortisol levels (ref 57), whereas cortisol exposure decreases TH levels in European eel (ref 58). Given this scattered evidence, the existence of a crosstalk active during teleost metamorphosis has never been formally demonstrated. The results we obtained in grouper are clearly indicating that HPI axis and cortisol synthesis are activated (i) during early development and (ii) during metamorphosis. This may suggest that in some aspect cortisol synthesis can work in concert with TH, as has been shown in several different contexts in amphibians (ref 17).” In the revised manuscript, we will also add the interesting case of the Senegal sole mentioned by the reviewer.

      In the last revision, we had also added that our results “brought a first insight into the potential role of corticoids in the metamorphosis of E. malabaricus and call for functional experiments directly testing a possible synergy” meaning that we clearly acknowledge that we are only revealing a hypothesis that remains to be tested. We later follow up with a discussion about the most novel observation and focus of our study, the increase in THs and cortisol during early development, which was unexpected and very intriguing. Again, these results suggest that there might be a link between the two, as has been shown in amphibians. This is typically the kind of results that should encourage more investigations into other fish species. Indeed, this has been pointed out by other authors and in particular by Bob Denver (probably the foremost expert on this topic) in Crespi and Denver 2012: “Elevation in HPA/I axis activity has been described prior to Metamorphosis in amphibians and fish, birth in mammals (reviewed in Crespi & Denver 2005a; Wada 2008)”. B. Denver also adds that: “Experiments in which GCs were elevated prior to metamorphosis or prior to hatching or birth (e.g. Weiss, Johnston & Moore 2007) or inhibited by treatments with GC synthesis blockers (e.g. metyrapone) or receptor antagonists (e.g. RU486, Glennemeir & Denver 2002) demonstrate that GCs play a causal role in precipitating these life-history transitions (also reviewed in Crespi & Denver 2005a; Wada 2008).” We believe the reviewer will be convinced by these elements coming from a colleague unanimously respected in the field.

      Furthermore, the authors need to recognise that the transcriptomic analysis is whole-body and that HPA axis genes are upregulated, which does not mean they are involved in regulating the HPT axis. The authors do not show that in thyrotrophs, any CRH receptor is expressed or in any other HPT axis-relevant cells and that changes in these genes correlate with changes in TSH expression. An in-situ hybridisation experiment showing co-expression on thyrotrophs of HPA genes and TSH could be a good start. However, the best scenario would be conducting cortisol treatment experiments to see if this hormone affects grouper metamorphosis.

      We agree that functional experiments are needed to validate our hypothesis. As the early peaks of expression levels observed for many genes were very intriguing for us, we did carry out thyroid hormones and goitrogenic treatment on young grouper larvae to test their effect on the morphological changes. Unfortunately, such experiments, already tricky on metamorphosing larvae, are even more risky on such tiny individuals just after hatching and we encountered high mortality rates. We must add that because we cannot establish a full grouper life cycle under lab conditions, we have done these experiment in the context of a commercial husbandry system in Japan, which while excellent limits the scope of possible experiments. We were thus not able to provide functional validation of our hypothesis. Such experiments will be a full project in itself, requiring setting up a rearing system suitable for both larval survival and economical constraints related to drug treatments. We were further limited by the spawning times of the grouper in the operational aquaculture farm, which are limited to a short time during each year. So even if we strongly agree with the necessity of conducting such experiments, we think that this is not in the scope of the present paper, but something future research can explore.

      High TSH and Tg levels usually parallel whole-body TH levels during teleost metamorphosis. However, in this study, high Tg expression levels are only achieved at the juvenile stage, whereas high TSH is achieved at D32, and at the juvenile stage, they are already at their lowest levels.

      This is exactly our point. We observe two peaks in TSH expression, one at D3 and one at D32. The peak at D3 coincides with high thyroid hormone levels on the same day, and while we have not measured TH at D32, existing literature shows that there is a peak in TH during that time (e.g., de Jesus et al., 1998). Similarly, there is a small peak of Tg at D3. Our manuscript focused more on the upregulation of these genes at D3, which has not been reported before in the literature and raised the question of the role of TH so early in the larval development, outside of the metamorphosis period.

      Regarding the respective levels of TSH and Tg, we first would like to add that their respective order of appearance before metamorphosis (TSH at D32, Tg after) is consistent with what we would expect. We agree however that the strong increase of Tg and TPO expression is later than expected. We will make this clear in the revised manuscript.

      It is very difficult to conclude anything with the TH and cortisol levels measurements. The authors only measured up until D10, whereas they argue that metamorphosis occurs at D32. In this way, these measurements could be more helpful if they focus on the correct developmental time. The data is irrelevant to their hypothesis.

      We respectfully disagree with the reviewer, considering that 1) TH levels have already been investigated in groupers coinciding with pigmentation changes and fin rays resorption, 2) that there is also evidence in numerous fish species that TH level increase is concomitant with increase of TH related genes, and 3) that we observed in our data an increase in the expression of TH related genes as well as pigmentation changes and fin rays resorption. Based on our experience in fish metamorphosis and the literature we can say confidently that those observations indicate that metamorphosis is occurring between D32 and the juvenile stage. To reinforce our point, we plan to add a figure to the revised manuscript, which puts our data in the context of earlier studies done in grouper. This will clearly show that our inference is correct. Additionally, we would like to point out that from our experience in several fish species transcriptomic data are more robust and precise than hormone measurements.

      However, as we were surprised by the activation of TH and corticoid pathway genes very early in the larval development (at D3), which is clearly outside of the metamorphosis period, we decided to measure TH and cortisol levels during this period of time to determine if whether or not there this surprising early activation was indeed corresponding to an increase in both TH and cortisol. As such observation has never been made in other teleost species (to our knowledge), and as we were wondering if gene activation was accompanied by hormonal increase, the measurements we did for TH and cortisol between D1 and D10 are relevant. We will make sure to improve the clarity of the revised version of the manuscript to avoid any confusion between the two periods we are studying: early larval development (between D1 and D10) and metamorphosis (between D32 and juvenile stage).

      Moreover, as stated in the previous review, a classical sign of teleost metamorphosis is the upregulation of TSHb and Tg, which does not occur at D32 therefore, it is very hard for me to accept that this is the metamorphic stage. With the lack of TH measurements, I cannot agree with the authors. I think this has to be toned down and made clear in the manuscript that D32 might be a putative metamorphic climax but that several aspects of biology work against it. Moreover, in D10, the authors show the highest cortisol level and lowest T4 and T3 levels. These observations are irreconcilable, with cortisol enhancing or participating in TH-driven metamorphosis.

      We thank the reviewer for this comment, but we think that there might be a misunderstanding here.

      (1) We clearly observed an increase of TSHb (that occurs between D18 and juvenile stage) and an increase of tg from D32 which coincide with the activation of other genes involved in TH pathway (dio2, dio3, and also a strong increase of TRb). All this and put in the context of what we know from previous grouper studies, clearly supports our conclusion that TH-regulated metamorphosis is starting at around D32 in grouper. We also observed morphological changes such as fin rays resorption and pigmentation changes between D32 and juvenile stage. Such morphological changes have already been associated as corresponding to metamorphosis in groupers (De Jesus et al 1998) as they occur during TH level increase, and they also happen to be under the control of TH in grouper (De Jesus et al 1998). Based on this study but also on studies (conducted on many other teleost species) showing that the increase of TH levels is always associated with an activation of TH pathway genes and morphological and pigmentation changes we concluded that metamorphosis of E. malabaricus occurs between D32 and juvenile stage. We will improve the clarity of the manuscript to make sure that our conclusion is based on our transcriptomic and morphological data plus the available literature.

      (2) We clearly observed another activation of TH related gene earlier in the development (between D1 and D10, with a surge of trhrs, tg and tpo at D3. As this activation was very unexpected for us, we decided to focus the analysis of TH levels between D1 and D10 and very interestingly we observed high level of T4 at D3 indicating that THs are instrumental very precociously in the larval development of the malabar grouper which has never been shown before. We declared line 195 that our “data reinforce the existence of two distinct periods of TH signalling activity, one early on at D3 and one late corresponding to classic metamorphosis at D32”. However, we agree that we could have been clearer and clearly explained that this early activation was very intriguing for us and that we wanted to investigate hormonal levels around that period. However, we never claimed anywhere in the manuscript that this early developmental period corresponds to metamorphosis. Something else is occurring and both TH and cortisol seem to be involved but further experiments need to be conducted to understand their role and their possible interaction.

      (3) Finally, regarding the comment about cortisol enhancing or participating in TH driven metamorphosis, our data clearly showed an activation of the corticoid pathway genes around metamorphosis (between D32 and juvenile stage) suggesting a potential implication of corticoids in metamorphosis, but we agree with the reviewer that further experiment are needed to test that. We never claimed that cortisol was enhancing or participating in metamorphosis, on the contrary we are “suggesting a possible interaction between TH and corticoid pathway during metamorphosis”. And we also say that our “results brought a first insight into the potential role of corticoids in the metamorphosis of E. malabaricus and call for functional experiments directly testing a possible synergy.” Nonetheless, we agree that some parts of our manuscript can be confusing in regards of cortisol synthesis during metamorphosis as we did not measure cortisol levels between D32 and juvenile stage. We will correct this in the revised version.

      Given this, the authors should quantify whole-body TH levels throughout the entire developmental window considered to determine where the peak is observed and how it correlates with the other hormonal genes/systems in the analysis.

      We did not measure TH levels at later stages as it has already been measured during Epinephelus coioides metamorphosis and the morphological changes observed in this species around the TH peak corresponds to what we observed in Epinephelus malabaricus around the peak of expression of TH pathway genes (see De Jesus et al., 1998 General and Comparative Endocrinology, 112:10-16). We are planning to add a figure reconciling all these data together. However, the main focus of this manuscript is the novel observation of the existence of an early activation period observed at D3, and for which we needed TH levels to determine if they were involved in another early developmental process (not related to metamorphosis). Our hypothesis is that this early activation might be related to the growth of fin rays necessary to enhance floatability during the oceanic larval dispersal. As we may have arrived at the explanation of this hypothesis too rapidly without setting up the context well enough, we will pay attention to improve that part too.

      Even though this is a solid technical paper and the data obtained is excellent, the conclusions drawn by the authors are not supported by their data, and at least hormonal levels should be present in parallel to the transcriptomic data. Furthermore, toning down some affirmations or even considering the different hypotheses available that are different from the ones suggested would be very positive.

      We thank the reviewer for acknowledging the solidity of the method of our paper and the quality of the results. We agree that there were several parts where our message is unclear, which we will address in the revised version of the manuscript to make sure there is no more confusion between the two distinct periods we studied in this paper (early larval development and metamorphosis). We will also make sure that our claims about TH/corticoids interaction during both periods remain hypothetical as we cannot yet, despite trials, sustain them with functional experiment.

    1. Author Response

      We provide here a provisional response to the Public Comments and main issues raised by the reviewers. We appreciate the opportunity to submit a revision and will give all of the reviewers’ comments careful consideration when modifying the manuscript.

      (1) BioRxiv version history.

      Reviewer 1 correctly noted that we have posted different versions of the paper on bioRxiv and that there were significant changes between the initial version and the one posted as part of the eLife preprint process. Here we provide a summary of that history.

      We initially posted a bioRxiv preprint in November, 2021 (Version 1) that included the results of two experiments. In Experiment 1, we compared conditions in which the stimulation frequency was at 2 kHz, 3.5 kHz, or 5.0 kHz. In Experiment 2, we replicated the 3.5 kHz condition of Experiment 1 and included two amplitude-modulated (AM) conditions, with a 3.5 kHz carrier signal modulated at 20 Hz or 140 Hz. Relative to the sham stimulation, non-modulated kTMP at 2 kHz and 3.5 kHz resulted in an increase in cortical excitability in Experiment 1. This effect was replicated in Experiment 2.

      In the original posting, we reported that there was an additional boost in excitability in the 20 Hz AM condition above that of the non-modulated condition. However, in re-examining the results, we recognized that the 20 Hz AM condition included an outlier that was pulling the group mean higher. We should have caught this outlier in the initial submission given that the resultant percent change for this individual is 3 standard deviations above the mean. Given the skew in the distribution, we also performed a log transform on the MEPs (which improves the normality and homoscedasticity of MEP distributions) and repeated the analysis. However, even here the participant’s results remained well outside the distribution. As such, we removed this participant and repeated all analyses. In this new analysis, there was no longer a significant difference between the 20 Hz AM and nonmodulated conditions in Experiment 2. Indeed, all three true stimulation conditions (nonmodulated, AM 20 Hz, AM 140 Hz) produced a similar boost in cortical excitability compared to sham. Thus, the results of Experiment 2 are consistent with those of Experiment 1, showing, in three new conditions, the efficacy of kHz stimulation on cortical excitability. But the results fail to provide evidence of an additional boost from amplitude modulation.

      We posted a second bioRxiv preprint in May, 2023 (Version 2) with the corrected results for Experiment 2, along with changes throughout the manuscript given the new analyses.

      Given the null results for the AM conditions, we decided to run a third experiment prior to submitting the work for publication. Here we used an alternative form of amplitude modulation (see Kasten et. al., NeuroImage 2018). In brief, we again observed a boost in cortical excitability in from non-modulated kTMP at 3.5 kHz, but no additional effect of amplitude modulation. This work is included in the third bioRrxiv preprint (Version 3), the paper that was submitted and reviewed at eLife.

      (2) Statistical analysis.

      Reviewer 1 raised a concern with the statistical analyses performed on aggregate data across experiments. We recognize that this is atypical and was certainly not part of an a priori plan. Here we describe our goal with the analyses and the thought process that led us to combine the data across the experiments.

      Our overarching aim is to examine the effect of corticospinal excitability of different kTMP waveforms (carrier frequency and amplitude modulated frequency) matched at the same estimated cortical E-field (2 V/m). Our core comparison was of the active conditions relative to a sham condition (E-field = 0.01 V/m). We included the non-modulated 3.5 kHz condition in Experiments 2 and 3 to provide a baseline from which we could assess whether amplitude modulation produced a measurable difference from that observed with non-modulated stimulation. Thus, this non-modulated condition as well as the sham condition was repeated in all three experiments. This provided an opportunity to examine the effect of kTMP with a relatively large sample, as well as assess how well the effects replicate, and resulted in the strategy we have taken in reporting the results.

      As a first step, we present the data from the 3.5 kHz non-modulated and sham conditions (including the individual participant data) for all three experiments in Figure 4. We used a linear mixed effect model to examine if there was an effect of Experiment (Exps 1, 2, 3) and observed no significant difference within each condition. Given this, we opted to pool the data for the sham and 3.5 kHz non-modulated conditions across the three experiments. Once data were pooled, we examined the effect of the carrier frequency and amplitude modulated frequency of the kTMP waveform.

      (3) Carry-over effects

      As suggested by Reviewer 1, we will examine in the revision if there is a carry-over effect across sessions (for the most part, 2-day intervals between sessions). For this, we will compare MEP amplitude in baseline blocks (pre-kTMP) across the four experimental sessions.

      Reviewer 1 also commented that mixing the single- and paired-pulse protocols might have impacted the results. While our a priori focus was on the single-pulse results, we wanted to include multiple probes given the novelty of our stimulation method. Mixing single- and different paired-pulse protocols has been relatively common in the noninvasive brain stimulation literature (e.g., Nitsche 2005, Huang et al, 2005, López-Alonso 2014, Batsikadze et al 2013) and we are unaware of any reports suggested that mixed designs (single and paired) distort the picture compared to pure designs (single only).

      (4) Sensation and Blinding

      Reviewer 2 bought up concerns about the sham condition and blinding of kTMP stimulation. We do think that kTMP is nearly ideal for blinding. The amplifier does emit an audible tone (at least for individuals with normal hearing) when set to an intensity to produce a 2 V/m E-field. For this reason, the participants and the experimenter wore ear plugs. Moreover, we played a 3.5 kHz tone in all conditions, including the sham condition, which effectively masked the amplifier sound. We measured the participant’s subjective rating of annoyance, pain, and muscle twitches after each kTMP session (active and sham). Using a linear mixed effect model, we found no difference between active and sham for each of these ratings suggesting that sensation was similar for active and sham (Fig 8). This matches our experience that kHz stimulation in the range used here has no perceptible sensation induced by the coil. To blind the experimenters (and participants) we used a coding system in which the experimenter typed in a number that had been randomly paired to a stimulation condition that varied across participants in a manner unknown to the experimenter.

      Reviewer 1 asked why we did not explicitly ask participants if they thought they were in an active or sham condition. This would certainly be a useful question. However, we did not want to alert them of the presence of a sham condition, preferring to simply describe the study as one testing a new method of non-invasive brain stimulation. Thus, we opted to focus on their subjective ratings of annoyance, pain, and finger twitches after kTMP stimulation for each experimental session.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2024-02352

      Corresponding author(s): Elise, Belaidi

      1. General Statements

      We would like to thank the reviewers for their constructive suggestions and comments. We hope that the “point by point answer” and the revision plan proposed below will convince the reviewers and the editor.

      We thank the reviewer 1 for his/her comments. We would like to specify that the involvement of HIF-1 in IH-induced mitochondrial remodeling has indeed been initiated by RNA-seq analysis and confirmed in a cell-based model as well as in wild-type and HIF-1a+/- heterozygous mice subjected to intermittent hypoxia (IH). In vivo, we originally demonstrated that Metformin reversed IH-induced increase in myocardial infarct size through AMPKa2 and, we proposed that metformin could modify HIF-1 activity. Then, we validated our hypothesis in an in vitro model allowing to demonstrate that Metformin, by increasing HIF-1a phosphorylation decreases its activity. We acknowledge that we used several models and this is the reason why we detailed as much as possible the Materials and Methods section including all models, experimental sets designed and methods details. We hope that the point by point response that we made for the reviewer 1 will increase the clarity of our work and we hope that the new results provided will strengthen the evidences concerning the mechanisms by which metformin can inhibit and modulate the deleterious impact of HIF-1 on IH-induced an increase in myocardial infarct size.

      We thank the reviewer 2 for his/her conclusion highlighting that “our work opens new avenues for exploring the potential effects of metformin as a modulatory of HIF-1𝛂 activity in obstructive sleep apnea syndrome”. We hope that the clarifications and/or justifications brought will convince him/her.<br /> We thank the reviewer 3 for having underlined that “metformin induces HIF-1α phosphorylation, decreases its nuclear localization and subsequently HIF-1 transcriptional activity are very much interesting” and for having highlighting that “our study is convincing”. We hope that the justification and the corrections brought in the point by point answer will convince him/her.

      Alltogether, as underlined by the 3 reviewers, our study is very interesting for translational science in the fields of cardiovascular, respiratory and sleep medicine. We hope that the point by point answer and the revision plan proposed will allow the publication of our article in EMBO Molecular Medicine.

      2. Description of the planned revisions

      • *

      Please find below the revision that we plan to address to answer to the questions of the reviewer 1.


      Figure 1 - it would be helpful to list all of the DEGs (what genes are changed?). Including the expression of HIF-1α and PHD isoforms would be informative. If there is a robust HIF-1α signal, changes in the expression of HIF and PHD isoforms would be anticipated. Fig 1F - with regards to glycolysis and hypoxia pathway analysis, most of the DEGs are not canonical HIF-1α/hypoxia targets.

      Figure 1 aimed at better understanding and manipulate the well-recognized involvement of HIF-1 in response to our specific IH stimulus (Semenza, Physiology 2009; Belaidi, Pharmacol & Ther. 2016). __The results provided by the RNA-seq analysis shows that IH induces cardiac oxidative and metabolic stress which are inter-related with HIF-1 activation. __We did not claim that these genes are HIF-1 targets genes. The RNA seq analysis did not allow to reveal HIF-1a and PHD1-3 transcript as the most dysregulated genes of the panel. In case of publication, bulk data and DEGS will be provided in an online file. We agree with the reviewer that the list of the 40 up and down-regulated genes would be very informative and would increase the value of the paper. Thus, we plan to add the name of the 40 up and down-regulated genes on Figure 1B.

      Figure 5G-I, show cytoplasmic HIF1a as well as nuclear.

      Alternatively, why not use IHC for subcellular localization?

      We think that the comments of the reviewer 1 concern Fig.4G-I and not 5G-I. In this figure, we showed that IH increases nuclear HIF-1____a____ expression compared to N condition and that this IH-effect is abolished in mice treated with Metformin, suggesting that, upon IH, Metformin impacts HIF-1__a __nuclear content and subsequently, its activity. The nuclear localization of HIF-1a is the most relevant mean to indicate its activation. We agree with the reviewer that IHC also allows for the indication of the nuclear localization of HIF-1a. Indeed, we previously performed IHC on nuclear HIF-1a localization and demonstrated that IH increased HIF-1a nuclear localization by IHC that was corroborated by Western-blot (Moulin S, TACD, 2020). Western-blot and IHC are both semi-quantitative techniques with different process of analyses. In this study, we choose Western-blot because we have the material to perform this technique and because IHC is associated with an analysis process (size of a slice, areas to analyze, colorimetry…) that is more complex than the analysis process of Western-blot (densitometry solely).

      While the nuclear localization of HIF-1a is the most relevant mean to indicate its activation; it could be interesting to see that HIF-1a cytosolic content was neither modify by IH nor by Metformin. This would also corroborate the results of the RNA-seq that did not demonstrate any difference in DEGs of HIF-1a or of other members of the HIF family. This would also confirm that Metformin plays a major role on HIF-1 activaty regulation (and not transcription) in the context of IH.

      Thus, we plan to perform a Western-blot of HIF-1a on cytosolic extracts of hearts from mice exposed to N or IH and treated or not with Metformin. These extracts are already available and Western-blot would be performed and replicated in 3 weeks. We could also provide a Western-blot in order to show the purity of our extraction protocol (nucleus vs cytosol).

      Figure 5F, it would be important to show the levels of expression of HIF1a in these experiments. Are there positive and negative controls that the authors could use for HIF21a activity in this experiment?

      In our manuscript, we aimed at demonstrating that Metformin decreases HIF-1 activity in a context of strong HIF-1____a____expression and/or stabilizion those mimics what happens after chronic IH in mice (Belaidi E, Int J Cardiol 2016, Moulin S, Ther Adv Chronic Dis 2020) and in apneic patients (Moulin S, Can J Cardiol 2020). Thus, we used a transfection allowing to overexpress HIF-1a that is one of the best means to increase HIF-1 activity. In the Figure 1 below, HA-HIF-1α-WT Addgene AmpR and 5 HRE GFP AmpR plasmids co-transfection induced a decrease in H9c2 viability and an increase in GFP-positive cells that were not observed in H9c2 transfected with pcDNA 3.1 HA-C AmpR (negative control). __This validates our in vitro model as a good positive control to mimic IH consequences. __ However, we agree with the reviewer that we could add a supplemental figure or a panel demonstrating that our transfection induced an increase in HIF-1a expression. Thus, we will perform a Western-blot targeting HIF-1a on H9c2 transfected with the control plasmid (pcDNA 3.1 HA-C AmpR) or the plasmid allowing the overexpression of HIF-1a (HA-HIF-1α-WT AmpR). This work would be performed in 2 months.

      Moreover, we already improved the lisibility of the Figure 5F to clarify the experimental conditions (table inserted under the graphic); we also completed the Materials and Methods section to specify the plasmid used (modifications are in red in the manuscript).

      Figure to see on the downloaded file.



      Figure 1 : GFP fluorescence in H9c2 cells transfected with pcDNA 3.1 HA-C AmpR (control condition) or HA-HIF-1α-WT AmpR (positive control, overexpression of HIF-1a) and 5 HRE-GFP AmpR plamids and treated with CoCl2 (1mM, 2h); magnification x100.

      • *

      This paragraph concerns only the point 4 of the fifth question.

      In these experiments, as well as subsequent studies, it would be very informative to use a specific AMPK activator e.g. MK-8772, to compare with metformin. It is well known that metformin has a number of other targets in addition to AMPK.

      We agree with the reviewer that metformin has pleiotropic effect. Very interestingly, we demonstrated that the reduced-infarct size is not related to the metabolic systemic effect of metformin since it failed to improve the IH-induced insulin resistance while it improves the answer to insulin in normoxic mice (supplemental Figure S3B). This demonstrates that in our model, the cardioprotective effects of Metformin are independent of a potential systemic effect. Then, we demonstrated that metformin protects the heart against ischemia-reperfusion through AMPK____a____2 activation by using AMPK____a____2KO exposed to IH in which Metformin failed to decrease infarct size (Fig.4N). MK-8772 is not widely used in vivo models. Moreover a recent study indicates that chronic treatment with MK-8772 (14 days 1 month in mice and rats, respectively) induces cardiac hypertrophy characterized by an increase in heart weight (Myers R, Science 2017). In vivo experiments with MK-8772 would be not clinically relevant as the use of metformin that is already used in clinic. However, in order to improve the mechanistic investigation concerning the role of AMPKa2 activation on inhibiting HIF-1 activity, we propose to perform the in vitroexperiments performed in Figure 5 with a specific allosteric small-molecule activator of AMPKa2 such as 991.

      We plan to:

      -Expose H9c2 to CoCl2 and treat them with 991 in order to measure HIF-1a phosphorylation.

      -Transfect H9c2 with our plasmids HA-HIF-1α-WT AmpR and 5 HRE GFP AmpR and treat them or not with 991 in order to measure HIF-1 activity (GFP fluorescence). These experiments would be performed in 2 months.

      __Please find below the revision that we plan to address to answer to the second question of the reviewer 2. __

      • *

      2) The WB images were cut and pasted. Please add the original images

      We acknowledge the reviewer's comment and will address it by submitting a supplementary file containing the uncropped immunoblot images. Since this file already exists, our plan is to standardize it by providing, for each slide (immunoblot), all relevant information pertaining to our experiments, including groups, molecular weight markers, cutting, membrane stripping, and other pertinent details.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      • *

      __Please find below the answers or the revisions that have already been incorporated in response to the comments of the reviewer 1. Please note that we provided new results and new figures at the discretion of the reviewer, but we are ready to insert them as figures or supplemental figures in a new revised manuscript if the reviewers and the editor think that it would improve our message. __

      Was mitochondrial content in the hearts after IH experiment measured e.g. mtDNA measurements? IH results in mitochondrial dysfunction/reduced mitochondrial content. It would have been good to show mitochondrial dysfunction by doing basic functional experiments (e.g. TMRM/MitoROS imaging etc.) by isolating cardiomyocytes from the N and IH experiments.

      We thank the reviewer for these questions about the mitochondrial function and content. The impact of IH on mitochondrial function has already been demonstrated in heart (Moulin S, Antioxydants 2022, Wei Q, Am J Physiol 2012). __Indeed, we previously showed that mitochondria isolated from hearts of mice exposed to IH had a decrease in maximal respiration in complex I and II that was not observed in HIF-1_a_+/- ____mice (Moulin S, Antioxidants 2022), indicating that HIF-1 is responsible for IH-induced mitochondrial dysfunction. __

      Figure 4 shows that Metformin abolished IH-induced mitochondrial remodeling similar to what we observed in HIF-1a+/-(Figure 2). This means that treating with Metformin or partially deleting the gene encoding for HIF-a induce the same impact on IH. Then, we demonstrated that Metformin can control HIF-1 activation and we concluded that metformin could be cardioprotective through inhibiting HIF-1 activation and subsequent mitochondrial stress and remodeling. In this study, we focused on the effects of Metformin on HIF-1 and we did not aim at directly test the effect of metformin on mitochondrial function. Actually, metformin exhibits biphasic effects on bioenergetics of cardiac tissue depending on the modality of administration (i. e. single injection, time of administration during an ischemia-reperfusion procedure); the dose administered and the tissue studied (i. e. hiPSC-CMs, isolated mitochondria…) (Emelyanova N, Transl Res 2021). But, we collected some data that we would like to submit at the discretion of the reviewer. Using oximetry, we measured maximal respiration in complex 1 and 2 on isolated mitochondria from hearts of mice exposed to N, IH and treated or not with metformin during the exposure__. While we observed that IH decreases maximal respiration in complex 1 and 2, we did not find any effect of metformin on mitochondrial respiration alteration induced by IH (Figure 2A, B). Using spectrofluorometry, we measured the mitochondrial membrane potential using TMRM; __we did not find any modification of membrane potential in IH or Metformin-treated mice (Figure 2C). Because we previously did not observe any impact of IH on mtDNA/gDNA ratio (Figure 2D), we did not test metformin on this parameter.

      To conclude, we think that these results are not directly in the scope of our work but if the reviewer thinks that they deserve to be discussed, we could add them in a supplementary figure.

      Please, see the figure on the dowloaded file

      Figure 2 : Mice were exposed to 21 days of Normoxia (N) or Intermittent Hypoxia (IH) (1-min cycle of FiO2 5%-21%) and treated with vehicle (Vh, CmCNa 0.01%, 0,1ml.10g-1) or Metformin (Met, 300mg.kg-1.d-1). (A, B) Mitochondrial function __was measured by oximetry with sequential addition of substrate (state 2), ADP (200mM, state 3, maximal respiration) and oligomycin (12.5 mM, state 4) ; quantification of O2 consumption for NADH-linked mitochondrial respiration (complex I-glutamate-malate, GM, 20mM) (A), and for FADH2-linked mitochondrial respiration (complex II-succinate, S, 5mM, in presence of complex I inhibition by rotenone, 6.25mM) (B) (n=8). (C) Mitochondrial membrane potential measured by spectrofluorometry after Tetramethylrhodamine Methyl Ester (TMRM, 0.2mM) in presence of GM (basal condition), maximal respiration (ADP) and uncoupling condition Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone, FCCP, 3mM); fluorescence intensity is expressed relative to fuorescence at baseline (before GM) (n=8). (D__) Mitochondrial content assessed by the expression of mitochondrial DNA (mtDNA, COX1) relative to genomic DNA (gDNA, ApoB) measured after PCR (n=6); *p

      Fig 2A-D - CoCl2 is not a good model to mimic hypoxia due its effect on disrupting iron homeostasis in cells, which can mean that some of the effects are due to changes in iron levels and not HIF stabilisation.

      The capacity of CoCl2 to chelate iron is the main property of CoCl2 that we used in order to stabilize HIF-1____a____. Actually, prolyl-4-hydroxylases need Fe2+ to hydroxylate HIF-1____a____ and induce its degradation. __Then, intermittent hypoxia (IH) is characterized by very rapid changes in PO2. This stimulus was designed to reproduce sleep apnea syndrome and its associated disorders (i. e. insulin-resistance, hypertension, increase in myocardial infarct size). This model was firstly developed and validated in rodents (Dematteis M, ILARJ 2008, Belaidi E, Eur Resp Rev 2022, Harki , Eur Resp J 2022). Compelling evidence indicate that the involvement of HIF-1 in IH-deleterious consequences is related to the repetitive phases of oxygenation and especially to IH-induced oxidative stress (Semenza Physiology 2009, Belaidi Pharmacol. & Ther 2016). In order to increase the level of mechanistic insights on HIF-1, we next attempted to optimize in vitro models. A device was developed by Minoves et al. (Minoves M, Am J Physiol, 2017) to expose endothelial and cancer cells to IH. __However, as illustrated below, this device does not mimic efficient rapid hypoxia-reoxygenation cycles able to induce cardiac cell death (Figure 3A). However, CoCl2 decreases H9C2 viability by 60% (Figure 3B) that is associated with a sustained stabilization of HIF-1____a (Figure 3C,D). Thus, we choose this in vitro model as it replicates cardiac cell death and HIF-1_a_overexpression or stabilization which we similarly observe in our in vivo model and in apneic patients (Moulin S, Can J Cardiol 2020).

      Please see the figure on the dowloaded file

      Figure 3 : (A-B) Cell viability measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H- tetrazolium bromide (MTT) of H9c2 cells exposed to 6 hours (h) of repetitive cycles of Intermittent hypoxia (2 minutes (min) PO2 16% - 2 min PO2 2%) (n=3) (A) or treated with CoCl2 (1mM, 2h) (n=6) (B). (C-D), __quantification of __total ____HIF-1____𝛂 expression relative to tubulin (C) and representative image of Wetsern-blot (D) (n=2-3); *p****p*

      Fig. 2K, change in BNIP3 expression is modest, but change in Parkin is very dramatic. BNIP3 is a HIF-1α target but Parkin is not, so it is plausible that mitophagy could be occurring through a HIF-1α independent mechanism.

      Fig. 2K is a representative panel of 2-3 independent experiments. The quantification reported in Figures 2I and 2J demonstrated a significant decrease in BNIP3 and Parkin expressions in HIF-1a heterozygous mice exposed to Intermittent Hypoxia (IH) compared to HIF-1a+/+ mice exposed to IH. While we acknowledge that only BNIP3 is a direct target of HIF-1, the role of HIF-1 in IH-induced auto/mitophagy is demonstrated by our experiments performed in HIF-1____a____heterozygous mice. This shows an important role for HIF-1 without excluding any impact of HIF-1a independent mechanisms.

      What are we meant to be looking at in Fig 2L?

      Figure 2L aims at illustrating mitochondrial remodeling under IH. Stars indicate that mitochondria have abnormal fate in IH conditions and arrows point autophagosomal membrane and formation. This figure was magnified to be clearer (please see new Figure 2L).

      Figure 3B-C, the reduction in pT172 on AMPK is modest. It would be good to include pACC as a downstream target for AMPK.

      As recommended by the reviewer, we inserted in the manuscript the quantification of 79Ser-P-ACC/ACC western-blot as well as a representative image of the Western-blot (See new Figure 3). We also modified the legend of the figure. 79Ser-P-ACC is an important target of AMPK; however, in our experimental conditions, its phosphorylation is not associated to the decrease in AMPK phosphorylation. This could be explained by many points. First, Metformin was administered every day and hearts were harvested 24 hours later after the last administration. Most studies demonstrating a modification of AMPK and ACC phosphorylation are experiments performed in vitro or directly (less than 1 hour) after a single dose of Metformin administration. In the context of myocardial ischemia-reperfusion, Yin et al. showed an increase in P-AMPK/AMPK directly after Metformin treatment without showing any data on P-ACC/ACC (Yin M; Am J Physiol 2011); similar data were published in models of chronic cardiac diseases (Soraya H, Eur J Pharmacol, Gundewar S, Circ Res 2009). Second, in line with the previous explanation, the lack of effect of metformin on P-AMPK and/or P-ACC in rodent models could be explained by its rapid distribution (Sheleme T Clin Pharmacokinetics of Metformin 2021) and its short half-life that is around 3.7 hours in mice (Junien N, Arch Int Pharmacodyn Ther 1979).

      To conclude, since we performed all our analysis 24h after the last treatment and exposure to hypoxia, we argue that the slight but significative decrease in AMPK phosphorylation that we observed in our study highlight a robust impact of chronic IH. However, this would be elegant to confirm this result by measuring AMPK through its phosphorylation capacity (Cool B, Cell Metab 2006, Ducommun S, Am J Physiol 2014). We already sent hearts from mice exposed to Normoxia or Intermittent Hypoxia to Luc Bertrand’s lab (IREC, Belgium) where they used to perform this assay.

      Fig. E-G, show data for mice treated with vehicle.

      In Figure 4 I-J­­, we demonstrated that Metformin significantly decreases infract size in IH condition only and this validates our main hypothesis regarding the specific beneficial effect of this drug in the context of chronic IH. __In order to show that the cardioprotective effect of Metformin is relative to AMPKa2 activation, we first showed that 79Ser-PACC/ACC, one of the main downstream targets of AMPKa2 was increased (Fig. E-G). We did not find it necessary to does not exhibit cardioprotective effects. However, as shown in Figure 3 below, __Metformin also increases 79Ser-PACC/ACC in Normoxic mice validating the treatment. Thus, in normoxic conditions, AMPK____a____2 activation does not exert any cardioprotective effect. We acknowledge that this reinforces our result about the specificity of AMPK____a____2 activation by Metformin under chronic IH condition. We could add this Figure in supplemental results.

      Please, see the figure on the dowloaded file

      Figure 4 : AMPK activation in Normoxic mice treated with vehicle (Vh, CmCNa 0.01%, 0,1ml.10g-1) or __ __metformin (Met, 300mg.kg-1.d-1 : __ __172Thr-P-AMPK/AMPK (A) and 79Ser-P-ACC/ACC (B) ratio and representative image of Western-blot (C) (n=3-6); *p

      Fig. 3K - what cre is used for the a2 KO mice?

      As written in the Materials and methods section, AMPKa2KO mice are not inducible Knock-out mice. Constitutive AMPKα2 knockout mice were kindly generated by Benoit Viollet (Viollet B, JCI, 2003).

      Include normoxia data for the a2 KO mice studies.

      The question of the reviewer concerning the cardioprotective effects of metformin is interesting but is not aligned with the objectives of the study. Indeed, we did not treat normoxic mice with Met for several reasons. First, the objective of the study was to find a cardioprotective strategy against IH-induced an increase in infarct size. Second, Fig. 3I shows that Met significantly reduced infarct size upon IH only; this suggests that AMPK____a____2 activation is specifically involved in IH-induced increase in infarct size but not in reducing infarct size in normoxic mice. Moreover, the beneficial impact of metformin in standard models of myocardial ischemia-reperfusion is controversial and has been extensively discussed (Foretz et al. Cell Metab. 2014). Overall, using AMPKa2 mice was legitimated in the context of IH only. We validated the involvement of AMPKa2 in the cardioprotective effect of metformin especially in IH conditions.

      Figure 5G, what is the rationale for switching to CoCl2 in the mice to prove metformin reduced HIF-1α expression? Why not use reduced O2 tension in mice.

      We respectfully disagree with the reviewer since mice were exposed to N and IH and treated or not with Metformin to demonstrate that this drug abolished IH-induced increase in HIF-1a nuclear expression (Figure 4 H, I). The same model was used in Figure 3 to demonstrate the impact of Metformin on infarct size. Fig. 5G was conducted to demonstrate the potential link between AMPKa2 and HIF-1a phosphorylation in basal conditions of AMPKa2 content or in absence of AMPKa2 (AMPKa2-/- mice). The single presence of AMPK____a____2 demonstrates an increase in HIF-1____a____ phosphorylation if its stabilization is increased by CoCl2; this was not observed in AMPK____a____2-/- mice highlighting that AMPK____a____2 plays an important role in HIF-1____a phosphorylation.


      Please find below the answer to the first question asked by the reviewer 2.

      1) Why did authors choose the IH protocol illustrated in Fig. S1A

      The choice of the hypoxic stimulus was based on literature and mainly on our recognized expertise in preclinical studies aiming at better understanding obstructive sleep apnea syndrome (OSA); a chronic pathology associated with several comorbidities such as diabetes, hypertension… We are conscious that the hypoxic stimulus used in this study is very severe, with a nadir arterial oxygen saturation (SaO2) around 60%. However, this experimental design is required to induce detrimental cardiovascular effects __in the absence of any confounding factors (i.e., obesity) or genetic susceptibility for complications (i. e. genetic susceptibility to hypertension) (Dematteis M, ILARJ 2008). Especially in the context of myocardial infarction, exposing rodents to 14 to 21 days of IH at 5% and subjected them to a myocardial ischemia-reperfusion protocol allows us to reproduce the increase in infarct size in rats (Belaidi E, J. Am. Coll. Cardiol., 2009; Bourdier G, Am J Physiol, 2016) and in mice (Belaidi E, Int J Cardiol 2016; Moulin S, Can J Cardiol 2020) similar to what has been observed in apneic patients (Buchner S, EHJ 2014). __Moreover, we recently conducted a meta-analysis based on 23 preclinical studies aiming at investigating the impact of the IH pattern (duration, FiO2, repetition of cycles…) on infarct size and cardiomyocyte death (Belaidi E, Eur. Resp. J 2022). We showed that IH significantly increases infarct size when IH is applied several days (especially 14 to 21 days) and when FiO2 is around 5%; whereas IH decreases infarct size when it is applied a single day at a FiO2 at 10%. This meta-analysis provided the confirmation that we need to apply a chronic and severe stimulus to reproduce an increase in infarct size that is observed in apneic patients which are exposed every day, during several days to a decrease in SaO2. If the reviewers and the editor consider that this point should be discussed in the discussion section, we will be happy to include it.

      • *

      Please find below the answers or the revisions that have already been incorporated in response to the comments of the reviewer 3. They appear in red in the new manuscript except the modifications performed in the “references” section which appear in black.

      1 The authors used H9c2 rat cardiac cells in vitro experiments although they used mouse model in vivo experiments. Using mouse P19.CL6 cardiac cells instead of rat H9c2 cells may much clearer. Why the authors did not use P19.CL6 cells should be explained.

      We thank the reviewer for his/her suggestion. P19CL6 cell line has been isolated from pluripotent P19 embryonal carcinoma (EC) cells after long term culture under conditions for mesodermal differentiation (Habara-Ohkubo A, Cell Struc Funct 1996). Therefore, these cells are mostly used to ____study the differentiation of cardiac muscle. Indeed, they were recognized to avoid large variations in the differentiation rates which were extensively reported (Mueller I, J Biomed Biotechnol 2010). To our knowledges no ventricular non-beating mice cell line. In this study, we used H9c2 which are extensively used and recognized as a gold standard cellular model to study the biology of cardiomyocytes including mechanisms involved in cardiac ischemia-reperfusion injury (Paillard M, Circulation, 2013; Zhang G Circulation 2021__), cardiac hypertrophy__ (Zhang N, Cell Death Diff 2020; Hu H, Cardiovasc Res 2020), intra-organites calcium exchanges (Moulin S, Antioxydants, 2022, Paillard M, Circulation, 2013) as drug testing (Beshay NM, J Pharm and Tox Methods, 2007). Recently, H9c2 and P19.CL6 were exposed to intermittent hypoxia (70 cycles of FiO2 1% (5 min) - FiO2 21% (10%)) in order to “mimic OSA” and investigate the transcription level of a pool of genes. The authors show some similiraties and differences of mRNA expression between the two cell lines that, indeed, could be attributed to variations in the cell origin (Takasawa S, IJMS 2022). However, in this study, there are no experiment allowing to assess the state of cardiac cells (apoptosis, life, metabolism, remodeling) questioning the pathophysiologic transposability of the model. Moreover, the number of experiments conducted on H9C2 (pubmed references : 7000 vs 100 for P19.CL6) to understand the mechanism involved in acute and chronic cardiac pathologies makes our choice confident and relevant.

      2 The authors described, "The protocol was approved by the French minister (APAFIS#23725-2020012111137561.v2)." in Animals (page 16) without showing approval date, the authors should clearly show the approval date together with their approval numbers.

      We added the approvement date in the materials and methods section. It was approved on February 20, 2020.

      3 In Figure 2 L, scale bar(s) should be added because figures are magnified and/or reduced by printer.

      We agree with the reviewer that scale bars were not visible; we highlighted them.

      4 In Figure 3J and 3N, scale bar(s) should be added.

      Scale bars have been now added on figures 3J and 3N. All pictures were acquired at the maximal zoom of a camera placed at an equal distance from the slices. Then, analyses were performed, slice per slice, with Image J with the same zoom (x5 to get an image at 100%). In this context, scale was added based on a photo of slice taken close to a ruler.

      5 In introduction, "HIF-1" should be changed to "hypoxia-inducible factor 1 (HIF-1)".

      Thank you, we replaced HIF-1 by Hypoxia Inducible Factor-1 (HIF-1) in the introduction section.

      6 In Results, "Angpt1, Txnip, Nmrk2, Nuak1 or Pfkfb1" should be changed to "Angiopoietin 1 (Angpt1), Thioredoxin-interacting protein (Txnip), Nicotinamide riboside kinase 2 (Nmrk2), NUAK family SNF1-like kinase 1 (Nuak1) or 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkb1)".

      We did the modification and let the abbreviation in italic since it concerns genes name.

      7 In Chronic intermittent hypoxia, "FiO2" should be changed to "FiO2".

      Thank you, we changed it in the materials and methods section.

      8 In Western-blot, "Bio-Rad, California, USA" should be changed to "Bio-Rad, Hercules, CA".

      Thank you, we did it.

      9 In Western-blot, what "tubulin" (α-tubulin or β-tubulin) should be clarified.

      We agree with the reviewer that this point should be specified; α-tubulin was stained, we added the “α”.

      As mentioned below, we have done all the modifications required by the reviewer in the references section.

      10 In Ref. 8, "Antioxidants (Basel) 11 (2022)" should be changed to "Antioxidants (Basel) 11, 2326 (2022)".

      Done

      11 In Ref.10, "Pharmacol Ther (2016)" should be changed to "Pharmacol Ther 168, 1-11 (2016)".

      Done

      12 In Ref.13, "Antioxidants (Basel) 11 (2022)" should be changed to "Antioxidants (Basel) 11, 1462 (2022).

      Done

      13 In Ref. 21, "Diabetes (2017)" should be changed to "Diabetes 66, 2942-2951 (2017)".

      Done

      14 In Ref. 28, "Adv Biol (Weinh), e2300292 (2023)" should be changed to "Adv Biol (Weinh), 8, 2300292 (2023)".

      Done

      15 In Ref. 37, "J Am Heart Assoc 6 (2017)" should be changed to "J Am Heart Assoc 6, e006680 (2017)".

      Done

      16 In Ref. 41, "Eur Respir Rev 32 (2023)" should be changed to "Eur Respir Rev 32, 230083 (2023)".

      Done

      17 In Ref. 46, "Int J Mol Sci 22 (2020)" should be changed to "Int J Mol Sci 22, 268 (2021)".

      Done

      18 In Ref. 47, "Int J Mol Sci 21 (2020)" should be changed to "Int J Mol Sci 21, 2428 (2020)". Done

      4. Description of analyses that authors prefer not to carry out

      __Please find below the answers to the comment of the reviewer 3 that we cannot provide and that is not in the scope of the study. __

      • *

      Figure 5, multiple phosphorylation sites have been identified on HIF1a. What is the nature of the Thr/SerP-HIF1a antibody? It would be far more preferable (essential?) to identify the site(s) within HIF1a that are phosphorylated by AMPK.

      The antibody was provided by Cell Signalling, ref. 9631. Phospho-(Ser/Thr) Phe Antibody detects phospho-serine or threonine in the context of tyrosine, tryptophan or phenylalanine.

      The identification of the Phosphorylation sites will require a long-time consuming phosphoproteomic analysis and subsequent functional validation in vivo and in vitro (directed mutagenesis, knock-in mice, …) which are out of the scope of our paper.

    1. The document provides a fascinating overview of the technologies available to monitor personal health and sports performance, showing how the sector is rapidly evolving towards increasingly intelligent and personalized solutions. Exploring a wide range of devices and mobile applications designed to record and analyze physiological and biomechanical parameters, we highlight how we are moving from simple measurement of standard vital signs to sophisticated predictive algorithms based on population information and artificial intelligence.

      Personally, I believe it is crucial to highlight the importance of independent validation of these technologies. It is worrying to know that many of them have not yet been adequately tested, raising questions about their effectiveness and usefulness for the average consumer. In a world where we are increasingly surrounded by data and devices, it is essential that the technologies we choose to use are reliable and based on solid scientific evidence.

      Analyzing specific devices such as the Zephyr Sensor, E4 Wristband, and Reign Active Recovery Band offers a detailed look at their capabilities and potential benefits. Furthermore, the reference to the Mio SLICE™ bracelet, with its "Personal Activity Score" calculated via an algorithm, is particularly interesting. The clinical study that demonstrated a reduction in the risk of cardiovascular disease for those with a score ≥100 is tangible evidence of the benefits these technologies can offer when tested properly.

      Finally, discussion of sleep and cardiorespiratory monitoring technologies, such as the OURA ring and Fitbit Charge2™, highlights current challenges and limitations. Personally, I think sleep tracking is one of the most interesting aspects of these technologies, as rest is critical to overall health and physical performance. However, it is important to be aware of limitations in the precision and interpretation of the data collected, as this may affect our confidence in the results provided.

      Overall, the document offers a comprehensive overview of an ever-expanding sector. As technologies for monitoring health and sports performance continue to evolve, it is essential to stay informed about their potential and limitations. Innovation is exciting, but careful research and development is essential to ensure that these technologies are truly useful and reliable for improving our lives and performance.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Response to Public Reviewer Comments

      We again thank the reviewers for the time and effort they clearly put into reviewing our manuscript. We have revised our manuscript to take into account the majority of their suggestions, primary among them being refinements of our model and classification approach, detailed sensitivity analysis of our model, and several new simulations. Their very constructive feedback has resulted in what we feel is a much-improved paper. In what follows, we respond to each of their points.

      Reviewer #1:

      COMMENT: The reviewer suggested that our control policy classification thresholds should be increased, especially if the behavioral labels are to be subsequently used to guide analyses of neural data which “is messy enough, but having trials being incorrectly labeled will make it even messier when trying to quantify differences in neural processing between strategies.”

      REPLY: We appreciate the observation and agree with the suggestion. In the revised manuscript, we simplified the model (as another reviewer suggested), which allowed for better training of the classifier. This enabled an increase in the threshold to 95% to have more confidence in the identified control strategies. Figures 7 and 8 were regenerated based on the new threshold.

      COMMENT: The reviewer asked if we could discuss what one might expect to observe neurally under the different control policies, and also suggested that an extension of this work could be to explore perturbation trials, which might further distinguish between the two control policies.

      REPLY: It is indeed interesting to speculate what neural activity could underlie these different behavioral signatures. As this task is novel to the field, it is difficult to predict what we might observe once we examine neural activity through the lens of these control regimes. We hope this will be the topic of future studies, and one aspect worthy of investigation is how neural activity prior to the start of the movement may reflect two different control objectives. Previous work has shown that motor cortex is highly active and specific as monkeys prepare for a cued movement and that this preparatory activity can take place without an imposed delay period (Ames et al., 2014; Cisek & Kalaska, 2005; Dekleva et al., 2018; Elsayed et al., 2016; Kaufman et al., 2014; Lara et al., 2018; Perich et al., 2018; Vyas et al., 2018; Zimnik & Churchland, 2021). It seems possible that the control strategies we observed correspond to different preparatory activity in the motor cortex. We added these speculations to the discussion.

      The reviewer’s suggestion to introduce perturbations to probe sensory processing is very good and was also suggested by another reviewer. We therefore conducted additional simulations in which we introduced perturbations (Supplementary Material; Figure S10). Indeed, in these model simulations the two control objectives separated more. However, testing these predictions via experiments must await future work.

      COMMENT: “It seems like a mix of lambda values are presented in Figure 5 and beyond. There needs to be some sort of analysis to verify that all strategies were equally used across lambda levels. Otherwise, apparent differences between control strategies may simply reflect changes in the difficulty of the task. It would also be useful to know if there were any trends across time?”

      REPLY: We appreciate and agree with the reviewer’s suggestion. We have added a complementary analysis of control objectives with respect to task difficulty, presented in the Supplementary Material (Figures S7 and S8). We demonstrate that, overall, the control objectives remain generally consistent throughout trials and difficulty levels. Therefore, it can be concluded that the difference in behavior associated with different control objectives does not depend on the trial sequence or difficulty of the task. A statement to this extent was added to the main text.

      COMMENT: “Figure 2 highlights key features of performance as a function of task difficulty. …However, there is a curious difference in hand/cursor Gain for Monkey J. Any insight as to the basis for this difference?”

      REPLY: The apparently different behavior of Monkey J in the hand/cursor RMS ratio could be due to subject-to-subject variability. Given that we have data from only two monkey subjects, we examined inter-individual variations between human subjects in the Supplementary Material by presenting individual hand/cursor gain data for all individual human subjects (Figure S1). As can be seen, there was indeed variability, with some subjects not exhibiting the same clear trend with task difficulty. However, on average, the RMS ratio shows a slight decrease as trials grow more difficult, as was earlier shown in Figure 2. We added a sentence about the possibility of inter-individual variations to address the difference in behavior of monkey J with reference to the supplementary material.

      Reviewer #2:

      (Reviewer #2's original review is with the first version of the Reviewed Preprint. Below is the authors' summary of those comments.)

      COMMENT: The reviewer commends the care and effort taken to characterize control policies that may be used to perform the CST, via dual human and monkey experiments and model simulations, noting the importance of doing so as a precursor to future neural recordings or BMI experiments. But the reviewer also wondered if it is all that surprising that different subjects might choose different strategies: “... it makes sense that different subjects might choose to favor different objectives, and also that they can do so when instructed. But has this taught us something about motor control or simply that there is a natural ambiguity built into the task?”

      REPLY: The redundancy in the task that allowed different solutions to achieve the task was deliberate, and the motivation for choosing this task for this study. We therefore did not regard the resulting subject-to-subject variability as a finding of our study. Rather, redundancy and inter-individual variability are features ubiquitous in all everyday actions and we explicitly wanted to examine behavior that is closer to such behavior. As commended by the reviewers, CST is a rich task that extends our research beyond the conventional highly-constrained reaching task. The goal of our study was to develop a computational account to identify and classify such differences to better leverage future neural analyses of such more complex behaviors. This choice of task has now been better motivated in the Introduction of the revised manuscript.

      COMMENT: The reviewer asks about our premise that subjects may use different control objectives in different trials, and whether instead a single policy may be a more parsimonious account for the different behavioral patterns in the data, given noise and instability in the system. In support of this view, the reviewer implemented a simple fixed controller and shared their own simulations to demonstrate its ability to generate different behavioral patterns simply by changing the gain of the controller. The reviewer concludes that our data “are potentially compatible with any of these interpretations, depending on which control-style model one prefers.”

      REPLY: We first address the reviewer’s concern that a simple “fixed” controller can account for the two types of behavioral patterns observed in Experiment 2 (instructed groups) by a small change in the control gain. We note that our controller is also fixed in terms of the plant, the actuator, and the sensory feedback loop; the only change we explore is in the relative weights of position vs. velocity in the Q matrix. This determines whether it is deviations in position or in velocity that predominate in the cost function. This, in turn, generates changes in the gain vector L in our model, since the optimal solution (i.e. the gains L that minimize the cost function) depends on the Q matrix as well as the dynamics of the plant (specifically, the lambda value). Hence, one could interpret the differences arising from changes in the control objective (the Q matrix) as changes in the gains of our “fixed” controller.

      More importantly, while the noise and instability in the system may indeed occasionally result in distinct behavioral patterns (and we have observed such cases in our simulations as well), these factors are far from giving an alternative account for the structural differences in the behavior that we attribute to the control objective. To substantiate this point, we performed additional simulations that are provided in the Supplementary Material (Figures S4—6). These simulations show that neither a change in noise nor in the relative cost of effort can account for the two distinct types of behavior. These differences are more consistently attributed to a change in the control objective.

      In addition, our approach provides a normative account of the control gains needed to simulate the observed data, as well as the control objectives that underlie those gains. As such, the two control policies in our model (Position and Velocity Control) resulted in control gains that captured the differences in the experimental groups (Experiment 2), both at the single trial and aggregate levels and across different task difficulties. Figure S9 in the Supplementary Material shows how the control gains differ between Position and Velocity Control in our model across different difficulty levels.

      We agree,with the reviewer’s overall point, that there are no doubt many models that can exhibit the variability observed in our experimental data, our simulations, or the reviewer’s simulations. Our study aimed to explore in detail not only the model’s ability to generate the variable behavior observed in experimental data, but also to match experimental results in terms of performance levels, gains, lags and correlations across a wide range of lambda values, wherein the only changes in the model were the lambda value and the control objective. Without the details of the reviewer’s model, we are unable to perform a detailed analysis of that model. Even so, we are not claiming that our model is the ‘ground truth,’ only that it is certainly a reasonable model, adopted from the literature, that provides intuitive and normative explanation about the performance of humans and monkeys over a range of metrics, system dynamics, and experimental conditions.

      Finally, we understand the reviewer’s concern regarding whether the trial-by-trial identification of control strategy in Figure 8 suggests that (uninstructed) subjects constantly switch control objectives between Position and Velocity. Although it is not unreasonable to imagine that individuals would intuitively try different strategies between ‘keeping the cursor still’ and ‘keeping the cursor at the center’ across trials, we agree that it is generally difficult to determine such trial-to-trial changes, especially when the behavior lies somewhere in between the two control objectives. In such cases, as we originally discussed in the manuscript, an alternative explanation could be a mixed control objective that generates behavior at the intersection of Position and Velocity Control, i.e., between the two slopes in Figure 8. We believe, however, that our modeling approach is still helpful in cases where performance is predominantly based on Position or Velocity Control. After all, the motivation for this study was to parse neural data into two classes associated with each control objective to potentially better identify structure underlying these behaviors.

      We clarified these points in the main text by adding further explanation in the Discussion section.

      COMMENT: The reviewer suggested additional experiments, such as perturbation trials, that might be useful to further explore the separability of control objectives. They also suggested that we temper our conclusion that our approach can reliably discriminate amongst different control policies on individual trials. Finally, the reviewer suggested that we modify our Introduction and/or Discussion to note past human/monkey research as well as investigations of minimization of velocity-error versus position-error in the smooth pursuit system.

      REPLY: We have expanded our simulations to investigate the effects of perturbation on the separability of different control objectives (Figure S10 in Supplementary Materials). We demonstrated that introducing perturbations more clearly differentiated between Position and Velocity Control. These results provide a good basis for further experimental verifications of the control objectives, but we defer these for future work.

      We also appreciate the additional past work that bridges human and monkey research that the reviewer highlights, including the related discussions in the eye movement literature on position versus velocity control. We have modified our Introduction and Discussion accordingly.

      Reviewer #3:

      COMMENT: The reviewer asked whether the observed differences in behavior might be due to some other factors besides the control policy, such as motor noise or effort cost, and suggested that we more systematically ruled out that possibility.

      REPLY: We appreciate and have heeded the reviewer’s suggestion. The revised manuscript now includes additional simulations in which the control objective was fixed to either Position or Velocity Control, while other parameters were systematically varied. Specifically, we examined the influence of the relative effort cost, the sensory delay, and motor noise, on performance. The results of these sensitivity analyses are presented in the Supplementary Material, Figures S4—6. In brief, we found that changing the relative effort cost, delay, or noise levels, mainly affected the success rate in performance (as expected), but did not affect the behavioral features originally associated with control objectives. We include a statement about this result in the main text with reference to the details provided in the Supplementary Material.

      COMMENT: The reviewer questioned our choice of classification features (RMS position and velocity) and wondered if other features might yield better class separation, such as the hand/cursor gain. In a similar vein, reviewer 2 suggested in their recommendations that we examine the width of the autocorrelation function as a potentially better feature.

      REPLY: We note first that our choice of cursor velocity and position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. However, we also explored other features as suggested. We found that they, too, exhibited overlap between the two different control objectives, and did not provide any significant improvement in classification performance (Figures S2 and S3; Supplementary Materials). Of course, that is not to say that a more exhaustive examination of features may not find ones that yield better classification performance than those we investigated, but that is beyond the scope of our study. We refer to this consideration of alternative metrics in the discussion.

      COMMENT: The reviewer notes that “It seems that the classification problem cannot be solved perfectly, at least on a single-trial level.” To address this point, the reviewer suggested that we conduct additional simulations under the two different control objectives, and quantify the misclassifications.

      REPLY: We appreciate the reviewer’s suggestion, and have conducted the additional simulations as suggested, the results of which are included in the revised manuscript.

      COMMENT: “The problem of inferring the control objective is framed as a dichotomy between position control and velocity control. In reality, however, it may be a continuum of possible objectives, based on the relative cost for position and velocity. How would the problem differ if the cost function is framed as estimating a parameter, rather than as a classification problem?”

      REPLY: A blended control strategy, formulated as a cost function that is a weighted combination of position and velocity costs, is indeed a possibility that we briefly discussed in the original manuscript. This possibility arises particularly for individuals whose performance metrics lie somewhere between the purely Position or purely Velocity Control. While our model allows for a weighted cost function, which we will explore in future work, we felt in this initial study that it was important to first identify the behavioral features unique to each control objective.

      Response to Recommendations for the Authors:

      Reviewer #1 (Recommendations For The Authors):

      None beyond those stated above.

      Reviewer #2 (Recommendations For The Authors):

      COMMENT: Line 166 states "According to equation (1), this behavior was equivalent to reducing the sum (𝑝 + 𝑥) when 𝜆 increased, so as to prevent rapid changes in cursor velocity". This doesn't seem right. In equation 1, velocity (not acceleration) depends on p+x. So a large p+x doesn't create a "rapid change in cursor velocity", but rather a rapid change in cursor position.

      REPLY: The reviewer is correct and we have corrected this misworded sentence; thank you for catching that.

      COMMENT: The reviewer points out the potential confusion readers may have, given our unclear use of ‘control strategy’ vs. ‘control policy’ vs. ‘control objective’. The reviewer suggests that “It would be helpful if this could be spelled out early and explicitly. 'Control strategy' seems perilously close to 'control policy', and it would be good to avoid that confusion. The authors might prefer to use the term 'cost function', which is really what is meant. Or they might prefer 'control objective', a term that they introduce as synonymous with 'control strategy'.”

      REPLY: We thank the reviewer for noting this ambiguity. We have clarified the language in the Introduction to explicitly note that by strategy, we mean the objective or cost function that subjects attempt to optimize. We then use ‘control objective’ consistently and removed the term ‘policy’ from the paper to avoid confusion. We also now use Position Control and Velocity Control as the labels for our two control objectives.

      COMMENT: The reviewer notes that in Figure 2B and the accompanying text in the manuscript, we need to be clearer about what is being correlated; namely, cursor and hand position.

      REPLY: Thank you for pointing out this lack of clarity, which we have corrected as suggested.

      COMMENT: The reviewer questions our attribution of decreasing lag with task difficulty as a consequence of subjects becoming more attentive/responsive when the task is harder, and points out that our model doesn’t include this possible influence yet the model reproduces the change in lag. The reviewer suggests that a more likely cause is due to phase lead in velocity compared to position, with velocity likely increasing with task difficulty, resulting in a phase advance in the response.

      REPLY: Our attribution of the decrease in lag with task difficulty being due to attention/motivation was a recapitulation of this point made in the paper by Quick et al. [2018]. But as noted by the reviewer, this potential influence on lag is not included in our model. Accordingly, the change in lag is more likely a reflection of the phase response of the closed loop system, which does change with task difficulty since the optimal gains depend upon the plant dynamics (i.e., the value of lambda). We have, therefore, deleted the text in question.

      COMMENT: “The Methods tell us rather a lot about the dynamics of the actual system, and the cost functions are also well defined. However, how they got from the cost function to the controller is not described. I was also a bit confused about the controller itself. Is the 50 ms delay assumed when deriving the controller or only when simulating it (the text seems to imply the latter, which might make sense given that it is hard to derive optimal controllers with a hard delay)? How similar (or dissimilar) are the controllers for the two objectives? Is the control policy (the matrix that multiplies state to get u) quite different, or only subtly?”

      REPLY: Thanks for pointing this out. For brevity, we had omitted the details and referred readers to the original paper (Todorov, 2005). However, we now revised the manuscript to now include all the details in the Methods section. Hence, the entire section on the model is new. This also necessitated updating all data figures (Figures 3, 4, 5, 6, 7, 8) as they contain modeling results.

      COMMENT: “Along similar lines, I had some minor to moderate confusions regarding the OFC model as described in the main text. Fig 3 shows a model with a state estimator, but it isn't explained how this works. …Here it isn't clear whether there is sensory noise, or a delay. The methods say a delay was included in the simulation (but perhaps not when deriving the controller?). Noise appears to have been added to u, but I'm guessing not to x or x'? The figure legend indicates that sensory feedback contains only some state variables, and that state estimation is used to estimate the rest. Presumably this uses a Kalman filter? Does it also use efference copy, as would be typical? My apologies if this was stated somewhere and I missed it. Either way, it would be good to add a bit more detail to the figure and/or figure legend.”

      REPLY: As the lack of detail evidently led to some confusion, we now more clearly spell out the details of the model in the Methods, including the state estimation procedure.

      COMMENT: The reviewer wondered why we chose to plot mean velocity vs. mean position as in Figure 5, noting that, “ignoring scale, all scatter plots would be identical if the vertical axis were final position (because mean velocity determines final position). So what this plot is really examining is the correlation between final position and average position. Under position control, the autocorrelation of position is short, and thus final position tends to have little to do with average position. Under velocity control, the autocorrelation of position is long, and thus final position tends to agree with average position. Given this, why not just analyze this in terms of the autocorrelation of position? This is expected to be much broader under velocity control (where they are not corrected) than under position control (where they are, and thus disappear or reverse quickly). To me, thinking of the result in terms of autocorrelation is more natural.”

      REPLY: The reviewer is correct that the scatter plots in Fig. 5 would be the same (to within a scale factor of the vertical axis) had we plotted final position vs. mean position instead of mean velocity vs. mean position as we did. Our preference for mean velocity vs. mean position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. We now mention these perspectives in the revised manuscript for the benefit of the reader.

      As suggested, we also investigated the width of the (temporal) autocorrelation function (acf) of cursor position for 200 simulated position control trials and 200 simulated velocity control trials, at four different lambda values (50 simulated trials per lambda). Figs. S2A and B (Supplementary Materials) show example trials and histograms of the acf width, respectively. As the reviewer surmised, velocity control trials tend to have wider acfs than position control trials. However, as with the metrics we chose to analyze, there is overlap and there is no visible benefit for the classification.

      COMMENT: “I think equation ten is incorrect, but would be correct if the identity matrix were added? Also, why is the last term of B set to 1/(Tau*M). What is M? Is it mass (which above was lowercase m)? If so, mass should also be included in A (it would be needed in two places in the last column). Or if we assume m = 1, then just ignore mass everywhere, including here and equation 5. Or perhaps I'm confused, and M is something else?”

      REPLY: Thanks for pointing this out. The Matrix A shown in the paper is for the continuous-time representation of the model. However, as the reviewer correctly mentioned, for the discrete-time implementation of the model, a modification (identity matrix) was added in our simulations. We have now clarified this in the Methods section of the revised manuscript. Also, as correctly pointed out, M is the mass of the hand, which depending on whether the hand acceleration (d^2 p/dt^2) or hand force (F) are taken as the state, it can be included in the A matrix. In our case, the A matrix is modified according to the state vector. Similarly, the B matrix is also modified. This is now clarified in the Methods section of the manuscript.

      Reviewer #3 (Recommendations For The Authors):

      COMMENT: “Equations 4-8 are written in continuous time, but Equation 9 is written in discrete time. Then Equation 10 is in discrete time. This needs to be tidied up. … I would suggest being more detailed and systematic, perhaps formulating the control problem in continuous time and then converting to discrete time.”

      REPLY: Thank you for this helpful suggestion. The model section in the Methods has been expanded to provide further details of the equation of motion, the discretization process, the control law calculation and the state estimation process.

      COMMENT: “It seems slightly odd for the observation to include only position and velocity of the cursor. Presumably participants can also observe the state of their own hand through proprioception (even if it were occluded). How would it affect the model predictions if the other states were observable?”

      REPLY: Thanks for pointing this out. We initially included only cursor position and velocity since we felt that was the most prominent state feedback, and the system is observable in that case. Nevertheless, we revised the manuscript and repeated all simulations using a full observability matrix. Our findings and conclusions remain unchanged. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

      COMMENT: “It seems unnecessary to include the acceleration of the cursor in the formulation of the model. …the acceleration is not even part of the observed state according to line 668… I think the model could therefore be simplified by omitting cursor acceleration from the state vector.”

      REPLY: We agree. We have simplified the model, and generated new simulations and figures. Our results and conclusions were unchanged by this modification. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

      COMMENT: “In the cost function, it's not clear why any states other than position and velocity of the cursor need to have non-zero values. …The choice to have the cost coefficient for these other states be 1 is completely arbitrary… If the point is that the contribution of these other costs should be negligible, then why not just set them to 0?”

      REPLY: We agree, and have made this change in the Methods section. Our findings and conclusions were unaffected.

      COMMENT: “It seems that the cost matrices were specified after transforming to discrete-time. It is possible however (and perhaps recommended) to formulate in continuous time and convert to discrete time. This can be done cleanly and quite straightforwardly using matrix exponentials. Depending on the discretization timestep, this can also naturally lead to non-zero costs for other states in the discrete-time formulation even if they were zero under continuous time. … A similar comment applies to discretization of the noise.”

      REPLY: Thanks for the suggestion. We have expanded on the discretization process in our Methods section, which uses a common approximation of the matrix exponentiation method.

      COMMENT: “Most of the parameters of the model seem to be chosen arbitrarily. I think this is okay as the point is to illustrate that the kinds of behaviors observed are within the scope of the model. However, it would be helpful to provide some rationale as to how the parameters were chosen. e.g. Were they taken directly from prior literature, or were they hand-tuned to approximately match observed behavior?”

      REPLY: We have revised the manuscript to more clearly note that the noise parameters, as well as parameters of the mechanical system (mass, muscle force, time scale, etc) in our model were taken from previous publications (Todorov, 2005, Cluff et al. 2019). As described in the manuscript, the parameter values of the cost function (Q matrix) were obtained by tuning the parameters to achieve a similar range of success rate with the model as observed in the experimental data. This is now clarified in the Methods section.

      COMMENT: “The ‘true’ cost function for this task is actually a 'well' in position space - zero cost within the screen and very high cost elsewhere. In principle, it might be possible to derive the optimal control policy for this more veridical cost function. It would be interesting to consider whether or not this model might reproduce the observed behaviors.”

      REPLY: This is indeed a very interesting suggestion, but difficult to implement based on the current optimal feedback control framework. However, this is interesting to consider in future work.

      Minor Comments:

      COMMENT: “In Figs 4 and 5, the data points are drawn from different conditions with varying values of lambda. How did the structure of this data depend on lambda? Might it be possible to illustrate in the figure (e.g. the shade/color of each dot) what the difficulty was for each trial?”

      REPLY: We performed additional analyses to show the effects of task difficulty on the choice of control objective. Overall, we found that the main behavioral characteristics of the control objective remained fairly unchanged across different task difficulties or across time. The results of this analysis are included in Fig. S7 and S8 of the Supplementary Materials.

      COMMENT: “Should mention trial duration (6s) in the main narrative of the intro/results.”

      REPLY: We now mention this detail when we describe the task for the first time.

      COMMENT: “As an alternative to training on synthetic data (which might not match behavior that precisely, and was also presumably fitted to subject data at some level) it might be worth considering to do a cross-validation analysis, i.e. train the classifier on subsets of the data with one participant removed each time, and classify on the held-out participant.”

      REPLY: This is indeed a valid point. The main reason to train the classifier based on model simulations was two-fold: first, to have confidence in the training data, as the experimental data was limited and noisy, which would result in less reliable classifications; and second, the model simulations are available for different contexts and conditions, where experimental data is not necessarily available. The latter is a more practical reason to be able to identify control objectives for any subject (who received no instructions), without having to collect training data from matching control subjects who received explicit instructions. Nonetheless, we appreciate the reviewer’s recommendation and will consider that for our future studies.

      COMMENT: “line 690 - Presumably the optimal policy was calculated without factoring in any delay (this would be tricky to do), but the 50ms delay was incorporated at the time of simulation?”

      REPLY: The discretization of the system equations allowed us to incorporate the delay in the system dynamics and solve for the optimal controller with the delay present. This was done simply by system augmentation (e.g., Crevecoeur et al., 2019), where the states of the system in the current time-step were augmented with the states from the 5 preceding time-steps to form the new state vector x(t)_aug =[x(t) , x(t-1) , … , x(t-d) ]. Similarly, the matrices A, B, and H from the system dynamics could be expanded accordingly to form the new dynamical system:

      $$x(t+1){aug} = A{aug} * x(t){aug} + B{aug} * u$$

      Then, the optimal control was implemented on the new (augmented) system dynamics.

      We have revised the manuscript (Methods) to clarify this issue.

    1. Author Response

      The following is the authors’ response to the previous reviews.

      eLife assessment

      This important study identifies the gene mamo as a new regulator of pigmentation in the silkworm Bombyx mori, a function that was previously unsuspected based on extensive work on Drosophila where the mamo gene is involved in gamete production. The evidence supporting the role of Bm-nano in pigmentation is convincing, including high-resolution linkage mapping of two mutant strains, expression profiling, and reproduction of the mutant phenotypes with state-of-the-art RNAi and CRISPR knock-out assays. While the discussion about genetic changes being guided or accelerated by the environment is extremely speculative and has little relevance for the findings presented, the work will be of interest to evolutionary biologists and geneticists studying color patterns and evolution of gene networks.

      Response: Thank you very much for your careful work. In the revised version, we conducted a comparative genomic analysis of the upstream regions of the Bm-mamo gene in 51 wild silkworms and 171 domesticated local silkworms. The analysis of nucleotide diversity (pi) and the fixation index (FSTs) of the Bm-mamo genome sequences in the wild and domesticated silkworm populations were also performed. The results showed that the Bm-mamo genome sequence of local silkworms was relatively conserved, while the upstream sequence of wild silkworms exhibited high nucleotide diversity. This finding suggested a high degree of variability in the regulatory region of the Bm-mamo gene, in wild strains. Additionally, the sequence in this region may have been fixed by domestication selection. We have optimized the description in the discussion section.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

      The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

      Strengths:

      The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

      This revision is a much improved manuscript and I command the authors for many of their edits.

      Response: Thank you very much for your careful work. With the help of reviewers and editors, we have revised the manuscript to improve its readability.

      I find the last part of the discussion, starting at "It is generally believed that changes in gene expression patterns are the result of the evolution of CREs", to be confusing.

      In this section, I believe the authors sequentially:

      • emphasize the role of CRE in morphological evolution (I agree)

      • emphasize that TF, and in particular their own CRE, are themselves important mutational targets of evolution (I agree, but the phrasing need to insist the authors are here talking about the CRE found at the TF locus, not the CRE bound by the TF).

      • use the stickleback Pel enhancer as an example, which I think is a good case study, but the authors also then make an argument about DNA fragility sites, which is hard to connect with the present study.

      • then continue on "DNA fragility" using the peppered moth and butterfly cortex locus. There is no evidence of DNA fragility at these loci, so the connection does not work. "The cortex gene locus is frequently mutated in Lepidoptera", the authors say. But a more accurate picture would be that the cortex locus is repeatedly involved in the generation of color pattern variants. Unlike for Pel fragile enhancer, we don't know if the causal mutations at this locus are repeatedly the same, and the haplotypes that have been described could be collateral rather than causal. Overall, it is important to clarify the idea that mutation bias is a possible factor explaining "genetic hotspots of evolution" (or genetic parallelism sensu 10.1038/nrg3483), but it is also possible that many genetic hotspots are repeated mutational targets because of their "optimal pleiotropy" (e.g. hub position in GRNs, such as mamo might be), or because of particularly modular CRE region that allow fine-tuning. Thus, I find the "fragility" argument misleading here. In fact the finding that "bd" and "bdf" alleles are different in nature is against the idea of a fragility bias (unless the authors can show increased mutation rates at this locus in a wild silkmoth species?). These alleles are also artificially-selected ie. they increased in frequency by breeding rather than natural selection in the wild, so while interesting for our understand of the genotype-phenotype map, they are not necessarily representative of the mutations that may underlie evolution in the wild.

      Response: Thank you very much for your careful work. DNA fragility is an interesting topic, but some explanations for DNA fragility are confusing. One study measured the rate of DNA double-strand breaks (DSBs) in yeast artificial chromosomes (YACs), which are chromosomes containing marine Pel that broke ~25 to 50 times more frequently than did the control. These authors believe that the increase in the mutation rate is caused by DNA sequence characteristics, particularly TG-dinucleotide repeats. Moreover, they found that adding a replication origin on the opposite side of Pel did not cause the fungus to switch fragile, making the forward sequence stable and the reverse complement fragile. Thus, Pel fragility is also dependent on the direction of DNA replication. In summary, they suggested that the special DNA sequence is the cause of DNA fragility. In addition, the sequence features associated with DNA fragility in the Pel region are also found in thousands of other positions in the stickleback and human genomes (Xie KT et al, 2019, science).

      In yeast artificial chromosomes (YACs), the characteristics of DNA sequences, such as TG-dinucleotide repeat sequences, may be important reasons for DNA fragility, and these breaks occur during DNA replication. However, the inserted sequence of YAC often undergoes deletion or recombination during cultivation and passage. In addition, yeast is a single-celled organism. Therefore, the results in yeast cannot represent the situation in multicellular organisms. If multicellular organisms are like this, there are several issues as follows:

      (1) The DNA replication process occurs separately in different multicellular organisms. Because DNA breakage and repair are independent, they can lead to the presence of different alleles in different cells. This can potentially lead to the occurrence of extensive chimeric organisms. However, we have not found such a situation in the genome sequencing of many multicellular organisms.

      (2) If the DNA sequence, TG-dinucleotide repeats, is the determining factor, the mutations near the sequence lose their strong correlation with environmental changes. The researchers conducted yeast artificial chromosome experiments in the same environment and found that the frequency of DNA breaks containing TG dinucleotide repeat sequences was 25 to 50 times greater than that of the control group. This means that, whether in the marine population or the lake population, this part of the sticklebacks’ genome has undergone frequent mutations. However, according to related research, populations of lake sticklebacks, rather than marine populations, often exhibit a decrease in the pelvic phenotype.

      (3) Researchers have found thousands of loci in the genome of sticklebacks and humans that contain such sequences (TG-dinucleotide repeats). This means that thousands of sites undergo frequent mutations during DNA replication. Unless these sites do not possess functionality, they will have some impact on the organism, even causing damage. Even if they are not functional sequences, these sequences will gradually be discarded or replaced during frequent mutations rather than being present in large quantities in the genome.

      Therefore, the study of DNA fragility in yeast cannot explain the situation in multicellular organisms.

      As you noted, we want to express that the frequent variation in the cortex gene should be regulated by targeted regulation involving the GRN in Lepidoptera. In addition, studies on specific epigenetic modifications discovered through the referenced fragile DNA sites suggest that DNA fragility is not determined by the DNA sequence (Ji F, 2020, Cell Res) but rather by other factors, such as epigenetic factors. The sequence features discovered at fragile DNA sites are traces of frequent mutations, not causes.

      In this revision, we analyzed the nucleotide diversity of the mamo genome in 51 wild and 171 domestic silkworms. We found high nucleic acid diversity from the third exon to the upstream region of this gene in wild silkworms. We randomly selected 12 wild silkworms and 12 domestic silkworms and compared their upstream sequences to approximately 1 kb. In wild silkworms, there is significant diversity in their upstream sequences. In domestic silkworms, the sequences are highly conserved, but in some silkworms, a long interspersed nuclear element (LINE) is inserted. This finding suggested that there is frequent variation in the sequence of this region in wild silkworms, while fixation occurs in domesticated silkworms. These genomic data are sourced from the pangenome of silkworms (Tong X, 2022, Nat Commun.). In the pangenomic research, 1078 strains (205 local strains, 194 improved strains, 632 mutant strains, and 47 wild silkworms), which included 545 third-generation sequencing genomes, were obtained. An online website was built to utilize these data (http://silkmeta.org.cn/). We warmly welcome you to use these data.

      In summary, for clearer expression, we have rewritten this section.

      Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC, Schluter D, Bell MA, Vasquez KM, Kingsley DM. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science. 2019 Jan 4;363(6422):81-84. doi: 10.1126/science.aan1425.

      Ji F, Liao H, Pan S, Ouyang L, Jia F, Fu Z, Zhang F, Geng X, Wang X, Li T, Liu S, Syeda MZ, Chen H, Li W, Chen Z, Shen H, Ying S. Genome-wide high-resolution mapping of mitotic DNA synthesis sites and common fragile sites by direct sequencing. Cell Res. 2020 Nov;30(11):1009-1023. doi: 10.1038/s41422-020-0357-y.

      Tong X, Han MJ, Lu K, Tai S, Liang S, Liu Y, Hu H, Shen J, Long A, Zhan C, Ding X, Liu S, Gao Q, Zhang B, Zhou L, Tan D, Yuan Y, Guo N, Li YH, Wu Z, Liu L, Li C, Lu Y, Gai T, Zhang Y, Yang R, Qian H, Liu Y, Luo J, Zheng L, Lou J, Peng Y, Zuo W, Song J, He S, Wu S, Zou Y, Zhou L, Cheng L, Tang Y, Cheng G, Yuan L, He W, Xu J, Fu T, Xiao Y, Lei T, Xu A, Yin Y, Wang J, Monteiro A, Westhof E, Lu C, Tian Z, Wang W, Xiang Z, Dai F. High-resolution silkworm pan-genome provides genetic insights into artificial selection and ecological adaptation. Nat Commun. 2022 Sep 24;13(1):5619. doi: 10.1038/s41467-022-33366-x.

      Lu K, Pan Y, Shen J, Yang L, Zhan C, Liang S, Tai S, Wan L, Li T, Cheng T, Ma B, Pan G, He N, Lu C, Westhof E, Xiang Z, Han MJ, Tong X, Dai F. SilkMeta: a comprehensive platform for sharing and exploiting pan-genomic and multi-omic silkworm data. Nucleic Acids Res. 2024 Jan 5;52(D1):D1024-D1032. doi: 10.1093/nar/gkad956.

      Curiously, the last paragraph ("Some research suggests that common fragile sites...") elaborate on the idea that some sites of the genome are prone to mutation. The connection with mamo and the current article are extremely thin. There is here an attempt to connect meiotic and mitotic breaks to Bm-mamo, but this is confusing: it seems to propose Bm-mamo as a recruiter of epigenetic modulators that may drive higher mutation rates elsewhere. Not only I am not convinced by this argument without actual data, but this would not explain how the mutations at the Bm-mamo itself evolved.

      Response: Thank you very much for your careful work. This section mainly illustrates that DNA fragility is not determined by sequence but is regulated by other factors in animals. In fruit flies, they found that mamo is an important candidate gene for recombination hotspot setting in meiosis. First, we evaluated PRDM9, which plays an important role in setting recombination hotspots during meiosis. Our purpose in mentioning this information is to illustrate that chromosome recombination is a process of programmed double strand breaks and to answer another reviewer's question about programmed events in the genome. In summary, we suggest that some variations in DNA sequences are procedural results. We have optimized the description of this section in this version.

      On a more positive note, I find it fascinating that the authors identified a TF that clearly articulates or orchestrate larval pattern development, and that when it is deleted, can generate healthy individuals. In other words, while it is a TF with many targets, it is not too pleiotropic. This idea, that the genetically causal modulators of developmental evolution are regulatory genes, has been described elsewhere (e.g. Fig 4c in 10.1038/s41576-020-0234-z, and associated refs). To me, the beautiful findings about Bm-mamo make sense in the general, existing framework that developmental processes and regulatory networks "shape" the evolutionary potential and trajectories of organisms. There is a degree of "programmability" in the genomes, because some loci are particularly prone to modulate a given type of trait. Here, Bm-mamo, as a potentially regulator of both CPs and melanin pathway genes, appear to be a potent modulator of epithelial traits. Claiming that there are inherent mutational biases behind this is unwarranted.

      Response: Thank you very much for your careful work. I completely agree with your statement that the genome exhibits a certain degree of programmability. On the one hand, some transcription factors can precisely control the spatiotemporal expression levels of some structural genes (such as pigment synthesis genes). On the other hand, these transcription factors are also subject to strict expression regulation. Because the color pattern is complex, changes in single or minority structural genes result in incomplete or imprecise changes in coloring patterns. Nevertheless, several regulatory factors can regulate multiple downstream target genes. Changes in their expression patterns can lead to holistic and significant changes in color patterns. There are long intergenic regions upstream of many important transcription factors, dozens of kilobase pairs (Kb) to hundreds of Kb, which may contain many different regulatory elements for better control of their expression patterns. Therefore, gene regulatory networks can directly regulate transcription factors to modulate a given type of trait. Transcription factors and their downstream target genes can form a functional module, which is similar to a functional module in software or operating systems. This regulation of transcription factors is simpler in terms of steps, which are similar to a single click switch button. The gene regulatory network regulates these modules in response to environmental changes and is widely recognized.

      Some people do not agree that genetic variations can also be regulated. They claim that this is completely random. The infinite monkey theorem (Félix-Édouard-Justin-Émile Borel, 1909) states that if an infinite number of monkeys were given typewriters and an infinite amount of time, they would eventually produce the complete works of Shakespeare. Although this theory advocates randomness on the surface, its conclusions are full of inevitability (tail event). In nature, some things we observe do not have obvious regularity because they involve relatively complex factors, and the underlying logic is obscure and difficult to understand. We often name them random. However, as we gradually understand the logic behind this complex event, we can also recognize the procedural nature of this randomness.

      Previously, chromosomal recombination during meiosis was believed to be a random event. However, currently, it is believed that the process is procedural. The occurrence of meiotic recombination mentioned earlier indicates that the genome has the ability to self-set the position of double-strand breaks to form new allelic forms. Because meiotic recombination is programmed, transcription factors that recognize DNA sites, enzymes that cleave double strands, and DNA repair systems exist, programming can also introduce genetic variation. A study in plants has provided insights into this programmed mutation (Monroe JG, 2023, nature). Frequent changes in the expression patterns of some transcription factors occur between and/or within species. In this article, we only discuss the possible reasons for variations in the expression patterns of some transcription factors in a general manner and simple reasoning. We have added an analysis of the response of wild silkworms and improved the relevance of the discussion.

      Monroe JG, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, Klein M, Hildebrandt J, Neumann M, Kliebenstein D, Weng ML, Imbert E, Ågren J, Rutter MT, Fenster CB, Weigel D. Mutation bias reflects natural selection in Arabidopsis thaliana. Nature. 2022 Feb;602(7895):101-105. doi: 10.1038/s41586-021-04269-6. Epub 2022 Jan 12. Erratum in: Nature. 2023 Aug;620(7973):

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      • Please structure your Discussion with section headers.

      Response: Thank you very much for your careful work. We have added relevant section headers.

      • As explained in my public review, I found the two last sections of the Discussion to be dispersed and confusing. I also must say that I carefully read the Response to Reviewers on this, which helped me to better understand the authors' intentions here. Please consider the revision of this Discussion as this feels extremely speculative difficult to connect with Bm-mamo.

      Response: Thank you very much for your careful work. We have rewritten this part of the content.

      • typo: were found near the TTS of yellow --> TSS

      Response: Thank you very much for your careful work. We have made these modifications.

      • l. 234 :"expression level of the 18 CP genes in the integument". Consider adding a mention of Figure 7 here, as only Fig. S10 is cited here.

      Response: Thank you very much for your careful work. We have made these modifications.

      • Editorial comment on the second half of the Abstract:

      Wu et al : "We found that Bm-mamo can comprehensively regulate the expression of related pigment synthesis and cuticular protein genes to form color patterns. This indicates that insects have a genetic basis for coordinate regulation of the structure and shape of the cuticle, as well as color patterns. This genetic basis provides the possibility for constructing the complex appearances of some insects. This study provides new insight into the regulation of color patterns."

      I respectfully suggest a more accurate rephrasing, where the methods are mentioned, and where the logical argument is more straightforward. For example

      "Using RNAi and CRISPR we show that Bm-mamo is a repressor or dark melanin patterns in the larval epithelium. Using in-vitro binding assays and gene expression profiling in wild-type and mutant larvae, we also show that Bm-mamo likely regulate the expression of related pigment synthesis and cuticular protein genes in a coordinated manner to mediate its role in color pattern formation. This mechanism is consistent with a dual role of this transcription factor in regulating both the structure and shape of the cuticle and pigments that are embedded within it. This study provides new insight into the regulation of color patterns as well as in the construction more complex epithelial features in some insects."

      I hope this let the ideas of the original version transpire as the authors intended.

      Response: Thank you very much for your careful work. We have made these modifications.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1

      • Albeit the link between CSA and NE integrity the work is in my eyes too preliminary. Although the data presented are well done and carefully evaluated they mostly (except Fig 1A) rely on direct comparisons of one patient cell line (CS-A or CS-B) to the same cells expression the wildtype protein. It remains thus open whether the effects seen on LEM2 expression, LEM2-LaimA/C interaction, stress fibre formation, cGAS/STING signaling pathway activation in the CS-A cells are representative for a number of different CS patient derived cells. This is especially important given the small changes observed. Please note that there are alos clear differences between the CSA-wt and CSB-wt cells. Would the HPA CSA KO cells show in addition to NE irregularities (not even quantified) the same phenotypes and can they be reverted by re-expression of the wildtype CSA protein? We would like to thank the reviewer for this comment. Indeed we have previously observed nuclear circularity defects in CSA KO HAP1 cells but we haven’t investigated the other phenotypes in this cell line. One of the main reasons behind this is the technical difficulties associated with performing immunofluorescence with HAP1 cells who are very small and tend to grow in aggregates.

      Proposed experimental plan: To address this reviewer’s comment, we will attempt again to use HAP1 cells (WT and CSA KO) and look at nuclear circularity (with quantification), stress fiber formation and cGas foci. If we don’t succeed, we will use an alternative isogenic cell model consisting of fibroblasts in which we will knock out CSA using CRISPR/Cas9. We will then repeat the same experiments as proposed above for the HAP1 cells.

      • The link between CSA and SUN1 is not well worked out. What is the effect of SUN2 downregulation and that of nespirins? It remains unclear whether the observed effects are indeed LINC mediated. Proposed experimental plan: To address this point, we will downregulate SUN2 and nesprins using siRNAs in two different cell models (as described above) and assess nuclear shape as well as cGas/STING pathway activation.

      Minor comments:

      * Fig 1B: Why is HA-Tagged CSA not shown on the CSA western? This would be helpful to compare to the endogenous levels at least in CSB cells. A western showing an housekeeping marker would allow better comparison. Judging from the proteins markers HA-tagged CSA seems much larger as endogenous CSA (first versus second row). Again, less cropped western blots would help.*

      We are sorry for the confusion and we have realised that the molecular weights on the western blots were incorrectly labeled on this figure. This will be modified, and the full, uncropped WB will be provided as a supplementary file.

      Fig 3A: Is CSA FLAG or HA-tagged? Or both? If both are expressed the question raises of why the CSA-LEM 2 interaction is only seen in an overexpression situation.

      • *

      CSA is HA tagged. To address this reviewer’s comment we will try performing immunoprecipitation on endogenous proteins.

      Fig 5: Inconsistency between figure and figure legend: 20 vs 25 nM Jasplakinolide. I assume Latrunculin A should read Cytochalasin D?

      Thank you for pointing this out, and yes this is indeed a mistake on our labeling. We will rectify this on the figure legend.

      Fig5B: Not clear why in "CSA-wT cells" Cytochalasin D and Jasplakinolide have the same effect on nuclear envelope shape yet only Jasplakinolide increases the number of blebs.

      Cyt D inhibits actin polymerization while jasplakinolide increases polymerization. Likely actin polymerization increases blebs through extra force being put on the nucleus through actin cables/ actin based motility. Both drugs decrease nuclear roundness as they disrupt the normal actin network leading to worsening of nuclear shape through different mechanisms.

      Page 10: Method for IF: 4% (v/v) paraformaldehyde and 2% /v/v) should likely read (w/v). Page 19: replace "withl" by "with".

      We will rectify both these points.

      Reviewer #2

      The paper is well-written and for the most part, the data support the conclusions of the authors. Some minor caveats could be addressed to improve the quality of the manuscript.

      We would like to thank the reviewer for their positive feedback on our manuscript.

      • The phenotype of decreased LEMD2 incorporation into the NE in CS-A cells is minor. Only ~20% and thus, it is not clear whether this is causal of any of the NE abnormalities. It should be better explained how these data add to the story.

      To address this point, we will overexpress LEMD2 in CS-A cells and assess whether the NE phenotype can be significantly rescued. This will add value to this part of story.

      • Inducing actin polymerization and depolarization impact nuclear morphological abnormalities and nuclear blebbing. Do these treatments impact nuclear fragility and cGAS accumulation at NE break sites?* This is a good point indeed. To address this question, we will include cGas foci staining and quantification upon treatment with these chemicals.

      • Depletion of SUN1 in CS-A cells increased nuclear circularity, decreased blebbing, and phosphorylation of TBK1. The impact of SUN1 depletion in cGAS foci formation at NE break sites and phosphorylation of STING is not shown. Such experiments will provide stronger evidence that CS-A activates the cGAS-STING pathway in a SUN1 (mechanical stress)-dependent manner.

      * We will address this question by analysing cGas foci and cGas-STING pathway activation upon SUN1 depletion by siRNA.

      Reviewer #3

      • *

      The data are generally clear, well performed and well interpreted with some exceptions:*

      1) I appreciate the use of isogenic cell lines (a big plus when dealing with patient-derived cell lines). However, these lines were established 30 years ago and the reported phenotypes might be due to genetic drifts. To exclude this, I suggest to complement the HAP-1 ERCC8 KO cell line with exogenously expressed CSA and assess if this rescues the phenotypes reported. Validation of the KO in these lines, either by western blotting or sequencing is needed.*

      This point has also been raised by the first reviewer, and will be addressed as described above (and pasted below):

      We would like to thank the reviewer for this comment. Indeed we have previously observed nuclear circularity defects in CSA KO HAP1 cells but we haven’t investigated the other phenotypes in this cell line. One of the main reasons behind this is the technical difficulties associated with performing immunofluorescence with HAP1 cells who are very small and tend to grow in aggregates.

      Proposed experimental plan: To address this reviewer’s comment, we will attempt again to use HAP1 cells (WT and CSA KO) and look at nuclear circularity (with quantification), stress fiber formation and cGas foci. If we don’t succeed, we will use an alternative isogenic cell model consisting of fibroblasts in which we will knock out CSA using CRISPR/Cas9. We will then repeat the same experiments as proposed above for the HAP1 cells.

      2) Related to the complementation of patient cell lines, the exogenous HA-CSA is not recognised by the anti-CSA in the CSA-null patient cell lines (Fig 1B, second blot). Shouldn't you be able to see this exogenous protein? HA-GFP-CSB in the complemented CSB-null patient cell line runs at the same weight as endogenous CSB (Fig 1B, fourth blot). This is also unexpected. I think you need better characterisation of your cell lines and need to demonstrate the level of exogenous transgenes that have been used to complement the cells and that they localise appropriately, presumably to the nucleus. You should also make sure to cite the paper where they were isolated and describe that they were immortalised (Troelstra et al., 1992) and the paper in which transgenes were stably overexpressed (Qiang et al., 2021).*

      *

      As mentioned above, we have realised that we made some mistakes with the labeling of the molecular weight on this western blot. This will be corrected and the full uncropped western blot will be provided as a supplementary figure.

      We will also cite the suggested papers accordingly.

      3) Immunolocalisation of INM proteins is notoriously tricky and the permeabilisation steps include only 0.2% Tx100, which can be insufficient to permeabilise the INM. I appreciate the Emerin and Lamin immunostaining seems to have worked, but in many cases successful immunostaining can be antibody-specific. Can you try harsher permeabilisation to expose LEM2 epitopes? I'm somewhat uncomfortable with the suggestion that there is a cytosolic (ER?) pool of endogenous LEM2 as this runs counter to the literature and feel that your antibody or fixation conditions are illuminating a non-specific protein. The WB in Fig 2E shows that there is virtually no LEM2 in the "soluble" fraction. I would be more cautious on this cytoplasmic/nuclear pool interpretation. Biochemical nuclear and cytoplasmic fractionation would help clarify the signal in a NE vs a non-NE pool.*

      *

      As suggested by the reviewer, we will try harsher permeabilisation conditions to test the LEMD2 antibody. As we suggest in the manuscript however, we think that the “cytoplasmic” LEMD2 pool we observed by IF in the absence of pre-extraction is indeed unspecific. This is why we have performed the rest of the experiments with a pre-extraction step, that we have shown to give a specific LEMD2 signal that disappear upon depleting LEMD2 by siRNA.

      4) Page 15: "Using a Proximity Ligation Assay (PLA), we showed a significant reduction in the number of PLA foci in CS-A cells compared to the WT(HA-CSA) cells, reflecting a reduced number of LEMD2-lamin A/C complexes (Figure 2G, 2H). This data suggests defects in the incorporation of LEMD2 into the NE and lamin protein complexes in CS-A cells". If you have less LEM2 in the NE, it is quite expected that you will have less "LEM2-laminA/C" complexes. To me the logic doesn't hold and this data does not suggest that there is an underlying defect in LEM2-lamin interaction. To ascertain whether there is such a defect one could perform an IP against LEM2 and quantify laminA/C, normalizing by the amount of LEM2 in the input.

      We feel we may not have been clear in how we interpreted this data. What we mean is that in each individual cell, the number of Lamin-LEMD2 complexes is decreased, probably indeed due to the fact that there is less LEMD2 altogether within the nucleus in the absence of CSA. We will clarify this in the text.

      5) "We overexpressed LEMD2-GFP and Flag-CSA constructs, followed by GFP pulldown in WT(HA-CSA) cells". Since the co-IP data are obtained in overexpression conditions (of both HA-CSA and Flag-CSA?), the authors should validate the interaction between LEM2 and CSA using an orthogonal approach. Perhaps anti-HA capture of the WT(HA-CSA) cells would allow you to immunoblot for endogenous LEM2?*

      *

      To address this point, we will try to immunoprecipitate HA-CSA and look at endogenous LEMD2.

      6) Related to the CSA-LEM2 binding in the above experiment, the procedure involves combining a native detergent-extracted cytoplasmic pool with a denatured (RIPA-extracted) nuclear pool for performing the GFP-trap. From which pool was the tagged CSA bound to LEM2 in?*

      *

      We are sorry about the confusion. We didn’t try to run the IP from the different pools but instead from the combined pools, to ensure we were looking in the whole cell extract. We would expect however that the interaction occurs in the nuclear pool as both CSA and LEMD2 are nuclear proteins.

      7) "The absence of CSA in CS-A patient cells does not affect the mobility of LEMD2 at the NE but instead decreases its interaction with A-type lamins". To me the fact that loss of CSA decreases LEM2-lamin interaction is not well supported (see point 3).

      • *

      See our response to point 4

      8) "Here, we showed by immunoprecipitation that LEMD2 also interacts with CSA. This suggests that the recruitment and stabilization of LEMD2 to the NE is mediated by an interaction with CSA, although the mechanism remains unclear". I think this is an overstatement: there are no data suggesting that CSA recruits or stabilises LEM2 at the NE.

      * *We will tone down this statement in the text

      9) As the authors suggest in the discussion, it would be worth checking whether LEM2 overexpression is able to rescue some of the NE defects reported, strengthening the hypothesis that LEM2 levels are at least in part responsible for the phenotypes reported.

      To address this point, we will perform LEMD2 overexpression in the CSA cells, and analyse the nuclear envelope defects and ruptures (shape and cGas foci quantification)

      10) To me it is not clear how the reported phenotypes are interrelated. The first part of the manuscript shows that CSA interacts with LEM2, and that loss-of-function CSA impacts on LEM2 levels and LEM2-lamin interaction, suggesting a direct role for CSA at the nuclear envelope. The second part of the manuscript shows that cells with defective CSA have more actin stress fibres and releasing the cytoskeleton-nuclear tethering is able per se to rescue the nuclear membrane and cGAS phenotypes. How do the authors reconciliate these two parts? Is CSA directly involved in both inner nuclear membrane homeostasis and actin cytoskeleton modulation or is this latter role upstream and the NE defects a mere consequence of increased cytoskeleton rigidity?

      At this point indeed we cannot draw definitive conclusions as to whether the two described phenotypes are inter-related. However, by addressing the other points raised by the reviewers, we hope this will help clarifying the mechanism.

      11) It is not clear how or why actin stress fibres are elevated in the CS-A cells. Can the authors provide any insight based on their RNAseq analysis? Demonstrating a link to ROCK, LIMK or Rho signalling would be interesting and verifying ppMLC2 levels would help explain why contractility is enhanced. Additionally, is the increase in contractility dependent upon any of the genes identified as up- or downregulated in RNAseq? Presently, the manuscript is missing a link between its two halves.*

      *

      We would like to reiterate that the RNASeq analysis we performed was done on previously published data from another group (as described in the text). To address the point raised by the reviewer, we will look more specifically into our analysis to look at ROCK, LIMK or Rho signalling to see if any of these pathways appear to be modulated by the absence of CSA.

      12) Related to point 1, the RNAseq comparison was performed on patient cells lacking CS-A and patient cells lacking CS-A and later over-expressing HA-CSA, and this comparison is used extensively for phenotype description in the manuscript. In isn't clear to me that this is the most insightful comparison to make; the rescue by overexpression is not as elegant as CRISPR reversion and the ko fibroblasts have presumably been surviving well in culture without CS-A before this protein was overexpressed. Can you validate the differential expression of any identified proteins in the acute HAP1 ko? Can you validate any of the differentially expressed proteins in comparison to normal fibroblasts (e.g., 13O6, as per Qiang et al., 2021)?

      As we will validate our experiments in an additional cell model (as described above), we will also indeed validate the level of expression of cytoskeletal proteins upon CSA KO/rescue.

      Minor comments

      * - Page 14: "To characterize the NE phenotypes further, we obtained CS patient-derived cell lines carrying loss-of-function mutations in CSA (CS-A cells) or CSB (CS-B cells), and their respective isogenic control cell lines (WT(HACSA) and WT(HA-GFP-CSB))." What type of loss-of-function? Is the mutant protein still produced? In Fig 6A there seem to be a band in the CS-A blot (second lane), but in Fig 1B, there isn't. I think this is important to know to interpret the phenotype related to LEM2 interaction.*

      We can clarify that in the text. Indeed, the loss of function mutation leads to the absence of CSA protein.

      - Figure 1B is poorly annotated. What do - and + stand for? In general, I find a bit confusing how the WB are presented throughout the manuscript, specifically how the antibodies are reported (e.g., HA-CSA instead of HA). Please mark up all western blots with antisera used. Please make sure all expected bands are within the crops - e.g., Fig 3B, the anti-LEM2 blot should be expanded vertically to show the LEM2-GFP relative to endogenous LEM2.

      We will correct these on the figures

      - From the methods, it appears that you obtained a Please provide clarity on which construct was used in which figure, and verify that an N-terminally tagged LEM2 still localises to the NE.

      We actually cloned LEMD2 into an empty pEGFP vector but still maintained LEM2-GFP. We will remove the C1plasmid from the methods to avoid confusion as we removed the MCS and GFP and just used the blank vector and inserted lem2-gfp as we obtained it.

      - Fig 1I: there is some text on top of the upper panels (DAPI, cGAS, Merge).

      • *

      * - "Through gene ontology analysis, we found that genes involved in endoplasmic reticulum (ER) stress were differentially expressed (Figure 4B)". I don't think that the way data are shown in Fig 4B is effective. Since GO has been performed, I would replace the table with a GO enrichment analysis graph. Ensure to report all the data in a supplementary .xls so that others can see and reuse it. Is there a mandated repository that accepts RNAseq data?*

      The RNAseq experiment and data was performed by another group and reported in a previous study, as referenced in the main text of the manuscript (Epanchintsev A, Costanzo F, Rauschendorf MA, Caputo M, Ye T, Donnio LM, et al. Cockayne’s Syndrome A and B Proteins Regulate Transcription Arrest after Genotoxic Stress by Promoting ATF3 Degradation. Mol Cell. 2017 Dec;68(6):1054-1066.e6.). Here, we only re-analysed their data using STRING pathway analysis, as detailed in the Material and Methods. However, as suggested by the reviewer, we will replace the table by a GO enrichment graph.

      - The volcano plot looks weird with many values at the maximum log10 (P-value) - is the data processed appropriately?

      As mentioned above, the RNA Seq analysis was performed and published in a different study. We think this is because the Y axis shows adjusted P values.

      - Figure 5B: the legend says "Latrunculin A". Please correct.

      We will correct this

      - For a Wellcome funded researcher, I'm surprised that the mandated OA statement and RRS is absent from the acknowledgements.

      We will of course comply with the open access policy of the Wellcome Trust. However, and based on the WT requirements detailed on their website, we believe the acknowledgement section complies with the funder’s policy: “All research publications must acknowledge Wellcome's support and list the grant reference number which funded the research reported.”

      Maybe mention the changes in nuclear shape is not a causative of nuclear blebbing. But maybe not say that they are completely mutually exclusive phenotype to each other.

      suggestion

      Maybe say that we will overexpress LEMD2 in CS-A cells and show that the NE phenotype can be significantly rescued. This will add value to this part of story. I remember when I did the FRAP experiment, CS-A cells with expression of LEMD2-GFP (that doesn’t form aggregates) looks better in term of shape.

      I think Anne, please check the plasmid map? According to the lab inventory (Plasmids Anne), it is LEMD2-GFP. So probably GFP is at C-terminus.

      I think there was a part in discussion was LEMD2-GFP was mistakenly written as GFP-LEMD… But I am sure I used LEMD2-GFP throughout the work

      We cloned it into an empty pEGFP vector but still maintained LEM2-GFP. Maybe remove the C1 in the methods to avoid confusion as we removed the MCS and GFP and just used the blank vector and inserted lem2-gfp as we obtained it.

      Same construct was used for GFP pulldown and for FRAP. And we can see in FRAp that they localise to the NE. SO it should localise to the NE. Maybe mention that we will do a LEMD2-GFP over expression experiment in CS-A cells and show that they do localise to the NE.

      I don’t remember fully if Denny did this and what came out. I thought he did and ER stress and cytoskeleton regulation came out as enriched terms?

      Denny will have to check this but I think this is because the Y axis shows adjusted P values?? I have the same in my data and Jack told me this is an artefact of the analysis if you adjust for multiple comparisons and is something more often seen in mass spec data

    1. Author Response

      Reviewer #1 (Public Review):

      Summary:

      The study investigates the role of cylicin-1 (CYLC1) in sperm acrosome-nucleus connections and its clinical relevance to male infertility. Using mouse models, the researchers demonstrate that cylicin-1 is specifically expressed in the post acrosomal sheath-like region in spermatids and plays a crucial role in mediating acrosome-nucleus connections. Loss of CYLC1 results in severe male subfertility, characterized by acrosome detachment and aberrant head morphology in sperm. Further analysis of a large cohort of infertile men reveals CYLC1 variants in patients with sperm head deformities. The study provides valuable insights into the role of CYLC1 in male fertility and proposes CYLC1 variants as potential risk factors for human male infertility, emphasizing the importance of mouse models in understanding the pathogenicity of such variants.

      We appreciate the comprehensive summary of reviewer 1.

      Strengths:

      This article demonstrates notable strengths in various aspects. Firstly, the clarity and excellent writing style contribute to the accessibility of the content. Secondly, the employed techniques are not only relevant but also complementary, enhancing the robustness of the study. The precision in their experimental design and the meticulous interpretation of results reflect the scientific rigor maintained throughout the study. Furthermore, the decision to create a second mouse model with the exact CYLC1 mutation found in humans adds significant qualitative value to the research. This approach not only validates the clinical relevance of the identified variant but also strengthens the translational impact of the findings.

      We appreciate the positive comment of reviewer 1.

      Weaknesses:

      There are no obvious weaknesses. While a few minor refinements, as suggested in the recommendations to authors, could enhance the overall support for the data and the authors' messages, these suggested improvements in no way diminish the robustness of the already presented data.

      In the recommendation for the authors, reviewer 1 mentioned a recent study (Schneider et al., eLife, 2023) showing that Cylc1-KO mice exhibits a reduced sperm count, an observation not noted in our current study. We would like to comment that that main and most important phenotype of Cylc1-KO mice in both studies is quite similar, including male subfertility and abnormal head morphology. We think the different targeting strategy and mouse strain may cause this discrepancy. In Schneider’s and our current studies, the total motility abnormality of Cylc1-KO mice are not observed. We appreciate the suggestion of reviewer 1 to further examine the detailed parameters of motility such as VCL, VSL, and ALH. Given that the head deformation is the most obvious phenotype of Cylc1-KO mice and the focus of our study, we feel sorry that this detailed analysis of sperm motility was not performed in the current stage. Reviewer 1 also asked whether Cylc1-KO female mice are fertile or not. Given that Cylc1 is an X chromosome gene and Cylc1-KO (Cylc1-/Y) mice are severely subfertile, we do not obtain enough Cylc1-KO female mice to examine their fecundity. We also would like to thank reviewer 1 to point out several inaccurate descriptions.

      Reviewer #2 (Public Review):

      Summary:

      To verify the function of PT-associated protein CYLC1, the authors generated a Cylc1-KO mouse model and revealed that loss of cylicin-1 leads to severe male subfertility as a result of sperm head deformities and acrosome detachment. Then they also identified a CYLC1 variant by WES analysis from 19 infertile males with sperm head deformities. To prove the pathogenicity of the identified mutation site, they further generated Cylc1-mutant mice that carried a single amino acid change equivalent to the variant in human CYLC1. The Cylc1-mutant mice also exhibited male subfertility with detached acrosomes of sperm cells.

      We appreciate the comprehensive summary of reviewer 2.

      Strengths:

      The phenotypes observed in the Cylc1-KO mice provide strong evidence for the function of CYLC1 as a PT-associated protein in spermatogenesis and male infertility. Further mechanistic studies indicate that loss of cylicin-1 in mice may disrupt the connections between the inner acrosomal membrane and acroplaxome, leading to detached acrosomes of sperm cells.

      We appreciate the positive comment of reviewer 2.

      Weaknesses:

      The authors identified a missense mutation (c.1377G>T/p. K459N) from 19 infertile males with sperm head deformities. The information for the variant in Table 1 is insufficient to determine the pathogenicity and reliability of the mutation site. More information should be added, including all individuals in gnomAD, East Asians in gnomAD, 1000 Genomes Project for allele frequency in the human population; MutationTaster, M-CAP, FATHMM, and more other tools for function prediction. Then, the expression of CYLC1 in the spermatozoa from men with CYLC1 mutation should be explored by qPCR, Western blot, or IF staining analyses. Although 19 infertile males were found carrying the same missense mutation (c.1377G>T/p. K459N), their phenotypes are somewhat different. For example, sperm concentrations for individuals AAX765, BBA344, and 3086 are extremely low but this is not observed in other infertile males. Then, progressive motility for individuals AAT812, 3165, 3172, 3203, and 3209 are extremely low but this is also not observed in other infertile males. It is worth considering why different phenotypes are observed in probands carrying the same mutation.

      We appreciate the suggestion of reviewer 2. First, Table 1 shows the information of the variant identified in CYLC1 gene, including allele frequency in gnomAD and functional prediction by SIFT, PolyPhen-2, and CADD. Given that mutant mice is a gold standard to confirm the pathogenicity of a variant, we generate Cylc1-mutant mice and Cylc1-mutant mice exhibit male subfertility with sperm acrosome detachment. The animal evidence is much more solid than bioinformatics prediction to confirm the pathogenicity of the identified variant in the CYLC1 gene. Second, the expression of CYLC1 in the spermatozoa from patients have been examined by IF staining (Fig. 5B). Unfortunately, the patients declined to continue in the project to donate more semen for qPCR and Western blot analyses. Third, the reviewer 2 asks why not all patients with CYLC1 gene mutation show the identical phenotype. Although some patients exhibit low sperm count or reduced motility, sperm head deformities are the shared phenotype of 19 patients. Many factors, such as way of life, may affect sperm quality. Perfectly identical phenotype of all 19 patients carrying the CYLC1 mutation is idealistic and will not always happen in clinical diagnosis. We also appreciate other suggestions from reviewer 2.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This manuscript presents a valuable approach to exploring CD4+ T-cell response in mice across stimuli and tissues through the analysis of their T-cell receptor repertoires. The authors use a transgenic mouse model, in which the possible diversity of the T-cell receptor repertoire is reduced, such that each of a diverse set of immune exposures elicits more detectably consistent T-cell responses across different individuals. However, whereas the proposed experimental system could be utilized to study convergent T-cell responses, the analyses done in this manuscript are incomplete and do not support the claims due to limitations in the statistical analyses and lack of data/code access.

      We worked to address the reviewers' concerns below, point-by-point.

      All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

      We added the Data availability statement to the manuscript.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors investigate the alpha chain TCR landscape in conventional vs regulatory CD4 T cells. Overall I think it is a very well thought out and executed study with interesting conclusions. The authors have investigated CDR3 alpha repertoires coupled with a transgenic fixed CDR3beta in a mouse system.

      Strengths:

      • One of a kind evidence and dataset.

      • State-of-the-art analyses using tools that are well-accepted in the literature.

      • Interesting conclusions on the breadth of immune response to challenges across different types of challenges (tumor, viral and parasitic).

      Thank you for the positive view.

      Weaknesses:

      • Some conclusions regarding the eCD4->eTreg transition are not so strong using only the data.

      The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

      • Some formatting issues.

      We are working on the manuscript to correct minor errors and formatting.

      Reviewer #2 (Public Review):

      This study investigates T-cell repertoire responses in a mouse model with a transgenic beta chain, such that all T-cells in all mice share a fixed beta chain, and repertoire diversity is determined solely by alpha chain rearrangements. Each mouse is exposed to one of a few distinct immune challenges, sacrificed, and T-cells are sampled from multiple tissues. FACS is used to sort CD4 and Treg cell populations from each sample, and TCR repertoire sequencing from UMI-tagged cDNA is done.

      Various analyses using repertoire diversity, overlap, and clustering are presented to support several principal findings: 1) TCR repertoires in this fixed beta system have highly distinct clonal compositions for each immune challenge and each cell type, 2) these are highly consistent across mice, so that mice with shared challenges have shared clones, and 3) induction of CD4-to-Treg cell type transitions is challenge-specific.

      The beta chain used for this mouse model was previously isolated based on specificity for Ovalbumin. Because the beta chain is essential for determining TCR antigen specificity, and is highly diverse in wildtype mice, I found it surprising that these mice are reported to have robust and consistently focused clonal responses to very diverse immune challenges, for which a fixed OVA-specific beta chain is unlikely to be useful. The authors don't comment on this aspect of their findings, but I would think it is not expected a priori that this would work. If this does work as reported, it is a valuable model system: due to massively reduced diversity, the TCR repertoire response is much more stereotyped across individual samples, and it is much easier to detect challenge-specific TCRs via the statistics of convergent responses.

      This was to some extent expected, since these mice live almost normally and have productive adaptive immune responses and protection. In real life, there are frequent TCR-pMHC interactions where the TCR-alpha chain dominates (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701794/; https://pubmed.ncbi.nlm.nih.gov/37047500/). On the fixed TCR-beta background this mechanics starts working full-fledged, essentially substituting TCR-beta diversity, at the extent of relatively simplified TCRab repertoire and probably higher cross-reactivity.

      We agree that this is a valuable model, for sure, and indicated this in the last sentence of our Discussion. Now we are also adding this point to the abstract.

      While the data and analyses present interesting signals, they are flawed in several ways that undermine the reported findings. I summarize below what I think are the most substantive data and analysis issues.

      (1) There may be systematic inconsistencies in repertoire sampling depth that are not described in the manuscript. Looking at the supplementary tables (and making some plots), I found that the control samples (mice with mock challenge) have consistently much shallower sampling-in terms of both read count and UMI count-compared with the other challenge samples. There is also a strong pattern of lower counts for Treg vs CD4 cell samples within each challenge.

      The immune response of control mice is less extensive, as it should be. Just like the fact that the number of Tregs in tissues is lower than CD4, this is normal. So this all follows the expectations. But please note that we were very accurate everywhere with appropriate data normalisation, using all our previous extensive experience (https://pubmed.ncbi.nlm.nih.gov/29080364/).

      In particular (now adding more relevant details to Methods):

      For diversity metrics calculations, we randomly sampled an equal number of 1000 UMI from each cloneset. Samples with UMI < 700 were excluded from analysis.

      For amino acid overlap metrics calculations, we selected top-1000 largest clonotypes from each cloneset. Samples with clonotype counts < 700 were excluded from analysis.

      For nucleotide overlaps metrics calculations (eCD4-eTreg), we selected top-100 clonotypes from each cloneset. Samples with clonotypes < 100 were excluded from analysis.

      The top N clonotypes were selected as the top N clonotypes after randomly shuffling the sequences and aligning them in descending order. This was done in order to get rid of the alphabetical order for clonotypes with equal counts (e.g. count = 1 or 2).

      Downsampling was carried out using software vdjtools v.1.2.1.

      (2) FACS data are not reported. Although the graphical abstract shows a schematic FACS plot, there are no such plots in the manuscript. Related to the issue above, it would be important to know the FACS cell counts for each sample.

      Yes, we agree that this is valuable information that should be provided. Unfortunately, this data has not been preserved.

      (3) For diversity estimation, UMI-wise downsampling was performed to normalize samples to 1000 random UMIs, but this procedure is not validated (the optimal normalization would require downsampling cells). What is the influence of possible sampling depth discrepancies mentioned above on diversity estimation? All of the Treg control samples have fewer than 1000 total UMIs-doesn't that pose a problem for sampling 1000 random UMIs?

      Indeed, I simulated this procedure and found systematic effects on diversity estimates when taking samples of different numbers of cells (each with a simulated UMI count) from the same underlying repertoire, even after normalizing to 1000 random UMIs. I don't think UMI downsampling corrects for cell sampling depth differences in diversity estimation, so it's not clear that the trends in Fig 1A are not artifactual-they would seem to show higher diversity for control samples, but these are the very same samples with an apparent systematic sampling depth bias.

      We evaluated this approach through all our work, and summarised in the ref: https://pubmed.ncbi.nlm.nih.gov/29080364/. Altogether, normalising to the same count of randomly sampled UMI seems to be the best approach (although, preferably, the initial sequencing depth should be essentially higher for all samples than the sampling threshold used). Initial sorting of identical numbers of cells and ideally uniform library preparation and sequencing is generally not realistic and does not work in the real world, while UMI downsampling does the same work much better.

      (4) The Figures may be inconsistent with the data. I downloaded the Supplementary Table corresponding to Fig 1 and made my own version of panels A-C. This looked quite different from the diversity estimations depicted in the manuscript. The data does not match the scale or trends shown in the manuscript figure.

      There was a wrong column for Chao1, now correcting. Also, please note that we only used samples with > 700 UMI. Supplementary Table now corrected accordingly. Also, please note that Figure 1 shows the results for lung samples only.

      (5) For the overlap analysis, a different kind of normalization was performed, but also not validated. Instead of sampling 1000 UMIs, the repertoires were reduced to their top 1000 most frequent clones. It is not made clear why a different normalization would be needed here. There are several samples (including all Treg control samples) with only a couple hundred clones. It's also likely that the noted systematic sampling depth differences may drive the separation seen in MDS1 between Treg and CD4 cell types. I also simulated this alternative downsampling procedure and found strong effects on MDS clustering due to sampling effects alone.

      That’s right, for the overlap analysis (which values are mathematically proportional to the clonotype counts in both compared repertoires, so the difference in the counts causes major biases) the right way to do it is to choose the same number of clonotypes. See Ref. https://pubmed.ncbi.nlm.nih.gov/29080364/.

      We kept only samples with > 700 for the overlap analyses. Some relatively poor samples are present in all challenges, while MDS1 localization has clear reproducible logic, so we are confident in these results.

      It is not made clear how the overlap scores were converted to distances for MDS. It's hard to interpret this without seeing the overlap matrix.

      This is a built-in feature in VDJtools software (https://pubmed.ncbi.nlm.nih.gov/26606115/). See also here: https://vdjtools-doc.readthedocs.io/en/master/overlap.html.

      (6) The cluster analysis is superficial, and appears to have been cherry-picked. The clusters reported in the main text have illegibly small logo plots, and no information about V/J gene enrichments. More importantly, as the caption states they were chosen from the columns of a large (and messier-looking) cluster matrix in the supplementary figure based on association with each specific challenge. There's no detail about how this association was calculated, or how it controlled for multiple tests. I don't think it is legitimate to simply display a set of clusters that visually correlate; in a sufficiently wide random matrix you will find columns that seem to correlate with any given pattern across rows.

      Particular CDR3 sequences and VJ segments do not mean much for the results of this manuscript. Logos are given just for visual explanation of how the consensus motifs of the clusters look like.

      We now add two more Supplementary Tables and a Supplementary Figure with full information about clusters.

      We disagree that the Supplementary Figure 1 (representing all the clusters) looks “messy”. Vice versa, it is surprisingly “digital”, showing the clear patterns of responses and homings. This becomes clear if you visually study it for a while. But yes, it is too big to let the reader focus on this or that aspect. That is why we need to select TCR clusters to illustrate this or that aspect discussed in the work, but they were selected from the overall already structured picture.

      (7) The findings on differential plasticity and CD4 to Treg conversion are not supported. If CD4 cells are converting to Tregs, we expect more nucleotide-level overlap of clones. This intuition makes sense. But it seems that this section affirms the consequent: variation in nucleotide-level clone overlap is a readout of variation in CD4 to Treg conversion. It is claimed, based on elevated nucleotide-level overlap, that the LLC and PYMT challenges induce conversion more readily than the other challenges. It is not noted in the textual interpretations, but Fig 4 also shows that the control samples had a substantially elevated nucleotide-level overlap. There is no mention of a null hypothesis for what we'd expect if there was no induced conversion going on at all. This is a reduced-diversity mouse model, so convergent recombination is more likely than usual, and the challenges could be expected to differ in the parts of TCR sequence space they induce focus on. They use the top 100 clones for normalization in this case, but don't say why (this is the 3rd distinct normalization procedure).

      Your point is absolutely correct: “This is a reduced-diversity mouse model, so convergent recombination is more likely than usual”. Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

      The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

      Although interpretations of the reported findings are limited due to the issues above, this is an interesting model system in which to explore convergent responses. Follow-up experimental work could validate some of the reported signals, and the data set may also be useful for other specific questions.

      Yes, thank you for your really thorough analysis. We fully agree with your conclusion.

      Reviewer #3 (Public Review):

      Nakonechnaya et al present a valuable and comprehensive exploration of CD4+ T cell response in mice across stimuli and tissues through the analysis of their TCR-alpha repertoires.

      The authors compare repertoires by looking at the relative overlap of shared clonotypes and observe that they sometimes cluster by tissue and sometimes by stimulus. They also compare different CD4+ subsets (conventional and Tregs) and find distinct yet convergent responses with occasional plasticity across subsets for some stimuli.

      The observed lack of a general behaviour highlights the need for careful comparison of immune repertoires across cell subsets and tissues in order to better understand their role in the adaptive immune response.

      In conclusion, this is an important paper to the community as it suggests several future directions of exploration.

      Unfortunately, the lack of code and data availability does not allow the reproducibility of the results.

      Thank you for your positive view.

      All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

      We added the Data availability statement to the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      • In the manuscript at "yielding 13,369 {plus minus} 1,255 UMI-labeled TCRα cDNA molecules and 3233 {plus minus} 310 TCRα CDR3 clonotypes per sample" I'm not sure how can there be fewer unique DNA molecules than clonotypes in each sample.

      That was our mistake for sure, now corrected.

      • In the manuscript at "This indicates that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

      I'm not sure it's possible to conclude that a drop in diversity in all conditions necessarily signals a focused nature. Since at this stage, the nature of the colotypes was not compared between conditions, it is not possible to claim a focused nature of the response.

      We have softened the wording:

      "This could indicate that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

      • What are your thoughts on why there is such a large overlap between Treg and Teff in the Lung in control? For some replicates it is almost as much as a post-LLC challenge!

      There is some natural dispersion in the data, which is generally expectable. The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

      • In the manuscript at "These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches" I'm not sure we can conclude this from the Figure. Wouldn't you expect the samples to be grouped by color (the different challenges)? Maybe I'm not understanding the sentence!

      This is a different story, about resident Tregs, irrespective of the challenge.

      The whole explanation is here in the text:

      “Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

      And in the abstract:

      “Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

      • In the manuscript at " Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells ":

      5b and 5d seem to have the same pattern: Spleen and MLN group together, AxLN and IgLN together and thymus is separate. Do you mean to say that the groups are more diffuse? I feel like the pattern really is the same and it's likely due to some noise in the data…

      Yes, we just mean here that eTreg groups are less diffuse - means more convergent.

      • I'm not sold on the eCD4 to eTreg conversion evidence. Why only limit to the top 100 clones? The top 1000 clones were used in previous analyses! Moreover, the authors claim that calculating relative overlap (via F2) of matching CDR3+V+J genes is evidence of a conversion between eCD4 and eTreg. I think to convince myself of a real conversion, I would track the cells between groups, unfortunately, I'm not sure how to track this.. Maybe looking at the thymus population? For example, what is the overlap in the thymus vs. after the challenge? I don't have an answer on how to verify but I feel that this conclusion is a bit on the weaker end.

      Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

      The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

      • There is a nuance in the analysis between Figure 3 and Figure 5 which I think I am not grasping. Both Figures use the same method and the same data but what is different? I think the manuscript would benefit from making this crystal clear. The conclusions will likely be more evident as well!

      As explained in the text and above, on Figure 5 “we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge.”

      The idea of this mini-chapter of the manuscript is to reveal tissue-resident Tregs, distinct for distinct tissues, resident there in all these mice, irrespectively of the challenge we applied. And they are really there (!).

      • Do the authors plan to share their R scripts?

      All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

      Minor typos and formatting issues to address:

      • Typo in Figure 2a the category should read "worm" instead of "warm"

      Corrected.

      • Figure 2a heatmap is missing a color bar indicating the value ranges

      The detailed information can be found in additional Supplementary materials.

      • Figure 2f is never mentioned in the manuscript!

      Corrected.

      • "eTreg repertoire upon lung challenge is reflected in the draining lymph node" - the word upon is of a lower size

      Corrected.

      • The authors should make the spelling of eTreg uniform across the manuscript (reg in subscript vs just lower case letters. Same goes for CDR3a vs CDR3\alpha

      Corrected.

      • Figure 4a-d p-values annotations are not shown. Is it because they are not significant?

      Corrected.

      • The spelling of FACS buffer should be uniform (FACs vs FACS, see methods)

      Corrected.

      • In the gating strategy, I would make a uniform annotation for the cluster of differentiation, for example, "CD44 high" vs "CD44^{hi}", pos vs + etc.

      Corrected.

      • Citation for MIGEC software (if available) is missing from methods

      Present in the text so probably sufficient.

      Reviewer #2 (Recommendations For The Authors):

      I noticed the data was made available via Figshare in the preprint, but there is no data availability statement in the current ms.

      We provided Data availability statement.

      The methods state that custom scripts were written to perform the various analyses. Those should be made available in a code repository, and linked in the ms.

      All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

      The title mentioned "TCR repertoire prism", so I thought "prism" was the name of a new method or software. But then the word "prism" didn't appear anywhere in the ms.

      We just mean viewing or understanding something from a different perspective or through a lens that reveals different aspects or nuances.

      Figure 1D lacks an x-axis label.

      Worked on the figures in general.

      Reviewer #3 (Recommendations For The Authors):

      • The paper is very concise, possibly a bit too much. It could use additional explanations to properly affirm its relevance, for example:

      why the choice of fixing the CDR3beta background?

      To make repertoire more similar across the mice, and to track all the features of repertoire using only one chain.

      to what it is fixed?

      As explained in Methods:

      “C57BL/6J DO11.10 TCRβ transgenic mice (kindly provided by Philippa Marrack) and crossed to C57BL/6J Foxp3eGFP TCRa-/- mice.”

      What do you expect to see and not to see in this specific system and why it is important?

      As stated above: we expected repertoire to be more similar across the mice, and it is important to find antigen-specific TCR clusters across mice, and to be able to track all the features of the TCR repertoire using only one chain.

      Does this system induce more convergent responses? If so, can we extrapolate the results from this system to the full alpha-beta response?

      Such a model, compared to conventional mice, is much more powerful in terms of the ability of monitoring convergent TCR responses. At the same time, it behaves natural, mice live almost normally, so we believe it reflects natural behaviour of the full fledged alpha-beta T cell repertoire.

      • Is the lack of similarity of other tissues to Lung/MLN due to a lack of a response?

      As indicated in the title of the corresponding mini-chapter: “eTreg repertoire upon lung challenge is reflected in the draining lymph node”. And conclusion of this mini-chapter is that “these results demonstrate the selective tissue localization of the antigen-focused Treg response. ”

      Can you do a dendrogram like 2a for the other tissues to better clarify what is going on there? There is space in the supplementary material.

      We built lots of those, but in such single dimension mostly they are less informative compared to 2D MDS plots.

      • Figure 5 seems a bit out of place as it looks more related to Figure 2. It could maybe be integrated there, sent to supplementary or become Figure 3?

      This is a different story, about resident Tregs, irrespective of the challenge.

      The whole explanation is here in the text:

      “Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

      And in the abstract:

      “Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

      • Have you explored more systematically the role of individual variability? If you stratify by individual, do you observe any trend? If not this is also an interesting observation to highlight and discuss.

      This is inside the calculations and figures/ one dot = 1 mice, so this natural variation is there inside.

      • Regarding the MDS plots: why are 2 dimensions the right amount? Maybe with 3, you can see both tissue specificity and stimuli contributions. Can you do a stress vs # dimensions plot to check what should be the right amount of dimensions to more accurately reproduce the distance matrix?

      Tissue specificity and stimuli contribution is hard to distinguish without focussing on appropriate samples, as we did on Fig. 3 and 5. The work is already not that simple as is, and attempting to analyse this in multidimensional space is far beyond our current abilities. But this is an interesting point for future work, thank you.

      • Figure 2: A better resolution is needed in order to properly resolve the logo plots at the bottom.

      Yes, we worked on Figures, and also provide new Supplementary Figure with all the logos.

      • No code or data are made available. There is also a lack of supplementary figures that complement and expand the results presented in the main text.

      We believe that the main text, although succinct, contains lots of information to analyse and conclusions (preliminary) to make. So we do not see it rational to overload it further.

    1. Author Response

      Reviewer #1 (Public Review):

      Response to reviewer 1 comments on “weaknesses”:

      “A weakness in the approach is the use of genetic models that do not offer complete deletion of the prolactin receptor from targeted neuronal populations...”

      We acknowledge that neither model used provided a complete deletion of the prolactin receptor (Prlr) from the targeted neuronal populations. We suspect that incomplete deletion of targeted genes is not uncommon in these sort of studies, but this remains the best approach to addressing our question, and we believe we have been thorough and transparent in reporting the degree of deletion observed. We thought we had appropriately discussed the implications of the low proportion of Kiss1 cells still expressing Prlr, but will certainly revisit to ensure it is discussed thoroughly. This does not detract, however, from the key conclusion that prolactin action is necessary for full suppression of fertility in lactation in the mouse.

      “Results showing no impact of progesterone on LH secretion during lactation are surprising, given the effectiveness of progesterone-containing birth control in lactating women...”

      We think that this comment misrepresents what has been done in our study. We did not report a lack of impact of progesterone, as exogenous progesterone was never administered to mice. We did, however, give mifepristone as a progesterone receptor antagonist to determine whether endogenous progesterone contributed to the suppression of kisspeptin neuronal activity. We found that mifepristone, at levels sufficient to terminate pregnancy, had no effect on pulsatile LH secretion in lactating mice. This is consistent with our prior observation that progesterone levels are low in mouse lactation, suggesting that progesterone does not contribute significantly to the suppression of kisspeptin neuronal activity during lactation in the mouse. We agree with the reviewer that if we had given exogenous progesterone, it likely would result in suppression of pulsatile LH secretion (as it does in women). Indeed, in other work, we have found that progesterone administration profoundly suppresses activity of the kisspeptin neurons in mice (https://doi.org/10.1210/en.2019-00193). But this was not the point of the present experiment. We will review how we have described this experiment to ensure that this is absolutely clear.

      “While the authors assert their findings may reflect an important role for prolactin in lactational infertility in other mammalian species, that remains to be seen….”

      We acknowledge that our study cannot address whether prolactin is necessary for the suppression of lactation in other mammalian species. We hope our data may stimulate a re-examination of this question in other species, however, as some of the prior methodology (such as using pharmacological suppression of prolactin) may have had off target effects that confound interpretation. We thought that this point was discussed appropriately in the manuscript but we will certainly check and make sure this is addressed suitably.

    1. Author Response

      The following is the authors’ response to the current reviews.

      eLife assessment

      This important study used Voltage Sensitive Dye Imaging (VSDI) to measure neural activity in the primary visual cortex of monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. The authors show convincingly that the initial effect of the mask ran counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. They interpret these results in terms of influences from the receptive field center, and although an alternative view that emphasizes the role of the receptive field surround also seems reasonable, this study stands as an interesting and important contribution to our understanding of mechanisms of visual perception.

      Public Reviews:

      Reviewer #1 (Public Review):

      This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings.

      The authors have done a good job responding to the main points of my previous review. One important question remains, as stated in that review:

      "My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry at al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here."

      Their rebuttal of my first review is not convincing -- I still believe that surround influences are important and perhaps predominant in determining the outcome of the experiments. This is particularly clear for the "paradoxical" dynamics that they observe, which seem exactly to reflect the behavior of the surround.

      The authors' arguments to the contrary are based on three main points. First, their stimuli cover the center and surround, unlike those of many previous experiments, so they argue that this somehow diminishes the impact of the surround. But the argument is not accompanied by data showing the effects of center stimuli alone or surround stimuli alone. Second, their model -- a normalization model -- does not need surround influences to account for the masking effect. Third, they cite human psychophysical masking results from their collaborators (Sebastian et al 2017), but do not cite an equally convincing demonstration that surround contrast creates potent orientation selective masking when presented alone (Petrov et al 2005, https://doi.org/10.1523/JNEUROSCI.2871-05.2005).

      At the end of the day, these issues will be resolved by further experiments, not argumentation. The paper stands as an excellent contribution, but it might be wise for the authors to be less doctrinaire in their interpretations.

      We thank the reviewer for their positive comments and constructive criticism. In general, we agree with the reviewer’s comments. Importantly, we do not claim that there is no effect from the surround. What we say in the discussion is:

      “Because our targets are added to the background rather than occluding it, it is likely that a significant portion of the behavioral and neural masking effects that we observe come from target-mask interactions at the target location rather than from the effect of the mask in the surround.”

      We still stand by this assessment. We also make the point that, at least within the framework of our delayed normalization model, there is no need for the normalization mechanism to extend beyond the center mechanism to account for our results, and even if the normalization mechanism is somewhat larger than the center, the overlap region at the center would still have a large contribution to the modulations. Overall, we agree that these issues will be need to be resolved by future experiments.

      For the reasons discussed in our previous reply, we disagree with the reviewers’ statement “…this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature”. For similar reasons we disagree with the statement “It is nice to see that VSDI results square well with those from prior extracellular recordings”.

      Reviewer #2 (Public Review):

      Summary

      In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

      Strengths

      The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

      We thank the reviewer for their positive comments.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      None, except perhaps for a more balanced representation of the "surround" possibility in the Discussion. The Petrov et al paper (https://doi.org/10.1523/JNEUROSCI.2871-05.2005) should be considered and cited.

      As discussed above, we believe that our discussion of possible contribution from the surround is balanced. While the paper by Petrov et al is interesting, the stimuli used to study the surround effects are quite different (e.g., gap between center and surround, and the sharp edge of the surround inner boundary) so direct comparison with our results is not possible.

      Reviewer #2 (Recommendations For The Authors):

      The authors have addressed the questions/suggestions I raised in my review.


      The following is the authors’ response to the original reviews.

      We thank the reviewers for their helpful comments and suggestions.

      eLife assessment

      This is an important contribution that extends earlier single-unit work on orientation-specific center-surround interactions to the domain of population responses measured with Voltage Sensitive Dye (VSD) imaging and the first to relate these interactions to orientation-specific perceptual effects of masking. The authors provide convincing evidence of a pattern of results in which the initial effect of the mask seems to run counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. It seems likely that the physiological effects of masking reported here can be attributed to previously described signals from the receptive field surround.

      We thank the reviewers for bringing up the relation of our results to findings from previous orientation-specific center-surround interactions studies. In our final manuscript, we added a paragraph discussing this important issue. Briefly, for multiple reasons, we believe that the orientation-dependent behavioral and neural masking effects that we observe are unlikely to depend on previously described center-surround interactions in V1. First, in human subjects, perceptual similarity masking effects are almost entirely accounted for by target-mask interactions at the target location and are recapitulated when the mask has the same size and location as the target (Sebastian et al 2017). Second, in our computational model, the effect of mask orientation on the dynamics of the response are qualitatively the same if the mask is restricted to the size and location of the target while mask contrast is increased (Fig. 8 – figure supplement 3). Third, in our model, the results are qualitatively the same when the spatial pooling region for the normalization signal is the same as that for the excitation signal (Fig. 8 – figure supplement figure 1). These considerations suggest that center-surround interactions may not be necessary for neural and behavioral similarity masking effects with additive targets.

      We would also like to point out some key differences between the stimuli that we use and the ones used in most previous center-surround studies. First, in our experiments, the target and the mask were additive, while in most previous center-surround studies the target occludes the background. Such studies therefore restrict the mask effect to the surround, while in our study we allow target-mask interactions at the center. Second, most center-surround studies have a sharp-edged target/surround, while in our experiments no sharp edges were present. Unpublished results from our lab suggest that such sharp edges have a large impact on V1 population responses. A third key difference is that our stimuli were flashed for a short interval of 250 ms corresponding to a typical duration of a fixation in natural vision, while most previous center-surround studies used either longer-duration drifting stimuli or very short-duration random-order stimuli for reverse-correlation analysis.

      In addition, we would like to emphasize that our results go beyond previous studies in two important ways. First, we study the effect of similarity masking in behaving animals and quantitatively compare the effect of similarity masking on behavior and physiology in the same subjects and at the same time. Second, VSD imaging allows us to capture the dynamics of superficial V1 population responses over the entire population of millions of neurons activated by the target at two important spatial scales. Such results therefore complement electrophysiological studies that examine the activity of a very small subset of the active neurons.

      Public Reviews:

      Reviewer #1 (Public Review):

      This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings. But the work may be less original than the authors propose, and their overall framing strikes me as odd. Some additional clarifications could make the contribution more clear.

      Please see our reply above regarding the agreement with previous studies and framing.

      My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry et al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here.

      We thank the reviewer for the pointing out this previous work which we now cite in the final version of the manuscript. For the reasons discussed above, while this study is interesting and related to our work, we believe that our results are quite distinct.

      • In the discussion (lines 315-316), they state "in order to account for the reduced neural sensitivity with target-background similarity in the second phase of the response, the divisive normalization signal has to be orientation selective." I wonder whether they observed this in their modeling. That is, how robust were the normalization model results to the values of sigma_e and sigma_n? It would be useful to know how critical their various model parameters were for replicating the experimental effects, rather than just showing that a good account is possible.

      Thank you for this suggestion. In the final manuscript we include a supplementary figure that shows how the model’s predictions are affected by the orientation tuning and spatial extent of the normalization signal, and by the size and contrast of the mask (Fig. 8 – figure supplement 1-4).

      • The majority of their target/background contrast conditions were collected only in one animal. This is a minor limitation for work of this kind, but it might be an issue for some.

      We agree that this is a limitation of the current study. These are challenging experiments and we were unable to collect all target/background contrast combinations from both monkeys. However, in the common conditions, the results appear similar in the two animals, and the key results seem to be robust to the contrast combination in the animal in which a wider range of contrast combinations was tested. We added these points to the discussion in the final manuscript.

      • The authors point out (line 193-195) that "Because the first phase of the response is shorter than the second phase, when V1 response is integrated over both phases, the overall response is positively correlated with the behavioral masking effect." I wonder if this could be explored a bit more at the behavioral level - i.e. does the "similarity masking" they are trying to explain show sensitivity to presentation time?

      We agree that testing the effect of stimulus duration on similarity masking is interesting, but unfortunately, it is beyond the scope of the current study. We would also like to point out that the duration of the presentation was selected to match the typical time of fixation during natural behaviors, so much shorter or much longer stimulus durations would be less relevant for natural vision.

      • From Fig. 3 it looks like the imaging ROI may include some opercular V2. If so, it's plausible that something about the retinotopic or columnar windowing they used in analysis may remove V2 signals, but they don't comment. Maybe they could tell us how they ensured they only included V1?

      We thank the reviewer for this comment. As part of our experiments, we extract a detailed retinotopic map for each chamber, so we were able to ensure that the area used for the decoding analysis lays entirely within V1. We now incorporate this information in the final manuscript (Fig. 3 – figure supplement 1).

      • In the discussion (lines 278-283) they say "The positive correlation between the neural and behavioral masking effects occurred earlier and was more robust at the columnar scale than at the retinotopic scale, suggesting that behavioral performance in our task is dominated by columnar scale signals in the second phase of the response. To the best of our knowledge, this is the first demonstration of such decoupling between V1 responses at the retinotopic and columnar scales, and the first demonstration that columnar scale signals are a better predictor of behavioral performance in a detection task." I am having trouble finding where exactly they demonstrate this in the results. Is this just by comparison of Figs. 4E,K and 5E,K? I may just be missing something here, but the argument needs to be made more clearly since much of their claim to originality rests on it.

      We thank the reviewer for this comment. In the final manuscript we are more explicit when we discuss this point and refer to the relevant panels in Figs. 4, 5 and their figure supplements. To substantiate this key claim, we also report the timing of the transition between the two phases in all temporal correlation panels and report the neural-behavioral correlation for the integration period.

      Reviewer #2 (Public Review):

      Summary

      In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

      Strengths

      The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

      Points to Consider / Possible Improvements

      The biphasic nature of the relationship between neural and behavioral modulation by the mask and the surprising finding that the two are anticorrelated in the initial phase are left as a mystery. The paper would be more impactful if this mystery could be resolved.

      We thank the reviewer for the positive comments. In our view, while our results are surprising, there may not be a remaining mystery that needs to be resolved. As our model shows, the biphasic nature of V1’s response can be explained by a delayed orientation-tuned gain control. Our results are consistent with the hypothesis that perception is based on columnar-scale V1 signals that are integrated over an approximately 200 ms long period that incorporates both the early and the late phase of the response, since such decoded V1 signals are positively correlated with the behavioral similarity masking effect (Fig. 5D, J; Fig. 5 – figure supplement 1). We now explain this more clearly in the discussion of our final manuscript.

      The finding is based on analyses of the correlation between behavior and neural responses. This appears in the main body of the manuscript and is detailed in Figures S1 and S2, which show the correlation over time between behavior and target response for the retinotopic and columnar scale.

      One possible way of thinking of this transition from anti- to positive correlation with behavior is that it might reflect the dynamics of a competitive interaction between mask and target, with the initial phase reflecting predominantly the mask response, with the target emerging, on some trials, in the latter phase. On trials when the mask response is stronger, the probability of the target emerging in the latter phase, and triggering a hit, might be lower, potentially explaining the anticorrelation in the initial phase. The sustained response may be a mixture of trials on which the target response is or is not strong enough to overcome the effect of the mask sufficiently to trigger target detection.

      It would, I think, be worth examining this by testing whether target dynamics may vary, depending on whether the monkey detected the target (hit trials) or failed to detect the target (miss trials). Unless I missed it I do not think this analysis was done. Consistent with this possibility, the authors do note (lines 226-229) that "The trajectories in the target plus mask conditions are more complex. For example, when mask orientation is at +/- 45 deg to the target, the population response is initially dominated by the mask, but then in mid-flight, the population response changes direction and turns toward the direction of the target orientation." This suggests (to this reviewer, at least) that the emergence of a positive correlation between behavioral and neural effects in the latter phase of the response could reflect either a perceptual decision that the target is present or perhaps deployment of attention to the location of the target.

      It may be that this transition reflected detection, in which it might be more likely on hit trials than miss trials. Given the SNR it would presumably be difficult to do this analysis on a trial-by-trial basis, but the hit and miss trials (which make each make up about 1/2 of all trials) could be averaged separately to see if the mid-flight transition is more prominent on hit trials. If this is so for the +/- 45 degree case it would be good to see the same analysis for other combinations of target and mask. It would also be interesting to separate correct reject trials from false alarms, to determine whether the mid-flight transition tends to occur on false alarm trials.

      If these analyses do not reveal the predicted pattern, they might still merit a supplemental figure, for the sake of completeness.

      We thank the reviewer for suggesting this interesting possibility. The original analysis in the manuscript was based on both correct and incorrect trials, raising the possibility that our results reflect some contribution from decision- and/or attention-related signals rather than from low-level nonlinear encoding mechanisms in V1 that we postulate in our model (Fig. 8). To explore this possibility, we re-examined our results while excluding error trials. We found that our key results from Figs 4 and 5 – namely that there is an early transient phase in which the neural and behavioral similarity effects are anti-correlated, and a later sustained phase in which they are positively correlated – hold even for the subset of correct trials, reducing the possibility that decision/attention-related signals play a major role in explaning our results. We now include the results of this analysis as a supplementary figure in the final manuscript (Fig. 4 – figure supplement 2). While there may be some interesting differences in the response dynamics between correct and incorrect trials, the current study was not designed to address this question and the large number of conditions and small number of repeats that it necessitated make this data set suboptimal for examining these phenomena.

      References

      Sebastian S, Abrams J, Geisler WS. 2017. Constrained sampling experiments reveal principles of detection in natural scenes. Proc Natl Acad Sci U S A 114: E5731-e40

    1. Reviewer #2 (Public Review):

      In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

      Comments on revised version:

      Regarding the Reviewer's 1 comment on the architecture details, I have now understood that the precise architecture (number/type of layers, activation functions, pooling operations, skip connections, upsampling choice...) might have remained relatively hidden to the authors themselves, as the U-net is built automatically by the fast.ai library from a given classical choice of encoder architecture (ResNet34 and ResNet101 here) to generate the decoder part and skip connections.

      Regarding the Major point 1, I raised the question of the generalisation potential of the method. I do not think, for instance, that the optimal number of frames to use, nor the optimal choice of their time-shift with respect to the division time (t-n, t+m) (not systematically studied here) may be generic hyperparameters that can be directly transferred to another setting. This implies that the method proposed will necessarily require re-labeling, re-training and re-optimizing the hyperparameters which directly influence the network architecture for each new dataset imaged differently. This limits the generalisation of the method to other datasets, and this may be seen as in contrast to other tools developed in the field for other tasks such as cellpose for segmentation, which has proven a true potential for generalisation on various data modalities. I was hoping that the authors would try themselves testing the robustness of their method by re-imaging the same tissue with slightly different acquisition rate for instance, to give more weight to their work.

      In this regard, and because the authors claimed to provide clear instructions on how to reuse their method or adapt it to a different context, I delved deeper into the code and, to my surprise, felt that we are far from the coding practice of what a well-documented and accessible tool should be.

      To start with, one has to be relatively accustomed with Napari to understand how the plugin must be installed, as the only thing given is a pip install command (that could be typed in any terminal without installing the plugin for Napari, but has to be typed inside the Napari terminal, which is mentioned nowhere). Surprisingly, the plugin was not uploaded on Napari hub, nor on PyPI by the authors, so it is not searchable/findable directly, one has to go to the Github repository and install it manually. In that regard, no description was provided in the copy-pasted templated files associated to the napari hub, so exporting it to the hub would actually leave it undocumented.

      Regarding now the python notebooks, one can fairly say that the "clear instructions" that were supposed to enlighten the code are really minimal. Only one notebook "trainingUNetCellDivision10.ipynb" has actually some comments, the other have (almost) none nor title to help the unskilled programmer delving into the script to guess what it should do. I doubt that a biologist who does not have a strong computational background will manage adapting the method to its own dataset (which seems to me unavoidable for the reasons mentioned above).

      Finally regarding the data, none is shared publicly along with this manuscript/code, such that if one doesn't have a similar type of dataset - that must be first annotated in a similar manner - one cannot even test the networks/plugin for its own information. A common and necessary practice in the field - and possibly a longer lasting contribution of this work - could have been to provide the complete and annotated dataset that was used to train and test the artificial neural network. The basic reason is that a more performant, or more generalisable deep-learning model may be developed very soon after this one and for its performance to be fairly compared, it requires to be compared on the same dataset. Benchmarking and comparison of methods performance is at the core of computer vision and deep-learning.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This study presents valuable findings on diabetogenic risk from colorectal cancer (CRC) treatment. The authors claim that postoperative screening for type 2 diabetes should be prioritized in CRC survivors with overweight/obesity, irrespective of the oncological treatment received. The evidence supporting the claims is solid but requires confirmation in different populations. These results have theoretical or practical implications and will be of interest to endocrinologists, oncologists, general practitioners, gastrointestinal surgeons, and policymakers working on CRC and diabetes.

      Author response: We thank you for taking the time to provide constructive feedback on our manuscript and for the useful suggestions. We have provided a point-by-point response to each of the reviewers’ comments with clearly marked changes to the manuscript.

      Public reviews

      Reviewer #1 (Public Review):

      Summary:

      In this study, the authors set out to determine whether colorectal cancer surgery site (right, left, rectal) and chemotherapy impact the subsequent risk of developing T2DM in the Danish national health register.

      Strengths:

      • The research question is conceptually interesting

      • The Danish national health register is a comprehensive health database

      • The data analysis was thorough and appropriate

      • The findings are interesting, and a little surprising that there was no impact of chemotherapy on the development of T2DM

      Weaknesses:

      This is not a weakness as such, but in the discussion, I would consider adding some brief comment on the international generalizability of the findings - e.g. demographic make up of the Danish population health register and background rates of DM and obesity in this population with CRC compared to countries on other continents.

      Author response: We agree that this information would be valuable. It has now been added in the Discussion section.

      Changes in manuscript: "In Denmark, the overall T2D prevalence is 6.9%25, lower than the global average in 2021 (10.5%) and also falls below the estimate of high-income countries (11.1%).26 Similarly, the obesity rate of 20% aligns with other Scandinavian countries and is below that of most high-income nations.27” (Page 8, line 256-258)

      A little more information would be helpful regarding how T2DM was diagnosed in the registry.

      Author response: We have now added a more thorough explanation of how T2D was diagnosed in the Methods section.

      Changes in manuscript: “Diabetes is defined as the second occurrence of any event across three types of inclusion events: 1) Diabetes diagnosed during hospitalisation 2) diabetes-specific services received at podiatrist 3) purchases of glucose lowering. Thus, if a patient developed transient T2D during chemotherapy treatment, it will only be an inclusion event if they purchase glucose lowering drugs. Individuals were classified as having T1D if they had received prescriptions for insulin combined with a diagnosis of type 1 from a medical hospital department. Otherwise, diabetes was classified as type 2.22” (Page 5, line 154-160)

      If someone did develop transient hyperglycemia requiring DM medications during chemotherapy, would the investigators have been able to identify these people?

      Author response: Yes, we have added a sentence in the Methods section.

      Changes in manuscript: “Thus, if a patient developed transient T2D during chemotherapy treatment, it will only be an inclusion event if they purchase glucose lowering drugs.” (Page 5, line 156-158)

      Would they have been classified as T2DM based on filling a prescription for DM meds for a period of time? Also, did the authors have information regarding time to development of T2DM after surgery?

      Author response: Yes, if they have 2 (or more) prescriptions of oral glucose lowering drugs. Yes, we have information regarding time to development of T2DM after surgery and found no difference between the groups.

      Changes in manuscript: Information on mean time to develop T2D post-surgery has now been added to Table 2.

      In the adjusted Models, the authors did not adjust for cancer stage, even though cancer stage appears to be very different between the chemo and no chemo groups. It would be interesting to know if it affects the results if the model adjusted for cancer stage

      Author response: We agree that adjustment for cancer stage would be a valuable information and we have performed the analysis and added a sentence in the Result section.

      Changes in manuscript: An adjusted analysis of cancer stage now appears in the Supplementary table 1.

      “Moreover, adjusting for cancer stage did not affect the results (Supplementary table 1).” (Page 7, line 219-220)

      It would be worthwhile to report if mortality rates were different between the groups during follow up, and if the authors investigated whether perhaps differences in mortality rates led to specific groups living longer, and therefore having more time to develop DM

      Author response: This situation is accounted for in the analysis by using Cox-regression analysis. This method accounts for the potential competing effect of mortality.

      Changes in manuscript: None.

      Overall, the authors achieved their aims, and the conclusions are supported by their results as reported.

      The results are unlikely to significantly change patient treatment or T2DM screening in this population. With some additional information, as described above, the results would be of interest to the community.

      Reviewer #2 (Public Review):

      Summary:

      The study showed the impact of cancer treatment on new onset of diabetes among patients with colorectal cancer using the national database. Findings reported that individuals with rectal cancer without chemotherapy were less likely to develop diabetes but among other groups, treatment didn't show any impact on the development of diabetes. BMI still played a significant role in developing diabetes regardless of treatment types.

      Strengths:

      One of the strengths of this study is innovative findings about the prognosis of colorectal cancer treatment stratified by treatment types. Especially, as it examined the impact of treatment on the risk of new chronic disease after diagnosis, it became significant evidence that suggests practical insights in developing a proper monitoring system for patients with colorectal cancer and their outcomes after treatment and diagnosis. It is imperative for providers to guide patients and caregivers to prevent adverse outcomes like new onset of chronic disease based on BMI and types of treatment. The next strength is the national database. As the study used the national database, the generalizability is validated.

      Weaknesses:

      Even though the study attempted to examine the impact of each treatment option, the dosage of chemotherapy and the types of chemotherapy were not able to be examined due to the data source.

      Author response: No unfortunately not. We agree that this would have been valuable information. This is stated in the original manuscript as a limitation. Please refer to page 10 line 305-306.

      Changes in manuscript: None.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Minor things:

      There are minor inconsistencies in the methods and results regarding BMI. In the methods, the authors state that BMI <18.5 and >/=40 were excluded, but these groups are included in Table 2.

      Author response: This has been corrected

      Changes in manuscript: BMI groups <18.5 and >/=40 are now excluded in Table 2. (Page 18)

      Line 204, I believe should be BMI 18.5-24.9, not 20-24.9.

      Author response: This has been corrected

      Changes in manuscript: “For each group (type of surgery ± chemotherapy), the HR for developing T2D depending on BMI subgroups was calculated by using Cox regression analysis adjusted for age, sex, year of surgery, and ASA score using normal weight (BMI:18.5-24.9) as the reference group.” (Page 6, line 184-186)

      Rather than showing the BMI mean in Table 1, it would be interesting to see the BMI breakdown by category.

      Author response: Yes, we agree. This analysis has now been added to Table 1

      Changes in manuscript: Please refer to Table 1

      Re line 215, I would consider rewriting to remove the multiple negatives -e.g. Radiation therapy in rectal resected had did not impact the incidence rate of T2D in the Rectal-No-Chemo group or Rectal-Chemo group

      Author response: This has been corrected. Please refer to the Result section.

      Changes in manuscript: “Radiation therapy in the rectal resected groups had no impact on the incidence rate of T2D (Table 2); and the unadjusted/adjusted HR of developing T2D was non-significant when comparing Rectal-No-Radiation patients with Rectal-Radiation patients (Table 3).” (Page 7, 223-225)

      Consider changing some of the "didn't"s in the discussion to "did not"

      Author response: This has been corrected.

      Changes in manuscript: Revised and corrected throughout the discussion.

      Reviewer #2 (Recommendations For The Authors):

      Some points need to be clarified and improved.

      In the method, patients with Type 1 Diabetes were excluded in the baseline but some patients were diagnosed with Type 1 diabetes after treatment and they were included in your analysis. It is interesting to identify Type 1 Diabetes after the treatment as an outcome, do you think that this diagnosis is caused by the treatment? And incidence rate or other HRs did not seem to include Type 1 Diabetes as stated in the methods. Did you exclude every Type 1 diabetes? If not, It needs to give further explanation about this outcome since the mechanism of Type 1 Diabetes and Type 2 Diabetes is different.

      Author response: This matter has now been clarified in the Methods section.

      Changes in manuscript: “Additionally, individuals diagnosed with Type 1 diabetes (T1D) either before or after surgery were excluded, along with those diagnosed with T2D preoperatively or within the first 2 weeks postoperatively, as the last group probably represents patients with preoperatively unknown pre-existing prediabetes or diabetes.22” (Page 4, line: 125-128)

      Despite limited existing findings, some studies actually reported the incidence rates of Type 2 Diabetes among patients with CRC (Singh S, Earle CC, Bae SJ, et al. Incidence of Diabetes in Colorectal Cancer Survivors. J Natl Cancer Inst. 2016;108(6):djv402. Published 2016 Feb 2. doi:10.1093/jnci/djv402; Khan NF, Mant D, Carpenter L, Forman D, Rose PW. Long-term health outcomes in a British cohort of breast, colorectal and prostate cancer survivors: a database study. Br J Cancer. 2011;105 Suppl 1(Suppl 1):S29-S37. doi:10.1038/bjc.2011.420; Jo A, Scarton L, O'Neal LJ, et al. New onset of type 2 diabetes as a complication after cancer diagnosis: A systematic review. Cancer Med. 2021;10(2):439-446. doi:10.1002/cam4.3666) whereas your study examined the impact of the different types of treatments.

      Author response: Our findings of T2D rate among CRC patients are now commented on in discussion section, and the abovementioned studies are included as references.

      Changes in manuscript: “This national cohort study demonstrated an IR of developing T2D after CRC surgery similar to previous studies.5,11” (Page 8, line 237-238)

      To strengthen the presentation, some places should be revised.

      • Line 216: it says that Table 1 showed no impact of radiation therapy on the incidence rate of T2D. However, either the interpretation or the table number seems wrong. Table 1 does not have this information. Correct this statement.

      • Line 239: There are typo and incomplete sentence. Check the sentence and correct the sentence.

      • Line 257-261: It may be a systematic issue to separate these two paragraphs. But two paragraphs seem related so make them one paragraph.

      Author response: These suggested changes have been made. Regarding line 216 the paragraph has been adjusted to the following:

      Changes in manuscript: “Radiation therapy in the rectal resected groups had no impact on the incidence rate of T2D (Table 2); and the unadjusted/adjusted HR of developing T2D was non-significant when comparing Rectal-No-Radiation patients with Rectal-Radiation patients (Table 3).” (Page 7, 223-225)

      Reference

      (1) Araghi M, Soerjomataram I, Jenkins M, et al. Global trends in colorectal cancer mortality: projections to the year 2035. Int J Cancer. 2019;144(12):2992-3000. doi:10.1002/ijc.32055

      (2) Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683-691. doi:10.1136/gutjnl-2015-310912

      (3) González N, Prieto I, del Puerto-Nevado L, et al. 2017 Update on the Relationship between Diabetes and Colorectal Cancer: Epidemiology, Potential Molecular Mechanisms and Therapeutic Implications. Vol 8.; 2017. www.impactjournals.com/oncotarget

      (4) Mills KT, Bellows CF, Hoffman AE, Kelly TN, Gagliardi G. Diabetes mellitus and colorectal cancer prognosis: A meta-analysis. Dis Colon Rectum. 2013;56(11):1304-1319. doi:10.1097/DCR.0b013e3182a479f9

      (5) Singh S, Earle CC, Bae SJ, et al. Incidence of Diabetes in Colorectal Cancer Survivors. J Natl Cancer Inst. 2016;108(6). doi:10.1093/jnci/djv402

      (6) Xiao Y, Wang H, Tang Y, et al. Increased risk of diabetes in cancer survivors: a pooled analysis of 13 population-based cohort studies. ESMO Open. 2021;6(4). doi:10.1016/j.esmoop.2021.100218

      (7) Colorectal D, Nordcan 2019. 5-Year Age-Standardised Relative Survival (%), Males and Females. Accessed September 12, 2022. “https://nordcan.iarc.fr/en/dataviz/survival?cancers=520&set_scale=0&sexes=1_2&populations=208”" has been copied into your clipboard

      (8) Nano J, Dhana K, Asllanaj E, et al. Trajectories of BMI Before Diagnosis of Type 2 Diabetes: The Rotterdam Study. Obesity. 2020;28(6):1149-1156. doi:10.1002/oby.22802

      (9) Maddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes. Translational Research. 2017;184:101-107. doi:10.1016/j.trsl.2017.02.004

      (10) Lega IC, Lipscombe LL. Review: Diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications. Endocr Rev. 2020;41(1). doi:10.1210/endrev/bnz014 (11) Jo A, Scarton L, O’Neal LTJ, et al. New onset of type 2 diabetes as a complication after cancer diagnosis: A systematic review. Cancer Med. 2021;10(2):439-446. doi:10.1002/cam4.3666

      (12) Feng JP, Yuan XL, Li M, et al. Secondary diabetes associated with 5-fluorouracil-based chemotherapy regimens in non-diabetic patients with colorectal cancer: Results from a single-centre cohort study. Colorectal Disease. 2013;15(1):27-33. doi:10.1111/j.1463-1318.2012.03097.x

      (13) Lee EK, Koo B, Hwangbo Y, et al. Incidence and disease course of new-onset diabetes mellitus in breast and colorectal cancer patients undergoing chemotherapy: A prospective multicenter cohort study. Diabetes Res Clin Pract. 2021;174. doi:10.1016/j.diabres.2021.108751

    1. Author Response

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public Review):

      Summary:

      This study by Lee et al. is a direct follow-up on their previous study that described an evoluBonary conservancy among placental mammals of two moBfs (a transmembrane moBf and a juxtamembrane palmitoylaBon site) in CD4, an anBgen co-receptor, and showed their relevance for T-cell anBgen signaling. In this study, they describe the contribuBon of these two moBfs to the CD4-mediated anBgen signaling in the absence of CD4-LCK binding. Their approach was the comparison of anBgen-induced proximal TCR signaling and distal IL-2 producBon in 58-/- T-cell hybridoma expressing exogenous truncated version of CD4 (without the interacBon with LCK), called T1 with T1 version with the mutaBons in either or both of the conserved moBfs. They show that the T1 CD4 can support signaling to the extend similar to WT CD4, but the mutaBon of the conserved moBfs substanBally reduced the signaling. The authors conclude that the role of these moBfs is independent of the LCK-binding.

      Strengths:

      The authors convincingly show that T1 CD4, lacking the interacBon with LCK supports the TCR signaling and also that the two studied moBfs have a significant contribuBon to it.

      Weaknesses:

      The study has several weaknesses.

      (1) The whole study is based on a single experimental system, geneBcally modified 58-/- hybridoma. It is unclear at this moment, how the molecular moBfs studied here contribute to the signaling in a real T cell. The evoluBonary conservancy suggests that these moBfs are important for T cell biology. However, the LCK-binding moBf is conserved as well (perhaps even more) and it plays a very minor role in their model. Without verifying their results in primary cells, the quanBtaBve, but even qualitaBve, importance of these moBfs for T-cell signaling and biology is unclear. Although the authors discuss this issue in the Discussion, it should be noted in all important parts of the manuscript, where conclusions are made (abstract, end of introducBon, perhaps also in the Btle) that the results are coming from the hybridoma cells.

      We appreciate the Reviewer’s thoughWul comments and suggesBon. We now state in the abstract and introducBon that wet-lab experiments were performed with T cell hybridomas. We have also beXer highlighted work from Killeen and LiXman (PMID: 8355789) wherein they showed that C-terminally truncated CD4, which lacked the moBfs that mediate CD4-Lck interacBons, can drive CD4+ T cell development, proliferaBon, and T-helper funcBon because we now provide mechanisBc data to help explain those in vivo results. Also, as noted by the reviewer, we discuss how the sum of our data provides jusBficaBon for the investment in and use of mouse models to interrogate how the funcBonally important residues/moBfs idenBfied and studied here influence T cell biology.

      We will take the opportunity to reiterate here that, while the study is based on a well characterized, albeit single, wet-lab experimental system, the whole study is based on two lines of invesBgaBon. The other approach was a systems biology computaBonal approach that analyzes data from real-world experiments in a variety of jawed vertebrate species over evoluBon. Specifically, we used a computaBonal reconstrucBon of the evoluBonary history of CD4 by performing mulBple analyses of CD4 from 99 jawed vertebrates spanning ~435 million years of evoluBon. This analysis allowed us to idenBfy residues, and networks of evoluBonarily coupled residues, that are predicted to be funcBonally important in vivo. Like other systems biology approaches, this allowed us to look at the larger picture by evaluaBng data points that have emerged from constant tesBng and adjustments of CD4 funcBon in vivo through selecBon on an evoluBonary Bmescale in more jawed vertebrate species, and under more real-world condiBons, than can be tested in the laboratory. Our structure-funcBon analysis provided a second, wet-lab reducBonist experimental system to cross-validate that the residues idenBfied by our evoluBonary analysis are funcBonally significant. This experimental validaBon is criBcal and elevates the relevance of our studies above ad hoc observaBons. Our work also provides mechanisBc insights for why the residues studied here are funcBonally significant (i.e., key determinants of pMHCII-specific signaling iniBaBon). In short, using both systems allowed us to cross-validate the funcBonal significance of the residues within the GGXXG and (C/F)CV+C moBfs studied here by two independent methods.

      (2) Many of the experiments lack the negaBve control. I believe that two types of negaBve controls should be included in all experiments. First, hybridoma cells without CD4 (or with CD4 mutant unable to bind MHCII). Second, no pepBde control, i.e., acBvaBon of the hybridoma cells with the APC not loaded with the cognate pepBde. These controls are required to disBnguish the basal levels of phoshorylaBon and CD4-independent anBgen-induced phosphorylaBon to quanBfy, what is the contribuBon of the parBcular moBfs to the CD4-mediated support. Although these controls are included in some of the experiments, they are missing in other ones. The binding mutant appears in some FC results as a horizontal bar (without any error bar/variability), showing that CD4 does not give a huge advantage in these readouts. Why don't the authors show no pepBde controls here as well? Why the primary FC data (histograms) are not shown? Why neither of these two controls is shown for the % of responders plots? Although the IL-2 producBon is a very robust and convincing readout, the phosphoflow is much less sensiBve. It seems that the signaling is elevated only marginally. Without the menBoned controls and showing the raw data, the precise interpretaBon is not possible.

      These comments, and those in point #3, concern our flow cytometry-based analysis of early intracellular signaling events where we asked: how do the moBfs under invesBgaBon impact phosphorylaBon of CD3z, ZAP-70, and PLCg1 in response to agonist pMHCII? Thank you for poinBng out areas of confusion regarding these analyses. We will try to clarify here and have worked to clarify the text.

      Our approach was to mutate consBtuent residues within the moBfs that our evoluBonary analysis predicted to be funcBonally significant, compare the performance of the mutants to that of controls bearing WT moBfs, and then infer the funcBon of the moBfs based on the differenBal phenotype of the mutants relaBve to their controls. In most cases, the C-terminally truncated CD4-T1 mutant served as the appropriate CD4 control backbone against which to evaluate the phenotypes of the GGXXG and (C/F)CV+C moBf mutants. This is a convenBonal structure-funcBon strategy.

      All experiments included APCs expressing null pMHCII (Hb:I-Ek) as negaBve controls. These were a necessary component of the data analysis, explained further below, which involved background subtracBon of the signal from control or mutant T cell hybridomas bound to these negaBve control APCs from those bound to the agonist pMHCII (MCC:I-Ek). Doing so allowed us to establish a true signal over background for calculaBng percent responders and signaling intensity. These negaBve controls served the same purpose of APCs expressing I-Ek not loaded with cognate pepBde requested by the reviewer. It is important to note that we previously published that TCR-CD3-pMHCII interacBons reciprocally increase CD4-pMHCII dwell Bme, and vice versa, such that dwell Bmes of the 5c.c7 TCR and CD4 to the null Hb:I-Ek are both basal in this system relaBve to antagonist, weak agonist, and agonist pMHCII (PMID 29386113). A recent study using different techniques also concluded that TCR-CD3 and CD4 cooperaBvely enhance signaling to pMHCII (PMID 36396644). The use of the null pMHCII, Hb:I-Ek, in each experiment thus serves as a well-characterized negaBve control for both TCR and CD4 engagement in this experimental system with regards to assembly of the TCR-CD3 and CD4 around pMHCII to drive signaling. In our view, it is the most important negaBve control for interpreBng our results, and it is present in each experiment. In Fig 1B and related supplemental figures we compare the Cterminally truncated CD4-T1 mutant to the full-length WT CD4 to evaluate the contribuBons of the intracellular domains to early signaling events. We found no significant differences for pCD3z, pZAP-70, and pPLCg1 levels demonstraBng that, in our system, CD4 WT and T1 are staBsBcally indisBnguishable.

      In Fig 1C we asked: what is the contribuBon of CD4-pMHCII interacBons made by CD4 T1, which lacks the intracellular domain, using our CD4 T1Dbind mutant. Fig 2C and Table 3 show that pCD3z levels for T1Dbind were ~54% of T1, meaning that CD4 binding to pMHCII roughly doubles pCD3z levels (even without the intracellular domain). We also showed that the percent of responders were not different between the CD4 T1 and T1Dbind mutant in Fig 2C. The impact on ZAP-70 and PLCg1 are shown in Figure 2—figure supplement 4. These differences, including the magnitude of the decrease, were observed reproducibly (p<0.001) in three independently generated sets of lines. We believe that this analysis saBsfies the request by the reviewer for an analysis of the contribuBons of CD4 binding to pMHCII. We did not include this as a negaBve control in experiments evaluaBng the contribuBons of the GGXXG and (C/F)CV+C moBfs to CD4 T1 signaling because the quesBon being asked in those experiments was how do the moBfs impact signaling in the absence of the intracellular domain (i.e., within the CD4 T1 backbone, making CD4 T1 the proper comparator for the quesBon we were asking). We showed the average normalized intensity for the T1Dbind mutant, relaBve to T1, for this lower bound of signaling mediated by TCR-CD3-only as a doXed line in those figures to provide a reference point for the readers to evaluate and put into perspecBve how the mutants we generated impacted the overall contribuBon of CD4 to these early signaling events. The T1Dbind mutants were not always measured in the same experiment at the same Bme with other mutants, because the cell lines used were not always made at the same Bme, so we did not think it appropriate to graph the results together.

      We do not know how to interpret the comment “Although the IL-2 producBon is a very robust and convincing readout, the phosphoflow is much less sensiBve. It seems that the signaling is elevated only marginally.” We will offer our perspecBve that we do not know how to equate the sensiBvity of the phos-flow to the IL-2. Because the IL-2 is a signaling output, it results from signaling amplificaBon from the membrane to the nucleus. If CD3z phosphorylaBon is the iniBaBng event for a signaling cascade that leads to IL-2 gene transcripBon and transducBon, as is widely believed, our data strongly suggests that the ~2-fold difference in pCD3z levels between CD4 T1 and T1Dbind (Fig 2C/Table 3 data) contributes to the difference between no IL-2 output for T1Dbind and IL-2 output by T1 in this experimental system. Because CD4 WT and T1 have significantly different levels of IL-2 output, but show no significant differences in pCD3z, pZAP-70, or pPLCg1 levels, there are likely to be other differences we did not measure via other pathways that intersect at the nucleus. At many levels, biology works on gradients such that small differences can Bp a system in one direcBon or another. The kineBc discriminaBon model (PMID 8643643), which is thought to be a reasonable descripBon of the relaBonship between pMHC engagement and signaling outcomes, suggests that very small differences in molecular interacBons at the earliest stages of a response can lead to big differences in signaling outcome. We therefore have no basis at this juncture to think that ~2-fold differences in pCD3z levels could not account for bigger differences in signaling output such as IL-2.

      (3) The processing of the data is not clear. Some of the figures seem to be overprocessed. For instance, I am not sure what "Normalized % responders of pCD3zeta" means (e.g., Fig. 1C and elsewhere)? Why do not the authors show the actual % of pCD3zeta+ cells including the gaBng strategy? Why do the authors subtract the two histograms in Fig. 2- Fig.S3? It is very unusual.

      We did develop and implement a novel strategy for measuring the impact of our mutaBons on CD3z, ZAP-70, and PLCg1 phosphorylaBon. This was explained in more detail in our prior study. The instrucBons to authors indicated that we should not repeat methods in the current manuscript. However, we will go through the approach here, and address why we did not show primary FC histograms for all experiments from above. First, we think that a brief explanaBon as to what moBvated us to develop our approach will add to a beXer understanding:

      (1) For experimental and staBsBcal rigor, our goal was to perform both experimental and biological replicates by measuring and comparing the average of at least three independently generated sets of paired WT/T1 control Vs. mutant cells lines generated at different Bmes to determine the staBsBcal significance of the difference, if any, between averages of the control and mutant lines.

      (2) Our quesBons necessitated that we measure signals generated naturally by the cooperaBve engagement of cognate pMHCII by TCR-CD3 and CD4 on APCs, rather than through aCD3/aCD4 crosslinking.

      (3) We chose to use flow cytometry rather than bulk cell analysis by Western Bloung to analyze signaling occurring in cells that were engaged to the agonist APC in order to avoid diluBon of that signal by cells that are not engaged to APCs and not signaling. 4. For each experiment, we wanted to subtract background signals from cells bound to APCs expressing a null pMHCII (Hb:I-Ek) from signals generated by cells bound to APCs expressing agonist pMHCII (MCC:I-Ek). Doing so allowed us to idenBfy cells that are signaling (responders) to agonist over null pMHCII. The goal here was to quanBtate the level of signaling in an objecBve manner with a method that can be applied to all samples uniformly rather than seung a flow cytometry gate on posiBve cells (e.g. pCD3z) because gaBng is subjecBve and can vary from experiment to experiment. To put that another way, as detailed below, we used our subtracBon method to idenBfy signaling responders rather than seung a signaling gate on the posiBve populaBon.

      Regarding gaBng schemes, controls, and data processing:

      Figure 2—figure supplement 3 of the current study and Figure 6—figure supplement 1 of our prior study are designed to walk the reader through our experimental design, gaBng, data processing and thinking. Here we will provide a detailed explanaBon to complement the figure legend as well as the methods provided in our prior manuscript (see pt #4 below).

      We will refer to Figure 2—figure supplement 3 here:

      Panel A. The dot plots show our approach to idenBfying 5c.c7+ CD4+ 58a-b- T cell hybridomas (yaxis, GFP posiBve) coupled to M12 cells (x-axis, TagIt Violet) expressing the null pMHCII Hb:I-Ek (lev) or agonist pMHCII MCC:I-Ek (right). The gaBng shows the frequency of GFP+ T cell hybridomas that are bound to TagIt violet posiBve APCs (i.e., cell couples). The histogram on the right then shows the staining intensity for pCD3z on the x-axis for the 10,000 coupled events collected wherein the APCs express the null pMHCII (filled cyan) or the agonist pMHCII (black line).

      Panel B. The data presented here is the same as in Panel A, but for CD4 T1 cells.

      Panel C. The data presented here walks through how we idenBfy 5c.c7+ CD4+ 58a-b- T cell hybridomas responding (i.e., signaling) to agonist pMHCII, as well as the mean signaling intensity of the responding populaBon, in a gaBng-independent manner aver background subtracBon. For the lev graph, we exported the data for the histograms shown in Panel A from FlowJo 10 sovware and ploXed them here using Prism 9 as smoothed lines (500 nearest neighbors). The cyan line is therefore a replicate of the flow cytometry histogram shown in Panel A for pCD3z intensity from 5c.c7+ CD4+ 58a-b- T cell hybridomas coupled to M12 cells expressing the null pMHCII (Hb:I-Ek), while the black histogram is a replicate of the pCD3z intensity for 5c.c7+ CD4+ 58a-b- T cell hybridomas coupled to M12 cells expressing the agonist pMHCII (MCC:I-Ek). Next, to idenBfy the responding populaBon in a gaBng-independent manner, we used Excel to subtract the pCD3z intensity for the null pMHCII (cyan) negaBve control populaBon on a bin-by-bin bases from the pCD3z intensity for the agonist pMHCII (black) responding populaBon. We then transferred the background subtracted values to Prism 9 for smoothing and ploung (grey line: MCC:I-Ek minus Hb:I-Ek). The middle graph shows the same data processing for the data from Panel B for the CD4 T1 cells. Please note that the background subtracted grey line has negaBve values and posiBve values. The negaBve values represent intensity bins where signaling in response to agonist pMHCII leads to fewer cells per bin than in the null pMHCII populaBon that is not signaling, while the posiBve values represent bins of intensity where signaling cells outnumber non-signaling cells. The right graph in this panel shows the populaBons aver background subtracBon for intensity bins that had more cells with pCD3z signal in the agonist pMHCII populaBon than the null pMHCII populaBon (grey = WT full length CD4 and blue = T1). In short, the right graph shows idenBficaBon of those cells that are signaling in response to agonist pMHCII. This approach miBgated the need for subjecBve gaBng in FlowJo to idenBfy signaling cells (i.e., pCD3z posiBve) and allowed for background subtracBon which could not be done in FlowJo. We used this approach for all analyses of pCD3z, pZAP-70, and pPLCg1 in this study.

      The number of cells in these background-subtracted populaBons were divided by 10,000 (the number of events collected and analyzed) to calculate the percent of responding 5c.c7+ CD4+ 58a-b- T cell hybridomas, while the mean fluorescent intensity for the cells within these populaBon represent the signaling intensity.

      Panel D. The graph on the lev shows the mean fluorescence intensity (MFI) ± SEM for the posiBve signaling populaBon from the right graph of panel C. We see in this example comparing a WT and T1 cell line, generated at the same Bme from the same parental 58a-b- T cell hybridoma populaBon, that the T1 MFI is significantly greater than the WT. These intensity values represent one of the paired intensity values used in the main Fig 2B (Lev graph), where we show the paired MFI analysis of responding populaBons from 5 independently generated sets of cell lines. Please note that these single MFI values are directly derived from the flow cytometry histograms aver background subtracBon. Figure 2B, and similar figures, therefore equate to a disBllaBon of all of the histograms for the populaBons tested in a manner that we consider easier to digest than either overlaying all histograms or showing mulBple panels individually. It also conserves more space. This is why we only showed representaBve flow cytometry histograms, rather than all histograms.

      The graph on the right shows the % responders for the posiBve signaling populaBon from the right graph of panel C. Specifically, the total number of cells that were determined to be signaling in response to agonist pMHCII was divided by 10,000 (the number of coupled cells collected by flow cytometry) to determine the percent responders. These values represent one of five sets of values used to determine the average normalized percent responders (all normalized to WT). There was no significant difference between these two populaBons in terms of percent responders.

      Regarding graphing normalized values for the mean MFI for signaling intensity or the percent responders: in our first manuscript, we presented the individual MFI intensity values for matched pairs of cells as well as the actual percent responders per group. The feedback we received from colleagues on this presentaBon was that it was confusing, distracBng, and otherwise hard to digest. It was suggested to us by mulBple individuals that the normalized values would be preferable because it is easier and faster to understand. Upon reflecBon, we agreed with this feedback because the normalized presentaBon with staBsBcs allows for the two key relevant quesBons to be quickly evaluated: 1. Are the mutants different than the control? 2. By how much? We have lev the raw intensity values and well as the normalized intensity values in the version of record. Given the Reviewer’s comments, we have now graphed the average % responders instead of normalized values in the figures, and lev the normalized values in Table 3.

      (4) The manuscript lacks Materials and Methods. It only refers to the previous paper, which is very unusual. Although most of the methods are the same, they sBll should be menBoned here. Moreover, some of the mutants presented here were not generated in the previous study, as far as I understand. Perhaps the authors plan to include Materials and Methods during the revision...

      Because we submiXed this as a Research Advances arBcle we followed the journal instrucBons to reference the Materials and Methods in our prior publicaBon, upon which this work builds, as the methods used are the same. They are detailed in that study. We have now included a copy of the Materials and Methods for the eLife staff to determine how best to link with this manuscript. We have also included the gene sequences for the novel constructs used in this study. Thank you for poinBng out the omission.

      (5) Membrane rafts are a very controversial topic. I recommend the authors stick to the more consensual term "detergent resistant microdomains" in all cases/occurances.

      We agree this is a controversial topic with a variety of viewpoints. Because we are not experts in the field of membrane composition, we turned to the literature to inform our view of how best to refer to these membrane subdomains. In our reading, we found a 2006 meeting report from a Keystone symposium on lipid rafts and cell function authored by Linda Pike (PMID 16645198). At this meeting, a central focus was reaching a consensus on how best to refer to these domains. The consensus term agreed upon by this group was “membrane rafts”. Specifically, we will quote from this report published in the Journal of Lipid Research, ‘Together, the discussions permitted the generation of a definition for “lipid rafts” in an ad hoc session on the final day of the meeting. All participants were invited to contribute to this effort, and the work product reflects the consensus of this broad-based group…… First and foremost, the term “lipid raft” was discarded in favor of the term “membrane raft.”’ We chose to use the term “membrane raft” based on this consensus opinion.

      (6) Last, but not least, the mechanistic explanation (beyond the independence of LCK binding) of the role of these motifs is very unclear at the moment.

      We agree with this comment. One goal in making these results, and those in our prior study, available to the field at large is to provide evidence in support of our view that the dominant paradigm that is thought to explain the earliest events in T cell signaling needs re-evaluating. How T cell signaling is initiated in response to pMHCII is clearly more complex than is currently thought. However, out data is inconsistent with the dominant paradigm in which CD4 recruits Lck to TCR-CD3 to phosphorylate ITAMs to initiate signaling.

      Reviewer #2 (Public Review):

      Summary:

      The paper by Kuhn and colleagues follows upon a 2022 eLife paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cyscontaining juxtamembrane (JM) motif that undergoes palmitoylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

      Using the same model system as in the earlier eLife paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule, and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

      Strengths:

      The strength of the paper is the further clear demonstration that the classical model of CD4 coreceptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier eLife paper, further implicate the gly-rich TM sequence and the palmitoylation targets in the JM region as having critical roles in productive co-receptordependent TCR activation.

      Weaknesses:

      The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitoylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

      We appreciate these thoughtful comments and suggestions. We will restate what we wrote in our preliminary response to the reviews to explain the scope of the current study:

      To address comments about the limited scope of this study and referencing of the Methods secBon to our prior study, we would like to note that we submiXed the current study via the Research Advance mechanism. Our goal was to build upon the conclusions of our 2022 eLife publicaBon (PMID: 35861317) and address an unresolved quesBon from that study (as nicely summarized by Reviewer #2). In the current manuscript we present data from reducBonist experiments that were designed specifically for this purpose and, as noted by the reviewers, we provide answers to the quesBon being asked. We think that the Research Advance mechanism is an ideal opportunity to make these results available to the field given the stated purpose of such arBcles (for reference: “A Research Advance might use a new technique or a different experimental design to generate results that build upon the conclusions of the original research by, for example, providing new mechanis=c insights or extend the pathway under inves=ga=on…”). Now that we have provided evidence that CD4 does not recruit Lck to phosphorylate TCR-CD3 ITAMs in our system, nor do the GGXXG and (C/F)CV+C motifs play a role in enabling CD4 to regulate Lck proximity to TCR-CD3, we agree that it is important to form and test alternative hypotheses for how TCR-CD3 signaling is initiated.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study addresses the fundamentally unresolved question of why many thousands of small-effect loci contribute more to the heritability of a trait than the large-effect lead variants. The authors explore resource competition within the transcriptional machinery as one possible explanation with a simple theoretical model, concluding that the effects of resource competition would be too small to explain the heritability effects. The topic and approximation of the problem are very timely and offer an intuitive way to think about polygenic variation, but the analysis of the simple model appears to be incomplete, leaving the main claims only partially supported.

      We thank eLife for recognizing the importance of our work. We hope the revised manuscript addresses the reviewers’ reservations.

      Public Reviews:

      Reviewer #1 (Public Review):

      This study explores whether the extreme polygenicity of common traits can be explained in part by competition among genes for limiting molecular resources (such as RNA polymerases) involved in gene regulation. The authors hypothesise that such competition would cause the expression levels of all genes that utilise the same molecular resource to be correlated and could thus, in principle, partly explain weak trans-regulatory effects and the observation of highly polygenic architectures of gene expression. They study this hypothesis under a very simple model where the same molecule binds to regulatory elements of a large number m of genes, and conclude that this gives rise to trans-regulatory effects that scale as 1/m, and which may thus be negligible for large m.

      We thank the reviewer for their thorough and thoughtful review of our manuscript.

      The main limitation of this study lies in the details of the mathematical analysis, which does not adequately account for various small effects, whose magnitude scales inversely with the number m of genes that compete for the limiting molecular resource. In particular, the fraction of "free" molecule (which is unbound to any of the genes) also scales as 1/m, but is not accounted for in the analysis, making it difficult to assess whether the quantitative conclusions are indeed correct.

      It is explicitly accounted for in the supplement.

      Second, the questions raised in this study are better analysed in the framework of a sensitivity or perturbation analysis, i.e., by asking how changes in expression level or binding affinity at one gene (rather than the total expression level or total binding affinity) affect expression level at other genes. In the context of complex traits, where an increase in gene expression can either increase or decrease the trait, we believe the most important quantity of interest is variation in expression and, therefore, trait variation. Nevertheless, our results do show that the relative change in expression due to competition is also small.

      Thus, while the qualitative conclusion that resource competition in itself is unlikely to mediate trans-regulatory effects and explain highly polygenic architectures of gene expression traits probably holds, the mathematical reasoning used to arrive at this conclusion requires more care.

      In my opinion, the potential impact of this kind of analysis rests at least partly on the plausibility of the initial hypothesis- namely whether most molecular resources involved in gene regulation are indeed "limiting resources". This is not obvious, and may require a careful assessment of existing evidence, e..g., what is the concentration of bound vs. unbound molecular species (such as RNA polymerases) in various cell types?

      We intentionally looked at the most extreme case of extreme resource limitation, and we conclude that since extreme resource limitation is a small effect, the same would be true of weak resource limitation, when unbound molecules play an important role. We put more emphasis on this point in our revised text.

      Reviewer #1 (Recommendations For The Authors):

      While the main conclusion that resource competition in itself is unlikely to mediate trans effects and explain high levels of polygenicity may well be correct, I am not convinced that the mathematical reasoning presented in support of this conclusion is entirely correct. I will attempt to outline my concerns mainly in the context of section 2, since the arguments in sections 3 and 4 build upon this.

      (a) The key assumption underlying the approximations in equations 3, 4, and 5 is that there is very little free polymerase, in other words /_0 is a small quantity. However, the second and third terms that emerge in equation 7 are also small quantities and (as far as I can see) of the same order as /_0. Thus, one cannot simply use equation 4 or 5 as a starting point to derive eq. 7 and should instead use the exact x_i = (g_i [G])/ (1+g_tot [G]), in order to make sure that all (and not just some) terms that are similar in order of magnitude are accounted for in the analysis.

      The concentration of free polymerase is marked as [P], and we explicitly assume (just before eq. 2) that [P]<<[P]0 with [P]0 being the overall concentration of polymerase. This is a conservative assumption – we consider extreme resource competition with little free polymerase and since we since only a small effect in this extreme scenario we assume it would be a small effect also for less extreme scenarios. We put more emphasis on this point in our revised text.

      More concretely, the difference between the exact x_i = (g_i [G])/ (1+g_tot [G]) and the approximate x_i = (g_i / g_tot) is precisely 1/m (for large m) in the example considered line 246 onwards. Thus, I suspect that the conclusion that Var[x_i] = (1-1/m)Var[g_i] in that example is just an artefact of starting with eqs. 4 and 5. As a sanity check, it may be useful to actually simulate resource competition explicitly (maybe using a deterministic simulation) under the explicit model [PG_i] = g_i [G] and _0 = + Sum[[PG]_i , i=1,m] without making any further approximations to see if perturbations in g_i actually produce Order [1/m] effects in the variance of x_i for the example considered line 246 onwards (this would require simulating with a few different m and plotting Var[x_i] vs. m for example).

      The exact equation the reviewer is alluding to describes a scenario of non-extreme resource competition. If g_tot [G]>>1, i.e. if most polymerase is bound to a gene then x_i is equal to g_i/g_tot and this is the scenario we are considering of extreme competition. If g_tot [G]<<1, then x_i=g_i [G] and competition has no effect. While the intermediate case is interesting, we see no reason for the effects to be larger than in the extreme competition case. We have added the results of simulations in the supplement to validate our arguments.

      Lines 231-239: Because of the concerns highlighted above and questions about the validity of equation 7, I am not convinced that the interpretations given here and also in section 4 are correct.

      (b) Lines 219-230 (including equations 6 and 7): I think to address the question of whether genetic changes in cis-regulatory elements for a given gene have an effect on other genes (under this model of resource competition), it is better to spell out the argument in terms of Var[ dx_i ] rather than Var[x_i], where dx_i is the change in expression level at gene i due to changes at all m genes, dg_i is the change in gene activity due to (genetic) changes in the relevant regulatory elements associated with gene i etc. Var[ dx_i ] can then be expressed as a sum of Var[dg_i], Var[dg_tot] and Cov[d g_i, dg_tot]. However, I suspect that to do this correctly, one should not start with the approximate x_i=g_i/g_tot : see previous comment.

      The variance of the deviation from the mean is mathematically identical to the overall variance, Var[ dx_i ]= Var[ x_i ]. Our analysis is therefore equivalent to the suggested analysis.

      Somewhere in all of this, there is also an implicit assumption that E[dg_i] is zero, i.e, mutations are as likely to increase as to decrease binding affinities so that one needs to only consider Var[dx_i] and not E[dx_i]; this assumption should be spelled out.

      Our results concern the variation around trait means and therefore we have not included a possible mean effect of mutation, which would not affect the results but just shift the mean.

      Some minor comments (mostly related to the introduction and general context):

      • I think it would be worth connecting more with the literature on molecular competition and gene regulation (see e.g., How Molecular Competition Influences Fluxes in Gene Expression Networks, De Vos et al, Plos One 2011). Even though this literature does not frame questions in terms of "polygenicity of traits", these analyses address the same basic questions: to what extent do perturbations in gene expression at one gene affect other genes, or to what extent is there crosstalk between different genes or pathways?

      We have expanded our introduction to refer to De Vos et al, as well as a few other papers we have recently become aware of. (e.g., Jie Lin & Ariel Amir Nature Communications volume 9, Article number: 4496 (2018))

      • Lines 88-89: "supports the network component of the model" is a vague phrase that does not convey much. It would be useful to clarify and make this more precise.

      We have clarified this phrasing in the text.

      • Lines 113-114: In the context of "selective constraint", it may also be worth discussing previous work by one of the authors: "A population genetic interpretation of GWAS findings for human quantitative traits". What implications would stabilizing selection on multiple traits (as opposed to simple purifying selection) have for the distribution of variances across trait loci and the extent to which trait architectures appear to be polygenic?

      While most definitely of great interest to some of the authors, the distribution of variance across loci does not affect our results.

      References: Barton and Etheridge 2018 in line 54 is not the correct reference; it should be Barton et al 2017 (paper with Amandine Veber). Fisher 1919 in line 52 is actually Fisher 1918. The formatting of references in the next paragraph (and in various other places in the paper) is also a bit unusual, with some authors referred to by their full names and others only by their last. I believe that it may be useful to crosscheck references throughout the paper.

      We have crosschecked the references in the paper.

      Line 164: Some word appears to be missing here. Maybe bound -> bound to ?

      Fixed

      Reviewer #2 (Public Review):

      The question the authors pose is very simple and yet very important. Does the fact that many genes compete for Pol II to be transcribed explain why so many trans-eQTL contribute to the heritability of complex traits? That is, if a gene uses up a proportion of Pol II, does that in turn affect the transcriptional output of other genes relevant or even irrelevant for the trait in a way that their effect will be captured in a genome-wide association study? If yes, then the large number of genetic effects associated with variation in complex traits can be explained but such trans-propagating has effects on the transcriptional output of many genes.

      This is a very timely question given that we still don't understand how, mechanistically, so many genes can be involved in complex traits variation. Their approach to this question is very simple and it is framed in classic enzyme-substrate equations. The authors show that the trans-propagating effect is too small to explain the ~70% of heritability of complex traits that are associated with trans-effects. Their conclusion relies on the comparison of the order of magnitude of a) the quantifiable transcriptional effects due to Pol II competition, and b) the observed percentage of variance explained by trans effects (data coming from Liu et al 2019, from the same lab).

      The results shown in this manuscript rule out that competition for limited resources in the cell (not restricted to Pol II, but applicable to any other cellular resource like ribosomes, etc) could explain the heritability of complex traits.

      We thanked the Reviewer for his resounding support of our paper!

      Reviewer #2 (Recommendations For The Authors):

      The authors rely on simulated data, and although the conclusions hold in a biologically-realistic scenario given the big difference in effect sizes, I wonder if the authors could provide data from the literature (if available) that give the reader a point of reference for the steady state of cells in terms of free/occupied Pol II molecules and/or free/occupied transcription binding sites. This information won't change the conclusion of the manuscript, but it will put it in the context of real biological data.

      We have scoured the literature, but have not found readily available data with which to validate our results (beyond that which is already referenced).

      Reviewer #3 (Public Review):

      Human complex traits including common diseases are highly polygenic (influenced by thousands of loci). This observation is in need of an explanation. The authors of this manuscript propose a model that competition for a single global resource (such as RNA polymerase II) may lead to a highly polygenic architecture of traits. Following an analytical examination, the authors reject their hypothesis. This work is of clear interest to the field. It remains to be seen if the model covers the variety of possible competition models.

      We thank the Reviewer for his assessment, support and comments.

      Reviewer #3 (Recommendations For The Authors):

      This manuscript provides a straightforward and elegant quantitative argument that the competition for the RNA polymerase is not a significant source of trans-eQTLs and, more generally, of genetic variance of complex polygenic phenotypes. This is an unusual manuscript because the authors propose a hypothesis that they confidently reject based on a calculation. This negative result is intuitive. Still, the manuscript is of interest. Progress in understanding the highly polygenic architecture of complex traits is welcome, and the resource competition hypothesis is quite natural. I have three specific comments/concerns listed below.

      (1) The manuscripts states that V(x_i)=V(g_i/g_tot). Unless I am missing something, this seems to result from a very strong implicit assumption that all genetic variance is due to variation in the binding of RNA polymerase, while x_i_max is a constant. I would expect that x_i_max may also be genetically variable due to many effects unrelated to the Pol II binding (e.g. transcription rate, bursting, presence of R-loops etc.). I guess that the assumption made by the authors is conservative.

      Indeed. We made conservative assumptions throughout, aiming to consider the most extreme scenario in which resource competition may affect trait variation. Our logic being that if even under the most extreme scenario resource competition is a small effect then it is a small effect in all scenarios. We put more emphasis on this point in our revised text.

      (2) The manuscript focuses on the competition for RNA polymerase but suggests that the lesson learned is highly generalizable. However, it is an example of a single global limiting resource resulting in first-order kinetics. What happens in a realistic scenario of competition for multiple resources associated with transcription and with downstream processes (free ribonucleotides, spliceosome, polyadenylation machinery, ribosome, post-translational modifications)? It is possible that in most cases a single resource is a limiting factor, but an investigation (or even a brief discussion) of this question would support the claim that the results are generalizable.

      We expect competition for multiple resource to result in similarly weak effects. Since there is not a great number of such resources, we do not expect it to change our qualitative result. We added language to that effect in the main text.

      (3) Alternatively, what happens in a scenario of competition for multiple local resources shared by a few genes (co-factors, substrates, chaperones, micro-RNAs, post-translational modification factors such as kinases, degradation factors, scaffolding proteins)? In this case, each gene would compete for resources with a few other genes increasing polygenicity without a global competition with all other genes. Intuitively, a large set of such local competitions may lead to a highly polygenic architecture.

      This is indeed a scenario in which competition may be a large effect which we mention in our discussion. “the conclusions may differ in contexts where a very small number of genes compete for a highly limited resource, such as access to a particular molecular transporter”

    2. Reviewer #1 (Public Review):

      This study explores whether the extreme polygenicity of common traits (the fact that variation in such traits is explained by a very large number of genetic variants) could be explained in part by competition among genes for limiting molecular resources involved in gene regulation, which would cause the expression of most genes to be correlated. While the hypothesis is interesting, I still have some concerns about the analysis and interpretation.

      As the authors say in their rebuttal, assuming extreme resource limitation, i.e., going from equation 2 to 5 essentially assumes assuming that 1/(gtot [G] ) <<1 and that terms that are order [ 1/(gtot [G] ) ] can neglected. However, then the authors derive so-called resource competition terms that are order (1/m) where m is the number of genes, so that gtot is proportional to m. My main criticism (which I am not sure was addressed) is thus: can we reliably derive small order (1/m) effects while neglecting order [ 1/(gtot [G] ) ] terms, when both are presumably similar in order of magnitude? Is this mathematically sound?

      I do not think the supplement that the authors have added actually gets to this. For example, section 7.1 just gives the textbook derivation of Michelis-Menten kinetics, and does not address my earlier criticism that the terms neglected in going from eq. 16 to eq. 17 (or from eq. 2 to 3) may be similar in magnitude to the terms being derived and interpreted in eqs. 6 and 7.<br /> Similarly, it is unclear from section 7.2 how the authors are doing the simulations. Are these true Michelis-Menten simulations involving equation 2? If yes, then what is the value of [G] and [P_0] in the simulations? If these are not true Michelis-Menten simulations, but instead something that already uses equation 5, then this still does not address my earlier criticism.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This manuscript provides important insights into the degradation of a host tRNA modification enzyme TRMT1 by SARS-CoV-2 protease nsp5. The data convincingly support the main conclusions of the paper. These results will be of interest to virologists interested in studying the alterations in tRNA modifications, host methyltransferases, and viral infections.

      Public Reviews:

      Response to Public Reviews

      We appreciate the reviewers’ assessment that our findings are well supported and provide important insight to the field. We also thank the reviewers for their comments and suggestions that have improved the quality of this manuscript. Through the requested edits and experiments, we provide additional results in this revision that further support and extend our original findings.

      We acknowledge the major questions that remain to be addressed, including the biological relevance of TRMT1 cleavage by Nsp5. We note that elucidating the biological role of host protein cleavage by viral proteases has been a long-standing challenge. For example, several endogenous proteins have been identified as cleavage targets of HIV protease, but the functional relevance for many of these cases took decades to resolve or remain unknown to this day. Nonetheless, we have added additional experiments that suggest a possible role for TRMT1 and TRMT1 cleavage in SARS-CoV-2 pathobiology.

      Key additions in the revised manuscript include:

      • Subcellular localization of full-length TRMT1 and TRMT1 fragments (Supplemental Figure 4).

      • Experiments demonstrating that TRMT1 levels are reduced to near background levels in SARS-CoV-2 infected human cells at higher MOI (Figure 6C and D).

      • Results showing that expression of the non-cleavable TRMT1 mutant can promote virion particle infectivity (Figure 8).

      • The addition of an “Ideas and Speculation” subsection that is now being offered to authors by eLife.

      Reviewer #1 (Public Review):

      Zhang et al. investigate the hypothesis that tRNA methyl transferase 1 (TRMT1) is cleaved by NSP5 (nonstructural protein 5 or MPro), the SARS-CoV-2 main protease, during SARS-CoV-2 infection. They provide solid evidence that TRMT1 is a substrate of Nsp5, revealing an Nsp5 target consensus sequence and evidence of TRMT1 cleavage in cells. Their conclusions are exceptionally strong given the co-submission by D'Oliveira et al showing cleavage of TRMT1 in vitro by Nsp5. Separately, the authors convincingly demonstrate widespread downregulation of RNA modifications during CoV-2 infection, including a requirement for TRMT1 in efficient viral replication. This finding is congruent with the authors' previous work defining the impact of TRMT1 and m2,2g on global translation, which is most likely necessary to support infection and virion production. What still remains unclear is the functional relevance of TRMT1 cleavage by Nsp5 during infection. Based on the data provided here, TRMT1 cleavage may be an act by CoV2 to self-limit replication, as the expression of a non-cleavable TRMT1 (versus wild-type TRMT1) supports enhanced viral RNA expression at certain MOIs. Theoretically, TRMT1 cleavage should inactivate the modification activity of TRMT1, which the authors thoroughly and elegantly investigate with rigorous biochemical assays. However, only a minority of TRMT1 undergoes cleavage during infection in this study and thus whether TRMT1 cleavage serves an important functional role during CoV-2 replication will be an important topic for future work. The authors fairly assess their work in this regard. This study pushes forward the idea that control of tRNA expression and functionality is an important and understudied area of host-pathogen interaction.

      We thank the reviewer for the thoughtful assessment of our study.

      We acknowledge that only a minority of TRMT1 undergoes cleavage during infection at the originally tested MOI. However, the ~40% reduction in TRMT1 levels after infection with SARS-CoV-2 is quite substantial considering that the TRMT1 in the nucleus and mitochondria are likely to be inaccessible to Nsp5. Moreover, we detected a reduction in m2,2G modification in the infected human cells, providing evidence for a functional impact on TRMT1 activity (Figure 1C).

      To further test the effects of SARS-CoV-2 infection on endogenous TRMT1, we infected 293T cells at a higher MOI and measured TRMT1 levels. At MOI=5, we found that SARS-CoV-2 infection led to near complete depletion of TRMT1 in human cells. This result suggests that SARS-CoV-2 infection could have a profound impact on TRMT1 levels during pathogenesis. We have added this new experiment as Figures 6C and D.

      Weaknesses noted:

      The detection of the N-terminal TRMT1 fragment by western blot is not robust. The polyclonal antibody used to detect TRMT1 in this work cross-reacts with a non-specific protein product. Unfortunately, this obstructs the visualization of the predicted N-terminal TRMT1 fragment. It is unclear how the authors were able to perform densitometry, given the interference of the nonspecific band. Additionally, the replicates in the source data make it clear that the appearance of the N-terminal fragment "wisp" under the non-specific band is not seen in every replicate. Though the disappearance of this wisp with mutant Nsp5 and uncleavable TRMT1 is reassuring, the detection of the N-terminal fragment with the TRMT1 antibody should be assessed critically. Considering this group has strong research interests in TRMT1, I assume that attempts to make other antibodies have proved unfruitful. Additionally, N-terminal tagging of TRMT1 is predicted to disrupt the mitochondrial targeting signal, eliminating the potential for using alternative antibodies to see the N-terminal fragment.

      We agree that the anti-TRMT1 antibody used here is sub-optimal for detection of the N-terminal TRMT1 fragment. However, as noted by the Reviewer, we provided multiple ways of corroborating that the lower-molecular weight band detected in human cells expressing Nsp5 corresponds to the N-terminal TRMT1 fragment. We have shown that the TRMT1 cleavage band is not detectable in human cells expressing GFP or inactive Nsp5. This indicates that the lower molecular weight TRMT1 band only arises when active Nsp5 protease is expressed. Moreover, the TRMT1 cleavage band is not detectable in TRMT1-KO cell lines, demonstrating that the band arises from TRMT1 cleavage rather than a non-specific protein. We have also detected the C-terminal fragment if TRMT1 is over-expressed with Nsp5. In addition, we have shown that the mutation of the predicted Nsp5 cleavage site in TRMT1 abolishes the appearance of the N- and Cterminal cleavage fragments.

      Despite the drawbacks of this antibody, we identified gel running conditions that resolves the non-specific band from the N-terminal TRMT1 cleavage fragment. Thus, for quantification, we measured the total signal of both the cleavage band and the nonspecific band in all lanes (Figure 3). After normalization to actin, the total signal from the cleavage band and the non-specific band in the control lane from cells expressing GFP was subtracted from the lanes with cells expressing Nsp5 to calculate the signal arising from the cleavage band. We have updated our Materials and Methods to provide details on how we quantified the TRMT1 cleavage band.

      While we did test other antibodies against TRMT1, none of them were sensitive enough to detect TRMT1 cleavage fragments at endogenous levels. For example, we included results with an antibody targeting the C-terminus of TRMT1 that could not detect TRMT1 cleavage products at endogenous levels (Supplemental Figure 3). However, the antibody could detect the C-terminal TRMT1 fragments if TRMT1 was overexpressed with Nsp5 (Supplemental Figure 3).

      These technical issues reiterate the fact that the functional significance of TRMT1 cleavage during CoV-2 infection remains unclear. However, this study demonstrates an important finding that the tRNA modification landscape is altered during CoV-2 infection and that TRMT1 is an important host factor supporting CoV-2 replication.

      We agree that the functional relevance of TRMT1 cleavage by Nsp5 remains an open question. Thus, we have added an experiment to test the functional impact of TRMT1 on virion particle production and infectivity (Figure 8). We find that TRMT1 expression is required for optimal virus production, consistent with our observation that TRMT1deficient cells exhibit reduced viral RNA replication. In addition, we find that expression of the non-cleavable TRMT1 mutant can promote virion particle infectivity (Figure 8, TRMT1-Q530N). These results are consistent with the Reviewer’s conclusion that “TRMT1 cleavage may be an act by CoV-2 to self-limit replication, as the expression of a non-cleavable TRMT1 (versus wild-type TRMT1) supports enhanced viral RNA expression at certain MOIs”. We discuss the potential implications of this result and their functional relevance in the “Ideas and Speculation” subsection.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript titled 'Proteolytic cleavage and inactivation of the TRMT1 tRNA modification enzyme by SARS-CoV-2 main protease' from K. Zhang et al. demonstrates that several RNA modifications are downregulated during SARS-CoV-2 infection including the widespread m2,2G methylation, which potentially contributes to changes in host translation. To understand the molecular basis behind this global hypomodification of RNA during infection, the authors focused on the human methyltransferase TRMT1 that catalyzes the m2,2G modification. They reveal that TRMT1 not only interacts with the main SARS-CoV-2 protease (Nsp5) in human cells but is also cleaved by Nsp5. To establish if TRMT1 cleavage by Nsp5 contributes to the reduction in m2,2G levels, the authors show compelling evidence that the TRMT1 fragments are incapable of methylating the RNA substrates due to loss of RNA binding by the catalytic domain. They further determine that expression of full-length TRMT1 is required for optimal SARS-CoV-2 replication in 293T cells. Nevertheless, the cleavage of TRMT1 was dispensable for SARS-CoV-2 replication hinting at the possibility that TRMT1 could be an off-target or fortuitous substrate of Nsp5. Overall, this study will be of interest to virologists and biologists studying the role of RNA modification and RNA modifying enzymes in viral infection.

      We thank the reviewer for the thoughtful assessment of our study.

      We agree with the possibility that TRMT1 could be a fortuitous substrate of Nsp5 due to the coincidental presence of a Nsp5 cleavage site in TRMT1. As considered in our Discussion section, TRMT1 cleavage could be a collateral effect of SARS-CoV-2 infection. While TRMT1 could be an off-target substrate during viral infection, the subsequent effect on tRNA modification levels could have physiological consequences on downstream processes that affect cellular health. This information could still be useful for understanding the pathophysiological consequences of SARS-CoV-2 infection in tissues.

      Strengths:

      • The authors use a state-of-the-art mass spectrometry approach to quantify RNA modifications in human cells infected with SARS-CoV-2.

      • The authors go to great length to demonstrate that SARS-CoV-2 main protease, Nsp5, interacts, and cleaves TRMT1 in cells and perform important controls when needed. They use a series of overexpression with strategically placed tags on both TRMT1 and Nsp5 to strengthen their observations.

      • The use of an inactive Nsp5 mutant (C145A) strongly supports the claim of the authors that Nsp5 is solely responsible for TRMT1 cleavage in cells.

      • Although the direct cleavage was not experimentally determined, the authors convincingly show that TRMT1 Q530N is not cleaved by Nsp5 suggesting that the predicted cleavage site at this position is most likely the bona fide region processed by Nsp5 in cells.

      • To understand the impact of TRMT1 cleavage on its RNA methylation activity, the authors rigorously test four protein constructs for their capacity not only to bind RNA but also to introduce the m2,2G modification. They demonstrate that the fragments resulting from TRMT1 cleavage are inactive and cannot methylate RNA. They further establish that the C-terminal region of TRMT1 (containing a zinc-finger domain) is the main binding site for RNA.

      • While 293T cells are unlikely an ideal model system to study SARS-CoV-2 infection, the authors use two cell lines and well-designed rescue experiments to uncover that TRMT1 is required for optimal SARS-CoV-2 replication.

      Weaknesses:

      • Immunoblo0ng is extensively used to probe for TRMT1 degradation by Nsp5 in this study. Regretfully, the polyclonal antibody used by the authors shows strong non-specific binding to other epitopes. This complicates the data interpretation and quantification since the cleaved TRMT1 band migrates very closely to a main non-specific band detected by the antibody (for instance Fig 3A). While this reviewer is concerned about the cross-contamination during quantification of the N-TRMT1, the loss of this faint cleaved band with the TRMT1 Q530N mutant is reassuring. Nevertheless, the poor behavior of this antibody for TRMT1 detection was already reported and the authors should have taken better precautions or designed a different strategy to circumvent the limitation of this antibody by relying on additional tags.

      We acknowledge the sub-optimal performance of the commercial anti-TRMT1 antibody used in our study. Nevertheless, we have provided multiple lines of evidence indicating that the lower molecular weight band detected using this antibody corresponds to the N-terminal TRMT1 fragment. As noted by the reviewer, we have shown that the lower molecular weight band disappears using the TRMT1-Q530N non-cleavable mutant. The lower molecular weight signal is also absent in TRMT1-KO cell lines expressing Nsp5. Moreover, we have shown that the TRMT1 cleavage band is undetectable in human cells expressing GFP or inactive Nsp5. We have also detected the C-terminal fragment when TRMT1 is over-expressed with Nsp5.

      As discussed in the response to Reviewer 1, we did consider alternative approaches for detecting the N-terminal fragment. We thought about tagging TRMT1 at the N-terminus so that we could detect the cleavage band using a different antibody. However, as noted by Reviewer 1, the tagging of TRMT1 at the N-terminus is likely to disrupt the mitochondrial targeting signal and alter the localization of TRMT1. In addition, we spent considerable time and effort testing alternative antibodies against TRMT1. However, none of them were effective at detecting the N- or C-terminal TRMT1 fragments. For example, we included results with a different antibody targeting the C-terminus of TRMT1 that could not detect TRMT1 cleavage products at endogenous levels but could detect them when TRMT1 was overexpressed with Nsp5 (Supplemental Figure 3).

      • While 293T cells are convenient to use, it is not a well-suited model system to study SARS-CoV2 infection and replication. Therefore, some of the conclusions from this study might not apply to better-suited cell systems such as Vero E6 cells or might not be observed in patient-infected cells.

      We acknowledge the potential caveats associated with using 293T human embryonic cells as a system for testing SARS-CoV2 replication. However, we note that 293T cells have been used as a physiological model for discovering and characterizing key aspects of SARS-CoV-2 biology, including viral replication. For example, SARS-CoV-2 has been shown to exhibit significant replication and virion production in 293T cells expressing ACE2 that can be inhibited by known SARS-CoV-2 antiviral compounds:

      https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(20)300045/fulltext

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444585/

      https://www.science.org/doi/10.1126/sciadv.add3867

      https://www.pnas.org/doi/full/10.1073/pnas.2025866118

      293T cells have also been demonstrated to exhibit cytopathic effects upon SARS-CoV-2 infection that are dependent upon the ACE2 receptor and mirror that of infected lung cells in culture and in patient tissues:

      https://www.embopress.org/doi/full/10.15252/embj.2020106267

      https://journals.asm.org/doi/full/10.1128/jvi.00002-22

      https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1009715

      https://www.nature.com/articles/s41559-021-01407-1

      In addition to 293T cells, we have demonstrated that infection of MRC5 human pulmonary fibroblast cells with SARS-CoV-2 results in a decrease in TRMT1 levels and m2,2G modification (Figure 1). The reduction in TRMT1 levels in MRC5 cells after SARS-CoV-2 infection is similar to that observed in 293T cells.

      • The reduction of bulk TRMT1 levels is minor during infection of MRC5 cells with SARS-CoV-2 (Fig 1). This does not seem to agree with the more dramatic reduction in m2,2G modification levels. Cellular Localization experiments of TRMT1 would help clarify this. While TRMT1 is found in the cytoplasm and nucleus, it is possible that TRMT1 is more dramatically degraded in the cytoplasm due to easier access by Nsp5.

      We agree that the processing of newly synthesized TRMT1 in the cytoplasm is likely to be the main cause for the reduction of TRMT1 levels in the infected MRC5 cells. Thus, we followed the Reviewer’s suggestion to conduct cellular localization experiments of TRMT1 (Supplemental Figure 4). Through these experiments, we show that full-length TRMT1 exhibits localization to the cytoplasm, mitochondria, and nucleus, consistent with prior findings from our group and others. This result supports the conclusion that cytoplasmic TRMT1 is the likely target of Nsp5 cleavage while TRMT1 in the nucleus and mitochondria are inaccessible to Nsp5. We also note that the decrease in cytoplasmic TRMT1 could account for the reduction in m2,2G modifications if the cytoplasmic pool of TRMT1 is responsible for modifying any exported tRNAs that were not modified in the nucleus.

      • In Fig 6, the authors show that TRMT1 is required for optimal SARS-CoV-2 replication. This can be rescued by expressing TRMT1 (Fig 7). Nevertheless, it is unknown if the methylation activity of TRMT1 is required. The authors could have expressed an inactive TRMT1 mutant (by disrupting the SAM binding site) to establish if the RNA modification by TRMT1 is important for SARS-CoV-2 replication or if it is the protein backbone that might contribute to other processes.

      We agree that it would be interesting to test if the methylation activity of TRMT1 is important for optimal SARS-CoV-2 replication. However, the present study focuses on the cleavage of TRMT1 by Nsp5 and the biological effects of this cleavage. Thus, we feel that generating another human cell line lies outside the scope of this paper and would be an excellent idea for future studies. We thank the reviewer for the proposed experiment.

      • Fig 7, the authors used the Q530N variant to rescue SARS-CoV-2 replication in TRMT1 KO cells. This is an important experiment and unexpectedly reveals that TRMT1 cleavage by Nsp5 is not required for viral replication. To strengthen the claim of the authors that TRMT1 is required to promote viral replication and that its cleavage inhibits RNA methylation, the authors could express the TRMT1 N-terminal construct in the TRMT1 KO cells to assess if viral replication is restored or not to similar levels as WT TRMT1. This will further validate the potential biological importance of TRMT1 cleavage by Nsp5.

      Indeed, we did not expect to find that human cells expressing the TRMT1-Q530N variant exhibit higher levels of viral replication. This suggests that cleavage of TRMT1 is inhibitory for viral replication. To provide further support for this observation, we analyzed the viral titer and infectivity of supernatants derived from human cells expressing wildtype TRMT1 or TRMT1-Q530N. Consistent with our finding that TRMT1-Q530N cells contain more viral RNA, the media supernatants from TRMT1Q530N expressing cells exhibit higher viral titer and infectivity compared to supernatants from TRMT1-KO cells expressing wildtype TRMT1. These results provide additional evidence that TRMT1 is required to promote viral replication. Moreover, these findings suggest that TRMT1 cleavage and reduced protein synthesis could selflimit viral replication. The additional results have been added as Figure 8.

      • Fig 7 shows that the TRMT1 Q530N variant rescues SARS-CoV-2 replication to greater levels then WT TRMT1. The authors should discuss this in greater detail and its possible implications with their proposed statement. For instance, are m2,2G levels higher in Q530N compared to WT? Does Q530N co-elute with Nsp5 or is the interaction disrupted in cells?

      These are excellent points brought up by the Reviewer. As noted above, we have added an additional experiment that tests the functional relevance of TRMT1 expression and cleavage on virion production and infectivity (Figure 8). Moreover, we have followed the Reviewer’s suggestion and discussed the potential implications of these findings in the “Ideas and Speculation” subsection.

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, the authors have used biochemical approaches to provide compelling evidence for the cleavage of TRMT1 by SARS-CoV-2 Nsp5 protease. This work is of wide interest to biochemists, cell biologists, and structural biologists in the coronavirus (CoV) field. Furthermore, it substantially advances the understanding of how CoV's interact with host factors during infection and modify cellular metabolism.

      We thank the reviewer for the thoughtful assessment of our study.

      Strengths:

      The authors provide multiple lines of biochemical evidence to report a TRMT1-Nsp5 interaction during SARS-CoV-2 infection. They show that the host enzyme TRMT1 is cleaved at a specific site and that it generates fragments that are incapable of functioning properly. This is an important result because TRMT1 is a critical player in host protein synthesis. This also advances our understanding of virus-host interactions during SARS-CoV-2 infections.

      Weaknesses:

      The major weakness is the lack of mechanistic insights into TRMT1-Nsp5 interactions. The authors have provided commendable biochemical data on proving the TRMT1-Nsp5 interaction but without clear mechanistic insights into when this interaction takes place in the context of SARS-CoV-2 propagation, what are the functional consequences of this interaction on host biology, and does this somehow benefit the infecting virus? I feel that the authors played it a bit safe despite having access to several reagents and an extremely promising research direction.

      We agree that our findings have prompted questions on the mechanistic and functional relevance of TRMT1 cleavage by Nsp5. To begin addressing the latter point, we have included a new experiment testing the impact of TRMT1 expression and cleavage on SARS-CoV-2 virus production and infectivity (Figure 8). We find that TRMT1-deficient cells infected with SARS-CoV-2 exhibit less virion production and the viruses produced are less infectious. Intriguingly, we find that expression of the non-cleavable TRMT1-Q530N variant in TRMT1-KO cells promotes an increase of viral titer as well as infectivity compared to expression of wildtype TRMT1. These results provide evidence for an unexpected role for TRMT1 expression in virus production and the generation of optimally infectious SARS-CoV-2 particles. We discuss the potential implications of this finding in the “Ideas and Speculation” subsection.

      We agree that understanding the timing and effects of Nsp5-TRMT1 interaction will be an important area of investigation moving forward. We would like to include additional time points beyond 24- and 48-hours post-infection. However, we have found that the MRC5-ACE2 cells exhibited increased levels of cell death at 72 and 96-hours postinfection that could confound results (Raymonda et al 2022). Moreover, we would like to know how the reduction in m2,2G modifications affects host tRNA biology and translation. However, these experiments involve large-scale methods such as tRNA sequencing and ribosome profiling which are outside the scope of our current studies and will be the subject of future efforts.

      We acknowledge the Reviewer’s assessment that we “played it a bit safe” in discussing the functional consequences of Nsp5-TRMT1 interaction. We aimed for a circumspect interpretation of our results and their biological implications, but might have been too cautious in our conclusions. Thus, we have added an “Ideas and Speculation” subsection that discusses possible reasons for how TRMT1 cleavage and interaction with Nsp5 could benefit the virus. We thank the Reviewer for pointing out this issue in our initial manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Having reviewed an earlier version of this manuscript, I appreciated the recent progress made by the authors. I felt the entire body of work is quite solid and the interpretations are clear and not overstated. One piece of data I thought deserved a sentence or two of discussion was the complementation assay with Q530N TRMT1. This experiment suggests the possibility that cleavage of TRMT1 by Nsp5 may be an act to self-limit replication, although this result could also be due to the elevated levels of Q530N TRMT1 expression compared to WT. I still think it is worthy of discussion. Another thing I would recommend is to include the length of infection by SARS-CoV-2 in the figure legends.

      We thank the reviewer for their positive response and constructive comments.

      We have followed the Reviewer’s suggestion to further discuss how cleavage of TRMT1 may act to self-limit replication in the “Ideas and Speculation” subsection. We have also included the length of infection by SARS-CoV-2 in the figure legends.

      Reviewer #2 (Recommendations For The Authors):

      In addition to the comments mentioned in the public review, this reviewer encourages the authors to address the following points:

      • Please clarify the rationale behind choosing 24 and 48 hours post-infection as time points for the analyses (Fig 1). One would expect even lower levels of TRMT1 and RNA modification after 72 and 96 hours post-infection.

      We chose the 24 and 48-hour time points since we have shown that MRC5 cells exhibit elevated accumulation of viral RNA at these time points (Raymonda et al 2022). However, at 72 and 96-hours post-infection, we have found that the MRC5-ACE2 cells exhibited cytopathic effects indicative of cell death that could confound results. We have included the rationale for these time points in our revised manuscript.

      • In Supplementary Figure 3, please add in the legend the meaning of the asterisk symbol.

      The asterisks denote non-specific bands that are still detectable in the TRMT1-KO cell line. We have updated the Figure Legend and thank the Reviewer for catching this omission.

      • In Supplementary Figure 3B, there is an intermediate band in lane 3 with C145A when using the antibody 609-659. The authors should clarify what that band is.

      The intermediate band in lane 3 (and in lane 6) of Supplemental Figure 3B represents non-specific detection of the Nsp5-C145A variant that exhibits extremely high levels of expression since it cannot self-cleave. We have clarified the identity of the band in the figure legend.

      Reviewer #3 (Recommendations For The Authors):

      I have only minor comments:

      Although the authors have done a commendable job of providing compelling biochemical evidence of TRMT1 cleavage by Nsp5, it is not clear how this enhances viral infection. The discussion presents the experimental findings and prior publications as a series of correlated observations without clearly specifying the mechanistic benefits of TRMT1 hijacking towards CoV propagation, or even proposing a mechanistic hypothesis to this end.

      We agree with the Reviewer that providing a mechanistic hypothesis on how TRMT1 cleavage impacts virus biology will help inform future studies. We have followed the Reviewer’s suggestion and discuss potential mechanisms in the “Ideas and Speculation” subsection.

      How do these experiments inform us about the cell biology of SARS-CoV- infections? Does Nsp5-mediated degradation start early in infection? Is the loss of TRMT1 sustained over the course of the infection? Do Nsp5 concentrations or relative amounts correlate with TRMT1 loss during this period? For instance, is there only a modest increase in Nsp5 levels from 24h to 48h? I would suggest adding a few more data points than just 24h and 48h in the cell culture experiments. As the manuscript stands right now, it will be a bit difficult for readers to appreciate the relevance of this study in its present form.

      These are excellent questions raised by the Reviewer. The temporal effects of SARSCoV-2 infection on TRMT1 levels will be an important area to dissect moving forward.

      As mentioned above, we would like to include additional time points beyond 24- and 48-hours post-infection. However, at 72 and 96-hours post-infection, we have found that the MRC5-ACE2 cells exhibited increased levels of cell death that could confound results.

      However, we do observe a correlation between the level of infection and the amount of TRMT1 depletion. In our newly added Figure 6C and 6D, we show that increasing the MOI leads to a concomitant increase in N-protein production that correlates with the amount of TRMT1 depletion. Moreover, we have added additional experiments to explore the biological relevance of our findings in terms of virion particle production and infectivity. We thank the reviewer for these insightful questions that have improved our manuscript and provide a foundation for future studies.

      Related to this previous comment: how do the authors rationalize their inference that TRMT1 is essential for SARS-CoV-2 infection, yet it is cleaved during the infection? What seems to be the advantage of this seemingly contradictory but possibly quite intriguing inference?

      We acknowledge the paradox that TRMT1 seems to be essential for SARS-CoV-2 replication but is cleaved during the infection. We propose several hypotheses to explain these findings:

      Hypothesis 1: TRMT1 could be a bystander target. The loss of TRMT1 expression leads to a decrease in modifications that impacts translation. This decrease in translation capacity of the infected cells would lead to decreased production of viral proteins and reduced viral replication. This could explain why TRMT1-deficient cells exhibit less virus production. This could also account for why the TRMT1-Q530N mutant might produce more virus. In this case, the cleavage of TRMT1 and biological effects on viral replication and virion production are coincidental. However, even if TRMT1 cleavage and inactivation does not impact viral replication or production, it would still be important to know the cellular impacts that contribute to disease pathogenesis.

      Hypothesis 2: The slight diminishment of viral replication due to host translation inhibition could outweigh the benefits of shutting down host responses dependent upon protein synthesis. The decrease in TRMT1-catalyzed tRNA modification caused by Nsp5 cleavage could severely inhibit host translation while viral translation can still be maintained through a tRNA pool optimized for viral translation, albeit at a slightly lower rate than if TRMT1 is not cleaved.

      Hypotheses 3: The Nsp5-TRMT1 interaction could allow the virus to bind tRNAs that are packaged in viral particles as suggested previously (Pena et al., 2022). The finding that expression of the non-cleavable TRMT1-Q530N variant enhances viral replication and infectivity supports the hypothesis that TRMT1 could facilitate tRNA uptake into viral particles. The packaging of specific tRNAs in viral particles could enhance viral translation in the subsequent round of infection, thereby enhancing infectivity and perhaps facilitating the species jump of SARS-CoV-2 towards hosts with incompatible codon bias.

      We have included these hypotheses in the new “Ideas and Speculation” subsection.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Response to reviewers:

      We would like to thank all the reviewers and the editors for their thorough and helpful feedback on our work. Before addressing specific questions and points, we would like to make a general comment on a mechanistic aspect of this study. The reviewers correctly pointed out that our study does not reveal the molecular mechanism that leads to centromeric histone depletion specifically from meiotic chromosomes. Identifying this mechanism requires a deep and thorough understanding of how centromeric histones are loaded and centromeres are established each cell cycle, and how they are maintained over time in different cell types. To our knowledge, these mechanisms have not been described in plants. To add a further layer of complexity, it appears that the mechanisms governing CENH3 maintenance may be (partially) different in plant mitotic and meiotic cells, and the mechanistic basis of this difference is unknown. Obviously, these are interesting but also complex questions and their resolution will require considerable resources and effort, which we believe is beyond the scope of this manuscript. Nevertheless, our finding that CENH3 maintenance and centromere function in meiotic cells are sensitive to heat stress is an unexpected discovery with profound implications for plant adaptation, which provides a strong incentive for further exploration of centromere maintenance mechanisms in plants.

      Furthermore, we would like to apologize to reviewers for poor quality of pictures in the original submission. It was decreased by conversion to a pdf format during submission.

      eLife assessment

      This important study reports how heat stress affects centromere integrity by compromising the loading of the centromere protein CENH3 and by prolonging the spindle assembly checkpoint during male meiosis in Arabidopsis thaliana. The evidence supporting the claims by live cell imaging is convincing, although deeper mechanistic insight is lacking, making the study overall somewhat preliminary in nature. This work will be of interest to a broad audience of biologists working on how chromatin states are affected by stress conditions.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Khaitova and co-workers present here an analysis of centromere composition and function during elevated temperatures in the plant Arabidopsis. The work relates to the ongoing climate change during which spikes in high temperatures will be found. Hence, the paper addresses a timely subject.

      The authors start by confirming earlier studies that high temperatures reduce the fertility of Arabidopsis plants. Interestingly, a hypomorphic mutant of the centromeric histone variant CENH3 (CENP-A), which was previously described by the authors, sensitizes plants to heat and results in a drop in viable pollen and silique length. The drop in fertility coincides with the formation of micronuclei in meiosis and an extension of meiotic progression as revealed by live cell imaging. Based on this finding, the authors then show that at high temperatures, the fluorescence intensity of a YFP:CENH3 declines in meiosis but remarkably not in the surrounding cells (tapetum cells). In addition, the amount of BMF1 (a Bub1 homolog and part of the spindle assembly checkpoint) also appears to decline on the kinetochores of meiocytes as judged by BMF1 reporter line. However, whether this is dependent on a decline of CENH3 or represents a separate pathway is not clear.

      We provide new data in Figure S6 showing that BMF1 loading on centromeres is substantially reduced in cenh3-4 mutants. Thus, efficient tethering of BMF1 to centromeres depends on CENH3.

      Finally, the authors measure the duration of the spindle checkpoint and find that it is extended under high temperatures from which they conclude that the attachment of spindle fibers to kinetochores is compromised under heat.

      Strengths:

      This is an interesting and important paper as it links centromere organization/function to heat stress in plants. A major conclusion of the authors is that weakened centromeres, presumably by heat, may be less effective in establishing productive interactions with spindle microtubules.

      Weaknesses:

      The paper does not explain the molecular reason why CENH3 levels in meiocyctes are reduced or why the attachment of spindle fibers to kinetochore is less efficient at high versus low temperatures.

      While we cannot explain the molecular mechanism underlying temperature-dependent depletion of CENH3 in meiocytes, the less efficient attachment of microtubules to the kinetochores at higher temperatures is likely caused by reduced levels of CENH3, which result in smaller centromeres that are less effective in establishing productive microtubule-kinetochore attachments. Here (new Figure S6) and in our previous study (Capitao et al. 2021), we have shown that amount of centromere/kinetochore proteins is reduced at centromeres in cenh3-4 mutants, and that these plants exhibit prolonged SAC and slower chromosome biorientation.

      Reviewer #2 (Public Review):

      Summary:

      This work investigates how increased temperature affects pollen production and fertility of Arabidopsis thaliana plants grown at selected temperature conditions ranging from 16C to 30C. They report that pollen production and fertility decline with increasing temperature. To identify the cause of reduced pollen and fertility, they resort to living cell imaging of male meiotic cells to identify that the duration of meiosis increases with an increase in temperature. They also show that pollen sterility is associated with the increased presence of micronuclei likely originating from heat stress-induced impaired meiotic chromosome segregation. They correlate abnormal meiosis to weakened centromere caused by meiosis-specific defective loading of the centromere-specific histone H3 variant (CenH3) to the meiotic centromeres. Similar is the case with kinetochore-associated spindle assembly checkpoint(SAC) protein BMF1. Intriguingly, they observe a reverse trend of strong CENH3 presence in the somatic cells of the tapetum in contrast to reduced loading of CENH3 in male meiocytes with increasing temperature. In contrast to CENH3 and BMF1, the SAC protein BMF3 persists for longer periods than the WT control, based on which authors conclude that the heat stress prolongs the duration of SAC at metaphase I, which in turn extends the time of chromosome biorientation during meiosis I. The study provides preliminary insights into the processes that affect plant reproduction with increasing temperatures which may be relevant to develop climate-resilient cultivars.

      Strengths:

      The authors have mastered the live cell imaging of male meiocytes which is a technically demanding exercise, which they have successfully employed to examine the time course of meiosis in Arabidopsis thaliana plants exposed to different temperature conditions. In continuation, they also monitor the loading dynamics and resident time of fluorescently tagged centromere/kinetochore proteins and spindle assembly checkpoint proteins to precisely measure the time duration of respective proteins to study their precise dynamics and function in male meiosis.

      Weaknesses:

      Here the authors use only one representative centromere protein CENH3, one kinetochore-associated SAC protein BMF1, and the SAC protein BMF3 to conclude that heat stress impairs centromere function and prolongs SAC with increased temperatures. Centromere and its associated protein complex the kinetochores and the SAC contain a multitude of proteins, some of which are well characterized in Arabidopsis thaliana. Hence the authors could have used additional such tagged proteins to further strengthen their claim.

      Indeed, several other proteins have recently been characterized as centromere/kinetochore components and could have been included in the study to further validate the results presented. To strengthen our argument, we have added new experimental data (Figure S4) showing temperature-induced depletion of CENH3 in wild-type plants by immunocytology. Thus, we convincingly show that temperature stress reduces the amount of CENH3. This is likely to affect the loading of most kinetochore and centromeric proteins. Here (new Figure S6) and in our previous study (Capitao et al., 2021), we have shown that genetic depletion of CENH3 in cenh3-4 mutants results in reduced loading of CENPC, MIS12 and BMF1 at mitotic centromeres and reduced loading of BMF3 and BMF1 at meiotic centromeres. We also attempted to assess the levels of CENPC and MIS12 on meiotic chromosomes by immunocytology, but our antibodies, which work on mitotic spreads, did not stain meiotic chromosomes.

      Though the results presented here are interesting and solid, the study lacks a deeper mechanistic understanding of what causes the defective loading of CenH3 to the centromeres, and why the SAC protein BMF3 persists only at meiotic centromeres to prolong the spindle assembly checkpoint. Also, this observation should be interpreted in light of the fact that SAC is not that robust in plants as several null mutants of plant SAC components are known to grow as healthy as wild-type plants at normal growth conditions without any vegetative and reproductive defects.

      Thank you for raising this point. We are of the opinion that SAC operates and it is important in plants - we have added a citation to a preprint from the Schnittger lab (Lampou et al., 2023, BioRxiv) that was published while this manuscript was under review. We think this is the most comprehensive analysis of plant SAC to date, clearly showing that SAC delays progression to anaphase in the presence of spindle inhibitors, although adaptation eventually occurs and the cell cycle progresses. This is very similar to the situation in animals, which also undergo spindle adaptation in similar situations. The difference between plants and animals may be due to subsequent events, where plants are better able to tolerate genome instability and resume cell division in the presence of abnormal chromosome numbers. Robustness and redundancy may be another reason why plant mutants deficient in SAC do not show obvious growth retardation.

      One of the immediate responses to heat stress is the production of heat shock proteins(Hsps), which act as molecular chaperones to safeguard the proteome. It will be interesting to see if the expression levels of known HsPs can be correlated with their role in stabilizing the structure of SAC proteins like BMF1 to prolong its presence at the meiotic kinetochores.

      Indeed, the heat stress response is likely to be involved in this process. We sought to investigate the role of this pathway by analyzing Arabidopsis mutants deficient in HEAT-SHOCK FACTOR BINDING PROTEIN (HSBP), which acts as a negative regulator of the heat shock response. This experiment was prompted by the observation that hsbp mutants have reduced fertility. We expected that an unrestricted heat stress response might affect meiosis and pollen formation. However, our initial experiments did not show altered pollen viability in response to heat stress in hsbp plants and we did not pursue this line of research further.

      Reviewer #3 (Public Review):

      Summary:

      Khaitova et al. report the formation of micronuclei during Arabidopsis meiosis under elevated temperatures. Micronuclei form when chromosomes are not correctly collected to the cellular poles in dividing cells. This happens when whole chromosomes or fragments are not properly attached to the kinetochore microtubules. The incidence of micronuclei formation is shown to increase at elevated temperatures in wild-type and more so in the weak centromere histone mutant cenH3-4. The number of micronuclei formed at high temperatures in the recombination mutant spo11 is like that in wild-type, indicating that the increased sensitivity of cenh3-4 is not related to the putative role of cenh3 in recombination. The abundance of CENH3-GFP at the centromere declines with higher temperature and correlates with a decline in spindle assembly checkpoint factor BMF1-GFP at the centromeres. The reduction in CENH3-GFP under heat is observed in meiocytes whereas CENH3-GFP abundance increases in the tapetum, suggesting there is a differential regulation of centromere loading in these two cell types. These observations are in line with previous reports on haploidization mutants and their hypersensitivity to heat stress.

      Strengths:

      This paper is an important contribution to our insights into the impact of heat stress on sexual reproduction in plants.

      Weaknesses:

      While it is highly significant, I struggled to interpret the results because of the poor quality of the figures and the videos.

      We apologize for the poor quality of the figures. The figure resolution was drastically reduced during the conversion of the manuscript to pdf on publisher web site.

      Reviewer #1 (Recommendations For The Authors):

      To complete the presented analysis, it would be great to analyze the signal strength of the here-presented BMF3 reporter at high temps, see below for further reasoning.

      Quantification of the BMF3 signal is difficult - it is only transiently associated with kinetochores and its level changes over time. Nevertheless, analysis of our movies taken under the same microscope settings indicates that the amount of BMF3 decreases with increasing temperature. This is illustrated in the new Figure S6C.

      Conversely, how is the BMF1 and BMF3 signal strength in cenh3-4 mutants?

      We performed an analysis of BMF1 and BMF3 signal in cenh3-4 mutants and observed a reduced level of signal from both proteins (Figure S6). In the case of BMF1, no signal was detectable in either somatic or meiotic cells.

      How do the authors explain the reduction in BMF1 signal at 26 and 30{degree sign}C versus the extension of the duration of the SAC as measured by the persistence of a BMF3 signal (line 192: "...reduces the amount of CENH3 and the kinetochore protein BMF1 on meiotic centromeres, potentially affecting their functionality..." versus line 213: "...We observed that while the BMF3:GFP signal persisted, on average, for about 22.7 min at 21 and 26{degree sign}C, its appearance was prolonged to 40.5 min at 30{degree sign}C..."). Is the BMF3 signal also reduced at high temps (see question above)?

      This is a very interesting point. While we see reduced levels of both proteins under heat stress or in cenh3-4 plants, the effect on BMF1 is much more pronounced and becomes undetectable under these conditions. This contrasts with BMF3, which appears to be reduced but is still clearly visible. These data suggest that BMF1 is more sensitive to reduced levels of CENH3 and it further corroborates the findings from the Schnittger lab that BMF1 is not the core component of SAC.

      Line 18-20: The observation that heat stress reduces fertility has been made by several research teams before this study. I propose to write "confirm"/"support" etc. instead of "reveal" to avoid a (presumably not intended) false priority claim in the abstract.

      We apologize, this was unintentional and we cite the relevant literature in the article. We have rewritten the abstract to avoid this impression.

      Figure 2: The panel/legend appears to be a bit mixed up. Panel C is described in legend under A. In addition, I cannot find any blue arrows in panel A (which is described as panel B). Correspondingly, the references to the panels in this figure (lines 134/135 and following) need to be updated. I am also not sure how the meiocytes in this figure were stained. The dots look like centromeres but then their intensity rather increases with increasing temperature. If correct, how can this be reconciled with the authors' statement that centromeres decrease in size at higher temps?

      We apologize for the mix up. An early version of the Figure was accidentally submitted and we now corrected it. The Panel B shows DAPI stained meiocytes at the tetrad stage and examples of micronuclei are indicated by arrowheads.

      Line 520: Should read "genotype" not "phenotype".

      Corrected

      Reviewer #2 (Recommendations For The Authors):

      (1) It is intriguing that heat stress impairs only the centromeres and segregation of meiotic chromosomes but not the mitotic chromosomes. No analysis of mitotic divisions is provided in the manuscript. As they have generated marker lines, it is reasonable to examine the mitotic time course as well by live monitoring of root tissues exposed to similar temperature conditions as done for meiotic analysis. This will help to address the effect of heat stress on mitotic centromeres and its comparison with meiosis will provide a better picture. There are two likely outcomes during mitosis:

      (a) It is possible that the heat stress also slows down mitotic progression as well as is the case in meiosis as shown in this paper and hence it is important to examine those as well to compare and contrast the CENH3/BMF1 dynamics in mitosis and meiosis.

      (b) The second scenario is that there is no effect of heat stress on the centromere integrity of mitotic chromosomes. In fact, the authors show indirect evidence in support of this wherein the eYFP: CENH3 showed a strong signal in the tapetal cells (somatic origin) surrounding the male meiocytes (generative origin). It is interesting that somatic cells of the tapetum show a strong signal whereas the meiocytes lack this. The authors should elaborate on this contrasting result.

      The effect we observed seems to be specific to meiosis. We analyzed the progression of mitosis in root cells and we see a negligible effect of temperature on mitotic progression and no micronuclei formation. Interestingly, in terms of CENH3 loading, root cells show a slight decrease in CENH3 at 30°C, in contrast to the situation in tapetum cells. These and other data suggest a tissue/cell specific behavior of centromere maintenance and deserve further analysis. We plan to publish data on mitosis and tissue-specific aspects of CENH3 loading in a separate manuscript.

      (2) Spindle assembly checkpoint (SAC) comprises several core proteins that are recruited to the kinetochores to correct the errors during the defective cell cycle. Here the authors demonstrate the prolonged presence of BMF3 as the only proof to claim that heat stress prolongs the spindle assembly checkpoint during metaphase I. Have the authors observed the dynamics of any other SAC core components such as MAD1, MAD2, MPS1, BUB3, and the like during heat stress?

      No, we did not. We provide several independent lines of evidence that centromere structure and functionality are affected, and spindle checkpoint analysis is only one of them. At the time we designed these experiments, the only experimentally validated and well-characterized component of the SAC was BMF3, and we used only on this protein as SAC reporter because a general analysis of the SAC was not the primary goal of our study. While this paper was under review, a preprint from the Schnittger lab focusing on plant SAC was published that comprehensively analyzed these SAC components in Arabidopsis and provided a solid foundation and resources for further research in this direction. This study also uses BMF3 as a reporter for SAC in meiotic cells. It is noteworthy that despite using different microscopic methods and different plant reporter lines, our labs independently arrived at exactly the same duration of BMF3 association with the kinetochore (i.e. 22 min).

      (3) Is BMF1 a component of SAC or the kinetochore? I understand that BMF1 is a part of the core SAC ( Komaki and Schnittger, 2017) although it localizes to the kinetochore. There are well-characterized kinetochore proteins in Arabidopsis such as Mis12, NUF2, NNF1, and SPC24(MUN1) which the authors could have used as a kinetochore marker. Regardless, here the authors used it as a kinetochore marker. Being a part of SAC, one would expect the prolonged presence of BMF1 similar to BMF3 in the meiotic kinetochores but it is the other way. How to explain these contrasting results?

      As discussed in the public section of the review, BMF1 does not seem to be the core component of SAC. Furthermore, this protein localizes to centromeres/kinetochore throughout the cell cycle and therefore, it cannot be used as SAC reporter.

      (4) Micronuclei can form as a result of chromosome missegregation as shown for spo11-1 and also due to segregation error caused by DNA repair defects. Here it is not clear what is the origin of micronuclei. It is very hard to decipher from live cell imaging. A simple meiotic spread of anthers of different treatments would address the origin of micronuclei.

      Cytology cannot easily determine the origin of micronuclei in meiotic cells. Acentric fragments produced from aberrant DNA repair will still be cytologically detectable only after metaphase I as they are tethered to the remaining chromatin via cohesion. Therefore, we took advantage of spo11 mutants that do not form any meiotic breaks, and hence cannot generate acentric fragments by aberrant repair, to discriminate the origin of micronuclei. We reason that all micronuclei produced in spo11 plants originate from chromosome mis-segregation and their increase at elevated temperature support the notion that heat stress further impairs chromosome segregation.

      (5) Fig.1 B The microspores are not clearly visible in the alexander-stained anthers. It is not clear which is fertile and which is sterile. A better quality picture would be ideal to appreciate the fact.

      Again, we apologize for poor quality of pictures due to manuscript conversion.

      Reviewer #3 (Recommendations For The Authors):

      (1) In Figure 2, it should be pointed out where the micronuclei are. I see here and there a single bright spot. In Arabidopsis, we have noticed bright spots under stress conditions that are autofluorescent signals. It needs to be shown that these spots are not observed in non-GFP lines. Better image quality may help too.

      The micronuclei in Figure 2 are visualized by DAPI staining, not with GFP. The nuclei are now indicated by arrowheads.

      (2) It was not possible to see the centromeres in Figure 3 hence I could not verify the fluorescence intensities of CENH3 and BMF1. There is also something wrong with the color codes blue and red in fig3B, C, and D.

      Again, we apologize for poor quality of pictures due to manuscript conversion.

      (3) Also in the videos it would help to point out where the micronuclei are seen. At what stage were these nuclei quantified? Given that meiosis progression in the cenh3-4 mutant is slower, it may be necessary to wait long enough to see established micronuclei. This information is supposed to be presented in Figure 2C. However, the X-axis shows time, not number. So I presume Fig 2C shows the duration of meiosis stages in the mutant. In Fig 2B, it shows the number of micronuclei per lobe. However, to correlate the incidence of micronuclei formation and the frequency of polyad formation (inviable microspores), one needs the quantification of the numbers of meiocytes carrying micronuclei. Then one can correlate the number of pollen per anther (shown in Fig 1c) with the incidence of micronuclei formation. The question of whether the degree of fertility reduction is due to micronuclei formation is a major issue that should be clarified.

      Then micronuclei were not quantified from the movies, but from DAPI stained whole anthers at the tetrad stage as indicated in the main text. We also apologize for confusion with the Figure 2 as we mixed up the panels in the original submission. This has been corrected in the new submission.

    1. Reviewer #1 (Public Review):

      Summary:

      In this study, Nishi et al. claim that the ratio of long-term hematopoietic stem cell (LT-HSC) versus short-term HSC (ST-HSC) determines the lineage output of HSCs and reduced ratio of ST-HSC in aged mice causes myeloid-biased hematopoiesis. The authors used Hoxb5 reporter mice to isolate LT-HSC and ST-HSC and performed molecular analyses and transplantation assays to support their arguments. How the hematopoietic system becomes myeloid-biased upon aging is an important question with many implications in the disease context as well. However, their study is descriptive with remaining questions.

      Weaknesses:

      (1) The authors may need conceptual re-framing of their main argument because whether the ST-HSCs used in this study are functionally indeed short-term "HSCs" is questionable. The data presented in this study and their immunophenotypic definition of ST-HSCs (Lineage negative/Sca-1+/c-Kit+/Flk2-/CD34-/CD150+/Hoxb5-) suggest that authors may find hematopoietic stem cell-like lymphoid progenitors as previously shown for megakaryocyte lineage (Haas et al., Cell stem cell. 2015) or, as the authors briefly mentioned in the discussion, Hoxb5- HSCs could be lymphoid-biased HSCs. The authors disputed the idea that Hoxb5- HSCs as lymphoid-biased HSCs based on their previous 4 weeks post-transplantation data (Chen et al., 2016). However, they overlooked the possibility of myeloid reprogramming of lymphoid-biased population during regenerative conditions (Pietras et al., Cell stem cell., 2015). In other words, early post-transplant ST-HSCs (Hoxb5- HSCs) can be seen as lacking the phenotypic lymphoid-biased HSCs. Thinking of their ST-HSCs as hematopoietic stem cell-like lymphoid progenitors or lymphoid-biased HSCs makes more sense conceptually as well. ST-HSCs come from LT-HSCs and further differentiate into lineage-biased multipotent progenitor (MPP) populations including myeloid-biased MPP2 and MPP3. Based on the authors' claim, LT-HSCs (Hoxb5- HSCs) have no lineage bias even in aged mice. Then these LT-HSCs make ST-HSCs, which produce mostly memory T cells. These memory T cell-producing ST-HSCs then produce MPPs including myeloid-biased MPP2 and MPP3. This differentiation trajectory is hard to accept. If we think Hoxb5- HSCs (ST-HSCs by authors) as a sub-population of immunophenotypic HSCs with lymphoid lineage bias or hematopoietic stem cell-like lymphoid progenitors, the differentiation trajectory has no flaw.

      (2) Authors' experimental designs have some caveats to support their claims. Authors claimed that aged LT-HSCs have no myeloid-biased clone expansion using transplantation assays. In these experiments, authors used 10 HSCs and young mice as recipients. Given the huge expansion of old HSC by number and known heterogeneity in immunophenotypically defined HSC populations, it is questionable how 10 out of so many old HSCs can faithfully represent the old HSC population. The Hoxb5+ old HSC primary and secondary recipient mice data (Figure 2C and D) support this concern. In addition, they only used young recipients. Considering the importance of the inflammatory aged niche in the myeloid-biased lineage output, transplanting young vs old LT-HSCs into aged mice will complete the whole picture.

      (3) The authors' molecular data analyses need more rigor with unbiased approaches. They claimed that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid or lymphoid gene set enrichment but aged bulk HSCs, which are just a sum of LT-HSCs and ST-HSCs by their gating scheme (Figure 4A), showed the "tendency" of enrichment of myeloid-related genes based on the selected gene set (Figure 4D). Although the proportion of ST-HSCs is reduced in bulk HSCs upon aging, since ST-HSCs do not exhibit lymphoid gene set enrichment based on their data, it is hard to understand how aged bulk HSCs have more myeloid gene set enrichment compared to young bulk HSCs. This bulk HSC data rather suggests that there could be a trend toward certain lineage bias (although not significant) in aged LT-HSCs or ST-HSCs. The authors need to verify the molecular lineage priming of LT-HSCs and ST-HSCs using another comprehensive dataset.

      (4) Some data are too weak to fully support their claims. The authors claimed that age-associated extramedullary changes are the main driver of myeloid-biased hematopoiesis based on no major differences in progenitor populations upon transplantation of 10 young HSCs into young or old recipient mice (Figure 7F) and relatively low donor-derived cells in thymus and spleen in aged recipient mice (Figure 7G-J). However, they used selected mice to calculate the progenitor populations in recipient mice (8 out of 17 from young recipients denoted by * and 8 out of 10 from aged recipients denoted by * in Figure 7C). In addition, they calculated the progenitor populations as frequency in c-kit positive cells. Given that they transplanted 10 LT-HSCs into "sub-lethally" irradiated mice and 8.7 Gy irradiation can have different effects on bone marrow clearance in young vs old mice, it is not clear whether this data is reliable enough to support their claims. The same concern applies to the data Figure 7G-J. Authors need to provide alternative data to support their claims.

    1. ment. Performance studies taught us t"acting" is not just something set apart from reality, but a model of and for the procethrough which real identities are constructed. What I suggest here is that we cbegin to think of animation as more than an entertainment medium, as a possimode of performative (real, social) world

      Just like how performance affects how someone's identity is constrcuted, animation may also change the real world.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements

      Regarding significance, we would like to highlight that the main finding and breakthrough of our manuscript is the discovery that intronic polyadenylation (IPA) isoforms are a source of microproteins (indeed, IPA was not known to induce sORF-encoded microproteins). We make the proof of principle of this concept (called miP-5’UTR-IPA) and of its functional relevance for one gene (PRKAR1B).

      A second finding of this study is that IPA (including miP-5’UTR-IPA) isoforms are widely upregulated in cell response to cisplatin, and therefore we show the functional relevance of miP-5’UTR-IPA isoforms in this biological context.

      Regarding the generality of the miP-5’UTR-IPA concept, we provide evidence that many genes generate miP-5’UTR-IPA isoforms, by crossing our 3’-seq data with available Ribo-Seq and mass spectrometry datasets, which were generated without cisplatin treatment. Also, the miP-5’UTR-IPA isoforms of PHF20 and PRKAR1B are detected both in the presence and absence of cisplatin. Thus, the novel concept of microprotein-coding IPA isoforms opens wide perspectives, way beyond cisplatin response.

      2. Description of the planned revisions

      REVIEWER #1

      Evidence, reproducibility and clarity

      Microporteins originating from coding and non-coding transcript are increasingly understood to control various cellular processes. In the present study, the authors investigated whether intronic polyadenylation (IPA) contributes to the formation of transcript isoforms encoding microproteins. Using genotoxic stress by cisplatin as a model in cell cultures, the authors detect abundant IPA. IPA in a subset of such transcripts leads to short 5'UTR transcript isoforms that are poorly associated with heavy polysomes and encode microproteins. For PRKAR1B, they demonstrate the expression of a corresponding microprotein and a function in modulating the cisplatin response. Based on depletion experiments of FANCD2 and STX1, the authors propose that impaired transcription processivity after cisplatin is one mechanism leading to IPA and microprotein production.

      While this is an interesting manuscript, I felt the support for the claimed generalization falls a bit short.

      Our response: The generality of the miP-5’UTR-IPA concept is supported by the large-scale analysis that we presented (Fig. 6): indeed, by crossing our 3’-seq data with Ribo-Seq and MS data (both of which originate from multiple cell types and tissues), we identified 156 genes with cisplatin-regulated miP-5’UTR-IPA isoforms. To strengthen this part and highlight the generality of the miP-5’UTR-IPA concept, we will provide the cell type/ tissue distribution of our set of 156 miP-5’UTR-IPA isoforms, by exploiting available 3’-seq datasets from various cells/tissues. (Please also see major point 1 below.)

      Major:

        • If I see it correctly, the authors mainly refer to existing riboSeq data and evidence from mass spectrometry/proteomics to infer the generality of the mechanism (beyond PRKAR1B). It is important to back this up with further experiments and validate this for the set-up used in this manuscript. This concerns the existence of the microproteins but also the downstream functional impact. Our response: In our study, we make the proof of principle of miP-5’UTR-IPA (that is, a microprotein-encoding IPA isoform) for the PRKAR1B gene and its sORF#2 (microprotein detection by WB and IF, functional evidence by siRNA and CRISPR of IPA site and sORF initiation codon). If I understand well (also based on minor point 3 below), this reviewer is requesting further evidence of microprotein existence (in addition to Ribo-Seq and mass spectrometry [MS] data) and function, for a second IPA-derived sORF that we study in this manuscript (either PHF20 sORF or PRKAR1B sORF#1). To the best of our knowledge, a proof of principle for a new concept is usually done on a single gene. Nevertheless, for the miP-5’UTR-IPA isoform of PHF20*, we already provided evidence for its function by using siRNAs (Fig. 3A-C) and for its translation by polysome profiling (Fig. 4C) in addition to Ribo-Seq and MS evidence (Fig. 4A). The fact that for PHF20 we did not detect the transfected Flag-tagged microprotein in HEK cells could be due to several reasons (as discussed on page 16); __we will __try this approach again with different biological conditions (cell lines, stress) or construct designs (as the sORF context may be important).
      1. Also, I wonder is this limited to cisplatin-induced genotoxic stress and the specific cell line used or is this a more global mechanism?*

      Our response: We provided evidence of IPA isoform regulation by cisplatin in two lung cancer cell lines (A549 and H358; Fig. 1A-B) but we agree that our analyses of miP-5’UTR-IPA were mainly done in A549 cells. We will: (i) clarify that we detected the miP-5’UTR-IPA isoforms of PRKAR1B and PHF20 in A549 cells (total cytosol and light polysomes) both in the presence and absence of cisplatin (Fig. 2D, 3A and S3D); (ii) add RT-qPCR validation of their cisplatin regulation in H358 cells; (iii) try to detect the PRKAR1B-encoded microprotein in a second cell line (Fig. 4); (iv) test the impact of PRKAR1B and PHF20 miP-5’UTR-IPA isoforms on cell survival in a second cell line and with a second genotoxic agent; (v) clarify in Fig. 6 that miP-5’UTR-IPA isoforms are regulated by cisplatin in both A549 and H358 cells (our 3’-seq data) and that the Ribo-Seq and MS datasets supporting their translation originate from multiple cell types and tissues without cisplatin treatment; and (vi) provide the cell type/ tissue distribution of our set of 156 miP-5’UTR-IPA isoforms (by exploiting available 3’-seq datasets from various cells/tissues).

      Minor:

        • While the rest of the paper reads well, the abstract could be improved/simplified to increase accessibility* Our response: We will improve the abstract.

      Page 11: Pertaining to Figure 3 and the functional impact: The authors analyze the IPA effect by probing cell viability and cell survival. It would be important to define the effects in further detail, as the mere regulation of cell cycle and/or apoptosis could also result in such outcome (which is then not necessarily a direct cisplatin response). Does this also impact the response to other genotoxic stress (also pertains to the effects studied and shown in Fig. 5)?

      Our response: Because cisplatin effects on cell growth are usually mediated by effects on cell cycle and cell death, we will determine which aspect is impacted by PRKAR1B and PHF20 miP-5’UTR-IPA isoforms, by carrying out FACS analysis of PI/BrdU and Annexin V (both in the presence and absence of cisplatin). As mentioned in major point 2, we will also test the impact of these isoforms on cell survival to a second genotoxic agent.

      Page 12 concerning the microprotein expression: the authors refer to data from other resources to claim that the microproteins are expressed, however they fail to demonstrate this for their setup (at least for 2 out of three they study here). I think this is a weak point as it does not directly support the general claim.

      Our response: Please see major point 1 above.

      Also, I did not understand what the authors intended to demonstrate with the immunoflourescence (Fig. 4E). What should a defined nuclear expression imply versus the diffuse staining throughout after cisplatin? How does this relate to the functional effects?

      Our response: We included in Fig. 4E the observation that the subcellular localization of the PRKAR1B-encoded micropotein is altered in response to cisplatin, because this supports the notion that this micropotein plays a role in cell response to cisplatin. We can remove this data if requested.

      Page 13/Fig. 5E: the different clones of the mATG show very high variability. To my understanding it is difficult to draw a clear conclusion from this heterogeneity.

      Our response: The statistical analysis shows a significant difference between the mATG and Control groups (p Page 15 on the mechanism: SETX has been demonstrated to control poly(A) site choice (PMID: 21700224, 32976578). However the quantitative role of SETX in poly(A) site choice regulation (compared to other regulators) seems to be rather marginal and not strictly unidirectional, i.e after SETX depletion also longer transcript isoforms can be detected (PMID: 32976578). How does this relate to the proposed mechanism of SETX-dependent processivity? Interestingly, from PMID: 32976578 it also appears that PRKAR1A has a 5'UTR poly(A) site that is regulated in a SETX-dependent manner.

      Our response: We will add in the discussion statements that (i) the role of SETX in cisplatin regulation of IPA:LE isoform ratio and processivity might be different from its role in APA regulation in the absence of genotoxic treatment (citing PMID 32976578; keeping in mind that we did not compare them side by side on a genome-wide scale) and (ii) PRKAR1A seems to have a 5'UTR poly(A) site regulated by SETX in TREND-DB (PMID 32976578).

      • Page 16, discussion first paragraph. While refs 1-4 are nice reviews that could be quoted here a study that appeared later represents the most comprehensive analysis to date covering the different facets from transcription to RNA processing and the resulting impact on poly(A) site choice (PMID: 30552333).*

      Our response: We will cite PMID 30552333 and 32976578 as resources of APA regulation by various regulators of gene expression (keeping in mind, however, that for most factors these studies do not exclude indirect effects).

      Significance

      This could be a very significant report, provided the generality of the claims and mechanistic insigths are further strengthend.

      Overall it targets a rather specialized readership. This could be improved by simplifing the abstract, additional experimental evidence for the generality of the proposed mechanism, and a stringent rewording of the main text drawing a clear line, omitting unnecessary details and focussing on the novel findings.

      Our response: Please see our responses above. In addition, we will reword the main text where necessary.

      REVIEWER #2

      Evidence, reproducibility and clarity

      Summary:

      *In this manuscript, Devaux et al. report that the anti-cancer drug cisplatin upregulates intronic polyadenylation (IPA) isoforms in non-small cell lung cancer cell lines. Their finding was based on 3' end sequencing and long-read sequencing. Through polysome profiling they confirmed that many of the IPA isoforms are translated, despite being inefficient in most cases. *

      Our response: There is some misunderstanding here. We will clarify in the text that inefficient association with heavy polysomes is observed for a minority (not the majority) of IPA isoforms. For this, in Fig. 2B and S2A, we will add the information that for the majority of IPA sites, the IPA:LE ratio is not significantly different (neither up or down) between total cytosol and heavy polysomes.

      They validated functions of IPA isoforms from two genes, PHF20 and PRKAR1B, in cell survival upon cisplatin treatment, based on an array of methods, including siRNA knockdown, CRISPR knockout of IPA polyA site, and CRISPR mutation of the start codon. They further found that FANCD2 and Senataxin can regulate cisplatin-mediated IPA activation. The authors advocate a new paradigm of expression of IPA-encoding microproteins in cisplatin-treated cells.

      Our response: We would like to point out that our data indicate that cisplatin upregulation of the IPA:LE isoform ratio is mediated at least in part by an inhibition of transcription processivity (explaining the decrease of LE isoforms), and that we do not claim an ‘IPA activation’ (that is, enhanced used of IPA sites) by cisplatin. This remark is also related to major point 1 below.

      Major comments:

        • While the phenomenon of IPA isoform upregulation by Cisplatin is quite convincing, the underlying mechanism is largely elusive. The authors indicated processivity as a potential mechanism and the effects of FACD2 and Senataxin appear in line with this hypothesis. However, they cannot rule out other possibilities based on the data presented in the manuscript. For example, it is not clear if the elongation rate of Pol II (distinct from its processivity) or nuclear RNA degradation is affected by cisplatin, which could also lead to increased expression of IPA isoforms. In addition, enhanced 3' end processing activity has been previously shown to activate IPA sites. Therefore, the underlying mechanism is mostly speculative. Our response: As explained on page 14, the reason why we focused on transcription processivity is that the cisplatin-induced upregulation of the IPA:LE isoform ratio was enriched in long genes and was accompanied by a decrease of LE isoform levels. Importantly, our data (e.g., for the PHF20 and PRKAR1B genes) indicate that the cisplatin-induced decrease of processivity explains –at least in part– the selective decrease of the LE but not IPA isoform levels and therefore the increase of the IPA:LE isoform ratio; we will clarify this in the manuscript (on pages 14 and 15). Our data also show that cisplatin effects on both processivity and IPA:LE isoform ratio are dependent on FANCD2 and SETX. We agree with the reviewer that we cannot exclude that IPA:LE isoform ratio upregulation by cisplatin might also be mediated in part by additional mechanisms (e.g.*, ‘factors involved in cleavage/ polyadenylation, splicing, transcription elongation and termination, and epigenetic marks’, as mentioned in the discussion on page 16) and we will add nuclear RNA degradation to the list of potential factors. However, we want to emphasize that the role of processivity is not speculative.

      The authors used the polysome:cytosolic ratio to indicate translational efficiency. However, because the CDS size affects the number of ribosomes per mRNA, the translational efficiency should be based on polysome:cytosolic ratio normalized to CDS size. Ideally, the authors should calculate number of ribosome per transcript based on monosome, light polysome and heavy polysome.

      Our response: We cannot normalize ribosome number by CDS size because (i) heavy polysomes are not a precise number of ribosomes and (ii) sORFs are not annotated as CDS.

      The functions of PHF20 and PRKAR1B IPA isoforms are based on knockdown or knockout mutations. Because of its gain-of-function property, overexpression of the isoforms in cisplatin-treated cells would be necessary to definitively confirm their funcitons.

      Our response: For PRKAR1B sORF#2, we ____will carry out overexpression of the sORF microprotein in A549 cells and CRISPR clones and analyze its effects on cell growth and cisplatin survival. We have appropriate constructs for this.

      Minor:

        • Fig. 1H, the numbers of IPA and LE transcripts should be provided. The statistical significance for the difference should also be included.* Our response: The numbers of IPA and LE transcripts were provided in Fig. S1I and we will provide the statistical significance (which is good), as requested.

      Fig. 1I, the image should be accompanied with fold difference as indicated in the text. Some statistics for difference between vehicle only and CisPt only is necessary.

      Our response: We will indicate the fold differences and provide the statistical significance, as requested.

      • Fig. 6, the authors did data mining of ribo-seq data and mass-spec data and identified 156 genes whose IPA isoforms have potentials of protein expression. The enriched GO terms for the 156 IPA genes are different than the overall IPA isoforms shown in Fig. 1C. Does this mean some genes, like those in DNA damage stimulus, produce IPA isoforms with different consequences, such as to inhibit their expression? *

      Our response: We think it is difficult to compare the enriched GO terms between overall IPA and miP-5’UTR-IPA. Indeed, differences could be due in part to trivial reasons (e.g., different number of genes in the lists). As suggested by this reviewer, it could be that for some gene sets enriched in particular functions, IPA may serve to downregulate the expression level of the full-length (canonical) mRNA. We discuss that this may be the case for the PRIM2 gene involved in DNA replication (page 17), but expanding on this would be speculative. Likewise, IPA isoforms encoding carboxy-terminal isoforms of canonical proteins, or IPA isoforms with a noncoding function (like ASCC3 or SPUD), might be enriched in particular gene functions, but again this idea is speculative and it goes beyond the scope of our manuscript.

      In addition, the authors need to use ribo-seq and mass spec data as a validation tool for their polysome profiling data to indicate the reliability of using polysome data to call protein expression.

      Our response: This comment seems to concern those IPA isoforms that are abundant in heavy polysomes. We do not wish to validate protein production from such isoforms, because they are not the focus of our study.

      Significance

      The significance of this work is its novelty in reporting IPA isoform activation by cisplatin. More importantly, some IPA isoform give rise to microproteins that have functional roles in cell survival upon cisplatin treatment.

      Our response: We would like to highlight that the main finding of our manuscript is the discovery that IPA isoforms are a source of microproteins. Cisplatin response is the biological context in which we did the study, and therefore our functional and mechanistic analyses.

      REVIEWER #3

      Evidence, reproducibility and clarity

      Devaux et al. report how cisplatin treatment changes the abundance of mRNA isoforms, favoring the expression of short transcripts originating from intronic polyadenylation (IPA) events relative to the expression of the corresponding mRNA isoform that includes the last annotated exon (LE). To detect IPA events the authors performed 3' end sequencing of polyadenylated mRNAs, long-read sequencing and conventional total RNA sequencing experiments in control and cisplatin treated cells. Analysis of the 3' end sequencing data revealed numerous genes showing an increase in the IPA:LE ratio upon cisplatin treatment, whereas few events with a decreased IPA:LE ratio were detected. Many of the identified events could be corroborated by the long-read sequencing data, sequencing of total RNA, and an existing polyA database. Furthermore, the authors validate IPA:LE ratios for a few selected genes using quantitative PCR. Subsequently, the authors continue to analyze if IPA isoforms are translated with a specific focus on IPA isoforms that do not contain any parts of the LE isoform coding sequence but terminated transcription in what is annotated as 5' untranslated region (UTR). These experiments show that IPA isoforms (including 5' UTR-IPAs) are translated but frequently associated with fewer ribosomes than the corresponding LE isoform. For two selected 5' UTR-IPA isoforms the authors identified potential small open reading frames (sORFs) that could give rise to microproteins with a potential function during cisplatin treatment. siRNA experiments targeting either the 5' UTR-IPA or the LE mRNA isoform of selected genes identified a small but significant differential effect on cell viability upon cisplatin treatment. Similar results were obtained when the endogenous IPA locus was deleted or the start codon of the potential sORF was mutated. Finally, the authors shed some light onto the molecular mechanisms of how cisplatin affects the IPA:LE ratio by decreasing transcription processivity.

      *This is an interesting manuscript suggesting a link between IPA, sORFs and cancer treatment. The manuscript offers valuable datasets as a resource for the research community. While the authors generally present a well-analyzed and validated dataset supporting their claims, some aspects require further evidence or clearer presentation for robustness and reader comprehension. In addition, the manuscript would benefit from improving data visualization and we have several suggestions (see below) on how to make the representation of the data in the figures more appealing to the reader. We encourage the authors to reconsider several of their bar plots and instead plot their data on a continuous axis, e.g. using a scatter plot (fold change versus FDR) instead of a bar chart that can only represent up/down total numbers. *

      Our response: Please see our responses below.

      Main points:

        • We disagree with one of the data interpretations concerning the high polysome (HP) versus total cytosolic polysomes (cytosol) localized IPA and LE mRNA isoforms in the paragraph "A subset of IPA isoforms are depleted in heavy polysomes and terminate in the annotated 5'UTR part of genes". Preferential IPA isoform localization to cytosol versus HP in comparison to the LE isoform does not mean that the IPA isoform translation efficiency is lower than that of the LE isoform. It just reflects the fact that IPA isoform coding sequence is considerably shorter than the coding sequence of the LE isoform (and thus can accommodate fewer ribosomes!). The authors mention that point later in the text but it should already be made clear at this point in the manuscript. They should make sure not to confuse translation efficiency (ribosome density across an open reading frame) and open reading frame length. * Our response: We will modify the text of this section (pages 10-11). We will __state that ‘the HP:cytosol ratio is usually considered as a proxy for translation efficiency’ and __we will only make conclusions in terms of ‘HP:cytosol ratio’ or ‘HP recruitment efficiency’, instead of ‘translation efficiency’ (we had used this term in a few sentences for the sake of simplicity). Please note that these changes will not alter the main conclusion of this part, because both the title (‘a subset of IPA isoforms are depleted in heavy polysomes and terminate in the annotated 5'UTR part of genes’) and the end of this section (page 11), as well as the legend of Fig. 2, were already written in such terms. Thus, in this section, we do not need to discuss ORF length (and we cannot, because sORFs are not annotated as CDS and we introduce sORFs only two sections later [Fig. 4]).
      1. In Figure 5, the authors claim that the "cisplatin survival phenotype of the PRKAR1B 5'UTR-IPA isoform is attributable to its small ORF#2". This is an interesting phenotype but the authors only present a WST1 assay to support these claims. Given that it is an important Figure in their manuscript and links the observations made earlier to cisplatin-induced survival, it would be critical to bolster these claims with additional data, e.g. AnnexinV/PI staining and flow cytometry to distinguish changes in cisplatin-induced apoptosis from proliferation.*

      Our response: We will make the requested experiments with FACS analysis of Annexin V and PI/BrdU to distinguish changes in cisplatin-induced apoptosis from proliferation (cell cycle).

      • Along the same line, it would be important to test the overexpression of the sORF microprotein upon cisplatin treatment. Changes in the mRNA sequence (such as the AUG mutation) could potentially also alter the mRNA structure. It would therefore be critical to show that the sORF microprotein is indeed responsible for the changes in cisplatin-induced viability (for instance by expression of a sORF::P2A::GFP construct). *

      Our response: As requested, we will test whether overexpression of the sORF microprotein can rescue the cisplatin survival phenotype of our PRKAR1B IPA and ATG mutants. We have appropriate constructs for this.

      • Figure 5C: Please show the Western blot of PRKAR1B and GAPDH and not just the quantification. There is plenty of space in Figure 5. *

      Our response: We will show the Western blots for PRKAR1B and GAPDH.

      • In the following, we list suggestions to improve different figures where the data could be more adequately presented:*

      - Figure 1A and B: We suggest representing the data in a scatter plot log fold change on the x-axis and FDR on the y-axis. The authors decided for an FDR cutoff of 10%. This is quite high. Why did the authors decide for this cutoff? How many genes would be identified with a more stringent cutoff (1% for example)? Please list the corresponding FDR values in TableS4.

      Our response: We have never seen in the literature 3’-seq (or related) data of IPA:LE ratio regulation plotted as a scatter plot with log fold change on the x-axis and FDR on the y-axis. Instead, we propose to provide scatter plots with IPA fold change on the x-axis and LE fold change on the y-axis, as in many previous studies. We were not very stringent on the FDR or adjusted p values, in order to reduce the rate of false negatives, because we then cross our lists of regulated IPAs in different compartments (e.g., cytosol and heavy polysomes; Fig. 2C). We provided adjusted p values in Table S4; with an adjusted p value of 1%, we observe 1986 upregulated IPA sites and 33 downregulated ones.

      *-Figure 1C: There are many ways to visualize fold change, p value and number of genes of a GO term analysis. The authors could choose one of the common ways to represent such data instead of just showing raw numbers in a table. *

      Our response: We like showing GO terms as tables, but we can provide a figure if necessary.

      -Figure 1E-G: Add to the figure that PRIM2 was assayed. It is only written in the figure legend.

      Our response: We will write ‘PRIM2’ in the figure.

      *-Figure 2A and B: Same suggestion as for Figure 1A and B, a scatter plot log fold change on the x-axis and FDR on the y-axis would visualize the data much better. *

      Our response: Same response as for Fig 1A-B above.

      -Figure S1B: Where does the number of 2118 cisplatin regulated genes come from? It was not described anywhere else. Should it not be 1987 regulated genes?

      Our response: We will clarify that 2118 is the union of genes with cisplatin upregulated IPA:LE ratio in H358 and/or A549 cells.

      -Figure S1H: Typo in the y-axis.

      Our response: This typo will be corrected, thanks.

      -Figure S2A: Same suggestion as for Figure 1A and B, a scatter plot log fold change on the x-axis and FDR on the y-axis.

      Our response: Same response as for Fig 1A-B above.

      -Figure S3C: If possible, show the plotted digital data of the polysome curves.

      Our response: We do not have digital data for the polysome curves, just the printed graph shown at the bottom of the figure.

      • Data availability: The provided UCSC genome browser link unfortunately does not load the data bam files. Please fix.*

      Our response: We will fix this upon submission to journal.

      Minor points:

      • Please check the text for typos, e.g. page 8: artefacts instead of artifacts. *

      Our response: We will check for typos.

      Significance

      The manuscript describes an interesting link between intronic polyadenylation, sORFs and cancer treatment and will be of interest to the gene expression regulation and RNA communities. As a relatively unknown mechanism to induce sORF-encoded microproteins, the study could lead to follow-up studies tackling intronic polyadenylation and their role in sORF expression.

      Our response: We would like to highlight that IPA was not previously known to induce sORF-encoded microproteins.

      While the authors generally present a well-analyzed and validated dataset, the link between sORF function and cisplatin response will require additional experiments to strengthen the sORF's impact for cellular survival.

      Our response: Please see our responses to main points 2 and 3 above.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      None.

      4. Description of analyses that authors prefer not to carry out

      REVIEWER #2

      Major point #2: The authors used the polysome:cytosolic ratio to indicate translational efficiency. However, because the CDS size affects the number of ribosomes per mRNA, the translational efficiency should be based on polysome:cytosolic ratio normalized to CDS size. Ideally, the authors should calculate number of ribosome per transcript based on monosome, light polysome and heavy polysome.

      Our response: We cannot normalize ribosome number by CDS size because (i) heavy polysomes are not a precise number of ribosomes and (ii) sORFs are not annotated as CDS.

      *Minor point #3: Fig. 6, the authors did data mining of ribo-seq data and mass-spec data and identified 156 genes whose IPA isoforms have potentials of protein expression. The enriched GO terms for the 156 IPA genes are different than the overall IPA isoforms shown in Fig. 1C. Does this mean some genes, like those in DNA damage stimulus, produce IPA isoforms with different consequences, such as to inhibit their expression? *

      Our response: We think it is difficult to compare the enriched GO terms between overall IPA and miP-5’UTR-IPA. Indeed, differences could be due in part to trivial reasons (e.g., different number of genes in the lists). As suggested by this reviewer, it could be that for some gene sets enriched in particular functions, IPA may serve to downregulate the expression level of the full-length (canonical) mRNA. We discuss that this may be the case for the PRIM2 gene involved in DNA replication (page 17), but expanding on this would be speculative. Likewise, IPA isoforms encoding carboxy-terminal isoforms of canonical proteins, or IPA isoforms with a noncoding function (like ASCC3 or SPUD), might be enriched in particular gene functions, but again this idea is speculative and it goes beyond the scope of our manuscript.

      In addition, the authors need to use ribo-seq and mass spec data as a validation tool for their polysome profiling data to indicate the reliability of using polysome data to call protein expression.

      Our response: This comment seems to concern those IPA isoforms that are abundant in heavy polysomes. We do not wish to validate protein production from such isoforms, because they are not the focus of our study.

      REVIEWER #3

      -Figure S3C: If possible, show the plotted digital data of the polysome curves.

      Our response: We do not have digital data for the polysome curves, just the printed graph shown at the bottom of the figure.

    1. Author Response:

      We would like to sincerely thank the referees and the editor for their time in considering our manuscript. The electrophysiology of bacteria is a fast-moving complex

      field and is proving contentious in places. We believe the peer review process of eLife provides an ideal mechanism to address the issues raised on our manuscript in an open and transparent manner. Hopefully we will encourage some more consensus in the field and help understand some of the inconsistencies in the current literature that are

      hampering progress.

      The editors stress the main issue raised was a single referee questioning the use of ThT as an indicator of membrane potential. We are well aware of the articles by the Pilizota group and we believe them to be scientifically flawed. The authors assume there are no voltage-gated ion channels in E. coli and then attempt to explain motility

      data based on a simple Nernstian battery model (they assume E. coli are unexcitable matter). This in turn leads them to conclude the membrane dye ThT is faulty, when in

      fact it is a problem with their simple battery model.

      In terms of the previous microbiology literature, the assumption of no voltage-gated ion channels in E. coli suggested by referee 2 is a highly contentious niche ideology. The majority of gene databases for E. coli have a number of ion-channels annotated as voltage sensitive due to comparative genetics studies e.g. try the https://bacteria.ensembl.org/ database (the search terms ‘voltage-gated coli’ give 2521 hits for genes, similarly you could check www.uniprot.org or www.biocyc.org) and M.M.Kuo, Y.Saimi, C.Kung, ‘Gain of function mutation indicate that E. coli Kch form a functional K + conduit in vivo’, EMBO Journal, 2003, 22, 16, 4049. Furthermore, recent microbiology reviews all agree that E. coli has a number of voltage-gated ion channels S.D.Beagle, S.W.Lockless, ‘Unappreciated roles for K + channels in bacterial physiology’,Trends in microbiology, 2021, 29, 10, 942-950. More emphatic experimental data is seen in spiking potentials that have been observed by many groups for E. coli, both directly using microelectrodes and indirectly using genetically expressed fluorophores, ‘Electrical spiking in bacterial biofilms’ E.Masi et al, Journal of the Royal Society Interface, 2015, 12, 102, ‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, J.M.Kralj, et al, Science, 2011, 333, 6040, 345 and ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 2023, 120, 3, e2208348120. The only mechanism currently known to cause spiking potentials in cells is due to positive feedback from voltage-gated ion channels (you need a mechanism to induce the oscillations). Indeed, people are starting to investigate the specific voltage-gated ion channels in E. coli and a role is emerging for calcium in addition to potassium e.g. ‘Genome-wide functional screen for calcium transients in E. coli identifies increased membrane potential adaptation to persistent DNA damage’, R.Luder, et al, J.Bacteriology, 2021, 203, 3, e00509.

      In terms of recent data from our own group, electrical impedance spectroscopy (EIS) experiments from E. coli indicate there are large conductivity changes associated with the Kch ion channels (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior',

      E.Akabuogu et al, ACS Nanoletters, 2024, in print). EIS experiments pr be the electrical phenomena of bacterial biofilms directly and do not depend on fluorophores i.e. they can’t be affected by ThT.

      Attempts to disprove the use of ThT to measure hyperpolarisation phenomena in E. coli using fluorescence microscopy also seem doomed to failure based on comparative control experiments. A wide range of other cationic fluorophores show similar behaviour to ThT e.g. the potassium sensitive dye used in our eLife article. Thus the behaviour of ThT appears to be generic for a range of cationic dyes and it implies a simple physical mechanism i.e. the positively charged dyes enter cells at low potentials. The elaborate photobleaching mechanism postulated by referee 2 seems most unlikely and is unable to explain our data (see below). ThT is photostable and chemically well- defined and it is therefore used almost universally in fluorescence assays for amyloids.

      A challenge with trying to use flagellar motility to measure intracellular potentials in live bacteria, as per referee 2’s many publications, is that a clutch is known to occur with E. coli e.g. ‘Flagellar brake protein YcgR interacts with motor proteins MotA and FliG to regulate the flagellar rotation speed and direction’, Q.Han et al, Frontiers in Microbiology, 2023, 14. Thus bacteria with high membrane potentials can have low motility when their clutch is engaged. This makes sense, since otherwise bacterial motility would be enslaved to their membrane potentials, greatly restricting their ability to react to their environmental conditions. Without quantifying the dynamics of the clutch (e.g. the gene circuit) it seems challenging to deduce how the motor reacts to Nernstian potentials in vivo. As a result we are not convinced by any of the Pilizota group articles. The quantitative connection between motility and membrane potential is too tenuous.

      In conclusion, the articles questioning the use of ThT are scientifically flawed and based on a niche ideology that E. coli do not contain voltage-gated ion channels. The current work disproves the simple Nernstian battery (SNB) model expounded by Pilizota et al, unpersuasively represented in multiple publications by this one group in the literature (see below for critical synopses) and demonstrates the SNB models needs to be replaced by a model that includes excitability (demonstrating hyperpolarization of the membrane potential).

      In the language of physics, a non-linear oscillator model is needed to explain spiking potentials in bacteria and the simple battery models presented by Pilizota et al do not have the required non-linearities to oscillate (‘Nonlinear dynamics and chaos’, Steve Strogatz, Westview Press, 2014). Such non-linear models are the foundation for describing eukaryotic electrophysiology, e.g. Hodgkin and Huxley’s Nobel prize winning research (1963), but also the vast majority of modern extensions (‘Mathematical physiology’, J.Keener, J.Sneyd, Springer, 2009, ‘Cellular biophysis and modelling: a primer on the computational biology of excitable cells’, G.C.Smith, 2019, CUP, ‘Dynamical systems in neuroscience: the geometry of excitability and bursting’, E.M.Izhikevich, 2006, MIT and ‘Neuronal dynamics: from single neurons to networks and models of cognition’, W.Gerstner et al, 2014, CUP). The Pilizota group is using modelling tools from the 1930s that quickly were shown to be inadequate to describe eukaryotic cellular electrophysiology and the same is true for bacterial electrophysiology (see the ground breaking work of A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 7576, 59 for the use of Hodgkin-Huxley models with bacterial biofilms). Below we describe a critical synopsis of the articles cited by referee 2 and we then directly answer the specific points all the

      referees raise.

      Critical synopsis of the articles cited by referee 2:

      1) ‘Generalized workflow for characterization of Nernstian dyes and their effects on bacterial physiology’, L.Mancini et al, Biophysical Journal, 2020, 118, 1, 4-14.

      This is the central article used by referee 2 to argue that there are issues with the calibration of ThT for the measurement of membrane potentials. The authors use a simple Nernstian battery (SNB) model and unfortunately it is wrong when voltage-gated ion channels occur. Huge oscillations occur in the membrane potentials of E. coli that cannot be described by the SNB model. Instead a Hodgkin Huxley model is needed, as shown in our eLife manuscript and multiple other studies (see above). Arrhenius kinetics are assumed in the SNB model for pumping with no real evidence and the generalized workflow involves ripping the flagella off the bacteria! The authors construct an elaborate ‘work flow’ to insure their ThT results can be interpreted using their erroneous SNB model over a limited range of parameters.

      2) ‘Non-equivalence of membrane voltage and ion-gradient as driving forces for the bacterial flagellar motor at low load’, C.J.Lo, et al, Biophysical Journal, 2007, 93, 1, 294.

      An odd de novo chimeric species is developed using an E. coli chassis which uses Na + instead of H + for the motility of its flagellar motor. It is not clear the relevance to wild type E. coli, due to the massive physiological perturbations involved. A SNB model is using to fit the data over a very limited parameter range with all the concomitant errors.

      3) Single-cell bacterial electrophysiology reveals mechanisms of stress-induced damage’, E.Krasnopeeva, et al, Biophysical Journal, 2019, 116, 2390.

      The abstract says ‘PMF defines the physiological state of the cell’. This statement is hyperbolic. An extremely wide range of molecules contribute to the physiological state of a cell. PMF does not even define the electrophysiology of the cell e.g. via the membrane potential. There are 0.2 M of K + compared with 0.0000001 M of H + in E. coli, so K + is arguably a million times more important for the membrane potential than H + and thus the electrophysiology! Equation (1) in the manuscript assumes no other ions are exchanged during the experiments other than H + . This is a very bad approximation when voltage-gated potassium ion channels move the majority ion (K + ) around! In our model Figure 4A is better explained by depolarisation due to K + channels closing than direct irreversible photodamage. Why does the THT fluorescence increase again for the second hyperpolarization event if the THT is supposed to be damaged? It does not make sense.

      4) ‘The proton motive force determines E. coli robustness to extracellular pH’, G.Terradot et al, 2024, preprint.

      This article expounds the SNB model once more. It still ignores the voltage-gated ion channels. Furthermore, it ignores the effect of the dominant ion in E. coli, K + . The manuscript is incorrect as a result and I would not recommend publication. In general, an important problem is being researched i.e. how the membrane potential of E. coli is related to motility, but there are serious flaws in the SNB approach and the experimental methodology appears tenuous.

      Answers to specific questions raised by the referees:

      Reviewer #1:

      Summary:<br /> Cell-to-cell communication is essential for higher functions in bacterial biofilms. Electrical signals have proven effective in transmitting signals across biofilms. These signals are then used to coordinate cellular metabolisms or to increase antibiotic tolerance. Here, the authors have reported for the first time coordinated oscillation of membrane potential in E. coli biofilms that may have a functional role in photoprotection.

      Strengths:<br /> - The authors report original data.<br /> - For the first time, they showed that coordinated oscillations in membrane potential occur in E. Coli biofilms.<br /> - The authors revealed a complex two-phase dynamic involving distinct molecular response mechanisms.<br /> - The authors developed two rigorous models inspired by 1) Hodgkin-Huxley model for the temporal dynamics of membrane potential and 2) Fire-Diffuse-Fire model for the propagation of the electric signal.<br /> - Since its discovery by comparative genomics, the Kch ion channel has not been associated with any specific phenotype in E. coli. Here, the authors proposed a functional role for the putative K+ Kch channel : enhancing survival under photo-toxic conditions.

      We thank the referee for their positive evaluations and agree with these statements.

      Weaknesses:<br /> - Since the flow of fresh medium is stopped at the beginning of the acquisition, environmental parameters such as pH and RedOx potential are likely to vary significantly during the experiment. It is therefore important to exclude the contributions of these variations to ensure that the electrical response is only induced by light stimulation. Unfortunately, no control experiments were carried out to address this issue.

      The electrical responses occur almost instantaneously when the stimulation with blue light begins i.e. it is too fast to be a build of pH. We are not sure what the referee means by

      Redox potential since it is an attribute of all chemicals that are able to donate/receive electrons. The electrical response to stress appears to be caused by ROS, since when ROS scavengers are added the electrical response is removed i.e. pH plays a very small minority role if any.

      - Furthermore, the control parameter of the experiment (light stimulation) is the same as that used to measure the electrical response, i.e. through fluorescence excitation. The use of the PROPS system could solve this problem.

      We were enthusiastic at the start of the project to use the PROPs system in E. coli as presented by J.M.Krajl et al,‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, Science, 2011, 333, 6040, 345. However, the people we contacted in the microbiology community said that it had some technical issues and there have been no subsequent studies using PROPs in bacteria after the initial promising study. The fluorescent protein system recently presented in PNAS seems more promising, ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 120, 3, e2208348120.

      - Electrical signal propagation is an important aspect of the manuscript. However, a detailed quantitative analysis of the spatial dynamics within the biofilm is lacking. In addition, it is unclear if the electrical signal propagates within the biofilm during the second peak regime, which is mediated by the Kch channel. This is an important question, given that the fire-diffuse-fire model is presented with emphasis on the role of K+ ions.

      We have presented a more detailed account of the electrical wavefront modelling work and it is currently under review in a physical journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

      - Since deletion of the kch gene inhibits the long-term electrical response to light stimulation (regime II), the authors concluded that K+ ions play a role in the habituation response. However, Kch is a putative K+ ion channel. The use of specific drugs could help to clarify the role of K+ ions.

      Our recent electrical impedance spectroscopy publication provides further evidence that Kch is associated with large changes in conductivity as expected for a voltage-gated ion channel (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

      - The manuscript as such does not allow us to properly conclude on the photo-protective role of the Kch ion channel.

      That Kch has a photoprotective role is our current working hypothesis. The hypothesis fits with the data, but we are not saying we have proven it beyond all possible doubt.

      - The link between membrane potential dynamics and mechanosensitivity is not captured in the equation for the Q-channel opening dynamics in the Hodgkin-Huxley model (Supp Eq 2).

      Our model is agnostic with respect to the mechanosensitivity of the ion channels, although we deduce that mechanosensitive ion channels contribute to ion channel Q.

      - Given the large number of parameters used in the models, it is hard to distinguish between prediction and fitting.

      This is always an issue with electrophysiological modelling (compared with most heart and brain modelling studies we are very conservative in the choice of parameters for the bacteria). In terms of predicting the different phenomena observed, we believe the model is very successful.

      Reviewer #2:

      Summary of what the authors were trying to achieve:<br /> The authors thought they studied membrane potential dynamics in E.coli biofilms. They thought so because they were unaware that the dye they used to report that membrane potential in E.coli, has been previously shown not to report it. Because of this, the interpretation of the authors' results is not accurate.

      We believe the Pilizota work is scientifically flawed.

      Major strengths and weaknesses of the methods and results:<br /> The strength of this work is that all the data is presented clearly, and accurately, as far as I can tell.

      The major critical weakness of this paper is the use of ThT dye as a membrane potential dye in E.coli. The work is unaware of a publication from 2020 https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] that demonstrates that ThT is not a membrane potential dye in E. coli. Therefore I think the results of this paper are misinterpreted. The same publication I reference above presents a protocol on how to carefully calibrate any candidate membrane potential dye in any given condition.

      We are aware of this study, but believe it to be scientifically flawed. We do not cite the article because we do not think it is a particularly useful contribution to the literature.

      I now go over each results section in the manuscript.

      Result section 1: Blue light triggers electrical spiking in single E. coli cells

      I do not think the title of the result section is correct for the following reasons. The above-referenced work demonstrates the loading profile one should expect from a Nernstian dye (Figure 1). It also demonstrates that ThT does not show that profile and explains why is this so. ThT only permeates the membrane under light exposure (Figure 5). This finding is consistent with blue light peroxidising the membrane (see also following work Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] on light-induced damage to the electrochemical gradient of protons-I am sure there are more references for this).

      The Pilizota group invokes some elaborate artefacts to explain the lack of agreement with a simple Nernstian battery model. The model is incorrect not the fluorophore.

      Please note that the loading profile (only observed under light) in the current manuscript in Figure 1B as well as in the video S1 is identical to that in Figure 3 from the above-referenced paper (i.e. https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com]), and corresponding videos S3 and S4. This kind of profile is exactly what one would expect theoretically if the light is simultaneously lowering the membrane potential as the ThT is equilibrating, see Figure S12 of that previous work. There, it is also demonstrated by the means of monitoring the speed of bacterial flagellar motor that the electrochemical gradient of protons is being lowered by the light. The authors state that applying the blue light for different time periods and over different time scales did not change the peak profile. This is expected if the light is lowering the electrochemical gradient of protons. But, in Figure S1, it is clear that it affected the timing of the peak, which is again expected, because the light affects the timing of the decay, and thus of the decay profile of the electrochemical gradient of protons (Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com]).

      We think the proton effect is a million times weaker than that due to potasium i.e. 0.2 M K+ versus 10-7 M H+. We can comfortably neglect the influx of H+ in our experiments.

      If find Figure S1D interesting. There authors load TMRM, which is a membrane voltage dye that has been used extensively (as far as I am aware this is the first reference for that and it has not been cited https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914430 [ncbi.nlm.nih.gov]/). As visible from the last TMRM reference I give, TMRM will only load the cells in Potassium Phosphate buffer with NaCl (and often we used EDTA to permeabilise the membrane). It is not fully clear (to me) whether here TMRM was prepared in rich media (it explicitly says so for ThT in Methods but not for TMRM), but it seems so. If this is the case, it likely also loads because of the damage to the membrane done with light, and therefore I am not surprised that the profiles are similar.

      The vast majority of cells continue to be viable. We do not think membrane damage is dominating.

      The authors then use CCCP. First, a small correction, as the authors state that it quenches membrane potential. CCCP is a protonophore (https://pubmed.ncbi.nlm.nih.gov/4962086 [pubmed.ncbi.nlm.nih.gov]/), so it collapses electrochemical gradient of protons. This means that it is possible, and this will depend on the type of pumps present in the cell, that CCCP collapses electrochemical gradient of protons, but the membrane potential is equal and opposite in sign to the DeltapH. So using CCCP does not automatically mean membrane potential will collapse (e.g. in some mammalian cells it does not need to be the case, but in E.coli it is https://www.biorxiv.org/content/10.1101/2021.11.19.469321v2 [biorxiv.org]). CCCP has also been recently found to be a substrate for TolC (https://journals.asm.org/doi/10.1128/mbio.00676-21 [journals.asm.org]), but at the concentrations the authors are using CCCP (100uM) that should not affect the results. However, the authors then state because they observed, in Figure S1E, a fast efflux of ions in all cells and no spiking dynamics this confirms that observed dynamics are membrane potential related. I do not agree that it does. First, Figure S1E, does not appear to show transients, instead, it is visible that after 50min treatment with 100uM CCCP, ThT dye shows no dynamics. The action of a Nernstian dye is defined. It is not sufficient that a charged molecule is affected in some way by electrical potential, this needs to be in a very specific way to be a Nernstian dye. Part of the profile of ThT loading observed in https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] is membrane potential related, but not in a way that is characteristic of Nernstian dye.

      Our understanding of the literature is CCCP poisons the whole metabolism of the bacterial cells. The ATP driven K+channels will stop functioning and this is the dominant contributor to membrane potential.

      Result section 2: Membrane potential dynamics depend on the intercellular distance

      In this chapter, the authors report that the time to reach the first intensity peak during ThT loading is different when cells are in microclusters. They interpret this as electrical signalling in clusters because the peak is reached faster in microclusters (as opposed to slower because intuitively in these clusters cells could be shielded from light). However, shielding is one possibility. The other is that the membrane has changed in composition and/or the effective light power the cells can tolerate (with mechanisms to handle light-induced damage, some of which authors mention later in the paper) is lower. Given that these cells were left in a microfluidic chamber for 2h hours to attach in growth media according to Methods, there is sufficient time for that to happen. In Figure S12 C and D of that same paper from my group (https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com]) one can see the effects of peak intensity and timing of the peak on the permeability of the membrane. Therefore I do not think the distance is the explanation for what authors observe.

      Shielding would provide the reverse effect, since hyperpolarization begins in the dense centres of the biofilms. For the initial 2 hours the cells receive negligible blue light. Neither of the referee’s comments thus seem tenable.

      Result section 3: Emergence of synchronized global wavefronts in E. coli biofilms

      In this section, the authors exposed a mature biofilm to blue light. They observe that the intensity peak is reached faster in the cells in the middle. They interpret this as the ion-channel-mediated wavefronts moved from the center of the biofilm. As above, cells in the middle can have different membrane permeability to those at the periphery, and probably even more importantly, there is no light profile shown anywhere in SI/Methods. I could be wrong, but the SI3 A profile is consistent with a potential Gaussian beam profile visible in the field of view. In Methods, I find the light source for the blue light and the type of microscope but no comments on how 'flat' the illumination is across their field of view. This is critical to assess what they are observing in this result section. I do find it interesting that the ThT intensity collapsed from the edges of the biofilms. In the publication I mentioned https://www.sciencedirect.com/science/article/pii/S0006349519308793#app2 [sciencedirect.com], the collapse of fluorescence was not understood (other than it is not membrane potential related). It was observed in Figure 5A, C, and F, that at the point of peak, electrochemical gradient of protons is already collapsed, and that at the point of peak cell expands and cytoplasmic content leaks out. This means that this part of the ThT curve is not membrane potential related. The authors see that after the first peak collapsed there is a period of time where ThT does not stain the cells and then it starts again. If after the first peak the cellular content leaks, as we have observed, then staining that occurs much later could be simply staining of cytoplasmic positively charged content, and the timing of that depends on the dynamics of cytoplasmic content leakage (we observed this to be happening over 2h in individual cells). ThT is also a non-specific amyloid dye, and in starving E. coli cells formation of protein clusters has been observed (https://pubmed.ncbi.nlm.nih.gov/30472191 [pubmed.ncbi.nlm.nih.gov]/), so such cytoplasmic staining seems possible.

      It is very easy to see if the illumination is flat (Köhler illumination) by comparing the intensity of background pixels on the detector. It was flat in our case. Protons have little to do with our work for reasons highlighted before. Differential membrane permittivity is a speculative phenomenon not well supported by any evidence and with no clear molecular mechanism.

      Finally, I note that authors observe biofilms of different shapes and sizes and state that they observe similar intensity profiles, which could mean that my comment on 'flatness' of the field of view above is not a concern. However, the scale bar in Figure 2A is not legible, so I can't compare it to the variation of sizes of the biofilms in Figure 2C (67 to 280um). Based on this, I think that the illumination profile is still a concern.

      The referee now contradicts themselves and wants a scale bar to be more visible. We have changed the scale bar.

      Result section 4: Voltage-gated Kch potassium channels mediate ion-channel electrical oscillations in E. coli

      First I note at this point, given that I disagree that the data presented thus 'suggest that E. coli biofilms use electrical signaling to coordinate long-range responses to light stress' as the authors state, it gets harder to comment on the rest of the results.

      In this result section the authors look at the effect of Kch, a putative voltage-gated potassium channel, on ThT profile in E. coli cells. And they see a difference. It is worth noting that in the publication https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] it is found that ThT is also likely a substrate for TolC (Figure 4), but that scenario could not be distinguished from the one where TolC mutant has a different membrane permeability (and there is a publication that suggests the latter is happening https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2958.2010.07245.x [onlinelibrary.wiley.com]). Given this, it is also possible that Kch deletion affects the membrane permeability. I do note that in video S4 I seem to see more of, what appear to be, plasmolysed cells. The authors do not see the ThT intensity with this mutant that appears long after the initial peak has disappeared, as they see in WT. It is not clear how long they waited for this, as from Figure S3C it could simply be that the dynamics of this is a lot slower, e.g. Kch deletion changes membrane permeability.

      The work that TolC provides a possible passive pathway for ThT to leave cells seems slightly niche. It just demonstrates another mechanism for the cells to equilibriate the concentrations of ThT in a Nernstian manner i.e. driven by the membrane voltage.

      The authors themselves state that the evidence for Kch being a voltage-gated channel is indirect (line 54). I do not think there is a need to claim function from a ThT profile of E. coli mutants (nor do I believe it's good practice), given how accurate single-channel recordings are currently. To know the exact dependency on the membrane potential, ion channel recordings on this protein are needed first.

      We have good evidence form electrical impedance spectroscopy experiments that Kch increases the conductivity of biofilms  (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

      Result section 5: Blue light influences ion-channel mediated membrane potential events in E. coli

      In this chapter the authors vary the light intensity and stain the cells with PI (this dye gets into the cells when the membrane becomes very permeable), and the extracellular environment with K+ dye (I have not yet worked carefully with this dye). They find that different amounts of light influence ThT dynamics. This is in line with previous literature (both papers I have been mentioning: Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] and https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com] especially SI12), but does not add anything new. I think the results presented here can be explained with previously published theory and do not indicate that the ion-channel mediated membrane potential dynamics is a light stress relief process.

      The simple Nernstian battery model proposed by Pilizota et al is erroneous in our opinion for reasons outlined above. We believe it will prove to be a dead end for bacterial electrophysiology studies.

      Result section 6: Development of a Hodgkin-Huxley model for the observed membrane potential dynamics

      This results section starts with the authors stating: 'our data provide evidence that E. coli manages light stress through well-controlled modulation of its membrane potential dynamics'. As stated above, I think they are instead observing the process of ThT loading while the light is damaging the membrane and thus simultaneously collapsing the electrochemical gradient of protons. As stated above, this has been modelled before. And then, they observe a ThT staining that is independent from membrane potential.

      This is an erroneous niche opinion. Protons have little say in the membrane potential since there are so few of them. The membrane potential is mostly determined by K+.

      I will briefly comment on the Hodgkin Huxley (HH) based model. First, I think there is no evidence for two channels with different activation profiles as authors propose. But also, the HH model has been developed for neurons. There, the leakage and the pumping fluxes are both described by a constant representing conductivity, times the difference between the membrane potential and Nernst potential for the given ion. The conductivity in the model is given as gK*n^4 for potassium, gNa*m^3*h sodium, and gL for leakage, where gK, gNa and gL were measured experimentally for neurons. And, n, m, and h are variables that describe the experimentally observed voltage-gated mechanism of neuronal sodium and potassium channels. (Please see Hodgkin AL, Huxley AF. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116:449-72 and Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500-44).

      In the 70 years since Hodgkin and Huxley first presented their model, a huge number of similar models have been proposed to describe cellular electrophysiology. We are not being hyperbolic when we state that the HH models for excitable cells are like the Schrödinger equation for molecules. We carefully adapted our HH model to reflect the currently understood electrophysiology of E. coli.

      Thus, in applying the model to describe bacterial electrophysiology one should ensure near equilibrium requirement holds (so that (V-VQ) etc terms in authors' equation Figure 5 B hold), and potassium and other channels in a given bacterium have similar gating properties to those found in neurons. I am not aware of such measurements in any bacteria, and therefore think the pump leak model of the electrophysiology of bacteria needs to start with fluxes that are more general (for example Keener JP, Sneyd J. 2009. Mathematical physiology: I: Cellular physiology. New York: Springer or https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000144 [journals.plos.org])

      The reference is to a slightly more modern version of a simple Nernstian battery model. The model will not oscillate and thus will not help modelling membrane potentials in bacteria. We are unsure where the equilibrium requirement comes from (inadequate modelling of the dynamics?)

      Result section 7: Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli.

      The results that Mcs channels affect the profile of ThT dye are interesting. It is again possible that the membrane permeability of these mutants has changed and therefore the dynamics have changed, so this needs to be checked first. I also note that our results show that the peak of ThT coincides with cell expansion. For this to be understood a model is needed that also takes into account the link between maintenance of electrochemical gradients of ions in the cell and osmotic pressure.

      The evidence for permeability changes in the membranes seems to be tenuous.

      A side note is that the authors state that the Msc responds to stress-related voltage changes. I think this is an overstatement. Mscs respond to predominantly membrane tension and are mostly nonspecific (see how their action recovers cellular volume in this publication https://www.pnas.org/doi/full/10.1073/pnas.1522185113 [pnas.org]). Authors cite references 35-39 to support this statement. These publications still state that these channels are predominantly membrane tension-gated. Some of the references state that the presence of external ions is important for tension-related gating but sometimes they gate spontaneously in the presence of certain ions. Other publications cited don't really look at gating with respect to ions (39 is on clustering). This is why I think the statement is somewhat misleading.

      We have reworded the discussion of Mscs since the literature appears to be ambiguous. We will try to run some electrical impedance spectroscopy experiments on the Msc mutants in the future to attempt to remove the ambiguity.

      Result section 8: Anomalous ion-channel-mediated wavefronts propagate light stress signals in 3D E. coli biofilms.

      I am not commenting on this result section, as it would only be applicable if ThT was membrane potential dye in E. coli.

      Ok, but we disagree on the use of ThT.

      Aims achieved/results support their conclusions:

      The authors clearly present their data. I am convinced that they have accurately presented everything they observed. However, I think their interpretation of the data and conclusions is inaccurate in line with the discussion I provided above.

      Likely impact of the work on the field, and the utility of the methods and data to the community:

      I do not think this publication should be published in its current format. It should be revised in light of the previous literature as discussed in detail above. I believe presenting it in it's current form on eLife pages would create unnecessary confusion.

      We believe many of the Pilizota group articles are scientifically flawed and are causing the confusion in the literature.

      Any other comments:

      I note, that while this work studies E. coli, it references papers in other bacteria using ThT. For example, in lines 35-36 authors state that bacteria (Bacillus subtilis in this case) in biofilms have been recently found to modulate membrane potential citing the relevant literature from 2015. It is worth noting that the most recent paper https://journals.asm.org/doi/10.1128/mbio.02220-23 [journals.asm.org] found that ThT binds to one or more proteins in the spore coat, suggesting that it does not act as a membrane potential in Bacillus spores. It is possible that it still reports membrane potential in Bacillus cells and the recent results are strictly spore-specific, but these should be kept in mind when using ThT with Bacillus.

      ThT was used successfully in previous studies of normal B. subtilis cells (by our own group and A.Prindle, ‘Spatial propagation of electrical signal in circular biofilms’, J.A.Blee et al, Physical Review E, 2019, 100, 052401, J.A.Blee et al, ‘Membrane potentials, oxidative stress and the dispersal response of bacterial biofilms to 405 nm light’, Physical Biology, 2020, 17, 2, 036001, A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 59-63). The connection to low metabolism pore research seems speculative.

      Reviewer #3:

      It has recently been demonstrated that bacteria in biofilms show changes in membrane potential in response to changes in their environment, and that these can propagate signals through the biofilm to coordinate bacterial behavior. Akabuogu et al. contribute to this exciting research area with a study of blue light-induced membrane potential dynamics in E. coli biofilms. They demonstrate that Thioflavin-T (ThT) intensity (a proxy for membrane potential) displays multiphasic dynamics in response to blue light treatment. They additionally use genetic manipulations to implicate the potassium channel Kch in the latter part of these dynamics. Mechanosensitive ion channels may also be involved, although these channels seem to have blue light-independent effects on membrane potential as well. In addition, there are challenges to the quantitative interpretation of ThT microscopy data which require consideration. The authors then explore whether these dynamics are involved in signaling at the community level. The authors suggest that cell firing is both more coordinated when cells are clustered and happens in waves in larger, 3D biofilms; however, in both cases evidence for these claims is incomplete. The authors present two simulations to describe the ThT data. The first of these simulations, a Hodgkin-Huxley model, indicates that the data are consistent with the activity of two ion channels with different kinetics; the Kch channel mutant, which ablates a specific portion of the response curve, is consistent with this. The second model is a fire-diffuse-fire model to describe wavefront propagation of membrane potential changes in a 3D biofilm; because the wavefront data are not presented clearly, the results of this model are difficult to interpret. Finally, the authors discuss whether these membrane potential changes could be involved in generating a protective response to blue light exposure; increased death in a Kch ion channel mutant upon blue light exposure suggests that this may be the case, but a no-light control is needed to clarify this.

      In a few instances, the paper is missing key control experiments that are important to the interpretation of the data. This makes it difficult to judge the meaning of some of the presented experiments.

      1. An additional control for the effects of autofluorescence is very important. The authors conduct an experiment where they treat cells with CCCP and see that Thioflavin-T (ThT) dynamics do not change over the course of the experiment. They suggest that this demonstrates that autofluorescence does not impact their measurements. However, cellular autofluorescence depends on the physiological state of the cell, which is impacted by CCCP treatment. A much simpler and more direct experiment would be to repeat the measurement in the absence of ThT or any other stain. This experiment should be performed both in the wild-type strain and in the ∆kch mutant.

      ThT is a very bright fluorophore (much brighter than a GFP). It is clear from the images of non-stained samples that autofluorescence provides a negligible contribution to the fluorescence intensity in an image.

      2. The effects of photobleaching should be considered. Of course, the intensity varies a lot over the course of the experiment in a way that photobleaching alone cannot explain. However, photobleaching can still contribute to the kinetics observed. Photobleaching can be assessed by changing the intensity, duration, or frequency of exposure to excitation light during the experiment. Considerations about photobleaching become particularly important when considering the effect of catalase on ThT intensity. The authors find that the decrease in ThT signal after the initial "spike" is attenuated by the addition of catalase; this is what would be predicted by catalase protecting ThT from photobleaching (indeed, catalase can be used to reduce photobleaching in time lapse imaging).

      Photobleaching was negligible over the course of the experiments. We employed techniques such as reducing sample exposure time and using the appropriate light intensity to minimize photobleaching.

      3. It would be helpful to have a baseline of membrane potential fluctuations in the absence of the proposed stimulus (in this case, blue light). Including traces of membrane potential recorded without light present would help support the claim that these changes in membrane potential represent a blue light-specific stress response, as the authors suggest. Of course, ThT is blue, so if the excitation light for ThT is problematic for this experiment the alternative dye tetramethylrhodamine methyl ester perchlorate (TMRM) can be used instead.

      Unfortunately the fluorescent baseline is too weak to measure cleanly in this experiment. It appears the collective response of all the bacteria hyperpolarization at the same time appears to dominate the signal (measurements in the eLife article and new potentiometry measurements).

      4. The effects of ThT in combination with blue light should be more carefully considered. In mitochondria, a combination of high concentrations of blue light and ThT leads to disruption of the PMF (Skates et al. 2021 BioRXiv), and similarly, ThT treatment enhances the photodynamic effects of blue light in E. coli (Bondia et al. 2021 Chemical Communications). If present in this experiment, this effect could confound the interpretation of the PMF dynamics reported in the paper.

      We think the PMF plays a minority role in determining the membrane potential in E. coli. For reasons outlined before (H+ is a minority ion in E. coli compared with K+).

      5. Figures 4D - E indicate that a ∆kch mutant has increased propidium iodide (PI) staining in the presence of blue light; this is interpreted to mean that Kch-mediated membrane potential dynamics help protect cells from blue light. However, Live/Dead staining results in these strains in the absence of blue light are not reported. This means that the possibility that the ∆kch mutant has a general decrease in survival (independent of any effects of blue light) cannot be ruled out.

      Both strains of bacterial has similar growth curve and also engaged in membrane potential dynamics for the duration of the experiment. We were interested in bacterial cells that observed membrane potential dynamics in the presence of the stress. Bacterial cells need to be alive to engage in membrane potential  dynamics (hyperpolarize) under stress conditions. Cells that engaged in membrane potential dynamics and later stained red were only counted after the entire duration. We believe that the wildtype handles the light stress better than the ∆kch mutant as measured with the PI.

      6. Additionally in Figures 4D - E, the interpretation of this experiment can be confounded by the fact that PI uptake can sometimes be seen in bacterial cells with high membrane potential (Kirchhoff & Cypionka 2017 J Microbial Methods); the interpretation is that high membrane potential can lead to increased PI permeability. Because the membrane potential is largely higher throughout blue light treatment in the ∆kch mutant (Fig. 3AB), this complicates the interpretation of this experiment.

      Kirchhoff & Cypionka 2017 J Microbial Methods, using fluorescence microscopy, suggested that changes in membrane potential dynamics can introduce experimental bias when propidium iodide is used to confirm the viability of tge bacterial strains, B subtilis (DSM-10) and Dinoroseobacter shibae, that are starved of oxygen (via N2 gassing) for 2 hours. They attempted to support their findings by using CCCP in stopping the membrane potential dynamics (but never showed any pictoral or plotted data for this confirmatory experiment). In our experiment methodology, cell death was not forced on the cells by introducing an extra burden or via anoxia. We believe that the accumulation of PI in ∆kch mutant is not due to high membrane potential dynamics but is attributed to the PI, unbiasedly showing damaged/dead cells. We think that propidium iodide is good for this experiment. Propidium iodide is a dye that is extensively used in life sciences. PI has also been used in the study of bacterial electrophysiology (https://pubmed.ncbi.nlm.nih.gov/32343961/, ) and no membrane potential related bias was reported.

      Throughout the paper, many ThT intensity traces are compared, and described as "similar" or "dissimilar", without detailed discussion or a clear standard for comparison. For example, the two membrane potential curves in Fig. S1C are described as "similar" although they have very different shapes, whereas the curves in Fig. 1B and 1D are discussed in terms of their differences although they are evidently much more similar to one another. Without metrics or statistics to compare these curves, it is hard to interpret these claims. These comparative interpretations are additionally challenging because many of the figures in which average trace data are presented do not indicate standard deviation.

      Comparison of small changes in the absolute intensities is problematic in such fluorescence experiments. We mean the shape of the traces is similar and they can be modelled using a HH model with similar parameters.

      The differences between the TMRM and ThT curves that the authors show in Fig. S1C warrant further consideration. Some of the key features of the response in the ThT curve (on which much of the modeling work in the paper relies) are not very apparent in the TMRM data. It is not obvious to me which of these traces will be more representative of the actual underlying membrane potential dynamics.

      In our experiment, TMRM was used to confirm the dynamics observed using ThT. However, ThT appear to be more photostable than TMRM (especially towars the 2nd peak). The most interesting observation is that with both dyes, all phases of the membrane potential dynamics were conspicuous (the first peak, the quiescent period and the second peak). The time periods for these three episodes were also similar.

      A key claim in this paper (that dynamics of firing differ depending on whether cells are alone or in a colony) is underpinned by "time-to-first peak" analysis, but there are some challenges in interpreting these results. The authors report an average time-to-first peak of 7.34 min for the data in Figure 1B, but the average curve in Figure 1B peaks earlier than this. In Figure 1E, it appears that there are a handful of outliers in the "sparse cell" condition that likely explain this discrepancy. Either an outlier analysis should be done and the mean recomputed accordingly, or a more outlier-robust method like the median should be used instead. Then, a statistical comparison of these results will indicate whether there is a significant difference between them.

      The key point is the comparison of standard errors on the standard deviation.

      In two different 3D biofilm experiments, the authors report the propagation of wavefronts of membrane potential; I am unable to discern these wavefronts in the imaging data, and they are not clearly demonstrated by analysis.

      The first data set is presented in Figures 2A, 2B, and Video S3. The images and video are very difficult to interpret because of how the images have been scaled: the center of the biofilm is highly saturated, and the zero value has also been set too high to consistently observe the single cells surrounding the biofilm. With the images scaled this way, it is very difficult to assess dynamics. The time stamps in Video S3 and on the panels in Figure 2A also do not correspond to one another although the same biofilm is shown (and the time course in 2B is also different from what is indicated in 2B). In either case, it appears that the center of the biofilm is consistently brighter than the edges, and the intensity of all cells in the biofilm increases in tandem; by eye, propagating wavefronts (either directed toward the edge or the center) are not evident to me. Increased brightness at the center of the biofilm could be explained by increased cell thickness there (as is typical in this type of biofilm). From the image legend, it is not clear whether the image presented is a single confocal slice or a projection. Even if this is a single confocal slice, in both Video S3 and Figure 2A there are regions of "haze" from out-of-focus light evident, suggesting that light from other focal planes is nonetheless present. This seems to me to be a simpler explanation for the fluorescence dynamics observed in this experiment: cells are all following the same trajectory that corresponds to that seen for single cells, and the center is brighter because of increased biofilm thickness.

      We appreciate the reviewer for this important observation. We have made changes to the figures to address this confusion. The cell cover has no influence on the observed membrane potential dynamics. The entire biofilm was exposed to the same blue light at each time. Therefore all parts of the biofilm received equal amounts of the blue light intensity. The membrane potential dynamics was not influenced by cell density (see Fig 2C). 

      The second data set is presented in Video S6B; I am similarly unable to see any wave propagation in this video. I observe only a consistent decrease in fluorescence intensity throughout the experiment that is spatially uniform (except for the bright, dynamic cells near the top; these presumably represent cells that are floating in the microfluidic and have newly arrived to the imaging region).

      A visual inspection of Video S6B shows a fast rise, a decrease in fluorescence and a second rise (supplementary figure 4B). The data for the fluorescence was carefully obtained using the imaris software. We created a curved geometry on each slice of the confocal stack. We analyzed the surfaces of this curved plane along the z-axis. This was carried out in imaris.

      3D imaging data can be difficult to interpret by eye, so it would perhaps be more helpful to demonstrate these propagating wavefronts by analysis; however, such analysis is not presented in a clear way. The legend in Figure 2B mentions a "wavefront trace", but there is no position information included - this trace instead seems to represent the average intensity trace of all cells. To demonstrate the propagation of a wavefront, this analysis should be shown for different subpopulations of cells at different positions from the center of the biofilm. Data is shown in Figure 8 that reflects the velocity of the wavefront as a function of biofilm position; however, because the wavefronts themselves are not evident in the data, it is difficult to interpret this analysis. The methods section additionally does not contain sufficient information about what these velocities represent and how they are calculated. Because of this, it is difficult for me to evaluate the section of the paper pertaining to wave propagation and the predicted biofilm critical size.

      The analysis is considered in more detail in a more expansive modelling article, currently under peer review in a physics journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

      There are some instances in the paper where claims are made that do not have data shown or are not evident in the cited data:

      1. In the first results section, "When CCCP was added, we observed a fast efflux of ions in all cells"- the data figure pertaining to this experiment is in Fig. S1E, which does not show any ion efflux. The methods section does not mention how ion efflux was measured during CCCP treatment.

      We have worded this differently to properly convey our results.

      2. In the discussion of voltage-gated calcium channels, the authors refer to "spiking events", but these are not obvious in Figure S3E. Although the fluorescence intensity changes over time, it's hard to distinguish these fluctuations from measurement noise; a no-light control could help clarify this.

      The calcium transients observed were not due to noise or artefacts.

      3. The authors state that the membrane potential dynamics simulated in Figure 7B are similar to those observed in 3D biofilms in Fig. S4B; however, the second peak is not clearly evident in Fig. S4B and it looks very different for the mature biofilm data reported in Fig. 2. I have some additional confusion about this data specifically: in the intensity trace shown in Fig. S4B, the intensity in the second frame is much higher than the first; this is not evident in Video S6B, in which the highest intensity is in the first frame at time 0. Similarly, the graph indicates that the intensity at 60 minutes is higher than the intensity at 4 minutes, but this is not the case in Fig. S4A or Video S6B.

      The confusion stated here has now been addressed. Also it should be noted that while Fig 2.1 was obtained with LED light source, Fig S4A was obtained using a laser light source. While obtaining the confocal images (for Fig S4A ), the light intensity was controlled to further minimize photobleaching. Most importantly, there is an evidence of slow rise to the 2nd peak in Fig S4B. The first peak, quiescence and slow rise to second peak are evident.

    2. Author Response

      We would like to sincerely thank the referees and the editor for their time in considering our manuscript. The electrophysiology of bacteria is a fast-moving complex field and is proving contentious in places. We believe the peer review process of eLife provides an ideal mechanism to address the issues raised on our manuscript in an open and transparent manner. Hopefully we will encourage some more consensus in the field and help understand some of the inconsistencies in the current literature that are hampering progress.

      The editors stress the main issue raised was a single referee questioning the use of ThT as an indicator of membrane potential. We are well aware of the articles by the Pilizota group and we believe them to be scientifically flawed. The authors assume there are no voltage-gated ion channels in E. coli and then attempt to explain motility data based on a simple Nernstian battery model (they assume E. coli are unexcitable matter). This in turn leads them to conclude the membrane dye ThT is faulty, when in fact it is a problem with their simple battery model.

      In terms of the previous microbiology literature, the assumption of no voltage-gated ion channels in E. coli suggested by referee 2 is a highly contentious niche ideology. The majority of gene databases for E. coli have a number of ion-channels annotated as voltage sensitive due to comparative genetics studies e.g. try the https://bacteria.ensembl.org/ database (the search terms ‘voltage-gated coli’ give 2521 hits for genes, similarly you could check www.uniprot.org or www.biocyc.org) and M.M.Kuo, Y.Saimi, C.Kung, ‘Gain of function mutation indicate that E. coli Kch form a functional K+ conduit in vivo’, EMBO Journal, 2003, 22, 16, 4049. Furthermore, recent microbiology reviews all agree that E. coli has a number of voltage-gated ion channels S.D.Beagle, S.W.Lockless, ‘Unappreciated roles for K+ channels in bacterial physiology’, Trends in microbiology, 2021, 29, 10, 942-950. More emphatic experimental data is seen in spiking potentials that have been observed by many groups for E. coli, both directly using microelectrodes and indirectly using genetically expressed fluorophores, ‘Electrical spiking in bacterial biofilms’ E.Masi et al, Journal of the Royal Society Interface, 2015, 12, 102, ‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, J.M.Kralj, et al, Science, 2011, 333, 6040, 345 and ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 2023, 120, 3, e2208348120. The only mechanism currently known to cause spiking potentials in cells is due to positive feedback from voltage-gated ion channels (you need a mechanism to induce the oscillations). Indeed, people are starting to investigate the specific voltage-gated ion channels in E. coli and a role is emerging for calcium in addition to potassium e.g. ‘Genome-wide functional screen for calcium transients in E. coli identifies increased membrane potential adaptation to persistent DNA damage’, R.Luder, et al, J.Bacteriology, 2021, 203, 3, e00509.

      In terms of recent data from our own group, electrical impedance spectroscopy (EIS) experiments from E. coli indicate there are large conductivity changes associated with the Kch ion channels (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print). EIS experiments probe the electrical phenomena of bacterial biofilms directly and do not depend on fluorophores i.e. they can’t be affected by ThT.

      Attempts to disprove the use of ThT to measure hyperpolarisation phenomena in E. coli using fluorescence microscopy also seem doomed to failure based on comparative control experiments. A wide range of other cationic fluorophores show similar behaviour to ThT e.g. the potassium sensitive dye used in our eLife article. Thus the behaviour of ThT appears to be generic for a range of cationic dyes and it implies a simple physical mechanism i.e. the positively charged dyes enter cells at low potentials. The elaborate photobleaching mechanism postulated by referee 2 seems most unlikely and is unable to explain our data (see below). ThT is photostable and chemically well-defined and it is therefore used almost universally in fluorescence assays for amyloids.

      A challenge with trying to use flagellar motility to measure intracellular potentials in live bacteria, as per referee 2’s many publications, is that a clutch is known to occur with E. coli e.g. ‘Flagellar brake protein YcgR interacts with motor proteins MotA and FliG to regulate the flagellar rotation speed and direction’, Q.Han et al, Frontiers in Microbiology, 2023, 14. Thus bacteria with high membrane potentials can have low motility when their clutch is engaged. This makes sense, since otherwise bacterial motility would be enslaved to their membrane potentials, greatly restricting their ability to react to their environmental conditions. Without quantifying the dynamics of the clutch (e.g. the gene circuit) it seems challenging to deduce how the motor reacts to Nernstian potentials in vivo. As a result we are not convinced by any of the Pilizota group articles. The quantitative connection between motility and membrane potential is too tenuous.

      In conclusion, the articles questioning the use of ThT are scientifically flawed and based on a niche ideology that E. coli do not contain voltage-gated ion channels. The current work disproves the simple Nernstian battery (SNB) model expounded by Pilizota et al, unpersuasively represented in multiple publications by this one group in the literature (see below for critical synopses) and demonstrates the SNB models needs to be replaced by a model that includes excitability (demonstrating hyperpolarization of the membrane potential).

      In the language of physics, a non-linear oscillator model is needed to explain spiking potentials in bacteria and the simple battery models presented by Pilizota et al do not have the required non-linearities to oscillate (‘Nonlinear dynamics and chaos’, Steve Strogatz, Westview Press, 2014). Such non-linear models are the foundation for describing eukaryotic electrophysiology, e.g. Hodgkin and Huxley’s Nobel prize winning research (1963), but also the vast majority of modern extensions (‘Mathematical physiology’, J.Keener, J.Sneyd, Springer, 2009, ‘Cellular biophysics and modelling: a primer on the computational biology of excitable cells’, G.C.Smith, 2019, CUP, ‘Dynamical systems in neuroscience: the geometry of excitability and bursting’, E.M.Izhikevich, 2006, MIT and ‘Neuronal dynamics: from single neurons to networks and models of cognition’, W.Gerstner et al, 2014, CUP). The Pilizota group is using modelling tools from the 1930s that quickly were shown to be inadequate to describe eukaryotic cellular electrophysiology and the same is true for bacterial electrophysiology (see the ground breaking work of A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 7576, 59 for the use of Hodgkin-Huxley models with bacterial biofilms). Below we describe a critical synopsis of the articles cited by referee 2 and we then directly answer the specific points all the referees raise.

      Critical synopsis of the articles cited by referee 2:

      (1) ‘Generalized workflow for characterization of Nernstian dyes and their effects on bacterial physiology’, L.Mancini et al, Biophysical Journal, 2020, 118, 1, 4-14.

      This is the central article used by referee 2 to argue that there are issues with the calibration of ThT for the measurement of membrane potentials. The authors use a simple Nernstian battery (SNB) model and unfortunately it is wrong when voltage-gated ion channels occur. Huge oscillations occur in the membrane potentials of E. coli that cannot be described by the SNB model. Instead a Hodgkin Huxley model is needed, as shown in our eLife manuscript and multiple other studies (see above). Arrhenius kinetics are assumed in the SNB model for pumping with no real evidence and the generalized workflow involves ripping the flagella off the bacteria! The authors construct an elaborate ‘work flow’ to insure their ThT results can be interpreted using their erroneous SNB model over a limited range of parameters.

      (2) ‘Non-equivalence of membrane voltage and ion-gradient as driving forces for the bacterial flagellar motor at low load’, C.J.Lo, et al, Biophysical Journal, 2007, 93, 1, 294.

      An odd de novo chimeric species is developed using an E. coli chassis which uses Na+ instead of H+ for the motility of its flagellar motor. It is not clear the relevance to wild type E. coli, due to the massive physiological perturbations involved. A SNB model is using to fit the data over a very limited parameter range with all the concomitant errors.

      (3) Single-cell bacterial electrophysiology reveals mechanisms of stress-induced damage’, E.Krasnopeeva, et al, Biophysical Journal, 2019, 116, 2390.

      The abstract says ‘PMF defines the physiological state of the cell’. This statement is hyperbolic. An extremely wide range of molecules contribute to the physiological state of a cell. PMF does not even define the electrophysiology of the cell e.g. via the membrane potential. There are 0.2 M of K+ compared with 0.0000001 M of H+ in E. coli, so K+ is arguably a million times more important for the membrane potential than H+ and thus the electrophysiology!

      Equation (1) in the manuscript assumes no other ions are exchanged during the experiments other than H+. This is a very bad approximation when voltage-gated potassium ion channels move the majority ion (K+) around!

      In our model Figure 4A is better explained by depolarisation due to K+ channels closing than direct irreversible photodamage. Why does the THT fluorescence increase again for the second hyperpolarization event if the THT is supposed to be damaged? It does not make sense.

      (4) ‘The proton motive force determines E. coli robustness to extracellular pH’, G.Terradot et al, 2024, preprint.

      This article expounds the SNB model once more. It still ignores the voltage-gated ion channels. Furthermore, it ignores the effect of the dominant ion in E. coli, K+. The manuscript is incorrect as a result and I would not recommend publication. In general, an important problem is being researched i.e. how the membrane potential of E. coli is related to motility, but there are serious flaws in the SNB approach and the experimental methodology appears tenuous.

      Answers to specific questions raised by the referees

      Reviewer #1 (Public Review):

      Summary: Cell-to-cell communication is essential for higher functions in bacterial biofilms. Electrical signals have proven effective in transmitting signals across biofilms. These signals are then used to coordinate cellular metabolisms or to increase antibiotic tolerance. Here, the authors have reported for the first time coordinated oscillation of membrane potential in E. coli biofilms that may have a functional role in photoprotection.

      Strengths:

      • The authors report original data.

      • For the first time, they showed that coordinated oscillations in membrane potential occur in E. Coli biofilms.

      • The authors revealed a complex two-phase dynamic involving distinct molecular response mechanisms.

      • The authors developed two rigorous models inspired by 1) Hodgkin-Huxley model for the temporal dynamics of membrane potential and 2) Fire-Diffuse-Fire model for the propagation of the electric signal.

      • Since its discovery by comparative genomics, the Kch ion channel has not been associated with any specific phenotype in E. coli. Here, the authors proposed a functional role for the putative K+ Kch channel : enhancing survival under photo-toxic conditions.

      We thank the referee for their positive evaluations and agree with these statements.

      Weaknesses:

      • Since the flow of fresh medium is stopped at the beginning of the acquisition, environmental parameters such as pH and RedOx potential are likely to vary significantly during the experiment. It is therefore important to exclude the contributions of these variations to ensure that the electrical response is only induced by light stimulation. Unfortunately, no control experiments were carried out to address this issue.

      The electrical responses occur almost instantaneously when the stimulation with blue light begins i.e. it is too fast to be a build of pH. We are not sure what the referee means by Redox potential since it is an attribute of all chemicals that are able to donate/receive electrons. The electrical response to stress appears to be caused by ROS, since when ROS scavengers are added the electrical response is removed i.e. pH plays a very small minority role if any.

      • Furthermore, the control parameter of the experiment (light stimulation) is the same as that used to measure the electrical response, i.e. through fluorescence excitation. The use of the PROPS system could solve this problem.

      We were enthusiastic at the start of the project to use the PROPs system in E. coli as presented by J.M.Krajl et al, ‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, Science, 2011, 333, 6040, 345. However, the people we contacted in the microbiology community said that it had some technical issues and there have been no subsequent studies using PROPs in bacteria after the initial promising study. The fluorescent protein system recently presented in PNAS seems more promising, ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 120, 3, e2208348120.

      Electrical signal propagation is an important aspect of the manuscript. However, a detailed >quantitative analysis of the spatial dynamics within the biofilm is lacking. In addition, it is unclear if the electrical signal propagates within the biofilm during the second peak regime, which is mediated by the Kch channel. This is an important question, given that the fire-diffuse-fire model is presented with emphasis on the role of K+ ions.

      We have presented a more detailed account of the electrical wavefront modelling work and it is currently under review in a physical journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

      • Since deletion of the kch gene inhibits the long-term electrical response to light stimulation (regime II), the authors concluded that K+ ions play a role in the habituation response. However, Kch is a putative K+ ion channel. The use of specific drugs could help to clarify the role of K+ ions.

      Our recent electrical impedance spectroscopy publication provides further evidence that Kch is associated with large changes in conductivity as expected for a voltage-gated ion channel (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

      • The manuscript as such does not allow us to properly conclude on the photo-protective role of the Kch ion channel.

      That Kch has a photoprotective role is our current working hypothesis. The hypothesis fits with the data, but we are not saying we have proven it beyond all possible doubt.

      • The link between membrane potential dynamics and mechanosensitivity is not captured in the equation for the Q-channel opening dynamics in the Hodgkin-Huxley model (Supp Eq 2).

      Our model is agnostic with respect to the mechanosensitivity of the ion channels, although we deduce that mechanosensitive ion channels contribute to ion channel Q.

      • Given the large number of parameters used in the models, it is hard to distinguish between prediction and fitting.

      This is always an issue with electrophysiological modelling (compared with most heart and brain modelling studies we are very conservative in the choice of parameters for the bacteria). In terms of predicting the different phenomena observed, we believe the model is very successful.

      Reviewer #2 (Public Review):

      Summary of what the authors were trying to achieve:

      The authors thought they studied membrane potential dynamics in E.coli biofilms. They thought so because they were unaware that the dye they used to report that membrane potential in E.coli, has been previously shown not to report it. Because of this, the interpretation of the authors' results is not accurate.

      We believe the Pilizota work is scientifically flawed.

      Major strengths and weaknesses of the methods and results:

      The strength of this work is that all the data is presented clearly, and accurately, as far as I can tell.

      The major critical weakness of this paper is the use of ThT dye as a membrane potential dye in E.coli. The work is unaware of a publication from 2020 https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] that demonstrates that ThT is not a membrane potential dye in E. coli. Therefore I think the results of this paper are misinterpreted. The same publication I reference above presents a protocol on how to carefully calibrate any candidate membrane potential dye in any given condition.

      We are aware of this study, but believe it to be scientifically flawed. We do not cite the article because we do not think it is a particularly useful contribution to the literature.

      I now go over each results section in the manuscript.

      Result section 1: Blue light triggers electrical spiking in single E. coli cells

      I do not think the title of the result section is correct for the following reasons. The above-referenced work demonstrates the loading profile one should expect from a Nernstian dye (Figure 1). It also demonstrates that ThT does not show that profile and explains why is this so. ThT only permeates the membrane under light exposure (Figure 5). This finding is consistent with blue light peroxidising the membrane (see also following work Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] on light-induced damage to the electrochemical gradient of protons-I am sure there are more references for this).

      The Pilizota group invokes some elaborate artefacts to explain the lack of agreement with a simple Nernstian battery model. The model is incorrect not the fluorophore.

      Please note that the loading profile (only observed under light) in the current manuscript in Figure 1B as well as in the video S1 is identical to that in Figure 3 from the above-referenced paper (i.e. https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com]), and corresponding videos S3 and S4. This kind of profile is exactly what one would expect theoretically if the light is simultaneously lowering the membrane potential as the ThT is equilibrating, see Figure S12 of that previous work. There, it is also demonstrated by the means of monitoring the speed of bacterial flagellar motor that the electrochemical gradient of protons is being lowered by the light. The authors state that applying the blue light for different time periods and over different time scales did not change the peak profile. This is expected if the light is lowering the electrochemical gradient of protons. But, in Figure S1, it is clear that it affected the timing of the peak, which is again expected, because the light affects the timing of the decay, and thus of the decay profile of the electrochemical gradient of protons (Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com]).

      We think the proton effect is a million times weaker than that due to potasium i.e. 0.2 M K+ versus 10-7 M H+. We can comfortably neglect the influx of H+ in our experiments.

      If find Figure S1D interesting. There authors load TMRM, which is a membrane voltage dye that has been used extensively (as far as I am aware this is the first reference for that and it has not been cited https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914430 [ncbi.nlm.nih.gov]/). As visible from the last TMRM reference I give, TMRM will only load the cells in Potassium Phosphate buffer with NaCl (and often we used EDTA to permeabilise the membrane). It is not fully clear (to me) whether here TMRM was prepared in rich media (it explicitly says so for ThT in Methods but not for TMRM), but it seems so. If this is the case, it likely also loads because of the damage to the membrane done with light, and therefore I am not surprised that the profiles are similar.

      The vast majority of cells continue to be viable. We do not think membrane damage is dominating.

      The authors then use CCCP. First, a small correction, as the authors state that it quenches membrane potential. CCCP is a protonophore (https://pubmed.ncbi.nlm.nih.gov/4962086 [pubmed.ncbi.nlm.nih.gov]/), so it collapses electrochemical gradient of protons. This means that it is possible, and this will depend on the type of pumps present in the cell, that CCCP collapses electrochemical gradient of protons, but the membrane potential is equal and opposite in sign to the DeltapH. So using CCCP does not automatically mean membrane potential will collapse (e.g. in some mammalian cells it does not need to be the case, but in E.coli it is https://www.biorxiv.org/content/10.1101/2021.11.19.469321v2 [biorxiv.org]). CCCP has also been recently found to be a substrate for TolC (https://journals.asm.org/doi/10.1128/mbio.00676-21 [journals.asm.org]), but at the concentrations the authors are using CCCP (100uM) that should not affect the results. However, the authors then state because they observed, in Figure S1E, a fast efflux of ions in all cells and no spiking dynamics this confirms that observed dynamics are membrane potential related. I do not agree that it does. First, Figure S1E, does not appear to show transients, instead, it is visible that after 50min treatment with 100uM CCCP, ThT dye shows no dynamics. The action of a Nernstian dye is defined. It is not sufficient that a charged molecule is affected in some way by electrical potential, this needs to be in a very specific way to be a Nernstian dye. Part of the profile of ThT loading observed in https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] is membrane potential related, but not in a way that is characteristic of Nernstian dye.

      Our understanding of the literature is CCCP poisons the whole metabolism of the bacterial cells. The ATP driven K+ channels will stop functioning and this is the dominant contributor to membrane potential.

      Result section 2: Membrane potential dynamics depend on the intercellular distance

      In this chapter, the authors report that the time to reach the first intensity peak during ThT loading is different when cells are in microclusters. They interpret this as electrical signalling in clusters because the peak is reached faster in microclusters (as opposed to slower because intuitively in these clusters cells could be shielded from light). However, shielding is one possibility. The other is that the membrane has changed in composition and/or the effective light power the cells can tolerate (with mechanisms to handle light-induced damage, some of which authors mention later in the paper) is lower. Given that these cells were left in a microfluidic chamber for 2h hours to attach in growth media according to Methods, there is sufficient time for that to happen. In Figure S12 C and D of that same paper from my group (https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com]) one can see the effects of peak intensity and timing of the peak on the permeability of the membrane. Therefore I do not think the distance is the explanation for what authors observe.

      Shielding would provide the reverse effect, since hyperpolarization begins in the dense centres of the biofilms. For the initial 2 hours the cells receive negligible blue light. Neither of the referee’s comments thus seem tenable.

      Result section 3: Emergence of synchronized global wavefronts in E. coli biofilms

      In this section, the authors exposed a mature biofilm to blue light. They observe that the intensity peak is reached faster in the cells in the middle. They interpret this as the ion-channel-mediated wavefronts moved from the center of the biofilm. As above, cells in the middle can have different membrane permeability to those at the periphery, and probably even more importantly, there is no light profile shown anywhere in SI/Methods. I could be wrong, but the SI3 A profile is consistent with a potential Gaussian beam profile visible in the field of view. In Methods, I find the light source for the blue light and the type of microscope but no comments on how 'flat' the illumination is across their field of view. This is critical to assess what they are observing in this result section. I do find it interesting that the ThT intensity collapsed from the edges of the biofilms. In the publication I mentioned https://www.sciencedirect.com/science/article/pii/S0006349519308793#app2 [sciencedirect.com], the collapse of fluorescence was not understood (other than it is not membrane potential related). It was observed in Figure 5A, C, and F, that at the point of peak, electrochemical gradient of protons is already collapsed, and that at the point of peak cell expands and cytoplasmic content leaks out. This means that this part of the ThT curve is not membrane potential related. The authors see that after the first peak collapsed there is a period of time where ThT does not stain the cells and then it starts again. If after the first peak the cellular content leaks, as we have observed, then staining that occurs much later could be simply staining of cytoplasmic positively charged content, and the timing of that depends on the dynamics of cytoplasmic content leakage (we observed this to be happening over 2h in individual cells). ThT is also a non-specific amyloid dye, and in starving E. coli cells formation of protein clusters has been observed (https://pubmed.ncbi.nlm.nih.gov/30472191 [pubmed.ncbi.nlm.nih.gov]/), so such cytoplasmic staining seems possible.

      It is very easy to see if the illumination is flat (Köhler illumination) by comparing the intensity of background pixels on the detector. It was flat in our case. Protons have little to do with our work for reasons highlighted before. Differential membrane permittivity is a speculative phenomenon not well supported by any evidence and with no clear molecular mechanism.

      Finally, I note that authors observe biofilms of different shapes and sizes and state that they observe similar intensity profiles, which could mean that my comment on 'flatness' of the field of view above is not a concern. However, the scale bar in Figure 2A is not legible, so I can't compare it to the variation of sizes of the biofilms in Figure 2C (67 to 280um). Based on this, I think that the illumination profile is still a concern.

      The referee now contradicts themselves and wants a scale bar to be more visible. We have changed the scale bar.

      Result section 4: Voltage-gated Kch potassium channels mediate ion-channel electrical oscillations in E. coli

      First I note at this point, given that I disagree that the data presented thus 'suggest that E. coli biofilms use electrical signaling to coordinate long-range responses to light stress' as the authors state, it gets harder to comment on the rest of the results.

      In this result section the authors look at the effect of Kch, a putative voltage-gated potassium channel, on ThT profile in E. coli cells. And they see a difference. It is worth noting that in the publication https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] it is found that ThT is also likely a substrate for TolC (Figure 4), but that scenario could not be distinguished from the one where TolC mutant has a different membrane permeability (and there is a publication that suggests the latter is happening https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2958.2010.07245.x [onlinelibrary.wiley.com]). Given this, it is also possible that Kch deletion affects the membrane permeability. I do note that in video S4 I seem to see more of, what appear to be, plasmolysed cells. The authors do not see the ThT intensity with this mutant that appears long after the initial peak has disappeared, as they see in WT. It is not clear how long they waited for this, as from Figure S3C it could simply be that the dynamics of this is a lot slower, e.g. Kch deletion changes membrane permeability.

      The work that TolC provides a possible passive pathway for ThT to leave cells seems slightly niche. It just demonstrates another mechanism for the cells to equilibriate the concentrations of ThT in a Nernstian manner i.e. driven by the membrane voltage.

      The authors themselves state that the evidence for Kch being a voltage-gated channel is indirect (line 54). I do not think there is a need to claim function from a ThT profile of E. coli mutants (nor do I believe it's good practice), given how accurate single-channel recordings are currently. To know the exact dependency on the membrane potential, ion channel recordings on this protein are needed first.

      We have good evidence form electrical impedance spectroscopy experiments that Kch increases the conductivity of biofilms (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

      Result section 5: Blue light influences ion-channel mediated membrane potential events in E. coli

      In this chapter the authors vary the light intensity and stain the cells with PI (this dye gets into the cells when the membrane becomes very permeable), and the extracellular environment with K+ dye (I have not yet worked carefully with this dye). They find that different amounts of light influence ThT dynamics. This is in line with previous literature (both papers I have been mentioning: Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] and https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com] especially SI12), but does not add anything new. I think the results presented here can be explained with previously published theory and do not indicate that the ion-channel mediated membrane potential dynamics is a light stress relief process.

      The simple Nernstian battery model proposed by Pilizota et al is erroneous in our opinion for reasons outlined above. We believe it will prove to be a dead end for bacterial electrophysiology studies.

      Result section 6: Development of a Hodgkin-Huxley model for the observed membrane potential dynamics

      This results section starts with the authors stating: 'our data provide evidence that E. coli manages light stress through well-controlled modulation of its membrane potential dynamics'. As stated above, I think they are instead observing the process of ThT loading while the light is damaging the membrane and thus simultaneously collapsing the electrochemical gradient of protons. As stated above, this has been modelled before. And then, they observe a ThT staining that is independent from membrane potential.

      This is an erroneous niche opinion. Protons have little say in the membrane potential since there are so few of them. The membrane potential is mostly determined by K+.

      I will briefly comment on the Hodgkin Huxley (HH) based model. First, I think there is no evidence for two channels with different activation profiles as authors propose. But also, the HH model has been developed for neurons. There, the leakage and the pumping fluxes are both described by a constant representing conductivity, times the difference between the membrane potential and Nernst potential for the given ion. The conductivity in the model is given as gKn^4 for potassium, gNam^3*h sodium, and gL for leakage, where gK, gNa and gL were measured experimentally for neurons. And, n, m, and h are variables that describe the experimentally observed voltage-gated mechanism of neuronal sodium and potassium channels. (Please see Hodgkin AL, Huxley AF. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116:449-72 and Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500-44).

      In the 70 years since Hodgkin and Huxley first presented their model, a huge number of similar models have been proposed to describe cellular electrophysiology. We are not being hyperbolic when we state that the HH models for excitable cells are like the Schrödinger equation for molecules. We carefully adapted our HH model to reflect the currently understood electrophysiology of E. coli.

      Thus, in applying the model to describe bacterial electrophysiology one should ensure near equilibrium requirement holds (so that (V-VQ) etc terms in authors' equation Figure 5 B hold), and potassium and other channels in a given bacterium have similar gating properties to those found in neurons. I am not aware of such measurements in any bacteria, and therefore think the pump leak model of the electrophysiology of bacteria needs to start with fluxes that are more general (for example Keener JP, Sneyd J. 2009. Mathematical physiology: I: Cellular physiology. New York: Springer or https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000144 [journals.plos.org])

      The reference is to a slightly more modern version of a simple Nernstian battery model. The model will not oscillate and thus will not help modelling membrane potentials in bacteria. We are unsure where the equilibrium requirement comes from (inadequate modelling of the dynamics?)

      Result section 7: Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli.

      The results that Mcs channels affect the profile of ThT dye are interesting. It is again possible that the membrane permeability of these mutants has changed and therefore the dynamics have changed, so this needs to be checked first. I also note that our results show that the peak of ThT coincides with cell expansion. For this to be understood a model is needed that also takes into account the link between maintenance of electrochemical gradients of ions in the cell and osmotic pressure.

      The evidence for permeability changes in the membranes seems to be tenuous.

      A side note is that the authors state that the Msc responds to stress-related voltage changes. I think this is an overstatement. Mscs respond to predominantly membrane tension and are mostly nonspecific (see how their action recovers cellular volume in this publication https://www.pnas.org/doi/full/10.1073/pnas.1522185113 [pnas.org]). Authors cite references 35-39 to support this statement. These publications still state that these channels are predominantly membrane tension-gated. Some of the references state that the presence of external ions is important for tension-related gating but sometimes they gate spontaneously in the presence of certain ions. Other publications cited don't really look at gating with respect to ions (39 is on clustering). This is why I think the statement is somewhat misleading.

      We have reworded the discussion of Mscs since the literature appears to be ambiguous. We will try to run some electrical impedance spectroscopy experiments on the Msc mutants in the future to attempt to remove the ambiguity.

      Result section 8: Anomalous ion-channel-mediated wavefronts propagate light stress signals in 3D E. coli biofilms.

      I am not commenting on this result section, as it would only be applicable if ThT was membrane potential dye in E. coli.

      Ok, but we disagree on the use of ThT.

      Aims achieved/results support their conclusions:

      The authors clearly present their data. I am convinced that they have accurately presented everything they observed. However, I think their interpretation of the data and conclusions is inaccurate in line with the discussion I provided above.

      Likely impact of the work on the field, and the utility of the methods and data to the community:

      I do not think this publication should be published in its current format. It should be revised in light of the previous literature as discussed in detail above. I believe presenting it in it's current form on eLife pages would create unnecessary confusion.

      We believe many of the Pilizota group articles are scientifically flawed and are causing the confusion in the literature.

      Any other comments:

      I note, that while this work studies E. coli, it references papers in other bacteria using ThT. For example, in lines 35-36 authors state that bacteria (Bacillus subtilis in this case) in biofilms have been recently found to modulate membrane potential citing the relevant literature from 2015. It is worth noting that the most recent paper https://journals.asm.org/doi/10.1128/mbio.02220-23 [journals.asm.org] found that ThT binds to one or more proteins in the spore coat, suggesting that it does not act as a membrane potential in Bacillus spores. It is possible that it still reports membrane potential in Bacillus cells and the recent results are strictly spore-specific, but these should be kept in mind when using ThT with Bacillus.

      ThT was used successfully in previous studies of normal B. subtilis cells (by our own group and A.Prindle, ‘Spatial propagation of electrical signal in circular biofilms’, J.A.Blee et al, Physical Review E, 2019, 100, 052401, J.A.Blee et al, ‘Membrane potentials, oxidative stress and the dispersal response of bacterial biofilms to 405 nm light’, Physical Biology, 2020, 17, 2, 036001, A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 59-63). The connection to low metabolism pore research seems speculative.

      Reviewer #3 (Public Review):

      It has recently been demonstrated that bacteria in biofilms show changes in membrane potential in response to changes in their environment, and that these can propagate signals through the biofilm to coordinate bacterial behavior. Akabuogu et al. contribute to this exciting research area with a study of blue light-induced membrane potential dynamics in E. coli biofilms. They demonstrate that Thioflavin-T (ThT) intensity (a proxy for membrane potential) displays multiphasic dynamics in response to blue light treatment. They additionally use genetic manipulations to implicate the potassium channel Kch in the latter part of these dynamics. Mechanosensitive ion channels may also be involved, although these channels seem to have blue light-independent effects on membrane potential as well. In addition, there are challenges to the quantitative interpretation of ThT microscopy data which require consideration. The authors then explore whether these dynamics are involved in signaling at the community level. The authors suggest that cell firing is both more coordinated when cells are clustered and happens in waves in larger, 3D biofilms; however, in both cases evidence for these claims is incomplete. The authors present two simulations to describe the ThT data. The first of these simulations, a Hodgkin-Huxley model, indicates that the data are consistent with the activity of two ion channels with different kinetics; the Kch channel mutant, which ablates a specific portion of the response curve, is consistent with this. The second model is a fire-diffuse-fire model to describe wavefront propagation of membrane potential changes in a 3D biofilm; because the wavefront data are not presented clearly, the results of this model are difficult to interpret. Finally, the authors discuss whether these membrane potential changes could be involved in generating a protective response to blue light exposure; increased death in a Kch ion channel mutant upon blue light exposure suggests that this may be the case, but a no-light control is needed to clarify this.

      In a few instances, the paper is missing key control experiments that are important to the interpretation of the data. This makes it difficult to judge the meaning of some of the presented experiments.

      (1) An additional control for the effects of autofluorescence is very important. The authors conduct an experiment where they treat cells with CCCP and see that Thioflavin-T (ThT) dynamics do not change over the course of the experiment. They suggest that this demonstrates that autofluorescence does not impact their measurements. However, cellular autofluorescence depends on the physiological state of the cell, which is impacted by CCCP treatment. A much simpler and more direct experiment would be to repeat the measurement in the absence of ThT or any other stain. This experiment should be performed both in the wild-type strain and in the ∆kch mutant.

      ThT is a very bright fluorophore (much brighter than a GFP). It is clear from the images of non-stained samples that autofluorescence provides a negligible contribution to the fluorescence intensity in an image.

      (2) The effects of photobleaching should be considered. Of course, the intensity varies a lot over the course of the experiment in a way that photobleaching alone cannot explain. However, photobleaching can still contribute to the kinetics observed. Photobleaching can be assessed by changing the intensity, duration, or frequency of exposure to excitation light during the experiment. Considerations about photobleaching become particularly important when considering the effect of catalase on ThT intensity. The authors find that the decrease in ThT signal after the initial "spike" is attenuated by the addition of catalase; this is what would be predicted by catalase protecting ThT from photobleaching (indeed, catalase can be used to reduce photobleaching in time lapse imaging).

      Photobleaching was negligible over the course of the experiments. We employed techniques such as reducing sample exposure time and using the appropriate light intensity to minimize photobleaching.

      (3) It would be helpful to have a baseline of membrane potential fluctuations in the absence of the proposed stimulus (in this case, blue light). Including traces of membrane potential recorded without light present would help support the claim that these changes in membrane potential represent a blue light-specific stress response, as the authors suggest. Of course, ThT is blue, so if the excitation light for ThT is problematic for this experiment the alternative dye tetramethylrhodamine methyl ester perchlorate (TMRM) can be used instead.

      Unfortunately the fluorescent baseline is too weak to measure cleanly in this experiment. It appears the collective response of all the bacteria hyperpolarization at the same time appears to dominate the signal (measurements in the eLife article and new potentiometry measurements).

      (4) The effects of ThT in combination with blue light should be more carefully considered. In mitochondria, a combination of high concentrations of blue light and ThT leads to disruption of the PMF (Skates et al. 2021 BioRXiv), and similarly, ThT treatment enhances the photodynamic effects of blue light in E. coli (Bondia et al. 2021 Chemical Communications). If present in this experiment, this effect could confound the interpretation of the PMF dynamics reported in the paper.

      We think the PMF plays a minority role in determining the membrane potential in E. coli. For reasons outlined before (H+ is a minority ion in E. coli compared with K+).

      (5) Figures 4D - E indicate that a ∆kch mutant has increased propidium iodide (PI) staining in the presence of blue light; this is interpreted to mean that Kch-mediated membrane potential dynamics help protect cells from blue light. However, Live/Dead staining results in these strains in the absence of blue light are not reported. This means that the possibility that the ∆kch mutant has a general decrease in survival (independent of any effects of blue light) cannot be ruled out.

      Both strains of bacterial has similar growth curve and also engaged in membrane potential dynamics for the duration of the experiment. We were interested in bacterial cells that observed membrane potential dynamics in the presence of the stress. Bacterial cells need to be alive to engage in membrane potential dynamics (hyperpolarize) under stress conditions. Cells that engaged in membrane potential dynamics and later stained red were only counted after the entire duration. We believe that the wildtype handles the light stress better than the ∆kch mutant as measured with the PI.

      (6) Additionally in Figures 4D - E, the interpretation of this experiment can be confounded by the fact that PI uptake can sometimes be seen in bacterial cells with high membrane potential (Kirchhoff & Cypionka 2017 J Microbial Methods); the interpretation is that high membrane potential can lead to increased PI permeability. Because the membrane potential is largely higher throughout blue light treatment in the ∆kch mutant (Fig. 3AB), this complicates the interpretation of this experiment.

      Kirchhoff & Cypionka 2017 J Microbial Methods, using fluorescence microscopy, suggested that changes in membrane potential dynamics can introduce experimental bias when propidium iodide is used to confirm the viability of tge bacterial strains, B subtilis (DSM-10) and Dinoroseobacter shibae, that are starved of oxygen (via N2 gassing) for 2 hours. They attempted to support their findings by using CCCP in stopping the membrane potential dynamics (but never showed any pictoral or plotted data for this confirmatory experiment). In our experiment methodology, cell death was not forced on the cells by introducing an extra burden or via anoxia. We believe that the accumulation of PI in ∆kch mutant is not due to high membrane potential dynamics but is attributed to the PI, unbiasedly showing damaged/dead cells. We think that propidium iodide is good for this experiment. Propidium iodide is a dye that is extensively used in life sciences. PI has also been used in the study of bacterial electrophysiology (https://pubmed.ncbi.nlm.nih.gov/32343961/, ) and no membrane potential related bias was reported.

      Throughout the paper, many ThT intensity traces are compared, and described as "similar" or "dissimilar", without detailed discussion or a clear standard for comparison. For example, the two membrane potential curves in Fig. S1C are described as "similar" although they have very different shapes, whereas the curves in Fig. 1B and 1D are discussed in terms of their differences although they are evidently much more similar to one another. Without metrics or statistics to compare these curves, it is hard to interpret these claims. These comparative interpretations are additionally challenging because many of the figures in which average trace data are presented do not indicate standard deviation.

      Comparison of small changes in the absolute intensities is problematic in such fluorescence experiments. We mean the shape of the traces is similar and they can be modelled using a HH model with similar parameters.

      The differences between the TMRM and ThT curves that the authors show in Fig. S1C warrant further consideration. Some of the key features of the response in the ThT curve (on which much of the modeling work in the paper relies) are not very apparent in the TMRM data. It is not obvious to me which of these traces will be more representative of the actual underlying membrane potential dynamics.

      In our experiment, TMRM was used to confirm the dynamics observed using ThT. However, ThT appear to be more photostable than TMRM (especially towars the 2nd peak). The most interesting observation is that with both dyes, all phases of the membrane potential dynamics were conspicuous (the first peak, the quiescent period and the second peak). The time periods for these three episodes were also similar.

      A key claim in this paper (that dynamics of firing differ depending on whether cells are alone or in a colony) is underpinned by "time-to-first peak" analysis, but there are some challenges in interpreting these results. The authors report an average time-to-first peak of 7.34 min for the data in Figure 1B, but the average curve in Figure 1B peaks earlier than this. In Figure 1E, it appears that there are a handful of outliers in the "sparse cell" condition that likely explain this discrepancy. Either an outlier analysis should be done and the mean recomputed accordingly, or a more outlier-robust method like the median should be used instead. Then, a statistical comparison of these results will indicate whether there is a significant difference between them.

      The key point is the comparison of standard errors on the standard deviation.

      In two different 3D biofilm experiments, the authors report the propagation of wavefronts of membrane potential; I am unable to discern these wavefronts in the imaging data, and they are not clearly demonstrated by analysis.

      The first data set is presented in Figures 2A, 2B, and Video S3. The images and video are very difficult to interpret because of how the images have been scaled: the center of the biofilm is highly saturated, and the zero value has also been set too high to consistently observe the single cells surrounding the biofilm. With the images scaled this way, it is very difficult to assess dynamics. The time stamps in Video S3 and on the panels in Figure 2A also do not correspond to one another although the same biofilm is shown (and the time course in 2B is also different from what is indicated in 2B). In either case, it appears that the center of the biofilm is consistently brighter than the edges, and the intensity of all cells in the biofilm increases in tandem; by eye, propagating wavefronts (either directed toward the edge or the center) are not evident to me. Increased brightness at the center of the biofilm could be explained by increased cell thickness there (as is typical in this type of biofilm). From the image legend, it is not clear whether the image presented is a single confocal slice or a projection. Even if this is a single confocal slice, in both Video S3 and Figure 2A there are regions of "haze" from out-of-focus light evident, suggesting that light from other focal planes is nonetheless present. This seems to me to be a simpler explanation for the fluorescence dynamics observed in this experiment: cells are all following the same trajectory that corresponds to that seen for single cells, and the center is brighter because of increased biofilm thickness.

      We appreciate the reviewer for this important observation. We have made changes to the figures to address this confusion. The cell cover has no influence on the observed membrane potential dynamics. The entire biofilm was exposed to the same blue light at each time. Therefore all parts of the biofilm received equal amounts of the blue light intensity. The membrane potential dynamics was not influenced by cell density (see Fig 2C).

      The second data set is presented in Video S6B; I am similarly unable to see any wave propagation in this video. I observe only a consistent decrease in fluorescence intensity throughout the experiment that is spatially uniform (except for the bright, dynamic cells near the top; these presumably represent cells that are floating in the microfluidic and have newly arrived to the imaging region).

      A visual inspection of Video S6B shows a fast rise, a decrease in fluorescence and a second rise (supplementary figure 4B). The data for the fluorescence was carefully obtained using the imaris software. We created a curved geometry on each slice of the confocal stack. We analyzed the surfaces of this curved plane along the z-axis. This was carried out in imaris.

      3D imaging data can be difficult to interpret by eye, so it would perhaps be more helpful to demonstrate these propagating wavefronts by analysis; however, such analysis is not presented in a clear way. The legend in Figure 2B mentions a "wavefront trace", but there is no position information included - this trace instead seems to represent the average intensity trace of all cells. To demonstrate the propagation of a wavefront, this analysis should be shown for different subpopulations of cells at different positions from the center of the biofilm. Data is shown in Figure 8 that reflects the velocity of the wavefront as a function of biofilm position; however, because the wavefronts themselves are not evident in the data, it is difficult to interpret this analysis. The methods section additionally does not contain sufficient information about what these velocities represent and how they are calculated. Because of this, it is difficult for me to evaluate the section of the paper pertaining to wave propagation and the predicted biofilm critical size.

      The analysis is considered in more detail in a more expansive modelling article, currently under peer review in a physics journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

      There are some instances in the paper where claims are made that do not have data shown or are not evident in the cited data:

      (1) In the first results section, "When CCCP was added, we observed a fast efflux of ions in all cells"- the data figure pertaining to this experiment is in Fig. S1E, which does not show any ion efflux. The methods section does not mention how ion efflux was measured during CCCP treatment.

      We have worded this differently to properly convey our results.

      (2) In the discussion of voltage-gated calcium channels, the authors refer to "spiking events", but these are not obvious in Figure S3E. Although the fluorescence intensity changes over time, it's hard to distinguish these fluctuations from measurement noise; a no-light control could help clarify this.

      The calcium transients observed were not due to noise or artefacts.

      (3) The authors state that the membrane potential dynamics simulated in Figure 7B are similar to those observed in 3D biofilms in Fig. S4B; however, the second peak is not clearly evident in Fig. S4B and it looks very different for the mature biofilm data reported in Fig. 2. I have some additional confusion about this data specifically: in the intensity trace shown in Fig. S4B, the intensity in the second frame is much higher than the first; this is not evident in Video S6B, in which the highest intensity is in the first frame at time 0. Similarly, the graph indicates that the intensity at 60 minutes is higher than the intensity at 4 minutes, but this is not the case in Fig. S4A or Video S6B.

      The confusion stated here has now been addressed. Also it should be noted that while Fig 2.1 was obtained with LED light source, Fig S4A was obtained using a laser light source. While obtaining the confocal images (for Fig S4A ), the light intensity was controlled to further minimize photobleaching. Most importantly, there is an evidence of slow rise to the 2nd peak in Fig S4B. The first peak, quiescence and slow rise to second peak are evident.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      (1) Fig. 3C needs the "still" for the movie of control C. owczarzaki (in Movie S1).

      We have now added a WT control in this figure panel.

      (2) The elongated cell shape is seen infrequently in control cells, and I wonder whether these events are transient inactivation of coHpo or coWts in these cells. Perhaps the authors could comment on this in the discussion.

      This is an interesting possibility and we have now included it in our discussion (Lines 401403).

      (3) Does C. owczarzaki normally aggregate or this is a lab-specific phenotype? For example, the slime mold Dictyostelium discoideum forms aggregates during its life cycle. Could some additional information about C. owczarzaki be added to the introduction?

      Unfortunately little is known about Capsaspora “in the wild”, as it was isolated as an endosymbiont from a laboratory strain of snails. However, some related filasterians isolated from natural environments also show aggregatve ability, indicating that aggregation is in fact a physiological process in this group of organisms. We have updated our introduction to include this fact (Line 78-80).

      Reviewer #2 (Recommendations For The Authors):

      The studies on Hippo signalling in Capsaspora are currently limited to genetic experiments and analysis of Yki/YAP localisation. Biochemical evidence that Co Wts phosphorylates Co Yki/YAP on a conserved serine residue(s) would give important further evidence that this essential signalling step in the animal Hippo pathway is conserved in Capsaspora. However, such experiments require antibodies that detect specific phosphorylation events, which might not be available at present. Is mass spectrometry of the phospho-proteome a potential approach that could be employed to investigate this? The benefit of this approach is it would give information on other Hippo pathway proteins and could be used to probe signalling events under different culture conditions (e.g., aggregate, non-aggregate).

      In response to this recommendation, we attempted to detect Phospho-coWts and PhosphocoHpo using commercial antibodies against mammalian their homologs, in the hope of cross-species reactivity. However, we could not detect a signal by Western blot. Thus better reagents or refinement of techniques beyond the scope of this article may be required to examine the phosphorylation of these Capsaspora proteins. There was a published report of Capsaspora phosphoproteome analysis (Sebe Pedros et al., 2016 Dev Cell), although phosphorylation of the conserved sites on coYki, coWts, and coHpo was not reported in this analysis, suggesting more targeted approaches may be needed to examine phosphorylation of these core Hippo pathway components.

      The following statement that Wts LOF is stronger than Hpo LOF Capsaspora is consistent with overgrowth phenotypes in flies and mammals:

      "Interestingly, we found that coWts-/- cells were significantly more likely to show nuclear mScarlet-coYki localization than coHpo-/- cells (Figure 1D), which is consistent with Hpo/MST independent activity of Wts/LATS previously reported in Drosophila and mammals (Zheng et al., 2015)."

      However, the following statement describes a stronger phenotype in Hpo LOF Capsaspora than Wts LOF:

      "As contractile cells in the coHpo mutant background tended to show a more extreme elongated morphology than the coWts mutant, we focused on the coHpo mutant for further analysis."

      Does this mean that Hpo can regulate actomyosin contractility in both Wts/Yki-dependent and independent manners? A genetic experiment, similar to those that have been performed in Drosophila and mammals could help to address this, e.g., what is the phenotype of Hpo, Yki Capsaspora and Wts, Yki double mutant Capsaspora? Do they phenocopy Yki LOF Capsaspora and are the actomyosin phenotypes associated with Hpo and Wts mutant Capsaspora completely or partially suppressed? The authors indicate that generation of double mutant Capsaspora is not technically possible at present, however.

      Indeed given available techniques the generation of such double mutants is not currently possible. With this phenotype (aberrant cytoskeletal dynamics), it is hard to say what a “stronger” phenotype is, and which mutant has the “stronger” phenotype. We have edited this statement to try and reflect this point (Line 208-209).

      Another outstanding question is whether the Hpo/Wts/Yki-related actomyosin phenotypes are linked to regulation of transcription by Yki, or are regulated non-transcriptionally. Indeed, a non-transcriptional role for Drosophila Yki in promoting actomyosin contractility has been reported (Fehon lab, Dev Cell, 2018). Generation of Scalloped/TEAD mutant Capsaspora would allow this question to be investigated. Alternatively, this could be explored using variant Co Yki transgenes, e.g., one a Co Yki transgene does not form a physical complex with Co Sd/TEAD and a Co Yki transgene that is targeted to the cell cortex.

      To address this point, we tested whether a conserved amino acid residue in coYki (F123) that is required for transcriptional activity of human YAP (in this case, F95) is required for the phenotypic effects of the coYki 4SA mutant. We found that, in contrast to expression of coYki 4SA, expression of a coYki 4SA F123A mutant showed no effect on cell or aggregate morphology. These new results, which support a requirement for transcriptional activity for coYki function, have now been added to Figure 7.

      Reviewer #3 (Recommendations For The Authors):

      Repetition from previous publication:

      (1) ej: last sentences of the abstract in both works: From Phillips et al. eLife 2022;0:e77598: "Taken together, these findings implicate an ancestral role for the Hippo pathway in cytoskeletal dynamics and multicellular morphogenesis predating the origin of animal multicellularity, which was co-opted during evolution to regulate cell proliferation".

      From this manuscript: "Together, these results implicate cytoskeletal regulation but not proliferation as an ancestral function of the Hippo pathway and uncover a novel role for Hippo signaling in regulating cell density in a proliferation-independent manner "

      Our two papers deal with different components of the Hippo pathway: Yorkie/YAP/coYki in Phillips et al. eLife 2022;0:e77598 and upstream kinases in the current paper. The fact that perturbing different components of the pathway leads to similar conclusions actually strengthens the overall conclusion. Nevertheless, to be more clear about the novelty of the current manuscript, we have now changed the current text from “Hippo pathway” to “Hippo kinase cascade”, to emphasize that the current analysis deals with kinases upstream of Yorkie/YAP/coYki (Lines 35, 368-371).

      (2) The authors claim that the change in localization of coYki in Hpo -/- and Wts -/- , being now able to enter the nucleus, is the demonstration that the nuclear regulation of Yki by the Hippo pathway is ancestral to animals. Nevertheless, the authors had already made this claim in their publication of eLife 2022, when they made a mutant version of Yki with the four conserved phosphorylation sites (Sebé-Padrós 2012) mutated. Figure 5 A to F in Phillips et al. eLife 2022;0:e77598. In their words "This regulation of coYki nuclear localization, along with the previous finding that coYki can induce the expression of Hippo pathway genes when expressed in Drosophila (Sebé-Pedrós et al., 2012), suggests that the function of coYki has a transcriptional regulator and Hippo pathway effector is conserved between Capsaspora and animals. ".

      I understand that the localization of Yki in the coHpo-/- and coWts-/- is needed as part of final proof that Hpo and Wts are the kinases that control Yki phosphorylation in C. owczarzaki, but does not constitute a completely new message and should be written like that. Figure 1C of the actual manuscript drives to the same conclusion as Figure 5 A to F in Phillips et al. eLife 2022;0:e77598

      We think that demonstrating that Hippo and Warts orthologs specifically are responsible for regulation of coYki localization is a very important finding: Many unicellular organisms encode Hippo, Warts, and/or Yorkie’s transcriptional factor partner Sd, but not Yorkie. Our understanding is that in these earlier-branching unicellular organisms, the Hippo/Warts kinase module and Sd-like proteins functioned in distinct signaling modules. Thus Yorkie has the interesting property of “fusing” these two distinct signaling modules when it emerged. In this framework, it is interesting to show that this “fusion” occurred in Capsaspora, the most distant known relative of animals with a Yorkie ortholog, indicating that this “fusion” event is very ancient. Although fleshing out of this idea is beyond the scope of this manuscript and we plan to write about it elsewhere, we have modified our discussion to point out the importance that Hippo and Warts specifically are upstream regulators of coYki.

      In Drosophila among the genes transcriptionally regulated by Yki, are the positive regulators of the Hippo pathway in order to down regulate the Yki production.

      (1) The authors don't explain if these upstream regulators of the Hippo pathway are conserved in C. owczarzaki.

      We have now indicated the conservation of some upstream Hippo pathway components (Line 69-71).

      (2) Also it would be important to know how much coYki is being active in the C. owczarzaki in the mutant lines of coHpo-/- and coWts-/- in respect to wt and also in respect to coYki 4SA, and how this is impacting the transcription and protein production of down stream genes of coYki. I think some transcriptional and proteomic data would be informative. At least for those genes related with cytoskeleton.

      We have now performed RNA-seq on the coHpo and coWts mutants to address the concerns above (See Figure 8 and the final section of Results).

      Related with the above. Among the downstream targets of coYki, the authors mentioned in their previous work (Phillips et al. eLife 2022;0:e77598) that B-integrins were up regulated in coYki -/- suggesting that B-integrins could be behind the stronger cell-substrate attachment observed in the coYki-/- mutant. It would be important to investigate if the integrin adhesome is now down regulated and how previous and new results are related to the stronger cellsubstrate attachment in the coHpo-/- and coWts-/- lines. It would be important that previous results on coYki-/-, a mutant line of the same pathway, are discussed in these two new mutant contexts.

      Two Capsaspora integrin beta genes were previously found to be upregulated in the coYki mutant (CAOG_05058 and CAOG_01283, from Phillips et al., 2022 eLife). In our coWts and coHpo mutant RNAseq data, we see that CAOG_05058 is upregulated in both coHpo and coWts mutants, whereas CAOG_01283 does not show significantly different expression in either the coHpo or coWts mutant. Because the CAOG_05058 expression data seems to go in the “opposite” direction than you might expect (i.e. not “down regulated” as the reviewer predicts), and because we see no change in expression in CAOG_01283, these results are difficult to interpret. Therefore the role of integrins in Capsaspora Hippo pathway mutant phenotypes is thus still an open question.

      Some cells from the coHpo-/- and coWts-/- mutant lines, show higher attachment to the substrate, which results in an elongated shape while the cell detaches from the substrate. The authors claim this phenotype as a contractile behavior in these cells. This behavior would be caused by changes in cytoskeleton regulation or increased number of microvilli or a change in the distribution of microvilli.

      (1) In my opinion, this phenotype can not be considered a behavior per se (the cells become round once they are free from the substrate, so the elongation is temporal and the contractile behavior is a consequence from this attachment to the substrate), so I would not say that the Hippo pathway controls a contractile behavior as the authors state as one of the main conclusions of the manuscript.

      Many cell behaviors are known to depend on external conditions, such as substrates, growth factors, nutrients, chemokines, etc., and are therefore “temporal” by the reviewer’s criteria. We therefore feel that the phenotype we describe here can be considered a cell behavior.

      (2) On the other hand I think that further efforts on microscopy or immunocytochemistry could be performed in order to discern among the different causes; more microvilli? change in microvilli distribution? change in the acto-myosin cytoskeleton? Moreover these options are not mutually exclusive and very likely the explanation is multifactorial.

      (3) coWts-/- has a different phenotype at the periphery of the aggregates than coHpo-/-. The authors use stable transfected lines with NMM-Venus to visualize microvilli. It would be interesting that further experiments using this tool would be performed in order to visualize putative differences of the cell membrane at the periphery in the two mutant genotypes.

      We have now performed experiments examining filopodia in round vs elongated cells using the NMM-venus marker, as well as differences in filopodial morphology within aggregates in the different genotypes. Our data and conclusions are included in our updated manuscript (Figure 3- figure supplement 1).

      The authors nicely inspect the consequences of the mutant lines coHpo-/- and coWts-/- in the formation of the aggregates. They find that the aggregates in these cases are more densely packed likely due to the higher attachment from microvilli, which they are able to revert by using myosin inhibitors.

      (1) As mentioned above, it would be interesting that further experiments are performed by using NMM-Venus transfection into the coHpo-/- and coWts-/-genotypes in order to visualize putative differences of the strength and distribution of the microvilli in the aggregates of these two mutant genotypes. These experiments would inform if more or less microvilli contacts are created in these lines and support a mechanical explanation of the denser aggregates in the mutant lines, as they now suggest in the discussion.

      We have now performed these experiments, and our data and conclusions are described in the updated manuscript (Figure 5- figure supplement 1).

      (2) On the other hand, myosin inhibition through blebbistatin increases the number of elongated cells in the mutant lines, demonstrating that myosin is necessary for the cells to resolve their substrate attachment and become round. In my view is confusing that myosin is needed for cells to become round again (wt phenotype) and at the same time myosin inhibition is needed for aggregates to become less dense (wt phenotype). Do they lose density because more elongated cells are now in the aggregate? These results look confusing to me and I think they should be better discussed. Again the above transfections of NMM-Venus into the coHpo-/- and coWts-/-genotypes could be informative.

      We have attempted to detect cells with an “elongated” morphology within WT and mutant aggregates but so far have been unable to visualize such cells. More advanced microscopy techniques at extended time scales may allow us to observe such things, but we believe such studies are beyond the scope of this manuscript.

      The authors do not connect and discuss their results with a very relevant study done in Drosophila, Xu J et al. Dev Cell. 2018; 46(3): 271-284.e5, where a transcriptionally independent role of Yki is characterized. In Drosophila, Yki has an important role in a positive feedback loop with myosin at the cortical part of the cell, which is especially relevant for cytoskeleton regulation.

      The results encountered by the authors in their previous study with coYki-/-, indicated that coYki was important for proper actin dynamics and cell shape in C. owczarzaki. At that moment they did not interrogate if this phenotype could be due to the lack of a possible role of coYki in the cortex and they argue that the phenotype was caused by the lack of transcription regulation of downstream genes of coYki, which actually many were cytoskeleton related.

      Because the cortex function of Yki is independent of regulation of Hpo and Wts, the authors could use these genotypes by comparing them with WT (where the cortical role of Yki should be the same) and coYki-/- to investigate if the cortex role of Yki, is conserved in C. owczarzaki. In Drosophila the cortex role of Yki has been suggested to control tension at the cell surface. Drosophila Yki at the cortex activates myosin II through the N-terminal part of the protein and establishes a positive feedback loop by down regulating the Hippo pathway and obtaining therefore more active DmYki into the nucleus. This mechanism has been proposed by Xu et al. to work as the link between sensing cell tensions at the surface with control of tissue proliferation.

      In my opinion these are relevant results in the field that should be addressed in this study or at least well discussed. Actually, I think they could be a great opportunity for investigating if a putative cortex role of Yki is ancestral to its role linked to the Hippo pathway.

      We have now addressed this study in our manuscript- please see our response to reviewer #2’s last comment above.

      It would be informative to understand how stable expression through hygromicin selection is achieved in the transfection experiments. Is the recombinant plasmid integrated in the genome? Or is it stable as an episome?

      We believe that the plasmids stably integrate, as we never lose fluorescent signal once established in a clonal line, even after extended culturing (>6 months). It may be worthwhile to definitely determine integration vs. episome in future studies.

      The authors do not speculate or discuss how cell tension and cell proliferation is different for a unicellular organism or a tissue (multicellular) and I think should be addressed since the contexts are different.

      This is an interesting and important point, which we plan to discuss in detail in an upcoming review article, as a proper discussion of this idea, we think, is beyond the scope of this manuscript.

      Minor point. The study should cite other unicellular holozoans that have been also developed into treatable organisms such as Monosiga brevicollis (Woznica A, Kumar A, et al 2021eLife 10:e70436) and Abeoforma whisleri (Faktorová, D., Nisbet, R.E.R., Fernández Robledo, J.A. et al. Nat Methods17, 481-494 (2020) in line 83 of the manuscript. I am sure the authors appreciate how much effort there is behind every non-model organism put forward as experimentally treatable and should be properly acknowledged.

      We agree, and we have now included these examples of non-model organism development in our manuscript.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      We thank all the reviewers for taking the time to assess and provide valuable feedback on the manuscript. We believe these comments helped clarify the manuscript’s prose, and the suggestions on the functionality and aim of the toolbox were globally incorporated into the following updates of the toolbox. Particularly, we would like to point out some changes that will help all reviewers, independently of their individual comments, to understand the current state of the toolbox and some systematic changes that were made to the manuscript.

      We have received a significant amount of feedback asking for a PyTorch implementation of the toolbox. Consequently, we decided to enact this, and the next version of the toolbox will be exclusively in PyTorch. We will maintain the Application Programming Interface (API) and tutorial documentation for the TensorFlow version of the toolbox on the online website. However, going forward we will focus exclusively on bug-fixing and expanding from the latest version of MotorNet, which will be in PyTorch. We now believe that the greater popularity of PyTorch in the academic community makes that choice more sustainable while helping a greater proportion of research projects.

      These changes led to a significant alteration of the MotorNet structure, which is reflected by changes made throughout the manuscript, most particularly in Figure 3 and Table 1. A beneficial side-effect of this is a much simpler structure for MotorNet which ought to contribute positively toward its usability by researchers in the neuroscience community.

      We also refactored some terminology to be more in line with current computational neuroscience vocabulary:

      • The term “plant”, which comes from industrial engineering and is more niche in neuroscience, has been replaced by “effector”.

      • The term “task” has been replaced by “environment” to match the gymnasium toolbox terminology, which MotorNet is now compatible with. Task objects essentially performed the same function as environment objects from the gymnasium toolbox.

      • The term “controller” has been replaced by “policy” throughout, as this term is more general.

      • The term “motor command” is very specific to the motor control subfield of neuroscience, and therefore is replaced by “action”, which is more commonplace for this modelling component in computational neuroscience and machine learning.

      Reviewer #1 (Public Review):

      Summary:

      Codol et al. present a toolbox that allows simulating biomechanically realistic effectors and training Artificial Neural Networks (ANNs) to control them. The paper provides a detailed explanation of how the toolbox is structured and several examples that demonstrate its usefulness.

      Main comments:

      (1) The paper is well written and easy to follow. The schematics help in understanding how the toolbox works and the examples provide an idea of the results that the user can obtain.

      We thank the reviewer for this comment.

      (2) As I understand it, the main purpose of the paper should be to facilitate the usage of the toolbox. For this reason, I have missed a more explicit link to the actual code. As I see it, researchers will read this paper to figure out whether they can use MotorNet to simulate their experiments, and how they should proceed if they decide to use it. I'd say the paper provides an answer to the first question and assures that the toolbox is very easy to install and use. Maybe the authors could support this claim by adding "snippets" of code that show the key steps in building an actual example.

      This is an important point, which we also considered when writing this paper. We instead decided to focus on the first approach, because it is easier to illustrate the scientific use of the toolbox using code or interactive (Jupyter) notebooks than a publication format. We find the “how to proceed” aspect of the toolbox can more easily and comprehensively be covered using online, interactive tutorials. Additionally, this allows us to update these tutorials as the toolbox evolves over different versions, while it is more difficult to update a scientific article. Consequently, we explicitly avoided code snippets on the article itself. However, we appreciate that the paper would gain in clarity if this was more explicitly stated early. We have modified the paper to include a pointer to where to find tutorials online. We added this at the last paragraph of the introduction section:

      “The interested reader may consult the full API documentation, including interactive tutorials on the toolbox website at https://motornet.org.”

      (3) The results provided in Figures 1, 4, 5 and 6 are useful, because they provide examples of the type of things one can do with the toolbox. I have a few comments that might help improving them:

      (a) The examples in Figures 1 and 5 seem a bit redundant (same effector, similar task). Maybe the authors could show an example with a different effector or task? (see point 4).

      The effectors from figures 1 and 5 are indeed very similar. However, the tasks in figure 1 and 5 present some important differences. The training procedure in figure 1 never includes any perturbations, while the one from figure 5 includes a wide range of perturbations of different magnitudes, timing and directions. The evaluation procedure of figure 1 includes center-out reaches with permanent viscous (proportional to velocity) external dynamics, while that of figure 5 are fixed, transient, square-shaped perturbation orthogonal to the reach direction. Finally, the networks in figure 1 undergo a second training procedure after evaluation while the network of figure 5 do not. While we agree that some variation of effectors would be beneficial, we do show examples of a point-mass effector in figure 6. Overall, figure 5 shows a task that is quite different from that of figure 1 with a similar effector, while the opposite is true for figure 6. We have modified the text to clarify this for the reader, by adding the following.

      End of 1st paragraph, section 2.4.

      “Therefore, the training protocol used for this task largely differed from section 2.1 in that the networks are exposed to a wide range of mechanical perturbations with varying characteristics.”

      1st paragraph of section 2.5

      […] this asymmetrical representation of PMDs during reaching movements did not occur when RNNs were trained to control an effector that lacked the geometrical properties of an arm such as illustrated in Figure 4c-e and section 2.1.

      (b) I missed a discussion on the relevance of the results shown in Figure 4. The moment arms are barely mentioned outside section 2.3. Are these results new? How can they help with motor control research?

      We thank the reviewer for this comment. This relates to a point from reviewer 2 indicating that the purpose of each section was sometimes difficult to grasp as one reads. Section 2.3 explains the biomechanical properties that the toolbox implements to improve realism of the effector. They are not new results in the sense that other toolboxes implement these features (though not in differentiable formats) and these properties of biological muscles are empirically well-established. However, they are important to understand what the toolbox provides, and consequently what constraints networks must accommodate to learn efficient control policies. An example of this is the results in figure 6, where a simple effector versus a more biomechanically complex effector will yield different neural representations.

      Regarding the manuscript itself, we agree that more clarity on the goal of every paragraph may improve the reader’s experience. Consequently, we ensured to specify such goals at the start of each section. Particularly, we clarify the purpose of section 2.3 by adding several sentences on this at the end of the first paragraph in that section. We also now clearly state the purpose of section 2.3 with the results of figure 6 and reference figure 4 in that section.

      (c) The results in Figure 6 are important, since one key asset of ANNs is that they provide access to the activity of the whole population of units that produces a given behavior. For this reason, I think it would be interesting to show the actual "empirical observations" that the results shown in Fig. 6 are replicating, hence allowing a direct comparison between the results obtained for biological and simulated neurons.

      These empirical observations are available from previous electrophysiological and modelling work. Particularly, polar histograms across reaching directions like panel C are displayed in figures 2 and 3 of Scott, Gribble, Graham, Cabel (2001, Nature). Colormaps of modelled unit activity across time and reaching directions like panel F are also displayed in figure 2 of Lillicrap, Scott (2013, Neuron). Electrophysiological recordings of M1 neurons during a similar task in non-human primates can also be seen on “Preserved neural population dynamics across animals performing similar behaviour” figure 2 B (https://doi.org/10.1101/2022.09.26.509498) and “Nonlinear manifolds underlie neural population activity during behaviour” figure 2 B as well (https://doi.org/10.1101/2023.07.18.549575). Note that these two pre-prints use the same dataset.

      We have added these citations to the text and made it explicit that they contain visualizations of similar modelling and empirical data for comparison:

      “This heterogeneous set of responses matches empirical observations in non-human primate primary motor cortex recordings (Churchland & Shenoy, 2007; Michaels et al., 2016) and replicate similar visualizations from previously published work (Fortunato et al., 2023; Lillicrap & Scott, 2013; Safaie et al., 2023).”

      (4) All examples in the paper use the arm26 plant as effector. Although the authors say that "users can easily declare their own custom-made effector and task objects if desired by subclassing the base Plant and Task class, respectively", this does not sound straightforward. Table 1 does not really clarify how to do it. Maybe an example that shows the actual code (see point 2) that creates a new plant (e.g. the 3-joint arm in Figure 7) would be useful.

      Subclassing is a Python process more than a MotorNet process, as python is an object-oriented language. Therefore, there are many Python tutorials on subclassing in the general sense that would be beneficial for that purpose. We have amended the main text to ensure that this is clearer to the reader.

      Subclassing a MotorNet object, in a more specific sense, requires overwriting some methods from the base MotorNet classes (e.g., Effector or Environment classes, which correspond to the original Plant and Task object, respectively). Since we made the decision (mentioned above) to not include code in the main text, we added tutorials to the online documentation, which include dedicated tutorials for MotorNet class subclassing. For instance, this tutorial showcases how to subclass Environment classes:

      https://colab.research.google.com/github/OlivierCodol/MotorNet/blob/master/examples/3-environments.ipynb

      (5) One potential limitation of the toolbox is that it is based on Tensorflow, when the field of Computational Neuroscience seems to be, or at least that's my impression, transitioning to pyTorch. How easy would it be to translate MotorNet to pyTorch? Maybe the authors could comment on this in the discussion.

      We have received a significant amount of feedback asking for a PyTorch implementation of the toolbox. Consequently, we decided to enact this, and the next version of the toolbox will be exclusively in PyTorch. We will maintain the Application Programming Interface (API) and tutorial documentation for the TensorFlow version of the toolbox on the online website. However, going forward we will focus exclusively on bug-fixing and expanding from the latest version of MotorNet, which will be in PyTorch. We now believe that the greater popularity of PyTorch in the academic community makes that choice more sustainable while helping a greater proportion of research projects.

      These changes led to a significant alteration of the MotorNet structure, which are reflected by changes made throughout the manuscript, notably in Figure 3 and Table 1.

      (6) Supervised learning (SL) is widely used in Systems Neuroscience, especially because it is faster than reinforcement learning (RL). Thus providing the possibility of training the ANNs with SL is an important asset of the toolbox. However, SL is not always ideal, especially when the optimal strategy is not known or when there are different alternative strategies and we want to know which is the one preferred by the subject. For instance, would it be possible to implement a setup in which the ANN has to choose between 2 different paths to reach a target? (e.g. Kaufman et al. 2015 eLife). In such a scenario, RL seems to be a more natural option Would it be easy to extend MotorNet so it allows training with RL? Maybe the authors could comment on this in the discussion.

      The new implementation of MotorNet that relies on PyTorch is already standardized to use an API that is compatible with Gymnasium. Gymnasium is a standard and popular interfacing toolbox used to link RL agents to environments. It is very well-documented and widely used, which will ensure that users who wish to employ RL to control MotorNet environments will be able to do so relatively effortlessly. We have added this point to accurately reflect the updated implementation, so users are aware that it is now a feature of the toolbox (new section 3.2.4.).

      Impact:

      MotorNet aims at simplifying the process of simulating complex experimental setups to rapidly test hypotheses about how the brain produces a specific movement. By providing an end-to-end pipeline to train ANNs on the simulated setup, it can greatly help guide experimenters to decide where to focus their experimental efforts.

      Additional context:

      Being the main result a toolbox, the paper is complemented by a GitHub repository and a documentation webpage. Both the repository and the webpage are well organized and easy to navigate. The webpage walks the user through the installation of the toolbox and the building of the effectors and the ANNs.

      Reviewer #2 (Public Review):

      MotorNet aims to provide a unified interface where the trained RNN controller exists within the same TensorFlow environment as the end effectors being controlled. This architecture provides a much simpler interface for the researcher to develop and iterate through computational hypotheses. In addition, the authors have built a set of biomechanically realistic end effectors (e.g., an 2 joint arm model with realistic muscles) within TensorFlow that are fully differentiable.

      MotorNet will prove a highly useful starting point for researchers interested in exploring the challenges of controlling movement with realistic muscle and joint dynamics. The architecture features a conveniently modular design and the inclusion of simpler arm models provides an approachable learning curve. Other state-of-the-art simulation engines offer realistic models of muscles and multi-joint arms and afford more complex object manipulation and contact dynamics than MotorNet. However, MotorNet's approach allows for direct optimization of the controller network via gradient descent rather than reinforcement learning, which is a compromise currently required when other simulation engines (as these engines' code cannot be differentiated through).

      The paper could be reorganized to provide clearer signposts as to what role each section plays (e.g., that the explanation of the moment arms of different joint models serves to illustrate the complexity of realistic biomechanics, rather than a novel discovery/exposition of this manuscript). Also, if possible, it would be valuable if the authors could provide more insight into whether gradient descent finds qualitatively different solutions to RL or other non gradient-based methods. This would strengthen the argument that a fully differentiable plant is useful beyond improving training time / computational power required (although this is a sufficiently important rationale per se).

      We thank the reviewer for these comments. We agree that more clarity on the section goals may improve the reader’s experience and ensured this is the case throughout the manuscript. Particularly, we added the following on the first paragraph of section 2.3, for which an explicit goal was most missing:

      “In this section we illustrate some of these biomechanical properties displayed by MotorNet effectors using specific examples. These properties are well-characterised in the biology and are often implemented in realistic biomechanical simulation software.”

      Regarding the potential difference in solutions obtained from reinforcement or supervised learning, this would represent a non-trivial amount of work to do so conclusively and so may not be within the scope of the current article. We do appreciate however that in some situations RL may be a more fitting approach to a given task design. In relation to this point we now specify in the discussion that the new API can accommodate interfacing with reinforcement learning toolboxes for those who may want to pursue this type of policy training approach when appropriate (new section 3.2.4.).

      Reviewer #3 (Public Review):

      Artificial neural networks have developed into a new research tool across various disciplines of neuroscience. However, specifically for studying neural control of movement it was extremely difficult to train those models, as they require not only simulating the neural network, but also the body parts one is interested in studying. The authors provide a solution to this problem which is built upon one of the main software packages used for deep learning (Tensorflow). This allows them to make use of state-of-the-art tools for training neural networks.

      They show that their toolbox is able to (re-)produce several commonly studied experiments e.g., planar reaching with and without loads. The toolbox is described in sufficient detail to get an overview of the functionality and the current state of what can be done with it. Although the authors state that only a few lines of code can reproduce such an experiment, they unfortunately don't provide any source code to reproduce their results (nor is it given in the respective repository).

      The possibility of adding code snippets to the article is something we originally considered, and which aligns with comment two from reviewer one (see above). Hopefully this provides a good overview of the motivation behind our choice not to add code to the article.

      The modularity of the presented toolbox makes it easy to exchange or modify single parts of an experiment e.g., the task or the neural network used as a controller. Together with the open-source nature of the toolbox, this will facilitate sharing and reproducibility across research labs.

      I can see how this paper can enable a whole set of new studies on neural control of movement and accelerate the turnover time for new ideas or hypotheses, as stated in the first paragraph of the Discussion section. Having such a low effort to run computational experiments will be definitely beneficial for the field of neural control of movement.

      We thank the reviewer for these comment.

    1. Author Response

      This important work presents a new methodology for the statistical analysis of fiber photometry data, improving statistical power while avoiding the bias inherent in the choices that are necessarily made when summarizing photometry data. The reanalysis of two recent photometry data sets, the simulations, and the mathematical detail provide convincing evidence for the utility of the method and the main conclusions, however, the discussion of the re-analyzed data is incomplete and would be improved by a deeper consideration of the limitations of the original data. In addition, consideration of other data sets and photometry methodologies including non-linear analysis tools, as well as a discussion of the importance of the data normalization are needed.

      Thank you for the thorough and positive review of our work! We will incorporate this feedback to strengthen the manuscript. Specifically, we plan to revise the Discussion section to include a deeper consideration of the limitations of the original data, a description of the capacities of our method for conducting non-linear analyses, and the role data normalization plays in applicability of our tool.

      Reviewer 1:

      Strengths:

      The framework the authors present is solid and well-explained. By reanalyzing formerly published data, the authors also further increase the significance of the proposed tool opening new avenues for reinterpreting already collected data.

      Weaknesses:

      However, this also leads to several questions. The normalization method employed for raw fiber photometry data is different from lab to lab. This imposes a significant challenge to applying a single tool of analysis.

      Thank you for the positive feedback, we will address your comments in our revision. We agree that any data pre-processing steps will have down-stream consequences on the statistical inference from our method. Note, though, that this would also be the case with standard analysis approaches (e.g., t-tests, correlations) applied to summary measures like AUCs. For that reason, we do not believe that variability in pre-processing is an impediment to widespread adoption of a standard analysis procedure. Rather, we argue that the sensitivity of analysis results to pre-processing choices underscores the need for establishing statistical techniques that reduce the need for pre-processing, and properly account for structure in the data arising from experimental designs. The reviewer brings up an excellent point that we can further elaborate on how our methods actually reduce the need for such pre-processing steps. Indeed, our method provides smooth estimation results along the functional domain (i.e., across trial timepoints), has the ability to adjust for between-trial and -animal heterogeneity, and provides a valid statistical inference framework that quantifies the resulting uncertainty. For example, adjustment for session-to-session variability in signal magnitudes or dynamics could be accounted for, at least in part, through the inclusion of session-level random effects. This heterogeneity would then influence the width of the confidence intervals. This stands in contrast to “sweeping it under the rug” with a pre-processing step that may have an unknown impact on the final statistical inferences. Similarly, the level of smoothing is at least in part selected as a function of the data, and again is accounted for directly in the equations used to construct confidence intervals. In sum, our method provides both a tool to account for challenges in the data, and a systematic framework to quantify the additional uncertainty that accompanies accounting for those data characteristics.

      Does the method that the authors propose work similarly efficiently whether the data are normalized in a running average dF/F as it is described in the cited papers? For example, trace smoothing using running averages (Jeong et al. 2022) in itself may lead to pattern dilution. The same question applies if the z-score is calculated based on various responses or even baselines.

      This is an important question given how common this practice is in the field. Briefly, application of pre-processing steps will change the interpretation of the results from our analysis method. For example, if one subtracts off a pre-trial baseline average from each trial timepoint, then the “definition of 0”, and the interpretation of coefficients and their statistical significance, changes. Similarly, if one scales the signal (e.g., divides the signal magnitude by a trial- or animal-specific baseline), then this changes the interpretation of the FLMM regression coefficients to be in terms of an animal-specific signal unit as opposed to a raw dF/F. This is, however, not specific to our technique, and pre-processing would have a similar influence on, for example, linear regression (and thus t-tests, ANOVAs and Pearson correlations) applied to summary measures. We agree with the reviewer that explicitly discussing this point will strengthen the paper.

      While it is difficult to make general claims about the anticipated performance of the method under all the potential pre-processing steps taken in the field, we believe that most common pre-processing strategies will not negatively influence the method’s performance or validity; they would, instead, change the interpretation of the results. We are releasing a series of vignettes to guide analysts through using our method and, to address your comment, we will add a section on interpretation after pre-processing.

      How reliable the method is if the data are non-stationary and the baselines undergo major changes between separate trials?

      This is an excellent question. We believe the statistical inferences will be valid and will properly quantify the uncertainty from non-stationarities, since our framework does not impose stationarity assumptions on the underlying process. It is worth mentioning that non-stationarity and high trial-to-trial variability may increase variance estimates if the model does not include a rich enough set of covariates to capture the source of the heterogeneity across trial baselines. However, this is a feature of our framework, rather than a bug, as it properly conveys to the analyst that high unaccounted for variability in the signal may result in high model uncertainty. Finally, mixed effects modeling provides a transparent, statistically reasonable, and flexible approach to account for between-session, and between-trial variability, a type of non-stationarity. We agree with the reviewer that this should be more explicitly discussed in the paper, and will do so.

      Finally, what is the rationale for not using non-linear analysis methods? Following the paper's logic, non-linear analysis can capture more information that is diluted by linear methods.

      Functional data analysis assumes that the function varies smoothly along the functional domain (i.e., across trial timepoints). It is a type of non-linear modeling technique over the functional domain since we do not assume a linear model (straight line). Therefore, our functional data analysis approach is able to capture more information that is diluted by linear models. While the basic form of our model assumes a linear change in the signal at a fixed trial timepoint, across trials/sessions, our package allows one to easily model changes with non-linear functions of covariates using splines or other basis functions. One must consider, however, the tradeoff between flexibility and interpretability when specifying potentially complex models.

      Reviewer 2

      Strengths:

      The open-source package in R using a similar syntax as the lme4 package for the implementation of this framework on photometry data enhances the accessibility, and usage by other researchers. Moreover, the decreased fitting time of the model in comparison with a similar package on simulated data, has the potential to be more easily adopted.

      The reanalysis of two studies using summary statistics on photometry data (Jeong et al., 2022; Coddington et al., 2023) highlights how trial-by-trial analysis at each time-point on the trial can reveal information obscured by averaging across trials. Furthermore, this work also exemplifies how session and subject variability can lead to opposite conclusions when not considered.

      Thank you for the positive assessment of our work!

      Weaknesses:

      Although this work has reanalyzed previous work that used summary statistics, it does not compare with other studies that use trial-by-trial photometry data across time-points in a trial.

      As described by the authors, fitting pointwise linear mixed models and performing t-test and Benjamini-Hochberg correction as performed in Lee et al. (2019) has some caveats. Using joint confidence intervals has the potential to improve statistical robustness, however, this is not directly shown with temporal data in this work. Furthermore, it is unclear how FLMM differs from the pointwise linear mixed modeling used in this work.

      We agree with the reviewers that providing more detail about the drawbacks of the approach applied in Lee et al., 2019 will strengthen the paper. We will add an example analysis applying the method proposed by Lee et al., 2019 to show how the set of timepoints at which coefficient estimates reach statistical significance can vary dramatically depending on the sampling rate one subsamples their data at, a highly undesirable property of this strategy. Our approach is robust to this, and still provides a multiple comparisons correction through the joint confidence intervals.

      In this work, FLMM usages included only one or two covariates. However, in complex behavioral experiments, where variables are correlated, more than two may be needed (see Simpson et al. (2023), Engelhard et al. (2019); Blanco-Pozo et al. (2024)). It is not clear from this work, how feasible computationally would be to fit such complex models, which would also include more complex random effects.

      This is a good point. In our experience, the code is still quite fast (often taking seconds to tens of seconds in our experience) on a standard laptop when fitting complex models that include, for example, 10 covariates, or complex random effect specifications on dataset sizes common in fiber photometry. In the manuscript, we included results from simpler models with few covariates in an attempt to show results from the FLMM versions of the standard analyses (e.g., correlations, t-tests) applied in Jeong et al., 2022. Our goal was to show that our method reveals effects obscured by standard analyses even in simple cases. Some of our models did, however, include complex nested random effects (e.g., the models described in Section 4.5.2).

      Like other mixed-model based analyses, our method becomes slower when the number of observations in the dataset is on the order of tens of thousands of observations. However, we coded the methods to be memory efficient so that even these larger analyses can be run on standard laptops. We thank the reviewer for this point, as we worked extremely hard to scale the method to be able to efficiently fit models commonly applied in neuroscience. Indeed, challenges with scalability were one of the main motivations for applying the estimation procedure that we did; in the appendix we show that the fit time of our approach is much faster than existing FLMM software such as the refund package function pffr(), especially for large sample sizes. While pffr() appears to scale exponentially with the number of clusters (e.g., animals), our method appears to scale linearly. We will more explicitly emphasize the scalability in the revision, since we agree this will strengthen the final manuscript.

      Reviewer #3

      Strengths:

      The statistical framework described provides a powerful way to analyze photometry data and potentially other similar signals. The provided package makes this methodology easy to implement and the extensively worked examples of reanalysis provide a useful guide to others on how to correctly specify models.

      Modeling the entire trial (function regression) removes the need to choose appropriate summary statistics, removing the opportunity to introduce bias, for example in searching for optimal windows in which to calculate the AUC. This is demonstrated in the re-analysis of Jeong et al., 2022, in which the AUC measures presented masked important details about how the photometry signal was changing.

      Meanwhile, using linear mixed methods allows for the estimation of random effects, which are an important consideration given the repeated-measures design of most photometry studies.

      Thank you for the positive assessment of our work!

      Weaknesses:

      While the availability of the software package (fastFMM), the provided code, and worked examples used in the paper are undoubtedly helpful to those wanting to use these methods, some concepts could be explained more thoroughly for a general neuroscience audience.

      We appreciate this and, to address your and other reviewers’ comments, we are creating a series of vignettes walking users through how to analyze photometry data with our package. We will include algebraic illustrations to help users gain familiarity with the regression modeling here.

      While the methodology is sound and the discussion of its benefits is good, the interpretation and discussion of the re-analyzed results are poor:

      In section 2.3, the authors use FLMM to identify an instance of Simpson's Paradox in the analysis of Jeong et al. (2022). While this phenomenon is evident in the original authors' metrics (replotted in Figure 5A), FLMM provides a convenient method to identify these effects while illustrating the deficiencies of the original authors' approach of concatenating a different number of sessions for each animal and ignoring potential within-session effects. The discussion of this result is muddled. Having identified the paradox, there is some appropriate speculation as to what is causing these opposing effects, particularly the decrease in sessions. In the discussion and appendices, the authors identify (1) changes in satiation/habitation/motivation, (2) the predictability of the rewards (presumably by the click of a solenoid valve) and (3) photobleaching as potential explanations of the decrease within days. Having identified these effects, but without strong evidence to rule all three out, the discussion of whether RPE or ANCCR matches these results is probably moot. In particular, the hypotheses developed by Jeong et al., were for a random (unpredictable) rewards experiment, whereas the evidence points to the rewards being sometimes predictable. The learning of that predictability (e.g. over sessions) and variation in predictability (e.g. by attention level to sounds of each mouse) significantly complicate the analysis. The FLMM analysis reveals the complexity of analyzing what is apparently a straightforward task design.

      While we are disappointed to hear the reviewer felt our initial interpretations and discussion were poor, the reviewer brings up an excellent point that we had not considered. They have convinced us that acknowledging and elaborating on this alternative perspective will strengthen the paper. We agree that the ANCCR/RPE model predictions were made for unpredictable rewards and, as the reviewer rightly points out, there is evidence that the animals sense the reward delivery. Regardless of the learning theory one adopts (RPE, ANCCR or others), we agree that this (potentially) learned predictability alone could account for the increase in signal magnitude across sessions.

      After reading the reviewer’s comments, we consulted with a number of researchers in this area, and several felt that a CS+ can serve as a reward, within itself. From this perspective, the rewards in the Jeong et al., 2022 experiment might still be considered unexpected. After discussing extensively with the authors of Jeong et al., 2022, it is clear that they went to enormous trouble to prevent the inadvertent generation of a CS+, and it is likely changes in pressure from the solenoid (rather than a sound) that served as a cue. This underscores the difficulty of preventing perception of reward delivery in practice. As this paper is focused on analysis approaches, we feel that we can contribute most thoughtfully to the dopamine–learning theory conversation by presenting both sides.

      Overall, we agree with the reviewer that future experiments will be needed for testing the accuracy of the models’ predictions for random (unpredicted) rewards. While we understand that our attempt to document our conversations with the Jeong et al., 2022 authors may have room for improvement, we hope the reviewer can appreciate that this was done with the best of intentions. We wish to emphasize that we also consulted with several other researchers in the field when crafting the discussion. The Jeong et al., 2022 authors could easily have avoided acknowledging the potential incompleteness of their theory, by claiming that our results do not invalidate their predictions for a random reward, as the reward was not unpredicted in the experiment (as a result of the inadvertent solenoid CS+). Instead, they went out of their way to emphasize that their experiment did test a random reward, and that our results do present problems for their theory. We think that engagement with re-analyses of one’s data, even when findings are inconvenient, is a good demonstration of open science practice. For that reason as well, we feel providing readers with a perspective on the entire discussion will contribute to the scientific discourse in this area.

      Finally, we would like to reiterate that this conversation is happening because our method, by analyzing the signal at every trial timepoint, revealed a neural signal that appears to indicate that the animals sense reward delivery. Ultimately, this was what we set out to do: help researchers ask questions of their data that they could not ask before. We believe that having a demonstration that we can indeed do this for a “live” issue is the most appropriate way of demonstrating the usefulness of the method.

      It is clear the reviewer put a lot of time into understanding what we did, and was very thoughtful about the feedback. We would like to thank the reviewer again for taking such care in reviewing our paper.

      If this paper is not trying to arbitrate between RPE and ANCCR, as stated in the text, the post hoc reasoning of the authors of Jeong et al 2022 provided in the discussion is not germane.

      While we appreciate that the post hoc reasoning of the authors of Jeong et al., 2022 may not seem germane, we would like to provide some context for its inclusion. As statisticians and computer scientists, our role is to create methods, and this often requires using open source data and recreating past analyses. This usually involves extensive conversation with authors about their data and analysis choices because, if we cannot reproduce their findings using their analysis methods, we cannot verify that results from our own methods are valid. As such, we prefer to conduct method development in a collaborative fashion, and we strive to constructively, and respectfully, discuss our results with the original authors. We feel that giving them the opportunity to suggest analyses, and express their point of view if our results conflict with their original conclusions, is important, and we do not want to discourage authors from making their datasets public. As such, we conducted numerous analyses at the suggestion of Jeong et al., 2022 and discussed the results over the course of many months. Indeed the analyses in the Appendix that the reviewer is referring to were conducted at the suggestion of the authors of Jeong et al., 2022, in an attempt to rule out alternative explanations. We nevertheless appreciate that our interpretations of these results can include some of the caveats suggested by the reviewer, and we will strive to improve these sections.

      Arbitrating between the models likely requires new experimental designs (removing the sound of the solenoid, satiety controls) or more complex models (e.g. with session effects, measures of predictability) that address the identified issues.

      We agree with the reviewer that the results suggest that new experimental designs will likely be necessary to adjudicate between models. It is our hope that, by weighing the different issues and interpretations, our paper might provide useful suggestions into what experimental designs would be most beneficial to rule out competing hypotheses in future data collection efforts. We believe that our methodology will strengthen our capacity to design new experiments and analyses. We will make the reviewer’s suggestions more explicit in the discussion by emphasizing the limitations of the original data.

      Of the three potential causes of within-session decreases, the photobleaching arguments advanced in the discussion and expanded greatly in the appendices are not convincing. The data being modeled is a processed signal (ΔF/F) with smoothing and baseline correction and this does not seem to have been considered in the argument.

      We are disappointed to hear that this extensive set of analyses, much of which was conducted at the suggestion of Jeong et al., 2022, was not convincing. We agree that acknowledging any pre-processing would provide useful context for the reader. We do wish to clarify that we analyzed the data that were made available online (raw data was not available). Moreover, for comparison with the authors’ results, we felt it was important to maintain the same pre-processing steps as they did. These conditions were held constant across analysis approaches; therefore, we think that the changes within-trial are likely not influenced substantially by these pre-processing choices. While we cannot speak definitively to the impact any of the processing conducted by the authors had on the results, we believe that it was likely minor, given that the timing of signals at other points in the trial, and in other experiments, were as expected (e.g., the signal rose rapidly after cue onset in Pavlovian tasks).

      Furthermore, the photometry readout is also a convolution of the actual concentration changes over time, influenced by the on-off kinetics of the sensor, which makes the interpretation of timing effects of photobleaching less obvious than presented here and more complex than the dyes considered in the cited reference used as a foundation for this line of reasoning.

      We appreciate the nuance of this point, and we will add it to our discussion. In response to your criticism, we have consulted with more experts in the field regarding the potential for bleaching in this data, and it is not clear to us why photobleaching would be visible in one time-window of a trial, but not at another (less than a second away), despite high dF/F magnitudes in both time-windows. We do wish to point out that, at the request of the authors, we analyzed many experiments from the same animals and in most cases did not observe other indications of photobleaching. Hence, it is not clear to us why this particular set of experiments would garner additional skepticism regarding the potential for photobleaching to invalidate results. While the role of photobleaching may be more complicated with this sensor than others in the references, that citation was included, at the suggestion of Jeong et al., 2022 simply as a way of acknowledging that non-linearities in photobleaching can occur.

      Within this discussion of photobleaching, the characterization of the background reward experiments used in part to consider photobleaching (appendix 7.3.2) is incorrect. In this experiment (Jeong et al., 2022), background rewards were only delivered in the inter-trial-interval (i.e. not between the CS+ and predicted reward as stated in the text). Both in the authors' description and in the data, there is a 6s before cue onset where rewards are not delivered and while not described in the text, the data suggests there is a period after a predicted reward when background rewards are not delivered. This complicates the comparison of this data to the random reward experiment.

      Thank you for pointing this out!! We will remove the parenthetical on page 18 of the appendix that incorrectly stated that rewards can occur between the CS+ and the predicted reward.

      The discussion of the lack of evidence for backpropagation, taken as evidence for ANCCR over RPE, is also weak.

      This point was meant to acknowledge that, although our method yields results that conflict with the conclusions described by Jeong et al., 2022 on data from some experiments, on other experiments our method supports their results. Again, we believe that a critical part of open science is acknowledging both areas where analyses support and conflict with those of the original authors. We agree with the reviewer that qualifying our results so as not to emphasize support for/against RPE/ANCCR will strengthen our paper, and we will make these changes.

      A more useful exercise than comparing FLMM to the methods and data of Jeong et al., 2022, would be to compare against the approach of Amo et al., 2022, which identifies backpropagation (data publicly available: DOI: 10.5061/dryad.hhmgqnkjw). The replication of a positive result would be more convincing of the sensitivity of the methodology than the replication of a negative result, which could be a result of many factors in the experimental design. Given that the Amo et al. analysis relies on identifying systematic changes in the timing of a signal over time, this would be particularly useful in understanding if the smoothing steps in FLMM obscure such changes.

      Thank you for this suggestion, and we agree this could be a useful analysis for the field. Your thoughtful review has convinced us that focusing on our statistical contribution will strengthen the paper, and we will make changes to further emphasize that we are not seeking to adjudicate between RPE/ANCCR. We only had space in the manuscript to include a subset of the analyses conducted on Jeong et al., 2022, and had to relegate the results from the Coddington et al., data to an appendix. Realistically, it would be hard for us to justify analyzing a third dataset. As you may surmise from the one we presented, reanalyzing a new dataset is usually very time consuming, and invariably requires extensive communication with the original authors. We did include numerous examples in our manuscript where we already replicated positive results, in a way that we believe demonstrates the sensitivity of the methodology. We have also been working with five groups at NIH and elsewhere using our approach, in experiments targeting different scientific questions. In fact, one paper that extensively applies our method and compares the results from those yielded by standard analysis of AUCs is already accepted and in press. Hence there should soon be additional demonstrations of what the method can do in less controversial settings. Finally, our forthcoming vignettes include additional analyses, not included in the manuscript, that replicate positive results. We take your point that our description of the data supporting one theory or the other should be qualified, and we will correct that. Again, your review was very thorough, and we appreciate your taking so much time to help us improve our work.

      Reviewer #2 (Recommendations For The Authors):

      First, I would like to commend the authors for the clarity of the paper, and for creating an open-source package that will help researchers more easily adopt this type of analysis.

      Thank you!

      I would suggest the authors consider adding to the manuscript, either some evidence or some intuition on how feasible would be to use FLMM for very complex model specifications, in terms of computational cost and model convergence.

      This is an excellent point and we will make this suggested change in the Methods and Discussion section in the next draft.

      From my understanding, this package might potentially be useful not just for photometry data but also for two-photon recordings for example. If so, I would also suggest the authors add to the discussion this potential use.

      We appreciate your thinking on this point, as it would definitely help expand use of the method. We included a brief point in the Discussion that this package would be useful for other techniques, but we will expand upon this.

      Reviewer #3 (Recommendations For The Authors):

      The authors should define 'function' in context, as well as provide greater detail of the alternate tests that FLMM is compared to in Figure 7. Given the novelty of estimating joint CIs, the authors should be clearer about how this should be reported and how this differs from pointwise CIs (and how this has been done in the past).

      Thank you, this is a very good point and will be critical for helping analysts describe and interpret results. We will add more detail to the Methods section on this point.

      The authors identify that many photometry studies are complex nested longitudinal designs, using the cohort of 8 animals used in five task designs of Jeong et al. 2022 as an example. The authors miss the opportunity to illustrate how FLMM might be useful in identifying the effects of subject characteristics (e.g. sex, CS+ cue identity).

      This is a great suggestion and we will add this important point to the discussion , especially in light of the factorial designs common in neuroscience experiments.

      In discussing the delay-length change experiment, it would be more accurate to say that proposed versions of RPE and ANCCR do not predict the specific change.

      We will make this change and agree this is a better phrasing.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      The brain-machine interface used in this study differs from typical BMIs in that it's not intended to give subjects voluntary control over their environment. However, it is possible that rats may become aware of their ability to manipulate trial start times using their neural activity. Is there any evidence that the time required to initiate trials on high-coherence or low-coherence trials decreases with experience?

      This is a great question. First, we designed the experiment to avoid this possibility. Rats were experienced on the sequence of the automatic maze both pre and post implantation (totaling to weeks of pre-training and habituation). As such, the majority of the trials ever experienced by the rat were not controlled by their neural activity. During BMI experimentation, only 10% of trials were triggered during high coherence states and 10% for low coherence states, leaving ~80% of trials not controlled by their neural activity. We also implemented a pseudo-randomized trial sequence. When considered together, we specifically designed this experiment to avoid the possibility that rats would actively use their neural activity to control the maze.

      Second, we had a similar question when collecting data for this manuscript and so we conducted a pilot experiment. We took 3 rats from experiment #1 (after its completion) and we required them to perform “forced-runs” over the course of 3-4 days, a task where rats navigate to a reward zone and are rewarded with a chocolate pellet. The trajectory on “forced-runs” is predetermined and rats were always rewarded for navigating along the predetermined route. Every trial was initiated by strong mPFC-hippocampal theta coherence. We were curious as to whether time-to-trial-onset would decrease if we repeatedly paired trial onset to strong mPFC-hippocampal theta coherence. 1 out of 3 rats (rat 21-35) showed a significant correlation between time-to-trial onset and trial number, indicating that our threshold for strong mPFC-hippocampal theta coherence was being met more quickly with experience (Figure R1A). When looking over sessions and rats, there was considerable variability in the magnitude of this correlation and sometimes even the direction (Figure R1B). As such, the degree to which rat 21-35 was aware of controlling the environment by reaching strong mPFC-hippocampal theta coherence is unclear, but this question requires future experimentation.

      Author response image 1.

      Strong mPFC-hippocampal theta coherence was used to control trial onset for the entirety of forced-navigation sessions. Time-to-trial onset is a measurement of how long it took for strong coherence to be met. A) Time-to-trial onset was averaged across sessions for each rat, then plotted as a function of trial number (within-session experience on the forced-runs task). Rat 21-35 showed a significant negative correlation between time-to-trial onset and trial number, indicating that time-to-coherence reduced with experience. The rest of the rats did not display this effect. B) Correlation between trial-onset and trial number (y-axis; see A) across sessions (x-axis). A majority of sessions showed a negative correlation between time-to-trial onset and trial number, like what was seen in (A), but the magnitude and sometimes direction of this effect varied considerably even within an animal.

      Is there any evidence that rats display better performance on trials with random delays in which HPC-PFC coherence was naturally elevated?

      This question is now addressed in Extended Figure 5 and discussed in the section titled “strong prefrontal-hippocampal theta coherence leads to correct choices on a spatial working memory task”.

      The introduction frames this study as a test of the "communication through coherence" hypothesis. In its strongest form, this hypothesis states that oscillatory synchronization is a pre-requisite for inter-areal communication, i.e. if two areas are not synchronized, they cannot transfer information. Recent experimental evidence shows this relationship is more likely inverted-coherence is a consequence of inter-areal interactions, rather than a cause. See Schneider et al. (DOI: 10.1016/j.neuron.2021.09.037) and Vinck et al. (10.1016/j.neuron.2023.03.015) for a more in-depth explanation of this distinction. The authors should expand their treatment of this hypothesis in light of these findings.

      Our introduction and discussions have sections dedicated to these studies now.

      Figure 6 - It would be much more intuitive to use the labels "Rat 1", "Rat 2", and "Rat 3"; the "21-4X" identifiers are confusing.

      This was corrected in the paper.

      Figure 6C - The sub-plots within this figure are rather small and difficult to interpret. The figure would be easier to parse if the data were presented as a heatmap of the ratio of theta power during blue vs. red stim, with each pixel corresponding to one channel.

      This suggestion was implemented in the paper. See Fig 6C. Extended Fig. 8 now shows the power spectra as a function of recording shank and channel.

      Ext. Figure 2B - What happens during an acquisition failure? Instead of "Amount of LFP data," consider using "Buffer size".

      Corrected.

      Ext. Figure 2D-E - Instead of "Amount of data," consider using "Window size"

      Referred to as buffer size.

      Ext. Figure 2E - y-axis should extend down to 4 Hz. Are all of the last four values exactly at 8 Hz?

      Yes. Values plateau at 8Hz. These data represent an average over ~50 samples.

      Ext. Figure 2F - consider moving this before D/E, since those panels are summaries of panel F

      Corrected.

      Ext. Figure 4A - ANOVA tells you that accuracy is impacted by delay duration, but not what that impact is. A post-hoc test is required to show that long delays lead to lower accuracy than short ones. Alternatively, one could compute the correlation between delay duration and proportion correctly for each mouse, and look for significant negative values.

      We included supplemental analyses in Extended Fig. 4

      Reviewer #2 (Recommendations For The Authors):

      The authors should replace terms that suggest a causal relationship between PFC-HPC synchrony and behavior, such as 'leads to', 'biases', and 'enhances' with more neutral terms.

      Causal implications were toned down and wherever “leads” or “led” remains, we specifically mean in the context of coherence being detected prior to a choice being made.

      The rationale for the analysis described in the paragraph starting on line 324, and how it fits with the preceding results, was not clear to me. The authors also write at the start of this paragraph "Given that mPFC-hippocampal theta coherence fluctuated in a periodical manner (Extended Fig. 5B)", but this figure only shows example data from 2 trials.

      The reviewer is correct. While we point towards 3 examples in the manuscript now, we focused this section on the autocorrelation analysis, which did not support our observation as we noticed a rather linear decay in correlation over time. As such, the periodicity observed was almost certainly a consequence of overlapping data in the epochs used to calculate coherence rather than intrinsic periodicity.

      Shortly after the start of the results section (line 112), the authors go into a very detailed description of how they validated their BMI without first describing what the BMI actually does. This made this and the subsequent paragraphs difficult to follow. I suggest the authors start with a general description of the BMI (and the general experiment) before going into the details.

      Corrected. See first paragraph of “Development of a closed-loop…”.

      In Figure 2C, as expected, around the onset of 'high' coherence trials, there is an increase in theta coherence but this appears to be very transient. However, it is unclear what the heatmap represents: is it a single trial, single session, an average across animals, or something else? In Figure 3F, however, the increase appears to be much more sustained.

      The sample size was rats for every panel in this figure. This was clarified at the end of Fig. 3.

      In Figure 2D, it was not clear to me what units of measurement are used when the averages and error bars are calculated. What is the 'n' here? Animals or sessions? This should be made clear in this figure as well as in other figures.

      The sample size is rats. This is now clarified at the end of Fig 2.

      Describing the study of Jones and Wilson (2005), the authors write: "While foundational, this study treated the dependent variable (choice accuracy) as independent to test the effect of choice outcome on task performance." (line 83) It was not clear to me what is meant by "dependent" and "independent" here. Explaining this more clearly might clarify how the authors' study goes beyond this and other previous studies.

      The reviewer is correct. A discussion on independent/dependent variables in the context of rationale for our experiment was removed.

      Reviewer #3 (Recommendations For The Authors):

      As explained in the public review, my comments mainly concern the interpretation of the experimental paradigm and its link with previous findings. I think modifying these in order to target the specific advance allowed by the paradigm would really improve the match between the experimental and analytical data that is very solid and the author's conclusions.

      Concerning the paradigm, I recommend that the authors focus more on their novel ability to clearly dissociate the functional role of theta coherence prior to the choice as opposed to induced by the choice. Currently, they explain by contrasting previous studies based on dependent variables whereas their approach uses an independent variable. I was a bit confused by this, particularly because the task variable is not really independent given that it's based on a brain-driven loop. Since theta coherence remains correlated with many other neurophysiological variables, the results cannot go beyond showing that leading up to the decision it correlates with good choice accuracy, without providing evidence that it is theta coherence itself that enhances this accuracy as they suggest in lines 93-94.

      The reviewer is correct. A discussion on independent/dependent variables in the context of rationale for our experiment was removed.

      Regarding previous results with muscimol inactivation, I recommend that the authors expand their discussion on this point. I think that their correlative data is not sufficient to conclude as they do that despite "these structures being deemed unnecessary" (based on causal muscimol experiments), they "can still contribute rather significantly" since their findings do not show a contribution, merely a correlation. This extra discussion could include possible explanations of the apparent, and thought-provoking discrepancies that they uncover such as: theta coherence may be a correlate of good accuracy without an underlying causal relation, theta coherence may always correlate with good accuracy but only be causally important in some tasks related to spatial working memory or, since muscimol experiments leave the brain time to adapt to the inactivation, redundancy between brain areas may mask their implication in the physiological context in certain tasks (see Goshen et al 2011).

      The second paragraph of the discussion is now dedicated to this.

      Possible further analysis :

      • In Extended 4A the authors show that performance drops with delay duration. It would be very interesting to see this graph with the high coherence / low coherence / yoked trials to see if the theta coherence is most important for longer trials for example.

      This is a great suggestion. Due to 10% of trials being triggered by high coherence states, our sample size precludes a robust analysis as suggested. Given that we found an enhancement effect on a task with minimal spatial working memory requirements (Fig. 4), it seems that coherence may be a general benefit or consequence of choice processes. Nonetheless, this remains an important question to address in a future study.

      • Figure 6: The authors explain in the text that although the effect of stimulation of VMT is variable, overall VMT activation increased PFC-HPC coherence. I think in the figure the results are only shown for one rat and session per panel. It would be interesting to add a figure including their whole data set to show the overall effect as well as the variability.

      The reviewer is correct and this comment promoted significant addition of detail to the manuscript. We have added an extended figure (Ext. Fig. 9) showing our VMT stimulation recording sessions. We originally did not include these because we were performing a parameter search to understanding if VMT stimulation could increase mPFC-hippocampal theta coherence. The results section was expanded accordingly.

      Changes to writing / figures :

      • The paper by Eliav et al, 2018 is cited to illustrate the universality of coupling between hippocampal rhythms and spikes whereas the main finding of this paper is that spikes lock to non-rhythmic LFP in the bat hippocampus. It seems inappropriate to cite this paper in the sentence on line 65.

      We agree with the reviewer and this citation was removed.

      • Line 180 when explaining the protocol, it would help comprehension if the authors clearly stated that "trial initiation" means opening the door to allow the rat to make its choice. I was initially unfamiliar with the paradigm and didn't figure this out immediately.

      We added a description to the second paragraph of our first results section.

      • Lines 324 and following: the analysis shows that there is a slow decay over around 2s of the theta coherence but not that it is periodical (as in regularly occurring in time), this would require the auto-correlation to show another bump at the timescale corresponding to the period of the signal. I recommend the authors use a different terminology.

      This comment is now addressed above in our response to Reviewer #2.

      • Lines 344: I am not sure why the stable theta coherence levels during the fixed delay phase show that the link with task performance is "through mechanisms specific to choice". Could the authors elaborate on this?

      We elaborated on this point further at the end of “Trials initiated by strong prefrontal-hippocampal theta coherence are characterized by prominent prefrontal theta rhythms and heightened pre-choice prefrontal-hippocampal synchrony”

      • Line 85: "independent to test the effect of choice outcome on task performance." I think there is a typo here and "choice outcome" should be "theta coherence".

      The sentence was removed in the updated draft.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      "Expanding the Drosophila toolkit for dual control of gene expression" by Zirin et al. aims to develop resources for simultaneous independent manipulation of multiple genes in Drosophila. The authors use CRISPR knock-ins to establish a collection of T2A-LexA and T2A-QF2 transgenes with expression patterns in a number of commonly studied organs and tissues. In addition to the transgenic lines that are established, the authors describe a number of plasmids that can be used to generate additional transgenes, including a plasmid to generate a dual insert of LexA and QF that can be resolved into a single insert using FLP/FRT-mediated recombination, and plasmids to generate RNAi reagents for the LexA and QF systems. Finally, the authors demonstrate that a subset of the LexA and QF lines that they generated can induce RNAi phenotypes when paired with LexAop or QUAS shRNA lines. In general, the claims of the paper are well supported by the evidence and the authors do a thorough job of validating the transgenic lines and characterizing their expression patterns.

      Strengths:

      • Numerous Gal4 lines allow for highly specific genetic manipulation in a wide range of organs and tissues, however, similar tissue-specific drivers using alternative binary expression systems are not currently well developed. This study provides a large number of tissue and organ-specific LexA and QF2 driver lines that should be broadly useful for the Drosophila community.

      • While a minority of the driver lines do not express the expected pattern (likely due to cryptic regulatory elements in the LexA or QF2 sequences), the ability to generate drivers using two different Gal4 alternatives mitigates this issue (as in nearly all cases at least one of the two systems produces a clean driver line with the expected expression pattern).

      • The use of LexA-GAD provides an additional degree of control as it is subject to Gal80 repression. This could prove to be particularly useful in cases where a researcher wishes to manipulate multiple genes using Gal4 and LexA-GAD drivers as the Gal80(ts) system could be used for simultaneous temporal control of both constructs.

      • The use of Fly Cell Atlas information to generate novel oenocyte-specific driver lines provides a useful proof-of-concept for constructing additional highly tissue-specific drivers.

      Weaknesses:

      • Since these reagents will most commonly be paired with existing Gal4 lines, adding information about corresponding Gal4 lines targeting these tissues and how faithfully the LexA and QF2 lines recapitulate these Gal4 patterns would be highly beneficial.

      It is outside the scope of this paper to analyze the expression patterns of the corresponding publicly available Gal4 lines. It is clear from the tissue specificity of the LexA-GAD and QF2 lines that they are expressed in the expected larval tissues based on the target genes. We have added a sentence in the discussion section noting “Further, we expect that there will also be differences between the expression pattern of corresponding Gal4 and the LexA-GAD/QF lines, as the latter were made by knock-in, while the former are often enhancer traps. However, based on our larval mounts and dissections, the stocks generated in this paper are highly specific to the expression pattern of the targeted genes.”

      • It is not stated in the manuscript if these transgenic lines and plasmids are currently publicly available. Information about how to obtain these reagents through Bloomington, Addgene, or TRiP should be added to the manuscript.

      We have added to the materials section that “All vectors described here that are required to produce new driver lines will be made available at Addgene.” And “All transgenic fly stocks described here will be made available at the Bloomington Drosophila Stock Center.”

      Reviewer #2 (Public Review):

      Zirin, Jusiak, and Lopes et al presented an efficient pipeline for making LexA-GAD and QF2 drivers. The tools can be combined with a large collection of existing GAL4 drivers for a dual genetic control of two cell populations. This is essential when studying inter-organ communications since most of the current genetic drivers are biased toward the expression of the central nervous system. In this manuscript, the authors described the methodology for efficiently generating T2A-LexA-GAD and T2A-QF2 knock-ins by CRISPR, targeting a number of genes with known tissue-specific expression patterns. The authors then validated and compared the expression of double as well as single drivers and found the tissue-specific expression results were largely consistent as expected. Finally, a collection of plasmids for LexA-GAD and QF,2 as well as the corresponding LexAop and QUAS plasmids were generated to facilitate the expansion of these tool kits. In general, this study will be of considerable interest to the fly community and the resources can be readily generalized to make drivers for other genes. I believe this toolkit will have a significant, immediate impact on the fly community.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      • Lines 56-57: Janelia Flylight lines are not necessarily brain-specific - this collection has or could be screened in other tissues.

      Correct. We have altered this sentence to read: However, these lines were developed primarily for brain expression. Although they are often expressed in other tissues, they are not well suited for experiments targeting non-neuronal cell types

      • Line 197 - I don't see the referenced Figure S1 in the reviewer materials. It appears this is actually referencing panels LL and MM in Figure 2.

      Correct. We have fixed this error.

      • No information on the injection efficiency to create the CRISPR knock-in lines is presented. I am guessing the efficiency will be similar to that of other reported HDR-based CRISPR knock-ins, but if this information is available it would be useful to include it so that others know what to expect when injecting these vectors.

      We did not systematically assay the injection efficiency. However, we can say that it was in line with previous descriptions of CRISPR-based plasmid and ‘drop-in’ HDR methods. We have added a note in the methods that “Knock-in efficiencies were comparable to previous reports (Kanca et al. 2019; Kanca et al. 2022).”

      • Demonstration of successful multi-manipulation would strengthen the paper.

      We do not feel that this is necessary as there have been many papers showing combinatorial Gal4+LexA/QF experiments. An example from our lab can be seen in PMID: 37582831.

      • Also, are there approaches for efficiently constructing pairs of UAS/LexAOp or UAS/QUAS shRNA lines that would potentially streamline the genetics for multi-manipulation? Otherwise, this could be rather cumbersome to implement as one needs to combine a Gal4 line, a LexA/QF2 line (which will be constrained as to its chromosomal location by the target gene), and separate UAS-shRNA and LexAop/QUAS-shRNA constructs into the same fly.

      There are some recent innovations that are useful in this respect. We have added a sentence to the discussion that says: “There remains an unmet need for a single vector that would allow for UAS/LexAop/QUAS control of different shRNAs. However, recent innovations in multi module vectors and multiplexed drug-based genetics allow researchers to more efficiently generate UAS/QUAS/lexAop transgenic fly strains (Matinyan et al. 2021; Wendler et al. 2022).”

      • In Figure 5 - is the difference for the hh inserts attributable to the driver line or the GFP/mCherry construct (or differential ability to detect GFP/mCherry)? One could try visualizing hhL(-Q) with the LexAop-GFP line. I guess that the correspondence between the nubbin and hh result suggests that maybe QF2 is suppressed in the wing pouch, but this could also be the difference in the reporter constructs and it would be interesting to know if this difference is truly attributable to the driver constructs from the standpoint of knowing how consistent the QF/LexA patterns are expected to be.

      The difference is not attributable to GFP versus mCherry or the specific LexAop and QUAS lines that we used in figure 5. We tested the double knock-in and derivative single knock-ins with various QUAS and lexAop reporters and always observed the same pattern.

      Reviewer #2 (Recommendations For The Authors):

      There are a few points that should be clarified. A list of these specific points is provided below with the view that this could help the preparations of a stronger, improved paper.

      Line 50-51: "There have been no systematic studies comparing the two systems, with only anecdotal evidence to support one system over the other." It is unclear to me what the anecdotal evidence the authors referred to. Could the authors elaborate more on this part?

      Based on an examination of QUAS brains, Potter et al, 2010 (PMID 20434990) makes the claim that “The low basal expression of QUAS and UAS reporters provides significant advantage compared to the lexA binary expression system.”

      Shearin et al., 2014 (PMID: 24451596) compared Gal4/UAS, LexA/LexAop, and QF/QUAS reporter strength with the nompC driver and found that the QF system produced the strongest expression.

      While these observations might be true in the nervous system, it isn’t clear that this extends to other tissues, nor what effect this would have on gene knockdown experiments.

      There have been some reports that have explored swapping out a Gal4 insertion for a LexA or QF at the same locus. For example, Gohl et al. 2011 PMID: (PMID 21473015) mentions that “the majority of the swaps captured most features of the original GAL4 expression patterns. In some cases, however, either prominent features of the GAL4 pattern were lost or we observed new expression patterns. These changes may have resulted from differences in the strength or responsiveness of reporter lines. Alternately, the swap may have modified some combination of enhancer spacing and sequence composition flanking the promoter.”

      Line 61-62: "On average, each StanEx line expresses LexA activity in five distinct cell types, with only one line showing expression in just one tissue..." What's the evidence to support this claim?

      This observation comes from Figure S3 of Kockel et al. 2016 (PMID: 27527793), where the authors “analyzed a subset of 76 StanEx lines that are unambiguously inserted within, or adjacent to, a single known gene.” We cited this reference in the preceding sentence. To clarify, we have added the citation again for line 61-62.

      Line 63-65: "These findings are consistent with prior studies indicating that enhancers very rarely produce expression patterns that are limited to a single cell type in a complex organism (Jenett et al. 2012)." It might be worth expanding on the use of the split system to achieve high cell-type-specificity. Especially, there are growing resources using split-intein and T2A-split-GAL4 with the prediction of genes from single-cell RNA sequencing datasets.

      We agree that the split system is currently the premier method to produce the most specific driver lines. Indeed, our group has recently published a paper on the split-intein Gal4 system (see PMID 37276389). However, the tradeoff is that split systems usually require generation of transgenic lines, which becomes impractical for research involving two independent binary transcriptional systems, as the user would need to combine at least three driver components into single stocks, plus the UAS/QUAS/LexAop insertions. The ideal would be to generate complementary split insertions on the same chromosome, but we think a discussion of this is tangential to the thrust of our work here.

      The authors did not fully discuss the rationale of using LexA-GAD vs LexA-p65 or VP16AD throughout the manuscript. I assumed the main reason for choosing LexA-GAD was to be compatible with GAL80 suppression. It might be worth explicitly stating in the result (e.g., line 123 or in the introduction). Also, did the authors observe weak transcriptional activation using LexA-GAD? It has been shown that the strength of transactional activation is much weaker for GAL4AD than the p65 or VP16AD. This might be worth noting in the manuscript as well.

      We did briefly mention in the introduction that one disadvantage of the Flylight lines is that they “use a p65 transcriptional activation domain and therefore are not compatible with the Gal80 temperature sensitive Gal4 repression system.” We have expanded on this issue in the introduction which now says: “We chose to use LexA with the Gal4 activation domain, rather than the p65 or VP16 activation domains to allow for temporal control by Gal80 (Lai and Lee 2006; Pfeiffer et al. 2010). We chose to use QF2 variant over the original QF, to avoid the toxicity reported for the latter (Riabinina et al. 2015).”

      We did not have any problems visualizing gene expression with fluorescent reporters. Nor did we have any difficulty obtaining knock-down phenotypes with ubiquitous drivers.

      Line 125-127. Is there a specific reason why the authors chose the SV40 terminator for the double driver construct but the Hsp70 terminator for the single driver construct?

      We found that the Hsp70 terminator gave slightly lower expression and decided to use this for the singles to avoid toxicity. For the doubles we chose the SV40, to compensate for reduced protein expressiojn of the second gene position.

      Line 144-146: "To verify the knock-ins, we PCR-amplified the genomic regions flanking the insertion sites and confirmed that the insertions were seamless and in-frame." Did the authors recover lines with indel introduced, resulting in out-of-frame insertion?

      Yes, we did see indels, which sometimes resulted in out of frame insertions, which were discarded. This result is in line with what we have observed with other CRISPR HDR knock-in experiments.

      The underlying reason might be out of the scope of this manuscript. However, it would still be helpful for the authors to speculate the potential reasons why the T2A-LexA-GAD and T2A-QF2 targeting the same insertion site showed very distinct expressions.

      It is outside the scope of this report to test this issue experimentally. We have a section in the discussion which does speculate as to the reason: “While we had no difficulty obtaining knock-ins for both types of activators, we did observe that for some target genes, the T2A-QF2 was only active in a subset of the expected gene expression pattern. In particular, we found that T2A-QF2 was difficult to express in the wing pouch. It may be that toxicity is an issue, and the weaker QF2w may be a better option for generating drivers in some organs (Riabinina and Potter 2016). Alternatively, differences in the LexA-GAD and QF2 sequences, and sequence length, could impact the function of nearby gene regulatory regions.”

      Regarding the observation that the existence of 3XP3-RFP marker can interfere with the expression of T2A-LexA-GAD and T2A-QF2 expression in a case-by-case manner, it might be worth emphasizing in the discussion that the proper removal of 3XP3-RFP marker by Cre/LoxP recombination is important.

      We have added the following to the discussion: “Importantly, our knock-in constructs contain the 3XP3-RFP cassette for screening transformants. Perhaps due to interaction between the 3XP3 promoter and the regulatory regions of the target gene, we occasionally saw misexpression of the LexA-GAD/QF2 in the 3XP3 domain. We have therefore prioritized Cre-Lox removal of the 3XP3-RFP cassette from our knock-in stocks, and advise that users of the plasmids described here likewise remove the marker, following successful knock-in.”

      For Fig. 5B, 5F-G, the authors should elaborate more in the result section. For example, lines 215-217: "We tested this with the hh and dpp lines and observed robust generation of both T2A-QF2 and T2A-LexA-GAD from hs-Flp; T2A-QF2-T2A-LexA-GAD parents (Figure 5B)." It is unclear what the authors mean by "robust generation". Also, there is no description of the results in Fig. 5F-G.

      We have expanded this section for figure 5B, which now reads: “We tested this with the hh and dpp lines and observed robust generation of both T2A-QF2 and T2A-LexA-GAD from hs-Flp; T2A-QF2-T2A-LexA-GAD parents (Figure 5B). In the case of the hh line, 15 out of 36 heat-shocked parents gave rise to at least one T2A-LexA-GAD progeny, with a mean of 14% recombinant offspring per parent. 20 out of 36 gave rise to at least one T2A-QF2 progeny, with a mean of 9% recombinant offspring per parent. In the case of the dpp line, 31 out of 32 heat-shocked parents gave rise to at least one T2A-LexA-GAD progeny, with a mean of 30% recombinant offspring per parent. 17 out of 32 gave rise to at least one T2A-QF2 progeny, with a mean of 9% recombinant offspring per parent.

      We have also added a description for Figure 5F-G, which reads: “Recombinants were also independently verified by PCR of the insertions (Figure 5F-G), where we observed the expected smaller band sizes in the derivative T2A-QF2 and T2A-LexA-GAD relative to the parental double driver.”

      Line 229, minor error: "Into these vectors, ..."

      We have edited this to read: “We cloned shRNAs targeting forked (f) and ebony (e) genes into these vectors and assayed their phenotypes when crossed to ubiquitous LexA-GAD and QF2 drivers.”

      Line 238-240: "Both Tub-LexA-GAD and Tub-QF2 drivers generated knockdown phenotypes in the thorax when crossed to f and e shRNA lines. However, the Tub-LexA-GAD phenotypes were stronger than those of Tub-QF2 (Figure 6C-D, F-G, I-J)." The stated "stronger phenotypes" are not clear to me. It might be worth elaborating more.

      We have further clarified this by changing it to: “However, the Tub-LexA-GAD phenotypes were stronger than those of Tub-QF2 (Figure 6C-D, F-G, I-J). For example, Tub-LexA-GAD produced a fully penetrant f bristle phenotype (Figure 6F) while some wild-type bristles remained on the thoraces of Tub-QF2 f knockdown (Figure 6G). Neither Tub-LexA-GAD or Tub-QF2 was able to achieve the strength of phenotype generated by the T2A-LexA-GAD da knock-in line (compare the darkness of the cuticle caused by e knockdown in Figure 6H-J).”

      Line 257-250: "Our collection of T2A-LexA-GAD and T2A-QF2 and double driver vectors can be easily adapted to target any gene for CRISPR knock-in, with a high probability that the resulting line will accurately reflect the expression of the endogenous locus" The authors could refer to the recent gene-specific Trojan GAL4/split-GAL4 work to support the idea that these gene-specific T2A-GAL4/split-GAL4 drivers reflect better than the enhancer-based drivers.

      We have added the following sentence to the discussion: “The specificity achieved with this approach can also be seen in recent efforts to build collections of gene specific T2A-Split-Gal4 and T2A-Gal4 insertions (Kanca et al. 2019; Chen et al. 2023; Ewen-Campen et al. 2023).”

      Line 630: "Removal of 3XP3-RFP eliminated gut and anal pad misexpression and did not affect glial cell expression." It would be helpful to add the annotation on Fig. 3B to show the location of glial cell expression.

      We have added arrowheads on Figure 3 and the legend now reads: “Removal of 3XP3-RFP eliminated gut and anal pad misexpression and did not affect glial cell expression (white arrowheads).

      Line 650-651: "The fat body mCherry expression is also present in the reporter stock and does not indicate LexA-GAD activity." I did not get what the authors were trying to convey. Where did the fat body mCherry expression come from? Please elaborate more.

      We have changed this section to explain that “The fat body mCherry expression (yellow arrowhead) is from leakiness of the reporter stock and does not indicate LexA-GAD activity.”

      Line 679-680: "forked shRNA produced a forked bristles phenotype." Please add the annotation on the figures to show where the phenotypes were.

      We have added arrowheads and asterisks to the figure. The legend now reads: “(E-G) forked shRNA produced a forked bristles phenotype (white arrowheads). Note that some bristles retain a more elongated wild-type morphology with the Tub-QF2 driven forked knockdown (G, yellow asterisk).”

      Fig 1D-E and 4A-B. There is no description throughout the manuscript about QA, QS regulation as well as little GAL80ts regulation. It will confuse readers with a little fly genetic background. Please include the introductions of these regulations of different binary expression systems.

      We have added a section in the introduction, which states: “We chose to use LexA with the Gal4 activation domain, rather than the p65 or VP16 activation domains to allow for temporal control by the temperature sensitive Gal4 repressor, Gal80 (Lai and Lee 2006; Pfeiffer et al. 2010). We chose to use QF2 variant over the original QF, to avoid the toxicity reported for the latter (Riabinina et al. 2015). Like Gal80-based modulation of LexA-GAD, QF2 activity can also be regulated temporally by expressing QS, a QF repressor. QS repression of QF can be released by feeding flies quinic acid (Riabinina and Potter 2016).”

      Fig. 2, there are several ND in the figure without any explanation in the manuscript (e.g. Mef2 and He). In addition, the expression patterns look quite different between T2A-LexA-GAD and T2A-QF2 for some genes (e.g., mex1, Myo31DF), but the authors did not mention any of them in the manuscript. Please elaborate more.

      We have altered the Figure 2 legend as follows: “(A-KK) T2A-LexA-GAD knock-in lines crossed to a LexAop-GFP reporter and T2A-QF2 knock-in lines crossed to a QUAS-GFP reporter. Panels show 3rd instar larva. GFP shows the driver line expression pattern. RFP shows the 3XP3 transformation marker, which labels the posterior gut and anal pads of the larva. Gene names and tissues are on the left. We failed to obtain LexA-GAD knock-ins for Mef2 (E) and He (DD). (LL-MM) 3rd instar imaginal disc from the insertions in the nubbin (nub) gene. Note that most of the lines are highly tissue-specific and are comparable between the LexA-GAD and QF2 knock-ins. Insertions in the daughterless gene (da) and nub are an exception, as the T2A-LexA-GAD, but not the T2A-QF2, gives the expected expression pattern. Insertions in the gut-specific genes mex1 (X-Y) and Myo31Df (Z-AA) also differed between the LexA-GAD and QF2 drivers.”

      We have also added a note on the inconsistency of mex1 and Myo31Df in the discussion: “While we had no difficulty obtaining knock-ins for both types of activators, we did observe that for some target genes, the T2A-QF2 was only active in a subset of the expected gene expression pattern. In particular, we found that T2A-QF2 was difficult to express in the wing pouch. Additionally, we found that the driver expression in the gut-specific genes, mex1 and Myo31Df differed between the LexA-GAD and QF2 transformants. In both cases the LexA-GAD was more broadly expressed along the length of the gut than the QF2. It may be that toxicity is an issue, and the weaker QF2w may be a better option for generating drivers in some organs (Riabinina and Potter 2016).”

      Fig. 4B, it is unclear why the hsp70 is present downstream of the enhancer of interest (upstream of T2A). Is it the molecular mark resulting from the cloning steps? Does it serve any specific purpose?

      This is the Drosophila hsp70 gene minimal promoter and is standard for many expression constructs in Drosophila. In the methods section we described how we made versions of the pMCS-T2A-QF2-T2A-LexA-GAD-WALIUM20 with and without tis minimal promoter: “We used pMCS-T2A-QF2-T2A-lexA0GAD-WALIUM20 for dpp-blk and pMCS-T2A-QF2-T2A-lexGAD-WALIUM20-alt (which lacks the hsp70 promoter) for Ilp2, since dpp-blk does not have a basal promoter, but the Ilp2 enhancer does.”

      Fig 5A. The resulting single T2A-QF2 and T2A LexA-GAD from the double driver parental lines retain the sequence of FRT3 upstream of the QF2 and LexA-GAD. I assume the FRT3 part will be translated and remain attached to QF2 and LexA-GAD. Is that correct? If so, would this cause any adverse effect?

      Correct. The FRT3 sequence is present in both the parental double and single derivatives. We can say that the additional amino acids do not prevent LexA-GAD or QF2 transcriptional activation. We do not know whether there may be other adverse effects, though we did not observe any.

      Fig. 5C-C'. It seems like the images of Fig. 5C-C' were the same as Fig. 4D-D'. If so, the authors should indicate that in the figure legend.

      We have made a note of this in the figure legend.

    1. Then Gawain bethought him, and it came into his heart that this were a jewel for the jeopardy that awaited him when he came to the Green Chapel to seek the return blow–could he so order it that he should escape unslain, ’twere a craft worth trying

      Here the green girdle serves as a symbol of life, however we come to see the meaning of the girdle changes throughout the text. We see Sir Gawain not follow with his chivalrous values and duty of faith as the presentation of the girdle challenges his beliefs as it becomes a stronger symbol of faith than God himself. This is an interesting development considering how previously in the text Sir Gawain was devoted to his faith, turning to it in his moments of need by praying "that Mary may be his guide". I think this is a key moment in the text because it reveals the humanity in Sir Gawain and his fear for death. As time goes by and the day to which he is interred to meet with the Green Knight approaches, he resorts to supernatural objects in the midst of fear for his own life.

      Works cited: Malarkey, Stoddard, and J. Barre Toelken. “Gawain and the Green Girdle.” The Journal of English and Germanic Philology, vol. 63, no. 1, 1964, pp. 14–20. JSTOR, http://www.jstor.org/stable/27714339. Accessed 6 Mar. 2024.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      Comment 1.1: The distinction of PIGS from nearby OPA, which has also been implied in navigation and ego-motion, is not as clear as it could be.

      Response1.1: The main “functional” distinction between TOS/OPA and PIGS is that TOS/OPA responds preferentially to moving vs. stationary stimuli (even concentric rings), likely due to its overlap with the retinotopic motion-selective visual area V3A, for which this is a defining functional property (e.g. Tootell et al., 1997, J Neurosci). In comparison, PIGS does not show such a motion-selectivity. Instead, PIGS responds preferentially to more complex forms of motion within scenes.

      Moreover, PIGS and TOS/OPA are located in differently relative to the retinotopic visual areas. Briefly, PIGS is located adjacent to areas IPS3-4 while TOS/OPA overlaps with areas V3A/B and IPS0 (V7). This point is now highlighted in the new experiment 3b and the new Figure 6. In this revision, we also tried to better highlight these point in sections 4.3, 4.4 and 4.5. (see also the response to the first comment from Reviewer #2).

      Reviewer 2:

      Comment 2.1: First, the scene-selective region identified appears to overlap with regions that have previously been identified in terms of their retinotopic properties. In particular, it is unclear whether this region overlaps with V7/IPS0 and/or IPS1. This is particularly important since prior work has shown that OPA often overlaps with v7/IPS0 (Silson et al, 2016, Journal of Vision). The findings would be much stronger if the authors could show how the location of PIGS relates to retinotopic areas (other than V6, which they do currently consider). I wonder if the authors have retinotopic mapping data for any of the participants included in this study. If not, the authors could always show atlas-based definitions of these areas (e.g. Wang et al, 2015, Cerebral Cortex).

      Response 2.1: We thank the reviewers for reminding us to more clearly delineate this issue of possible overlap, including the information provided by Silson et al, 2016. The issue of possible overlap between area TOS/OPA and the retinotopic visual areas, both in humans and non-human primates, was also clarified by our team in 2011 (Nasr et al., 2011). As you can see in Figure 6 (newly generated), and consistent with those previous studies, TOS/OPA overlaps with visual areas V3A/B and V7. Whereas PIGS is located more dorsally close to IPS3-4. As shown here, there is no overlap between PIGS and TOS/OPA and there is no overlap between PIGS and areas V3A/B and V7.

      To more directly address the reviewer’s concern, in this revision, we have added a new experiment (Experiment 3b) in which we have shown the relative position of PIGS and the retinotopic areas in two individual subjects (Figure 6). All the relevant points are also discussed in section 4.3.

      Comment 2.2: Second, recent studies have reported a region anterior to OPA that seems to be involved in scene memory (Steel et al, 2021, Nature Communications; Steel et al, 2023, The Journal of Neuroscience; Steel et al, 2023, biorXiv). Is this region distinct from PIGS? Based on the figures in those papers, the scene memory-related region is inferior to V7/IPS0, so characterizing the location of PIGS to V7/IPS0 as suggested above would be very helpful here as well. If PIGS overlaps with either of V7/IPS0 or the scene memory-related area described by Steel and colleagues, then arguably it is not a newly defined region (although the characterization provided here still provides new information).

      Response 2.2: The lateral-place memory area (LPMA) is located on the lateral brain surface, anterior relative to the IPS (see Figure 1 from Steel et al., 2021 and Figure 3 from Steel et al., 2023). In contrast, PIGS is located on the posterior brain surface, also posterior relative to the IPS. In other words, they are located on two different sides of a major brain sulcus. In this revision we have clarified this point, including the citations by Steel and colleagues in section 4.3.

      Comments 2.3: Another reason that it would be helpful to relate PIGS to this scene memory area is that this scene memory area has been shown to have activity related to the amount of visuospatial context (Steel et al, 2023, The Journal of Neuroscience). The conditions used to show the sensitivity of PIGS to ego-motion also differ in the visuospatial context that can be accessed from the stimuli. Even if PIGS appears distinct from the scene memory area, the degree of visuospatial context is an alternative account of what might be represented in PIGS.

      Response 2.3: The reviewer raises an interesting point. One minor confusion is that we may be inadvertently referring to two slightly different types of “visuospatial context”. Specifically, the stimuli used in the ego-motion experiment here (i.e. coherently vs. incoherently changing scenes) represent the same scenes, and the only difference between the two conditions is the sequence of images across the experimental blocks. In that sense, the two experimental conditions may be considered to have the same visuospatial “context”. However, it could be also argued that the coherently changing scenes provide more information about the environmental layout. In that case, considering the previous reports that PPA/TPA and RSC/MPA may also be involved in layout encoding (Epstein and Kanwisher 1998; Wolbers et al. 2011), we expected to see more activity within those regions in response to coherently compared incoherently changing scenes. These issues are now more explicitly discussed in the revised article (section 4.6).

      Reviewer 3:

      Comment 3.1: There are few weaknesses in this work. If pressed, I might say that the stimuli depicting ego-motion do not, strictly speaking, depict motion, but only apparent motion between 2s apart photographs. However, this choice was made to equate frame rates and motion contrast between the 'ego-motion' and a control condition, which is a useful and valid approach to the problem. Some choices for visualization of the results might be made differently; for example, outlines of the regions might be shown in more plots for easier comparison of activation locations, but this is a minor issue.

      Response 3.1: We thank the reviewer for these constructive suggestions, and we agree with their comment that the ego-motion stimuli are not smooth, even though they were refreshed every 100 ms. However, the stimuli were nevertheless coherent enough to activate areas V6 and MT, two major areas known to respond preferentially to coherent compared to incoherent motion.

      Reviewer #1 (Recommendations For The Authors):

      I enjoyed reading this article. I have a few suggestions for improvement:

      (1) Delineation from OPA: The OPA has been described in quite similar terms as PIGS, with its involvement in ego-motion (e.g., crawling, walking) and navigation in general (e.g., Dilks' recent work; Bonner and Epstein). The authors address the distinction in section 4.4. Unlike Kamps et al. (2016) and Jones et al. (2023), the authors found weak or no evidence for ego-motion in OPA. They explain this discrepancy with differences in refresh rates and different levels of spatial smoothing of the fMRI data. It is not clear why these fairly small methodological differences would lead to different findings of ego-motion in the OPA. Arguably, the OPA is the closest of the "established" scene areas to PIGS, both in anatomical location and in function. I would therefore appreciate a more detailed discussion of the differences between these two areas.

      Response: Jones et al. have also shown that ego-motion TOS/OPA activity when compared to scrambled scenes. This is fundamentally different than what we have shown here, which coherently vs. incoherently changing scenes (i.e. not a small difference). Also, Kamps et al. used static scenes as a control which, considering TOS/OPA motion-selectivity, have a large impact on TOS/OPA response.

      (2) Random effects analysis: The authors mention using a "random effects analysis" for several of their experiments. I would ask them to provide more details on what statistical models were used here. Were they purely random-effects models or actually mixed-effects models? What were the factors that entered into the analysis? Providing more detail would make the analysis techniques more transparent.

      Response: This point is now clarified in the Methods section.

      (3) Data and code availability: The authors write that data and code "are ready to be shared upon request." (section 2.5) In the spirit of transparency and openness, I strongly encourage the authors to make the data publicly available, e.g., on OSF or OpenNeuro. In particular, having probabilistic maps of PIGS available will allow other researchers to include PIGS in their analysis pipelines, making the current work more impactful.

      Response: We have made the probabilistic labels available to the public. This point is now highlighted in section 2.5.

      (4) Minor comments on the writing that caught my eye while reading the article:

      • Line 27: "in the human brain".

      Response: Done.

      -Line 30: I don't agree with the characterization of the previous model of scene perception as "simplistic." Adding one additional ROI makes it no less simplistic. Perhaps the authors can rephrase to make this slightly less antagonistic?

      Response: Done.

      • Line 71: it is not clear why NHPs are relevant here.

      Response: We decided to keep the text intact.

      • Line 138" "were randomized".

      Response: Done.

      • Line 152: "consisting".

      Response: Done.

      • Line 155: "sets" (plural).

      Response: Done.

      • Lines 253-255: Why were the 3T spatially smoothed but not the 7T data? This seems odd.

      Response: We kept the text intact.

      • Line 481: "we found strong motion selectivity" (remove "a").

      Response: Done.

      • Line 564: a word is missing, probably: "a stronger effect of ego-motion".

      Response: Done.

      • Line 591: "controlling spatial attention" (remove "the").

      Response: Done.

      • Line 591 and 594: Both sentences start with "However". I think the first of these should not because it is setting up the contrast for the second sentence.

      Response: Done.

      • Line 607: "higher-level" (hyphen).

      Response: Done.

      • Throughout the manuscript: adverbial phrases such as "(in)coherently changing" or "probabilistically localized" do not get a hyphen.

      Response: Done.

      Reviewer #2 (Recommendations For The Authors):

      The authors state that "All data, codes and stimuli are ready to be shared upon request". Ideally, these materials should be deposited in appropriate repositories (e.g. OpenMRI, GitHub) and not require readers to contact the authors to obtain such materials.

      Other Comments:

      (a) The title ("A previously undescribed scene-selective site is the key to encoding ego-motion in natural environments") is potentially misleading - the work was not conducted in a natural environment. At best, you could say they are 'naturalistic stimuli'. Also, in what sense is PIGS "key" to encoding ego-motion - the study just shows sensitivity to this factor.

      Response: We changed the title to “naturalistic environments”.

      (b) Figure 1 - I'm not sure what point the authors are trying to make with Figure 1. The comparison is between a highly smoothed, group fixed-effects analysis and a less-smoothed individual subject analysis. The differences between the two could reflect group vs. individual, highly-smoothed (5 mm) versus less-smoothed (2 mm), or differences in thresholding. If the thresholding were lower for the group analysis, it would probably start to look more similar to the individual subject. As it stands, this figure isn't particularly informative, it seems redundant with Figure 2, and Figure 1A is not even referenced in the main text. Further, fixed effects analyses are relatively uncommon in the recent literature, so their inclusion is unusual.

      Response: Figure 1A is a replication of the data/method used in Nasr et al., 2011 and it will help the readers see the difference between the “traditional” scene-selectivity maps generated based on group-averaging” vs. data from individual subjects. In this case, we decided not to change the Figure.

      (c) Figure 3 - why are the two sets of maps shown at different thresholds? For 3B given the larger sample size, it is expected that the extent of the significant activations will increase. Currently the higher threshold for 3B and the smaller range for 3A is making the sets of maps look more comparable.

      Response: As the reviewer noticed, the number of subjects is larger in Figure 3B compared to 3A. The main point of this figure is to show that the PIGS activity center does not vary across populations. Considering this point, we decided not to change this figure.

      (d) Figure 10 - why is the threshold lower than used for other figures? It would be helpful if there was consistent thresholding across figures.

      Response: Experiment 6 and Experiment 1 are based on different stimuli (see Methods). Also, among those subjects who participated in Experiment 1, two subjects did not participate in Experiment 6. These points are already highlighted in the text.

      (e) Figures - how about the AFNI approach of thresholding and showing sub-threshold data at the same time? (Taylor et al, 2023, Neuroimage).

      Response: We highly appreciate the methodology suggested by Taylor and colleagues. However, our main point here is to show the center of PIGS activity. In this condition, showing an unthresholded activity map doesn’t have any advantage over the current maps. Considering these points, we decided not to change the figures.

      (f) Coherent versus incoherent scenes - there are many differences between the coherent and incoherent scenes. Arguing that it must be ego-motion seems a little premature without further investigation. Activity anterior to OPA has been associated with the construction of an internal representation of a spatial environment (Steel et al., 2023, The Journal of Neuroscience). Could it be that this is the key effect, not really the ego-motion?

      Response: In this revision, we discussed the study by Steel et al., 2021 and 2023 in section 4.3.

      Reviewer #3 (Recommendations For The Authors):

      Overall, I think this is already an excellent contribution. The suggestions I have are minor and may help with the clarity of the results.

      (1) My main request of the authors would be to provide more points of reference in some of the figures with cortical maps. In many cases, the authors use arrows to point to the locations of activations of interest. However, the arrows in adjacent figures are often not placed in exactly the same places on maps that are meant to be compared. It would very much help the viewer to compare activations if the arrows pointing to activations or regions of interest were placed in identical locations for the same brains appearing in different sub-panels (e.g. in panels A and B of Figure 1). The underlying folds of the cortical surface provide some points of reference, but these are often occluded to different extents by data in figures that are meant to be compared.

      Response: To address the reviewer’s concern, we regenerated Figure 8 (Figure 7 in the previous submission) and we tried to put arrowheads in identical locations, as much as possible. Especially for PIGS, this point was also considered in Figures 2 and 3.

      (2) Outlines (such as those in Figure 5) are also very useful, and I would encourage broader use of them in other figures (e.g. Figures 7, 10, and 12). Figures 10 and 12 are on the fsaverage surface, so the same outlines could be used for them as for Figure 5.

      To be clear, it's possible to apprehend the results with the figures as they are, but I think a few small changes could help a lot.

      Response: In this revision, we added outlines to Figures 11 and 13 (Figure 10 and 12 in the previous submission). We did not add the outline to Figure 8 because it made it hard to see PIGS. Rather we used arrows (see the previous comment).

      Other minor points:

      In the method for Experiment 4, the authors write: "Other details of the experiment were similar to those in Experiment 1.". Similar or the same? The authors should clarify this statement, e.g. "the number of images per block, the number of blocks, the number of runs were the same as Experiment 1" - with any differences noted.

      Response: This point is now addressed in the Methods section.

      In Figure 8, it would be better to have the panel labels (A, B, C, D) in the upper left of each panel rather than the lower left.

      Response: We tried to keep the panels arrangement consistent across the figures. That is why letters are positioned like this.

      A final gentle suggestion: pycortex (http://github.com/gallantlab/pycortex) provides a means to visualize the flattened fsaveage surface with outlines for localized regions of interest and overlaid lines for major sulci. Though it is by no means necessary for publication, It would be lovely to see these results on that surface, which is freely available and downloadable via a pycortex command (surface here: https://figshare.com/articles/dataset/fsaverage_subject_for_pycortex/9916166)

      Response: We thank the reviewer for bringing pycortex to our attention. We will consider using it in our future studies.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The emergence of catalytic self-replication of polymers is an important question in the context of the origin of life. Tkachenko and Maslov present a model in which such a catalytic polymer sequence emerges from a random pool of replicating polymers.

      Strengths:

      The model is part of a theme from many previous papers from the same authors and their colleagues. The model is interesting, technically correct, and demonstrates qualitatively new phenomena. It is good that the paper also makes a connection with possible experimental scenarios -- specifically, concrete proposals are made for testing the core ideas of the model. It would indeed be an exciting demonstration when such an experiment does indeed materialize.

      Weaknesses:

      Unlike the rest of the paper which is very tight in its arguments, I find that the discussion section is not so. Specifically, sentences such as " In fact, this can be seen as a special case of the classical error catastrophe" are a bit loose and not well substantiated -- although these are in the discussion section, I find this to be a weakness of an otherwise good paper. Tightening some of the arguments here will make it an excellent paper in my opinion.

      We followed the reviewer's recommendations by streamlining the discussion and removing the potentially confusing comparison to the classic error catastrophe.

      Reviewer #2 (Public Review):

      Summary:

      The replication of information-coding polymers and the emergence of catalytic ribozymes pose significant challenges, both experimentally and theoretically, in the study of the RNA world hypothesis. In this context, Tkachenko et al. put forth a novel hypothesis regarding a replication oligomer system based on a cleavage ribozyme. They initially highlighted that the breakage of oligomers could contribute to self-replication, provided that these fragments function as primers for subsequent replications. Next, they proposed a self-replicating system of oligomers founded on a hammerhead structure that catalyzes cleavage. By a simple dynamical model, they demonstrated that such a system is self-sustainable in certain parameter regimes. Furthermore, they delved into discussions regarding the potential emergence of such a system and the evolution toward further optimized ribozymes.

      Strengths: Although the cleavage (hammerhead) ribozyme has been discussed in the context of the origins of life, the authors are the first to discuss how they could be selected using a mathematical model as far as I know. The idea is simple: ribozyme activity creates fragments by breakage of an oligomer, which works as a primer for the ribozyme itself, resulting in a positive feedback system (i.e., autocatalytic sets in a broader sense). This potentially enables us to resolve at the same time problems on the (i) supply of new primers (but note that there is a major concern on this as described in the 'weakness'), and (ii) the sustaining of the cleavage ribozyme.

      Weaknesses:

      The major weakness of their theory is that the ends of the new primers, formed through the breakage/cleavage of polymers, must be chemically active (as the authors have already emphasized in the last paragraph of their discussion) to enable further elongation. Reactivating the ends of preexisting oligomers without enzymes, to the best of our current knowledge, could be a challenging task. Although their model heavily relies on this aspect, the authors do not elaborate on it.

      We have added a discussion of the need for chemical activation: "It is important to note that in the context of RNA, such bidirectional elongation requires chemical activation of the phosphate group at the 5' end of the primer to provide free energy for the newly formed covalent bond. Like the polymerization process itself, achieving this without enzymes is biochemically challenging. One might speculate that prebiotic evolution relied on inorganic catalysis, such as on mineral surfaces, or involved polymers other than today's RNA."

      We also included in the discussion a comment on a possible combination of our mechanism and the Virtual Circle Genome model that would avoid the need for bidirectional growth: "It may be possible to incorporate the selection mechanism proposed in this paper into the Virtual Circle Genome model. Such a hybrid approach would avoid the need for the biochemically problematic bidirectional growth while explaining the emergence of early catalytic activity unaffected by sequence scrambling"

      Another weakness is in the setup of their discussion on evolutionary dynamics. While they claim that their model is robust against replication errors, their approach to evolutionary dynamics appears unconventional, and it remains unclear under what conditions their assumptions are founded. They treat a whole set of oligos as a subject of evolution, rather than each individual oligo. This may necessitate more complex assumptions, such as the encapsulation of sets of oligos inside a protocell, to be adequately rationalized. Thus, it remains uncertain whether the system is indeed robust against replication errors in a more natural context. For example, if a mutant oligo, denoted as b', arises due to an error in the replication of oligo b, and if b' has lower catalytic activity but replicates more rapidly than b, it may ultimately come to dominate the system.

      We agree with the reviewer that the evolutionary dynamics in multi-species ecosystems are somewhat complicated and potentially confusing. To this end, we have added the following text and citations to our discussion: "Note that this fitness is defined at the level of the ecosystem, comprising all sequences in the chemostat, and is not necessarily attributable to individual members of that population. Over time, similar to microbial ecosystems, this population changes according to the laws of competitive exclusion [34, 35]". However, we would like to point out that we assume that our model operates in a chemostat-like environment, which can be realized, for example, in a prebiotic pool supplied with a constant flux of monomers. Thus, the evolutionary dynamics described by our equations do not require encapsulation of sets of oligos in a protocell followed by selection of these protocells.

      Reviewer #3 (Public Review):

      Summary:

      Non-enzymatic replication of RNA or a similar polymer is likely to be important for the origin of life. The authors present a model of how a functional catalytic sequence could emerge from a mixture of sequences undergoing non-enzymatic replication.

      Strengths:

      Interesting model describing details of the proposed replication mechanism.

      Weaknesses:

      A discussion of the virtual circular genome idea proposed in [33] is included in the discussion section together with the problem of sequence scrambling faced by this mechanism that was raised in [34]. However, the authors state that sequence scrambling is a special case of the classical error catastrophe. This should be reworded, because these phenomena are completely different. The error catastrophe occurs due to single-point mutational errors in a model that assumes that a complete template is being copied in one cycle. Sequence scrambling arises in models that assume cycles of melting and reannealing, in which case only part of a template is copied in one cycle. Scrambling is due to the many alternative ways in which pairs of sequences can reanneal. Many of these alternatives are incorrect and this leads to the disappearance of the original sequence. This problem exists even in the limit where there is zero mutational error rate. Therefore, it cannot be called a special case of the error catastrophe problem.

      We followed the reviewer's recommendations and removed the potentially confusing comparison to the classic error catastrophe.

      The authors seem to believe that their model avoids the scrambling problem. If this is the case, a clear explanation should be added about why this problem is avoided. Two possible points are mentioned.

      (i) Replication is bidirectional in this model. This seems like a small detail to me. I don't think it makes any difference to whether scrambling occurs.

      (ii) The functional activity is located in a short sequence region. I can imagine that if the length of a strand that is synthesized in a single cycle is long enough to cover the complete functional region, then sometimes the complete functional sequence can be copied in one cycle. Is this what is being argued? If so, it depends a lot on rates of primer extension and lengths of melting cycles etc, and some comment on this should be made.

      As we now explain in the text, while the scrambling problem itself is not completely avoided in our model, it does not affect the replication of the functionally relevant regions of the oligomers. Our key observation is that, due to the simplicity of the cleaving enzymes, the length of the functionally relevant region is much smaller than the scrambling-free length. This can be seen from a back-of-the-envelope estimate of the scrambling-free length added to the text: "...assuming the minimal hybridization length l_0=6 and random statistics of the master sequence, one gets the scrambling free length \sqrt{2 x 4^l_0}+l_0 ~100. This is an order of magnitude larger than both l_0 and the length of the core region of the hammerhead ribozyme."

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I have evaluated that the authors have proposed a novel mechanism potentially relevant to the origins of life, and they have explained it with a sufficiently simple model. However, I recommend that they address the following issues, including those I raised in the public review:

      • Title: I believe that the title "Emergence of catalytic activity in ..." is rather broad. Could it be more specific to accurately represent the system described in the paper? For instance, "Selective advantage (or selection) of the hammerhead cleavage ribozyme in..." may better encapsulate the paper's focus.

      We thank the reviewer for this suggestion. However, our mechanism is not unique to hammerhead ribozymes. So we decided to keep the old title.

      • One theoretically non-trivial aspect is the stability of the cooperative structure. Could the authors provide a more detailed explanation of what drives the instability of the system and what mechanisms restore its stability? For example, in a similar self-reproducing oligomer system with ribozymes and their fragments (Kamimura et al. PLoS Comp. 2019), the symmetry of fragments breaks because they effectively suppress each other's replication. Also, it would be beneficial to clarify the necessary assumptions for stability. (For instance, the authors assumed that a_L can serve as a primer for only a, while a_R can serve for both a and b.).

      We thank the reviewer for bringing this interesting paper to our attention. The cooperative fixed point in our model is intrinsically dynamically stable. It is an interesting point why the replicase in Kamimura et al can be dynamically unstable, while the ligase in our model is always stable. However, it goes beyond the scope of our study. We added the following discussion to the manuscript: "Note that the stability of our cooperative fixed point is a non-trivial result. For example, in a related model by Kamimura et al. [34], the fixed point corresponding to a viable composite replicase is dynamically unstable and requires additional stabilization, e.g., by cell-like compartments."

      • As mentioned in the public review, a critical aspect of the practical applicability of the theory is whether cleaved oligos can be reactivated and further elongated, especially through non-enzymatic pathways. Alternatively, is it possible with the presence of enzymes? While I appreciate the conceptual beauty of their model, I recommend that they at least address the difficulty or feasibility of achieving this.

      We addressed this point in response to the public review

      • As also mentioned, in the section on evolutionary dynamics, it's essential to clarify the unit of evolution and the assumptions made. For a system-level evolution (i.e., all the sets of oligos, a and b can be the unit of evolution), more detailed assumptions are required, such as the presence of compartments whose growth is coupled with the replication of oligos inside, and the competition between these compartments. I recommend the authors clarify these points.

      We addressed this point in response to the public review

      Reviewer #3 (Recommendations For The Authors):

      Assuming that the above points can be addressed, this reviewer would support publication with minor modifications.

      We addressed all points in response to the public review

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their kind works and helpful insights and suggestions. Below, we have pasted the reviews (in italics), with our responses:


      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The study provides insights into how polyploidization via endomitosis may arise in human hepatocytes by studying fetal liver cell line-derived organoids. Using live cell imaging and LSM microscopy, binculeation was consistently observed in two independent cell line systems, at frequencies seen in human liver and sensitive to pharmacological inhibition (GSK3i) and genetic manipulation (E2F7 & E2F8 editing). The findings presented are in line with earlier data, largely gathered studying rodents. The data is convincing and robust indicating that these systems can be used to study cause and consequences of polyploidy in human hepatocytes.

      1. While the authors do suggest that they provide a mechanisms how polyploidy is initiated in human hepatocytes undergoing endomitosis, ie. loss of membrane association of membrane-anchoring proteins at the midbody (e.g. Anillin, RacGAP1), I do feel that the data provided is rather descriptive and does not address a particular mechanism that may account for loss of membrane anchoring. As such, the title is making a too strong point, as, in my point of view, it associates with loss of membrane anchorage, but may not drive endomitosis. Whether this is a "passive" process in response to changes in physical forces and tension, or regulated via signalling intermediates to initiate regression of the cleavage furrow is not addressed experimentally (mislocalizing these proteins on a larger scale). Discussion seems warranted.

      We agree with the reviewer that our mechanistic insights into the molecular mechanisms of endomitosis are limited, and we cannot currently prove that the loss of membrane-anchoring drives endomitosis. We have therefore toned down this conclusion and changed the title to “Binucleated human hepatocytes arise through late cytokinetic regression during endomitosis M phase”. Furthermore, we have expanded the Discussion to reflect on the gaps in knowledge and speculate about possible molecular mechanisms of endomitosis, see pages 12-16 (in particular, lines 404-423, lines 433-443, and 445-472.

      I do not see the need for additional experiments, as I believe the data is robust and introduces an interesting new model where the role of ploidy can be studied in human hepatocytes ex vivo. However, if the authors wish to extend their studies and document further similarities with pathways engaged in rodents, some E2F7/8 targets relevant for ploidy control such as Anillin or PIDDosome components, or, maybe MDM2 processing for p53 activation, could be tested in wt and E2F mutant cell lines.

      Unfortunately, we have not been able to look at E2F7/8 targets and their expression in E2F mutant Hep-Orgs. We performed qPCRs for some cytokinesis regulators such as Ect2, RacGap1 and Mklp1 in Hep-Orgs, however these genes are so lowly expressed that we can hardly detect them. This is likely because these transcripts are only expressed in a short period of the cell cycle during S/G2 phase, whereas the vast majority of cells in Hep-Orgs are in G1. Therefore, differences in gene expression are very difficult (if not impossible) to detect by qPCR. We also tried to perform single molecule FISH on Hep-Orgs, which would allow us to quantify lowly expressed transcripts in single cells, however despite that the smFISH stainings work well on cholangiocyte organoids and intestinal organoids, we could not get good signals in Hep-Orgs. Taken together, we are unable at this point to look into downstream targets of E2F7/8.

      A minor suggestion is to clarify the term M-CDK activity in the introduction, as it may not be fully intuitive to all readers; similarly, ploidy reversal is still controversial in the field, but it is stated as a given fact.

      Thank you for these suggestions, we have clarified the term M-CDK on page 3, lines 60-61, and have rephrased the sentence on ploidy reversal on page 3, lines 81-82.

      Reviewer #1 (Significance (Required)):

      Polyploidy at the cellular and nuclear level is a key feature of hepatocytes albeit the physiological significance of the process is not entirely clear. Increased ploidy has been linked to cancer resistance in the liver, but may pose a threat to hepatocyte survival under conditions of repeated compensatory proliferation cycles. Curiously, during normal regeneration after single surgical intervention liver regeneration is not compromised, even though it may recover faster starting when starting from higher ploidy levels. Mechanistically, most data has been generates studying rodents where it is documented that the proliferation behaviour changes around the time of weaning in mice when hepatocytes start to fail cytokinesis and undergo endomitosis, leading to cellular and nuclear polyploidy. In rodents, insulin signalling / AKT appears involved as is the E2F network and p53, activated by the caspase-2-PIDDosome.

      The model system introduced here will allow mechanistic studies in human organoids and help to increase our understanding of this process in steady state and under conditions of stress.


      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      Polyploid cells arise within various human tissues by multiple different mechanisms. Here, Darmasaputra et al present a study of one such mechanism, endomitosis, in liver cells using fetal-derived human hepatocyte organoids. In this model, they demonstrate that binucleated cells arise through the late regression of the cytokinetic furrow prior to abscission. They identify a rare event in cytokinetic cells - loss of midbody association with the plasma membrane - that could explain the cytokinesis failure observed in a proportion of these cells. Finally, they show that loss of Wnt signalling increases the number of binucleation events in a manner that depends on E2F7 and E2F8, similar to what has been observed in murine hepatocytes.

      Major comments:

      This is a compelling and well-presented study. The data presented are high quality, the experiments are well described and controlled and the conclusions are convincing. I am particularly impressed by the technical effort that the authors must have put into obtaining high quality live and IF images of dividing cells within organoids and their careful documentation of what are very rare mitotic events. In addition, the manuscript is extremely well written and I found it a pleasure to read.

      1. I do not think that there are additional experiments that are essential to justify the conclusions of the paper. However, I do have suggestions that I think would strengthen this work and increase its significance. As is, the authors present findings in two different areas: the documentation of cytokinesis failure in hepatocyte organoids and the role of Wnt and E2F7/8 on binucleation. It would be really nice if the two parts could be linked. For example, the authors could examine cell divisions in the organoids without Wnt either live or fixed and show that they have a higher proportion of cells undergoing cytokinetic regression or with membrane-midbody attachment defects. Alternatively, they could look at whether the expression levels of key cytokinetic genes are changed in the Wnt and E2F7/8 organoids. As I said, these experiments are not required for or the publication of this work and I will leave it up to the authors to decide if they have the time or capacity to add additional data.

      We thank the reviewer for this suggestion. Unfortunately, despite substantial effort, we have been unable to perform successful live imaging of Hep-Orgs under CHIR99021 removal conditions: these organoids become very sensitive to live imaging and they also proliferate very slowly. We have tried to look at the expression of cytokinetic genes by qPCR, however these experiments were inconclusive (see also our response to reviewer #1, point 2). Thus, we cannot rule out that the increase in binucleation that we see upon CHIR99021 removal is not due to increased endomitosis, but rather occurs independently, for example by an increased survival rate of binucleated cells upon WNT removal. We have now discussed this issue and explained the limitations of our study in the discussion, pages 14-15, lines 451-460.

      Finally, before publication, the authors should discuss further the mechanisms by which loss of membrane attachment during cytokinesis could occur - there is quite a lot of literature in this area on the role of RacGAP1 and Ect2 in membrane attachment that is not discussed, particularly from the lab of Mark Pentronczki (eg Kotynkova 2026 PMID: 27926870, Lekmotsev PMID: 23235882). It's surprising that the authors haven't mentioned (or looked at) Ect2 at all, especially since Ect2 levels have been shown to control polyploidy in cardiomyocytes (Liu 2019 PMID: 31597755). This at least warrants some discussion.

      We thank the reviewer for pointing us to these articles. We have elaborated the discussion to include the work on rodent and human cardiomyocytes, and to explain why we think that there is no defect in ECT2 and RhoA signaling in human hepatocytes undergoing endomitosis, see pages 13-14, 404-423 and 433-443.

      Minor comments:

      Table 1 would be more striking as a graphical representation. I appreciate that the n numbers in the regressed cells means that statistical comparisons is not possible, but some kind of colour coding or graph would make this part clearer

      We agree that Table 1 was difficult to read – we now show the data schematically in a new figure, Fig.4.

      It's not clear what the difference between Hep-Org 1 and Hep-Org 2 are. Are these from different donors?

      Indeed Hep-Org1 and Hep-Org2 are from different donors. We have clarified this in the text, see page 5, lines 131-133.

      Reviewer #2 (Significance (Required)):

      This study is an important technical development in that it reports a new system to study in depth cell biology of liver endomitosis in non-transformed and, crucially, human 3D hepatocyte organoids. The findings reported using this system are potentially interesting although they could be further developed if they were mechanistically linked together (see major comments). This work is likely to be highly interesting to scientists studying cell division, cytokinesis and hepatocyte biology. It also has wider implications for liver biology and particularly liver regeneration. Additionally, given the role of polyploidisation in many different tissues, it will likely be of interest to scientists studying polyploidy and endomitosis more generally.

      My area of expertise is in cytokinesis and cell division in general, although not specifically in hepatocytes. I am not an expert in organoids.


      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Darmasaputra and colleagues took advantage of human hepatocyte organoids (Hep-Org) to investigate the formation of binucleated cells that naturally occurs in liver. So far, the mechanism of hepatocyte binucleation has been studied in rodents, where binucleated hepatocytes arise upon weaning through an insulin/akt pathway that inhibits furrow contraction in a fraction of cells (Ref. 21, 22). In addition, it is known that E2F7 and E2F8 downstream of the Wnt signaling repress the expression in mouse hepatocytes of several key cytokinetic proteins (AuroraB, Mklp1, Ect2, Racgap1) and thereby promote binucleation (Ref. 23).

      Advances:

      As seen in vivo, the authors first show that a fraction (5-15%) of cells are binucleated in two independently derived human Hep-Orgs. Live cell imaging reveals that binucleation is not due to furrow ingression defects after anaphase but rather arises from post-furrowing intercellular bridge regression. Fixed data suggest that the cytokinetic midbody formed normally but lost its anchorage to the bridge membrane. Activation of the Wnt signaling resulted in a modest but significant increase in the proportion of binucleated cells (4.5 to Major comments

      1. An outstanding question is whether human Hep-Orgs represent a bona-fide model to study the process of human liver binucleation. The absence of cholangiocytes, vascularization, other cell types and physiological hormones etc. might impact on the mechanism of binucleation, which is the main focus of this study. Since the mechanism of binucleation in human Hep-Orgs appears radically different from what has been reported in vivo in rodents, the authors should reproduce the lack of furrow ingression in mouse Hep-Orgs (that they were able to generate in Ref. 44). This could be done in fixed cells as in Fig. 3. Alternatively, they could use live cell imaging and chemical dyes such as SiR-Tubulin and Cell Mask to label microtubules and the plasma membrane, respectively, without the need of creating genome-edited reporter lines.

      The mechanism of endomitosis that we observe in human hepatocyte organoids is indeed different from what has been observed in mouse hepatocytes. Unfortunately, mouse Hep-Orgs are more difficult to generate as they require a two-step perfusion protocol from live mice (described in Hu et al., 2018). Additionally, mouse Hep-Orgs do not survive freezing, so to be able to perform the suggested experiments, we would need to generate new mouse Hep-Org lines. As our collaborators are currently not performing any experiments with mouse livers, we would need to request an ethical permit to generate these organoids, which would take several months. We have seriously considered this option, however due to the substantial investment in time and resources, we feel these experiments would be more suited for a follow-up study.

      To nonetheless better clarify the differences between what has been observed in rodents, and what we see in the Hep-Orgs, we have added a paragraph in the discussion, see pages 14-15 lines 433-460.

      The videos acquired in Fig. 2 contain much more information than presented. The authors should measure the rate of furrow ingression, the extend of spindle elongation, the time of MT severing and the time of furrow/bridge regression after cytokinesis onset. All these parameters are important since spindle elongation and furrow ingression are altered in rodents. Is this also the case in human Hep-orgs? Furthermore, the spindle seems very different (bent bridges) in endomitotic compared to canonical cytokinesis (Fig. 2A). Finally, the authors should provide more time points during the time of furrow regression to better show how this phenomenon occurs. It seems, based on fixed images, that the midbody stays attached to the plasma yhmembrane in an asymmetric manner (i.e. does not fully detach, contrary to what is stated in the text). 3D reconstructions in fixed cells and a further characterization of the movies would clarify this point.

      We thank the reviewer for this suggestion. Although there are some technical limitations that pose some restrictions (explained below), we have extended our analyses where possible. In our live imaging, we use 5-minute time intervals with 4 mm z-slices, which allows a delicate balance between having enough frames in M phase, and imaging for at least 48 hours, which is required to catch enough divisions. We are unable to image with smaller time intervals or smaller z-slices, as this leads to phototoxicity. Nonetheless, using these settings, we can get an indication of the rate of furrow ingression, time of severing and the time of furrow regression:

      • We find that the time of furrowing onset and the rate of furrow ingression is very similar between canonical M phases and endomitosis M phases: we have now added this data in the results section, page 7, lines 192-199 and Fig. 2D.
      • The time of cytokinetic regression is more variable between endomitoses events, and can range between 30 minutes and 2,5 hours. We have also added this information to page 7, lines 199-202and Fig. 2E
      • The time of MT severing is similar between endomitosis M phases, as we show in Fig. 2C
      • Unfortunately, we cannot accurately measure the extent of spindle elongation, as the divisions occur in 3D and our Z resolution is not good enough. Regarding the observation that the spindle looks different in the endomitosis example in Fig. 2A: we have quantified how often we observe bent midzones in endomitosis versus canonical M phases, and this occurs in 60% of canonical (n=12/20) and 83% of endomitosis M phases (n=15/18). We have now added this information in the results section, page 6, lines 1862-185.
      • We have quantified how often we see the midbody remaining attached to one side of the plasma membrane versus fully detaching: we find that in 6 out of 9 late stage endomitotic regressions, the membrane is detached from both sides, and in 3 out of 9, it remains attached to one side. We have added this information to the results section, page 8 line 249-251.

        DAPI staining is not sensitive enough to detect thin chromatin bridges. To rule out that post-furrowing regression is not merely due to the present of DNA bridges, the authors should confirm their results with LAP2b staining (see PMID 19203582).

      To exclude the presence of ultrafine DNA bridges during anaphase, we have performed a staining for RIF1, a factor that localizes to ultrafine DNA bridges in anaphase and is required for their resolution (Hengeveld et al, 2015, PMID: 26256213). In early anaphase, we find many RIF1-positive thread-like structures, as has been described before in other non-transformed and non-stressed cells. However, in late anaphase and telophase, we never observe these fibers (n=57/57), suggesting that they are fully resolved and are not the cause of cytokinetic regression. We have added this data to the results section, see page 8, lines 226-234, and Fig. S1.

      The authors shows that binucleation results from defective anchorage of the bridge membrane to the midbody, but the molecular mechanism remains elusive and should be further probed. In Fig. 3, there is no obvious changes in the investigated markers. Are the intensities of RACGAP1, Anillin, CIT-K reduced in regressing cells? Are ECT2, activated (phospho) Myosin II, CEP55/ESCRT-III, (activated) AuroraB and MKLP1 normally localized/concentrated? ECT2, AuroraB and MKLP1 are regulated by E2F7/8 (Ref. 23) and AuroraB inactivation after bridge formation leads to late regression (PMID 19203582).

      We agree with the reviewer that the molecular mechanism by which midbodies lose their attachment to the membrane is currently unclear. We do not see any clear differences in the intensities of RACGAP1, Anillin, or CIT-K in cells undergoing endomitotic regression. We also do not expect large differences in localization or abundancies of ECT2, AuroraB or MKLP1, because if this were the case, you would expect differences in early cytokinesis in endomitosis, such as a delay or a slower rate of furrow ingression. We did perform additional IF experiments to investigate the localization of SEPT9, a septin that is expressed in human hepatocytes and that has essential functions in membrane anchorage during cytokinesis. Although we find that SEPT9 exhibits more variable localizations than RACGAP1, Anillin, and CIT-K, we find that in the majority of endomitotic regressions, it is also absent from the regressed membrane (n=5/7 cells). We have added this data to the results section on page 9, and in the figures Fig. 3C and Fig.4C.

      The results of Fig. 4F indicate that the increased proportion of binucleated cells upon CHIR99021 removal depends on E2F7/8. Without live cell imaging (or FISH experiments) the authors cannot conclude that conclude that the increase in endomitosis is dependent on E2F7/8. A decrease in binucleation could indeed not imply a reduced occurrence of endomitosis. For instance, it is possible that E2F7/8 KO induces the formation of mononucleated 4n cells due to early mitotic failure. This issue should be clarified.

      The reviewer raises an important point. Unfortunately, we were unable to generate E2F7/8 KO lines containing fluorescent nuclear and membrane markers, which would allow us to perform live-imaging and confirm that these organoids perform less endomitosis. As an alternative, we tried to use SiR-Tubulin dyes for live imaging, but even at very low concentrations these dyes are toxic for the organoids. To exclude the possibility that E2F7/8 KO induces the formation of mononucleated 4n cells, we have measured the DNA content in wildtype, E2F7 and E2F8 lines, and found that the distribution of ploidies is very similar between these lines, both in normal growth conditions as well as upon removal of CHIR99021 (see the new supplemental figure, Fig. S3). We thus think it is unlikely that E2F7/8 KO induces the formation of mononucleated 4n, however it remains possible that the differences in percentage of binucleated cells arise independently of endomitosis. We have now toned down our conclusions on the function of WNT signaling and E2F7/8 in endomitosis, and discussed alternative explanations for our findings in the discussion, see page 14, lines 451-460.

      Binucleation increases with age both in humans and rodents. Could this feature be mimicked in the human Hep-Org by leaving the organoids longer in culture? (optional but would reinforce the value of the model).

      We do not see an increase in binucleation percentages in organoids that are kept longer in cultures, and we have now also tested the effect of growing the organoids in a “differentiation medium”, which was previously described to give rise to more mature hepatocyte gene expression (Hu et al. 2018), however we see no significant differences in the percentages of binucleated cells per organoid. We have now included this data, as well as our analyses of the effect of insulin in the growth medium (see our response to point 12 below) in the results section on page 11 lines 341-353 and we further discuss this point in the Discussion, pages 12-13, lines 389-397.

      Minor comments

      The results of Table 1 are based on very few fixed cells (3 to 6). The authors should consider increasing the number of regressing cells.

      We are aware that the number of endomitotic regressions is very low. Unfortunately, it is extremely challenging to catch these events by IF: cells in Hep-Orgs cycle very slowly (they divide once every ±50 hours), and thus very few cells are in M phase at any given moment (only 1 or 2 cells per organoid) – the chance that this cell is then also in telophase is even lower, and then only ± 5% of the telophase are actually undergoing endomitosis. Due to technical limitations of the organoid IF staining protocol, it is not trivial to scale up these experiments, making it very difficult to find more than 3-5 endomitotic regressions per condition. Despite the low numbers of endomitotic regressions that we have identified, we find that RacGAP1, Anillin and CIT-K localize in a very similar manner in cells undergoing endomitosis. For SEPT9, we see a little bit more variation in the localizations, but also here the majority of cells in undergoing endomitosis have lost SEPT9 membrane association on the regressed membrane (see Fig. 4C).

      Is WNT signaling modified by E2F7/8 mutations? To conclude that "WNT signaling inhibits binucleation in an E2F7/8-dependent manner", the authors should check that E2F7/8 KO does not impair the increase of WNT signaling upon CHIR99021 removal.

      We had not thought of this option, but it is indeed possible that E2F7/8 influences the ability of cells to respond to CHIR99021 removal. WNT regulators are not known to be targets of E2F7 or E2F8 in mice (see PMIDs: 22180533, 18194653, and 23064264), however as we have not analyzed the gene expression changes in E2F7 or E2F8 mutant organoids, we cannot exclude the possibility that CHIR99021 has different effects in E2F7/E2F8 knock-out cells. We now discuss this possibility in the discussion, page 15, lines 459-460.

      Please provide movies of the cells presented in Fig. 2A.

      We have included movies of these cells, see Supplemental Movie 1 and Supplemental Movie 2.

      1. Removal of CHIR99021 induces major shape changes and lumen formation (rather than "exhibited some morphological changes" as stated). Could the author speculate on this?

      WNT signaling is likely important for many aspects of hepatocyte growth and differentiation, and it is possible that upon CHIR99021 removal, Hep-Orgs are starting to differentiate and become more secretory, which would explain why they start forming larger lumens. We now discuss this in more detail in the final part of the results section, see page 11 lines 341-353, and in the discussion, page 15 lines 462-472.

      1. Fig. 4: Why do the authors use the cell line-1 that has the lowest level of binucleation in this experiment? Would the results be the same in cell line 2? (optional)

      We perform most experiments in Hep-Org line 1 because this line is easier to maintain in culture, and we have been unable to generate CRISPR knock-outs in Hep-Org line 2.

      1. Would insulin increase the proportion of binucleated cells, as in rodents? (optional)

      We have tested this, but do not see a difference in the percentage of binucleated cells when we either increase or decrease the concentration of insulin in the growth medium. We have now added this data in the results section, see page 11, lines 347-350 and Fig. 5J.

      Reviewer #3 (Significance (Required)):

      Strengths and limitations:

      The manuscript is well written, easy to follow, and the quality of the data is overall high. A clear strength of this study is the use of state-of-the-art human hepatocyte organoids and genome editing (to generate reporter lines and to KO E2F7/8). This allows the authors to address the mechanism of binucleation in a human context. Interestingly, it revealed both similarities (e.g. E2F7/8 depends for binucleation) and striking mechanistic differences (e.g. post-furrowing regression) between rodent and human systems. The study is rather descriptive -which is fine- but deeper mechanistic insights would strengthen the conclusions of the manuscript. For instance, "our results identify how human hepatocytes inhibit cell division in endomitosis" appears as an overstatement since the molecular reason of midbody anchorage defects remains elusive.

      We thank the reviewer for their kind words. Unfortunately, we have been unable to gain deeper mechanistic insights into the molecular mechanism of membrane regression in endomitosis. We have therefore toned down our conclusions, see the new concluding sentence in the abstract, page 2, lines 35-36.

    1. In a recent AMS webinar on CRT, Dr. Valaida Wise makes the important distinction that as a theoretical framework, CRT helps us think about and critically analyze systems; as such, it can help teachers think about the right questions to ask regarding potential or actual inequities that may be present in our classrooms. Our concern at the classroom level, therefore, is not the theoretical work of CRT, but that we have a culturally responsive pedagogy (CRP) in place. We will come back to this.

      In a webinar, Dr. Valaida Wise points out that CRT helps teachers analyze systems and spot potential issues. The main focus should be on using culturally responsive teaching methods in classrooms.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      In this valuable study, the discovery and subsequent design of the AF03-NL chimeric antibody yielded a tool for studying filoviruses and provides a possible blueprint for future therapeutics. However, the data are incomplete and not presented clearly, which obscures flaws in the analyses and leaves unexplained phenomena. The work will be of interest to virologists studying antibodies.

      Author response: Thank for your very valuable comments. The ms has been revised substantially and some new data have been added to further support the conclusions.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary and Strengths:

      Zhang et al. conducted a study in which they isolated and characterized a Marburg virus (MARV) glycoprotein-specific antibody, AF-03. The antibody was obtained from a phage-display library. The study shows that AF-03 competes with the previously characterized MARV-neutralizing antibody MR78, which binds to the virus's receptor binding site. The authors also performed GP mutagenesis experiments to confirm that AF-03 binds near the receptor binding site. In addition, the study confirmed that AF-03, like MR78, can neutralize Ebola viruses with cleaved glycoproteins. Finally, the authors demonstrated that NPC2-fused AF-03 was effective in neutralizing several filovirus species.

      Weaknesses:

      (1) The main premise of this study is unclear. Flyak et al. in 2015 described the isolation and characterization of a large panel of neutralizing antibodies from a Marburg survivor (Flyak et al., Cell, 2015). Based on biochemical and structural characterization, Flyak proposed that the Marburg neutralizing antibodies bind to the NPC1 receptor binding side. In the same study, it has been shown that several MARV-neutralizing antibodies can bind to cleaved Ebola glycoproteins that were enzymatically treated to remove the mucin-like domain and glycan cap. In the following study, it has been shown that the bispecific-antibody strategy can be used to deliver Marburg-specific antibodies into the endosome, where they can neutralize Ebola viruses (Wec et al., Science 2016). Finally, the use of lysosome-resident protein NPC2 to deliver antibody cargos to late endosomes has been previously described (Wirchnianski et al., Front. Immunol, 2021). The above-mentioned studies are not referenced in the introduction. The authors state that "there is no licensed treatment or vaccine for Marburg [virus] infection." While this is true, there are human antibodies that recognize neutralizing epitopes - that information can't be excluded while providing the rationale for the study. Furthermore, the authors use the word "novel" to describe the AF-03 antibody. How novel is AF-03 if multiple Marburg-neutralizing antibodies were previously characterized in multiple studies? Since AF-03 competes with previously characterized MR78, it binds to the same antigenic region as MR78. AF-03 also has comparable neutralization potency as MR78.

      Author response: Thank for your valuable advice. In terms of the novelty of AF-03, the inhibition assay indicates that Q128/N129/C226 functions as key amino acids responsible for AF-03 neutralization given that the neutralizing capacity of AF-03 to pesudotyped virus harboring these mutants is impaired (see revised Fig. 2A left panel). Furthermore, ELISA assays show that mutation of Q128S-N129S or C226Y significantly disrupts the binding of GP to AF-03, while the neutralizing and binding capacity of MR78 to mutant GP and pseudovirus harboring C226Y instead of Q128S-N129S is not almost affected (see revised Fig. 2A right panel and 2B). Considering the fact that AF-03 and MR78 could compete with each other to bind to MARV GP (Fig. 2D). we thus make a conclusion that the epitopes of these two mAbs overlapped partially. Therefore, AF-03 is not a clone of MR78 and is a novel neutralizing mAb to MARV.

      The work from Wirchnianski and colleagues has been referenced actually in the ms (see Ref. 38). Although our strategy for the design of broad-spectrum neutralizing antibody refers to their work, we further expand the species being evaluated including RAVN and mutated EBOV strains. The results show that NPC2-fused AF-03 exhibits neutralizing activity to 10 filovirus species and 17 EBOV mutants (Fig. 6A and B). The work by Flyak et al. in 2015 that described the isolation and characterization of a large panel of neutralizing antibodies from a Marburg survivor has been cited in Introduction section accordingly.

      (2) Without the AF-03-MARV GP crystal structure, it's unclear how van der Waals interactions, H-bonds, and polar and electrostatic interactions can be evaluated. While authors use computer-guided homology modeling, this technique can't be used to determine critical interactions. Furthermore, Flyak et al. reported that binding to the NPC1 receptor binding site is the main mechanism of Marburg virus neutralization by human monoclonal antibodies. Since both AF-03 (this study) and MR78 (Flyak study) competed with each other, that information alone was sufficient for GP mutagenesis experiments that identified the NPC1 receptor binding site as the main region for mutagenesis.

      Author response: Computer-guided homology modeling has been exploited successfully in our lab to determine key residues responsible for the interaction between antigen and mAbs (Immunol Res. 2015, 62:377; Scand J Immunol. 2019, 90:e12777; Sci Rep. 2022, 12:8469; Front Immunol. 2022, 13:831536). We refer to the crystal structure of MARV GP and the complex of MR78 and GP reported previously (Cell 2015, 160:904) and then model the complex of MARV GP and AF-03. Although AF-03 and MR78 compete with each other, we show that the epitopes of these two mAbs just overlap partially (Fig. 2A-D).

      (3) The AF-03-GP affinity measurements were performed using bivalent IgG molecules and trimeric GP molecules. This format does not allow accurate measurements of affinity due to the avidity effect. The reported KD value is abnormally low due to avidity effects. The authors need to repeat the affinity experiments by immobilizing trimeric GPs and then adding monovalent AF-03 Fab.

      Author response: As shown in Fig. 1A, GP protein used in this work is not trimer but largely monomer composed of MLD-deleted GP1 and GP2, which may at a certain extent weaken the engagement between GP and AF-03. It is noteworthy that we re-done the SPR assays for the binding of AF-03 to GP and show that KD value is 4.71x10-11M (see revised Fig. 1C). This GP protein is thus available to the evaluation of mAb affinity. In addition, it is reasonable to utilize bivalent IgG to detect the affinity of mAb to monomeric GP since the affinity likely decreases significantly when monovalent Fab is used.

      Reviewer #2 (Public Review):

      Summary:

      The authors describe the discovery of a filovirus neutralizing antibody, AF03, by phage display, and its subsequent improvements to include NPC2 that resulted in a greater breadth of neutralization. Overall, the manuscript would benefit from considerable grammatical review, which would improve the communication of each point to the reader. The authors do not convincingly map the AF03 epitope, nor do they provide any strong support for their assumption that AF03 targets the NPC1 binding site. However, the authors do show that AF03 competes for MR78 binding to its epitope, and provides good support for the internalization of AF03-NL as the mechanism for improved breadth over the original AF03 antibody.

      Strengths:

      This study shows convincing binding to Marburgvirus GP and neutralization of Marburg viruses by AF03, as well as convincing neutralization of Ebolaviruses by AF03-NL. While there are no distinct populations of PE-stained cells shown by FACS in Figure 5A, the cell staining data in Figure 5C are compelling to a non-expert in endosomal staining like me. The control experiments in Figure 7 are compelling showing neutralization by AF03-NL but not AF03 or NPC2 alone or in combination. Altogether these data support the internalisation and stabilisation mechanism that is proposed for the gain in neutralization breadth observed for Ebolaviruses by AF03-NL over AF03 alone.

      Weaknesses:

      Overall, this reviewer is of the opinion that this paper is constructed haphazardly. For instance, the neutralization of mutant pseudoviruses is shown in Figure 2 before the concept of pseudovirus neutralization by AF03 is introduced in Figure 3. Similarly, the control experiments for AF03+NPC2 are described in Figure 7 after the data for breadth of neutralization are shown in Figure 6. GP quality controls are shown in Figure 2 after GP ELISAs / BLI experiments are done in Figure 1. This is disorienting for the reader.

      Author response: AF-03 production and its binding capacity to GP is determined in Fig. 1. The epitopes of AF-03 is identified in Fig. 2. The neutralizing activity of AF-03 to pseudotyped MARV in vitro and in vivo is detected in Fig. 3. The neutralizing activity of AF-03 to pseudotyped ebolavirus harboring cleaved GP is detected in Fig. 4. The endosome-delivering ability of AF03-NL is examined in Fig. 5. The neutralization of filovirus species and EBOV mutants by AF03-NL is detected in Fig. 6. The requirement of CI-MPR for neutralization activity of AF03-NL is determined in Fig. 7. We think that this arrangement is suitable.

      Figure 1: The visualisation of AF03 modelling and docking endeavours is extremely difficult to interpret. Firstly, there is no effort to orient the non-specialist reader with respect to the Marburgvirus GP model. Secondly, from the figures presented it is impossible to tell if the Fv docks perfectly onto the GP surface, or if there are violent clashes between the deeply penetrating AF03 CDRs and GP. This information would be better presented on a white background, perhaps showing GP in surface view from multiple angles and slices. The authors attempt to label potential interactions, but these are impossible to read, and labels should be added separately to appropriately oriented zoomed-in views.

      Author response: To be readily understood the rationale of computer-guided modeling, the descriptions in the Methods and Results section have been refined accordingly. In addition, the information of the theoretical structure was presented on white background (see revised Fig. 1D-F).

      Figure 2: The neutralization of mutant pseudoviruses cannot be properly assessed using bar graphs. These data should be plotted as neutralization curves as they were done for the wild-type neutralization data in Figure 3. The authors conclude that Q128 & N129 are contact residues, but the neutralization data for this mutant appear odd as the lowest two concentrations of AF03 show higher neutralization than the second highest AF03 concentration. Neutralization of T204/Q205/T206 (green), Y218 (orange), K222 (blue), or C226 (purple) appears to be better than neutralization of the wild-type MARV. The authors do not discuss this oddity. What are the IC50's? The omission of antibody concentrations on the x-axis and missing IC50 values give a sense of obscuring the data, and the manuscript would benefit from greater transparency, and be much easier to interpret if these were included. I am intrigued that the Q128S/N129S mutant is reported as having little effect on the neutralization of MR78. The bar graph appears to show some effect (difficult to interpret without neutralization curves and IC50 data), and indeed PDB:5UQY seems to suggest that these amino acids form a central component of the MR78 epitope (Q128 forms potential hydrogen bonds with CDRH1 Y35 and CDRL3 Y91, while N129 packs against the MR78 CDRH3 and potentially makes additional polar contact with the backbone). Lastly, since neutralization was tested in both HEK293T cells and Huh7 cells in Figure 3, the authors should clarify which cells were used for neutralization in Figure 2.

      Author response: Thank for your advice. Accordingly, in the revised ms, the neutralization curve of AF-03 and MR78 is presented in revised Fig. 2A. The neutralization of AF-03 to pseudotyped MARV harboring Q128S/N129S or C226Y is impaired significantly compared with WT MARV and those bearing other indicated mutations, while Q128S/N129S instead of C226Y mutation affect the neutralizing capacity of MR78 at a certain extent. This is consistent with the data on the binding of AF-03 or MR78 to MARV GP protein assayed by ELISA (see revised Fig. 2B). Overall, these results show that Q128/N129/C226 functions as key amino acids responsible for AF-03 neutralization.

      Figure 3: The first two images in Figure 3C showing bioluminescent intensity from pseudovirus-injected mice pretreated with either 10mg/kg or 3mg/kg AF03 are identical images. This is apparent from the location, shape, and intensity of the bioluminescence, as well as the identical foot placement of each mouse in these two panels. Currently, this figure is incomplete and should be corrected to show the different mice treated with either 10mg/kg or 3mg/kg of AF03.

      Author response: Thank for your carefulness. Indeed, it is our mistake. In the revised ms, this fault has been corrected. The correct images have been added (see revised Fig. 3C).

      Figure 4 would benefit from a control experiment without antibodies comparing infection with GP-cleaved and GP-uncleaved pseudoviruses. The paragraph describing these data was also difficult to read and would benefit from additional grammatical review.

      Author response: Accordingly, a control experiment comparing the infection of GP-cleaved with GP-uncleaved pseudoviruses is performed. The results show that The infection of pseudotyped ebolavirus harboring cleaved GP to host cells is comparable or stronger than those containing intact GP(see revised Fig. s1). Therefore, the data in Fig. 4 support the inhibition of cell entry of ebolavirus species harboring cleaved GP by AF-03, which is not attributed to the possible impairment of cell entry capacity of GPcl-containing ebolavirus. In addition, the sentences have been modified to be read smoothly.

      Figure 5: The authors should clarify in the methods section that the "mock" experiment included the PE anti-human IgG Fc antibody. Without this clarification, the lack of a distinct negative population in the FACS data could be interpreted as non-specific staining with PE. If the PE antibody was added at an equivalent concentration to all panels, what does the directionality of the arrowheads in Figure 5A (labelled PE) and 5B (labelled pHrodo Red) indicate?

      Author response: Thank for your advice. In the revised version, we denote that the mock is actually a human IgG isotype in the figure legend. The arrowheads denote the fluorescence intensity of PE or pHrodo on the lateral axis of the plots. Of course, herein the percentage of PE or pHrodo-positive cells is shown.

      Figure 6B: These data would benefit from the inclusion of IC50, transparency of antibody concentrations used, and consistency in the direction of antibody concentrations (increasing to the right or left of the x-axis) when compared to Figure 2.

      Author response: The concentration of antibody titrated is shown in figure legends. The direction of antibody concentrations is unified throughout the paper. Although IC50 is not included, these data clearly show that AF03-NL rather than AF-03 prominently inhibits the cell entry of EBOV mutants.

      Reviewer #1 (Recommendations For The Authors):

      Line 143: anti-human should be anti-human.

      Line 223: From the SDS-PAGE results, it's not clear that the AF-03 was expressed in the eukaryotic cell line. Please, rephrase the sentence.

      Line 263: ELISA experiments can't be used to determine affinity.

      Line 394: Flyak et al. generated human antibodies from PBMC samples of Marburg survivors, not plasma samples.

      Author response: According to reviewer's advice, the sentences have been modified or corrected to more accurately describe the results. As well, the grammatic errors in the ms have been corrected carefully.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this manuscript, Lee et al. compared encoding of odor identity and value by calcium signaling from neurons in the ventral pallidum (VP) in comparison to D1 and D2 neurons in the olfactory tubercle (OT).

      Strengths:

      They utilize a strong comparative approach, which allows the comparison of signals in two directly connected regions. First, they demonstrate that both D1 and D2 OT neurons project strongly to the VP, but not the VTA or other examined regions, in contrast to accumbal D1 neurons which project strongly to the VTA as well as the VP. They examine single unit calcium activity in a robust olfactory cue conditioning paradigm that allows them to differentiate encoding of olfactory identity versus value, by incorporating two different sucrose, neutral and air puff cues with different chemical characteristics. They then use multiple analytical approaches to demonstrate strong, low-dimensional encoding of cue value in the VP, and more robust, high-dimensional encoding of odor identity by both D1 and D2 OT neurons, though D1 OT neurons are still somewhat modulated by reward contingency/value. Finally, they utilize a modified conditioning paradigm that dissociates reward probability and lick vigor to demonstrate that VP encoding of cue value is not dependent on encoding of lick vigor during sucrose cues, and that separable populations of VP neurons encode cue value/sucrose probability and lick vigor.

      Weaknesses:

      The conclusions of the data are mostly well supported by the analyses, but the statistical analysis is somewhat limited and needs to be clarified and extended.

      (1) The manuscript includes limited direct statistical comparison of the neural populations, and many of the comparisons between the subregions are descriptive, including descriptions of the percentage of neurons having specific response types, or differences in effect sizes or differing "levels" of significance. An additional direct comparison of data from each subpopulation would help to confirm whether the differences reported are statistically meaningful.

      Response: We thank the reviewer for their helpful suggestions. As the reviewer noted, the first version of our manuscript had limited direct comparisons of single-neuron metrics across subpopulations. These analyses were also limited to the supplementary figures: 1) {SK vs. XK} and {SK vs. ST} decoder auROC (S10F), 2) Valence scores (S10G), and 3) S-cue confusion after MNR classification (S11D). We have now included the following statistical comparisons of single-neuron metrics across subpopulation: 1) % of neurons that respond to both S cues (Tables S10, S11), 2) % of neurons that have auROC >0.75 for {SK vs. XK}, {SK vs. PK}, and {SK vs. ST} (Tables S12-S17), 3) response magnitudes to S cues (Table S38), and 4) valence scores (Tables S44-46).

      (2) When hypothesis tests are conducted between the neural populations, it is not clear whether the authors have accounted for the random effect of the subject, or whether individual units were treated as fully independent. For instance, pairwise differences are reported in Figures 4I, 5G/I/L, and others, but the statistical methods are unclear. Assessment of the statistics is further limited by the lack of reporting of degrees of freedom. If the individual neurons are treated as independent in these analyses, it could increase the likelihood of

      Response: We have clarified when statistical analyses are comparing individual neurons vs. simultaneously recorded populations. Per the reviewer’s recommendation, we have also incorporated linear mixed-effects models when statistically analyzing individual neurons. Lastly, to further clarify the statistical analyses used, we have added multiple supplementary tables that better describe the statistical tests used and the relevant outputs.

      Reviewer #2 (Public Review):

      Summary:

      This work is interesting since the authors provide an in vivo analysis into how odor-associations may change as represented at the level of olfactory tubercle (presynaptic) and next at the level of the ventral pallidum (postsynaptic). First the authors start-off with a seemingly careful characterization of the anterograde and retrograde connectivity of dopamine 1 receptor (D1) and dopamine 2 receptor (D2) expressing medium spiny neurons in the olfactory tubercle and neurons in the ventral pallidum. From this work they claim that regardless of D1 or D2 expression, tubercle neurons mainly project to the lateral portion of the ventral pallidum. Next, to compare how odor-associated neuronal activity in the ventral pallidum and the olfactory tubercle (D1 vs D2 MSNs) transforms across association learning, the authors performed 2photon calcium imaging while mice engaged in a lick / no-lick task wherein two odors are associated with reward, two odors are associated with no outcome, and two odors are associated with an air puff.

      This manuscript builds off of prior work by several groups indicating that the olfactory tubercle neurons form flexible learned associations to odors by looking at outputs into the pallidum (but without looking specifically at palladial neurons that truly get input from tubercle I should highlight) and with that, this work is novel. We appreciated the use of a straight-forward odoroutcome behavioral paradigm and the careful computational methods and analyses utilized to disentangle the contributions of single neurons vs population level responses to behavior. With one exception from the Murthy lab, 2P imaging in the tubercle is a new frontier and that is appreciated - as is the 2P imaging in the pallidum which was well-supported by the histology. The anatomical work is also well presented.

      Overall the approach and methods are superb. The issues come when considering how the authors present the story and what conclusions are made from these data. Several key points before going into specifics about each are: 1) The authors can not conclude that their results are contradictory to prior results, 2) The authors over-interpret the results and do not discuss several key methodological issues. We were concerned with the ability to make strong claims regarding the circuitry presented, especially given how much the presented claims contradict prior work. There were also issues with the interpretability of neuronal encoding of value vs valence based on the present behavior (in which a distinction between the air puff and neutral trial types was not clear) and the imaging methodology (in which the neuronal populations analyzed were not clearly defined). In addition to toning down and rectifying some of the language and interpretations, we suggest including a study limitations section where these methodological and interpretation issues are discussed. Over-interpreting and playing up the significance of this work is unnecessary, especially given eLife's new review and publication policy. Readers should be given a sufficiently detailed and nuanced presentation of these thought-provoking results, and from there allowed to interpret the results as they want.

      Strengths:

      State-of-the-art approaches (as detailed above)

      Possible conceptual innovation in terms of looking into output from the olfactory tubercle which has yet to be investigated in this avenue.

      Weaknesses:

      On the first point regarding the authors repeated and unsupported claims that their results are contradictory. There are papers by numerous groups, in respected journals including this one, all together which used 5 different methods (cfos, photometry, 2P, units, fMRI), in animals ranging from humans to mice, which support that tubercle neurons reflect the emotional association of an odor, whether spontaneous or learned. With that, it is on the authors to not claim that their results contradict as if the other papers are suspect, but instead, from our standpoint it is on the authors to explain how and why their results differ from these other papers versus just simply saying they found something different [which at present is framed in a way that is 'correct' due to primacy if nothing else].

      Response: We acknowledge that the first version of the manuscript contained unnecessary disagreeing language. We do not think that our results are broadly in disagreement with the existing literature, but we do come to different conclusions about what the OT is representing. Namely, our comparison of valence encoding in OT to that in the VP strongly indicates that the anteromedial OT has a less robust representation of valence, and we argue that this reflects either an intermediate form of valence representation or potentially might not be important for valence representation at all. We have toned down our conclusions, made clear that we are only recording from one domain of the OT, limited our speculation to the discussion and added a “speculations” section.

      Second, onto the points of interpretation of results, there are several specific areas where this should be rectified. As is, the authors overinterpret their results and draw too far-reaching conclusions. This needs to be corrected.

      In particular, the claims that D1 and D2 neurons of the olfactory tubercle nearly exclusively send projections to the ventral pallidum must be interpreted with caution given that the authors injected an anterograde AAV into the anteromedial olfactory tubercle, and did not examine the projections from either the posterior or lateral portions of the olfactory tubercle. This is especially significant since the retrograde tracing performed from the ventral pallidum indicates that the lateral olfactory tubercle, not the medial olfactory tubercle, primarily projects to the ventral pallidum (Fig 1D-F), however this may be due to leakage into the nucleus accumbens, as seen in the supplementary figure, S1G.

      Response: We thank the reviewer for the point of caution. We have now made it clear that our conclusions are limited to the anteromedial portion of the OT, and other areas may have other projections.

      The same caution must be advised when interpreting the retrograde tracing performed in Fig 1G-I, since the neuronal tracer used and the laterality and rostral-caudal injection site within the VTA could result in different projection patterns and under- or over-labelling. Additionally, the metric used, %Fiber Density (Figure 1C), as in the percentage of 16-bit pixels within the region of interest with an intensity greater than 200, is semi-quantitative, and is more applicable for examining axonal fibers that pass through a region rather than the synaptic terminals (like with a synaptophysin fusion protein-based tracing paradigm) found within a region (puncta). The statements made in contrast to prior studies should therefore be softened, and these concerns should be addressed in the introduction, discussion, and the limitations section if added.

      Response: We have added statements to address these limitations.

      The other major concern is whether the behavioral data generated is indicative of the full spectrum of valence. The authors appropriately state that the mice "perceive" the air puff, yet based on their data the mice did not clearly experience the puff-associated odor as emotionally aversive (viz., negative valence). The way the authors describe these results, it seems they agree with this. With that, the authors can't say the puff is aversive without data to show such - that is an assumption which, while seemingly intuitive, is not supported by the data unfortunately. To elaborate more since this is important to the messaging of the paper: The authors utilized a simple behavioral design, wherein two molecular classes of odors were included in either a sucrose rewarded, neutral no outcome, or air puff punished trial type. The odor-outcome pairs were switched after three days, allowing the authors to compare neuronal responses on the basis of odor identity and the later associated outcome. While the mice showed clear learning of the rewarded trial types by an increase in anticipatory licking during the odor, they did not show any significant changes in behavior that indicated learning of the air puff trial type (change in running velocity or % maximal eye size), especially in contrast to the neutral trial type. This brings up the concern that either the odor-air puff aversive associations (to odors) were not learned, or that the neutral trial types, in which a reward was omitted, were just as aversive as the air puff to the rear, despite the lack of startle response - perhaps due to stimulus generalization between neutral and air puff odor. The possibility of lack of learning is addressed in the paragraph starting at line 578, but does not account for the possibility that the lack of reward is also sufficiently punishing. The authors also address the possibility that laterality in the VP contributed to the lack of neural responsivity observed, but should also include a statement regarding laterality in the olfactory tubercle, as described in https://doi.org/10.7554/eLife.25423 and https://doi.org/10.1523/JNEUROSCI.0073-15.2015, since the effects of modulating the lateral portion of the olfactory tubercle are not yet reported. Lastly, use of the term "reward processing" should be avoided/omitted since the authors did not specifically study the processing of reinforcers.

      Response: As the reviewer points out, we tried to be cautious interpreting the “aversive” odor response, and focused mainly on the reward association. This was discussed in the discussion. We don’t see the need to further add a redundent statement to a “limitations section”. We have also added a note about the previously identified laterality of the OT, which might account for lack of aversive responsive neurons in the OT. The reviewer makes an interesting suggestion that behavioral responses to airpuff-associated odors are not significantly different from un-associated because the lack of reward in this context is already aversive. We note that the walking velocity between reward- and puff-associated odor is significantly different, but not that to unassociated. This is in agreement with the suggestion, and we have added a statement to reflect this.

      Also, I would appreciate justification of the term "value". How specifically does the assay used assess value versus a more simplistic learned association which influences perceived hedonics or valence of the odors.

      Response: We have removed the term “value” with the exception of areas where we cite the work of others. We acknowledge that the word value is complicated in the incentive learning field and appreciate the suggestion. Our experimental design was meant to investigate learned association for positive and negative stimuli, thus valence is more appropriate and we have used this term.

      More information is needed regarding how neurons are identified day-to-day, both in textual additions to the Methods and also in terms of elaborating more in the results and/or figure legends about what neurons are included:

      (a) The ROI maps for identifying/indicating cells in the FOVs are nice to see and at the same time raise some concerns about how cells are identified and/or borders for those specific ROIs drawn. For instance, Figure 4, A & D, ROI #13 (cell #13) between those two panels is VERY different in shape/size. Also see ROIs 15 and 4. Why was an ROI map not made on day 1 and then that same map applied and registered to frames from consecutive imaging days in that same mouse? As it is new ROIs are drawn, smaller for some "cells" and larger for others. And at least in ROI #13 above, one ROI is about twice as large as the other. This inconsistency in the work flow and definition of the ROIs is needing to be addressed in Methods. Also, the authors should address if and how this could possibly impact their results.

      Response: We have added details and clarified the methods section to make this more clear. We note that we extracted calcium transients from the raw data with the the widely used Constrained Nonnegative Matrix Factorization (CNMF) algorithm. This processing algorithm simultaneously identifies spatial and temporal components using modeled kinetics of calcium transients and pre-trained CNN classifiers. Using 2-photon microscopy the optical resolution in the z plane is narrow and we may not always capture components of a neuron that look like “neurons”, but all ROIs were confirmed manually to ensure they were not artifacts.

      (b) Also, more details are needed in results and/or figure legends regarding the changes in cell numbers over days that are directly compared in the results. Some days there are 10% or more or less cells. Why? It is not the same population being compared in this case and so some Discussion of this is needed.

      Response: The shapes of the spatial components can vary across days due to nonrigid motion in the brain and/or miniscule differences in the imaging angle across days. Although we visually verified that we are imaging approximately the same z plane across days, we cannot (and do not) claim to image identical populations of neurons across days.

      Reviewer #3 (Public Review):

      Summary:

      This manuscript describes a study of the olfactory tubercle in the context of reward representation in the brain. The authors do so by studying the responses of OT neurons to odors with various reward contingencies and compare systematically to the ventral pallidum. Through careful tracing, they present convincing anatomical evidence that the projection from the olfactory tubercle is restricted to the lateral portion of the ventral pallidum.

      Using a clever behavioral paradigm, the authors then investigate how D1 receptor- vs. D2 receptor-expressing neurons of the OT respond to odors as mice learn different contingencies. The authors find that, while the D1-expressing OT neurons are modulated marginally more by the rewarded odor than the D2-expressing OT neurons as mice learn the contingencies, this modulation is significantly less than is observed for the ventral pallidum. In addition, neither of the OT neuron classes shows significant modulation by the reward itself. In contrast, the OT neurons contained information that could distinguish odor identities. These observations have led the authors to conclude that the primary feature represented in the OT is not reward.

      Strengths:

      The highly localized projection pattern from olfactory tubercle to ventral pallidum is a valuable finding and suggests that studying this connection may give unique insights into the transformation of odor by reward association.

      Comparison of olfactory tubercle vs. ventral pallidum is a good strategy to further clarify the olfactory tubercle's position in value representation in the brain.

      Weaknesses:

      The authors' interpretation of the physiologic results - that a novel framework is needed to interpret the OT's role - requires more careful treatment.

      Response: We thank the reviewer for their recommendation. We have toned down the conclusiveness of our language in the discussion. Additionally, we have removed several speculative sentences from the concluding paragraph.

      Reviewer recommendations for Authors:

      We thank the reviewers for this helpful list of recommended changes to the manuscript.<br /> Regrettably, a few of the recommendations were overlooked in the revision, as indicated below.<br /> We do agree with the suggestions and plan to add appropriate changes to the version of record.

      Reviewer #1 (Recommendations For The Authors):

      If the comparisons mentioned in point 2 in the public review do not account for the lack of independence of individual neurons, I suggest the authors do so by either running linear mixed effects models with a random effect for subject, or one-way ANOVAs with a random effect of subject, where appropriate. The authors could also run analyses on summarized individual subject data (averages, % of neurons, etc.), though the authors would lose substantial power when assessing whether average changes differ between subjects in each recording group.

      We have clarified when statistical analyses are comparing individual neurons vs. simultaneously recorded populations. Per the reviewer’s recommendation, we have also incorporated linear mixed-effects models when statistically analyzing individual neurons. Lastly, to further clarify the statistical analyses used, we have added supplementary tables for every statistical test that better describe the parameters used and the relevant outputs.

      Reviewer #2 (Recommendations For The Authors):

      Of minor note, there are some symbols/special characters that did not translate in the figure caption for Figure 6C, repeated text between lines 700-705 and 707-712, and some other small grammatical errors. Additionally, the source of the anterograde tracing virus (AAV9-phSyn1FLEX-tdTomato-T2A-SypEGFP-WPRE) needs to be stated.

      Thank you for pointing these out. We have added description to the figure legend, and deleted the repeated lines and fixed grammatical errors. During the revision, we Regrettably overlooked the request to provide the source for the AAV9-phSyn1-FLEX-tdTomato-T2A-SypEGFP-WPRE. We agree that this small detail is important and will add it before publication of the version of record. This viral vector was purchased from The Salk Institute GT3 Core.

      Reviewer #3 (Recommendations For The Authors):

      The authors' interpretation of the physiologic results - that a novel framework is needed to interpret the OT's role - requires more careful treatment. As the authors note, there is rewardcontingency modulation in OT, especially when D1 neurons are compared against D2, as shown in Fig. 3D,E, Fig. 4I, and Fig. F,J. Though small in effect size, presumably, these modulations cannot be explained by the odor identity. These observations, to this reviewer, suggest the D1 neurons of OT have a component of cue-reward representation. In other words, rather than developing an entirely new framework, an alternative possibility that D1 neurons of OT occupy an intermediate stage in associating cues with reward (i.e., under the same framework, but occupying a different position in the emergence of value representation) should be considered.

      We thank the reviewer for this thoughtful comment. We have eliminated the statement that “novel framework is needed” and have been more conservative in our interpretations. We have also acknowledged that our results are not necessarily in conflict with existing literature, but we do draw different conclusions, namely that the anteromedial OT is not a robust valence encoding population in comparison to that in the VP. We appreciate the suggestion of the term “intermediate stage” in reward association and have now included this in the discussion. Lastly, we have limited broader speculation to a “speculation” section of the discussion.

      Related to the above point, have the authors analyzed if the similarities in the chemical structures correspond to perceptual and neural similarities? In the data presented in Figure S4, there are greater similarities in the population patterns within the same rewarding condition than within chemical groups. A comparison of the reward vs. chemical group (a simpler version of Fig. 5B) may be beneficial and take full advantage of the experimental design.

      This comparison already exists in 5B and lines 285-289 of results. In VP populations, the distribution was structured such that intervalence pairwise comparisons between sucrose-paired and not sucrose-paired odors (e.g. ||SK-PK|| and ||SK-XK||) were larger than intravalence pairwise comparisons (e.g. ||SK-ST||, or ||XK-XT||). OTD1 populations showed an intermediate trend where most intravalence pairwise distances were smaller than intervalence pairwise distances with the exception of ||SK-ST||.

      Related to the point about chemical similarities - is the smaller effect size (amount of modulation associated with reward contingency) in this study, compared to the study by Martiros et al, explained by the similarities of odorants used?

      This is an interesting point. Although the odorants we use are different from those in Martiros et al, we think it is unlikely to the basis of smaller effect size due to reward modulation. If OT represents odor in a population code, whereby identity is encoded in unique ensembles of activity, then variation in the expression of D1R between OT neurons could account for different effects in different ensembles. However, there is no evidence for such varied expression and it doesn’t seem like an ideal mechanism for the OT to broadly associate odor with reward. Moreover, we do not observe any differences in effect size of reward association between the different odorants used in our study. Rather, we think the difference between our findings is more likely to result from recording in different populations of neurons, which is addressed in lines 522-535.

      Regarding the data presented in Fig. 3I - the rewarded odor responses (Sk) are compared against neutral ones (Xk responses), but an S vs. P comparison may be informative, too. Even though the authors mention that the effect of air puff is subtle, the behavioral data presented in Fig. 2F and G suggest that these serve as aversive stimuli. For example, on day 4, the first day after the reward contingency switch, the licking levels seem the lowest for the P odors.

      We have added the S vs P comparison. Indeed, we had originally omitted this because the neural and behavioral response to puff cues was not robust. This is discussed in the discussion (lines 563-579), and our conclusions about aversive conditioning are cautious.

      Regarding the data presented in Fig. 4G: it is difficult to interpret the data when the data for day 1 reward period and day 3 reward cue period are combined. Or do the authors mean day 1 S cue and day 3 S cue?

      These data were based on an observation that some neurons in the VP only responded to sucrose (not odor) on day 1, but later became responsive to the associated odor on day 4. To quantify this, Fig. 4G shows the percentage of these neurons by reporting the percentage that were both responsive to sucrose (not odor) on day 1 and also rewarded odor on day 3. This is described in lines 260-274.

      Figure 6 presentation would benefit from a revision. For example, it is unclear if the water port becomes available for the "N" odors with 100% or 50% chance of reward delivery, and if so, how that happens. There are some errors e.g., colormap used for panel G; odors listed may be wrong in line 752 etc. It was unfortunately not possible to understand what was presented.

      We have added a schematic (Fig 6B) to better describe the movement of the port and details to the methods. The color scale was indeed inverted in panel G (now H), and it has been corrected. We have verified that the odors listed in the methods are correct. Although not included in the revision, in the version of record we will also add corresponding descriptors (e.g., LHi & Lx) to the odors in the methods for easier comparison.

      Minor comments

      For Figure 2H, an alternative description in the legend may be beneficial, as the phrasing is not intuitive. A suggested alternative is "licks in response to sugar-associated odors expressed as fraction of all odors".

      We appreciate the suggestion and have changed this to “licks during either sucrose cue expressed as a fraction of all licks during any odor.”

      Figure 2H: please explain the color code for crosses in the legend and the statistical comparison shown in the figure.

      We have added a legend to explain the color code and included a statement about the statistics in the legend with a link to a supplemental table for statistical parameters.

      Figure 3D: may contain mislabeling in the legend - the legend for 3D does not match the plot (legend refers to bar graph while plot shows line graphs)

      Unclear what is meant. 3D legend says: “Percentage of total neurons that were significantly excited or inhibited by each odor (Bonferroni- adjusted FDR < 0.05) as a function of time relative to odor. Lines represent the mean across biological replicates and the shaded area reflects the mean ± SEM.” This is not a bar plot and is not referred to as one. 3E does show bar plots and is correctly described in the legend.

      Figure 3M: uses letters to refer to cell populations that are identical to the roman numerals used in Fig 3 A-C as well as colours similar to the ones in Fig 3C. However, the cell groups are unrelated; splitting the figures or using a different nomenclature might help

      We have adapted a different color code that we think makes this more distinct.

      Figure 4I: statistical comparison shown in figure not explained (neither in main text nor legend)

      We have added a statement about the statistical comparison and referenced a supplementary table.

      Figure 5 D: color code appears to have a different range than the values shown (i.e. lower limit is 0.7 while the plot shows values below 0.7)

      We confirm this is not a mistake but a stylistic choice. The displayed color scale does only show values to lower limit of 0.7, while the lower limit of values is 0.67. Although the color for 0.67 is not shown in the scale it is approximately the same as the lower limit. The values are reported for full transparency and accuracy.

      Figure 5 G, I, & L: statistical comparison shown in figure not explained

      The comparisons have been explained in supplemental tables (S22-29) and referenced in the legend.

      Figure 5 I: meaning of symbols overlayed over bars not explained

      “Markers represent the mean across biological replicates” has been added.

      Figure 5 J&K: please state if error bars show SEM or SD; also please describe individual thinner lines in the legend

      This has been added to describe 5I. The same format applies to J&K.

      Figure 5L: please describe the individual crosses overlayed over bars in the legend

      Described in 5I.

      Figure S6A-C: please mention the odors used.

      S6A-C shows kinetics for the odor a-terpinene, which is now indicated in the legend.

      Line 129: mentions a 70 psi airpuff but methods say 75 psi - please clarify This has been corrected. 70 psi is the correct value.

      Line 134 typo: SP should be PK

      This has been corrected.

      Line 428: typo; should be cluster 3, not 2

      This has been corrected.

      Line 474 (and figure 6O): please explain what "P" is

      “P” is probability, used as P(S), as in probability of sucrose. This is defined in in line 466.

      Line 692: please describe the staining protocol in the methods (rather than just listing the antibodies and concentrations)

      We have added more details (lines 692-699).

      Line 707-712: duplicate text (identical to Line 700-705)

      This has been deleted.

    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript the authors are interested in understanding how fission yeast respond to a Nitrogen Signaling Factor (NSF) that has previously been shown to allow Leucine auxotrophs to grow in the presence of Leucine when Nitrogen Catabolite Repression (NCR) is triggered by the presence of a high quality Nitrogen source such as Ammonium Chloride (NH4Cl).

      The authors begin with a screen to identify genes that affect the ability of wild type cells grown near cells with leucine auxotrophy to enhance or abolish NCR phenotype. They screened the non-essential gene deletion library which they manipulate so that it only contains a leucine auxotrophy (unlike the original gene deletion library which contains additional auxotrophies). They identify 137 genes whose deletion allows growth of Leu auxotrophs in the presence of Leucine and Ammonia without the presence of WT cells. These genes are required for NCR. They further identify 203 genes which do not bypass NCR even in the presence of wild type cells, and are thus important for bypassing NCR in the presence of WT cells.

      They then conduct a second screen to identify which of these genes are important for bypassing NCR in response to the Synthetic NSF, 10(R)-hydroxy-8(Z)-octadecenoic acid, by looking for genes which grow in the presence of leucine when ammonia is not present, but do not grow in the presence of leucine when ammonia is present, even when NSF is added. This second screen identifies 117 strains carrying deletions in a gene set enriched for genes related to cellular respiration and mitochondria. They then show that the NSF bypass of NCR is linked to respiration by showing that it is abolished in the presence of the respiration inhibitor Antimycin A, that growth in low levels of glucose can bypass NCR in the absence of NSF< and that cells supplemented with NSF have a higher oxygen consumption rate.

      To gain insight into how the cell responds to NSF, the authors then gather RNA expression data from cells grown in high ammonium concentrations following treatment with NSF relative to a negative control treated only with Methanol (the vehicle into which NSF is dissolved). They argue that the gene expression pattern resembles gene expression data from cells undergoing respiration in glycerol relative to cells undergoing fermentation in glucose. They show that the upregulated genes relate to trehalose synthesis, detoxification of Reactive Oxygen Species, and cellular fusion and the downregulated genes are related to cellular adhesion and flocculation.

      They validate their RNA-seq measurements by showing that the two most highly induced and two most highly repressed genes respond to NSF addition in a dose dependent manner and do not respond oleic acid which is chemically similar to NSF. The most highly responsive gene they identify is an uncharacterized gene, SPBPB2B2.01, which they suggest naming "NSF-responsive amino acid transporter 1" (nrt1). They also show that the nrt1 response is dependent on the culture density, and that the response is present (though the magnitude varies) in YES and in EMM under varying nitrogen concentrations, and that yfp driven by the nrt1 promoter is induced by NSF.

      The authors then investigate the 8 transcription factors that were present in their list of genes required for NSF-mediated adapted growth. They note that Hsr1 was the only one of these transcription factors, indeed the only gene, that was a hit in their screen for NSF-mediated adapted growth and whose expression was induced upon NSF treatment. To see if the activity of the other transcription factors changed in response to NSF treatment, the authors then gathered ChIP-seq data using 6 of these transcription factors as targets for IP. They saw that for Hsr1 and Php3, targets that had increased RNA-seq expression showed an increase in promoter occupancy while for Hsr1, Php3, Adn2, and Atf1, genes that had decreased RNA-seq expression showed a decrease in promoter activity.

      Finally the authors attempt to identify the mode of action of NSF by generating a functionalized NSF with an alkyne tag (AlkNSF) which they then use as a probe to identify NSF binding partners. They first show that AlkNSF does allow bypass of NCR, although at 30-fold higher concentration. Also AlkNSF induces nrt1 expression in a dose dependent manner, although the expression saturates at a lower level and requires a much higher concentration for induction. They then look for proteins that co-purify with AlkNSF compared to a control that was pre-incubated with NSF which was expected to compete off AlkNSF. The only significant protein they saw was Ayr1, which was not identified in their screen and which did not abrogate NSF bypass of NCR when deleted independantly. They saw that Ayr1 deletion actually increases the response of nrt1 and mei2 targets to NSF, and speculate that Ayr1 metabolises NSF and reduces the cell's ability to respond to NSF to bypass NCR.

      They then repeat the affinity purification / mass spec protocol in an Ayr1 delete cells to identify other interaction partners, this time incubating with a higher concentration of NSF, and also comparing to an experiment using Alkeyne Oleic Acid as a control for non-specific binding. The top two specific hits from this assay are Hmt2 and Gst3. NSF was still able to rescue NCR in gst3 deletes, indicating that it was not relevant for the phenotype. Cells lacking hmt2 did not grow in EMM, but did grow in YES when not supplemented with ammonium and when supplemented with ammonium did not grow, and addition of NSF did not rescue growth. They also see that nrt1 and mei2 gene induction in response to NSF is abolished when hmt2 is deleted. They then argue that hmt2, a sulfide:quinone oxidoreductase localized in the inner membrane of mitochondria is a direct target of NSF that triggers a switch to respiratory metabolism and allows bypass of NCR.

      Below are comments that I think ought to be addressed prior to publication (Major comments)

      1. In line 70, the authors state that "S. pombe cells rely on their own BCAA synthesis to sustain growth" when grown alongside Leucine when ammonium is supplied in the media. If prototrophs can inhibit NCR via NSFs in neighboring auxotrophic cells on the same plate, couldn't they also inhibit NCR within their own colony? How do we know that prototrophic cells grown in high quality nitrogen sources along with, say leucine, are not taking up leucine? The fact that leucine auxotrophs cannot grow in high quality nitrogen sources when leucine is present does not imply that wild type cells must use be synthesizing BCAAs rather than importing them. In a recent paper (Kamrad et al Nat. Microbiol. 2023, https://www.nature.com/articles/s41564-022-01304-8), it was shown that S. cerevisiae cells grown in lysine and in high concentrations of ammonium uptake lysine rather than synthesize it as lysine concentrations in the media are increased. I am aware via unpublished results that this is the case for Leucine as well. I would be surprised if the same isn't true in S. pombe. The authors should caveat or remove this assertion.
      2. It is important for the authors to put their observation linking respiration to rescue from NCR in context with findings from a closely related study (Chiu et al 2022) which included some authors from this manuscript and which the authors cite. In that paper, it was shown that the siderefore ferrichrome can also rescue NCR in fission yeast. That paper stated "It is likely that ferrichrome increased mitochondrial activity, which enabled efficient utilization of glucose downstream of the glycolytic pathway" based on experiments in different concentrations of glucose. This evidence seems to support the link between respiration and rescue from NCR proposed by the authors of this manuscript. The authors should acknowledge this closely related and earlier work as it strengthen's the case they are trying to make. They could even test if ferrichrome addition makes cells sensitive to antimycin A (as in fig 1E), but that extra experiment would be optional in my opinion.
      3. In figure 1B for the second screen I do not understand what the photos represent. For the photos, two rows are meant to have no NH4 and also no NSF and the label on that image makes no mention of Leucine supplementation. In the diagram there are two rows that have NH4 and leucine and one row that has no NH4 but does have leucine. I assume the diagram is correct and the labels on the images are incorrect.
      4. It would be important for the authors to put their observation linking respiration to rescue from NCR in context with findings from Chiu et al 2022 which the authors cite. In that paper, it was shown that the siderefore Ferrichrome can also rescue NCR in fission yeast which the authors site which found that a siderephore rescues NCR. Also the authors of that paper stated "It is likely that ferrichrome increased mitochondrial activity, which enabled efficient utilization of glucose downstream of the glycolytic pathway." based on experiments in different concentrations of glucose. This evidence seems to support the link between respiration and rescue from NCR proposed by the authors of this manuscript.
      5. In line 133. The authors state that the 29 mutants that didn't grow under Leucine supplementation either without NH4CL or with NH4Cl whether or not NSF was present were "related to EMM Growth, leucine uptake, or utilization of ammonium as the sole nitrogen source." The first two make sense, but I can't see why a a strain with deletion of a gene related to utilization of ammonium as a sole nitrogen source wouldn't grow when supplemented with leucine. In fact for all the leucine auxotrophs in the screen, if one was to try to grow them with ammonium as the sole nitrogen source they would not grow, so it isn't clear that this screen can identify genes responsible for utilization of ammonium as a sole nitrogen source. The authors should clarify or remove this point.
      6. 203 strains are important for avoidance of NCR (because in the presence of Ammonium and Leucine, as well as a WT strain, they cannot grow). Of these 57 strains can't grow in the presence of a WT strain but they can grow in the presence of NSF. The authors conclude in line 138 that these strains are "likely to respond to a transmissible signal that is different from NSF". This is confusing because deletion of these genes still does allow cells to respond to NSF, however when these cells are growing in the presence of wild type cells (which in their model are releasing NSF), the cells don't grow. I am confused about the nature of the transmissible signal that the authors suggest. It would appear that when these genes are deleted and grown next to a wild type cell which sends the alternative signal and the NSF, the other transmissible signal would inhibits the ability of NSF to release NCR (as NSF can still rescue the gene). It is not clear how the other transmissible signal would work when the gene is present as it is clearly not necessary to rescue growth.

      A simpler explanation might be that there was contamination in the second screen, or that there was a threshold effect - perhaps in the first screen the strains grew just below a threshold and in the second screen it grew just above that level.

      The authors should clarify their interpretation for these strains, and acknowledge any alternative technical explanations.<br /> 7. The authors' efforts to removed confounding effects that might stem from additional auxotrophic alleles made the screen more convincing. However, Fig 1E, 1F, 5B, and 5E were done with EMM+Leu+Ade+Ura, while the initial strain was just done in the presence of additional Leucine. It is unclear why this was done from the text and captions, but I assume it was because they used a strain that was ade- and ura- in addition to being leu-. Given that they had strains without these additional mutations, this seems like a strange choice. The authors should acknowledge that there are possible confounding effects of adding adenine and uracil to the media, and, if they did have additional metabolic deletions, acknowledge that that could possibly be confounding.<br /> 8. Fig 1E, it appears that cells can grow without NSF in the presence of ammonium and additional amino acids after 10 days (although NSF is required for growth at 5 days). This is not a problem for the screen as that was taken at 5-6 days, but it appears as though NSF does not rescue growth so much as speed it up. The authors should acknowledge this when describing the phenotype. It also argues for a quantitative time course growth experiment to compare growth over the course of 10 days with and without NSF, although this would not be necessary to the paper's main argument.<br /> 9. In line 191 and 192, the authors suggest that the "downregulation of flocculation/adhesion related genes by NSF could serve to avoid undesirable mating during growth". If this is the case, I don't understand why mating genes and cellular fusion genes would be upregulated. What do the authors mean by undesirable mating? Wouldn't flocculation increase desirable mating as well? If all mating is undesirable, wouldn't upregulation of mating and cellular fusion genes be detrimental? 10. The authors mention that trehalose is an antioxidant, for which they reference Malecki 2019, however that paper shows no direct evidence of trehalose functioning as an antioxidant under respiratory conditions. It only shows that some trehalose synthesis genes are upregulated when cells are grown under glucose. The authors should identify primary literature to back this statement up, or soften the wording. Also trehalose is known to be a storage metabolite (which is mentioned in Malicki et al 2019, but not in this manuscript). In fact work in budding yeast has show that trehalose can be a shared metabolite that can be produced by respiring cells and used as a fermentable carbon source in communities of budding yeast cells that consist of fermenting and non-fermenting cells (Varahan et al, eLife 2019 https://doi.org/10.7554/eLife.46735). It seems that this role should be considered as an alternative explanation for the induction of trehalose in respiratory cells.<br /> 11. Line 208: The stimulatory effect of NSF on NRT1 decreased with cell density, thus cell density is likely to be an important factor in terms of gene expression. The methods section, text and figure captions do not mention the density at which cells were inoculated/harvested for RNA-seq and other experiments. If that density was more than OD 0.1, then this would be inconsistent with the measurements from Fig 3. Also in fig 3D, The culture density is not mentioned in the figure or the caption, even though the text suggests that for that experiment cells were grown at low density (Lines 212-213). The authors should provide information on density for their experiments in order for them to be reproducible, as they show it is a key factor. 12. In suggesting a name for NRT1 (NSF-responsive amino acid transporter 1), the authors assume that the gene has a role in amino acid transmembrane transport, but they have no experiments showing this phenotype. They mention that it is Inferred from homology with other amino acid transporters. I presume this name has already been approved by Pombase and is not provisional, but it seems that including phenotypes inferred from homology, rather than from experiments is unwise. Do the authors have any other direct evidence that this is a bona fide Amino Acid Transporter? Perhaps a name like "NSF-responsive gene" would be more appropriate.

      Related to this, it appears that the expression level of Nrt1 may be very low (see Fig S2B in which the scale of the RNA-seq track is very small [-1,1] and the amount of expression is very small even when NSF is added). Looking at Fig 2A, the total transcript abundance did not appear to be very low in terms of counts per million (over 100) is this a discrepancy in fig S2B? Perhaps the large fold change is the result of counts very close to zero in the control condition? Also in Fig 3 the nrt1 expression levels did not appear to be especially low and they appeared repeatable. Is the RNA-seq data shown in fig S2B for nrt1 a fluke or am I misinterpreting it? <br /> 13. To show that their Chip-seq worked, the authors showed specific examples of Chip-seq reads for target genes Line 240, "Previously determined target genes of these TFs were significantly enriched in our data set, demonstrating that the experiment has worked (Figure S2A)." Is the significance here, the threshold from fig S2B? If so that threshold should be clearly stated here in the text. If it is the fact that asn1 shows up as "Fil1 bound" is strange as there are no genes that had significant changes in ChIP-seq signals for fig S2B. If there is another threshold the authors should describe it. While some of the examples they showed were convincing (e.g. php3-flag for the php3 regulated gene gln1 and the increased reads for srw1 for the reb1 target srw1), there were some targets that didn't seem to be especially enriched for their designated transcription factor. For example, the gene trx1 which was identified as an Hsr1 binding target had some binding from Hsr1, but more from Php3 and equivalent amounts for many of the other transcription factors. A clear description of how genes are chosen to be significant in the text, alongside references/selection criteria the authors used to select the specific genes shown should be provided to improve reproducability. <br /> 14. In lines 244-246 the authors state that "These differences in TF occupancy were positively correlated with target gene expression changes. That is, individual genes that were upregulated by NSF tended to be more strongly bound by the TFs, whereas downregulated genes were less occupied by the respective TFs (Figure 4A)." This is far from a general trend. The trend is not there for reb1 and fil1. In fact fil1 looks to the eye like it shows a decrease in occupancy for genes with increased expression, and I worry that the authors did a one sided test for significance that would have missed this, although the variability of the genes that don't change in this case is very high, so there could be no significant effect. The authors elaborate on some of the detail in following statements, but they should soften or remove this statement.

      Related to this, in line 254, the authors state: "These results imply that NSF exposure rewires the recipient cell's transcriptional program, for which the TFs Atf1, Adn2, Adn3, Fil1, Hsr1, Php3, Php5, and Reb1 are indispensable (Table S3)." While I am convinced from the RNA-seq evidence and some of the chip-seq evidence that NSF exposure rewires cell's transcriptional program, I am not convinced that the 8 transcription factors they mention are indespensable for rewiring the transcriptional program. While they may be indespensible for the phenotype itself, Reb1, and Fil1 show no no siginificant enrichment in occupancy of upregulated or downregulated targets (Fig 4A) and, along with Atf1, Reb1, and Fil1, have very few genes in which ocupancy is changed significantly (Fig S2B), while no chip-seq experiments were shown for Php5 and Adn3.

      The more specific summary of the data (Lines 250-253) from Fig S2B describing how hsr1 and adn2 have the strongest effects of the transcription factors required for NSF-mediated NCR bypass is a much stronger message for this section. 15. In line 335, the authors state that "in contrast to other communication systems, NSF does not induce noticeable changes in S. pombe's morphology", referring to changins in mating, filamentation, and bacterial biofilm formation. However they do show very clearly that NSF does cause a large decrease in expression in flocculation/adhesion genes. The fact that they do not see a change in morphology is likely due to the fact that the lab strain in the conditions used for this assay do not flocculate. We have recently identified conditions and strains which do exhibit flocculation in this preprint [https://www.biorxiv.org/content/10.1101/2023.12.15.571870v2]. It is likely that if they had a strain and conditions that did flocculate addition of NSF would break up flocculation and thus change the morphology based on their evidence. The authors should remove or caveat this point.<br /> 16. Line 270 Fig 5B: The concentration of NH4Cl listed in the text (374mM) does not match the concentration shown on the figure (748mM). I assume this is a typo but it should be corrected prior to publication.

      Also I have several minor comments to help improve the manuscript.

      m1: Lines 66-70- state that "uptake of the branched-chain amino acids (BCAA) isoleucine (Ile), leucine (Leu), and valine (Val) is suppressed in the presence of high-quality nitrogen sources such as ammonium or glutamate, because the expression of transporters or permeases that are needed for the uptake of poorer nitrogen sources are down regulated (Zhang et al, 2018)." This reference is for S. cerevisiae and is a review. The authors should cite original results in S. pombe if possible, and if that is not available, alert the reader that this result is from a different species.

      m2: It is unclear from the methods section how the images taken for the screens were analyzed. Were they analayzed and scored by hand, or using custom image analysis software. Either way, when publishing the authors should publish the scores for each deletion mutant in their screen. If there was custom image analysis, the authors should mention in their methods the cutoffs which they used to score growth, and consider plotting the data as a supplement so readers can get a sense of how sensitive the screen was.

      m3: The authors identify 137 mutants that did not require NSF signaling to bypass NCR and claimed these genes were required for NCR. It would be helpful and give more confidence in this screen to demonstrate the extent to which the genes identified in this study overlap with any previous genes required for NCR, and whether there was any GO-term enrichment in this set.

      m4: It would be interesting if the authors could speculate a bit in their discussion on why mitochondrial respiration counteracts NCR. Is there something about cells undergoing respiration that would make it easier for them to use BCAAs than to produce them, or conversely something about fermenting cells that makes it easier for them to produce BCAAs rather than importing them?

      m5: It is unclear why Figure 1F has 'MP biomedicals TM' listed in the figure. It doesn't seem to be listed in the caption or the methods. Is this different media than in other experiments? If so, the authors should add that information to the methods or the caption.

      m6: In Line 160, positively influenced is strange wording, do the authors mean "induced"?

      m7: In the section on gene expression change upon exposure to NSF, the authors use a + after each gene name. My understanding is that that notation is meant to refer to strains with the wild type genotype of that gene, and not the gene itself. Shouldn't the gene be italicised in lower case to represent the gene? See: Lera-Ramirez et al 2023 https://doi.org/10.1093/genetics/iyad143.

      m8: In Fig 2A, genes are displayed on a plot that depicts level vs log2FC, but a comparison between the fold change and p-value would be more useful, and I believe DESeq2 should provide an adjusted p-value for these genes. A related issue is that it appears as though there were no biological replicates, though there was data gathered at different time points. In these genome wide experiments, replicates can give confidence to data and help distinguish true change from intrinsic variability of expression in specific genes. Though the authors did qPCR to validate specific results, it would have improved the quality of their systems-level data to have replicates for these and other key experiments (Chip-seq, affinity purification and even the screen).

      m9: Supp Fig S1: To show that similar gene expression profiles exist for other time points, it would be more convincing to show Log fold change 2h vs 4h and 2h vs 6h and show correlation, or else to make a heat map with all genes to see that genes that go up in one condition go up in the other conditions. It is not clear if the red and blue colors are defined for the 2h dataset and then mapped onto the 4 and 6h dataset, or if they are independently assigned for each plot.

      m10: Mbx2 is a key transcription factor related to flocculation and adhesion genes, and its expression is correlated with expression of its targets. If this transcription factor's expression levels decreased in response to NSF, that might strengthen and help explain the decrease in expression the authors observe in flocculation/adhesion genes when cells encounter NSF. If it it does not change, it might also be interesting for readers interested in these phenotypes.

      m11: In Fig 3D, The notation for the Ammonium concentrations for EMM and YES are inconcistent (+ vs parentheses), also the units (mM from the caption) are not on the figure, but the abbreviation "N" is which is confusing and inconsistent with the other plots in which NH4CL is not abbreviated. Additionally, the caption lists additional nutrients in the media for the EMM conditions (Leu, Ade, Ura) which ought to also be listed.

      m12: In lines 233-235, the authors say "One possibility is that they remain bound to their target genes but become activated or deactivated by NSF directly, or posttranslational modification, such as phosphorylation in the case of Atf1". I don't think the authors intend this, but this sentence could be taken to mean that Atf1 has been shown to be phorphorylated by NSF in the reference they site. I think the authors should clarify, i.e. by saying "..such as phophorylation which is known to regulate activity of Aft1 in response to oxidative and osmotic stress [Lawrence et al 2009]".

      m13: In Fig 4B and Fig S2A, there are grey and colored tracks for the chip-seq (- and + NSF), but they are very difficult to see. If grey is in front it is hard to tell how close the colored peak wehn the colored peak is lower. For example, grey is in front for pex7 while color is in front for yhb1. Could the authors add some transparancy so that the data for both conditions could be seen at once? Also there is little information on the control. My assumption for the input(ChIP) sample was that it was cross-linked and sonicated but not immunoprecipitated, but it is not clear what conditions it was in. I would assume it was done without NSF treatment in WT cells, but those details should be added in the caption or methods. In particular, in the input there is a large spike for Gsf2. Do the authors have any explanation for this and does it have anything to do with that gene's NSF responsiveness?

      m14: The authors might consider putting something like Fig S2B (or even a corresponding volcano plot) as a main figure for Fig 4 in addition to the other two panels, as the individual examples from fig 4B are nice to see, but do not give a broad overview of the data.

      m15: In line 348, the wording "Would score" might be better replaced by "would be identified."

      Significance

      Assessment:

      In general I find the authors arguments compelling and their experiments convincing. The initial and follow on screens were well designed and the authors linked respiration and the action of NSF in a convincing way. The analysis of RNA-seq data was also convincing, especially regarding the decreased expression of flocculation and adhesion genes, and the follow up of specific targets gives confidence in the data (though see Major point 12 below regarding the naming and expression levels of nsf1). The identification of hmt2 as a functional target of NSF was compelling and rigorous, and the authors offer an interesting hypothesis to connect this to respiration that could form the basis of future studies.

      At times I thought that some of the interpretation of the results was hard to follow, poorly worded, or off the mark (see comments below). The presentation of the CHiP seq data also felt incomplete, though the influence of Hsr1 and Adn2 on expression of NSF1 targets was convincing. The genome wide assays (RNA-seq, CHiP seq, screen and pull-down/mass spec) could have done with replicates which would have improved statistics and reliability of the results presented for those experiments, although for key messages, the authors followed up with convincing targeted experiments.

      The study represents an advance on recent work in NCR in fission yeast in linking this with the broad metabolic switch between fermentation and respiration, and in that sense makes this of interest to a broader swathe of the microbiology community, outside those interested in metabolic regulation in microbes. In addition to being of interest to applied researchers interested in producing metabolites with yeast and other microbes, the link to cell signaling and, via flocculation and adhesion genes, to microbial multicellular-like phenotypes would make this work of interest to those interested in microbial communities.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This study investigated transcriptional profiles of midbrain dopamine neurons using single nucleus RNA (snRNA) sequencing. The authors found more nuanced subgroups of dopamine neurons than previous studies, and idenfied some genes that are preferenally expressed in subpopulaons that are more vulnerable to neurochemical lesions using 6-hydroxydopamine (6OHDA). The reviewers found the results are solid, and the study is overall valuable, providing crical informaon on the heterogeneity and vulnerability of dopamine neurons although the scope is somewhat limited because the result with snRNA is similar to previous results and cell deaths were induced by 6OHDA injecons.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this study by Yaghmaeian Salmani et al., the authors performed single-nuclei RNA sequencing of a large number of cells (>70,000) in the ventral midbrain. The authors focused on cells in the ventral tegmental area (VTA) and substana nigra (SN), which contain heterogeneous cell populaons comprising dopaminergic, GABAergic, and glutamatergic neurons. Dopamine neurons are known to consist of heterogeneous subtypes, and these cells have been implicated in various neuropsychiatric diseases. Thus, idenfying specific marker genes across different dopamine subpopulaons may allow researchers in future studies to develop dopamine subtype-specific targeng strategies that could have substanal translaonal implicaons for developing more specific therapies for neuropsychiatric diseases.

      A strength of the authors' approach compared to previous work is that a large number of cells were sequenced, which was achieved using snRNA-seq, which the authors found to be superior compared to scRNA-seq for reducing sampling bias. A weakness of the study is that relavely litle new informaon is provided as the results are largely consistent with previous studies (e.g., Poulin et al., 2014). Nevertheless, it should be noted that the authors found some more nuanced subdivisions in several genecally idenfied DA subtypes.

      On this point we respectfully disagree with the reviewer. In this study, over 30,000 mDA neurons have been analyzed at the genome-wide gene expression level, idenfying mDA territories and neighborhoods (that some may call “subtypes”), a descripon of the mDA neuron diversity that goes far beyond what has been published previously.

      Although several single-cell RNA sequencing studies of mDA neurons have added to our understanding of mDA diversity, they have been limited by the low numbers of sequenced mDA neurons. As the reviewer specifically referred to the study by Poulin et al., 2014, it should be noted that in this report, 159 mDA neurons were analyzed by qPCR – not by RNAseq – of 96 previously identified marker genes. Despite those limitaons, this was indeed a highly impressive study, suggesng five different mDA neuron subtypes (as compared to the 16 neighborhoods described here), published before the era of single-cell genome-wide gene expression methods and advanced bioinformac tools were available. On average, the following scRNAseq studies typically captured a few hundred mDA neurons - compared to over 30,000 in this study. None of the studies menoned in our manuscript were close to capturing the full diversity, and the informaon on mDA neuron diversity is, for this reason, somewhat fragmented in the scienfic literature. Indeed, the seven mDA “subtypes” described in the excellent reviews by Poulin et al., 2020 in Trends in Neurosciences and Garritsen et al., 2023 in Nature Neuroscience are integrated interpretaons of the results from numerous independent studies, each methodologically unique. Several previously idenfied groups, especially Vglut2+ populaons in VTA and SNpc, have been considered poorly defined. As menoned above, our findings in this study could reliably idenfy, by computaonal analyses and combinatorial marker expression in situ, 16 different neighborhoods within the mDA populaon and localize them in the ssue (Figure 4, Supplementary figures 4-1 to 4-3, described further in Supplementary Results). To menon three examples: Within Sox6+ SNpc, we idenfied four different variants (neighborhoods) with partly unique anatomical localizaon. In addion, the large group of mDA neurons referred to as the Pcsk6 territory has not been clearly defined in earlier studies. We also idenfied a novel mDA neuron group that is related to the previously well described Vip-expressing mDA neurons. These and other novel features are menoned in the manuscript and in Supplementary Figure 4-1 to 4-3.

      Although we have, for the consideraon of the space and intelligibility, characterized the 16 neighborhoods with only a few selected key marker genes, we have idenfied numerous addional novel markers, some of which are shown in dot plots in Figure 3 and Supplementary Figure 3, which can be used to characterize these groups further. We also provide all our sequencing data and our Padlock probe ISS data for anyone to download and analyze further, and we have made a web-based tool, CELLxGENE, available on our group’s website to facilitate exploraon of the different aspects of our dataset.

      Lastly, the authors performed molecular analysis of ventral midbrain cells in response to 6-OHDA exposure, which leads to the degeneraon of SN dopamine neurons, whereas VTA dopamine neurons are mainly unaffected. Based on this analysis, the authors idenfied several candidate genes that may be linked to neuronal vulnerability or resilience.

      Overall, the authors present a comprehensive mouse brain atlas detailing gene expression profiles of ventral midbrain cell populaons, which will be important to guide future studies that focus on understanding dopamine heterogeneity in health and disease.<br /> We thank the reviewer for poinng this out.

      Reviewer #2 (Public Review):

      In the manuscript by Salmani et al., the authors explore the transcriptomic characterizaon of dopamine neurons in order to explore which neurons are parcularly vulnerable to 6-OHDA-induced toxicity. To do this they perform single nucleus RNA sequencing of a large number of cells in the mouse midbrain in control animals and those exposed to 6-OHDA. This manuscript provides a detailed atlas of the transcriptome of various types of ventral midbrain cells - though the focus here is on dopaminergic cells, the data can be mined by other groups interested in other cell types as well.

      The results in terms of cell type classificaon are largely consistent with previous studies, though a more nuanced picture of cellular subtypes is portrayed here, a unique advantage of the large dataset obtained. The major advance here is exploring the transcriponal profile in the ventral midbrain of animals treated with 6-OHDA, highlighng potenal candidate genes that may influence vulnerability. This approach could be generalizable to invesgate how various experiences and insults alter unique cell subtypes in the midbrain, providing valuable informaon about how these smuli impact DA cell biology and which cells may be the most strongly affected.

      We appreciate these comments. We want to state that the study not only gives a more nuanced picture but goes far beyond previously published studies and provides a highly resolved and detailed atlas of mDA neurons. Thus, it clarifies poorly described diversity and idenfies enrely novel groups of diverse mDA neurons at the genome-wide gene expression level.

      Overall, the manuscript is relavely heavy on characterizaon and comparavely light on funconal interpretaon of findings. This limits the impact of the proposed work. It also isn't clear what the vulnerability factors may be in the neurons that die. Beyond the characterizaon of which neurons die - what is the reason that these neurons are suscepble to lesion? Also, the interpretaon of these findings is going to be limited by the fact that 6-OHDA is an injectable, and the effects depend on the accuracy of injecon targeng and the equal access of the toxin to access all cell populaons. Though the site of injecon (MFB) should hit most/all of the forebrain-projecng DA cells, the injecon sites for each animal were not characterized (and since the cells from animals were pooled, the effects of injecon targeng on the group data would be hard to determine in any case).

      We agree that the results are presented to provide a comprehensive and valuable resource rather than explaining molecular mechanisms. The reviewer points out that “what the vulnerability factors may be in the neurons that die” is unclear. However, our study was designed to answer the queson: What genes are enriched in clusters of mDA neurons that are parcularly likely to die aer toxic stress? Using single-cell analysis, we believe this queson had higher priority than atempng to idenfy gene expression changes occurring during the cell death process. We agree that we cannot answer why neurons are suscepble to lesions, only idenfy genes that correlate with either high or low sensivity. Thus, the genes we refer to as “vulnerability genes” and “resilience genes” are candidates for influencing differenal vulnerability. Hard evidence for such influence will require addional and extensive funconal analysis. As for the variability of injecon and the characterizaon of individual animals, we wish to menon the online interacve explorer available at htps://perlmannlab.org/resources/. It allows visualizaon of nuclei distribuon per territory and neighborhood for each mouse, making it easy to determine the cell loss rao and cell distribuon per animal. There is indeed variance in the proporons of intact/lesioned total nuclei per animal. This is also evident from the DAT autoradiographs shown for each lesioned animal and presented in Figure Supplement 5-1 A. Importantly, the relave UMAP distribuon of nuclei is quite similar between individual animals. To further invesgate this, we used Pearson’s Chi square test of independence with a conngency table for animals, each with two categorical variables as the proporon of nuclei from intact vs lesioned parts of the vMB (see added Supplementary figure 5-1 C ). This shows that – while there is a difference in the number of nuclei remaining aer lesioning – the relave distribuon among clusters and neighborhoods is similar between animals. We have clarified this point in the manuscript (see page 12 ).

      I am also not clear why the authors don't explore more about what the genes/pathways are that differenate these condions and why some cells are parcularly vulnerable or resilient. For example, one could run GO analyses, weighted gene co-expression network analysis, or any one of a number of analysis packages to highlight which genes/pathways may give rise to vulnerability or resilience. Since the manuscript is focused on idenfying cells and gene expression profiles that define vulnerability and resilience, there is much more that could have been done with this based on the data that the authors collected.

      We performed GO analysis for the genes upregulated and downregulated in the ML clusters (specific to the lesion condion) in the original manuscript (Please see figure supplement 7-1 C-E, and the newly added Supplementary file 10), but we agree with the reviewer that we could also have analyzed funconal categories of genes correlang with differenal vulnerability. Thus, we have used tools recently developed by Morabito et al., Cell Reports Methods (2023), and their hdWGCNA package to address this queson. This method is parcularly suitable for analyzing high-dimensional transcriptomics data such as single-cell RNA-seq or spaal transcriptomics. We calculated the coexpression network based on the lesioned nuclei of the mDA territories. Of the 9 co-expression modules calculated, one has the highest expression in Sox6 territory and has genes in common with the vulnerability module. Another co-expression module has genes in common with the resilience module and is most highly expressed in Otx2 and Ebf1 territories. We also did GO analysis for these co-expression modules and added addional GO analysis of the ML-enriched genes (see Supplementary Figure 7-1 D,E, the newly added Supplementary Figure 6-3, and the newly added Supplementary file 9). Text describing these addional analyses are menoned on page 15 and 17.

      In addition, we wish to emphasize our idenficaon of the genes we refer to as vulnerability and resilience modules in the previous version of the manuscript. Several of the genes were discussed in the previous version of the manuscript but we have now included more informaon on these genes, based on previously published studies and discuss their potenal funconal roles (see pages 22 & 23 in the Discussion).

      Another limitation of this study as presented is the missed opportunity to integrate it with the rich literature on midbrain dopamine (and non-dopamine) neuron subtypes. Many subtypes have been explored, with divergent funcons, and can usually be disnguished by either their projecon site, neurotransmiter identy, or both. Unfortunately, the projecon site does not seem to track parcularly well with transcriptomic idenes, aside from a few genes such as DAT or the DRD2 receptor. However, this could have been more thoroughly explored in this manuscript, either by introducing AAVretro barcodes through injecon into downstream brain sites, or through exisng evidence within their sequencing dataset. There are likely clear interpretaons from some of that literature, some of which may be more excing than others. For example, the authors note that vGluT2-expressing cells were part of the resilient territory. This might be because this is expressed in medially-located DA cells and not laterally-located ones, which tends to track which cells die and which don't.

      The manuscript consists of a comprehensive descripon of transcriponal diversity. Although of clear value, we believe that addional, comprehensive analysis that combines snRNAseq with, e.g., AAVretro barcodes must be done in a separate study. It should also be noted that we describe each territory and neighborhoods in the further detail in the Supplementary Results, which contains references to the relevant literature. In line with the comments, this secon has now been expanded with further references to relevant studies (see Supplementary Results related to Figure 4-figure supplements 1-3).

      It is not immediately clear why the authors used a relaxed gate for mCherry fluorescence in Figure 1. This makes it difficult to definively isolate dopaminergic neurons - or at least, neurons with a DATCre expression history. While the expression of TH/DAT should be able to give a fairly reliable idenficaon of these cells, the reason for this decision is not made clear in the text.

      We used a relaxed gang to ensure that we could capture nuclei expressing low levels of RFP, which we believe could be especially relevant for the lesioned dataset (see page 5). We did not find that it would be advantageous to use a more stringent gang that would risk losing all cells expressing no (or very low levels) RFP. Idenfying mDA neurons based on their typical markers is straighorward, as their transcriponal relaonship is evident from the expression profile of several markers, including transcripon factors such as Nr4a2, Pitx3, and En1. In addion, as pointed out in response to Reviewer #1, point 5, atypical DA neurons expressing Th and other mDA markers with no or low levels of Slc6a3 (DAT) were isolated. We believe the study is more complete by the inclusion of these cells. Moreover, we included a sufficiently large number of cells, which ensured a comprehensive analysis of mDA neurons in relaon to other cell types dissected from the ventral midbrain.

      Reviewer #1 (Recommendations For The Authors):

      (1) The authors state that a major advantage of their approach is that it prevents biased datasets when compared to methods that rely on capturing certain cell types. I was wondering if the authors could follow up on this topic with a more detailed descripon of their methodological advantages regarding potenal sampling bias. This is somewhat unclear to me, given that the results of the present study are largely consistent with previous work on this topic.

      As expanded on above (see response to the inial comment in the public review), we strongly disagree that there is litle novelty in our study. None of the previous studies come close to describing the mDA neuron populaon with a similar resoluon, which is unsurprising given the differences in the number of analyzed mDA neurons in this versus previous reports. We agree with the reviewer that our data is consistent with previous studies, when they are all combined. Thus, we idenfied mDA neuron groups that correspond (or roughly correspond) to major DA neuron groups idenfied in previous studies (see pages 8-14 in the Supplementary Results). However, the atlas presented here goes well beyond anything published in scope and resoluon. The diversity we define is comparable to findings that, with careful cross-paper analyses, can be stched together from previous single-cell studies. However, even such a combined analysis does not unravel the resoluon and diverse categorizaon of what we have demonstrated herein (16 neighborhoods in midbrain dopaminergic territories). Considering the well-established problems of dissociang and isolang whole neurons from adult brain ssue, this is likely due to sampling bias, resulng in an almost complete exclusion of some sub-populaons of neurons. We have added text on page 20 to clarify this point.

      (2) In the abstract, the authors state that their "results showed that differences between mDA neuron group could best be understood as a connuum without sharp differences between subtypes". However, I am not sure whether this is the most appropriate descripon of the authors' results, parcularly when looking at the schemac overview shown in Fig. 4F. To me, it seems more likely that genecally-defined DA subtypes overlap with discrete ventral midbrain subnuclei - parcularly in the case of Sox6-expressing cells, which are almost exclusively located in the SNc. In the case of genes that are specific for the VTA, there also seems to be a strong bias toward certain VTA subnuclei, although I agree that arguments can be made that there is some topographic organizaon along a dorso-ventral and medio-lateral gradient, which seems to be largely consistent with the anatomical locaon of projecon-defined dopamine neurons as described previously by Poulin et al., 2018 (Nature Neuroscience).

      What was meant by connuum must be interpreted in the context of the transcriponal landscape of mDA neurons and not their anatomical localizaon. As stated in the paper, the dendrogram depicon of mDA neurons’ transcriptome can be misinterpreted as an indicaon of sharp boundaries and discrete groups in transcriponal profiles. In contrast, we assert that differences between developmentally related mDA neurons are beter described as a connuum with areas in the gene expression landscape defined by the expression of shared genes but without sharp borders between them. We decided to name different areas within this connuum as “territories” at the higher hierarchical level and “neighborhoods” at the more highly resolved level. Hypothecally, such categorizaon can be even more fine-grained, but we find it unlikely that a resoluon beyond the neighborhood level is biologically relevant. As pointed out, the Sox6 territory is the territory that best qualifies as a disncve subtype, while mDA neurons in, e.g., the VTA consist of much higher and nuanced diversity. Importantly, all mDA neurons are much more related to each other than cell types lacking a common developmental origin, including hypothalamic DA neurons. Thus, our effort to define differences in such a gene expression connuum is, in our opinion, more accurate than conveying the message that the diversity consists of subtypes comparable in difference to other cell types that lack a close developmental relaonship with the mDA neuron populaon. Such disnct neuron types, despite using the same neurotransmiter as hypothalamic DA neurons, appear as disnct islands in the UMAP snRNA-seq landscape and typically harbor hundreds of differenally expressed genes. As pointed out in the Discussion, several other studies have noted similar difficules in defining different subtypes among related neurons in e.g. the cortex, striatum, and hippocampus (Kozareva et al., 2021; Saunders et al., 2018; Tasic et al., 2018; Yao et al., 2021). For example, Yao et al., 2021, used a similar hierarchical definion to avoid the implicaon that different groups (“neighborhoods” in this study) should be defined as disnct subtypes of neurons with obvious disncve funcons.

      (3) I recommend that the authors revise the introducon to include more current literature on this topic. The review by Bjoerklund and Dunnet, 2006, is very informave and important, but there is more current literature available that discusses anatomical, molecular, and funconal heterogeneity in the ventral midbrain. For example, it would be nice to incorporate recent work from the Awatramani lab on the mapping of the projecon of molecularly defined dopamine neurons (Poulin et al., 2018; Nature Neuroscience).

      We deliberately avoided including primary references to previously described diversity in the Introducon since numerous papers are relevant to cite. Instead, we refer to three essenal reviews, including the recent arcles from Awatramani and Pasterkamp. In the Supplementary Results related to Figure 4 (pages 8-14 in the Supplementary Results), we include many references and the Poulin 2018 paper. We believe that this is the appropriate place for a comprehensive discussion on anatomical, molecular, and funconal heterogeneity. In the revised manuscript's main body, we now emphasize that previous literature is discussed in the Supplementary Results (see page 11).

      (4) In Fig. 1C, the authors show a sample image demonstrang overlap between TH and mCherry, but this has not been quanfied. Similarly, there seem to be no sample images and quanficaon for the contralateral side that was exposed to 6-OHDA.

      The mouse lines used here (Dat-Cre and Rpl10a-mCherry) have been characterized before (Toskas et al., Science Advances 2022). The labelling colocalizes nearly fully with TH, with some excepons (see response below to point #5). We have now complemented with addional data showing an IHC image of one of the midbrain of a unilaterally lesioned mouse in Figure Supplement 5-1E.

      (5) The authors state that they focused their analysis on 33,052 nuclei expressing above-threshold levels of either Th OR Slc6a3. However, there seem to be cell populaons in the ventral midbrain of mice that express TH mRNA but not TH protein, and these cells do not seem to be bona fide dopamine neurons (see work from the Morales lab). Similarly, not all dopamine neurons may express DAT mRNA. I was wondering how these discrepancies may influence the authors' analysis and interpretaon.

      Indeed, the presence of cells lacking TH protein despite Th mRNA being expressed has been previously described. We also detected these cells across SNpc and VTA and now show these data as a newly added supplementary figure 2-1. In our dataset, the Gad2 territory, located in the ventromedial VTA, contains cells that express many typical mDA markers, such as Pitx3, but very low levels of TH protein. We have idenfied these based on Pitx3-EGFP and Gad2 mRNA co-expression (figure supplement 4-3). In other parts of VTA and SNpc, most cells seem to co-express Th mRNA and protein and are labeled with Dat-Cre. Also scatered in these areas, we could detect some rare mDA cells that lack TH protein. It should be noted that in our mDA territories other typical mDA neuron genes were expressed, such as Slc18a2, Ddc, Nr4a2 and Pitx3, and thus, they were not solely defined by the presence of Th and/or Slc6a3. Cells that do not have a history of DAT-expression, and therefore were not mCherry labelled, were also included in the analysis due to the relaxed gang used during FANS isolaon.

      (6) The sex and age of the mice that are used for the experiments are not stated in the Materials and Methods secon under "Mouse lines and genotyping".

      Thank you for pointing this out. This informaon has been added to the updated manuscript in the methods secon.

      Reviewer #2 (Recommendations For The Authors):

      I think that the manuscript can be significantly improved just by providing deeper analyses of the exisng data and linking them to the current state of the art in terms of defining midbrain dopamine neurons (e.g., by projecon). The dataset is likely richer than was explored in the manuscript and more valuable insights could be gleaned with a deeper analysis.

      Please see our response to Reviewer #2 (Public Review), regarding WGCNA analysis, and the comments on ML-based GO analysis, as well as the comments on the added secons in the supplementary results file.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      The delineation of MBOAT function is important with theoretical and practical implications in MAFLD, alcohol-induced hepatic steatosis, and lysosomal diseases. The strength of evidence is convincing using methodology in line with current state-of-the-art, with good support for the claims.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors provide mechanistic insights into how the loss of function of MBOAT7 promotes alcoholassociated liver disease. They showed that hepatocyte-specific genetic deletion of Mboat7 enhances ethanol-induced hepatic steatosis and increased ALT levels in a murine model of ethanol-induced liver disease. Through lipidomic profiling, they showed that mice with Mboat7 deletion demonstrated augmented ethanol-induced endosomal and lysosomal lipids, together with impaired transcription factor EB (TFEB)-mediated lysosomal biogenesis and accumulation of autophagosomes.

      Strengths:

      Alcohol-induced liver disease (ALD) and metabolic-associated steatotic liver disease (MASLD) are major global health problems, and polymorphism near the gene encoding MBOAT7 has been associated with these conditions. This paper is timely as it is important to gain insights on how loss of MBOAT function contributes to liver disease as this may eventually lead to therapeutic strategies. -The conclusions of the paper are mostly well supported by data.

      We sincerely thank Reviewer #1 for constructive feedback on this work.

      Weaknesses:

      (1) In regards to circulating levels of MBOAT7 products, a comparison of heavy drinkers with ALD versus heavy drinkers without ALD would be more clinically relevant.

      We agree this comparison would be an important comparison to make in future studies, but given the difficulties in accessing well-matched samples such as these we see this as beyond the scope of the current work.

      (2) A few typos need to be addressed. For Figure 1 - figure supplement 1, should the second column heading be "Heavy drinkers" instead of "Healthy drinkers"? Also, in the same figure, it is unclear what the "healthy" subcategory under MELD means.

      The typographical error was addressed in the main text and in all associated tables and figures.

      (3) Some of the data in the tables need to be addressed/discussed. For instance, the white blood cell count (WBC) in Figure 1 - figure supplement 1 for "healthy controls" is 34, compared to 13.51 for drinkers. A WBC of 34 is not at all healthy and should be explained. The vast difference between BMI and also between racial distribution within the two cohorts should also be explained. Is it possible that some of these differences contributed to the different levels of circulating MBOAT7 products that were measured?

      Sincere thanks for catching this error. In follow up, we found that some of our patient recruitment sites were using different units to report WBC counts (percent vs 1000/ml) and at this time we cannot retrospectively correct that difference. Therefore, we have incomplete WBC values for the cohort so elected to exclude that information to avoid confusing readers. A revised table is provided in revision reflecting these changes/ If we look at each site separately, values for WBC were in the normal range, so we do not think this is a major limitation of our studies. In regards to BMI and race: Race is not actually significant, but close. For BMI, there are 2 very low BMIs in the Heavy drinkers which bring that average down. We agree with Reviewer # 1 that race and / or BMI could impact MBOAT7, but larger cohorts are needed to detect such potential differences.

      (4) The representation of the statistical difference between the bars in the results figures by using alphabets is a bit confusing. For instance, in figure 2C, does that mean all the bars labelled A are significantly different from B? The solid black bar seems to be very similar to the open red bar; please double check.

      We apologize for this confusing presentation. Using the letter system, groups not sharing a common superscript differ statistically. Given this concern, we have gone back and reviewed all statistical comparisons and realized that there were several mistakes in the graph Figure 2C, Figure 3F and G, Figure 3-Supplementary Figure 1 F and Figure 3-Supplementary Figure 10H. The graphs themselves were not altered, but the denotation of statistical significance was updated with the correct letter superscripts.

      Reviewer #2 (Public Review):

      Summary:

      The work by Varadharajan et. al. explored a previously known genetic variant and its pathophysiology in the development of alcohol-associated liver injury. It provides a plausible mechanism for how varying levels of MBOAT7 could impact the lipid metabolomics of the cell, leading to a deleterious phenotype in MBOAT7 knockout. The authors further characterized the impact of the lipidomic changes and raised lysosomal biogenesis and autophagic flux as mechanisms of how MBOAT7 deletion causes the progression of ALD.

      Strengths:

      Connecting the GWAS data on MBOAT7 variants with plausible pathophysiology greatly enhances the translational relevance of these findings. The global lipidomic profiling of ALD mice is also very informative and may lead to other discoveries related to lipid handling pathways.

      We sincerely thank Reviewer #1 for constructive feedback on this work.

      Weaknesses:

      The rationale of why MBOAT7 metabolites are lower in heavy drinkers than in normal individuals is not well explained. MBOAT7 loss of function drives ALD, but unclear if MBOAT7 deletion also drives preference for alcohol or if alcohol inhibits MBOAT7 function. Presuming most individuals studied here were WT and expressed an appropriate level of MBOAT7?

      Although we were unable to genotype for the rs641738 SNP in the human subjects studied here, the original study by Buch et al. published in Nature Genetics performed cis expression quantitative trait lock (cis-eQTL) analyses to demonstrate that the minor disease-associated allele was associated with reduced MBOAT7 expression in subjects with alcohol-related cirrhosis. It is important to note that we did not see any evidence that alcohol preference was altered in either myeloid- or hepatocyte-specific Mboat7-knockout mice, given ethanol intake was similar in all genotypes. Additional studies are needed to address the possibility that MBOAT7 loss of function may promote alcohol preference, but we agree that this should be further investigated.

      Also, the discussion of mechanisms of MBOAT7-induced dysregulation of lysosomal biogenesis/autophagy, while very interesting, seems incomplete. It is not clear how MBOAT7 an enzyme involved in membrane phospholipid remodeling increases mTOR which leads to decreased TFEB target gene transcription.

      Although we agree with Reviewer #2 that mechanistic understanding by which MBOAT7 loss of function impacts mTOR activity and TFEB-driven lysosomal biogenesis is still incomplete, we do feel that the results published here will inform downstream investigation linking phosphatidylinositol remodeling to mTOR and TFEB. The MBOAT7 gene encodes an acyltransferase enzyme that specifically esterifies arachidonyl-CoA to lysophosphatidylinositol (LPI) to generate the predominant molecular species of phosphatidylinositol (PI) in cell membranes (38:4). It is well established that PI-related lipids can regulate membrane dynamics and signal transduction pathways. For instance PI-phosphates (PIPs) are dynamically shaped by PI kinases and phosphatases to play crucial roles in the regulation of a wide variety of cellular processes via specific interactions of PIP-binding proteins. Among PI phosphates, PI 3phosphate (PI3P) regulates vesicular trafficking pathways, including endocytosis, endosome-toGolgi retrograde transport, autophagy and mTOR signaling. Although additional work is needed to understand the molecular details of how MBOAT7-driven LPI acylation impacts mTOR and TFEB, it is not particularly surprising that PI lipid remodeling could broadly impact cell signaling.

      Furthermore, given the significant disturbances of global lipidomic profiling in MBOAT7 knockout, many pathways are potentially affected by this deletion. Further in vivo modeling that specifically addresses these pathways (TFEB targeting, mTOR inhibitor) would help strengthen the conclusions of this paper.

      We agree that further in vivo studies are needed that are beyond the scope of the current work.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      (1) p values are rather hard to read. For example, Figure 2c, Hepatocyte-specific deletion of Mboat7 resulted in enhanced ethanol-induced increases in liver weight. However, doesn't look like there is a significant difference between the 2 EtOH groups in Figure 2C? Same comment for Figure 2e, not sure if pair-fed groups had a significant difference.

      (2) Figure 2 Supp fig 1, what is the top band on the MBOAT7 WB?

      We have addressed these statistical comparison comments as described above. Although we cannot be sure, it is likely that the top band on the MBOAT7 Western blot is a non-specific band that shows up with the antibody combination used given there is equal intensity in the Mboat7flox/flox and the MSKO mice (Mboat7flox/flox+LysM-Cre).

    1. Reviewer #2 (Public Review):

      Summary:

      This study looks into the complex dominance patterns of S-allele incompatibilities in Brassicaceae, through which it attempts to learn more about the sheltering of deleterious load. I found several weak points in the analyses that diminished my excitement about the results. In particular, the way in which deleterious mutations were classified lacked the ability to distinguish the severity of the mutations and thus their expected associated dominance. Furthermore, the simulation approach could have provided this exact sort of insight but was not designed to do so, making this comparison to the empirical data also less than exciting for me.

      Major and minor comments:

      I think the introduction (or somewhere before we dive into it in the results) of the dominance hierarchy for the S-alleles needs a more in-depth explanation. Not being familiar with this beforehand really made this paper inaccessible to me until I then went to find out more before continuing. I would expect this paper to be broad enough that self-contained information makes it accessible to all readers. For example, lines 110-115 could be in the Introduction.

      Along with my above comment, perhaps it is not my place to comment, but I find the paper not of a broad enough scope to be of interest to a broad readership. This S-allele dominance system is more than simple balancing selection, it is a very complex and specific form of dominance between several haplotypes, and the mechanism of dominance does not seem to be genetic. I am not sure that it thus extrapolates to broad comments on general dominance and balancing selection, e.g. it would not be the same as considering inversions and this form of balancing selection where we also expect recessive deleterious mutations to accumulate.

      It would have been particularly interesting, or a nice addition, to see deleterious mutations classed by something like SNPeff or GERP where you can have different classes of moderate to severe deleterious variants, which we would expect also to be more recessive the more deleterious they are. In line with my next comment on the simulations, I think relative differences between mutations expected to be more or less dominant may be even more insightful into the process of sheltering which may or may not be going on here.

      In the simulations, h=0 and s=0.01 (as in Figure 5) for all deleterious mutations seems overly simplistic, and at the convenient end for realistic dominance. I think besides recessive lethals which we expect to be close to h=0 would have a much larger selection coefficient, and other deleterious mutations would only be partially recessive at such an s value. I expect this would change some of the simulation results seen, though to what degree I am not certain. It would be nice to at least check the same exact results for h=0.3 or 0.2 (or additionally also for recessive lethals, e.g. h=0 and s=-0.9). I would also disagree with the statement in line 677, many studies have shown, particularly those on balancing selection, that partially recessive deleterious mutations are not eliminated by natural selection and do play a role in population genetic dynamics. I am also not surprised that extinction was found for higher s values when the mutation rate for such mutations was very high and the distribution of s values was constant. An influx of such highly deleterious mutations is unlikely to ever let a population survive, yet that does NOT mean that in nature, the rare influx of such mutations does lead to them being sheltered. I find overall that the simulation results contribute very little, to none, to this paper, as without something more realistic, like a simultaneous distribution of s and h values, you cannot say which, if any class of these mutations are the ones expected to accumulate because of S-allele dominance. Rather they only show the disappointing or less exciting result that fully recessive, weakly deleterious mutations (which I again think do not even exist in nature as I said above) have minor, to no effect across the classes of S-allele dominance. They provide no insight into whether any type of recessive deleterious mutation can accumulate under the S-allele dominance hierarchy, and that is the interesting question at hand. I would either remove these simulations or redo them in another approach. The authors never mention what simulation approach was used, so I can only assume this is custom, in-house code. Yet I do not find that code provided on the github page. I do not know if the lack of a distribution for h and s values is then a choice or a programming limitation, but I see it as one that should be overcome if these simulations are meant to be meaningful to the results of the study.

  3. Feb 2024
    1. level 1 vs level 2

      (#14) (*14)J11(Rita) Is there a specific sampling design for multilevel regression analysis? Are there any assumptions to take into consideration when looking at multilevel regression?

      Response: Anytime the data is clustered you have a multilevel design. I.e., Add health data is clustered by schools. Longitudinal data like the NLSY is clustered at the individual level. The WVS is clustered by countries. Clusters provide the context→and context, for a sociologist, may actually be the point. (More on this in class).

      (*14)J12(Osamudia): A very broad question as I start the reading–is there ever any debate about how we define the levels in multilevel modeling? Reading table 1.1 of the reading, I found myself questioning whether the various levels were appropriately characterized and distinguished.

      Response: You are right to critique this. The levels depend on how we are conceptualizing context, and that depends on sociological theory. I.e., variation in social structure and context might be a level 2 variable, but the way we define that will affect how we think about the model. I.e., is religion a level 1 or level 2 variable?

    1. Author Response

      The following is the authors’ response to the original reviews.

      Thank you and the two reviewers for the thorough review of our manuscript. We found the reviewer’s comments highly valuable and addressed them by the following additional experiments and changes in the text and the figures:

      (1) We measured the effect of ROCK MASO’s on the ROCK expression by immunostaining and observed a reduction in ROCK signal, supporting the downregulation of ROCK protein level under ROCK MASO’s (new Fig. S3).

      (2) We measured the effect of lower concertation of ROCK inhibitor, Y27632 (10µM), and observe the same phenotypes of skeletal loss, skeletal reduction and ectopic branching in this concentration (Fig. 2, S4). Importantly, these phenotypes were not observed when directly inhibiting PKA and PKC, in whole sea urchin embryos (1) and in skeletogenic cell cultures (2), further supporting the specificity of ROCK inhibitor.

      (3) We added a time course of Pl-ROCK expression and immunostaining of ROCK in the fertilized egg, that show that this gene is maternal and the protein is present in the egg Fig. 2SA-C.

      (4) We recorded F-actin in ROCK MASO’s and demonstrate that it is still detected around the spicules and their tips, similarly to ROCK inhibited embryos (new Fig.S3).

      (5) We revised the paper text and figures to provide a better description of our results, distinguish clearly between our data and our interpretations and emphasize the novelty of our findings.

      This paper demonstrates that ROCK, F-actin polymerization and actomyosin contractility play critical roles in biomineral growth and in shaping biomineral morphology in the sea urchin embryo, and that ROCK activity affects skeletogenic gene expression. Our findings together with previous reports of the role of actomyosin in Eukaryotes biomineralization, suggest that this molecular machinery is a part of the common molecular tool-kit used in biomineralization. The identification of a common molecular mechanism within the diverse gene regulatory networks, organic scaffolds and minerals that Eukaryote use to build their biominerals will be of high interest to the field of biomineralization and evolutionary biology. Furthermore, our paper portrays the interplay between the cellular and the genetic machinery that drives morphogenesis. We believe it would be of great interest to the broad readership of eLife and particularly to the fields of biomineralization, cell, developmental and evolutionary biology.

      Thank you very much for the helpful review of our paper.

      Reviewer #1 (Public Review):

      We thank the reviewer for the appreciation of our work the helpful comments that guided us to strengthen the experimental evidence for our conclusions and increase the paper’s clarity. Below are our responses to the specific comments:

      Major comments

      One MASO led to reduced skeleton formation while the other one additionally induced ectopic branching. How was the optimum concentration for the MASOs determined? Did the authors perform a dose-response curve? What is the reason for this difference? Which of the two MASOs can be validated by reduced ROCK protein abundance? Since the ROCK antibody works, I would like to see a control experiment on Rock protein abundance in control and ROCK MO injected larvae which is the gold-standard for validating the knock-down.

      We tested several MASO concentrations to identify a concentration where the control embryos injected with Random MASO were overall healthy and ROCK MASO’s showed clear phenotypes.

      To test the effect of ROCK MASO’s on ROCK protein levels we did immunostaining experiments that are now presented in new Fig. S3. We could not do Western blot for injected embryos since ROCK antibody requires thousands of embryos for Western blot, which is not feasible for injected embryos. Therefore, we tested the effect of the two translation ROCK MASO’s on ROCK abundance compared to uninjected and Random MASO injected embryos using immunostaining. We observed a reduction of ROCK signal, supporting the downregulation of ROCK protein level in these genetic perturbations (new Fig. S3).

      L212 "Together, these measurements show that ROCK is not required for the uptake of calcium into cells." But what about trafficking and exocytosis? As mentioned earlier, I think this is a really important point that needs to be confirmed to understand the function of ROCK in controlling calcification. In their previous study (reference 45) the authors demonstrated that they have superior techniques in measuring vesicle dynamics in vivo. Here an acute treatment with the ROCK inhibitor would be sufficient to test if calcein-positive vesicle motion, including the observed reduction in velocity close to the tissue skeleton interface, is affected by the inhibitor.

      We thank the reviewer for the appreciation of our previous work where we studied calcium vesicle dynamics in whole embryos (Winter et al, Plos Com Biol 2021). We agree with the reviewer that the best way to test directly the effect of ROCK on mineral deposition and vesicle kinetics is to observe it in live skeletogenic cells. However, in Winter et al 2021, we found that the skeleton (spicules) doesn’t grow when the embryos are immobilized in either control or treated embryos. We have to immobilize the embryos to record live timelapses of whole embryos. Hence, this means that we can not determine the role of ROCK or any other perturbation in vesicle trafficking and exocytosis based on experiments conducted in immobilized whole embryos, since skeletogenesis is arrested. We believe that we can do it in skeletogenic cell cultures and we are currently developing this assay for vesicle tracking, but this is beyond the scope of this current work.

      Is there a colocalization of ROCK and f-actin in the tips of the spicules? This would support the mechano-sensing-hypothesis by ROCK.

      Our studies show that F-actin is localized around the spicule cavity and in the cortex of the cells (Figs. 5 and 6) while ROCK is enriched in the skeletogenic cell bodies, with some localization near the skeletogenic cell membranes (Fig. 1). To directly address the reviewer question we immune-stained ROCK and F-actin in the same embryos, and showed that their sub-cellular localizations does not show a strong overlap (Fig. S3 Q-T). However, ROCK does not bind F-actin directly: ROCK activates another kinase, LimK that phosphorylates Cofilin that interacts with F-actin. Therefore, the fact that ROCK is not colocalized with F-actin does not support nor contradicts the possible role of ROCK in mechano-sensing.

      L 283. "F-actin is enriched at the tips of the spicules independently of ROCK activity" The results of this paragraph clearly demonstrate that ROCK inhibition has no effect on the localization of f-actin at the tips of the growing spicules. In addition, the new cell culture experiments underline this observation. Still, the central question that remains is, what is the interaction between ROCK, f-actin, and the mineralization process, that leads to the observed deformations? What does the f-actin signal look like in a branched phenotype or in larvae that failed to develop a skeleton (inhibition from Y20)?

      As we report in Fig. 6, and now on new Fig. S3, under ROCK late inhibition or in ROCK morphants, we still detect F-actin around the spicule and enriched at the tips. When ROCK is inhibited and the embryo fails to develop a skeleton, we observe Factin accumulation in the skeletogenic cells, but the F-actin is not organized (Fig. 5). As the spicule is absent in this condition, it is hard to conclude whether the effect on F-actin organization is direct or due to the absence of spicule in this condition. We stated that explicitly in the current version in the results, lines 324-326 and in the discussion, lines 405-408.

      Immunohistochemical analyses on f-actin localization and abundance should be additionally performed with ROCK knock-down phenotypes to confirm the pharmacological inhibition.

      We did that in our new Figure S3 and showed that ROCK morphant show the same F-actin localization at the tips like control and ROCK inhibited embryos.

      L 365 "...supporting its role in mineral deposition..." "...Overall, our studies indicate that ROCK activity....is essential for the formation of the spicule cavity......which could be essential for mineral deposition..." I think the authors need to do a better job in clearly separating between the potential processes impacted by ROCK perturbation. Is it stabilization and mechano-sensing in the spicule tip or the intracellular trafficking and deposition of the ACC? If the dataset does not allow for a definite conclusion, I suggest clearly separating the different possibilities combined with thorough discussion-based findings from other mineralizing systems where the interaction between ROCK and F-actin has been described.

      We thank the reviewer for this important comment. We believe that ROCK and the actomyosin are involved in both, mechano-sensing of the rigid biomineral and in the transport and exocytosis of mineral-bearing vesicles. In the current version we provide explicit explanations of these two hypotheses in the discussion section. The possible role in exocytosis and the experiments that are required to assess this role are described in lines 427-439, and the possible mechano-sensing role and effect on gene expression is described in lines 440-453.

      Reviewer #1 (Recommendations For The Authors):

      Minor comments

      L185 "These SR-µCT measurements show that the rate of mineral deposition is significantly reduced under ROCK inhibition." To correctly support this statement I would suggest to calculate the real growth rates (µm3 time-1). For example, an increase in volume from 6,850 µm3 at 48 hpf to 14,673 µm3 at 72 hpf would result in a growth rate of 7823 µm3 24h-1.

      We thank the reviewer for this suggestion. We calculated the rate of spicule growth as the reviewer suggested and we added this information in lines 218-221.

      L343: "This implies that....within the skeletogenic lineage." This concluding sentence is very speculative and therefore misplaced in the results section.

      We removed this sentence from the results section into the discussion, lines 443-445.

      L382: "The participation of F-actin and ROCK in polarized tip-growth and vesicle exocytosis has been observed in both, animals and plants." L407-409: "...F-actin could be regulating the localized exocytosis of mineral-bearing vesicles...." I think this is exactly the core question that remains unresolved in this study. To reduce speculations I strongly recommend addressing the effect of ROCK inhibition on vesicle trafficking and exocytosis (Monitoring of calcein-positive Vesicles in PMCs).

      We agree with the reviewer that this is a critical question that we would have address, but as we explained above, is beyond the scope of this study.

      Figure 5: The values below the scale bars in the newly added figures U+V are extremely small. Also, the Legend for this figure sounds incorrect. Should read: "...and skeletogenic cell cultures that were treated with 30µM ROCK inhibitor that was added at 48hpf and recorded at 72hpf.

      We increased the font near the scale bars and corrected the figure caption. Thanks for this and your other helpful comments!

      Reviewer #2 (Public Review):

      We thank the reviewer for raising the important issue of inhibitor concentration which led us to do additional experiments with lower concentration that were valuable and strengthen the manuscript. We also thank the reviewer for asking us to be clearer with the interpretation of the results. Below are our responses to the specific comments:

      My concerns are the interpretation of the experiments. The main overriding concern is a possible over-interpretation of the role of ROCK. In the literature that ROCK participates in many biological processes with a major contribution to the actin cytoskeleton. And when a function is attributed to ROCK, it is usually based on the determination of a protein that is phosphorylated by this kinase. Here that is not the case. The observation here is in most cases stunted growth of the spicule skeleton and some mis-patterning occurs or there is an absence of skeleton if the inhibitor is added prior to initiation of skeletal growth. They state in the abstract that ROCK impairs the organization of F-actin around the spicules. The evidence for that as a direct role is absent.

      We agree with the reviewer that since the spicule doesn’t form under ROCK continuous inhibition, it is unclear if the absence of F-actin around the spicule in this condition is a direct outcome of the lack of ROCK activation of F-actin polymerization, or an indirect outcome due to the lack of spicule to coat. We therefore deleted this line in the abstract and explicitly stated that we cannot conclude whether the impaired F-actin organization is directly due to ROCK effect on actin polymerization in the results, lines 324-326 and in the discussion, lines 405-408.

      They use morpholino data and ROCK inhibitor data to draw their conclusion. My main concern is the concentration of the inhibitor used since at the high concentrations used, the inhibitor chosen is known to inhibit other kinases as well as ROCK (PKA and PKC). They indicate that this inhibition is specifically in the skeletogenic cells based on the isolation of skeletogenic cells in culture and spicule production either under control or ROCK inhibition and they observe the same - stunting and branching or absence of skeletons if treated before skeletogenesis commences. Again, however, the high concentrations are known to inhibit the other kinases.

      In the previous version of the paper we used the range of 30-80µM Y-27632 to block ROCK activity. These concentrations are commonly used in mammalian systems and in Drosophila to block ROCK activity (3-8). The reviewer is correct stating that at high concentration, this inhibitor can block PKA and PKC. However, the affinity of the inhibitor for these kinases is more than 100 times lower than its affinity to ROCK as indicated by the biochemical Ki values reported in the manufactory datasheet: 0.14-0.22 μM for ROCK1, 0.3 μM for ROCK2, 25 μM for PKA and 26 μM for PKC.

      Importantly, these Ki values are based on biochemistry assays where the activity of the inhibitor is tested in-vitro with the purified protein. Therefore, these concentrations are not relevant to cell or embryo cultures where the inhibitor has to penetrate the cells and affect ROCK activity in-vivo. Y-27632 activity was studied both in-vitro and in-vivo in Narumiya, Ishizaki and Ufhata, Methods in Enzymology 2000 (9). This paper reports similar concentrations to the ones indicated in the manufactory datasheet for the in-vitro experiments, but shows that 10µM concentration or higher are effective in cell cultures. We therefore tested the effect of 10µM Y-27632 added at 0hpf (continuous inhibition) and at 25hpf (late inhibition) and added this information to Figs. 2 and S3. Continuous inhibition at this concentration resulted with three major phenotypes: skeletal loss, spicule initiations and small spicules with ectopic branching. This result supports our conclusion that ROCK activity is necessary for spicule formation, elongation and prevention of branching. Late inhibition in this concentration resulted with the majority of the embryos developing branched spicules, which is very similar to the effect of MyoII inhibition with Blebbistatin. This result again, supports the inference that ROCK activity is required for normal skeletal growth and the prevention of ectopic branching. Importantly, there are two papers were PKA and PKC were directly inhibited in whole sea urchin embryos (1) and in skeletogenic cell cultures (2). In both assays, PKC inhibition resulted with mild reduction of spicule length while PKA inhibition did not affect skeletal formation. Neither skeletal loss nor ectopic branching were ever observed under PKC or PKA inhibition, supporting the specific inhibition of ROCK by Y-27362. Furthermore, both genetic and pharmacological perturbations of ROCK resulted with significant reduction of skeletal growth and with the enhancement of ectopic branching. Therefore, we believe we provide convincing evidence for the role of ROCK in spicule formation, growth and prevention of branching. We revised Fig. 2 and S3 to include the 10µM Y-27632 data and the text describing the inhibition to include the explanations and references we provided here.

      They use blebbistatin and latrunculin and show that these known inhibitors of actin cytoskeleton lead to abnormal spiculogenesis, This coincidence is suggestive but is not proof that it is ROCK acts on the actomyosin cytoskeleton given the specificity concerns.

      As stated above, we believe that in the current vesion we overcame the specificity concerns and provided solid evidence that ROCK activity is necessary for spicule formation, growth and prevention of branching. Furthermore, the skeletogenic phenotypes of late 10µM Y-27632 are highly similar to those of MyoII inhibition (Blebbistatin) while the phenotypes of higher concetrations resemble the inhibition of actin polymerization by Latrunculin. We agree with the reviewer that: “This coincidence is suggestive but is not proof that ROCK acts on the actomyosin cytoskeleton” and we revise the discussion paragraph to differentiate between our solid findings and our speculations (lines 421-426): “These correlative similarities between ROCK and the actomyosin perturbations lead us to the following speculations: the low dosage of late ROCK inhibition is perturbing mostly ROCK activation of MyoII contractility while the higher dosage affects factors that control actin polymerization (Fig. 8F). Further studies in higher temporal and spatial resolution of MyoIIP activity and F-actin structures in control and under ROCK inhibition will enable us to test this.”

      Reviewer #2 (Recommendations For The Authors):

      The following areas require attention:

      (1) You begin and end the abstract with statements on evolution in which the actomyosin cytoskeleton is associated with skeletogenesis despite different GRNs, different contributing proteins, etc. You then move to ROCK and claim to reveal that ROCK is a central player in the process. As above, in the judgement of this reviewer, you fail to establish a direct role of ROCK to the actomyosin role in skeletogenesis. Sure, the ROCK inhibitors suggest that ROCK plays some kind of role in the process but you also indicate that ROCK could act on many processes, none of which you directly associate with the necessary activity of ROCK.

      We agree that our paper provides correlative similarities between the phenotypes of ROCK and those of direct pertrubations of the actomyosin network, and lacks causal relationship. We made this point clear throughout the current version of the manuscript.

      (2) In the abstract you report that ROCK inhibition impairs the actin cytoskeleton around the skeleton. In examining your images in Fig. 5 that is not the case. Based on Phalloidin staining, actin surrounds both the control and the ROCK-inhibited skeleton. The distribution of actin is the same in both cases. Myosin is also stained in this figure and it too shows similar staining both in experimental and control. So, to this reviewer, there is insufficient evidence to suggest that the actin cytoskeleton is impaired, and there is no evidence directly relating ROCK with that cytoskeleton. I'm not questioning the observation that inhibition of ROCK causes stunting and mispatterning of the skeleton. That you show and quantify well. The issue is the precise target of ROCK. Your data does not establish the specific cause. It could be the actin cytoskeleton but your experiments do not directly address that.

      Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

      (3) In parts of the manuscript you use the term filopodia and in other parts I think you use pseudopodia to refer to the same structure. Since Ettensohn has provided the most evidence on the organization of the skeletogenic syncytia, I suggest you use the same term he used for those cellular extensions.

      The filopodia and the pseudopodia are two distinct structures generated by the skeletogenic cells. The filopodia is the common cellular extension described in many cells, while the term “pseudopodia cable” describes the specific structure that forms between the skeletogenic cells in which the spicule cavity forms, in agreement with Prof. Ettensohn terminology.

      (4) In trying to find relationships you cite a number of previous papers at the end of the introduction. I went back to those papers and they describe (from your work) calcium exocytosis, plus filopodia formation, plus planar cell polarity, plus CDC42, any one of which could involve an actin cytoskeleton. You even cite a paper saying that perturbations of ROCK prevent spicule formation. I went back to that paper and that isn't the case. You then summarize the Introduction by relating ROCK and the actin cytoskeleton, thereby raising reader expectation that the two will be connected. As above, in reality, your evidence here does not connect the two.

      We thank the reviewer for giving us credit for all these works, but only the paper on vesicle kinetics is from our lab (winter et al 2021). As for Croce et al, 2006 that the reviewer refers to: in Fig. 9A, 75µM of Y-27632 is used to inhibit ROCK in the same sea urchin species that we use, and the phenotype is identical to what we observe – the skeletogenic cells are there, but the spicule is not formed. As mentioned above, in the current version we distinguished clearly between our solid findings and our interpretations.

      (5) You emphasize in Fig. 1 the inhibition of ROCK in the presence of VEGFR inhibition. However, at no place in the manuscript do you say anything about how VEGFR is inhibited, when it is inhibited, or how you know it is inhibited. That oversight must be corrected. You mention axitinib but don't say anything about what it does. Some readers may know its activity but many will not.

      We now indicate that we use Axitinib to block VEGFR in the results section (line 104) and in the methods section (lines 470-471).

      (6) Fig. 2. The use of Y27632 as a selective inhibitor of ROCK. According to data sheets from the manufacturer, at the levels used in your experiments, 120 µm, 80 µm and 30 µm, those levels of inhibitor also inhibit the activity of PKA and PKC (both inhibited at around 25 µm). This is concerning because of the literature indicating that activation of the VEGFR operates through PKA. Inhibition of PKA, then, would inhibit the activity of VEGF signaling. Thus, the inhibitory effects of Y27632 may actually not be attributed specifically to ROCK. Furthermore, the heading of this section states that ROCK activity controls initiation, growth, and morphology of the spicule. Yet, even in high levels of inhibitor spicule production is initiated. Yes, the growth and the morphology are compromised, but the initiation doesn't seem to be.

      The spicule fails to form under ROCK continuous inhibition in all concentrations (Fig. 2). Also, as we explained in details above, these Ki values are based on biochemical experiments with purified proteins and are not relevant to in-vivo use of the inhibitor. Yet, these Ki values demonstrate that the affinity of the inhibitor to ROCK is 100 higher than of its affinity to PKA and PKC. Specifically to the reviewer suggestion here: direct inhibition of PKA does not have skeletogenic phenotypes, not in whole embryos (1) and not in skeletogenic cell culture (2). Since we see the same skeletogenic phenotypes at low Y-27362 concentration and the genetic and pharmacological pertrubations of ROCK reconcile, we believe that these phenotypes can be atributed directly to ROCK.

      (7) The synchrotron study is very nice with two points that should be addressed. Again, a high concentration of Y27632 was used giving a caveat on ROCK specificity. And second, the blue and green calcein pulses are very nice but the recent paper by the Bradham group should be cited.

      We added a reference to Bradham recent paper on two calcein pulses (10).

      (8) Fig. 5 is where an attempt is made to associate ROCK inhibition to alterations in actomyosin. Again, a high concentration of the inhibitor is used casting doubt on whether it specifically inhibits ROCK. However, even if the inhibition is specific to ROCK the images do not provide convincing evidence that ROCK activity normally is directed toward actomyosin. This is crucial to the manuscript.

      As stated above, we addressed the specificity in this version and we modified the text to emphasize the correlation and not cuasation: Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

      (9) Again in Fig. 6 the inhibitor is used with the same concern about whether the effects noted are due to ROCK.

      Fig. 6 is now Fig. 7 – the effect of ROCK on gene expression and as explained above, we addressed the specificity in this version.

      (10) Lines 350-358. This interpretation falls apart without showing that the inhibitor is specific for ROCK as indicated above. Also, Fig. 5 is unconvincing in showing a difference in actin or myosin distribution in control vs ROCK inhibited embryos. Yes, the spicules are stunted, but whether actin or myosin have anything to do with that as a result of lack of ROCK activity is not demonstrated.

      As stated above, we addressed the specificity in the revised version and we modified the text to emphasize the correlation and not cuasation: Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

      (11) Throughout, the manuscript spelling, grammar, and sentence structure will require extensive editing. The mistakes are numerous.

      We did our best to correct the spelling and grammar. If we still missed some mistakes, we would be happy to further correct them.

      References

      (1) Mitsunaga K, Shinohara S, Yasumasu I. Probable Contribution of Protein Phosphorylation by Protein Kinase C to Spicule Formation in Sea Urchin Embryos: (sea urchin/protein kinase C/spicule formation/H-7/HA1004). Dev Growth Differ. 1990;32(3):335-42.

      (2) Mitsunaga K, Shinohara S, Yasumasu I. Does Protein Phosphorylation by Protein Kinase C Support Pseudopodial Cable Growth in Cultured MicromereDerived Cells of the Sea Urchin, Hemicentrotus pulcherrimus?: (sea urchin/protein kinase C/spicule formation/phorbol ester/H-7). Dev Growth Differ. 1990;32(6):647-55.

      (3) Su Y, Huang H, Luo T, Zheng Y, Fan J, Ren H, et al. Cell-in-cell structure mediates in-cell killing suppressed by CD44. Cell Discov. 2022;8(1):35.

      (4) Kagawa H, Javali A, Khoei HH, Sommer TM, Sestini G, Novatchkova M, et al. Human blastoids model blastocyst development and implantation. Nature. 2022;601(7894):600-5.

      (5) Canellas-Socias A, Cortina C, Hernando-Momblona X, Palomo-Ponce S, Mulholland EJ, Turon G, et al. Metastatic recurrence in colorectal cancer arises from residual EMP1(+) cells. Nature. 2022;611(7936):603-13.

      (6) Becker KN, Pettee KM, Sugrue A, Reinard KA, Schroeder JL, Eisenmann KM. The Cytoskeleton Effectors Rho-Kinase (ROCK) and Mammalian DiaphanousRelated (mDia) Formin Have Dynamic Roles in Tumor Microtube Formation in Invasive Glioblastoma Cells. Cells. 2022;11(9).

      (7) Segal D, Zaritsky A, Schejter ED, Shilo BZ. Feedback inhibition of actin on Rho mediates content release from large secretory vesicles. J Cell Biol. 2018;217(5):1815-26.

      (8) Fischer RS, Gardel M, Ma X, Adelstein RS, Waterman CM. Local cortical tension by myosin II guides 3D endothelial cell branching. Curr Biol. 2009;19(3):2605.

      (9) Narumiya S, Ishizaki T, Uehata M. Use and properties of ROCK-specific inhibitor Y-27632. Methods Enzymol. 2000;325:273-84.

      (10) Descoteaux AE, Zuch DT, Bradham CA. Polychrome labeling reveals skeletal triradiate and elongation dynamics and abnormalities in patterning cue-perturbed embryos. Dev Biol. 2023;498:1-13.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The OSCA/TMEM63 channels have recently been identified as mechanosensitive channels. In a previous study, the authors found that OSCA subtypes (1, 2, and 3) respond differently to stretch and poke stimuli. For example, OSCA1.2 is activated by both poke and stretch, while OSCA3.1, responds strongly to stretch but poorly to poke stimuli. In this study, the authors use cryo-EM, mutagenesis, and electrophysiology to dissect the mechanistic determinants that underlie the channels' ability to respond to poke and stretch stimuli.

      The starting hypothesis of the study is that the mechanical activation of OSCA channels relies on the interactions between the protein and the lipid bilayer and that the differential responses to poke and stretch might stem from variations in the lipid-interacting regions of OSCA proteins. The authors specifically identify the amphipathic helix (AH), the fenestration, and the Beam Like Domain (BLD) as elements that might play a role in mechanosensing.

      The strength of this paper lies in the technically sound data - the structural work and electrophysiology are both very well done. For example, the authors produce a high-resolution OSCA3.1 structure which will be a useful tool for many future studies. Also, the study identifies several interesting mutants that seemingly uncouple the OSCA1.2 poke and stretch responses. These might be valuable in future studies of OSCA mechanosensation.

      However, the experimental approach employed by the authors to dissect the molecular mechanisms of poke and stretch falls short of enabling meaningful mechanistic conclusions. For example, we are left with several unanswered questions surrounding the role of AH and the fenestration lipids in mechanosensation: Is the AH really important for the poke response if mutating residues conserved between OSCA1.2 and OSCA3.1 disrupts the OSCA1.2 ability to respond to poke but mutating the OSCA1.2 AH to resemble that of OSCA3.1 results in no change to its "pokability"? Similar questions arise in response to the study of the fenestrationlining residues.

      We thank the reviewer for their feedback. We believe that the different OSCA1.2 mutants on their own suggest an involvement of the AH and fenestration-lining residues in its mechanosensitive response. We attribute the inability to restore the poke response of OSCA3.1 with similar mutations to its inherent high threshold to this particular stimulus and perhaps other structural differences, or a combination of them, that we did not probe in this study. We agree more work is required in the field to address these remaining questions and further dissect the difference between poke and stretch responses.

      Reviewer #2 (Public Review):

      Summary:

      Jojoa-Cruz et al. determined a high-resolution cryo-EM structure in the Arabidopsis thaliana (At) OSCA3.1 channel. Based on a structural comparison between OSCA3.1 and OSCA1.2 and the difference between these two paralogs in their mechanosensitivity to poking and membrane stretch, the authors performed structural-guided mutagenesis and tested the roles of three structural domains, including an amphipathic helix, a beam-like domain, and a lipid fenestration site at the pore domain, for mechanosensation of OSCA channels.

      Strengths:

      The authors successfully determined a structure of the AtOSCA3.1 channel reconstituted in lipid nanodiscs by cryo-EM to a high resolution of 2.6 Å. The high-resolution EM map enabled the authors to observe putative lipid EM densities at various sites where lipid molecules are associated with the channel. Overall, the structural data provides the information for comparison with other OSCA paralogs.

      In addition, the authors identified OSCA1.2 mutants that exhibit differential responses to mechanical stimulation by poking and membrane stretch (i.e., impaired response to poke assay but intact response to membrane stretch). This interesting behavior will be useful for further study on differentiating the mechanisms of OSCA activation by distinct mechanical stimuli.

      Major weakness:

      The major weaknesses of this study are the mutagenesis design and the functional characterization of the three structural domains - an amphipathic helix (AH), a beam-like domain (BLD), and the fenestration site at the pore, in OSCA mechanosensation.

      (1) First of all, it is confusing to the reviewer, whether the authors set out to test these structural domains as a direct sensor(s) of mechanical stimuli or as a coupling domain(s) for downstream channel opening and closing (gating). The data interpretations are vague in this regard as the authors tend to interpret the effects of mutations on the channel 'sensitivity' to different mechanical stimuli (poking or membrane stretch). The authors ought to dissect the molecular bases of sensing mechanical force and opening/closing (gating) the channel pore domain for the structural elements that they want to study.

      We agree with the reviewer that our data are unable to distinguish the transduction of a mechanical stimulus and channel gating. We set up to determine whether these features were involved in the mechanosensitive response. However, as the reviewer points out, evaluating whether they work as direct sensors or coupling domains would require a more involved experimental design that lies beyond the scope of this work. Thus, we do not claim in our study whether these features act as direct sensors of mechanosensitive stimuli or as coupling domains, only their involvement.

      Furthermore, the authors relied on the functional discrepancies between OSCA1.2 (sensitive to both membrane poking and stretch) and OSCA3.1 (little or weak sensitivity to poking but sensitive to membrane stretch). But the experimental data presented in the study are not clear to address the mechanisms of channel activation by poking vs. by stretch, and why the channels behave differently.

      We had hoped that when we switched regions of the OSCA1.2 and OSCA3.1 channels we would abolish poke-induced responses in OSCA1.2 and confer poke-induced sensitivity to OSCA3.1. We agree with the reviewer that we were not able to pinpoint the reason or multiple reasons, as it could be a compounded effect of several differences, that caused OSCA3.1 higher threshold and thus we could not confer to it an OSCA1.2-like phenotype. Yet, we shed some light on some of the structural differences that appear to contribute to OSCA3.1 behavior, as mutagenesis of OSCA1.2 to resemble this channel led to OSCA3.1-like phenotype.

      (2) The reviewer questions if the "apparent threshold" of poke-induced membrane displacement and the threshold of membrane stretch are good measures of the change in the channel sensitivity to the different mechanical stimuli.

      The best way to determine an accurate measure of sensitivity to mechanical stimuli is stretch applied to a patch of membrane. There are more complicating factors that influence the determination of "apparent threshold" in the whole cell poking assay, including visualizing when the probe first hits the cell (very difficult to see). With that said, the stretch assay has its own issues such as the creep of the membrane into the pipette glass which we try to minimize with positive pressure between tests.

      (3) Overall, the mutagenesis design in the various structural domains lacks logical coherence and the interpretation of the functional data is not sufficient to support the authors' hypothesis. Essentially the authors mutated several residues on the hotspot domains, observed some effects on the channel response to poking and membrane stretch, then interpreted the mutated residues/regions are critical for OSCA mechanosensation. Examples are as follows.

      In the section "Mutation of key residues in the amphipathic helix", the authors mutated W75 and L80, which are located on the N- and C-terminal of the AH in OSCA1.2, and mutated Pro in the OSCA1.2 AH to Arg at the equivalent position in OSCA3.1 AH. W75 and L80 are conserved between OSCA 1.2 and OSCA3.1. Mutations of W75 and/or L80 impaired OSCA1.2 activation by poking, but not by membrane stretch. In comparison, the wildtype OSCA3.1 which contains W and L at the equivalent position of its AH exhibits little or weak response to poking. The loss of response to poking in the OSCA1.2 W/L mutants does not indicate their roles in pokinginduced activation.

      Besides, the P2R mutation on OSCA1.2 AH showed no effect on the channel activation by poking, suggesting Arg in OSCA3.1 AH is not responsible for its weak response to poking. Together the mutagenesis of W75, L80, and P2R on OSCA1.2 AH does not support the hypothesis of the role of AH involved in OSCA mechanosensation.

      Mutagenesis of OSCA1.2 in the amphipathic helix for residues W75 and L80 suggests a role of the helix in the poke response in OSCA1.2, regardless of OSCA3.1 having the same residues. Furthermore, the lack of alteration in the response for mutant P77R suggests that specific residues of the helix are involved in this response and is not a case where any mutation in the helix will lead to a loss of function.

      OSCA3.1 WT exhibits a high-threshold response (near membrane rupture) in the poke assay without any mutations, and this could be due to other features, for example, the residues lining the membrane fenestration, as well as features not identified/probed in this study. We agree with the reviewer that the differences in the AH do not explain the different response to poke in OSCA1.2 and OSCA3.1, and we have added this statement explicitly in the discussion for clarification (line #251-252).

      In the section "Replacing the OSCA3.1 BLD in OSCA1.2", the authors replaced the BLD in OSCA 1.2 with that from OSCA3.1, and only observed slightly stronger displacement by poking stimuli. The authors still suggest that BLD "appears to play a role" in the channel sensitivity to poke despite the evidence not being strong.

      We agree with the reviewer that the experiments carried out show little difference between the response of OSCA1.2 WT and OSCA1.2 with OSCA3.1 BLD, and we have stated so (line #259: “Substituting the BLD of OSCA1.2 for that of OSCA3.1 had little effect on poke- or stretchactivated responses. Although these results suggest that the BLD may not be involved in modulating the MA response of OSCA1.2…”). However, the section of the discussion that the reviewer points out also considers evidence provided by recent reports from Zheng, et al. (Neuron, 2023) and Jojoa-Cruz, et al. (Structure, 2024) and we suggest an hypothesis to reconcile our findings with these new evidence.

      OSCA1.2 has four Lys residues in TM4 and TM6b at the pore fenestration site, which were shown to interact with the lipid phosphate head group, whereas two of the equivalent residues in OSCA3.1 are Ile. In the section "Substitution of potential lipid-interacting lysine residues", the authors made K435I/K536I double mutant for OSCA1.2 to mimic OSCA3.1 and observed poor response to poking but an intact response to stretch. Did the authors mutate the Ile residues in OSCA3.1 to Lys, and did the mutation confer channel sensitivity to poking stimuli resembling OSCA1.2? The reviewer thinks it is necessary to perform such an experiment, to thoroughly suggest the importance of the four Lys residues in lipid interaction for channel mechanoactivation.

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are no longer able to perform such experiments.

      Reviewer #3 (Public Review):

      Summary:

      Jojoa-Cruz et al provide a new structure of At-OSCA3.1. The structure of OSCA 3.1 is similar to previous OSCA cryo-em structures of both OSCA3.1 and other homologues validating the new structure. Using the novel structure of OSCA3.1 as a guide they created several point mutations to investigate two different mechanosensitive modalities: poking and stretching. To investigate the ability of OSCA channels to gate in response to poking they created point mutations in OSCA1.2 to reduce sensitivity to poking based on the differences between the OSCA1.2 and 3.1 structures. Their results suggest that two separate regions are responsible for gating in response to poking and stretching.

      Strengths:

      Through a detailed structure-based analysis, the authors identified structural differences between OSCA3.1 and OSCA1.2. These subtle structural changes identify regions in the amphipathic helix and near the pore that are essential for the gating of OSCA1.2 in response to poking and stretching. The use of point mutations to understand how these regions are involved in mechanosensation clearly shows the role of these residues in mechanosensation.

      Weaknesses:

      In general, the point mutations selected all show significant alterations to the inherent mechanosensitive regions. This often suggests that any mutation would disrupt the function of the region, additional mutations that are similar in function to the WT channel would support the claims in the manuscript. Mutations in the amphipathic helix at W75 and L80 show reduced gating in response to poking stimuli. The gating observed occurs at poking depths similar to cellular rupture, the similarity in depths suggests that these mutations could be a complete loss of function. For example, a mutation to L80I or L80Q would show that the addition of the negative charge is responsible for this disruption not just a change in the steric space of the residue in an essential region.

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I have several questions regarding some of the aspects of your study:

      Mutation of the hydrophobic W75 and L80 in OSCA1.2 to charged residues significantly decreases the poke response in OSCA1.2 without affecting the stretch response. However, W75 and L80 are also present in OSCA3.1, which does not respond efficiently to poke. You conclude that these two residues are important for the poke response, but do not delve into why, if these residues are important, OSCA3.1 is not poke-sensitive.

      In addition, mutation of the OSCA1.2 AH to resemble that of OSCA3.1 does not produce channels that are less poke-sensitive. Given the data presented, if AH were a universal "poke sensor", one could also expect WT OSCA3.1 to exhibit a robust poke response, like OSCA1.2. Here I think it would be important to explain in more detail how this data might fit together.

      We thank the reviewer for bringing up this issue. We decided to test the importance of the AH due to the presence of similar structures in other mechanosensitive channels. Our data showed that single and double mutants of the AH of OSCA1.2 affected its poke response but not stretch. This supports the idea of the AH involvement in the poke response. Yet, we agree that the differences in the AH between OSCA1.2 and OSCA3.1 (P77R mutation) do not explain the higher threshold of OSCA3.1, we have explicitly added this in line #255. The particular OSCA3.1 phenotype may be due to other differences in the structure, for example, differences in the membrane fenestration area, or a combined effect of several differences, which we believe is more likely.

      I also have some questions about the protein-lipid interactions in the fenestration. A lipid has been observed in this location in both OSCA1.2 and OSCA3.1 structures. Mutation of the two OSCA1.2 lysines to isoleucines results in channels that are resistant to poke which leads to the conclusion that the interactions between the fenestration lysines and lipids are important for the poke response.

      Here, there are several questions that arise but are not answered:

      It is not shown what happens when OSCA3.1 isoleucines are mutated to lysines - do these mutants result in poke-able channels? Is the OSCA3.1 mechanosensing altered?

      We performed a preliminary test on OSCA3.1 I423K/I525K double mutant (n = 3). However, we did not see an increase in poke sensitivity. We attributed this to other unexplored differences in OSCA3.1 having an effect in channel mechanosensitivity.

      It is implied that the poke response is predicated on the lysine-lipid interaction. However, lipid densities are present in both OSCA1.2 and OSCA3.1 structures, indicating that both fenestrations interact with lipids. How can we be certain that the mutation of lysine to isoleucine does not disrupt an inter-protein interaction rather than a protein-lipid one? For example, the K435I mutation might disrupt interactions with D523 or the backbone of G527?

      The reviewer brings up a good point. We believe the phenotype seen is due to a different strength in the interaction between lipids and proteins, however, disrupted interaction with other residues is a valid alternative explanation. We agree that the suggested experiments will further clarify the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

      Similarly, the effects of single lysine-to-isoleucine (K435I or K536I) mutations are not explored.

      The observed effect might be caused by only one of these substitutions.

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

      I also wanted to take this opportunity to ask a couple of philosophical (?) questions about using a mammalian system to study ion channels that have evolved to function in plants. Your study highlights the intimate relationship between the lipid bilayer and protein function/mechanosensitivity. Plant cells contain high levels of sterols and cerebrosides that would significantly affect both cell stiffness and the specific interactions that can be formed between the protein and the lipid bilayer. I wonder if the properties of the lipid bilayer might shift the thresholds for poke and/or stretch stimuli and if structural elements that do not appear to have a major role in mechanosensation in a mammalian cell (e.g., BLD) might be very influential in a lipid environment that more closely resembles that of a plant?

      Conversely, is it possible that OSCA channels are not poke-sensitive in plant cells? These questions are beyond the scope of your study, but they might be a nice addition to your discussion.

      The reviewer poses a great question. Electrophysiological approaches for studying plant mechanosensitive channels suffer the limitation of not being able to fully reconstitute the environment of a plant cell. To be able to patch the cell, the cell wall needs to be disposed of, which eliminates the tension generated from this structure onto the membrane. In that sense, performing these assays in plant cells or another system would not give us a fully accurate picture of the physiological thresholds of these channels. Given this limitation, we performed our study with mammalian cells given our expertise with them. Like the reviewer, we are also intrigued by the effect of different membrane compositions on the behavior of OSCA channels and how these channels will behave under physiological conditions, but we agree with the reviewer that these questions are out of the scope of our work. To address this point, in line #294 we have added: “It is also important to note that the membrane of a plant cell contains a different lipid composition than that of HEK293 cells used in our assays, and thus these lipids, or the plant cell wall, may alter how these channels respond to physiological stimuli.”

      Line 313 For structural studies, human codon-optimized OSCA3.1. Could you please clarify what this means?

      We have changed the phrase to “For structural studies, the OSCA3.1 (UniProt ID: Q9C8G5) coding sequence was synthesized using optimized codons for expression in human cells and subsequently cloned into the pcDNA3.1 vector” in line #327 to clarify this sentence.

      As a final comment, in the methods you use references to previously published work. I would strongly encourage you to replace these with experimental details.

      We understand the reviewer’s argument. However, this article falls under eLIFE’s Research Advances and will be linked to the original published work to which we reference the method. As suggested in the guidelines for this type of article, we only described the methods that were different from the original paper.

      Reviewer #2 (Recommendations For The Authors):

      (1) In line 85, provide C-alpha r.m.s.d. values for the structural alignment among OSCA3.1, OSCA1.1, and OSCA1.2 protomers.

      As requested, we have added the C-alpha RMSD in line #86.

      (2) In line 90, should the figure reference to Fig. 1d be Fig. 1e?

      We thank the reviewer for catching this error. We have corrected it in the manuscript.

      (3) In lines 89-94, what putative lipid is it resolved in the OSCA3.1 pore? Can the authors assign the lipid identity? Is this the same or different from the lipids resolved in OSCA1.2, OSCA1.1, and TMEM63?

      In the model, we have built the lipid as palmitic acid to represent a lipid tail, but the resolution in this area makes it difficult to ascertain the identity of said lipid, hence we cannot compare to lipids in other orthologs.

      (4) In lines 115-121, the authors describe the presence of AHs and their functional roles in MscL and TMEM16. It will be more informative if the authors can add figures to show the structure of MscL and highlight the analogous AH. In addition, the current Supplementary Fig. 6 is not informative so it should be improved. It is not clear to the reviewer why that stretch of helix in TMEM16 is equivalent or analogous to the AH in OSCAs, either sequence alignment or a detailed structural alignment is helpful to address this point. Also, in lines 120-121, it says this helix in TMEM16 "does not present amphipathic properties", please show the sequence or amphipathicity of the helix.

      We thank the reviewer for the feedback on this figure. Supplementary Fig. 6 has been thoroughly modified to address the reviewer’s concerns. We now include a panel showing the structure of MscL and its amphipathic helix. We have modified the alignment of OSCA3.1 to a TMEM16 homolog to make clearer the homologous positioning of the helices in question and zoom in to show their sequences.

      (5) In discussion, lines 249-257, the authors referred to a recent study that suggested three evolutionarily coupled residue pairs located on BLD and TM6b. The authors speculate that the reason they did not observe a significant effect of channel response to poke/stretch stimuli in the BLD swapping between OSCA1.2 and 3.1 is due to the 2 of 3 salt bridges remaining for the residue pairs. To test the importance of these residue pairs and their coupling for channel gating, instead of swapping the entire BLD, can the authors systematically mutate the residue pairs, disrupt the salt-bridge interactions, and analyze the effect on channel response to mechanical force?

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

      (6) The reviewer suggests the authors tone down the elaboration of polymodal activation of OSCA by membrane poking and stretch.

      We believe the idea of polymodal activation is sufficiently toned down as we only postulate it as a possibility and following we give an alternative explanation based on methodological limitations: “Nonetheless, the discrepancy could be due to inherent methodological differences between these two assays, as whole-cell recordings during poking involve channels in inaccessible membranes (at the cell-substrate interface) and channel interactions with extracellular and intracellular components, while the stretch assay is limited to recording channels inside the patch.”

      (7) In lines 81-83, the authors described the BLD as showing increased flexibility, and the EM map at this region is less well resolved for registry assignment. In the method for cryo-EM image processing and Supplementary Fig. 1, the authors only carried out 3D refinement and classification at the full channel level. Have the authors attempted to do focus refinement or classification at the BLD domain in order to improve the local resolution or to sort out conformational heterogeneity? The reviewer suggests doing so because the BLD domain is a hot spot that the authors have proposed to play an important role in OSCA mechanosensation. Conformational changes identified in this region might provide insights into its role in the channel function.

      We thank the reviewer for this suggestion. We have performed focused classification on the BLD with and without surrounding regions and, in our hands, it did not improve the resolution or provide further insights.

      Reviewer #3 (Recommendations For The Authors):

      Here are a few specific minor corrections that should be addressed

      (1) In lines 117-135, in the discussion of Figure 2, the data shows an apparent increase in the poking threshold to gate W75K/L80E. The substantial increase in the depth required to gate the channel suggests that these channels are less sensitive to poking. Would it be possible to compare the depth at which these two patches show activity and the depth at which the other 22 cells ruptured? Line 161 mentions that the rupture threshold of HEK cells is close to the gating of OSCA3.1 at 13.8 µm.

      The distance just before the cell ruptured in 22 cells with no response was 12.5 +/- 2.5 um. The distance at which the cells ruptured was 0.5 um more (13 +/- 2.5 n=22). We have added this last value in line #137.

      (2) Would it be possible in Figures 2 panels b and c, 3, and figure 4 to label the WT as WT OSCA1.2?

      We thank the reviewer for pointing this out. We agree this modification will improve the clarity of the figures and have changed the figures to follow the reviewer’s suggestion.

      (3) Can you provide a western blot of the mutations described in Figure 2? This would provide insight into the amount of protein at the cell surface and available to respond to poking, the stretch data shows that these channels are in the membrane but does not show if they are in the membrane in similar quantities.

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

      (4) The functional differences between the two channels are projected to be tied to several distinct point mutations, however, the data could be strengthened by additional point mutations at all sites to show that the phenotypes are due to the mutations specifically not just any mutation in the region.

      We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

    1. Limits of Reconciliation# When we think about repair and reconciliation, many of us might wonder where there are limits. Are there wounds too big to be repaired? Are there evils too great to be forgiven? Is anyone ever totally beyond the pale of possible reconciliation? Is there a point of no return? One way to approach questions of this kind is to start from limit cases. That is, go to the farthest limit and see what we find there by way of a template, then work our way back toward the everyday. Let’s look at two contrasting limit cases: one where philosophers and cultural leaders declared that repairs were possible even after extreme wrongdoing, and one where the wrongdoers were declared unforgivable.1 Nuremberg Trials# After the defeat of Nazi Germany, prominent Nazi figures were put on trial in the Nuremberg Trials. These trials were a way of gathering and presenting evidence of the great evils done by the Nazis, and as a way of publicly punishing them. We could consider this as, in part, a large-scale public shaming of these specific Nazis and the larger Nazi movement. Some argued that there was no type of reconciliation or forgiveness possible given the crimes committed by the Nazis. Hannah Arendt argued that no possible punishment could ever be sufficient: The Nazi crimes, it seems to me, explode the limits of the law; and that is precisely what constitutes their monstrousness. For these crimes, no punishment is severe enough. It may well be essential to hang Göring, but it is totally inadequate.

      I think the Nuremberg Trials illustrate a critical boundary in the concept of reconciliation. In my view, they show that while legal justice is vital, it may not always provide complete closure or moral resolution, especially for vast atrocities. This challenges us to think deeply about the limits of forgiveness and justice.

    1. Harassment in social media contexts can be difficult to define, especially when the harassment pattern is created by a collective of seemingly unconnected people. Maybe each individual action can be read as unpleasant but technically okay. But taken together, all the instances of the pattern lead up to a level of harm done to the victim which can do real damage. Because social media spaces are to some extent private spaces, the moderators of those spaces can ask someone to leave if they wish. A Facebook group may have a ‘policy’ listed in the group info, which spells out the conditions under which a person might be blocked from the group. As a Facebook user, I could decide that I don’t like the way someone is posting on my wall; I could block them, with or without warning, much as if I were asking a guest to leave my house. In the next section, we will look in more detail about when harassment tactics get used; how they get justified, and what all this means in the context of social media.

      in my opinion, this detailed exploration of the nuanced nature of violence and harassment underscores the complexity of defining and addressing these issues within the frameworks of law and social norms. It leads me to think the fine line between permissible actions and those that cause harm, which totally broaden my views.

    2. You might remember from Chapter 14 that social contracts, whether literal or metaphorical, involve groups of people all accepting limits to their freedoms. Because of this, some philosophers say that a state or nation is, fundamentally, violent. Violence in this case refers to the way that individual Natural Rights and freedoms are violated by external social constraints. This kind of violence is considered to be legitimated by the agreement to the social contract. This might be easier to understand if you imagine a medical scenario. Say you have broken a bone and you are in pain. A doctor might say that the bone needs to be set; this will be painful, and kind of a forceful, “violent” action in which someone is interfering with your body in a painful way. So the doctor asks if you agree to let her set the bone. You agree, and so the doctor’s action is construed as being a legitimate interference with your body and your freedom. If someone randomly just walked up to you and started pulling at the injured limb, this unagreed violence would not be considered legitimate. Likewise, when medical practitioners interfere with a patient’s body in a way that is non-consensual or not what the patient agreed to, then the violence is considered illegitimate, or morally bad. We tend to think of violence as being another “normatively loaded” word, like authenticity. But where authenticity is usually loaded with a positive connotation–on the whole, people often value authenticity as a good thing–violence is loaded with a negative connotation. Yes, the doctor setting the bone is violent and invasive, but we don’t usually call this “violence” because it is considered to be a legitimate exercise of violence. Instead, we reserve the term “violence” mostly for describing forms of interference that we consider to be morally bad. 17.4.2. A Bit of History# In much of mainstream Western thought, the individual’s right to freedom is taken as a supreme moral good, and so anything that is viewed as an illegitimate interference with that individual freedom is considered violence or violation. In the founding of the United States, one thing on people’s minds was the way that in a Britain riddled with factions and disagreement, people of one subgroup could not speak freely when another subgroup was in power. This case was unusual because instead of one group being consistently dominant, the Catholic and Protestant communities alternated between being dominant and being oppressed, based on who was king or queen. So the United States wanted to reinforce what they saw as the value of individual freedoms by writing it into the formal, explicit part of our social contract. Thus, we got the famous First Amendment to the Constitution, saying that individuals’ right to freely express themselves in speech, in their religion, in their gatherings, and so on could not legally be interfered with. As a principle, the concept is pretty clear: let people do their thing. But we do still live in a society which does not permit total freedom to do whatever one wants, with no consequences. Some actions do too much damage, and would undermine the society of freedom, so those actions are written into the law (that is, proscribed) as a basis for reprisals. This happens a few ways: Some are proscribed as crimes that lead to arrest, trial, and possibly incarceration. Some are proscribed as concepts or categories of thing, which a person could use to take someone else to court. For example, copyright infringement doesn’t usually result in someone showing up to arrest and imprison in the States. But if someone believes their copyrights have been violated, they can sue the offending party for damages pay, etc. The concept of copyright is proscribed in law, so it forms the basis for such lawsuits. Beyond what is proscribed by law, there are plenty of other actions and behaviors we don’t want people to be doing in our society, but they are not such as should be written into law. I don’t want my friends to lie to me, generally speaking, but this is not against the law. It would be weird if it was! Plain old lying isn’t proscribed, but perjury is (lying under oath in a court of law). The protections of freedom in the First Amendment were designed to help articulate a separation between what we might not like (e.g., someone having a different faith, or someone lying) and what is actually damaging enough to warrant formal legal mechanisms for reprisal (e.g. perjury). The Catholics and the Protestants don’t need to like each other, but they have the right to coexist in this society regardless of which group currently has a monarch on the throne. 17.4.3. So what is harassment?# One useful way to think about harassment is that it is often a pattern of behavior that exploits the distinction between things that are legally proscribed and things that are hurtful, but not so harmful as to be explicitly prohibit by law given the protection of freedoms. Let’s use an example to clarify. Suppose it’s been raining all day, and as I walk down the sidewalk, a car drives by, spraying me with water from the road. This does not make me happy. It makes me uncomfortable, since my clothes are wet, and it could hurt me if wet clothes means I get so cold I become ill. Or it could hurt me if I were on my way to an important interview, for which I will now show up looking sloppy. But the car has done nothing wrong, from a legal standpoint. There is no legal basis for reprisals, and indeed it would seem quite ridiculous if I tried to prosecute someone for having splashed me by driving near me. In a shared world, we sometimes wind up in each others’ splash zones. Now, suppose it was more dramatic than that. Suppose the car had to really veer to spray me with the puddle, such that they could be described as driving recklessly, if anyone happened to be describing it. This is not the splash zone of regular living; it’s malice. But it’s still not illegal, nor the basis for legal action. Finally, suppose it’s not just one car. There is a whole caravan of cars. I recognize the drivers as classmates whom I don’t get along with. They have planned a coordinated strike, each driving through the puddles so fast I can’t hardly catch a breath between splashes. My bag is soaked; my laptop and phone permanently damaged. Since damaging someone else’s private property is proscribed, I could try to prosecute the drivers. I have no idea if this hypothetical case would get anywhere in a real court, but if I could get a judge onside, they might issue a fine, to be paid by the drivers, to answer for my damages (that is, to pay for the replacement of my private property which was destroyed, specifically my laptop and phone). At a guess, I would suspect that it would be very difficult to get anywhere with such a suit in court. Puddle-based harassment isn’t something that is recognized by law. This is what harassment does: it uses a pattern of minorly hurtful actions, so that the harasser can maintain plausible deniability about intent to harm, or at least, failing that, can avoid formal consequences. When harassment concepts get proscribed, this situation shifts. Think about employment law in the States. Depending on what State you’re in and what sector, employment law does not permit racial harassment in the workplace. This means that if you can show a pattern of repeating behavior which is hurtful and based on racially coded comments, then you might have a viable case for a racial harassment suit. (Practically, this probably doesn’t mean suing. It means notifying HR that you have evidence of the pattern and request that they take disciplinary action. What the law does is say that if the harassing party subsequently sues for something like wrongful termination, the company has a legal basis for construing your evidence as showing a pattern of harassment.) If there were a rise in, or a new recognition of, widespread and harmful puddle-based harassment, we might gather with activists and fight to get puddle-based harassment recognized by law, in order to reduce its occurrence. Not that this would be easy, but it would give us the legal basis for pressing charges when coordinated puddle-attacks occur. Getting the action proscribed by the law doesn’t stop people from taking that action. They are still free to puddle-splash at will. But there would be a possibility of consequences, should their pedestrian victims seek reprisal. Harassment is behavior which uses a pattern of actions which are permissible by law, but still hurtful. Variations: Where a relevant harassment definition exists in law, there can be legal consequences. Other institutions can also make their own harassment policies. The consequences would not arise at the legal level, but at the social level. Many universities have policies about sexual harassment which are much richer and more detailed than statutory law. If behavior is reported which is defined by the university policy as harassment, then they can issue consequences such as suspension of the student. Implicit policies can be implemented as well. I don’t have a formal harassment policy that I require my houseguests to sign before entering my home; but it is my home, and if they start behaving in ways that I consider problematic, I do have the right to kick them out of my house. Harassment in social media contexts can be difficult to define, especially when the harassment pattern is created by a collective of seemingly unconnected people. Maybe each individual action can be read as unpleasant but technically okay. But taken together, all the instances of the pattern lead up to a level of harm done to the victim which can do real damage. Because social media spaces are to some extent private spaces, the moderators of those spaces can ask someone to leave if they wish. A Facebook group may have a ‘policy’ listed in the group info, which spells out the conditions under which a person might be blocked from the group. As a Facebook user, I could decide that I don’t like the way someone is posting on my wall; I could block them, with or without warning, much as if I were asking a guest to leave my house. In the next section, we will look in more detail about when harassment tactics get used; how they get justified, and what all this means in the context of social media.

      The comparison of harassment to being sprayed by oncoming cars effectively emphasizes how difficult it is to identify and deal with harassment, particularly in social media settings where people's individual actions may appear harmless but can have detrimental effects when combined. In the same way that a homeowner may ask someone to leave their home if their behavior becomes undesirable, platforms must have clear policies and procedures in place to deal with such behavior.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2023-02270

      Corresponding author(s): Usha Vijayraghavan

      General Statements

      We thank all three Reviewers for their thorough assessment of our manuscript and their constructive feedback and comments.

      Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Reviewer #1

      We are encouraged by the very positive comments made on the significance of our study that it provides convincing insights on alternative modes of nuclear positioning and division which is an important question in cell biology. We also took all possible suggestions to improve the interpretation of our results, have also added some newer data to address the constructive points raised by the reviewer.

      Major comments:

      1. A) I am concerned about the lethal phenotype caused by slu7 deprivation. Slu7 deficiency causes defective nuclear positioning at the bud in late G2. This phenotype per se should not cause defective mitosis, so slu7 deficiency may also be interfering with other aspects of mitosis which might indeed impinge on cell viability.

      Response: Our data indeed show Slu7 knockdown has severe growth defect when grown on non-permissive media (YPD) where a two-fold difference in O.D. was seen by 12 hours (Supplementary figure 2.B).

      We agree with the reviewer that defective mitosis, arises from several aspects of cell cycle including those in mitosis. The data we present show G2 arrest, small-budded cells with unsegregated nuclei and large-budded cells with segregated nuclei, all which do not progress through cell cycle phases and contribute to the severe growth defect. Further, GO enrichment analysis of deregulated pathways on knockdown of Slu7 support the above findings as various cell cycle related pathways are abnormal in their expression levels. In this study, we have focused on an in depth analysis of the role of Slu7 in a particular window and uncover how it controls nuclear position for progress G2-M phase cell cycle progression. The likely targets and mechanisms by which Slu7 regulates other phases of the cell cycle which needs similar other deeper investigations in future. Our detailed analysis of nuclear movement in Slu7 knockdown cells grown in YPD for 12 hours showed no nuclear movement (Supplementary figure 3B) which is the terminal phenotype. To examine events that lead to nuclear mispositioning phenotype we investigated the dividing slu7kd cells grown in non-permissive media for only 6 hours; under these conditions Slu7 protein is still detected at lower amount (Supplementary figure 1D). From the studies of nuclear position, mitotic spindle position and dynein distribution in mother and daughter cell, we propose that in the dividing cells, the nucleus does not experience enough force to move inside the daughter bud during mitosis. Further, we delineate the role of Slu7 in the splicing of transcripts for PAC1 encoding a protein whose homolog in S. cerevisiae has a proven role in nuclear migration. In live imaging of slu7kd cells that show nuclear segregation at the start of live imaging, new bud was not formed till the end of 60 minutes, implying that are arrested after transition to mitosis. We could speculate a role for Slu7 through regulation of genes involved in mitotic exit or cytokinesis.

      1. B) Supp. Fig4 shows defective mitosis in TBZ, so TBZ may be exacerbating defective mitosis of slu7-deficient cells.

      __Response: __Studies with yeast and mammalian model systems have revealed that the mobility and repair of damaged DNA are compromised upon disruption of microtubules (Wu et al, 2008; Chung et al, 2015; Lottersberger et al, 2015; Lawrimore et al, 2017; Oshidari et al, 2018; Laflamme et al, 2019). These data point to reasons why the mutants in DNA damage checkpoint genes are sensitive to TBZ. In this context, we observed that CnSlu7 knockdown is also sensitive to MMS stress (shown below). In addition, recent work on human Slu7 in Hela cell lines has elucidated the its role in the maintenance of genome integrity by preventing the formation of R-loops (Jiménez et al, 2019). We suggest that TBZ may exacerbate the defective mitosis of Slu7 depleted cells, however, whether it is particular only to mitosis or to the other cellular processes where the microtubules are involved needs further investigation.

      Throughout the figures it can be observed uneven chromosome/nuclear segregation in cells deprived of slu7, however, these mitotic defects have not been mentioned or explored in depth. From Supp Figure 3C it can be inferred that CENP-A segregation is uneven. Is this correct? Is CENP-A-GFP segregation normal?

      __Response: __ It should be noted that in Cryptococcus, the kinetochore remains unclustered during the early phase of cell cycle, cluster to a single punctum at the end of G2 phase and then de-cluster at the end of mitosis. Since this is a highly dynamic process, its technically challenging to measure the intensity CENP-A in mother and daughter cell. In the fixed cell imaging or live imaging data, there are no appreciable differences in intensity of the GFP signal of the tagged proteins (H4 and CENPA). The uneven chromosome/nuclear segregation observed in certain panels images presented are due to technical issues in that particular stack while generating the montage. This has been re-examined and we infer that there are no major differences in the signals from GFP-H4 and GFP - CENPA through mitosis.

      Additionally, taking the cue from the reviewer’s comment, we examined the likelihood of improper chromosome segregation by evaluating if there are any appreciable cell populations that are aneuploid. We revisited our flow cytometry data, we found no significant difference in the population of aneuploid cells between the knockdown strain and wildtype strain grown in non-permissive condition for 12 hours. This data was assessed again in new experiments where we also analyzed by flow cytometry the ipl1 mutant where aneuploidy is reported (Varshney et al, 2019). It has been reported in Cryptococcus neoformans that aneuploid cells are resistance to anti-fungal drug fluconazole. Preliminary experiments showed that slu7kd cells were sensitive to fluconazole and in this assay were similar to wildtype cells. Hence, we speculate that chromosome segregation is normal in Slu7 depleted cells.

      If chromosome segregation is altered upon slu7 deprivation, this might also explain the drop in cell viability and slow growth rates of this condition.

      __Response: __ From live microscopy imaging and flow cytometry data, we believe that the chromosome segregation is normal in Slu7 depleted cells. Dilution spotting in permissive media after growth in non-permissive media revealed that slu7kd cells resumed growth without losing viability, indicating the arrest phenotype associated with the depletion of Slu7 is largely reversible and does not cause chromosome mis-segregation (figure is now added to manuscript as supplementary figure 2D). Prolonged arrest at various cell cycle phase might lead to cell death and hence drop in cell viability.

      The manuscript will improve if authors analyse chromosome segregation for example, by showing time-lapse images of chromosome dynamics during mitosis.

      __Response: __Chromosome dynamics during the mitotic phase is given below. We observe that the chromosome segregation is equal in both mother and daughter bud. The uneven chromosome/nuclear segregation observed in certain panels images presented in original manuscript were due to technical issues while generating the montage.

      The authors perform an RNA seq comparing wild-type cells with slu7 deficiency and detect changes in gene expression, however, they do not explore from this data the percentage of un-spliced introns genome-wide which might be very informative, even more than changes in gene expression, which many of them, might be an indirect consequence of Slu7 deficiency. Authors should re-analyze the RNA seq data looking for unprocessed mRNAs and provide information about the overall impact of slu7 in intron processing.

      __Response: __ A very detailed bioinformatic analysis of the impact on slu7 on global transcriptome and splice pattern, is an ongoing study in the laboratory. The findings are indeed giving good leads which are being validated by further experiments using mini-gene exon-intron constructs. These studies are extensive and form a future manuscript identifying and characterizing intronic features which predispose an intron towards Slu7 dependency. Therefore, it falls outside the scope for this study on the cell biological role of Slu7 on mitosis, specifically nuclear position to ensure faithful mitotic segregation.

      Minor comments:

      __ __1. "Previous studies of slu7 mutants in S. cerevisiae and the conditional knockdown of its S. pombe homolog". Consider replacing homolog with Ortholog.

      Response: The suggestion is well taken, and the word “homolog” has been replaced with word “ortholog”.

      1. A) Taking these results together, we conclude that the inability of the conditional mutant to grow in the non-permissive media is due to impaired progression through the G2-M phase of the cell cycle. Is the G2/M delay the cause of the slow growth phenotype of the Slu7 deficiency?

      Response: From the live microscopy, we note that even when the budding index for mitosis has been reached the nucleus in slu7kd cells is still in the mother cell and spends more time here rather than reaching the bud or bud neck. We present G2/M delay as ONE of the reasons for the slow growth of Slu7 depleted cells. Although we have showed that Slu7 depletion does not activate MAD2 dependent Spindle Assembly Checkpoint, we have not investigated the activation of other cell cycle checkpoints such as G2 DNA damage checkpoint. These are potential new leads as we infer from our RNA seq datasets that CHK1, TEL1, BDR1 and RAD51 show increased expression in Slu7 knockdown condition when compared to wildtype. It is therefore reasonable to conclude that Slu7 might play a role at various cell cycle phases through direct or indirect effect on genes involved in these phases. Delayed positioning of the nucleus during G2/M is one of the major effects that is investigated in depth in this study.

      1. B) If so, growth defects of slu7 deficiency could be suppressed by ectopic expression of G2/M activators.

      Response: We have not tested this possibility, but we predict that expression of G2/M activators would at best offer only partial rescue the growth defect of Slu7 depleted cells since multiple pathways are adversely affected in cells depleted of Slu7.

      In this line of investigation, we have tested the consequences of PAC1 overexpression, as PAC1 expression levels and splicing are affected by loss of Slu7. We report a partial rescue of nuclear position defect during mitosis, yet these cells were arrested at cytokinesis. Further, the unavailability of an array of suitable auxotrophic (or other) markers in this model system makes it technically challenging to do rescue experiments by overexpression of multiple candidate downstream genes.

      Supp Figure 3C, remove the drawing on the right. Adjust times relative to panels.

      Response: The drawing has been removed and the time points have been adjusted.

      1. Tracking the nucleus in wild-type cells with a small bud showed that the nucleus moved into the daughter bud, divided into two, and one-half migrated to the mother bud (Supplementary Figure 3B, top row).

      Please replace the sentence: "one-half" with "one of the daughter nuclei". Additionally, as this nuclear positioning occurring during late mitosis is due to spindle elongation, I would not use the term migrated but "positioned" or "moved". Nuclear movement into the bud, which is referred to as "moved", can indeed be named "migrated".

      Response: The word “migrated” in the above sentence has been replaced with the word “moved”.

      1. Indicates in Figure 2B the marker used (GFP-H4), as in Fig Supp 3B.

      Response: The marker has been indicated in the figure.

      1. Nuclear division initiates in the bud, and one of the divided nuclei with segregated chromosomes migrates back to the mother cell (Figure 2B, top panel, wildtype, quantified in Figure 2C grey bar).

      As mentioned before, I would not name this, nuclear migration as it is the result of spindle elongation, and it can be confusing or misleading for non-expert readers.

      Response: The word “migrate” in the above sentence has been replaced with the word “move”.

      1. These two conclusions should be revised and described in temporal/sequential order.
      2. Thus, we identify that the depletion of CnSlu7 severely affects the temporal and spatial sequence of events during mitosis, particularly nuclear migration and division.
      3. Together, these results confirmed that without affecting the kinetochore clustering, depletion of Slu7 affects nuclear migration during the G2 to mitotic transition in Cryptococcus neoformans.

      Response: We thank the reviewer for bringing out the clarity in the concluding statements. These has now been revised to read as follows:

      “Together, these results confirm that without affecting the kinetochore clustering, depletion of Slu7 affects nuclear movement during the G2 to mitotic transition in Cryptococcus neoformans. Thus, we identify that the depletion of CnSlu7 severely affects the temporal and spatial sequence of events during mitosis, particularly nuclear migration, and division.”

      1. In slu7d cells, in cells with small buds, numerous cMTs were nucleated from the MTOCs, and as the cell cycle progressed, they organized to form the unipolar mitotic spindle (Figure 3A, slu7kd GFP-TUB1 panel, time point 55 mins).

      Please, revise whether the term unipolar mitotic spindle is correct here.

      Response: The word unipolar has been removed.

      1. I suggest including page and line numbers in the manuscript to facilitate revision.

      Response: We regret missing out this formatting guideline. The Page and line numbers have provided.

      Reviewer #2

      We are thankful by the very positive comments on the significance of our work, its novelty and findings being of broad interest to microbiology; splicing; cell cycle and cell division communities. We respond to all comments raised below.

      1. The authors test the Mad2-dependent spindle assembly checkpoint and show that it is not relevant for slu7-depletion. This is as expected if the defect is in nuclear positioning. They could test other checkpoint pathways that would monitor nuclear positioning in budding yeasts. Perhaps they have considered this: Bub2, Bfa1, Tem1, Lte1 mutants? I don't think this experiment is essential for publication, but it could strongly support their model.

      Response: We appreciate the comment on other checkpoints operating during mitosis. However, we have not done these experiments to examine role of components that arrest mitosis (Bub2, Tem1 etc.) in response to spindle or kinetochore damage. We hope the reviewer appreciates that this line of work would require the generation of bub2Δ strain and extensive characterization for their role in checkpoint in Cryptococcus before it can be brought into strains compromised for Slu7.

      __ Minor comments:__ 1. in Figure 3, Dyn1-GFP is imaged and in many of the cells in which Slu7 is depleted, nothing (or very little) can be seen. It is later argued that this is an indirect effect, due to defects in Pac1 and associated functions. Have the authors attempted a Dynein western blot (the 3xGFP tag should be quite sensitive)? It would be good to demonstrate that the Dynein motor complex hasn't simply fallen apart and Dynein been degraded in the slu7-depletion.

      Response: A study in S. cerevisiae has reported the dynein expression does not change in pac1Δ cells (Lee et al., 2003). Since the molecular weight of CnnDYN1 along with the tag is 630kDa, we did attempt the very challenging experiment of western blot to check for the expression levels this very large protein in wildtype and slu7kd cells. Based on the reviewer’s suggestion, we have attempted dot blot of protein lysates from wild type and from slu7kd cells probed with anti GFP antibody for estimating DYN-GFP levels. Untagged WT H99 strain was used as negative control. The same blot was stripped and re-probed for PSTAIRE which served as a loading control. This experiment revealed that dynein levels are same in both wildtype and slu7kd cells.

      in Figure 7: have any intronless genes been tested for rescue of the post-mitotic delay/arrest? This is not necessary for publication, but if any have been tested already, they could be listed here.

      Response: We have not tested intronless genes for their role in the rescue of post mitotic delay/arrest. From the RNA seq data, we observed that most of the genes involved in mitotic exit network (MEN) and cytokinesis were highly expressed in slu7kd cells as compared to the wildtype indicating and indirect role for Slu7 in their expression level. So, we had validated three candidates MOB2, CDC12 and DBF2 by qRT PCR (Supplementary 7.D) and found they were upregulated in slu7kd cells and hence speculate that deregulation of these transcript could contribute to the post mitotic arrest in slu7kd.

      In SFig2C legend make it clear that these cells are HU arrested at time zero. Are the cells in glucose or galactose during HU treatment.?

      Response: We regret the lack of clarity in the legend and the required details have been added. The cells were initially grown in non-permissive media for 2 hours to deplete Slu7 and then HU was added to the non-permissive media and the cell were allowed to grow for 4 hours.

      in SFig4, the TBZ sensitivity isn't very convincing as the slu7kd strain is struggling to grow at all on YPD.

      Response: We agree with the reviewer comment on the growth of slu7kd cells on media YPD containing TBZ. TBZ may exacerbate the defective mitosis of Slu7 depleted cells, however whether it pertains only to mitosis or any cellular processes where microtubules are involved requires further investigation.

      In SFig5 legend the volcano plot needs to be better explained. What are the dashed lines etc. ?

      Response: We regret missing these details on the volcano plot which has now been added to the legend.

      __Reviewer #3 __

      We appreciate the views that our work provides strong evidence to support out conclusions that Cryptococcus neoformans Slu7 controls mitotic progression by efficient splicing of cell cycle regulators and cytoskeletal elements. We have taken all comments of the reviewer into account to revise our manuscript with additional data, and by improving the presentation. The key additional data are summarized below.

      Major comments:

      1) The authors claimed that CnSlu7 is the most divergent among the fungal homologs and closer to its human counterpart (Fig. 1A, Supplementary Fig 1A). -Just based on the phylogenetic tree including limited members, as in Supplementary Fig. 1, it cannot be concluded that CnSlu7 is closer to its human counterpart since the basidiomycete yeast such as C. neoformans itself is more closely positions to humans compared to the ascomycete yeasts S. cerevisiae and Sch. pombe in phylogenetic tree analysis. It is strongly recommended to include other fungal species from the Basidiomycota, such as Ustilago maydis, in phylogenetic analysis in Supplementary Fig. 1. - Conservation analysis among diverse eukaryotes is more meaningful data that the conservation withing the fungi group, so that it is recommended that the data of Fig. 1 A would be replaced with the revised Supplementary Fig 1. -The analysis data on amino acid identities among Slu7 homologues should be presented to support the claim.

      Response: We agree with the reviewer that our data would be better served by an improved analysis of the phylogenetic relationship between various Slu7 homologs. We have therefore reconstructed the phylogenetic tree by including other fungal groups. This is presented here and also in the revised manuscript Supplementary Figure 1A. These data too, show that Cryptococcus (deneoformans and neoformans) Slu7 is the most diverged among its homologs from various fungal species with its closest homologs being other pathogens Puccinia graminis and Ustilago maydis.

      2) Despite that CnSlu7 is the main key subject, the comparative analysis of CnSlu7 to the previously reported Slu7 homologues, in the aspect of functional domain organization, is not provided in the present manuscript. - It was reported that Slu7 contains the four motifs that control its cellular localization and canonical function as a splicing factor, such as a nuclear location signal, a zinc knuckle motif, four stretches of leucine repeats and a lysine-rich domain. Notably, human Slu7 protein is 204 amino acids longer than S. cerevisiae homolog with only 24% identity in the zinc knuckle motif (Molecular Biology of the Cell Vol. 15, 3782-3795). Thus, it is strongly recommended to provide additional information on the conserved and diverged features of CnSlu7 compared to other Slu7 homologs as a part of revised Figure.

      Response: The multiple sequence alignment of Cryptococcus neoformans Slu7 with its fungal and higher eukaryote homologs such as human Slu7 and plant Slu7 proteins revealed that only the CCHC zinc finger motif is highly conserved. We do not detect conservation in the nuclear localization signal, stretch of leucine repeats and lysine rich domain except for leucine 3 stretch near the C terminal. This additional information is presented in revised Figure 1A.

      3) The manuscript clearly demonstrated that one of key targets of Slu7-mediated splicing is PAC1 in C. neoformans. Considering, Pac1 is also conserved from S. cerevisiae to human, it could be speculated that the defect of Slu7 can affect nuclear migration in other fungal species and human cells by inefficient splicing of PAC1, despite striking differences in their nuclear position during cell division. Please discuss this possibility or provide the qRT-PCR analysis data of PAC1 homologs in the available fungal Slu7 mutant strains.

      Response: Cell cycle arrest phenotypes of splicing factor mutants (studied largely in budding and fission yeast) results from inefficient pre-mRNA splicing of cell cycle-related genes. Slu7 is a well characterized second step splicing factor in S. cerevisiae where in vitro splicing assays with ACT1 minigene transcripts with a modified single intron showed ScSlu7 is dispensable for splicing when the branchpoint to 3'SS distance is less than seven nucleotides in the mini transcript (Brys and Schwer, 1996). In fission yeast we reported the effects of metabolic depletion of Slu7, which is an essential gene (Banerjee et al., 2013) and showed unexpectedly that in addition to BrP to 3'SS distance new intronic features contributors of dependency of fission yeast intron containing transcripts on Slu7 functions. The work also showed in multi-intronic transcripts its role is intron-specific and thus the candidate gene/ transcript is likely to be to dependent on Slu7 by virtue of the intronic features and not its biological function. In this study a splicing dependent role of CnSlu7 in cell cycle progression is investigated where based on a strong nuclear mis-positioning phenotype we narrowed on PAC1 transcripts as one of targets. We show PAC1, encoding a cytoskeletal factor, has introns dependent on CnSlu7 for efficient splicing and show partial rescue of nuclear position in strain complemented with expression of an intronless PAC1 gene. In this scenario, while it is likely that in other species where PAC1 exon-introns nucleotide sequences are similar to that in Cryptococcus a role for Slu7 may be predicted, for validation by other experimentalists.

      Interestingly, PAC1 in S. cerevisiae is an intronless gene and its homolog is not annotated in S. pombe. In human cell lines, knockdown of Slu7 by siRNA resulted in metaphase arrest by inefficient splicing of soronin – which is crucial in sister chromatid cohesion and correct spindle assembly, according to recent research in human cell lines (Jiménez et al., 2019).

      Hence the roles of splicing factor in cell cycle is through splicing of targets involved in cell cycle wherein the targets regulated by splicing factor may or may not be conserved in other species.

      Minor comments:

      General points 1) Provide information on the marker sizes in the data of qRT-PCR analysis presented in Figures 5 and 6, and Supplementary Fig 2A.

      Response: We regret the omission of this technical data and have corrected the same by providing the marker sizes in all the figures.

      2) Please unify the format of gene names. Some genes were written with superscript of "+", such as CLN1+ and PAC1+ in Fig. 4. What does "+" mean in the gene names?

      Response: We have taken the suggestion to carefully review the nomenclature of genes and their expressed transcripts as is typical for Cryptococcus neoformans. To depict the wildtype form of transcript we had used +. Thus CLN1+ was used to denote Cyclin 1 cellular transcript from expressed from its own locus without any modification of promoter or the intronic features.

      3) Supplementary Figure 1 C: Please correct "Slu7KD" 6 hrs YPD to "slu7kd" 6 hrs YPD.

      Response: This error has been corrected.

      4) Supplementary Figure 2A: What do "mRNA" and "No RT29X/", respectively, indicate?

      Response: The mRNA indicates the spliced form across any intron after intron is spliced out, so denotes exon-exon sequences in the mRNA. The reactions marked as “No RT 29 X” denote semi- quantitative PCR performed on DNase treated RNA sample, without reverse transcription to generate the cDNA. These reactions were done to confirm that there is no genomic DNA present in the RNA sample used for reverse transcription reaction of the cellular transcripts. Some of these details are now included in the Supp Fig 2A legend.

      5) Supplementary Figure 4C: Please provide brief explanation in the text on why the authors employed mad2Δ slu7kd cells.

      Response: In Page 8, line 6, we had provided the rationale for generating and studying mad2Δ slu7kd strain. This is recapitulated below:

      “To investigate whether Slu7 knockdown triggers the activation of spindle assembly checkpoint (SAC), we generated a strain with conditional slu7kd in cells with mad2Δ allele and the GFP-H4 nuclear marker.”

      6) Supplementary Figure 6D legend: Please correct the description of "slu7kd SH:Slu7 FL" from "expressing intronless PAC1" to "expressing full length of SLU7".

      Response: The error in the legend is regretted and this has been corrected.

      7) Supplementary Figure 7D: The authors confirmed that MOB2, CDC12, and DFB1 were expressed at higher levels in slu7kd when compared to wildtype. Please briefly explain in the text why the expression level of these genes in slu7kd was mentioned.

      Response: slu7kd cells expressing intronless Pac1 arrest post nuclear division. Revisiting our transcriptomic data, we found that genes involved in mitosis exit network and cytokinesis, such as DFB1, MOB2, CDC12, BUD4, and CHS2, were deregulated in slu7kd when compared to wildtype. We confirmed the same by performing qRT PCRs for three candidates, MOB2, DBF1 and CDC12 and that these transcript were expressed at high levels in knockdown when compared to wildtype.

      8) The species name should be written as abbreviation after the first mention. For example, please correct Cryptococcus neoformans to C. neoformans throughout manuscript.

      Response: The suggestion is well taken, and the required edits have been made throughout the text.

      9) Please unify the format of paper titles listed in References.

      Response: This formatting error is regretted and corrected to have all references in a single format.

      10) No page information for Hoffmann et al (2010) in References.

      Response: This omission is corrected.

      11) Update the information on the published journal of Chatterjee et al. (2021) in References.

      Response: This omission is regretted and is now corrected.

      12) Information on the authors, title, published journal and pages should be provided for the papers (Yadav and Sanyal, 2018; Sridhar et al., 2021) in Supplementary Table 1, which were not included in the main Reference list.

      Response: The references are now added to the main list.

      References used for addressing the reviewer’s comments:

      1. Chung DKC, Chan JNY, Strecker J, Zhang W, Ebrahimi-Ardebili S, Lu T, Abraham KJ, Durocher D, Mekhail K (2015) Perinuclear tethers license telomeric DSBs for a broad kinesin- and NPC-dependent DNA repair process. Nat Commun doi:10.1038/NCOMMS8742.
      2. Jiménez M, Urtasun R, Elizalde M, Azkona M, Latasa MU, Uriarte I, Arechederra M, Alignani D, Bárcena-Varela M, Alvarez-Sola G et al (2019) Splicing events in the control of genome integrity: Role of SLU7 and truncated SRSF3 proteins. Nucleic Acids Res 47: 3450–3466. doi:10.1093/nar/gkz014.
      3. Laflamme G, Sim S, Leary A, Pascariu M, Vogel J, D’Amours D (2019) Interphase Microtubules Safeguard Mitotic Progression by Suppressing an Aurora B-Dependent Arrest Induced by DNA Replication Stress. Cell Rep 26: 2875-2889.e3. doi:10.1016/J.CELREP.2019.02.051.
      4. Lawrimore J, Barry TM, Barry RM, York AC, Friedman B, Cook DM, Akialis K, Tyler J, Vasquez P, Yeh E et al (2017) Microtubule dynamics drive enhanced chromatin motion and mobilize telomeres in response to DNA damage. Mol Biol Cell 28: 1701–1711. doi:10.1091/MBC.E16-12-0846.
      5. Lee WL, Oberle JR, Cooper JA (2003) The role of the lissencephaly protein Pac1 during nuclear migration in budding yeast. J Cell Biol. doi:10.1083/jcb.200209022.
      6. Lottersberger F, Karssemeijer RA, Dimitrova N, De Lange T (2015) 53BP1 and the LINC Complex Promote Microtubule-Dependent DSB Mobility and DNA Repair. Cell 163: 880–893. doi:10.1016/J.CELL.2015.09.057.
      7. Oshidari R, Strecker J, Chung DKC, Abraham KJ, Chan JNY, Damaren CJ, Mekhail K (2018) Nuclear microtubule filaments mediate non-linear directional motion of chromatin and promote DNA repair. Nat Commun doi:10.1038/S41467-018-05009-7.
      8. Varshney N, Som S, Chatterjee S, Sridhar S, Bhattacharyya D, Paul R, Sanyal K (2019) Spatio-temporal regulation of nuclear division by Aurora B kinase Ipl1 in Cryptococcus neoformans. PLoS Genet doi:10.1371/journal.pgen.1007959.
      9. Wu G, Zhou L, Khidr L, Guo XE, Kim W, Lee YM, Krasieva T, Chen PL (2008) A novel role of the chromokinesin Kif4A in DNA damage response. Cell Cycle 7: 2013–2020. doi:10.4161/CC.7.13.6130.
    1. "I don't remember. Are you measuring yourself by that?""You waited six months, and you do too remember. And this is five months. And we're not measuring anything. William and I have known each other longer than five months, but we've been together - you know, as a couple - five months. And I'm almost twenty-three, which is two years older than Mom was. And don't tell me it was different when you guys did it.""No," he heard himself say. "It's pretty much the same, I imagine?"

      In this section, I noticed the dynamic both Ballinger and Melanie have is very odd, especially the way she assumes telling her father about her significant other when the truth is the contrary. By the beginning of the conversation, we can already see that Melanie and Ballinger have a close relationship with each other, but Melanie seems to have grown distant from her family and made some choices she knew her parents would not be proud of once she told them the truth. However, she has an idea; she can start the conversation by slowly introducing her father Coombs by talking a little bit more about him and quickly assuming they already had this conversation before which does not give her father the chance to think properly about the situation except to accept it. That strategy did not work for long once her father started to figure out how serious her relationship was with Coombs as well as how impossible it was to share Melanie his problems with his wife, Mary. Ballinger and Melanie seemed to have a close relationship with each other, but this situation may drift the, away for a long time.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      TRIP13/Pch2 is a conserved essential regulator of meiotic recombination from yeast to humans. In this manuscript, the authors generated TRIP13 null mice and Flag-tagged TRIP13 knock-in mice to study its role in meiosis. They demonstrate that TRIP13 regulates MORMA domain proteins and is essential for meiotic completion and fertility. The main impact of this manuscript is its clarification of the in vivo function of TRIP13 during mouse meiosis and its previously unrecognized role as a dose-sensitive regulator of meiosis.

      Strengths:

      Two previously reported Trip13 mutations in mice are both hypomorphic alleles with distinct phenotypes, precluding a conclusion on its function. This study for the first time generated the TRIP13 null mice, definitively revealing the function of TRIP13 in meiosis. The authors also show the novel localization of TRIP13 at SC and its independence from the axial element components. The finding of dose-sensitive regulation of meiosis by TRIP13 has implications in understanding human meiosis and disease phenotypes.

      Weaknesses:

      This manuscript would be more impactful if more mechanistic advancements could be made. For example, the authors could follow up with one of the new interactors identified by MS to offer new insight into the molecular function of TRIP13.

      We agree that it would be interesting to follow up on new candidate interactors but think that it would be more feasible to follow up on them in future studies.

      Reviewer #2 (Public Review):

      Summary and Strengths:

      In this manuscript, Chotiner and colleagues demonstrated the localization of TRIP13 and clarified the phenotypes of Trip13-null mice in mouse meiosis. The meiotic phenotypes of Trip13 have been well characterized using the hypomorph alleles in the literature. However, the null phenotypes have not been examined, and the localization of TRIP13 was not clearly demonstrated. The study fills these important knowledge gaps in the field. The demonstration of TRIP13 localization to SC in mice provides an explanation of how HOMRA domain proteins are evicted from SC in diverse organisms. This conclusion was confirmed in both IF and TRIP13-tagged Tg mice. Further, the phenotypes of Trip13-null mice are very clear. The manuscript is well crafted, and the discussion section is well organized and comprehends the topic in the field. All in all, the manuscript will provide important knowledge in the field of meiosis.

      Weaknesses:

      The heterozygous phenotypes demonstrate that TRIP13 is a dosage-sensitive regulator of meiosis. In relation to this conclusion, as summarized in the discussion section, other mutants defective in meiotic recombination showed dosage-sensitive phenotypes. However, the authors did not examine meiotic recombination in the Trip13-null mice.

      Meiotic recombination was extensively characterized in Trip13 severe hypomorph mutants in two previous studies: gamma-H2AX, BLM, BRCA1, ATR, RPA, RAD51, DMC1, MLH1 (Li and Schimenti, 2007; Roig et al., 2010). All the meiotic defects in our Trip13-null mice were also present in Trip13 severe hypermorph mutants: meiotic arrest, defects in chromosomal synapsis, asynapsis at chromosomal ends, and accumulation of HORMAD1/2 on the SC axis. Therefore, the defects in meiotic recombination in Trip13-null mice are expected to be similar to those in Trip13 severe hypermorph mutants and thus we did not examine the proteins involved in meiotic recombination in the Trip13-null mutant.

      Reviewer #3 (Public Review):

      Summary:

      The authors perform a thorough examination of the phenotypes of a newly generated Trip13 null allele in mice, noting defects in chromosome synapsis and impact on localization of other key proteins (namely HORMADs) on meiotic chromosomes. The vast majority of data confirms observations of several prior studies of Trip13 alleles (moderate and severe hypomorphs). The original or primary aims of the study aren't clear, but it can be assumed that the authors wanted to better study the role of this protein in evicting HORMADs upon synapsis by studying phenotypes of mutants and better characterizing TRIP13 localization data (which they find localizes to the central element of synapsed chromosomes using a new epitope-tagged allele). Their data confirm prior reports and are consistent with localization data of the orthologous Pch2 protein in many other organisms.

      Strengths:

      The quality of data is high. Probably the most important data the authors find is that TRIP13 is localized along the CE of synapsed chromosomes. However, this was not unexpected because PCH2 is also similarly localized. Also, the authors use a clear null (deletion allele), whereas prior studies used hypomorphs.

      Weaknesses:

      There is limited new data; most are confirmatory or expected (i.e., SC localization), and thus the impact of this report is not high. The claim that TRIP13 "functions as a dosage-sensitive regulator of meiosis" is exaggerated in my opinion. Indeed, the authors make the observation that hets have a phenotype, but numerous genes have haploinsufficient phenotypes. In my opinion, it is a leap to extrapolate this to infer that TRIP13 is a "regulator" of meiosis. What is the definition of a meiosis regulator? Is it at the apex of the meiosis process, or is it a crucial cog of any aspect of meiosis?

      TRIP13 is not haploinsufficient, as Trip13 heterozygotes were still viable and fertile (albeit with defects in meiosis). TRIP13 is an ATPase and changes the conformation of meiosis-specific proteins such as HORMAD proteins. TRIP13 is essential for meiosis and its mutations cause defects in both meiotic recombination and chromosomal synapsis. Reviewer 1 stated that “TRIP13/Pch2 is a conserved essential regulator of meiotic recombination from yeast to humans”. Therefore, we feel that TRIP13 can be called a regulator of meiosis.

      Reviewer #1 (Recommendations For The Authors):

      A schematic illustration of SC structure, the components involved, and the main finding, would be helpful for readers to better understand the advancement made by this study.

      We have now added a schematic illustration in a new panel - Figure 7C.

      Fig. 1B, the stage with diplotene cells should be XII.

      The pachytene cells (Pac) were mis-labelled as diplotene cells. Corrected.

      Fig. 1C, color mislabeled.

      Corrected.

      Reviewer #2 (Recommendations For The Authors):

      The manuscript will provide important knowledge in the field of meiosis. I support the publication of this study. I have some suggestions to improve and polish the manuscript.

      Major points:

      (1) The heterozygous phenotypes demonstrate that TRIP13 is a dosage-sensitive regulator of meiosis. In relation to this conclusion, as summarized in the discussion section, other mutants defective in meiotic recombination showed dosage-sensitive phenotypes. Given the function of HORMAD1 in meiotic recombination, it would be informative if the authors could examine how major makers of meiotic recombination behave in Trip13-null meiosis.

      Please see our response to Weaknesses from Reviewer #2.

      (2) Relating to the above point, the complete lack of synapsis on the sex chromosomes in the Trip13-null meiosis is impressive. This result raises a question as to whether the pathway to designate XY-obligatory crossover (which can be detected with large foci of ANKRD31 and MEI4/REC114 at PAR) is affected or not. It would be interesting to examine whether the ANKRD31 and MEI4/REC114 foci are present on PAR in Trip13-null meiosis.

      We have performed immunofluorescent analysis of REC114 in spermatocytes. In Trip13-null pachytene-like spermatocytes, X and Y chromosomes are not synapsed. REC114 still formed one focus each on the unsynapsed X and Y chromosomes. We have added this new data in the Results as a new supplementary figure (Figure 4 -supplement 1).

      (3) Figure 4 can be improved if there are quantified data for each phenotype. These phenotypes look nearly complete, but it would be informative to show the penetrance of these phenotypes.

      Because some chromosomes have unsynapsed ends, resulting in two centromere or telomere foci, the total number of centromere or telomere foci is always higher in Trip13-null pachytene-like spermatocytes than wild type pachytene spermatocytes. Therefore, we did not count the foci of centromeres and telomeres. Consistently, the centromere and telomere markers localized as expected in both wild type and Trip13-null spermatocytes.

      (4) I am not fully convinced by these photos: "synapsed sister chromatids (Figure 6B)" and "Sycp2-/- spermatocytes formed short stretches of synapsis (Figure 6C)". The authors may try confocal microscopy with super-resolution deconvolution as they did for other data.

      These have been previously demonstrated. The “synapsed sister chromatids (Figure 6B)” were previously demonstrated by confocal microscopy with super-resolution deconvolution (Guan et al., 2020). The short stretches of synapsis in Sycp2-/- spermatocytes was previously demonstrated by electron microscopy (Tripartite SC structure) and SYCP1 immunofluorescence (Yang et al., 2006). We have revised the text by citing the previous evidence and the publications.

      Minor points:

      (1) Line 19-21: "Loss of TRIP13 leads to meiotic arrest and thus sterility in both sexes. Trip13-null meiocytes exhibit abnormal persistence of HORMAD1 and HOMRAD2 on synapsed SC". These findings confirm the previously reported phenotypes of the Trip13 hypomorph alleles. This information can be added to the abstract. Otherwise, it sounds like these are totally new findings, as written.

      This information is now added to the abstract: “These findings confirm the previously reported phenotypes of the Trip13 hypomorph alleles.”

      (2) The introduction section seems too long and contains unnecessary information. Some molecular details that are not touched in the result section can be deleted (e.g., Line 65-73).

      We would like to keep the molecular details on the two conformation states, as it provides biochemical background on TRIP13-HORMAD interactions.

      (3) Introduction, Line 92. A rationale can be added as to why the authors characterized the Trip13-null allele.

      a rationale has been added as follows: “To determine the effect of complete loss of TRIP13, we characterized Trip13-null mice.”

      (4) Line 205: Typo "TRRIP13". Corrected.

      Reviewer #3 (Recommendations For The Authors):

      Just a few recommendations:

      (1) In my opinion, the title is an overreach. "Regulator" invokes other concepts such as transcription factors.

      Please see our explanation in response to weaknesses from Reviewer #3.

      (2) The first sentence of the results deals with TRIP13 expression in only 3 tissues. The authors might look at more comprehensive RNA-seq data from mice and humans.

      We examined TRIP13 protein expression in 8 mouse tissues by WB and found that TRIP13 protein was abundant in testis but present at a very low level in ovary and liver (Figure 1A). We feel that readers can easily look up the relative transcript levels of Trip13 in more tissues from mice and humans from NCBI database under “Gene”.

      (3) The null allele is semi-lethal. Is body size affected? Were the mice abnormal in any other ways, given that TRIP13 has been implicated in other diseases and processes, and is expressed in other tissues (TRIP13 stands for Thyroid receptor interacting protein).

      The body weight of 2-3 month-old males was not significantly different between wild type (24.3±2.8 g, n=5) and Trip13 KO mice (22.8±1.7 g, n=5, p=0.3, Student’s t-Test). We have included the body weight information in the revised manuscript. We didn’t observe abnormal somatic defects in the viable Trip13-null mice, nor did the authors report any in the Trip13 hypomorph mutants in two previous studies (Li and Schimenti, 2007; Roig et al., 2010).

      (4) Line 276 : It would be nice to elaborate on the "spatial explanation."

      We meant that TRIP13 localizes to SC while HORMAD proteins are removed from SC upon chromosomal synapsis, thus providing a spatial explanation. However, we have now deleted “spatial”.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      However, there are several concerns to be explained more in this study. In addition, some results should be revised and updated.

      Thank you for your comments. The concerns were addressed by the description and experiment.

      Some results were revised and updated accordingly.

      Reviewer #2 (Public Review):

      The minor weakness of the study is inconsistent use of terminology throughout the manuscript, occasional logic-jump in their flow, and missing detailed description in methodologies used either in the text or Materials and Methods section, which can be easily rectified.

      Thank you for your review. We have revised the manuscript and corrected errors according to your comments.

      Reviewer #3 (Public Review):

      Importantly, besides the Miwi ubiquitination experiment which is performed in a heterologous and therefore may not be ideal for extracting conclusions, the possible involvement of ubiquitination was not shown for any other proteins that the authors found that interact with FBXO24. Could histones and transition proteins be targets of the proposed ubiquitin ligase activity of FBXO24, and in its absence, histone replacement is abrogated?

      Thank you for your comments. The histones and transition proteins were not found in the immunoprecipitates of FBXO24, suggesting they are not the direct targets of FBXO24, shown in Figure S3G.

      Miwi should be immunoprecipitated and Miwi ubiquitination should be detected (with WB or mass spec) in WT testis.

      We agree with this suggestion. In the revision, the expression and ubiquitination of MIWI were detected in WT testis by the immunoprecipitation and ubiquitination assay, as shown in Figure 8H.

      Therefore, the claim that FBXO24 is essential for piRNA biogenesis/production (lines 308, 314) is not appropriately supported.

      We appreciate the comment. We have revised the description and modified the claim on page 11.

      Reviewing Editor's note for revision

      (1) As noted by all three reviewers, as currently written the rationale to focus on MIWI is not entirely clear. A transitional narrative to focus on MIWI needs to be provided as well as an explanation for how the absence of FBXO24 as an E3 ubiquitin ligase is responsible for the observed mRNA and protein differential expression.

      We appreciate your comments. We have supplemented the transitional narrative by focusing on MIWI and explained mRNA and protein differential expression upon FBXO24 deletion, shown on Page 7 and Page 13, respectively.

      (2) As it can be indirect, mass spec detection of MIWI in testis co-IP and MIWI ubiquitination should be detected (with WB or mass spec) in WT testis.

      In the revision, the expression and ubiquitination of MIWI were detected in WT testis by the immunoprecipitation and ubiquitination assay, as shown in Figure 8H.

      (3) Please tone down the claim that FBXO24 is essential for piRNA biogenesis/production as it requires further evidence.

      We have revised the description and modified the claim on page 11.

      (4) Ontology analysis of the genes with abnormally spliced mRNAs to provide an explanation for developmental defects.

      In the revision, we have performed the ontology analysis and provided new data regarding the abnormally spliced genes, as shown in Figure S4D.

      Reviewer #1 (Recommendations For The Authors):

      Major comments

      (1) The authors performed mainly with the WT (or knock-in) and Fbxo24-knockout mouse model. Do the heterozygous males and their sperm have any physiological defects like FBXO24-deficient mice?

      This is a good question. We did the phenotype analysis and found that heterozygous males are all fertile, and their sperm do not have any physiological defects.

      (2) Fbxo24-KO sperm carries swollen mitochondria. How do the mitochondria affect sperm function?

      Thank you for raising this interesting question. Based on our data and published literature, the defective mitochondria were associated with energetic disturbances and reduced sperm motility, as shown on Page 12.

      (3) TEM images show that Fbxo24-KO spermatids carry swollen mitochondria and enlarged chromatoid bodies. How the swollen mitochondria and enlarged chromatid are defective for sperm motility and flagellar development, requires more explanation. In addition, it is unclear how the enlarged diameter of the chromatoid body is critical for normal sperm development.

      Thank you for your comments. The chromatoid bodies are considered to be engaged in mitochondrial sheath morphogenesis. Analysis of the chromatoid bodies' RNA content reveals enrichment of PIWI-interacting RNAs (piRNAs), further emphasizing the role of the chromatoid bodies in post-transcriptional regulation of spermatogenetic genes. We added this explanation on Page 12-13.

      (4) The authors only show band images to compare the protein amounts between WT and KO sperm and round spermatids. As the blots for loading controls are not clear, the authors should quantify the protein levels and perform a statistical comparison.

      We quantified the protein levels and performed a statistical comparison, as shown in Figure S3B.

      (5) The authors show the defective sperm head structure from Fbxo24-KO sperm in Figure 5. However, the Fbxo24-KO sperm heads seem quite normal in Figure 3. How many sperm show defective sperm head structure? In addition, the authors observed altered histone-to-protamine conversion in sperm, but it is unclear whether the altered nuclear protein conversion causes morphological defects in the sperm head.

      We appreciate the comments. In our study, we found over 80% of Fbxo24 KO sperm showed defective structure in the sperm head. Altered histone-to-protamine conversion caused the decondensed nucleus of Fbxo24 KO sperm. Notably, in many knockout mice studies, impaired chromatin condensation is frequently associated with abnormal sperm head morphology, as shown in reference 15 of Page 8.

      (6) The authors compare the protein levels of RNF8, PHF7, TSSK6, which participate in nuclear protein replacement in sperm. However, considering the sperm is the endpoint for the nuclear protein conversion, it is unclear to compare the protein levels in mature sperm. The authors might want to compare the protein levels in developing germ cells.

      Thank you for your comment. Yes, we actually detected the protein levels of RNF8, PHF7, and TSSK6 in the testes, not in sperm. We have corrected it in the Figure 5E. We apologize for our carelessness.

      (7)This reviewer suggests describing more rationales for how the authors focus on the MIWI protein. Also, it is wondered whether MIWI is also detected from testis co-IP mass spectrometry.

      We agree with this suggestion. Since MIWI was a core component of CB and also identified as an FBOX24 interacting partner from our immunoprecipitation-mass spectrometry (IP-MS) (Table S1), we focused on the examination of MIWI expression between WT and Fbxo24 KO testes. We have added this description in the revision (see lines 191-193 on page 7).

      (8) The authors need to provide a more detailed explanation for how the altered piRNA production affects physiological defects in germ cell development. In addition, it will be good to describe more how the piRNAs affect a broad range of mRNA levels.

      Thank you for your comments. The previously published studies have demonstrated that piRNAs could act as siRNAs to degrade specific mRNAs during male germ cell development and maturation. We have cited these studies on lines 369-372 of Page 13.

      (9) The authors observed an altered splicing process in the absence of FBXO24. However, it is a little bit confusing how the altered splicing events affect developmental defects. Therefore, the authors should state which mRNAs have undergone abnormal splicing processes and provide ontology analysis for the genes.

      We have performed the ontology analysis and showed the new data in Figure S4D.

      Minor comments

      (1) Figure 1A-C - Statistical comparison is missed. Numbers for biological replication should be described in corresponding legends.

      Thank you for your careful review. We have provided the statistical comparison and the numbers for biological replication in the legends of Figure 1A-C.

      (2) Figure 1E, F - Current images can't clearly resolve the nuclear localization of the FBXO24 testicular germ cells. To clarify the intracellular localization, the authors should provide images with higher resolution.

      The resolution of Figure 1E, F was improved, as suggested. Thank you!

      (3) Figure 1E, F - Scale bar information is missing.

      The scale bars of Figure 1E, F were provided.

      (4) It will be much better to show the predicted frameshift and early termination of the protein translation in Fbxo24-knockout mice.

      The predicted frameshift of Fbxo24-knockout mice was added and shown in Figure S1B.

      (5) It is required to provide primer information for qPCR.

      The primer information for qPCR was provided, as shown in Table S7.

      (6) The authors describe that Fbxo24-KO sperm show abrupt bending of the tail. However, the description is unclear and the sperm shown in Figure 3C seems quite normal. The authors should clarify the abnormal bending pattern of the tail and show quantified results.

      Thank you for pointing out this issue. In Fbxo24 KO sperm, abnormal bending of the sperm tails mainly included neck bending and midpiece bending. We have shown them in Figure S3A.

      (7) The authors mention that Fbxo24-KO sperm have swollen mitochondria at the midpiece, but this is also unclear. How many mitochondria are swollen in Fbxo24-KO sperm?

      This is a good question. However, since it is very difficult to observe all of the mitochondria in each sperm using the electronic microscope, we could not quantify the swollen mitochondria in Fbxo24 KO sperm.

      (8) Scale bar information is missed - Fig 3C insets, Fig 3D, Fig 3F insets, 4A insets, Figure 4C insets.

      All the scale bars have been added.

      (9) How many sperm have annulus defects? In Figure 3F, WT sperm does not have an annulus, which could be damaged during sample preparation. Is the annulus defects in Fbxo24-KO sperm consistent?

      Thank you for asking these questions. Based on our results, about 30% of Fbxo24 KO sperm showed defective annulus structure. Since both TEM (Figure 3F) and SEM (Figure 3G) results clearly showed the defective annulus structure of Fbxo24 KO sperm, we believe the annulus defects are consistent and highly unlikely caused by sample preparation.

      (10) A Cross-section image for the endpiece of Fbxo24-KO sperm is not suitable. There is a longitudinal column structure of the principal piece.

      Thank you for your comments. It is difficult to observe a completely longitudinal structure of sperm tail under TEM. The cross-section of the endpiece and principal piece allowed us know the structure of the axoneme, ODFs and fibrous sheath (FS).

      (11) The endpiece of Fbxo24-KO sperm seems to have a normal axoneme. Do all endpieces of Fbxo24KO sperm have normal axoneme? Also, the authors need to describe whether an axonemal structure is damaged and disrupted in all Fbxo24-KO sperm.

      Our TEM data showed the axonemal structure was impaired in the endpiece of Fbxo24 KO sperm (See right panels of Figure 3H). Moreover, based on the ultrastructure analysis of TEM, we found over 90% of Fbxo24 sperm had a damaged axonemal structure.

      (12) Reference blots in Fig 3I, 3J, 4E (left), 5C and 5E are quite faint. The authors should replace the blot images.

      Thank you for pointing out this. We have rerun Western blot multiple times but could not obtain better images due to antibody sensitivity. However, we quantified the protein levels and performed a statistical comparison, as shown in Figure S3B, to establish a good readout from these images for the readers.

      (13) Loading controls are required - 7D-H.

      Done as suggested. Thanks!

      (14) How do the authors measure the midpiece length? From where to where? This should be clarified.

      Good question. We measured the midpiece length from the sperm neck to the sperm annulus by MitoTracker staining. We have clarified this on Page 16.

      (15) How are the bands for Fbxo24 shifted during IP in Fig 7A?

      The protein modification in the interaction may cause the band shift.

      (16) There are several typos throughout the manuscript. Please check carefully and fix them.

      Thank you for your careful review. We have corrected and fixed all the typos as far as we can.

      Reviewer #2 (Recommendations For The Authors):

      Major comments

      (1) Please provide a schematic of HA-Fbxo24 knock-in construct and strategy together with knockout (Figure S2) or even separately early in Figure S1. The description of using the transgenic mouse is mentioned even earlier than the knockout but there are no citations or methods provided in the text other than that listed in Materials and Methods.

      Thank you for your suggestion. As suggested, the schematic of the HA-Fbxo24 knock-in strategy has been supplemented in Figure S2A. The description of using the transgenic mouse has been added to the results, as shown on page 4 of lines 102-103.

      Also, it is not clear to what extent the phenotypic and molecular characterization of HA-transgenic mice is performed. For example, Lines 134-139: The use of Fbxo24-HA labeled transgenic mice results in the rescue of spermatogenesis and fertility as shown in Figure 2F by measuring the litter size. It is not clear how this observation leads the author to state that this rescues defects in spermiogenesis. Please clarify how and what other measures are taken to support this conclusion. Is the observed infertility due to defects in spermatogenesis or spermiogenesis?

      Thank you for your question. We crossed FBXO24-HATag males with FBXO24−/− females to obtain FBXO24−/−; FBXO24-HATag males. We examined the testes volume and histological morphology of FBXO24−/−; FBXO24-HATag males and found that they were similar to FBXO24+/−; FBXO24-HATag littermates, indicating that spermatogenesis was restored, as shown in Figure S2H.

      (2) Line 107 vs Line 114: Please use the terminology spermatogenesis and spermiogenesis consistently throughout the text. Earlier in the introduction, the authors clearly defined that spermatogenesis involves three phases, with the third phase referred to as spermiogenesis. However, the author concludes in the first line that "FBXO24 plays a role during spermatogenesis" while summarizing at the end of the paragraph that this protein is "expressed in haploid spermatids specifically during spermiogenesis". Therefore, it is not clear whether the authors conclude that FBXO24 is important for all of spermatogenesis (line 107) or only for part of spermiogenesis (line 114). Another example is line 219 vs. 238: At this point in the manuscript, it is again unclear whether the authors want to study molecular changes during spermatogenesis or spermiogenesis upon FBXO24 depletion. Many examples of such cases throughout the text, and it is recommended to be consistent in using more restrictive terminology whenever applicable for a clear interpretation.

      We thank you for your careful review. We have double-checked the terminology of spermatogenesis and spermiogenesis and made it consistent throughout the text of the revised manuscript.

      (3) It is not clear how rampant/frequent the Fbxo24-knockout sperm show defects in head morphology based on Figures 3C, 3F, and 5A since it seems that there are some sperm showing relatively normallooking sperm heads. Please provide quantification.

      We have performed the quantification and found that over 80% of Fbxo24 KO sperm showed defective structures in the sperm head.

      (4) Figure 3B: The authors describe in the figure legend that 3 mice were analyzed in each group. The standard deviation for the WT analysis is missing, or if the author wanted to set the WT value to 100%, the bar and scale shown on the y-axis do not fit. The value for WT looks more like 95%.

      We have indeed analyzed sperm motility based on the WT value set at 100% and have revised Figure 3B in the revision. We apologize for this oversight.

      (5) Figure 3 B and C: It is not clear how the motility is measured. Is CASA used (not described in Methods). The conclusion about abnormal flagellar bending in KO spermatozoa cannot be drawn from the static microscopic images alone. Please provide more details of motility analysis together with videos of live cell imaging.

      The sperm motility was measured manually using a hemocytometer, according to the reference.

      We provided the details of sperm motility analysis in the Materials and Methods section on Page 16.

      (6) Figure 3 I and J: These are one of a few figures that are not supported by statistical analysis. In particular, for 3I, GAPDH controls of WT and KO protein do not show equal loading, which could explain the lower expression of the KO protein. Please show normalized bar graphs with multiple biological replicates or at least show a representee technical replicat that shows equal loading of GAPDH to better support the conclusion.

      Thank you for your suggestion. Statistical comparison of relative protein expression was supplemented, as shown in new Figure S3B.

      (7) Line 184: It is not clear how the authors define a swollen mitochondrion? Are there any size criteria (roundness) that can be measured to distinguish between a swollen and a non-swollen mitochondrion? It is recommended to use another terminology as often 'swollen' implies there is a difference in osmolarity but there is no experiment to support this implication.

      Thank you for your comment. We have changed the “swollen” to “vacuolar” in the revision, as shown on Page 7.

      (8) Figure S4, without a bright field image, it is hard to see the purity and morphology of the isolated prep. Please provide the bright field images together or as overlaid images.

      We agree with your comment. We have provided the overlaid images in new Figure S4A.

      (9) There is a big logic jump in what prompts the authors to look MIWI protein level and link the observation to MIWI/piRNA pathway in both Introduction and Results while it is one of the main findings. It is recommended to provide a better rationale and logical flow in the text.

      Thank you for your suggestion. We have added a sentence explaining why we wanted to focus on studying MIWI expression (see lines 190-193 on page 7).

      Minor comments

      (1) Please keep all the conventions of gene vs. protein nomenclature. For example, write the genes mentioned in the figures in italics with the first letter in Capital, as it is done in the main part. Proteins should be in ALL CAPITAL like FBXO24.

      The names of gene and protein have been revised in the revision, as suggested.

      (2) In the MM section, the name of the manufacturer and the location of the materials used are missing in several sections. Please go back through the MM section and add this information in the appropriate places.

      Done as suggested. Thank you!

      (3) On page 4, the authors mentioned that "Further qPCR analysis of developmental testes and purified testicular cells showed that FBXO24 mRNA was highly expressed in the round spermatids and elongating spermatids (Fig 1B-C)". Please include statistical analyses for Fig 1B-C as well as for Fig 1A to support the written statements.

      Statistical comparison was supplemented, as shown in Figure 1. P-values are denoted in figures by *p < 0.05.

      (4) Figure 3E: Please describe in more detail how the length of the midpiece was measured. Was it based on TEM images or based on fluorescent images using MitoTracker?

      As we responded to Reviewer #1, we measured the midpiece length from the sperm neck to the sperm annulus by MitoTracker staining. We have clarified this in the Method and Material section on Page 16.

      (5) Line 431: In the "Electron Microscopy" section of the MM part, the author should indicate the ascending ethanol series (%) used.

      Done as suggested. Thank you!

      (6) Line 432: The thickness of the sections prepared is missing, as well as an indication of the microtome used.

      We have added thickness and the microtome in the Method and Material section on Page 16.

      (7) Line 433: If the generated tiff files have been processed with Adobe Photoshop, this information is missing.

      We have provided information on the usage of Adobe Photoshop for the generation of tiff files on Page 17.

      (8) Lines 445, 452, 467: In some places in the paper, the temperature is written with a space between the number and {degree sign}C, and sometimes it is not. Please go through the paper and make it consistent. The usual spelling is 4{degree sign}C.

      We have gone through the manuscript and checked all the spelling of temperature writing to make them consistent. Thank you for careful review.

      (9) Line 469: The gel documentation system used is not mentioned.

      Done as suggested. Thank you!

      (10) Line 469: The 'TM' should be superscripted.

      Done as suggested.

      (11) Line 489: A space is missing between the changes and the parenthesis.

      Done as suggested.

      (12) Line 495-496: The authors write that the fractions enriched with round spermatids after sedimentation were collected manually. Was a determination of cell concentration - e.g., 2 x106 cells/ml -performed after collection of the cells? How were the cells stored until use? Please add the sedimentation time and used temperature.

      Store the cell in the 1´ Krebs buffer on ice. The cell sediment was through a BSA density gradient for 1.5 h at 4°C. The cell concentration was determined after collection, as shown on Page 18.

      (13) Line 505: spelling error. Instead of " manufacturer's procedure" it is written manufactures' instructions.

      The spelling error was corrected.

      (14) Line 520: Please write a short sentence on how the purification of the 16-40 nt long RNA was performed.

      The length of 16–40 nt RNA was enriched by polyacrylamide gel electrophoresis. We added this information on Page 19 of line 531.

      (15) Line 528: The version of the used GraphPad software is missing.

      The version of GraphPad software was supplemented, as shown on Page 19.

      (16) Line 677: For qPCR analyses, the number of mice analyzed (N) and a statistical evaluation are missing.

      The statistical comparison and the numbers for biological replication were added, as shown on Page 26.

      (17) Figure 3D: Please add a scale bar.

      Done as suggested. Thanks!

      (18) Line 371 and Line 377: Two times "in summary" is written. Please make one summary for the whole paper.

      This sentence was revised, as shown in Page 13.

      (19) Line 382: To be consistent in the whole paper, please write Figure 10 in bold letters.

      Done as suggested.

      (20) Please make the size and font of the references consistent with the main text.

      Done as suggested. Thanks again for your careful review.

      Reviewer #3 (Recommendations For The Authors):

      I would like to see the description of the FBXO24 immunoprecipitation experiment performed in HEK293T cells. This somatic cell line does not normally express Miwi, so how Miwi was detected in FBXO24 mCherry IP beads? It is not mentioned if Miwi is expressed from a recombinant vector in this experiment. Similarly, I would like to see a better description of the experiment described in the same paragraph towards the end of it with the ubiquitin peptides, it is not clear.

      Thank you for your comments. FBXO24-mCherry was expressed in HEK293T cells and the immunoprecipitates was incubated with the protein lysate of the testes (see lines 268-272 on Page 10). The description of the ubiquitin experiment was added as well, as shown in lines 283-286 on Page 10.

      Line 263: I think the term ectopic here is not appropriate, a correction is needed.

      We have changed “ectopic” to “increased” in the revision (see line 268 on Page 10).

      I would like the authors to provide a tentative explanation or evidence of why FBXO24 KO males are completely sterile, even though there are still mature sperm produced with some motility. Since there are defects in nuclear condensation it will be very relevant to check DNA damage/fragmentation, which could contribute to the sterility phenotype.

      This is a good suggestion. We reanalyzed the sperm DNA damage by TUNEL staining and shown the new data in Figure S3E-F.

      Line 213: There have been some conflicting reports about the role of RNF8 in spermiogenesis, but a recent report has shown that RNF8 is not involved in histone PTMs that mediate histone to protamine transition (Abe et al Biol Reprod 2021 https://doi.org/10.1093%2Fbiolre%2Fioab132).

      Thank you for your comment. We have cited this critical reference and discussed it in Discussion section on Page 12.

      Figure 7: I would like to see zoomed-out views of the affected exons, so that flanking unaffected exons can be used as a reference for unaffected splicing. Most of the genome browser views in this image only show affected exons and it is impossible to see if these alone are affected or if the reduced RNAseq coverage in those exons is a result of overall reduced mapped reads in these genes. Also, a fixed Y axis with the same max value should be shown for these genome browser snapshots so that the expression level is comparable between the two genotypes.

      Thank you for your comments. Loading control of RT-PCR and scale range of Y axis were added in new Figure 7.

      Minor corrections:

      Line 70: correct "..functions as protein-protein interaction..".

      Thank you for your careful review. We have corrected this sentence (see line 69 on Page 3).

      Line 101: correct "..qPCR analysis of developmental testis..".

      We have corrected this sentence (see line 100 on Page 4). Thanks again.

      Line 116: correct "..results in detective..".

      Corrected.

      Line 186: correct ".. explored..".

      Corrected.

      Line 218: correct ".. gene expressions.

      Corrected.

      Line 221: correct "..genes significantly differentiated expressed".

      Corrected.

      Line 241: FBXO24 was shown earlier in both cytoplasm and nucleus.

      We have changed “FBXO24 is mainly confined to the nucleus” to “FBXO24 expressed in the nucleus”, as shown in line 247 on Page 9.

      Line 501-502: correct "..reverse transcriptional".

      “reverse transcriptional” was changed into “reverse transcription”, showing in Page 18.

      Line 686: correct ".. deficiency male..".

      Corrected.

      Line 769: correct "..Western blots were adopted..".

      Corrected.

      Line 784: correct "..WT tesis..".

      Corrected.

      I cannot understand exactly what is shown in Figure 9B. Some elements marked on the X-axis are single base locations (-2K, TSS, +2K) and others are stretches of sequences so they cannot be equivalent. Why there is only an intron shown? There should be a measure of normalized expression on the Y-axis.

      Thank you for your questions. The X-axis means that genome segments were scaled to the same size and were calculated the signal abundance, which was analyzed by computeMatrix. Aim to know the piRNA source, piRNA was mapped to the gene body, including introns, CDS and UTRs. The value of the Y-axis is the normalized count.

      Figure 6F is not needed.

      Figure 6F was used to illustrate the number of different types of mRNA splicing upon FBXO24 deletion in the round spermatids. To better understand the splicing for the reader, we decided to keep it.

      The last two paragraphs of the discussion seem to be redundant.

      Thank you for pointing out this. We have revised the last two paragraphs of the discussion.

    1. Author Response

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Maestri et al. use an integrative framework to study the evolutionary history of coronaviruses. They find that coronaviruses arose recently rather than having undergone ancient codivergences with their mammalian hosts. Furthermore, recent host switching has occurred extensively, but typically between closely related species. Humans have acted as an intermediate host, especially between bats and other mammal species.

      Strengths:

      The study draws on a range of data sources to reconstruct the history of virus-host codivergence and host switching. The analyses include various tests of robustness and evaluations through simulation.

      Weaknesses:

      The analyses are limited to a single genetic marker (RdRp) from coronaviruses, but using other sections of the genome might lead to different conclusions. The genetic marker also lacks resolution for recent divergences, which precludes the detailed examination of recent host switches. Careful and detailed reconstruction of the timescale would be helpful for clarifying the evolutionary history of coronaviruses alongside their hosts.

      The use of a single short genetic marker (the RdRp palmprint region) from coronaviruses is indeed a limitation. However, this marker is the one that is currently used for routinely delimiting operational taxonomic units in RNA viruses and reconstructing their evolutionary history (Edgar et al. 2022, see also the Serratus project; https://serratus.io/); therefore, we took the conscious decision early on to rely on this expertise. Unfortunately, this marker cannot provide robust timescale reconstructions for coronavirus evolution (previous estimates of coronavirus origin range from around 10 thousand years ago to 293 million years ago depending on modeling assumptions). Only future genomic work across Coronaviridae that will characterize multiple genetic regions with different evolutionary rates will allow us to precisely elucidate the timescale of the evolutionary history of coronaviruses alongside their hosts. In the meantime, we show here that, while the RdRp palmprint region cannot by itself resolve the precise timescale of coronavirus evolution, it strongly suggests, when used along with cophylogenetic approaches, a recent evolutionary origin in bats.

      R. C. Edgar, et al., Petabase-scale sequence alignment catalyses viral discovery. Nature 602, 142–147 (2022).

      Reviewer #2 (Public Review):

      Summary:

      In their study titled "Recent evolutionary origin and localized diversity hotspots of mammalian coronaviruses," authors Benoît Perez-Lamarque, Renan Maestri, Anna Zhukova, and Hélène Morlon investigate the complex evolutionary history of coronaviruses, particularly those affecting mammals, including humans. The study focuses on unraveling the evolutionary trajectory of these viruses, which have shown a high propensity for causing pandemics, as evidenced by the SARS-CoV2 outbreak.

      The research addresses a significant gap in our understanding of the evolutionary dynamics of coronaviruses, particularly their history, patterns of host-to-host transmission, and geographical spread. These aspects are important for predicting and managing future pandemic scenarios.

      Historically, studies have employed cophylogenetic tests to explore virus-host relationships within the Coronaviridae family, often suggesting a long history of virus-host codiversification spanning millions of years. However, the team led by Perez-Lamarque proposes a novel phylogenetic framework that contrasts this traditional view. Their approach, which involves adapting gene tree-species tree reconciliation, is designed to robustly test the validity of two competing scenarios: an ancient origination and codiversification versus a more recent emergence and diversification through host switching.

      Upon applying this innovative framework to the study of coronaviruses and their mammalian hosts, the authors' findings challenge the prevailing notion of a deep evolutionary history. Instead, their results strongly support a scenario where coronaviruses have a more recent origin, likely in bat populations, followed by diversification predominantly through host-switching events. This diversification, interestingly, seems to occur preferentially within mammalian orders.

      A critical aspect of their findings is the identification of hotspots of coronavirus diversity, particularly in East Asia and Europe. These regions align with the proposed scenario of a relatively recent origin and subsequent localized host-switching events. The study also highlights the rarity of spillovers from bats to other species, yet underscores the relatively higher likelihood of such spillovers occurring towards humans, suggesting a significant role for humans as an intermediate host in the evolutionary journey of these viruses.

      The research also points out the high rates of host-switching within mammalian orders, including between humans, domesticated animals, and non-flying wild mammals.

      In conclusion, the study by Perez-Lamarque and colleagues presents an important quantitative advance in our understanding of the evolutionary history of mammalian coronaviruses. It suggests that the long-held belief in extensive virus-host codiversification may have been substantially overestimated, paving the way for a reevaluation of how we understand, predict, and potentially control the spread of these viruses.

      Strengths:

      The study is conceptually robust, and its conclusions are convincing.

      Weaknesses:

      Despite the availability of a dated host tree the authors were only able to use the "undated" model in ALE, with the dated method (which only allows time-consistent transfers) failing on their dataset (possibly due to dataset size?). Further exploration of the question would be potentially valuable.

      Our intuition is that ALE in its “dated” version did not necessarily fail on our dataset due to its size (ALE ran, but provided unrealistic parameter estimates and was not able to output possible reconciliations, as mentioned in our Material and Methods section). We think it most likely did not run because there is no pattern of codiversification: the coronavirus and mammal trees are so distinct that finding a reconciliation scenario between these trees with time-consistent transfers is very difficult and ALE fails at estimating an amalgamated likelihood for such an unlikely scenario. Following a suggestion from reviewer #3, we are going to try running the dated version of ALE independently on the alpha and beta-coronaviruses, resulting in smaller datasets. This will help us elucidate whether the dated version of ALE fails due to data size or the absence of a codiversification pattern.

      Reviewer #3 (Public Review):

      Summary:

      This work uses tools and concepts from co-phylogenetic analyses to reconstruct the evolutionary and diversification history of coronaviruses in mammals. It concludes that cross-species transmissions from bats to humans are a relatively common event (compared to bats to other species). Across all mammals, the diversification history of coronaviruses suggests that there is potential for further evolutionary diversification.

      Strengths:

      The article uses an interesting approach based on jointly looking at the extant network of coronaviruses-mammals interactions, and the phylogenetic history of both these organisms. The authors do an impressive job of explaining the challenges of reconstructing evolutionary dynamics for RNA viruses, and this helps readers appraise the relevance of their approach.

      Weaknesses:

      I remain unconvinced by the argument that sampling does not introduce substantial biases in the analyses. As the authors highlight, incomplete knowledge of the extant interactions would lead to a biased reconstruction of the diversification history. In a recent paper (Poisot et al. 2023, Patterns), we look at sampling biases in the virome of mammals and suggest that is a fairly prominent issue, that is furthermore structured by taxonomy, space, and phylogenetic position. Case in point, even for betacoronaviruses, there have been many newly confirmed hosts in recent years. For organisms that have received less intense scrutiny, I think a thorough discussion of potential gaps in data would be required (see for example Cohen et al. 2022, Nat. Comms).

      I was also surprised to see little discussion of the differences between alpha and beta coronaviruses - there is evidence that they may differ in their cross-species transmission (see Caraballo et al. 2022 Micr. Spectr.), which could call into question the relevance of treating all coronaviruses as a single, homogeneous group.

      Some of the discussions in this paper also echo previous work by e.g. Geoghegan et al. (see 2017, PLOS Pathogens), which I was surprised to not see discussed, as it is a much earlier investigation of the relative frequencies of co-divergence and host switches for different viral families, with a deep discussion of how this may structure future evolutionary dynamics.

      We totally agree that sampling biases in the virome of mammals is a prominent issue, which is why we conducted a series of sensitivity analyses to test their effect on our main conclusions. We thoroughly tested the effect of (i) the unequal sampling effort across mammalian species that have been screened and (ii) the unequal screening of mammalian species across the mammalian tree of life by subsampling the data to correct for the unequal sampling effort (see Supporting Information Text). In both cases, we still reported low support for a scenario of codiversification, the origin in bats in East Asia, the preferential host switches within mammalian orders, and the rare spillovers from bats to humans. The robustness of our findings to sampling biases may be explained by the fact that the cophylogenetic approach we used (ALE) explicitly accounts for undersampling by assuming that all host transfers involve unsampled intermediate hosts. To address the reviewer's comment, we will better underline the importance of sampling biases in our main text and include the suggested references. We will also better highlight our sensitivity analyses by moving them from the Supporting Information Text to the main text.

      We agree that distinguishing between alpha and beta coronaviruses will provide useful additional insights; we are going to run separate cophylogenetic analyses for these two sub-clades. We will report the results of these additional analyses in the revised manuscript, and put them in context with the existing literature about the two sub-clades.

      We were not aware of the work of Geoghegan et al. (see 2017, PLOS Pathogens), thank you for providing this reference that we will now discuss.

    2. Reviewer #3 (Public Review):

      Summary:<br /> This work uses tools and concepts from co-phylogenetic analyses to reconstruct the evolutionary and diversification history of coronaviruses in mammals. It concludes that cross-species transmissions from bats to humans are a relatively common event (compared to bats to other species). Across all mammals, the diversification history of coronaviruses suggests that there is potential for further evolutionary diversification.

      Strengths:<br /> The article uses an interesting approach based on jointly looking at the extant network of coronaviruses-mammals interactions, and the phylogenetic history of both these organisms. The authors do an impressive job of explaining the challenges of reconstructing evolutionary dynamics for RNA viruses, and this helps readers appraise the relevance of their approach.

      Weaknesses:<br /> I remain unconvinced by the argument that sampling does not introduce substantial biases in the analyses. As the authors highlight, incomplete knowledge of the extant interactions would lead to a biased reconstruction of the diversification history. In a recent paper (Poisot et al. 2023, Patterns), we look at sampling biases in the virome of mammals and suggest that is a fairly prominent issue, that is furthermore structured by taxonomy, space, and phylogenetic position. Case in point, even for betacoronaviruses, there have been many newly confirmed hosts in recent years. For organisms that have received less intense scrutiny, I think a thorough discussion of potential gaps in data would be required (see for example Cohen et al. 2022, Nat. Comms).

      I was also surprised to see little discussion of the differences between alpha and beta coronaviruses - there is evidence that they may differ in their cross-species transmission (see Caraballo et al. 2022 Micr. Spectr.), which could call into question the relevance of treating all coronaviruses as a single, homogeneous group.

      Some of the discussions in this paper also echo previous work by e.g. Geoghegan et al. (see 2017, PLOS Pathogens), which I was surprised to not see discussed, as it is a much earlier investigation of the relative frequencies of co-divergence and host switches for different viral families, with a deep discussion of how this may structure future evolutionary dynamics.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      REPLY TO REVIEWERS

      Reviewer #1

      __Evidence, reproducibility and clarity: __Interesting results from exposing human brain organoids to FGF8 include suggestions that FGF8 contributes to the anterior to posterior patterning of the neocortex, as previously reported in mouse. Good, varied methods with reproducibility described well in the methods section. It would improve the reader's experience however to cite numbers of organoids used in specific experiments/assays in the main text.

      Response: We thank the Reviewer for the positive assessment of our study, and we agree that citing the number of organoids per experimental approach would better allow the readers to appreciate the intrinsic variability of organoid protocols. We will include the number of organoids per experiment both in figure legends and in Materials and Methods as a summary table.

      ....Organoids do not develop individual neocortical areas. To approach this issue of area identity, however, the authors compared control and FGF8-treated organoids against an existing dataset of transcriptomes of human fetal brains that separated pre-frontal, motor, somatosensory, and visual areas. This seems a good idea, but results showed both treated and untreated organoids alike expressed genes characteristic of somatosensory and pre-frontal cortical regions (anterior and midlevel areas) apparently suggesting that exogenous FGF8 had little effect. Because the previous dataset was not the authors' work, however, and because a comparison between organoids and actual human tissue is hard to interpret, this whole section is probably only confusing to include.

      Response: We would like to clarify to the reviewer that the effect of FGF8 on antero-posterior area identity is only partial in our organoid system, suggesting that different doses or temporal windows of FGF8 treatment may be necessary to achieve a stronger modulation of area identity genes. We agree with the Reviewer that, due to this partial effect, the transcriptomic comparison with fetal brain areas might be confusing for readers. Therefore, we plan to move this type of data to the Supplementary Material. We thank the Reviewer for bringing this to our attention.

      The authors further stress a dorsal/ventral effect in FGF8-treated organoids. The population of ventral telencephalic interneurons, produced in the lateral ganglionic eminence in mice, expand in the human organoids at the expense of glutamatergic neurons of the dorsal telencephalon. This may be consistent with the loss of ventral telencephalic structures in FGF8-deficient mice. The authors suggest that FGF8 expansion of interneurons is a novel finding not previously seen in animal research and may point to a human-specific characteristic. Readers may believe this part of the paper requires more support, just because multiple studies of FGF8 have not revealed this action. Overall, this paper would benefit from shortening, and by statements that some of the results suggest, but do not guarantee, particular conclusions.

      Response: We agree with the reviewer that before stating that FGF8-induced expansion of interneurons in dorsal telencephalic territories is a human-specific characteristic, more support in mouse studies would need to be performed. However, as suggested by reviewer 2 below, there is some evidence that ventral interneuron markers, such as ASCL1 and DLX2, are expressed in the dorsal telencephalon of the early fetal human cerebral cortex, even if at much lower levels than in the ventral telencephalon, and that individual human cortical progenitors can generate both excitatory neurons and inhibitory interneurons in culture. Thus, FGF8 might promote an intrinsic capacity of dorsal cortical neurons to induce the generation of ventral interneurons, which would indeed be a human (or maybe primate)-specific trait. We plan to better discuss this issue in the revised version of the manuscript.

      Significance

      The paper is for a fairly specialized audience interested in the development of the cerebral cortex, but also has interest regarding developmental human brain defects

      Response: Although the manuscript sounds upon first reading specific to a specialized audience interested in cortical development, we believe that the strength of our human organoid system is the formation of regionalized organoids including brain regions other than the cortex. Moreover, considering the increasing attention on brain organoids in general, and the lack of information on the action of FGF8 during human cortical development, we are confident that this study will attract a broader audience.

      Interesting results from exposing human brain organoids to FGF8 include suggestions that FGF8 contributes to the anterior to posterior patterning of the neocortex, as previously reported in mouse. Good, varied methods with reproducibility described well in the methods section. It would improve the reader's experience however to cite numbers of organoids used in specific experiments/assays in the main text.

      Response: We thank again the reviewer for acknowledging the potential of our study. As previously mentioned, we agree that providing information about the number of organoids used will enhance the statistical analysis. This will definitely be added in a revised version.

      Reviewer #2

      Evidence, reproducibility and clarity

      ……However, organoid technology offers a solution to this and the present study presents an elegant approach to addressing how FGF8 signalling directs both anterior/posterior and dorsal/ventral identity in neural progenitors and their offspring in human development. This has both biological and clinical relevance has the study demonstrates how FGF8 may be a key regulator of expression of susceptibility genes for neurodevelopmental conditions. The methods and approach are described clearly and in great detail and it serves as an exemplar for how studies like this might be pursued in the future. Likewise, the results are presented logically, using excellent figures with clear descriptions of the findings. It is positively entertaining to read and very thought provoking. We don't have any major issues with the conclusions.

      Response: We sincerely appreciate the reviewer’s enthusiastic and thoughtful feedback. The positive remarks on the clarity and detail of our methods and results are very encouraging, and we are pleased that the reviewer found our study both entertaining and thought-provoking.

      We have some minor issues over presentation and interpretation that we would like the authors to consider.

      1) Developmental staging. It is stated that the organoids have reached a developmental stage equivalent to 16.5 GW based on expression of key genes such as CRYAB. Firstly, we would prefer an unambiguous way of stating age such as post-conceptional age. It is never clear what gestational weeks exactly means (post-menstrual, post-ovulatory?). Secondly, in several figures, UMAPs generated from the organoids are presented alongside representative mouse brain sections from E13.5 which is equivalent to about 11 post conceptional weeks in human. Although we find the mouse sections helpful, perhaps the potential discrepancy in developmental stage should be pointed out.

      Response: We agree with the reviewer that the staging of human organoids in vitro can be very tricky. We will clarify this issue by using post-conceptional weeks (PCW) instead of gestational weeks in the revised version of the manuscript. It is true, that schematic representations of brain sections of mouse telencephalon of around E13.5 were used in the paper, but the idea was to choose an age where dorsal and ventral territories are clearly separated during embryogenesis to highlight the expression of the different genes. We will change the schematics to make sure they can be better compared with scRNA-seq data and will highlight that they represent early mid-gestation stages of mouse embryos.

      2) Dorso-ventral patterning. Firstly, we wondered why VGLUT2 was used as a marker for dorsal identity when it is generally regarded as being expressed by subcortical neurons, e.g. thalamus and midbrain, whereas VGLUT1 is the standard marker for cortical neurons :https://doi.org/10.1016/j.tins.2003.11.005? Potentially, VGLUT2 expression may be more an indicator of mid/hindbrain identity than cortical identity. Is there any evidence for VGLUT2 expression by cortical cells in development? Also, MASH1 (more correctly called ASCL1) is not exclusively ventral, having shown to be expressed in a subset of intermediate progenitor cells for glutamatergic neurons in rodent doi:10.1093/cercor/bhj168 and particularly human doi: 10.1111/joa.12971. We are surprised that the recent evidence that human cortical progenitors do have capacity to generate GABAergic neurons 10.1038/s41586-021-04230-7; 10.1101/2023.11.06.565899 is not mentioned in this section as perhaps FGF8 doesn't so much ventralise progenitor cells as promote an inherent property. This might explain why MGE-like identity is not observed, whereas LGE/CGE like is, as it has already been shown that MGE-like gene expression by dorsal progenitors is very much less likely than LGE/CGE like expression 10.1038/s41586-021-04230-7; DOI 10.1007/s00429-016-1343-5

      Response: We fully agree and thank the reviewer for bringing to our attention this interesting discussion and pointing to our confusion between VGLUT1 and VGLUT2 expression profiles. After checking our scRNA-seq data, we realized that the Reviewer is absolutely correct about the issue of using VGLUT2 as a dorsal telencephalic marker, as it is expressed in both dorsal and ventral cells. In contrast, VGLUT1 appears to be more specific for neocortical (dorsal) neurons (see UMAP images below). Moreover, it perfectly fits with our results showing a downregulation of VGLUT1 in dorsal glutamatergic neurons.

      We are currently conducting additional staining experiments to support this point. Specifically, our plan includes:

      • Performing immunostaining assays to validate the expression patterns of VGLUT2 in dorsal cortical neurons, notably triple VGLUT2/TRB1/CTIP2 and double VGLUT2/SATB2 stainings, to be added in Supplementary material. This will allow to confirm the use of VGLUT2 as a dorsal marker.
      • Performing additional immunostainings involving VGLUT1, either juxtaposed with GAD67 to assess dorso-ventral neuronal balance or in conjunction with dorsal cortical markers to examine co-expression. This new analysis will be quantified using AI and integrated into Figure 4. Notably, these experiments will provide a comprehensive understanding of the expression patterns of VGLUT1 and VGLUT2 in the dorsal or ventral telencephalon and will further elucidate their utility as markers for specific neuronal populations in human brain organoids.

      Furthermore, and importantly, we fully agree with the reviewer that human dorsal cortical progenitors do have the ability to generate GABAergic neurons, even if at lower efficiency than glutamatergic neurons, and that FGF8 might promote this inherent property in human organoids. This new discussion and the new references suggested by the reviewer will significantly contribute to our data interpretation about LGE/MGE development. Therefore, we intend to incorporate them into the revised version of the text. Again, thank you to the reviewer for these insightful suggestions.

      3) MEA recordings. The presentation of electrophysiological data is quite simple. Detection of spikes is claimed therefore representative traces of the spikes should be included and these can be easily generated with the Maxwell system software. It isn't clear how many times the experiments were repeated and there is no statistical analysis. For example, in the text they state on page 15 'Notably, WNTi+FGF8 organoids showed lower spike frequency (firing rate) and amplitude'. The amplitude difference is 43uV vs 41uV; we doubt this is significantly different. Threshold for detecting burst firing appears to be different between Figure 5C and 5d. Why? Shouldn't it be the same? The axonal tracking analysis in fig 5E/F needs more explanation. How many axons were tracked? Is there any statistical analysis beyond means and standard deviation?

      Response: We agree with the Reviewer that the presentation of our electrophysiological data need further improvement. We are currently repeating key recordings on four additional samples coming from two different batches, which will allow us to conduct a better statistical analysis.

      In detail, we plan to:

      • Extract representative traces of spikes from the Maxwell software, which will be included as Supplementary material. Footprints of action potentials will be extracted using the in-built analysis tool available in the software.
      • Perform axon tracking analysis on three control and three FGF8-treated samples coming from two distinct batches of organoids. Recordings and analyses will be conducted over a period of two weeks to monitor the growth of axonal tracts, enabling us to perform statistical analysis and observe the temporal evolution of axonal growth. Furthermore, placing the threshold for detecting bursts in the network analysis at different levels in control or treated samples seems to be a routine procedure in this MEA system. Indeed, while the user can set a fixed multiplying factor (that is, of course, the same for both control and treated samples), it is the software that multiplies such factor by the basal average activity of the sample. In this way, bursts can be detected as synchronized activity emerging from the basal one, which, of course, varies in every sample. We plan to better explain this point in the Materials and Methods section, and we thank the reviewer for raising this lack of clarity.

      4) Anterior/posterior patterning. Returning to the subject of cortical GABAergic neurons, it has been proposed that the prefrontal cortex contains a relatively higher proportion of GABAergic neurons, although the mechanism for this has not been elucidated (see https://doi.org/10.1111/joa.13055 and references therein). Might higher anterior FGF8 specifying cortical progenitors to produce GABA neurons have a role in this?

      Response: We thank the reviewer for citing this very interesting review. It is highly possible that FGF8 normally expressed anteriorly might have a role in inducing distinct GABAergic subtypes, such as Calretinin+ interneurons, which have been found to be more abundant in frontal cortices of the developing human fetal brain. Our organoids are too early in terms of developmental age to verify whether interneuron subtypes such as CalR+ are more or less represented, but we will definitely add this very interesting point to our discussion in the revised version.

      5) Nomenclature. As this study principally presents data on mRNA expression levels it might be preferable to use italicised capitals for all gene names (except where referring to mouse genes). Also, common names are used in places and standard gene names in others, e.g. COUPTF1 is referred to NR2F1 but VGLUT1 is not referred to SLC17A7 (also see above re MASH1). It would be good to see everything standardised.

      Response: We appreciate the Reviewer for highlighting these discrepancies. We will standardize gene names both in the text and figures accordingly.

      Significance

      This study involves a very imaginative use of organoids combined with a variety of approaches to test if fundamental principles of forebrain development, particularly cell specification and regional patterning, that we have learnt from mouse models are relevant to human brain development. It also has clinical relevance as it explores potential disruptions to development that leader to diseases of higher cognition, such as autism of schizophrenia. It is a very accessible manuscript that should have broad appeal. It makes several incremental additions to the field and points the way to future experiments in this area.

      Response: We sincerely thank the Reviewer's insightful comments and positive assessment of our study.

      __Reviewer #3 __

      __Evidence, reproducibility and clarity: __

      In the manuscript "FGF8-mediated gene regulation affects regional identity in human cerebral organoids" the authors used FGF8 to change cellular fate in human brain organoids. The experiments are well-performed and the authors used well-established protocols to generate brain organoids. The results clearly show that FGF8 addition induces an increase of diencephalon/midbrain markers (OTX2, EN2), suggesting that long-term FGF8 treatment can induce also posterior regional identities. These data are reinforced also by scRNAseq highlighting a possible mix of cellular identity.

      Response: We thank the reviewer for this encouraging report about our study highlighting the significance of our findings.

      Main concern:

      1. The authors should start using FGF8 at later stages than day 19-21, in trying to maintain the forebrain identity.

      Response: As the Reviewer correctly pointed out, the temporal window of FGF8 treatment seems of pivotal importance for the final outcome of regional identity acquisition. Indeed, while early treatment with FGF8 at day 5 disrupts FOXG1 expression in organoids, as demonstrated in Supplementary Figure 1, our first attempts at adding FGF8 at day 15 resulted in poor regulation of the major FGF8-target gene NR2F1. However, we noticed that high expression of FOXG1 was still maintained, supporting forebrain identity. We fully agree with the reviewer that it is worth treating organoids with FGF8 at later stages to test whether forebrain identity becomes enriched while midbrain one is reduced, which would highlight an FGF8-dependent dosage of forebrain identity acquisition. To this purpose, we have already started additional experiments to assess the effect of delayed FGF8 treatment on forebrain markers and FGF-target genes, such as ETV1, SPRY4, DUSP6, ETV4 and ETV5, but also on representative midbrain markers. Importantly, we will treat the same batch of organoids with the same amount of FGF8 but at different times to be able to compare the different treatments in parallel. We plan to incorporate these supplementary analyses into the Supplementary material to provide a more comprehensive characterization of the efficiency time windows of FGF8.

      In detail, we plan to structure these additional experiments as follows:

      • We will culture in parallel neural progenitors (cortical induction protocol, with XAV-939 as a WNT inhibitor) that will be treated with 100 ng/ML FGF8 starting at day5 (early treatment), at day10 (normal treatment) or at day 20 (late treatment).
      • Each condition will require at least n=6 organoids.
      • Samples will be cultured until day 30.
      • At day 30, we will fix n=3 organoids per condition to be processed by immunostaining, and harvest n=3 organoids per condition for RNA extraction and Real Time RT-PCR analysis.
      • By immunostaining, we will measure the number of FOXG1+ cells as a read-out of telencephalic identity and the intensity of NR2F1 staining to evaluate FGF8 action.
      • By RT-PCR, we will measure the expression level of the following regional identity markers and FGF8 target genes: FOXG1, EN2, OTX2, NR2F1, ETV1, SPRY4, DUSP6, ETV4 and ETV5. This experimental setup will allow us to further detail the efficiency of distinct temporal windows for FGF8 treatment and their effects on cell identity and FGF target gene modulation. However, based on the first data we already obtained, we expect poor FGF target gene modulation upon late FGF8 treatment. This is why we believe that the temporal window we selected for our study already represents an optimal compromise between maintaining high levels of FOXG1 while effectively modulating FGF8 targets in human organoids.

      To verify the identity of the neurons in the organoids the authors should check their ability to make projections in immunodeficient mice. Human iPSC-derived cortical neurons establish subcortical projections in the mouse brain after transplantation and the location of the different neuronal projections could reveal the rosto-caudal identity of the cortical neurons.

      Response: We agree with the reviewer that in general conducting in vivo transplants of human organoids offers an interesting approach to testing the identity of differentiated neurons by tracking their projections. However, we believe that due to the multi-regional character of FGF8-treated organoids (which includes also midbrain-like neurons), their transplant into the neocortex would be of difficult interpretation and would not reveal the precise rostrocaudal identity of transplanted human cortical neurons, as requested by the reviewer. Furthermore, this would almost constitute an entire project on its own, given the technical challenges associated with such experimental approaches. We think that our thorough scRNA sequencing analysis is powerful enough for assessing cell identity, as supported by the majority of organoid studies investigating cell identity through scRNA-seq without resorting to transplantation. In our study, the scRNA-seq analysis was subsequently validated by several steps of immunostainings, a simple but fundamental corroborative control approach that is sometimes overlooked in similar studies. Finally, we would like to emphasize that reviewers #1 and 2 found our complementary approaches (molecular, cellular, and functional) appropriate, well-performed, logical and reproducible.

      Significance:

      The proposed protocol is useful to generate brain organoids with mixed cell populations from different regions of the brain (forebrain, midbrain, hindbrain). However, has limited applications since is not clear whether the proposed structures have some kind of organization.

      Response: We agree with the Reviewer that each protocol comes with its own limitations and that a careful characterization of the proportion of different regional domains could definitively improve the significance and applicability of our protocol. To this aim, we are now using artificial intelligence-mediated detection of cortical versus midbrain-like domains in control and FGF8-treated organoids, to further improve the characterization of distinct cellular populations and quantify the extent of their domains in multi-regional organoids. These data will be added in Figure 3.

    1. Author Response

      The following is the authors’ response to the original reviews.

      We wish to thank the reviewers for their helpful insightful comments. Their concerns were mainly related to the interpretation of the data, help in clarifying our statements and improving our discussion.

      Reviewer #1 (Recommendations For The Authors):

      This is a very interesting study It involves the utilization of hippocampal neuronal cultures from syntaxin 1 knock-out mice. These cultures serve as a platform for monitoring changes in synaptic transmission through electrophysiological recording of postsynaptic currents, upon lentiviral infection with various isoforms, chimeras, and point mutations of syntaxins.

      The authors observe the following:

      (1) Syntaxin2 restores neuronal viability and can partially rescue Ca2+-evoked release in syntaxin1 knock-out neurons that it is much slower (cumulative charge transfer differences) and with a clearly smaller RRP than when rescued with syntaxin1. In contrast, syntaxin2-mediated rescue leads to a high increase in spontaneous release (Figure 1). Convincingly, the authors conclude that syntaxin 1 is optimized for fast phasic release and for clamping of spontaneous release, in comparison with syntaxin2.

      (2) The replacement of the SNARE domain (or its C-terminal part) of syntaxin1 by the SNARE domain of syntaxin2 (or its C-terminal part) rescues the fast kinetics, but not the amplitude, of Ca2+-evoked release. This is associated with a decrease in the size of the RRP and an increase in spontaneous release. The probability of vesicular release (PVR) is a little bit increased, which is intriguing because a little decrease would be expected instead according to the reduced RRP, indicating that an enhancement of Ca2-dependent fusion is occurring at the same time by unknown mechanisms as the authors properly point out. The replacement of the Analogous experiments in which the SNARE domain of syntaxin1 is replaced into syntaxin2, reveals the exitance of differential regulatory elements outside the SNARE domain.

      (3) Different constructs of syntaxin 1 and syntaxin 2 display different expression levels. On the other hand, the expression levels of Munc-18 are associated with the characteristics of the transfected specific syntaxin construct. In any case, the electrophysiological phenotypes cannot be consistently explained by changes in Munc-18.

      (4) Mutations in several residues of the outer surface of the C-terminal half of the syntaxin1 SNARE domain lead to alterations in the RRP and the frequency of spontaneous release, but the changes cannot attributed to a change in the net surface charge, because the alterations occur even in paired mutations in which electrical neutrality is conserved.

      Comments:

      (1) This is a comment regarding the interpretation of the results. In general, the decrease in the RRP size is associated with the increased frequency of spontaneous release due to unclamping. The authors claim that both phenomena seem to be independent of each other. In any case, how can the authors discard the possibility that the unclamping of spontaneous release leads to a decrease in the RRP size?

      The main argument against the reduction of the RRP being caused by the observed increase in the mEPSC frequency is based on kinetics of refilling and depletion. The average time a vesicle fuses spontaneously after it becomes primed is 500 – 1000 seconds (spontaneous vesicle release rate – STX1 Figure 1, Figure 2 and Figure 3). The time it takes to refill the RRP after depletion is in the order of 3 seconds (Rosenmund and Stevens, 1996). Therefore, the refilling of the RRP is more than 100 times faster. Even when the spontaneous release would increase 5 fold, this would lead to less than 5 % of the steady state depletion of the RRP.

      (2) The authors have analyzed the kinetics of mEPSCs and found differences (Fig2-Supp. Fig1; Fig2-Supp. Fig1). It would be interesting and pertinent to discuss these data in the context of potential phenotypes in the fusion pore kinetics involving syntaxin1 and syntaxin2 and their SNARE domains. Indeed, the figure will improve by including averaged traces of mEPSCs.

      We thank the reviewer for the idea. Upon closer examination of the changes in mEPSC rise time and mEPSC decay time we noticed a minor slowing in the mEPSC rise time from 0.443ms (SEM0.0067) of STX1A to 0.535ms (SEM0.0151) for STX1A-2(SNARE) or 0.507ms (SEM0.01251) for STX1A-2(Cter), while the mEPSC half widths did not change significantly. It is possible that the measured change is related to the detection algorithm as mEPSC detection at elevated frequencies becomes more difficult due to increased overlap of event, and we therefore prefer to refrain from making any mechanistic claims.

      Minor comments:

      (1) Fig2 J; Fig 3 J. It is difficult to distinguish between different colors and implementing a legend within the graph will be very helpful.

      (2) Fig3 H. Please change the color of the box plot for Stx1 A to improve the contrast with the individual data points.

      (3) Page 6. Line 225. "Figure 2D and E" should be corrected to "Figure 2C and D"

      (1) Colors were changed for clearer visualization. (2) Unfortunately, changing the color did not improve the contrast with the individual plots. However, the numerical data is all included in the data sheets of the corresponding figure. (3) The mistake was corrected.

      Reviewer #2 (Recommendations For The Authors):

      Line 135-136: Are cited numbers cited in the text mean and SEM? Please indicate.

      Line 139 and Figure 1G: The difference between purple and blue was very hard to see on my hard copy.

      Line 152: Reference to Figure 1L should probably be 1K.

      Line 183: Reference to Figure 2C should probably be Figure 2F.

      Line 225: Reference to Figure 2D and 2E should probably be 2C and 2D.

      Line 239: Reference to Figure 3I should probably be 3H.

      All typos were addressed and colors were changed for better visualization.

      Line 210-211: Sentence ("One of the benefits..") is hard to understand.

      Thank you for noticing this mistake, agreeably the the sentence did not add any important or new information and so it was deleted. Additionally, the message of the mentioned sentence was already clearly stated in lines 209-211.

      Figure 4E-H misses data for STX2, for the figure to be arranged like Figure 5.

      Given that STX1 is the endogenous syntaxin in hippocampal neurons, we use it at a control for all the analysis done in STX2 and STX2-chimera experimental groups, thus it is included in Figure 3 and 5.

      It appears that the authors do not present or discuss the Western Blot in Fig. 4D. Are the quantitative results of the Western Blot consistent with or different from the quantification of the immunostainings (Fig. 4B-C)? A similar question for Figure 5D, which also seems not to be presented.

      In terms of quantification, we have relied mainly on the ICC experiments because they test also for putative impairments in transport to the presynaptic compartment. Our WB data are overall consistent with the results, but were not used to quantitate expression of our syntaxin chimeras and mutations in the STX1-null hippocampal neuron model.

      Figure 6F-G: The normalization of spontaneous vesicular release rates is not clear, because the vesicular release rates already contain a normalization (mEPSC rate divided by RRP size). Is a further normalization of the STX1A condition informative? The authors should consider presenting the release rates themselves. In any case, the normalization should be presented/explained, at least in the legends.

      The reviewer is in principle correct. Due to the large number of experimental groups we had to perform recordings from multiple cultures, where not all experimental groups were present, while the WT STX1 was present as a consistent control. The reduce culture to culture variability, additional normalization to the WT control group was performed. However, we also included the raw data numerical values in the data-source sheets (Normalized and absolute), which produce a similar overall outcome.

      References to Figure 7 subpanels (A, B, and C) are missing.

      Thank you for the comment. We have integrated all panels into one for better representation and understanding since they are representative of one another.

      Lines 330-339 and Figure 7 in Discussion: the authors discuss that adding the non-cognate STX2 SNARE-domain to syntaxin-1 might destabilize the primed state and decrease the fusion energy barrier (as indicated in Figure 7C). What is the evidence that the decrease in RRP size is not caused solely by the depletion of the pool due to the increased spontaneous fusion?

      Please see the comments to major point 2 of reviewer 1.

      Statistics: Missing is the number of observations (n) for all data. Even if all data points are displayed, this should be stated.

      N numbers are included in the data sheets attached to each figure.

      The statement (start of Discussion,) that the SNARE-domain of STX1 'plays a minimal role in the regulation for Ca2+-evoked release' is somewhat puzzling, since without the SNARE-domain in STX1 there would be no Ca2+-evoked release. I guess these statements (similar statements are found elsewhere) are due to the interesting finding that STX2 leads to a decrease in release kinetics, compared to STX1, and this is not (entirely) due to differences in the SNARE-domain. I would suggest rephrasing the finding in terms of release kinetics. Also, the statement in the last sentence of the Abstract is not clear.

      Thank you for pointing this out and we agree that our experiments showed strong impact of the syntaxin isoform exchange on release kinetics and overall release output. A similar comment came also from reviewer #3 and so, we have addressed both comments as one.

      Our confusing statement resulted from the order of the presented results and our summarizing remarks for each section. Our statement reflected our finding that mutating residues in the C-terminal part of the STX1 SNARE motif affected only spontaneous release and RRP size but not release efficacy. We now state (pg. 6 lines 231-233) that the data observed from the comparison of “the results obtained from the Ca2+-evoked release between STX1 and STX2 support major regulatory differences of the domains outside of the SNARE domain between isoforms”.

      We have changed the abstract pg. 2 lines 55-56

      We have changed the introduction pg. 3 lines 102-105 for a better contextualization.

      We have changed the start of the discussion pg. 9 lines 250-252 for better contextualization.

      Reviewer #3 (Recommendations For The Authors):

      In this manuscript, Salazar-Lázaro et al. presented interesting data that C-terminal half of the Syx1 SNARE domain is responsible for clamping of spontaneous release, stabilizing RRP, and also Ca2+-evoked release. The authors routinely utilized the chimeric approach to replace the SNARE domain of Syx1 with its paralogue Syx2 and analyzed the neuronal activity through electrophysiology. The data are straightforward and fruitful. The conclusions are partly reasonable. One obvious drawback is that they did not explore the underlying mechanism. I think it is easy for the authors to carry out some simple assays to verify their hypothesis for the mechanism, instead of just talking about it in the discussion section. In all, I appreciate the data presented in the manuscript. If the authors could supply more data on the mechanisms, this would be important research in the field. Some critical comments are listed below:

      We thank the reviewer for his/her comments and suggestions.

      Major comments:

      (1) In pg.3, lines 102-104, the authors stated that 'We found that the C-terminal half of the SNARE domain of STX1.. ..while it is minimally involved in the regulation of Ca2+-evoked release.' But in pg.5, lines 174-176, they wrote that 'Replacement of the full-SNARE domain (STX1A-2(SNARE)) or the C-terminal half (STX1A-2(Cter)) of the SNARE domain of STX1A with the same domain from STX2 resulted in a reduction in the EPSC amplitude (Figure 2B).' and in pg.5-6, lines 197-199, they wrote that 'Taken together our results suggest that the C-terminal half of the SNARE domain of STX1A is involved in the regulation of the efficacy of Ca2+-evoked release, the formation of the RRP and in the clamping of spontaneous release.' It puzzles me a lot as to what the authors are really trying to express for the relationship between C-half of the SNARE complex and Ca2+-evoked release (i.e., minimally involved or significantly participate in the process?). Please clarify and reorganize the contexts.

      Please see our reply to the last comment of reviewer 2.

      (2) Figure 1-figure supplement 1, the authors should analyze Syx1/VGlut1 level additionally. And, if possible, compare the difference between Syx1/VGlut1 and Syx2/VGlut1.

      The levels of STX1/VGlut1 and STX2/VGlut1 were analyzed in detail in Figures 4 and 5.

      The direct comparison between the expression levels of these two proteins is not possible since affinities of the antibodies to the target proteins are different and can induce potential biases. While this could be overcome by the use of a FLAG-tag to the syntaxin proteins, we have not utilized this approach in this publication. We in addition inferred sufficient and comparable expression of both syntaxins from their ability to rescue some of syntaxin1 loss of function phenotypes.

      (3) Figure 2D only analyzed the EPSC half-width, could the author alternatively analyze the rise/decay time? Also, in Figure 3-figure supplement 1, does it refer to the kinetic parameters of Syx2-1A in Figure 3? It is very confused.

      We have changed the text accordingly and each parameter is referenced to its corresponding figure for clarity. As for the decay and rise time of STX1 and STX1-chimeras, they are in Figure 2-figure supplement 1A and B.

      (4) On pg.4, lines 151-152, 'Finally, no change was observed in the paired-pulse ratio (PPR) between STX1A and STX2 groups (Figure 1L).' does not contain any explanations and comments for this observation in the texts.

      The small EPSC amplitudes and altered kinetics on the STX2 constricts (Figure 1 and Figure 3) have made it more difficult to quantitate paired pulse experiments. Therefore, we preferred not to overinterpret these measurements. The findings that the paired pulse data were not significantly different, fit with the vesicular release probability measurements which showed no major changes. We have made our statement on this basis.

      (5) On pg.6, lines 235-236, the authors wrote that 'Additionally, we found that only STX2-1A(SNARE) and STX2-1A(Cter) could rescue the RRP to around double of what we measured from STX2 and STX2-1A(Nter) (figure 3F)'. However, in Figure 3F, the authors indicated 'n.s.' (p>0.05) for the differences between STX2 and STX2-1A(SNARE)/STX2-1A(Cter). It is perplexing how the authors interpret their data. Definitely, the p-value could not be arbitrarily used as a criterion of difference. An easier way is that indicating the exact p-values for each comparison (indicate in figure legends or list in tables).

      We apologize for any confusion, and hope the modification gives more clarity in our interpretation. The calculated p-values are included in attached data source tables and hope this will provide clarity to our comparative analysis. We have changed the text in pg 7 lines 238-241 and are cautious to overinterpret these results and rely more on the data observed in STX1A-chimeras, which show significant changes in the RRP.

      (6) I noticed that the authors preferred using 'xx% increase/decrease' or 'xx-fold increase/decrease' to interpret their inter-group data. I would doubt whether the interpretations are appropriate. First, it seems that most of the individual scatters from one set were not subject to Gaussian distribution; also, the authors utilized non-parameter tests to compare the differences. Second, the authors did not explicitly indicate the method to calculate the % or fold, e.g., by comparing mean value or median. I think it is a bad choice to use the median to calculate fold changes; meanwhile, the mean value would also be biased, given the fact that the data were not Gaussian-distributed. The authors should be cautious in interpreting their data.

      We thank the reviewer for pointing the inaccuracy of our descriptions and have included the parameter used to calculated the percentage and fold increase/decrease in the materials and methods section. Specifically, the mean. Our intention is to plainly state the amount of change seen in a parameter based on the observed changes in the mean value. We agree with the reviewer that interpreting this could be problematic if we are speculating possible mechanisms. Further test should be conducted as to state whether similar increase/decrease changes in a parameter are due to the disturbance of the same mechanisms or different. E.g., we discussed whether the regulation of SYT1 might be or not be the mechanism affected in some of the chimeras that show an increase in the spontaneous release rate, for the release rate observed in some is massively higher than that seen in SYT1-KO (Bouazza-Arostegui et al., 2022). It is tempting to speculate that it could be due to other mechanisms based on the differences in the changes. For this reason, we have given an array of possible mechanisms affected when we manipulate the SNARE domain of STX1.

      (7) The authors routinely analyzed the levels of Munc18-1 in neuronal lysates by WB and Munc18-1/VGlut1 by immunofluorescence in various Syx1 mutants. However, in my view, these assays were slightly indirect. It is evident that the SNARE domain of Syx1 participates in the binding to Munc18-1 according to the atomic structures (pdb entries: 3C98 and 7UDB). Meanwhile, Han et al. reported that K46E mutation (located in domain 1 of Munc18-1) strongly impairs Syx1 expression, Syx1-interaction, vesicle docking and secretion (Han et al., 2011, PMID: 21900502). Intriguingly, the residue K46 of Munc18-1, which is close to D231/R232 of Syx1, may have potential electrostatic contacts to D231 and R232 of Syx1. This is reminiscent of the possibility that Syx1D231/R232 and some Syx1-2 chimeras lost their normal function through their defective binding to Munc18-1.nmb, To better understand the underlying mechanism, the authors may need to carry out in vivo and/or in vitro binding analysis between syntaxin mutants/chimeras and Munc18-1. They also need to conduct more discussions about the issue.

      We express our gratitude for the identification of a previously overlooked aspect in our investigation of the interplay between Munc18-1 and STX1. In response, we have incorporated additional discourse on this matter in pg11 lines 419-431.

      Additionally, we appreciate the thoughtful suggestion regarding additional experiments to further explore the molecular relationship between Munc18-1 and STX1. We agree that co-immunoprecipitation experiments (either by using an antibody against Munc18-1 or STX1 and STX2) would offer greater insight into whether the binding of these proteins is affected in the isoform or the mutants. Notably, we performed immunoprecipitation experiments by using neuronal lysates of the corresponding groups and using STX1A and STX2 antibodies for the pull-downs. However, we were unable to co-IP Munc18-1 when doing so. Changing the conditions of the experiment did not yield better results and so these experiments remained inconclusive for the moment. For this reason, we included it as an open question and a potential concluding hypothesis of the molecular mechanism. However, Shi et al., 2021, have performed co-IP assays using Munc18-1-wt and a mutant form which affects the binding to the C-terminal half of the SNARE domain of STX, and STX1-wt and a STX mutants targeting some of our residues of interest and showed a decrease in the pulled-down levels of Munc18-1 using HeLa cells. We have made sure to mention the conclusion of this important publication in our discussion.

      (8) The third possible mechanism (i.e., interaction with Syt1) proposed by the authors seems more reasonable. However, the discussions raised by the authors were not enough. For instance, plenty of literature has indicated that Syt1 may participate in synaptic vesicle priming through stabilizing partially or fully assembled SNARE complex (Li et al., 2017, PMID: 28860966; Bacaj et al., 2015, PMID: 26437117; Mohrmann et al., 2013, PMID: 24005294; Wang et al., 2011; PMID: 22184197; Liu et al., 2009, PMID: 19515907); complexins are also SNARE binding modules that regulate synaptic exocytosis. Lack of complexins could lead to unclasping of spontaneous fusion of synaptic vesicles, though it causes severe Ca2+-triggered release at the same time (Maximov et al., 2009, PMID: 19164751). Meanwhile, different domains of complexin may accomplish different steps of SV fusion, early research had indicated that the C-terminal sequence of complexin is selectively required for clamping of spontaneous fusion and priming but not for Ca2+-triggered release (Kaeser-Woo et al., 2012, PMID: 22357870). Likewise, if possible, the authors may need to carry out in vivo and/or in vitro binding analysis to confirm their hypothesis.

      The exploration of complexin´s involvement was limited in our study primarily due to our methodological focus on comprehending molecular mechanisms concerning the sequence disparities between STX1 and STX2. Our laboratory has studied the role of Complexin extensively, and we certainly have had a possible involvement in mind. However, since the sites identified on syntaxin are either conserved between STX1 and STX2 or not close to the central or accessory helical domains of complexin, we did not perform experiments to test putative interactions, and we refrained from discussing complexin in this paper.

      (9) Lastly, I would suspect that whether the defects of Syx2 and Syx1 chimeras were caused by the SNARE complex itself, from another point of view that is different from the hypothesis raised by the authors. Changing the outward residues (or we say the solvent-accessible residues) of the SNARE complex may affect the stability, assembly kinetics, and energetics (Wang and Ma, 2022, PMID: 35810329; Zorman et al., 2014, PMID: 25180101), especially for the C-terminal halves. Is this another possible mechanism through which the C-terminus of Syx1 might contribute to SV priming and clamping of spontaneous release? The authors should at least conduct some discussions about the point.

      Thank you for this suggestion. We indeed assumed that since the hydrophobic layers of the SNARE domains that form the hydrophobic pocket of STX2 and STX1 are mainly conserved, that the intrinsic stability of the SNARE complex is largely unchanged. Additionally, Li et al., (2022) PMID: 35810329 examined the stability of the alfa-helix structure of the SNARE domain of SNAP25. And while they found no changes in the stability and formation of the alfa-helix when mutating outwards-facing residues for methodological purposes (bimane-tryptophan quenching), their study did not selectively explore the effect of mutations of outer-surface residues on the stability of the alfa-helix.

      Zorman et al., (2014) PMID: 25180101, as noted by the reviewer, observed that changes in the sequence of the SNARE domain (by using SNARE proteins from different trafficking systems (neuron, GLUT4, yeast…) correlated with changes in the step-wise SNARE complex assembly. However, they also did not selectively mutate the outer solvent-accessible residues, hindering conclusive speculations in the contribution of said residues on the kinetics and energetics of assembly and intrinsic stability of the SNARE complex.

      Upon petition of the reviewer, we have added this paragraph to discuss an additional mechanism:

      “As a final remark, it is possible that the changes in the spontaneous release rate and the priming stability may stem from a reduced stability of the SNARE complex itself through putative interactions between outer surface residues. Studies of the kinetics of assembly of the SNARE complex which mutate solvent-accessible residues in the C-terminal half of the SNARE domain of SYB2 have shown reduction in the stability of the SNARE complex assembly and are correlated with impaired fusion (Jiao et al., 2018). However, STX1 mutations of outward residues were inconclusive and were always accompanied by hydrophobic layer mutations (Jiao et al., 2018), which affect the assembly kinetics and energetics of the SNARE complex (Ma et al., 2015). Single molecule optical-tweezer studies have focused on the impact of regulatory molecules on the stability of assembly such as Munc18-1 (Ma et al., 2015; Jiao et al., 2018) and complexin (Hao et al., 2023), or on the intrinsic stability of the hydrophobic layers in the step-wise assembly of the SNARE complex (Gao et al., 2012; Ma et al., 2015; Zhang et al., 2017). Although the conserved hydrophobic layers in the SNARE domains of STX1A and STX2 (Figure 1) suggest unchanged zippering and intrinsic stability of the complex, further studies addressing the contribution of surface residues on the stability of the alfa-helix structure of the SNARE domain of STX1 (Li et al., 2022) or the stability of the SNARE complex should be conducted.”

      Minor comments:

      (1) In pg.6, line 236, 'figure 3F', the initial 'f' should be uppercased.

      (3) On pg.11, line 396, the section title 'The interaction of the C-terminus of de SNARE domain of STX1A with Munc18-1 in the stabilization of the primed pool of vesicles.' The word 'de' is confusing, please check.

      (4) In pg.12, line 446, the section title, should 'though' be 'through'?

      These comments have been acknowledged and changed. Thank you

      (2) In pg.7, line 239, '..had an increased PVR (Figure 3G), no change in the release rate (Figure 3I)', should Figure 3I be Figure 3H? and line 240, 'and an increase in short-term depression during 10Hz train stimulation (Figure 3I)', should Figure 3I be Figure 3J? If so, Figure 3I will not be cited in the texts and lack adequate interpretations. Please check.

      We apologize for the oversight in not referencing this specific subpanel of the figure and have incorporated the reference in the text. Additionally, our interpretation of this data is connected to the mechanisms that govern efficacy of Ca2+-evoked response, and its dependence on the integrity of the entire-SNARE domain. We wish to highlight the modifications made to the discussion on the regulation of the Ca2+-evoked response based on previous reviewer comment #1, and a similar comment from reviewer #2 (as stated previously).

    1. Author Response

      Reviewer #1 (Public Review):

      Summary:

      Visual Perceptual Learning (VPL) results in varying degrees of generalization to tasks or stimuli not seen during training. The question of which stimulus or task features predict whether learning will transfer to a different perceptual task has long been central in the field of perceptual learning, with numerous theories proposed to address it. This paper introduces a novel framework for understanding generalization in VPL, focusing on the form invariants of the training stimulus. Contrary to a previously proposed theory that task difficulty predicts the extent of generalization - suggesting that more challenging tasks yield less transfer to other tasks or stimuli - this paper offers an alternative perspective. It introduces the concept of task invariants and investigates how the structural stability of these invariants affects VPL and its generalization. The study finds that tasks with high-stability invariants are learned more quickly. However, training with low-stability invariants leads to greater generalization to tasks with higher stability, but not the reverse. This indicates that, at least based on the experiments in this paper, an easier training task results in less generalization, challenging previous theories that focus on task difficulty (or precision). Instead, this paper posits that the structural stability of stimulus or task invariants is the key factor in explaining VPL generalization across different tasks

      Strengths:

      • The paper effectively demonstrates that the difficulty of a perceptual task does not necessarily correlate with its learning generalization to other tasks, challenging previous theories in the field of Visual Perceptual Learning. Instead, it proposes a significant and novel approach, suggesting that the form invariants of training stimuli are more reliable predictors of learning generalization. The results consistently bolster this theory, underlining the role of invariant stability in forecasting the extent of VPL generalization across different tasks.

      • The experiments conducted in the study are thoughtfully designed and provide robust support for the central claim about the significance of form invariants in VPL generalization.

      Weaknesses:

      • The paper assumes a considerable familiarity with the Erlangen program and the definitions of invariants and their structural stability, potentially alienating readers who are not versed in these concepts. This assumption may hinder the understanding of the paper's theoretical rationale and the selection of stimuli for the experiments, particularly for those unfamiliar with the Erlangen program's application in psychophysics. A brief introduction to these key concepts would greatly enhance the paper's accessibility. The justification for the chosen stimuli and the design of the three experiments could be more thoroughly articulated.

      Response: We appreciate the reviewer's feedback regarding the accessibility of our paper. In response to this feedback, we plan to enhance the introduction section of our paper to provide a concise yet comprehensive overview of the key concepts of Erlangen program. Additionally, we will provide a more thorough justification for the selection of stimuli and the experimental design in our revised version, ensuring that readers understand the rationale behind our choices.

      • The paper does not clearly articulate how its proposed theory can be integrated with existing observations in the field of VPL. While it acknowledges previous theories on VPL generalization, the paper falls short in explaining how its framework might apply to classical tasks and stimuli that have been widely used in the VPL literature, such as orientation or motion discrimination with Gabors, vernier acuity, etc. It also does not provide insight into the application of this framework to more naturalistic tasks or stimuli. If the stability of invariants is a key factor in predicting a task's generalization potential, the paper should elucidate how to define the stability of new stimuli or tasks. This issue ties back to the earlier mentioned weakness: namely, the absence of a clear explanation of the Erlangen program and its relevant concepts.

      Response: Thanks for highlighting the need for better integration of our proposed theory with existing observations in the field of VPL. Unfortunately, the theoretical framework proposed in our study is based on the Klein’s Erlangen program and is only applicable to geometric shape stimuli. For VPL studies using stimuli and paradigms that are completely unrelated to geometric transformations (such as motion discrimination with Gabors or random dots, vernier acuity, spatial frequency discrimination, contrast detection or discrimination, etc.), our proposed theory does not apply. Some stimuli employed by VPL studies can be classified into certain geometric invariants. For instance, orientation discrimination with Gabors (Dosher & Lu, 2005) and texture discrimination task (F. Wang et al., 2016) both belong to tasks involving Euclidean invariants, and circle versus square discrimination (Kraft et al., 2010) belongs to tasks involving affine invariance. However, these studies do not simultaneously involve multiple geometric invariants of varying levels stability, and thus cannot be directly compared with our research. It is worth noting that while the Klein’s hierarchy of geometries, which our study focuses on, is rarely mentioned in the field of VPL, it does have connections with concepts such as 'global/local', 'coarse/fine', 'easy/difficulty', 'complex/simple': more stable invariants are closer to 'global', 'coarse', 'easy', 'complex', while less stable invariants are closer to 'local', 'fine', 'difficulty', 'simple'. Importantly, several VPL studies have found ‘fine-to-coarse’ or ‘local-to-global’ asymmetric transfer (Chang et al., 2014; N. Chen et al., 2016; Dosher & Lu, 2005), which seems consistent with the results of our study.

      In the introduction section of our revised version and subsequent full author response, we will provide a clear explanation of the Erlangen program and elucidate how to define the stability of new stimuli or tasks. In the discussion section of our revised version, we will compare our results to other studies concerned with the generalization of perceptual learning and speculate on how our proposed theory fit with existing observations in the field of VPL.

      • The paper does not convincingly establish the necessity of its introduced concept of invariant stability for interpreting the presented data. For instance, consider an alternative explanation: performing in the collinearity task requires orientation invariance. Therefore, it's straightforward that learning the collinearity task doesn't aid in performing the other two tasks (parallelism and orientation), which do require orientation estimation. Interestingly, orientation invariance is more characteristic of higher visual areas, which, consistent with the Reverse Hierarchy Theory, are engaged more rapidly in learning compared to lower visual areas. This simpler explanation, grounded in established concepts of VPL and the tuning properties of neurons across the visual cortex, can account for the observed effects, at least in one scenario. This approach has previously been used/proposed to explain VPL generalization, as seen in (Chowdhury and DeAngelis, Neuron, 2008), (Liu and Pack, Neuron, 2017), and (Bakhtiari et al., JoV, 2020). The question then is: how does the concept of invariant stability provide additional insights beyond this simpler explanation?

      Response: We appreciate the alternative explanation proposed by the reviewer and agree that it presents a valid perspective grounded in established concepts of VPL and neural tuning properties. However, performing in the collinearity and parallelism tasks both require orientation invariance. While utilizing the orientation invariance, as proposed by the reviewer, can explain the lack of transfer from collinearity or parallelism to orientation task, it cannot explain why collinearity does not transfer to parallelism.

      As stated in the response to the previous review, in the revised discussion section, we will compare our study with other studies (including the three papers mentioned by the reviewer), aiming to clarify the necessity of the concept of invariant stability for interpreting the observed data and understanding the mechanisms underlying VPL generalization.

      • While the paper discusses the transfer of learning between tasks with varying levels of invariant stability, the mechanism of this transfer within each invariant condition remains unclear. A more detailed analysis would involve keeping the invariant's stability constant while altering a feature of the stimulus in the test condition. For example, in the VPL literature, one of the primary methods for testing generalization is examining transfer to a new stimulus location. The paper does not address the expected outcomes of location transfer in relation to the stability of the invariant. Moreover, in the affine and Euclidean conditions one could maintain consistent orientations for the distractors and targets during training, then switch them in the testing phase to assess transfer within the same level of invariant structural stability.

      Response: Thanks for raising the issue regarding the mechanism of transfer within each invariant conditions. We plan to design an additional experiment that is similar in paradigm to Experiment 2, aiming to examine how VPL generalizes to a new test location within a single invariant stability level.

      • In the section detailing the modeling experiment using deep neural networks (DNN), the takeaway was unclear. While it was interesting to observe that the DNN exhibited a generalization pattern across conditions similar to that seen in the human experiments, the claim made in the abstract and introduction that the model provides a 'mechanistic' explanation for the phenomenon seems overstated. The pattern of weight changes across layers, as depicted in Figure 7, does not conclusively explain the observed variability in generalizations. Furthermore, the substantial weight change observed in the first two layers during the orientation discrimination task is somewhat counterintuitive. Given that neurons in early layers typically have smaller receptive fields and narrower tunings, one would expect this to result in less transfer, not more.

      Response: We appreciate the reviewer's feedback regarding the clarity of our DNN modeling experiment. We acknowledge that while DNNs have been demonstrated to serve as models for visual systems as well as VPL, the claim that the model provides a ‘mechanistic’ explanation for the phenomenon still overstated. In our revised version,

      We will attempt a more detailed analysis of the DNN model while providing a more explicit explanation of the findings from the DNN modeling experiment, emphasizing its implications for understanding the observed variability in generalizations.

      Additionally, the substantial weight change observed in the first two layers during the orientation discrimination task is not contradictory to the theoretical framework we proposed, instead, it aligns with our speculation regarding the neural mechanisms of VPL for geometric invariants. Specifically, it suggests that invariants with lower stability rely more on the plasticity of lower-level brain areas, thus exhibiting poorer generalization performance to new locations or stimulus features within each invariant conditions. However, it does not imply that their learning effects cannot transfer to invariants with higher stability.

      Reviewer #2 (Public Review):

      The strengths of this paper are clear: The authors are asking a novel question about geometric representation that would be relevant to a broad audience. Their question has a clear grounding in pre-existing mathematical concepts, that, to my knowledge, have been only minimally explored in cognitive science. Moreover, the data themselves are quite striking, such that my only concern would be that the data seem almost too clean. It is hard to know what to make of that, however. From one perspective, this is even more reason the results should be publicly available. Yet I am of the (perhaps unorthodox) opinion that reviewers should voice these gut reactions, even if it does not influence the evaluation otherwise. Below I offer some more concrete comments:

      (1) The justification for the designs is not well explained. The authors simply tell the audience in a single sentence that they test projective, affine, and Euclidean geometry. But despite my familiarity with these terms -- familiarity that many readers may not have -- I still had to pause for a very long time to make sense of how these considerations led to the stimuli that were created. I think the authors must, for a point that is so central to the paper, thoroughly explain exactly why the stimuli were designed the way that they were and how these designs map onto the theoretical constructs being tested.

      (2) I wondered if the design in Experiment 1 was flawed in one small but critical way. The goal of the parallelism stimuli, I gathered, was to have a set of items that is not parallel to the other set of items. But in doing that, isn't the manipulation effectively the same as the manipulation in the orientation stimuli? Both functionally involve just rotating one set by a fixed amount. (Note: This does not seem to be a problem in Experiment 2, in which the conditions are more clearly delineated.)

      (3) I wondered if the results would hold up for stimuli that were more diverse. It seems that a determined experimenter could easily design an "adversarial" version of these experiments for which the results would be unlikely to replicate. For instance: In the orientation group in Experiment 1, what if the odd-one-out was rotated 90 degrees instead of 180 degrees? Intuitively, it seems like this trial type would now be much easier, and the pattern observed here would not hold up. If it did hold up, that would provide stronger support for the authors' theory.

      It is not enough, in my opinion, to simply have some confirmatory evidence of this theory. One would have to have thoroughly tested many possible ways that theory could fail. I'm unsure that enough has been done here to convince me that these ideas would hold up across a more diverse set of stimuli.

      Response: (1) We appreciate the reviewer’s feedback regarding the justification for our experimental designs. We recognize the importance of thoroughly explaining how our stimuli were designed and how these designs correspond to the theoretical constructs being tested. In our revised version, we will enhance the introduction of Erlangen program and provide a more detailed explanation of the rationale behind our stimulus designs, aiming to enhance the clarity and transparency of our experimental approach for readers who may not be familiar with these concepts.

      (2) We appreciate the reviewer’s insight into the design of Experiment 1 and the concern regarding the potential similarity between the parallelism and orientation stimuli manipulations.

      The parallelism and orientation stimuli in Experiment 1 were first used by Olson & Attneave (1970) to support line-based models of shape coding and then adapted to measure the relative salience of different geometric properties (Chen, 1986). In the parallelism stimuli, the odd quadrant differs from the rest in line slope, while in the orientation stimuli, in contrast, the odd quadrant contains exactly the same line segments as the rest but differs in direction pointed by the angles. The result, that the odd quadrant was detected much faster in the parallelism stimuli than in the orientation stimuli, can serve as evidence for line-based models of shape coding. However, according to Chen (1986, 2005), the idea of invariants over transformations suggests a new analysis of the data: in the parallelism stimuli, the fact that line segments share the same slope essentially implies that they are parallel, and the discrimination may be actually based on parallelism. Thus, the faster discrimination of the parallelism stimuli than that of the orientation stimuli may be explained in terms of relative superiority of parallelism over orientation of angles—a Euclidean property.

      The group of stimuli in Experiment 1 has been employed by several studies to investigate scientific questions related to the Klein’s hierarchy of geometries (L. Chen, 2005; Meng et al., 2019; B. Wang et al., n.d.). Due to historical inheritance, we adopted this set of stimuli and corresponding paradigm, despite their imperfect design.

      (3) Thanks for raising the important issue of stimulus diversity and the potential for "adversarial" versions of the experiments to challenge our findings. We acknowledge the validity of your concern and recognize the need to demonstrate the robustness of our results across a range of stimuli. We plan to design additional experiments to investigate the potential implications of varying stimulus characteristics, such as different rotation angles proposed by the reviewer, on the observed patterns of performance.

    1. Author Response

      Reviewer #1 (Public Review):

      This study used a multi-day learning paradigm combined with fMRI to reveal neural changes reflecting the learning of new (arbitrary) shape-sound associations. In the scanner, the shapes and sounds are presented separately and together, both before and after learning. When they are presented together, they can be either consistent or inconsistent with the learned associations. The analyses focus on auditory and visual cortices, as well as the object-selective cortex (LOC) and anterior temporal lobe regions (temporal pole (TP) and perirhinal cortex (PRC)). Results revealed several learning-induced changes, particularly in the anterior temporal lobe regions. First, the LOC and PRC showed a reduced bias to shapes vs sounds (presented separately) after learning. Second, the TP responded more strongly to incongruent than congruent shape-sound pairs after learning. Third, the similarity of TP activity patterns to sounds and shapes (presented separately) was increased for non-matching shape-sound comparisons after learning. Fourth, when comparing the pattern similarity of individual features to combined shape-sound stimuli, the PRC showed a reduced bias towards visual features after learning. Finally, comparing patterns to combined shape-sound stimuli before and after learning revealed a reduced (and negative) similarity for incongruent combinations in PRC. These results are all interpreted as evidence for an explicit integrative code of newly learned multimodal objects, in which the whole is different from the sum of the parts.

      The study has many strengths. It addresses a fundamental question that is of broad interest, the learning paradigm is well-designed and controlled, and the stimuli are real 3D stimuli that participants interact with. The manuscript is well written and the figures are very informative, clearly illustrating the analyses performed.

      There are also some weaknesses. The sample size (N=17) is small for detecting the subtle effects of learning. Most of the statistical analyses are not corrected for multiple comparisons (ROIs), and the specificity of the key results to specific regions is also not tested. Furthermore, the evidence for an integrative representation is rather indirect, and alternative interpretations for these results are not considered.

      We thank the reviewer for their careful reading and the positive comments on our manuscript. As suggested, we have conducted additional analyses of theoretically-motivated ROIs and have found that temporal pole and perirhinal cortex are the only regions to show the key experience-dependent transformations. We are much more cautious with respect to multiple comparisons, and have removed a series of post hoc across-ROI comparisons that were irrelevant to the key questions of the present manuscript. The revised manuscript now includes much more discussion about alternative interpretations as suggested by the reviewer (and also by the other reviewers).

      Additionally, we looked into scanning more participants, but our scanner has since had a full upgrade and the sequence used in the current study is no longer supported by our scanner. However, we note that while most analyses contain 17 participants, we employed a within-subject learning design that is not typically used in fMRI experiments and increases our power to detect an effect. This is supported by the robust effect size of the behavioural data, whereby 17 out of 18 participants revealed a learning effect (Cohen’s D = 1.28) and which was replicated in a follow-up experiment with a larger sample size.

      We address the other reviewer comments point-by-point in the below.

      Reviewer #2 (Public Review):

      Li et al. used a four-day fMRI design to investigate how unimodal feature information is combined, integrated, or abstracted to form a multimodal object representation. The experimental question is of great interest and understanding how the human brain combines featural information to form complex representations is relevant for a wide range of researchers in neuroscience, cognitive science, and AI. While most fMRI research on object representations is limited to visual information, the authors examined how visual and auditory information is integrated to form a multimodal object representation. The experimental design is elegant and clever. Three visual shapes and three auditory sounds were used as the unimodal features; the visual shapes were used to create 3D-printed objects. On Day 1, the participants interacted with the 3D objects to learn the visual features, but the objects were not paired with the auditory features, which were played separately. On Day 2, participants were scanned with fMRI while they were exposed to the unimodal visual and auditory features as well as pairs of visual-auditory cues. On Day 3, participants again interacted with the 3D objects but now each was paired with one of the three sounds that played from an internal speaker. On Day 4, participants completed the same fMRI scanning runs they completed on Day 2, except now some visual-auditory feature pairs corresponded with Congruent (learned) objects, and some with Incongruent (unlearned) objects. Using the same fMRI design on Days 2 and 4 enables a well-controlled comparison between feature- and object-evoked neural representations before and after learning. The notable results corresponded to findings in the perirhinal cortex and temporal pole. The authors report (1) that a visual bias on Day 2 for unimodal features in the perirhinal cortex was attenuated after learning on Day 4, (2) a decreased univariate response to congruent vs. incongruent visual-auditory objects in the temporal pole on Day 4, (3) decreased pattern similarity between congruent vs. incongruent pairs of visual and auditory unimodal features in the temporal pole on Day 4, (4) in the perirhinal cortex, visual unimodal features on Day 2 do not correlate with their respective visual-auditory objects on Day 4, and (5) in the perirhinal cortex, multimodal object representations across Days 2 and 4 are uncorrelated for congruent objects and anticorrelated for incongruent. The authors claim that each of these results supports the theory that multimodal objects are represented in an "explicit integrative" code separate from feature representations. While these data are valuable and the results are interesting, the authors' claims are not well supported by their findings.

      We thank the reviewer for the careful reading of our manuscript and positive comments. Overall, we now stay closer to the data when describing the results and provide our interpretation of these results in the discussion section while remaining open to alternative interpretations (as also suggested by Reviewer 1).

      (1) In the introduction, the authors contrast two theories: (a) multimodal objects are represented in the co-activation of unimodal features, and (b) multimodal objects are represented in an explicit integrative code such that the whole is different than the sum of its parts. However, the distinction between these two theories is not straightforward. An explanation of what is precisely meant by "explicit" and "integrative" would clarify the authors' theoretical stance. Perhaps we can assume that an "explicit" representation is a new representation that is created to represent a multimodal object. What is meant by "integrative" is more ambiguous-unimodal features could be integrated within a representation in a manner that preserves the decodability of the unimodal features, or alternatively the multimodal representation could be completely abstracted away from the constituent features such that the features are no longer decodable. Even if the object representation is "explicit" and distinct from the unimodal feature representations, it can in theory still contain featural information, though perhaps warped or transformed. The authors do not clearly commit to a degree of featural abstraction in their theory of "explicit integrative" multimodal object representations which makes it difficult to assess the validity of their claims.

      Due to its ambiguity, we removed the term “explicit” and now make it clear that our central question was whether crossmodal object representations require only unimodal feature-level representations (e.g., frogs are created from only the combination of shape and sound) or whether crossmodal object representations also rely on an integrative code distinct from the unimodal features (e.g., there is something more to “frog” than its original shape and sound). We now clarify this in the revised manuscript.

      “One theoretical view from the cognitive sciences suggests that crossmodal objects are built from component unimodal features represented across distributed sensory regions.8 Under this view, when a child thinks about “frog”, the visual cortex represents the appearance of the shape of the frog whereas the auditory cortex represents the croaking sound. Alternatively, other theoretical views predict that multisensory objects are not only built from their component unimodal sensory features, but that there is also a crossmodal integrative code that is different from the sum of these parts.9,10,11,12,13 These latter views propose that anterior temporal lobe structures can act as a polymodal “hub” that combines separate features into integrated wholes.9,11,14,15” – pg. 4

      For this reason, we designed our paradigm to equate the unimodal representations, such that neural differences between the congruent and incongruent conditions provide evidence for a crossmodal integrative code different from the unimodal features (because the unimodal features are equated by default in the design).

      “Critically, our four-day learning task allowed us to isolate any neural activity associated with integrative coding in anterior temporal lobe structures that emerges with experience and differs from the neural patterns recorded at baseline. The learned and non-learned crossmodal objects were constructed from the same set of three validated shape and sound features, ensuring that factors such as familiarity with the unimodal features, subjective similarity, and feature identity were tightly controlled (Figure 2). If the mind represented crossmodal objects entirely as the reactivation of unimodal shapes and sounds (i.e., objects are constructed from their parts), then there should be no difference between the learned and non-learned objects (because they were created from the same three shapes and sounds). By contrast, if the mind represented crossmodal objects as something over and above their component features (i.e., representations for crossmodal objects rely on integrative coding that is different from the sum of their parts), then there should be behavioral and neural differences between learned and non-learned crossmodal objects (because the only difference across the objects is the learned relationship between the parts). Furthermore, this design allowed us to determine the relationship between the object representation acquired after crossmodal learning and the unimodal feature representations acquired before crossmodal learning. That is, we could examine whether learning led to abstraction of the object representations such that it no longer resembled the unimodal feature representations.” – pg. 5

      Furthermore, we agree with the reviewer that our definition and methodological design does not directly capture the structure of the integrative code. With experience, the unimodal feature representations may be completely abstracted away, warped, or changed in a nonlinear transformation. We suggest that crossmodal learning forms an integrative code that is different from the original unimodal representations in the anterior temporal lobes, however, we agree that future work is needed to more directly capture the structure of the integrative code that emerges with experience.

      “In our task, participants had to differentiate congruent and incongruent objects constructed from the same three shape and sound features (Figure 2). An efficient way to solve this task would be to form distinct object-level outputs from the overlapping unimodal feature-level inputs such that congruent objects are made to be orthogonal from the representations before learning (i.e., measured as pattern similarity equal to 0 in the perirhinal cortex; Figure 5b, 6, Supplemental Figure S5), whereas non-learned incongruent objects could be made to be dissimilar from the representations before learning (i.e., anticorrelation, measured as patten similarity less than 0 in the perirhinal cortex; Figure 6). Because our paradigm could decouple neural responses to the learned object representations (on Day 4) from the original component unimodal features at baseline (on Day 2), these results could be taken as evidence of pattern separation in the human perirhinal cortex.11,12 However, our pattern of results could also be explained by other types of crossmodal integrative coding. For example, incongruent object representations may be less stable than congruent object representations, such that incongruent objects representation are warped to a greater extent than congruent objects (Figure 6).” – pg. 18

      “As one solution to the crossmodal binding problem, we suggest that the temporal pole and perirhinal cortex form unique crossmodal object representations that are different from the distributed features in sensory cortex (Figure 4, 5, 6, Supplemental Figure S5). However, the nature by which the integrative code is structured and formed in the temporal pole and perirhinal cortex following crossmodal experience – such as through transformations, warping, or other factors – is an open question and an important area for future investigation.” – pg. 18

      (2) After participants learned the multimodal objects, the authors report a decreased univariate response to congruent visual-auditory objects relative to incongruent objects in the temporal pole. This is claimed to support the existence of an explicit, integrative code for multimodal objects. Given the number of alternative explanations for this finding, this claim seems unwarranted. A simpler interpretation of these results is that the temporal pole is responding to the novelty of the incongruent visual-auditory objects. If there is in fact an explicit, integrative multimodal object representation in the temporal pole, it is unclear why this would manifest in a decreased univariate response.

      We thank the reviewer for identifying this issue. Our behavioural design controls unimodal feature-level novelty but allows object-level novelty to differ. Thus, neural differences between the congruent and incongruent conditions reflects sensitivity to the object-level differences between the combination of shape and sound. However, we agree that there are multiple interpretations regarding the nature of how the integrative code is structured in the temporal pole and perirhinal cortex. We have removed the interpretation highlighted by the reviewer from the results. Instead, we now provide our preferred interpretation in the discussion, while acknowledging the other possibilities that the reviewer mentions.

      As one possibility, these results in temporal pole may reflect “conceptual combination”. “hummingbird” – a congruent pairing – may require less neural resources than an incongruent pairing such as “bark-frog”.

      “Furthermore, these distinct anterior temporal lobe structures may be involved with integrative coding in different ways. For example, the crossmodal object representations measured after learning were found to be related to the component unimodal feature representations measured before learning in the temporal pole but not the perirhinal cortex (Figure 5, 6, Supplemental Figure S5). Moreover, pattern similarity for congruent shape-sound pairs were lower than the pattern similarity for incongruent shape-sound pairs after crossmodal learning in the temporal pole but not the perirhinal cortex (Figure 4b, Supplemental Figure S3a). As one interpretation of this pattern of results, the temporal pole may represent new crossmodal objects by combining previously learned knowledge. 8,9,10,11,13,14,15,33 Specifically, research into conceptual combination has linked the anterior temporal lobes to compound object concepts such as “hummingbird”.34,35,36 For example, participants during our task may have represented the sound-based “humming” concept and visually-based “bird” concept on Day 1, forming the crossmodal “hummingbird” concept on Day 3; Figure 1, 2, which may recruit less activity in temporal pole than an incongruent pairing such as “barking-frog”. For these reasons, the temporal pole may form a crossmodal object code based on pre-existing knowledge, resulting in reduced neural activity (Figure 3d) and pattern similarity towards features associated with learned objects (Figure 4b).”– pg. 18

      (3) The authors ran a neural pattern similarity analysis on the unimodal features before and after multimodal object learning. They found that the similarity between visual and auditory features that composed congruent objects decreased in the temporal pole after multimodal object learning. This was interpreted to reflect an explicit integrative code for multimodal objects, though it is not clear why. First, behavioral data show that participants reported increased similarity between the visual and auditory unimodal features within congruent objects after learning, the opposite of what was found in the temporal pole. Second, it is unclear why an analysis of the unimodal features would be interpreted to reflect the nature of the multimodal object representations. Since the same features corresponded with both congruent and incongruent objects, the nature of the feature representations cannot be interpreted to reflect the nature of the object representations per se. Third, using unimodal feature representations to make claims about object representations seems to contradict the theoretical claim that explicit, integrative object representations are distinct from unimodal features. If the learned multimodal object representation exists separately from the unimodal feature representations, there is no reason why the unimodal features themselves would be influenced by the formation of the object representation. Instead, these results seem to more strongly support the theory that multimodal object learning results in a transformation or warping of feature space.

      We apologize for the lack of clarity. We have now overhauled this aspect of our manuscript in an attempt to better highlight key aspects of our experimental design. In particular, because the unimodal features composing the congruent and incongruent objects were equated, neural differences between these conditions would provide evidence for an experience-dependent crossmodal integrative code that is different from its component unimodal features.

      Related to the second and third points, we were looking at the extent to which the original unimodal representations change with crossmodal learning. Before crossmodal learning, we found that the perirhinal cortex tracked the similarity between the individual visual shape features and the crossmodal objects that were composed of those visual shapes – however, there was no evidence that perirhinal cortex was tracking the unimodal sound features on those crossmodal objects. After crossmodal learning, we see that this visual shape bias in perirhinal cortex was no longer present – that is, the representation in perirhinal cortex started to look less like the visual features that comprise the objects. Thus, crossmodal learning transformed the perirhinal representations so that they were no longer predominantly grounded in a single visual modality, which may be a mechanism by which object concepts gain their abstraction. We have now tried to be clearer about this interpretation throughout the paper.

      Notably, we suggest that experience may change both the crossmodal object representations, as well as the unimodal feature representations. For example, we have previously shown that unimodal visual features are influenced by experience in parallel with the representation of the conjunction (e.g., Liang et al., 2020; Cerebral Cortex). Nevertheless, we remain open to the myriad possible structures of the integrative code that might emerge with experience.

      We now clarify these points throughout the manuscript. For example:

      “We then examined whether the original representations would change after participants learned how the features were paired together to make specific crossmodal objects, conducting the same analysis described above after crossmodal learning had taken place (Figure 5b). With this analysis, we sought to measure the relationship between the representation for the learned crossmodal object and the original baseline representation for the unimodal features. More specifically, the voxel-wise activity for unimodal feature runs before crossmodal learning was correlated to the voxel-wise activity for crossmodal object runs after crossmodal learning (Figure 5b). Another linear mixed model which included modality as a fixed factor within each ROI revealed that the perirhinal cortex was no longer biased towards visual shape after crossmodal learning (F1,32 = 0.12, p = 0.73), whereas the temporal pole, LOC, V1, and A1 remained biased towards either visual shape or sound (F1,30-32 between 16.20 and 73.42, all p < 0.001, η2 between 0.35 and 0.70).” – pg. 14

      “To investigate this effect in perirhinal cortex more specifically, we conducted a linear mixed model to directly compare the change in the visual bias of perirhinal representations from before crossmodal learning to after crossmodal learning (green regions in Figure 5a vs. 5b). Specifically, the linear mixed model included learning day (before vs. after crossmodal learning) and modality (visual feature match to crossmodal object vs. sound feature match to crossmodal object). Results revealed a significant interaction between learning day and modality in the perirhinal cortex (F1,775 = 5.56, p = 0.019, η2 = 0.071), meaning that the baseline visual shape bias observed in perirhinal cortex (green region of Figure 5a) was significantly attenuated with experience (green region of Figure 5b). After crossmodal learning, a given shape no longer invoked significant pattern similarity between objects that had the same shape but differed in terms of what they sounded like. Taken together, these results suggest that prior to learning the crossmodal objects, the perirhinal cortex had a default bias toward representing the visual shape information and was not representing sound information of the crossmodal objects. After crossmodal learning, however, the visual shape bias in perirhinal cortex was no longer present. That is, with crossmodal learning, the representations within perirhinal cortex started to look less like the visual features that comprised the crossmodal objects, providing evidence that the perirhinal representations were no longer predominantly grounded in the visual modality.” – pg. 13

      “Importantly, the initial visual shape bias observed in the perirhinal cortex was attenuated by experience (Figure 5, Supplemental Figure S5), suggesting that the perirhinal representations had become abstracted and were no longer predominantly grounded in a single modality after crossmodal learning. One possibility may be that the perirhinal cortex is by default visually driven as an extension to the ventral visual stream,10,11,12 but can act as a polymodal “hub” region for additional crossmodal input following learning.” – pg. 19

      (4) The most compelling evidence the authors provide for their theoretical claims is the finding that, in the perirhinal cortex, the unimodal feature representations on Day 2 do not correlate with the multimodal objects they comprise on Day 4. This suggests that the learned multimodal object representations are not combinations of their unimodal features. If unimodal features are not decodable within the congruent object representations, this would support the authors' explicit integrative hypothesis. However, the analyses provided do not go all the way in convincing the reader of this claim. First, the analyses reported do not differentiate between congruent and incongruent objects. If this result in the perirhinal cortex reflects the formation of new multimodal object representations, it should only be true for congruent objects but not incongruent objects. Since the analyses combine congruent and incongruent objects it is not possible to know whether this was the case. Second, just because feature representations on Day 2 do not correlate with multimodal object patterns on Day 4 does not mean that the object representations on Day 4 do not contain featural information. This could be directly tested by correlating feature representations on Day 4 with congruent vs. incongruent object representations on Day 4. It could be that representations in the perirhinal cortex are not stable over time and all representations-including unimodal feature representations-shift between sessions, which could explain these results yet not entail the existence of abstracted object representations.

      We thank the reviewer for this suggestion and have conducted the two additional analyses. Specifically, we split the congruent and incongruent conditions and also investigated correlations between unimodal representations on Day 4 with crossmodal object representations on Day 4. There was no significant interaction between modality and congruency in any ROI across or within learning days. One possible explanation for these findings is that both congruent and incongruent crossmodal objects are represented differently from their underlying unimodal features, and all of these representations can transform with experience.

      However, the new analyses also revealed that perirhinal cortex was the only region without a modality-specific bias after crossmodal learning (e.g., Day 4 Unimodal Feature runs x Day 4 Crossmodal Object runs; now shown in Supplemental Figure S5). Overall, these results are consistent with the notion of a crossmodal integrative code in perirhinal cortex that has changed with experience and is different from the component unimodal features. Nevertheless, we explore alternative interpretations for how the crossmodal code emerges with experience in the discussion.

      “To examine whether these results differed by congruency (i.e., whether any modality-specific biases differed as a function of whether the object was congruent or incongruent), we conducted exploratory linear mixed models for each of the five a priori ROIs across learning days. More specifically, we correlated: 1) the voxel-wise activity for Unimodal Feature Runs before crossmodal learning to the voxel-wise activity for Crossmodal Object Runs before crossmodal learning (Day 2 vs. Day 2), 2) the voxel-wise activity for Unimodal Feature Runs before crossmodal learning to the voxel-wise activity for Crossmodal Object Runs after crossmodal learning (Day 2 vs Day 4), and 3) the voxel-wise activity for Unimodal Feature Runs after crossmodal learning to the voxel-wise activity for Crossmodal Object Runs after crossmodal learning (Day 4 vs Day 4). For each of the three analyses described, we then conducted separate linear mixed models which included modality (visual feature match to crossmodal object vs. sound feature match to crossmodal object) and congruency (congruent vs. incongruent)….There was no significant relationship between modality and congruency in any ROI between Day 2 and Day 2 (F1,346-368 between 0.00 and 1.06, p between 0.30 and 0.99), between Day 2 and Day 4 (F1,346-368 between 0.021 and 0.91, p between 0.34 and 0.89), or between Day 4 and Day 4 (F1,346-368 between 0.01 and 3.05, p between 0.082 and 0.93). However, exploratory analyses revealed that perirhinal cortex was the only region without a modality-specific bias and where the unimodal feature runs were not significantly correlated to the crossmodal object runs after crossmodal learning (Supplemental Figure S5).” – pg. 14

      “Taken together, the overall pattern of results suggests that representations of the crossmodal objects in perirhinal cortex were heavily influenced by their consistent visual features before crossmodal learning. However, the crossmodal object representations were no longer influenced by the component visual features after crossmodal learning (Figure 5, Supplemental Figure S5). Additional exploratory analyses did not find evidence of experience-dependent changes in the hippocampus or inferior parietal lobes (Supplemental Figure S4c-e).” – pg. 14

      “The voxel-wise matrix for Unimodal Feature runs on Day 4 were correlated to the voxel-wise matrix for Crossmodal Object runs on Day 4 (see Figure 5 in the main text for an example). We compared the average pattern similarity (z-transformed Pearson correlation) between shape (blue) and sound (orange) features specifically after crossmodal learning. Consistent with Figure 5b, perirhinal cortex was the only region without a modality-specific bias. Furthermore, perirhinal cortex was the only region where the representations of both the visual and sound features were not significantly correlated to the crossmodal objects. By contrast, every other region maintained a modality-specific bias for either the visual or sound features. These results suggest that perirhinal cortex representations were transformed with experience, such that the initial visual shape representations (Figure 5a) were no longer grounded in a single modality after crossmodal learning. Furthermore, these results suggest that crossmodal learning formed an integrative code different from the unimodal features in perirhinal cortex, as the visual and sound features were not significantly correlated with the crossmodal objects. * p < 0.05, ** p < 0.01, *** p < 0.001. Horizontal lines within brain regions indicate a significant main effect of modality. Vertical asterisks denote pattern similarity comparisons relative to 0.” – Supplemental Figure S5

      “We found that the temporal pole and perirhinal cortex – two anterior temporal lobe structures – came to represent new crossmodal object concepts with learning, such that the acquired crossmodal object representations were different from the representation of the constituent unimodal features (Figure 5, 6). Intriguingly, the perirhinal cortex was by default biased towards visual shape, but that this initial visual bias was attenuated with experience (Figure 3c, 5, Supplemental Figure S5). Within the perirhinal cortex, the acquired crossmodal object concepts (measured after crossmodal learning) became less similar to their original component unimodal features (measured at baseline before crossmodal learning); Figure 5, 6, Supplemental Figure S5. This is consistent with the idea that object representations in perirhinal cortex integrate the component sensory features into a whole that is different from the sum of the component parts, which might be a mechanism by which object concepts obtain their abstraction…. As one solution to the crossmodal binding problem, we suggest that the temporal pole and perirhinal cortex form unique crossmodal object representations that are different from the distributed features in sensory cortex (Figure 4, 5, 6, Supplemental Figure S5). However, the nature by which the integrative code is structured and formed in the temporal pole and perirhinal cortex following crossmodal experience – such as through transformations, warping, or other factors – is an open question and an important area for future investigation.” – pg. 18

      In sum, the authors have collected a fantastic dataset that has the potential to answer questions about the formation of multimodal object representations in the brain. A more precise delineation of different theoretical accounts and additional analyses are needed to provide convincing support for the theory that “explicit integrative” multimodal object representations are formed during learning.

      We thank the reviewer for the positive comments and helpful feedback. We hope that our changes to our wording and clarifications to our methodology now more clearly supports the central goal of our study: to find evidence of crossmodal integrative coding different from the original unimodal feature parts in anterior temporal lobe structures. We furthermore agree that future research is needed to delineate the structure of the integrative code that emerges with experience in the anterior temporal lobes.

      Reviewer #3 (Public Review):

      This paper uses behavior and functional brain imaging to understand how neural and cognitive representations of visual and auditory stimuli change as participants learn associations among them. Prior work suggests that areas in the anterior temporal (ATL) and perirhinal cortex play an important role in learning/representing cross-modal associations, but the hypothesis has not been directly tested by evaluating behavior and functional imaging before and after learning cross- modal associations. The results show that such learning changes both the perceived similarities amongst stimuli and the neural responses generated within ATL and perirhinal regions, providing novel support for the view that cross-modal learning leads to a representational change in these regions.

      This work has several strengths. It tackles an important question for current theories of object representation in the mind and brain in a novel and quite direct fashion, by studying how these representations change with cross-modal learning. As the authors note, little work has directly assessed representational change in ATL following such learning, despite the widespread view that ATL is critical for such representation. Indeed, such direct assessment poses several methodological challenges, which the authors have met with an ingenious experimental design. The experiment allows the authors to maintain tight control over both the familiarity and the perceived similarities amongst the shapes and sounds that comprise their stimuli so that the observed changes across sessions must reflect learned cross-modal associations among these. I especially appreciated the creation of physical objects that participants can explore and the approach to learning in which shapes and sounds are initially experienced independently and later in an associated fashion. In using multi-echo MRI to resolve signals in ventral ATL, the authors have minimized a key challenge facing much work in this area (namely the poor SNR yielded by standard acquisition sequences in ventral ATL). The use of both univariate and multivariate techniques was well-motivated and helpful in testing the central questions. The manuscript is, for the most part, clearly written, and nicely connects the current work to important questions in two literatures, specifically (1) the hypothesized role of the perirhinal cortex in representing/learning complex conjunctions of features and (2) the tension between purely embodied approaches to semantic representation vs the view that ATL regions encode important amodal/crossmodal structure.

      There are some places in the manuscript that would benefit from further explanation and methodological detail. I also had some questions about the results themselves and what they signify about the roles of ATL and the perirhinal cortex in object representation.

      We thank the reviewer for their positive feedback and address the comments in the below point-by-point responses.

      (A) I found the terms "features" and "objects" to be confusing as used throughout the manuscript, and sometimes inconsistent. I think by "features" the authors mean the shape and sound stimuli in their experiment. I think by "object" the authors usually mean the conjunction of a shape with a sound---for instance, when a shape and sound are simultaneously experienced in the scanner, or when the participant presses a button on the shape and hears the sound. The confusion comes partly because shapes are often described as being composed of features, not features in and of themselves. (The same is sometimes true of sounds). So when reading "features" I kept thinking the paper referred to the elements that went together to comprise a shape. It also comes from ambiguous use of the word object, which might refer to (a) the 3D- printed item that people play with, which is an object, or (b) a visually-presented shape (for instance, the localizer involved comparing an "object" to a "phase-scrambled" stimulus---here I assume "object" refers to an intact visual stimulus and not the joint presentation of visual and auditory items). I think the design, stimuli, and results would be easier for a naive reader to follow if the authors used the terms "unimodal representation" to refer to cases where only visual or auditory input is presented, and "cross-modal" or "conjoint" representation when both are present.

      We thank the reviewer for this suggestion and agree. We have replaced the terms “features” and “objects” with “unimodal” and “crossmodal” in the title, text, and figures throughout the manuscript for consistency (i.e., “crossmodal binding problem”). To simplify the terminology, we have also removed the localizer results.

      (B) There are a few places where I wasn't sure what exactly was done, and where the methods lacked sufficient detail for another scientist to replicate what was done. Specifically:

      (1) The behavioral study assessing perceptual similarity between visual and auditory stimuli was unclear. The procedure, stimuli, number of trials, etc, should be explained in sufficient detail in methods to allow replication. The results of the study should also minimally be reported in the supplementary information. Without an understanding of how these studies were carried out, it was very difficult to understand the observed pattern of behavioral change. For instance, I initially thought separate behavioral blocks were carried out for visual versus auditory stimuli, each presented in isolation; however, the effects contrast congruent and incongruent stimuli, which suggests these decisions must have been made for the conjoint presentation of both modalities. I'm still not sure how this worked. Additionally, the manuscript makes a brief mention that similarity judgments were made in the context of "all stimuli," but I didn't understand what that meant. Similarity ratings are hugely sensitive to the contrast set with which items appear, so clarity on these points is pretty important. A strength of the design is the contention that shape and sound stimuli were psychophysically matched, so it is important to show the reader how this was done and what the results were.

      We agree and apologize for the lack of sufficient detail in the original manuscript. We now include much more detail about the similarity rating task. The methodology and results of the behavioral rating experiments are now shown in Supplemental Figure S1. In Figure S1a, the similarity ratings are visualized on a multidimensional scaling plot. The triangular geometry for shape (blue) and sound (red) indicate that the subjective similarity was equated within each unimodal feature across individual participants. Quantitatively, there was no difference in similarity between the congruent and incongruent pairings in Figure S1b and Figure S1c prior to crossmodal learning. In addition to providing more information on these methods in the Supplemental Information, we also now provide a more detailed description of the task in the manuscript itself. For convenience, we reproduce these sections below.

      “Pairwise Similarity Task. Using the same task as the stimulus validation procedure (Supplemental Figure S1a), participants provided similarity ratings for all combinations of the 3 validated shapes and 3 validated sounds (each of the six features were rated in the context of every other feature in the set, with 4 repeats of the same feature, for a total of 72 trials). More specifically, three stimuli were displayed on each trial, with one at the top and two at the bottom of the screen in the same procedure as we have used previously27. The 3D shapes were visually displayed as a photo, whereas sounds were displayed on screen in a box that could be played over headphones when clicked with the mouse. The participant made an initial judgment by selecting the more similar stimulus on the bottom relative to the stimulus on the top. Afterwards, the participant made a similarity rating between each bottom stimulus with the top stimulus from 0 being no similarity to 5 being identical. This procedure ensured that ratings were made relative to all other stimuli in the set.”– pg. 28

      “Pairwise similarity task and results. In the initial stimulus validation experiment, participants provided pairwise ratings for 5 sounds and 3 shapes. The shapes were equated in their subjective similarity that had been selected from a well-characterized perceptually uniform stimulus space27 and the pairwise ratings followed the same procedure as described in ref 27. Based on this initial experiment, we then selected the 3 sounds from the that were most closely equated in their subjective similarity. (a) 3D-printed shapes were displayed as images, whereas sounds were displayed in a box that could be played when clicked by the participant. Ratings were averaged to produce a similarity matrix for each participant, and then averaged to produce a group-level similarity matrix. Shown as triangular representational geometries recovered from multidimensional scaling in the above, shapes (blue) and sounds (orange) were approximately equated in their subjective similarity. These features were then used in the four-day crossmodal learning task. (b) Behavioral results from the four-day crossmodal learning task paired with multi-echo fMRI described in the main text. Before crossmodal learning, there was no difference in similarity between shape and sound features associated with congruent objects compared to incongruent objects – indicating that similarity was controlled at the unimodal feature-level. After crossmodal learning, we observed a robust shift in the magnitude of similarity. The shape and sound features associated with congruent objects were now significantly more similar than the same shape and sound features associated with incongruent objects (p < 0.001), evidence that crossmodal learning changed how participants experienced the unimodal features (observed in 17/18 participants). (c) We replicated this learning-related shift in pattern similarity with a larger sample size (n = 44; observed in 38/44 participants). *** denotes p < 0.001. Horizontal lines denote the comparison of congruent vs. incongruent conditions. – Supplemental Figure S1

      (2) The experiences through which participants learned/experienced the shapes and sounds were unclear. The methods mention that they had one minute to explore/palpate each shape and that these experiences were interleaved with other tasks, but it is not clear what the other tasks were, how many such exploration experiences occurred, or how long the total learning time was. The manuscript also mentions that participants learn the shape-sound associations with 100% accuracy but it isn't clear how that was assessed. These details are important partly b/c it seems like very minimal experience to change neural representations in the cortex.

      We apologize for the lack of detail and agree with the reviewer’s suggestions – we now include much more information in the methods section. Each behavioral day required about 1 hour of total time to complete, and indeed, participants rapidly learned their associations with minimal experience. For example:

      “Behavioral Tasks. On each behavioral day (Day 1 and Day 3; Figure 2), participants completed the following tasks, in this order: Exploration Phase, one Unimodal Feature 1-back run (26 trials), Exploration Phase, one Crossmodal 1-back run (26 trials), Exploration Phase, Pairwise Similarity Task (24 trials), Exploration Phase, Pairwise Similarity Task (24 trials), Exploration Phase, Pairwise Similarity Task (24 trials), and finally, Exploration Phase. To verify learning on Day 3, participants also additionally completed a Learning Verification Task at the end of the session. – pg. 27

      “The overall procedure ensured that participants extensively explored the unimodal features on Day 1 and the crossmodal objects on Day 3. The Unimodal Feature and the Crossmodal Object 1-back runs administered on Day 1 and Day 3 served as practice for the neuroimaging sessions on Day 2 and Day 4, during which these 1-back tasks were completed. Each behavioral session required less than 1 hour of total time to complete.” – pg. 27

      “Learning Verification Task (Day 3 only). As the final task on Day 3, participants completed a task to ensure that participants successfully formed their crossmodal pairing. All three shapes and sounds were randomly displayed in 6 boxes on a display. Photos of the 3D shapes were shown, and sounds were played by clicking the box with the mouse cursor. The participant was cued with either a shape or sound, and then selected the corresponding paired feature. At the end of Day 3, we found that all participants reached 100% accuracy on this task (10 trials).” – pg. 29

      (3) I didn't understand the similarity metric used in the multivariate imaging analyses. The manuscript mentions Z-scored Pearson's r, but I didn't know if this meant (a) many Pearson coefficients were computed and these were then Z-scored, so that 0 indicates a value equal to the mean Pearson correlation and 1 is equal to the standard deviation of the correlations, or (b) whether a Fisher Z transform was applied to each r (so that 0 means r was also around 0). From the interpretation of some results, I think the latter is the approach taken, but in general, it would be helpful to see, in Methods or Supplementary information, exactly how similarity scores were computed, and why that approach was adopted. This is particularly important since it is hard to understand the direction of some key effects.

      The reviewer is correct that the Fisher Z transform was applied to each individual r before averaging the correlations. This approach is generally recommended when averaging correlations (see Corey, Dunlap, & Burke, 1998). We are now clearer on this point in the manuscript:

      “The z-transformed Pearson’s correlation coefficient was used as the distance metric for all pattern similarity analyses. More specifically, each individual Pearson correlation was Fisher z-transformed and then averaged (see 61).” – pg. 32

      (C) From Figure 3D, the temporal pole mask appears to exclude the anterior fusiform cortex (or the ventral surface of the ATL generally). If so, this is a shame, since that appears to be the locus most important to cross-modal integration in the "hub and spokes" model of semantic representation in the brain. The observation in the paper that the perirhinal cortex seems initially biased toward visual structure while more superior ATL is biased toward auditory structure appears generally consistent with the "graded hub" view expressed, for instance, in our group's 2017 review paper (Lambon Ralph et al., Nature Reviews Neuroscience). The balance of visual- versus auditory-sensitivity in that work appears balanced in the anterior fusiform, just a little lateral to the anterior perirhinal cortex. It would be helpful to know if the same pattern is observed for this area specifically in the current dataset.

      We thank the reviewer for this suggestion. After close inspection of Lambon Ralph et al. (2017), we believe that our perirhinal cortex mask appears to be overlapping with the ventral ATL/anterior fusiform region that the reviewer mentions. See Author response image 1 for a visual comparison:

      Author response image 1.

      The top four figures are sampled from Lambon Ralph et al (2017), whereas the bottom two figures visualize our perirhinal cortex mask (white) and temporal pole mask (dark green) relative to the fusiform cortex. The ROIs visualized were defined from the Harvard-Oxford atlas.

      We now mention this area of overlap in our manuscript and link it to the hub and spokes model:

      “Notably, our perirhinal cortex mask overlaps with a key region of the ventral anterior temporal lobe thought to be the central locus of crossmodal integration in the “hub and spokes” model of semantic representations.9,50 – pg. 20

      (D) While most effects seem robust from the information presented, I'm not so sure about the analysis of the perirhinal cortex shown in Figure 5. This compares (I think) the neural similarity evoked by a unimodal stimulus ("feature") to that evoked by the same stimulus when paired with its congruent stimulus in the other modality ("object"). These similarities show an interaction with modality prior to cross-modal association, but no interaction afterward, leading the authors to suggest that the perirhinal cortex has become less biased toward visual structure following learning. But the plots in Figures 4a and b are shown against different scales on the y-axes, obscuring the fact that all of the similarities are smaller in the after-learning comparison. Since the perirhinal interaction was already the smallest effect in the pre-learning analysis, it isn't really surprising that it drops below significance when all the effects diminish in the second comparison. A more rigorous test would assess the reliability of the interaction of comparison (pre- or post-learning) with modality. The possibility that perirhinal representations become less "visual" following cross-modal learning is potentially important so a post hoc contrast of that kind would be helpful.

      We apologize for the lack of clarity. We conducted a linear mixed model to assess the interaction between modality and crossmodal learning day (before and after crossmodal learning) in the perirhinal cortex as described by the reviewer. The critical interaction was significant, which is now clarified in the text as well as in the rescaled figure plots.

      “To investigate this effect in perirhinal cortex more specifically, we conducted a linear mixed model to directly compare the change in the visual bias of perirhinal representations from before crossmodal learning to after crossmodal learning (green regions in Figure 5a vs. 5b). Specifically, the linear mixed model included learning day (before vs. after crossmodal learning) and modality (visual feature match to crossmodal object vs. sound feature match to crossmodal object). Results revealed a significant interaction between learning day and modality in the perirhinal cortex (F1,775 = 5.56, p = 0.019, η2 = 0.071), meaning that the baseline visual shape bias observed in perirhinal cortex (green region of Figure 5a) was significantly attenuated with experience (green region of Figure 5b). After crossmodal learning, a given shape no longer invoked significant pattern similarity between objects that had the same shape but differed in terms of what they sounded like. Taken together, these results suggest that prior to learning the crossmodal objects, the perirhinal cortex had a default bias toward representing the visual shape information and was not representing sound information of the crossmodal objects. After crossmodal learning, however, the visual shape bias in perirhinal cortex was no longer present. That is, with crossmodal learning, the representations within perirhinal cortex started to look less like the visual features that comprised the crossmodal objects, providing evidence that the perirhinal representations were no longer predominantly grounded in the visual modality.” – pg. 13

      We note that not all effects drop in Figure 5b (even in regions with a similar numerical pattern similarity to PRC, like the hippocampus – also see Supplemental Figure S5 for a comparison for patterns only on Day 4), suggesting that the change in visual bias in PRC is not simply due to noise.

      “Importantly, the change in pattern similarity in the perirhinal cortex across learning days (Figure 5) is unlikely to be driven by noise, poor alignment of patterns across sessions, or generally reduced responses. Other regions with numerically similar pattern similarity to perirhinal cortex did not change across learning days (e.g., visual features x crossmodal objects in A1 in Figure 5; the exploratory ROI hippocampus with numerically similar pattern similarity to perirhinal cortex also did not change in Supplemental Figure S4c-d).” – pg. 14

      (E) Is there a reason the authors did not look at representation and change in the hippocampus? As a rapid-learning, widely-connected feature-binding mechanism, and given the fairly minimal amount of learning experience, it seems like the hippocampus would be a key area of potential import for the cross-modal association. It also looks as though the hippocampus is implicated in the localizer scan (Figure 3c).

      We thank the reviewer for this suggestion and now include additional analyses for the hippocampus. We found no evidence of crossmodal integrative coding different from the unimodal features. Rather, the hippocampus seems to represent the convergence of unimodal features, as evidenced by …[can you give some pithy description for what is meant by “convergence” vs “integration”?]. We provide these results in the Supplemental Information and describe them in the main text:

      “Analyses for the hippocampus (HPC) and inferior parietal lobe (IPL). (a) In the visual vs. auditory univariate analysis, there was no visual or sound bias in HPC, but there was a bias towards sounds that increased numerically after crossmodal learning in the IPL. (b) Pattern similarity analyses between unimodal features associated with congruent objects and incongruent objects. Similar to Supplemental Figure S3, there was no main effect of congruency in either region. (c) When we looked at the pattern similarity between Unimodal Feature runs on Day 2 to Crossmodal Object runs on Day 2, we found that there was significant pattern similarity when there was a match between the unimodal feature and the crossmodal object (e.g., pattern similarity > 0). This pattern of results held when (d) correlating the Unimodal Feature runs on Day 2 to Crossmodal Object runs on Day 4, and (e) correlating the Unimodal Feature runs on Day 4 to Crossmodal Object runs on Day 4. Finally, (f) there was no significant pattern similarity between Crossmodal Object runs before learning correlated to Crossmodal Object after learning in HPC, but there was significant pattern similarity in IPL (p < 0.001). Taken together, these results suggest that both HPC and IPL are sensitive to visual and sound content, as the (c, d, e) unimodal feature-level representations were correlated to the crossmodal object representations irrespective of learning day. However, there was no difference between congruent and incongruent pairings in any analysis, suggesting that HPC and IPL did not represent crossmodal objects differently from the component unimodal features. For these reasons, HPC and IPL may represent the convergence of unimodal feature representations (i.e., because HPC and IPL were sensitive to both visual and sound features), but our results do not seem to support these regions in forming crossmodal integrative coding distinct from the unimodal features (i.e., because representations in HPC and IPL did not differentiate the congruent and incongruent conditions and did not change with experience). * p < 0.05, ** p < 0.01, *** p < 0.001. Asterisks above or below bars indicate a significant difference from zero. Horizontal lines within brain regions in (a) reflect an interaction between modality and learning day, whereas horizontal lines within brain regions in reflect main effects of (b) learning day, (c-e) modality, or (f) congruency.” – Supplemental Figure S4.

      “Notably, our perirhinal cortex mask overlaps with a key region of the ventral anterior temporal lobe thought to be the central locus of crossmodal integration in the “hub and spokes” model of semantic representations.9,50 However, additional work has also linked other brain regions to the convergence of unimodal representations, such as the hippocampus51,52,53 and inferior parietal lobes.54,55 This past work on the hippocampus and inferior parietal lobe does not necessarily address the crossmodal binding problem that was the main focus of our present study, as previous findings often do not differentiate between crossmodal integrative coding and the convergence of unimodal feature representations per se. Furthermore, previous studies in the literature typically do not control for stimulus-based factors such as experience with unimodal features, subjective similarity, or feature identity that may complicate the interpretation of results when determining regions important for crossmodal integration. Indeed, we found evidence consistent with the convergence of unimodal feature-based representations in both the hippocampus and inferior parietal lobes (Supplemental Figure S4), but no evidence of crossmodal integrative coding different from the unimodal features. The hippocampus and inferior parietal lobes were both sensitive to visual and sound features before and after crossmodal learning (see Supplemental Figure S4c-e). Yet the hippocampus and inferior parietal lobes did not differentiate between the congruent and incongruent conditions or change with experience (see Supplemental Figure S4).” – pg. 20

      (F) The direction of the neural effects was difficult to track and understand. I think the key observation is that TP and PRh both show changes related to cross-modal congruency - but still it would be helpful if the authors could articulate, perhaps via a schematic illustration, how they think representations in each key area are changing with the cross-modal association. Why does the temporal pole come to activate less for congruent than incongruent stimuli (Figure 3)? And why do TP responses grow less similar to one another for congruent relative to incongruent stimuli after learning (Figure 4)? Why are incongruent stimulus similarities anticorrelated in their perirhinal responses following cross-modal learning (Figure 6)?

      We thank the author for identifying this issue, which was also raised by the other reviewers. The reviewer is correct that the key observation is that the TP and PRC both show changes related to crossmodal congruency (given that the unimodal features were equated in the methodological design). However, the structure of the integrative code is less clear, which we now emphasize in the main text. Our findings provide evidence of a crossmodal integrative code that is different from the unimodal features, and future studies are needed to better understand the structure of how such a code might emerge. We now more clearly highlight this distinction throughout the paper:

      “By contrast, perirhinal cortex may be involved in pattern separation following crossmodal experience. In our task, participants had to differentiate congruent and incongruent objects constructed from the same three shape and sound features (Figure 2). An efficient way to solve this task would be to form distinct object-level outputs from the overlapping unimodal feature-level inputs such that congruent objects are made to be orthogonal from the representations before learning (i.e., measured as pattern similarity equal to 0 in the perirhinal cortex; Figure 5b, 6, Supplemental Figure S5), whereas non-learned incongruent objects could be made to be dissimilar from the representations before learning (i.e., anticorrelation, measured as patten similarity less than 0 in the perirhinal cortex; Figure 6). Because our paradigm could decouple neural responses to the learned object representations (on Day 4) from the original component unimodal features at baseline (on Day 2), these results could be taken as evidence of pattern separation in the human perirhinal cortex.11,12 However, our pattern of results could also be explained by other types of crossmodal integrative coding. For example, incongruent object representations may be less stable than congruent object representations, such that incongruent objects representation are warped to a greater extent than congruent objects (Figure 6).” – pg. 18

      “As one solution to the crossmodal binding problem, we suggest that the temporal pole and perirhinal cortex form unique crossmodal object representations that are different from the distributed features in sensory cortex (Figure 4, 5, 6, Supplemental Figure S5). However, the nature by which the integrative code is structured and formed in the temporal pole and perirhinal cortex following crossmodal experience – such as through transformations, warping, or other factors – is an open question and an important area for future investigation. Furthermore, these anterior temporal lobe structures may be involved with integrative coding in different ways. For example, the crossmodal object representations measured after learning were found to be related to the component unimodal feature representations measured before learning in the temporal pole but not the perirhinal cortex (Figure 5, 6, Supplemental Figure S5). Moreover, pattern similarity for congruent shape-sound pairs were lower than the pattern similarity for incongruent shape-sound pairs after crossmodal learning in the temporal pole but not the perirhinal cortex (Figure 4b, Supplemental Figure S3a). As one interpretation of this pattern of results, the temporal pole may represent new crossmodal objects by combining previously learned knowledge. 8,9,10,11,13,14,15,33 Specifically, research into conceptual combination has linked the anterior temporal lobes to compound object concepts such as “hummingbird”.34,35,36 For example, participants during our task may have represented the sound-based “humming” concept and visually-based “bird” concept on Day 1, forming the crossmodal “hummingbird” concept on Day 3; Figure 1, 2, which may recruit less activity in temporal pole than an incongruent pairing such as “barking-frog”. For these reasons, the temporal pole may form a crossmodal object code based on pre-existing knowledge, resulting in reduced neural activity (Figure 3d) and pattern similarity towards features associated with learned objects (Figure 4b).” – pg. 18

      This work represents a key step in our advancing understanding of object representations in the brain. The experimental design provides a useful template for studying neural change related to the cross-modal association that may prove useful to others in the field. Given the broad variety of open questions and potential alternative analyses, an open dataset from this study would also likely be a considerable contribution to the field.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer 1

      Comment 1.1: “Did the UKB or HCHS datasets have information on accurate markers of insulin resistance, such as HbA1c or HOMA-IR (if fasting glucose was not available)? Looking at that data would allow us to determine the contribution of insulin resistance to the observed cortical phenotype.”

      Reply 1.1: We appreciate the insightful suggestion from the reviewer. In response, we incorporated the HbA1c into our analysis, enhancing its sensitivity to potential effects of insulin resistance. Subsequently, our analysis was reperformed, integrating HbA1c alongside non-fasting blood glucose in the PLS. This addition did not alter our main results, i.e., that of the PLS, virtual histology, and network contextualization analysis. Notably, as a result of the inclusion of HbA1c, the second latent variable now accounted for a greater shared variance (22.13%), with HbA1c showing the highest loading among MetS component variables. The manuscript has been thoroughly revised to incorporate these results.

      Comments 1.2: “(Results, p.13, 291-292) "A correlation matrix relating all considered MetS component measures is displayed in supplementary figure S12. Please clarify in this figure labels whether this was non-fasting glucose. If this is non-fasting glucose, it is not a MetS-related risk factor. The reader might be misled into thinking that fasting-glucose has a weak correlation, while its contribution (and the effect of insulin resistance) was not studied here.”

      “Table S8 and Table S9: Is the glucose metric here measured following fasting? If not, this should not be listed as a metabolic syndrome criterion. Or it should be specified that it isn't fasted glucose, otherwise, it sounds misleading.”

      Reply 1.2: We thank the reviewer for bringing this ambiguity to our attention. The initial analysis included only non-fasting plasma glucose in the PLS, as fasting plasma glucose data was unavailable for UKB and HCHS participants. Following your suggestion in reply 1.1, we have now incorporated HbA1c, a more indicative marker of insulin resistance. We retained non-fasting blood glucose in our analysis, recognizing its relevance as a diagnostic variable for type 2 diabetes mellitus, although it is less informative than fasting plasma glucose, HbA1c, or HOMA-IR. This decision is substantiated by the significant correlation found between non-fasting plasma glucose and HbA1c in our sample (r=.49).

      To enhance clarity, we have revised the methods section to explicitly mention that the study investigates non-fasting blood glucose. The revised sentence reads: “Here, we related regional cortical thickness and subcortical volumes to clinical measurements of MetS components, i.e., obesity (waist circumference, hip circumference, waist-hip ratio, body mass index), arterial hypertension (systolic blood pressure, diastolic blood pressure), dyslipidemia (high density lipoprotein, low density lipoprotein, total cholesterol, triglycerides) and insulin resistance (HbA1c, non-fasting blood glucose).”

      Additionally, we have updated the caption of supplementary figure S13 (formerly supplementary figure S12) to clearly indicate the investigation of non-fasting plasma glucose. The table detailing diagnostic MetS criteria (supplementary table S2) has also been amended to clarify the absence of fasting plasma glucose data in our study and to indicate that only data on antidiabetic therapy and diagnosis of type 2 diabetes mellitus were used as criteria for insulin resistance in the case-control analysis.

      Comment 1.3: “I do not understand how the authors can claim there is a deterministic relationship there if all the results are only correlational or comparative. Can the differences in functional connectivity and white matter fiber tracts observed not be caused by the changes in cortices they relate to? How can the authors be sure the network organisation is shaping the cortical effects and not the opposite (the cortical changes influence the network organisation)? This should be further discussed or explained.”

      Reply 1.3: We agree with the reviewer's comment on the non-causative nature of our data and have accordingly revised the discussion section to reflect a more cautious interpretation of our findings. We have carefully reframed our language to avoid any implications of causality, ensuring the narrative aligns with the correlational nature of our data. Nevertheless, we believe that exploring causal interpretations can offer valuable clinical insights. Therefore, while moderating our language, we have maintained certain speculative discussions regarding potential causative pathomechanistic pathways.

      Comment 1.4: “The hippocampus is also an area where changes have consistently been observed. Why did the authors limit their analysis to the cortex.”

      Reply 1.4: We appreciate this reviewer comment. In response, we have added volumes of Melbourne Subcortical Atlas parcels (including the hippocampus) to the analysis. Corresponding results are now shown in figure 2. The subcortical bootstrap ratios indicated that higher MetS severity was related to lower volumes across all investigated subcortical structures.

      Comment 1.5: “Which field ID of the UK biobank are the measures referring to? If possible, please specify the Field ID for each of the UKB metrics used in the study.”

      Reply 1.5: We thank the reviewer for the recommendation. The Field IDs used in our study are now listed in supplementary figure S1.

      Comment 1.6: “Several Figures were wrongly annotated, making it hard to follow the text.”

      Reply 1.6: Thank you for bringing the annotation issues to our awareness. We have thoroughly edited all annotations which should now correctly reference the figure content.

      Reviewer 2

      Comment 2.1: “Do the authors have the chance to see how the pattern relates to changes in cognitive function in the UKBB and possibly HCHS? This could help to provide some evidence about the directionality of the effect.” Reply 2.1: Thank you for your suggestion. We acknowledge the potential value of investigating gray matter morphometric data alongside longitudinal information on cognitive function. Although we concur with the significance of this approach, we are constrained by the ongoing processing of the UKB's imaging follow-up data and the pending release of the HCHS follow-up data. Consequently, our current analysis cannot incorporate this aspect for now. We plan to explore the relationship between MetS, cognition and brain morphology using longitudinal data as soon as it becomes available.

      Comment 2.2: “Also, you could project new data onto the component and establish a link with cognition in a third sample which would be even more convincing. I can offer LIFE-Adult study for this aim.”

      Reply 2.2: We are grateful for your recommendation to enhance our study's robustness by including a third sample to establish a cognitive link. While we recognize the merit of such a sensitivity analysis, we believe that our current dataset, derived from two large, independent cohorts, is sufficiently comprehensive for the scope of our current analysis. However, we are open to considering this approach in future studies and appreciate your offer of the LIFE-Adult study. We would welcome further conversation with you regarding future joint projects.

      Comment 2.3: “The sentences (p.17, ll.435 ff) seem to repeat: "Interestingly, we also observed a positive relationship between cortical thickness and MetS in the superior frontal, parietal and occipital lobe. Interpretation of this result is, however, less intuitive. We also noted a positive MetS-cortical thickness association in superior frontal, parietal and occipital lobes, a less intuitive finding that has been previously reported [60,61].”

      Reply 2.3: Thank you for making us aware of this duplication. We have deleted the first part of the section. It now reads “We also noted a positive MetS-cortical thickness association in superior frontal, parietal and occipital lobes, a less intuitive finding that has been previously reported.”

      Comment 2.4: “I would highly appreciate empirical evidence for the claim in ll. 442 "In support of this hypothesis, the determined cortical thickness abnormality pattern is consistent with the atrophy pattern found in vascular mild cognitive impairment and vascular dementia" Considering the previous reports about the co-localization of obesity-associated atrophy and AD neurodegeneration (Morys et al. 2023, DOI: 10.3233/JAD-220535), that most dementias are mixed and that MetS probably increases dementia risk through both AD and vascular mechanisms, I feel such "binary" claims on VaD/AD-related atrophy patterns should be backed up empirically.”

      Reply 2.4: Thank you for highlighting the need for clarity in differentiating between vascular and Alzheimer's dementia. We recognize the intricate overlap in dementia pathologies. Acknowledging the prevalence of mixed dementia and the influence of MetS on both AD and vascular mechanisms, we realize our original statement might have implied a specificity to vascular dementia, which was not intended.

      To address your concern, we have revised our statement to avoid an exclusive focus on vascular pathology, ensuring a more balanced representation of dementia types. Additionally, we have included Morys et al. 2023 as a reference. The section now reads: “In support of this hypothesis, the determined brain morphological abnormality pattern is consistent with the atrophy pattern found in vascular mild cognitive impairment, vascular dementia and Alzheimer’s dementia.”

      Comment 2.5: “I wonder how specific the cell-type results are to this covariance pattern. Maybe patterns of CT (independent of MetS) show similar associations with one or more of the reported celltypes? Would it be possible to additionally show the association of the first three components of general cortical thickness variation with the cell type densities?”

      Reply 2.5: Thank you for your query regarding the specificity of the cell-type results to the observed covariance pattern. To address this, we have conducted a virtual histology analysis of the first three latent variables of the main analysis PLS. The findings of this extended analysis have been detailed in the supplementary Figure S21. The imaging covariance profile of latent variable 2 was significantly associated with the density of excitatory neurons of subtype 3. The imaging covariance profile linked to latent variable 3 showed no significant association of cell type densities. Possibly, latent variable 3 represents only a noise component as it explained only 2.12% of shared variance. We hope this addition provides a clearer understanding of the specificity of our main results.

      Comment 2.6: “I agree that this multivariate approach can contribute to a more holistic understanding, yet I would like to see the discussion expanded on how to move on from here. Should we target the MetS more comprehensively or would it be best to focus on obesity (being the strongest contributor and risk factor for other "downstream" conditions such as T2DM)? A holistic approach is somewhat at odds with the in-depth investigation of specific mechanisms.”

      Reply 2.6: We value your suggestion to elaborate on the implications of our findings. Our study indicates that obesity may have the most pronounced impact on brain morphology among MetS components, suggesting it as a key contributor to the clinical-anatomical covariance pattern observed in our analysis. This highlights obesity as a primary target for future research and preventive strategies. However, we believe that our results warrant further validation, ideally through longitudinal studies, before drawing definitive clinical conclusions.

      Additionally, our study endorses a comprehensive approach to MetS, highlighting the importance of considering the syndrome as a whole to gain broader insights. We want to clarify, however, that such an approach is meant to complement, rather than replace, the study of individual cardiometabolic risk factors. The broad perspective our study adopts is facilitated by its epidemiological nature, which may not be as applicable in experimental settings that are vital for deriving mechanistic disease insights.

      To reflect these points, we have expanded the discussion in our manuscript to include a more detailed consideration of these implications and future research directions.

      Comment 2.7: “Please report the number of missing variables.”

      Reply 2.7: Thank you for your request to report the number of missing variables. We would like to direct your attention to table 1, where we have listed the number of available values for each variable in parentheses. To determine the number of missing variables, one can subtract these numbers from the total sample size.

      Comment 2.8: “Was the pattern similar in pre-clinical (pre-diabetes, pre-hypertension) vs. clinical conditions?“

      Reply 2.8: Thank you for your interest in the applicability of our findings across different MetS severity levels. Our analysis employs a continuous framework to encompass the entire range of vascular and cardiometabolic risks, including those only mildly affected by MetS. The linear relationship we observed between MetS severity and gray matter morphology patterns, as illustrated in Figure 2d, supports the interpretation that our findings apply to the entire spectrum of MetS severities.

      Comment 2.9: “How did you deal with medication (anti-hypertensive, anti-diabetic, statins..)?”

      Reply 2.9: Information on medication was considered for defining MetS for the case-control sensitivity analysis but was not included in the PLS. Detailed information can be found in table 1.

      Comment 2.10: “It would be really interesting to determine the genetic variations associated with the latent component. Have you considered doing a GWAS on this, potentially in the CHARGE consortium or with UKBB as discovery and HCHS as replication sample?”

      Reply 2.10: Thank you for your valuable suggestion regarding the implementation of a GWAS. We agree that incorporating a GWAS would provide significant insights, but we also recognize that it extends beyond the scope of our current analysis. However, we are actively planning a follow-up analysis. This subsequent analysis will encompass a comprehensive examination of both genetic variation and imaging findings in the context of MetS.

      Comment 2.11: “Please provide more information on which data fields from UKBB were used exactly (e.g. in github repository).”

      Reply 2.11: We appreciate your recommendation. The details regarding the Field IDs used in our study have been included as supplementary table S1.

      Reviewer 3

      Comments 3.1: “After a thorough review of the methods and results sections, I found no direct or strong evidence supporting the authors' claim that the identified latent variables were related to more severe MetS to worse cognitive performance. While a sub-group comparison was conducted, it did not adequately account for confounding factors such as educational level.”

      “Page 18-19 lines 431-446: the fifth paragraph in the discussion section. - As previously mentioned in the "Weaknesses" section, this study did not conduct a direct association analysis between MetS and cognitive levels without considering subgroup comparisons. Hence, I recommend the content of this paragraph warrants careful reconsideration.”

      Reply 3.1: We acknowledge the reviewer's constructive feedback regarding our analysis of cognitive data. We have performed a mediation analysis relating the subject-specific clinical PLS score of latent variable 1 representing MetS severity and cognitive test performances and testing for mediating effects of the imaging PLS score capturing the MetS-related brain morphological abnormalities. The imaging score was found to statistically mediate the relationship between the clinical PLS score and executive function and processing speed, memory, and reasoning test performance. These findings highlight brain structural differences as a relevant pathomechanistic correlate in the relationship of MetS and cognition. Corresponding information can now be found in figure 3, methods section 2.6.2, result section 3.3 and discussion section 4.2.

      Moreover, we would like to apologize for any confusion caused by previous unclear presentation. Our study further incorporates association analyses between MetS, brain structure, and cognition using MetS components, regional brain morphological measures, and cognitive performance data in a PLS to investigate whether cognitive measures contribute to the latent variable. These analyses were separately performed on the UK Biobank and HCHS datasets, due to their distinct cognitive assessments. We adjusted for age, sex, and education in the subgroup analyses by removing their effects from the input variables. These relationships are detailed in supplementary figures S16b and S17b, with loadings close to zero for age, sex, and education, confirming effective deconfounding.

      In sum, we greatly appreciate the suggestion to conduct a mediation analysis, which has substantially enhanced the strength and relevance of our analysis.

      Comment 3.2: “I would suggest the authors provide a more comprehensive description of the metrics used to assess each MetS component, such as obesity (incorporating parameters like waist circumference, hip circumference, waist-hip ratio, and body mass index) and arterial hypertension (detailing metrics like systolic and diastolic blood pressure), etc.”

      Reply 3.2: Thank you for your suggestion regarding a more detailed description of the metrics for assessing each component of MetS. We would like to point out that the specific metrics used, including those for obesity (such as waist circumference, hip circumference, waist-hip ratio, and body mass index) and arterial hypertension (including systolic and diastolic blood pressure), are comprehensively detailed in table 1 of our manuscript. We hope this table provides the clarity and specificity you are seeking regarding the MetS assessment metrics in our study.

      Comment 3.3: “I recommend the inclusion of an additional, detailed flowchart to further illustrate the procedure of virtual histology analysis. This would enhance the clarity of the methodological approach and assist readers in better comprehending the analysis method.”

      Reply 3.3: Thank you for your suggestion. Recognizing the challenges in visually representing many of our analysis steps, we have instead supplemented our manuscript with additional references. These references provide a clearer understanding of our virtual histology approach, particularly focusing on the processing of regional microarray expression data.

      The corresponding sentence reads: “Further details on the processing steps covered by ABAnnotate can be found elsewhere (https://osf.io/gcxun) [42]”

      Comment 3.4: “Why were both brain hemispheres used instead of solely utilizing the left hemisphere as the atlas, especially considering that the Allen Human Brain Atlas (AHBA) only includes gene data for the right hemisphere for two subjects?”

      Reply 3.4: Thank you for your query regarding our decision to use both brain hemispheres instead of solely the left hemisphere, especially considering the Allen Human Brain Atlas (AHBA) predominantly featuring gene data from the left hemisphere. Given the AHBA's limited spatial coverage of expression data in the right hemisphere, our approach involved mirroring the existing tissue samples across the left-right hemisphere boundary using the abagen toolbox,1 a practice supported by findings that suggest minimal lateralization of microarray expression.2,3 Further details are provided in previous work employing ABAnnotate.4 These studies are now referenced in our methods section.

      Comment 3.5: “The second latent variable was not further discussed. If this result is deemed significant, it warrants a more detailed discussion. "

      Reply 3.5: Thank you for the suggestion. We have added a paragraph to the discussion that discusses the second latent variable in greater detail. It reads: “The second latent variable accounted for 22.33% of shared variance and linked higher insulin resistance and lower dyslipidemia to lower thickness and volume in lateral frontal, posterior temporal, parietal and occipital regions. The distinct covariance profile of this latent variable, compared to the first, likely indicates a separate pathomechanistic connection between MetS components and brain morphology. Given that HbA1c and blood glucose were the most significant contributors to this variable, insulin resistance might drive the observed clinicalanatomical relationship.”

      Comment 3.6: “I suggest appending positive MetS effects after "..., insular, cingulate and temporal cortices;" for two reasons: a). The "positive MetS effects" might represent crucial findings that should not be omitted. b). Including both negative and positive effects ensures that subsequent references to "this pattern" are more precise.”

      Reply 3.6: We concur with the notion that the positive MetS effects should be highlighted as well. We modified the first discussion paragraph now mentioning them.

      Comment 3.7: “I would appreciate further clarification on this sentence and the use of the term "uniform" in this context. Does this suggest that despite the heterogeneity in the physiological and pathological characteristics of the various MetS components (e.g., obesity, hypertension), their impacts on cortical thickness manifest similarly? How is it that these diverse components lead to "uniform" effects on cortical thickness? Does this observation align with or deviate from previous findings in the literature?”

      Reply 3.7: Thank you for highlighting the ambiguity in our previous explanation. We agree that the complexity of the relationship between MetS components and brain morphology requires clearer articulation. To address this, we have revised the relevant sentence for better clarity. It now reads: „This finding indicates a relatively uniform connection between MetS and brain morphology, implying that the associative effects of various MetS components on brain structure are comparatively similar, despite the distinct pathomechanisms each component entails.“

      Comment 3.8: “Figure 1 does not have the labels "c)" and "d)". ”

      Reply 3.8: Thank you. We have modified figure 1 and made sure that the caption correctly references its content.

      Comment 3.10: “Incorrect figure/table citation:

      • Page 18 line 418: "(figure 2b and 1c)" à (figure 2b and 2c).

      • Page 18 line 419: "(supplementary figures S8 and S12-13)" à (supplementary figures S11 and S1516).

      • In the supplementary material, "Text S5 - Case-control analysis" section contains several figure or table citation errors. Please take a moment to review and correct them.”

      Reply 3.10: Thank you for bringing this to our attention. We have corrected the figure and table citation errors.

      Comment 3.11: “Page 8 line 184: The more commonly used term is "insulin resistance" rather than "insuline resistance.”

      Reply 3.11: We now use “insulin resistance” throughout the manuscript.

      Comment 3.12: “Nevertheless, variations in gene sets may introduce a degree of heterogeneity in the results (Seidlitz, et al., 2020; Martins et al., 2021). Consequently, further validation or exploratory analyses utilizing different gene sets can yield more compelling results and conclusions.”

      Reply 3.12: Thank you for your insightful comment regarding the potential heterogeneity introduced by variations in gene sets. We agree that exploring different gene sets could indeed enhance the robustness and generalizability of our findings. However, we think conducting a comprehensive methodological analysis of the available cell-type specific gene sets is a substantial effort and warrants its own investigation to thoroughly implement it and assess its implications. We also like to highlight that we are adhering to previous practices in our analysis setup.4,5

      References

      (1) Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Jbabdi S, Makin TR, Jbabdi S, Burt J, Hawrylycz MJ, eds. eLife. 2021;10:e72129. doi:10.7554/eLife.72129

      (2) Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391-399. doi:10.1038/nature11405

      (3) Hawrylycz M, Miller JA, Menon V, et al. Canonical genetic signatures of the adult human brain. Nat Neurosci. 2015;18(12):1832-1844. doi:10.1038/nn.4171

      (4) Lotter LD, Saberi A, Hansen JY, et al. Human cortex development is shaped by molecular and cellular brain systems. Published online May 5, 2023:2023.05.05.539537. doi:10.1101/2023.05.05.539537

      (5) Lotter LD, Kohl SH, Gerloff C, et al. Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion. Neuroscience & Biobehavioral Reviews. 2023;146:105042. doi:10.1016/j.neubiorev.2023.105042

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This study presents a useful characterization of the biochemical consequences of a disease-associated point mutation in a nonmuscle actin. The study uses solid and well-characterized in vitro assays to explore function. In some cases the statistical analyses are inadequate and several important in vitro assays are not employed.

      Public Reviews:

      Reviewer #1 (Public Review):

      Strengths:

      The authors first perform several important controls to show that the expressed mutant actin is properly folded, and then show that the Arp2/3 complex behaves similarly with WT and mutant actin via a TIRF microscopy assay as well as a bulk pyrene-actin assay. A TIRF assay showed a small but significant reduction in the rate of elongation of the mutant actin suggesting only a mild polymerization defect.

      Based on in silico analysis of the close location of the actin point mutation and bound cofilin, cofilin was chosen for further investigation. Faster de novo nucleation by cofilin was observed with mutant actin. In contrast, the mutant actin was more slowly severed. Both effects favor the retention of filamentous mutant actin. In solution, the effect of cofilin concentration and pH was assessed for both WT and mutant actin filaments, with a more limited repertoire of conditions in a TIRF assay that directly showed slower severing of mutant actin.

      Lastly, the mutated residue in actin is predicted to interact with the cardiomyopathy loop in myosin and thus a standard in vitro motility assay with immobilized motors was used to show that non-muscle myosin 2A moved mutant actin more slowly, explained in part by a reduced affinity for the filament deduced from transient kinetic assays. By the same motility assay, myosin 5A also showed impaired interaction with the mutant filaments.

      The Discussion is interesting and concludes that the mutant actin will co-exist with WT actin in filaments, and will contribute to altered actin dynamics and poor interaction with relevant myosin motors in the cellular context. While not an exhaustive list of possible defects, this is a solid start to understanding how this mutation might trigger a disease phenotype.

      We thank the reviewer for the positive evaluation of our work.

      Weaknesses:

      • Potential assembly defects of the mutant actin could be more thoroughly investigated if the same experiment shown in Fig. 2 was repeated as a function of actin concentration, which would allow the rate of disassembly and the critical concentration to also be determined.

      The polymerization rate of individual filaments observed in TIRFM experiments showed only minor changes, as did the bulk-polymerization rate of 2 µM actin in pyrene-actin based experiments. Therefore, we decided not to perform additional pyrene-actin based experiments, in which we titrate the actin concentration, as we expect only very small changes to the critical concentration. Instead, we focused on the disturbed interaction with ABPs, as we assume these defects to be more relevant in an in vivo context. Using pyrene-based bulkexperiments, we did determine the rate of dilution-induced depolymerization of mutant filaments and compare them with the values determined for wt (Figure 5A, Table 1).

      • The more direct TIRF assay for cofilin severing was only performed at high cofilin concentration (100 nM). Lower concentrations of cofilin would also be informative, as well as directly examining by the TIRF assay the effect of cofilin on filaments composed of a 50:50 mixture of WT:mutant actin, the more relevant case for the cell.

      The TIRF assay for cofilin severing was performed initially over the cofilin concentration range from 20 to 250 nM. The results obtained in the presence of 100 nM cofilin allow a particularly informative depiction of the differences observed with mutant and WT actin. This applies to the image series showing the changes in filament length, cofilin clusters, and filament number as well as to the graphs showing time dependent changes in the number of filaments and total actin fluorescence. We have not included the results for a 50:50 mixture of WT:mutant actin because its attenuating effect is documented in several other experiments in the manuscript.

      • The more appropriate assay to determine the effect of the actin point mutation on class 5 myosin would be the inverted assay where myosin walks along single actin filaments adhered to a coverslip. This would allow an evaluation of class 5 myosin processivity on WT versus mutant actin that more closely reflects how Myo5 acts in cells, instead of the ensemble assay used appropriately for myosin 2.

      Our results with Myo5A show a less productive interaction with mutant actin filaments as indicated by a 1.7-fold reduction in the average sliding velocity and an increase in the optimal Myo5A-HMM surface density from 770 to 3100 molecules per µm2. These results indicate a reduction in binding affinity and coupling efficiency, with a likely impact on processivity. We expect only a small incremental gain in knowledge about the extent of changes by performing additional experiments with an inverted assay geometry, given that under physiological conditions the motor properties of Myo5A and other cytoskeletal myosins are modulated by other factors such as the presence of tropomyosin isoforms and other actin binding proteins.

      Reviewer #2 (Public Review):

      Greve et al. investigated the effects of a disease-associated gamma-actin mutation (E334Q) on actin filament polymerization, association of selected actin-binding proteins, and myosin activity. Recombinant wildtype and mutant proteins expressed in sf9 cells were found to be folded and stable, and the presence of the mutation altered a number of activities. Given the location of the mutation, it is not surprising that there are changes in polymerization and interactions with actin binding proteins. Nevertheless, it is important to quantify the effects of the mutation to better understand disease etiology.

      We thank the reviewer for the positive evaluation of our work.

      Some weaknesses were identified in the paper as discussed below.

      • Throughout the paper, the authors report average values and the standard-error-of-the-mean (SEM) for groups of three experiments. Reporting the SEM is not appropriate or useful for so few points, as it does not reflect the distribution of the data points. When only three points are available, it would be better to just show the three different points. Otherwise, plot the average and the range of the three points.

      We have gone through the manuscript carefully to correct any errors in the statistics, as explained below.

      Figure 1B, 5B, 5C, 5D, 8D, 9B, and 8 – figure supplement 2 all show the mean ± SD, as also correctly reported for Figure 8E and 8F in the figure legend. The statement, that these figures show the mean ± SEM was inaccurate. We corrected this mistake for all the listed figures. Furthermore, we now give the exact N for every experiment in the figure legend.

      Figure 2C, 2E, 2F, 4B, 5A, 6B-E showed the mean ± SEM. As suggested by the reviewer, we corrected the figures to show the mean ± SD.

      We still refer to the mean ± SEM in Figure 2B, where elongation rates for more than 100 filaments were recorded, and in Figure 8B, where sliding velocities for several thousand actin filaments were measured.

      • The description and characterization of the recombinant actin is incomplete. Please show gels of purified proteins. This is especially important with this preparation since the chymotrypsin step could result in internally cleaved proteins and altered properties, as shown by Ceron et al (2022). The authors should also comment on N-terminal acetylation of actin.

      We added an additional figure showing the purification strategy for the recombinant cytoskeletal γ –actin WT and p.E334Q protein with exemplary SDS-gels from different stages of purification (Figure 1 – figure supplement 1).

      In a previous paper, we reported the mass spectrometric analysis of the post-translational modifications of recombinant human β- and γ-cytoskeletal actin produced in Sf-9 cells. (Müller et al., 2013, Plos One). Recombinant actin showing complete N-terminal processing resulting in cleavage of the initial methionine and acetylation of the following aspartate (β-actin) or glutamate (γ-actin) is the predominant species in the analyzed preparations (> 95 %). While the recombinant actin in the 2013 study was produced tag-free and purified by affinity chromatography using the column-immobilized actin-binding domain of gelsolin (G4-G6), we have no reason to assume that the purification strategy using the actin-thymosin-β4 changes the efficiency of the N-terminal processing in Sf-9 cells. This is supported by our, yet unpublished, mass-spectrometric studies on recombinant human α-cardiac actin purified using the actin- thymosin-β4 fusion construct, which revealed actin species with an acetylated aspartate-3. This N-terminal modification of α-cardiac actin is catalyzed by the same actinspecific acetyltransferase (NAA80) as the acetylation of asparate-2 or glutamate-2 in cytoskeletal actin isoforms (Varland et al., 2019, Trends in Biochemical Sciences). Furthermore, additional studies that used the actin-thymosin-β4 fusion construct for the production of recombinant human cytoskeletal actin isoforms in Pichia pastoris reported robust N-terminal acetylation, when the actin was co-produced with NAA80 (In contrast to Sf-9 cells, NAA80 is not endogenously expressed in Pichia pastoris) (Hatano et al., 2020, Journal of Cell Science).

      We therefore, added the following statement to the manuscript:

      “Purification of the fusion protein by immobilized metal affinity chromatography, followed by chymotrypsin–mediated cleavage of C–terminal linker and tag sequences, results in homogeneous protein without non–native residues and native N-terminal processing, which includes cleavage of the initial methionine and acetylation of the following glutamate. “

      • The authors do not use the best technique to assess actin polymerization parameters. Although the TIRF assay is excellent for some measurements, it is not as good as the standard pyrene-actin assays that provide critical concentration, nucleation, and polymerization parameters. The authors use pyrene-actin in other parts of the paper, so it is not clear why they don't do the assays that are the standard in the actin field.

      The polymerization rate of individual filaments observed in TIRFM experiments showed only minor changes, as did the bulk-polymerization rate of 2 µM actin in pyrene-actin based experiments. Therefore, we decided not to perform additional pyrene-actin based experiments, in which we titrate the actin concentration, as we expect only very small changes to the critical concentration. Instead, we focused on the disturbed interaction with ABPs, as we assume these defects to be more relevant in an in vivo context. Using pyrene-based bulkexperiments, we did determine the rate of dilution-induced depolymerization of mutant filaments and compare them with the values determined for WT (Figure 5A, Table 1).

      • The authors' data suggest that, while the binding of cofilin-1 to both the WT and mutant actins remains similar, the major defect of the E334Q actin is that it is not as readily severed/disassembled by cofilin. What is missing is a direct measurement of the severing rate (number of breaks per second) as measured in TIRF.

      The severing rate as measured in TIRF is dependent on a number of parameters in a nonlinear manner. Therefore, we opted to show the combination of images directly showing the progress of the reaction and graphs summarizing the concomitant changes in cofilin clusters, actin filaments, actin-related fluorescence intensity and cofilin-related fluorescence intensity.

      • Figure 4 shows that the E334Q mutation increases rather than decreases the number of filaments that spontaneously assemble in the TIRF assay, but it is unclear how reduced severing would lead to increased filament numbers, rather, the opposite would be expected. A more straightforward approach would be to perform experiments where severing leads to more nuclei and therefore enhances the net bulk assembly rate.

      Figure 4 shows polymerization experiments that were started from ATP-G-actin in the presence of cofilin-1. These experiments show clearly that, especially at the higher cofilin-1 concentration (100 nM), the filament number is strongly increased in experiments performed with mutant actin. Inspection of the corresponding videos of these TIRFM experiments suggest that the increased number of filaments must result from an increased number of de novo nucleation events and not primarily from a mutation-induced change in severing susceptibility. The observation of a cofilin-stimulated increase in the de novo nucleation efficiency of actin was initially described by Andrianantoandro & Pollard (2006, Molecular Cell) using TIRFMbased experiments and is thought to arise from the stabilization of thermodynamically unfavorable actin dimers and trimers by cofilin. While the exact role of this cofilin-mediated effect in vivo is not completely clear, it is thought to contribute to cofilin-meditated actin dynamics synergistically with cofilin-mediated severing. It is therefore necessary, to clearly distinguish between the two effects of cofilin in vitro: stimulation of de novo nucleation and stimulation of filament disassembly. Our data indicated that the E334Q mutation affects these two effects differentially, as we state in the abstract and in the discussion.

      Abstract: “E334Q differentially affects cofilin-mediated actin dynamics by increasing the rate of cofilin-mediated de novo nucleation of actin filaments and decreasing the efficiency of cofilin-mediated filament severing.”

      Discussion: “Cofilin-mediated severing and nucleation were previously proposed to synergistically contribute to global actin turnover in cells (Andrianantoandro & Pollard, 2006; Du & Frieden, 1998). Our results show that the mutation affects these different cofilin functions in actin dynamics in opposite ways. Cofilin-mediated filament nucleation is more efficient for p.E334Q monomers, while cofilin-mediated severing of filaments containing p.E334Q is significantly reduced. The interaction of both actin monomers and actin filaments with ADF/cofilin proteins involves several distinct overlapping reactions. In the case of actin filaments, cofilin binding is followed by structural modification of the filament, severing and depolymerizing the filament (De La Cruz & Sept, 2010). Cofilin binding to monomeric actin is followed by the closure of the nucleotide cleft and the formation of stabilized “long-pitch” actin dimers, which stimulate nucleation (Andrianantoandro & Pollard, 2006)”.

      We interpret the reviewer's suggestion to mean that additional pyrene-actin-based bulk polymerization experiments should be performed to investigate the bulk-polymerization rate of ATP-G-actin in the presence of cofilin-1. In our understanding, these experiment would not provide additional value as 1) An observed increase of the bulk-polymerization rate cannot be directly correlated to a change of the efficiency of de novo nucleation or severing and 2) the effect of the mutation on cofilin-mediated filament disassembly was extensively analyzed in other experiments starting from preformed actin filaments. Moreover, our results are consistent with in silico modelling and normal mode analysis of the WT and mutant actin-cofilin complex.

      • Figure 5 A: in the pyrene disassembly assay, where actin is diluted below its critical concentration, cofilin enhances the rate of depolymerization by generating more free ends. The E334Q mutation leads to decreased cofilin-induced severing and therefore lower depolymerization. While these data seem convincing, it would be better to present them as an XY plot and fit the data to lines for comparison of the slopes.

      We now present the data as suggested by the reviewer. Furthermore, we determined the apparent second-order rate constant for cofilin-induced F-actin depolymerization (kc) to quantify the observed differences between WT, mutant and heterofilaments, as suggested by the reviewer.

      The paragraph describing these results was changed accordingly:

      “The observed rate constant values are linearly dependent on the concentration of cofilin–1 in the range 0–40 nM, with the slope corresponding to the apparent second– order rate constant (kC) for the cofilin-1 induced depolymerization of F–actin. In experiments performed with p.E334Q filaments, the value obtained for kC was 4.2-fold lower (0.81 × 10-4 ± 0.08 × 10-4 nM-1 s-1) compared to experiments with WT filaments (3.42 × 10-4 ± 0.22 × 10-4 nM-1 s-1). When heterofilaments were used, the effect of the mutation was reduced to a 2.2-fold difference compared to WT filaments (1.54 × 10-4 ± 0.11 × 10-4 nM-1 s-1).”

      • Figure 5 B and C: the cosedimentation data do not seem to help elucidate the underlying mechanism. While the authors report statistical significance, differences are small, especially for gel densitometry measurements where the error is high, which suggests that there may be little biological significance. Importantly, example gels from these experiments should be shown, if not the complete set included in the supplement. In B, the higher cofilin concentrations would be expected to stabilize the filaments and thus the curve should be Ushaped.

      We do not completely agree with the reviewer on this point. We think the co-sedimentation experiments are useful, as they show that cofilin-1 efficiently binds to mutant filaments, but is less efficient in stimulating disassembly in these endpoint-experiments. This information is not provided by the analysis of the effect of cofilin-1 on the bulk-depolymerization rate and adds to our understanding of the defect of the actin-cofilin interaction for the mutant.

      While we agree with the reviewer on the point that co-sedimentation experiments must be repeated several times to produce reliable data, we cannot fully grasp the reasoning behind the statement “While the authors report statistical significance, differences are small, especially for gel densitometry measurements where the error is high, which suggests that there may be little biological significance.”. We interpret this statement as advice to be cautious when extrapolating the observed perturbances of cofilin-mediated actin dynamics in vitro to the in vivo context. We think we are cautious about this throughout the manuscript.

      The author expects a U-shape curve, as high cofilin concentrations are reported to stabilize actin filaments by completely decorating the filament before severing-prone boundaries between cofilin-decorated and undecorated regions are generated. We have also performed these experiment with cytoskeletal β-actin and human cofilin-1 and never observed this U shape. This indicates that significant filament disassembly also happens at high cofilin concentrations, most likely directly after mixing of F-actin and cofilin. We cannot rule out that the incubation time plays an important role and that the U-shape only appears after longer incubation times. We also want to direct the reviewer to the publication “A Mechanism for Actin Filament Severing by Malaria Parasite Actin Depolymerizing Factor 1 via a Low Affinity Binding Interface” (Wong et al. 2013, JBC) in which comparable co-sedimentation experiments were performed (Figure 5E-G) with rabbit skeletal α-actin and human cofilin-1 and also no Ushaped curves were observed, even at higher molar excess of cofilin-1 compared to our experiments and with longer incubation times (1 hour vs. 10 minutes).

      We now included an exemplary gel showing co-sedimentation experiments performed with WT, mutant actin and different concentrations of cofilin at pH 7.8 in the manuscript (Figure 5 – figure supplement 2)

      • Figure 5 D: these data show that the binding of cofilin to WT and E334Q actin is approximately the same, with the mutant binding slightly more weakly. It would be clearer if the two plots were normalized to their respective plateaus since the difference in arbitrary units distracts from the conclusion of the figure. If the difference in the plateaus is meaningful, please explain.

      As suggested by the reviewer, we normalized the data for a better understanding of the message conveyed.

      • Figure 6: It is assumed that the authors are trying to show in this figure that cofilin binds both actins approximately the same but does not sever as readily for E334Q actin. The numerous parameters measured do not directly address what the authors are actually trying to show, which presumably is that the rate of severing is lower for E334Q than WT. It is therefore puzzling why no measurement of severing events per second per micron of actin in TIRF is made, which would give a more precise account of the underlying mechanism.

      The severing rate as measured in TIRF is dependent on a number of parameters in a nonlinear manner. Therefore, we opted to show the combination of images directly showing the progress of the reaction and graphs summarizing the concomitant changes in cofilin clusters, actin filaments, actin-related fluorescence intensity and cofilin-related fluorescence intensity.

      • Actin-activated steady-state ATPase data of the NM2A with mutant and WT actin would have been extremely useful and informative. The authors show the ability to make these types of measurements in the paper (NADH assay), and it is surprising that they are not included for assessing the myosin activity. It may be because of limited actin quantities. If this is the case, it should be indicated.

      Indeed, the measurement of the steady-state actin-activated ATPase with recombinant cytoskeletal actin is very material-intensive and therefore costly, as a complete titration of actin is required for the generation of meaningful data. Since the vast majority of our assays involving a myosin family member were performed with NM2A-HMM, we decided to perform a full actin titration of the steady-state actin-activated ATPase of NM2A-HMM with WT and mutant filaments. The results of these experiments are now shown in Figure 8C. The panel showing the results used for determining the dissociation rate constants (k-A) for the interaction of NM2C-2R with p.E334Q or WT γ –actin in the absence of nucleotide was moved to the supplement (Figure 8 – figure supplement 2).

      We added the following paragraph to the Material and Methods section concerning the Steady-State ATPase assay:

      “For measurements of the basal and actin–activated NM2A–HMM ATPase, 0.5 µM MLCKtreated HMM was used. Phalloidin–stabilized WT or mutant F-actin was added over the range of 0–25 µM. The change in absorbance at 340 nm due to oxidation of NADH was recorded in a Multiskan FC Microplate Photometer (Thermo Fisher Scientific, Waltham, MA, USA). The data were fitted to the Michaelis-Menten equation to obtain values for the actin concentration at half-maximal activation of ATP-turnover (Kapp) and for the maximum ATP-turnover at saturated actin concentration (kcat).”

      Furthermore, we added a description of the results of the experiments to the Results section of the manuscript:

      “Using a NADH-coupled enzymatic assay, we determined the ability of p.E334Q and WT filaments to activate the ATPase of NM2A-HMM over the range of 0-25 µM F-actin (Figure 8C). While we observed no significant difference in Kapp, indicated by the actin concentration at half-maximal activation, in experiments with p.E334Q filaments (2.89 ± 0.49 µM) and WT filaments (3.20 ± 0.74 µM), we observed a 28% slower maximal ATP turnover at saturating actin concentration (kcat) with p.E334Q filaments (0.076 ± 0.005 s-1 vs. 0.097 ± 0.002 s-1).”

      • (line 310) The authors state that they "noticed increased rapid dissociation and association events for E334Q filaments" in the motility assay. This observation motivates the authors to assess actin affinities of NM2A-HMM. Although differences in rigor and AM.ADP affinities are found between mutant and WT actins, the actin attachment lifetimes (many minutes) are unlikely to be related to the rapid association and dissociation event seen in the motility assay. Rather, this jiggling is more likely to be related to a lower duty ratio of the myosins, which appears to be the conclusion reached for the myosin-V data. These points should be clarified in the text.

      We changed the text in accordance with the reviewer’ suggestion. It reads now: Cytoskeletal –actin filaments move with an average sliding velocity of 195.3 ± 5.0 nm s–1 on lawns of surface immobilized NM2A–HMM molecules (Figure 8A, B). For NM2A-HMM densities below about 10,000 molecules per μm2, the average sliding speed for cytoskeletal actin filaments drops steeply (Hundt et al, 2016). Filaments formed by p.E334Q actin move 5fold slower, resulting in an observed average sliding velocity of 39.1 ± 3.2 nm/s. Filaments copolymerized from a 1:1 mixture of WT and p.E334Q actin move with an average sliding velocity of 131.2 ± 10 nm s–1 (Figure 8A, B). When equal densities of surface-attached WT and mutant filaments were used, we observed that the number of rapid dissociation and association events increased markedly for p.E334Q filaments (Figure 8 – video supplement 7– 9).

      Using a NADH-coupled enzymatic assay, we determined the ability of p.E334Q and WT filaments to activate the ATPase of NM2A-HMM over the range of 0-25 µM F-actin (Figure 8C). While we observed no significant difference in Kapp, indicated by the actin concentration at halfmaximal activation, in experiments with p.E334Q filaments (2.89 ± 0.49 µM) and WT filaments (3.20 ± 0.74 µM), we observed a 28% slower maximal ATP turnover at saturating actin concentration (kcat) with p.E334Q filaments (0.076 ± 0.005 s-1 vs. 0.097 ± 0.002 s-1). To investigate the impact of the mutation on actomyosin–affinity using transient–kinetic approaches, we determined the dissociation rate constants using a single–headed NM2A–2R construct (Figure 8D). …..

      • (line 327) The authors report that the 1/K1 value is unchanged. There are no descriptions of this experiment in the paper. I am assuming the authors measured the ATP-induced dissociation of actomyosin and determined ATP affinity (K1) from this experiment. If this is the case, they should describe the experiment and show the data, provide a second-order rate constate for ATP binding, and report the max rate of dissociation (k2). This is a kinetic experiment done frequently by this group, so the absence of these details is surprising.

      In the previous version of the manuscript, the method used to determine 1/K1 (ATP-induced dissociation of the actomyosin complex) was described in the Material and Methods paragraph “Transient kinetic analysis of the actomyosin complex” and the values obtained for 1/K1 were given in Table 1. We now included the experimental data as an additional figure in the manuscript (Figure 8 – figure supplement 3). Furthermore, we also give the maximal dissociation rate k+2 and the apparent second-order rate constant for ATP-binding (K1k+2) for the WT and mutant actomyosin complex in Table 1. Therefore, we changed the paragraph in the Results section concerning this experiment to:

      “The apparent ATP–affinity (1/K1), the maximal dissociation rate of NM2A from F-actin in the presence of ATP (k+2), and the apparent second-order rate constant of ATP binding (K1k+2) showed no significant differences for complexes formed between NM2A and WT or p.E334Q filaments (Table 1, Figure 8 – figure supplement 3).”

      and the section in the Material and Methods to:

      “The apparent ATP–affinity of the actomyosin complex was determined by mixing the apyrase–treated, pyrene–labeled, phalloidin–stabilized actomyosin complex with increasing concentrations of ATP at the stopped–flow system. Fitting an exponential function to the individual transients yields the ATP–dependent dissociation rate of NM2A–2R from F–actin (kobs). The kobs–values were plotted against the corresponding ATP concentrations and a hyperbola was fitted to the data. The fit yields the apparent ATP–affinity (1/K1) of the actomyosin complex and the maximal dissociation rate k+2.

      The apparent second–order rate constant for ATP binding (K1k+2) was determined by applying a linear fit to the data obtained at low ATP concentrations (0 – 25 µM).”

      For a better understanding of the numerous rate and equilibrium constants, we have now included a figure showing the kinetic reaction scheme of the myosin ATPase cycle (Figure 8 – figure supplement 1).

      Recommendations for the authors:

      Reviewer #1:

      • The subdomains of actin are mislabeled in Fig. 1A.

      The labeling of the subdomains has been corrected.

      • Additional experimental data addressing the 3 weaknesses noted in the public review would be informative but are not essential in my opinion. Examining the effect of cofilin on severing by the TIRF assay in more detail and using a processivity assay for myosin V (immobilized actin) would be the two aspects I would most value.

      The TIRF assay for cofilin severing was performed initially over the cofilin concentration range from 20 to 250 nM. The results obtained in the presence of 100 nM cofilin allow a particularly informative depiction of the differences observed with mutant and WT actin. This applies to the image series showing the changes in filament length, cofilin clusters, and filament number as well as to the graphs showing time dependent changes in the number of filaments and total actin fluorescence. We have not included the results for a 50:50 mixture of WT:mutant actin because its attenuating effect is documented in several other experiments in the manuscript.

      Our results with Myo5A show a less productive interaction with mutant actin filaments as indicated by a 1.7-fold reduction in the average sliding velocity and an increase in the optimal Myo5A-HMM surface density from 770 to 3100 molecules per µm2. These results indicate a reduction in binding affinity and coupling efficiency, with a likely impact on processivity. Given that Myo5A is only one of many cytoskeletal myosin motors and that the motor properties of all myosins are modulated by the presence of tropomyosin isoforms and other actin binding proteins, we expect only a small incremental gain in knowledge by performing additional experiments with an inverted assay geometry.

      Reviewer #2:

      • The authors should address the concerns regarding the statistical methodologies.

      We have gone through the manuscript carefully to correct any errors in the statistics, as explained below.

      Figure 1B, 5B, 5C, 5D, 8D, 9B, and 8 – figure supplement 2 all show the mean ± SD, as also correctly reported for Figure 8E and 8F in the figure legend. The statement, that these figures show the mean ± SEM was wrong and we corrected this mistake for all the listed figures. Furthermore, we now give the exact N for every experiment in the figure legend.

      Figure 2C, 2E, 2F, 4B, 5A, 6B-E indeed showed the mean ± SEM. As the reviewer rightly points out, this is not the appropriate way to deal with such sample sizes. We therefore corrected the figures to show the mean ± SD.

      We still refer to the mean ± SEM in Figure 2B, where elongation rates for more than 100 filaments were recorded, and in Figure 8B, where sliding velocities for several thousand actin filaments were measured.

      • The authors should present the actin titration of the steady state ATPase activity for at least one of the myosins, or preferably all of them.

      An actin titration of the steady state ATPase activity of NM-2A has been included in the revised version of the manuscript (Fig 8C).

      • The authors should consider the use of pyrene-actin in measuring the assembly/disassembly of actin.

      Values for the rate of actin assembly/disassembly measured with pyrene-actin are given in Table 1. Based on the small changes observed, we did not determine the critical actin concentration for the mutant construct.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      We thank reviewer #1 for identifying the major caveats of the paper, and have split them out into separate comments below to address them.

      Comment 1) The caveats are that ecosystem processes beyond water availability are not investigated although they are brought into play in the title and in the paper

      Author response: We disagree that water availability is the only ecosystem process investigated in this study, as herbivory, plant mortality, and the maintenance of diversity in higher trophic levels are important processes within ecosystems. We have added text to the abstract and introduction clarifying that we consider these response measures to be ecosystem processes. Further language to this effect already exists in the abstract, methods, and discussion.

      Comment 2) That herbivory beyond leaf damage was not reported (there might be none, the reader needs to be shown the evidence for this)

      Author response: This is typically how herbivory is assessed in ecological studies, and our focus is on folivores. There may be additional herbivory in the form of fluid-sucking insects, shoot/root herbivory, etc., but these were not assessed. It would be interesting to assess these other forms of herbivory to see if they respond similarly with additional studies.

      Comment 3) That herbivore diversity is defined by leaf damage (authors need to give evidence that this is a valid inference)

      Author response: We thank reviewer #1 for pointing out the lack of written support for this claim. We have modified the methods (lines 138-139; 214-217) to clarify that this is a useful proxy for insect richness in the Piper system, and have added citations demonstrating it has been found to correlate well with insect richness in tropical forests.

      Comment 4) That the plots were isolated from herbivores beyond their borders

      Author response: This was not an assumption of the study. We have modified the methods (line 200) to make this clearer to the reader.

      Comment 5) That the effects of extreme climate events were isolated to Peru

      Author response: This was not an assumption of the study, rather it is an observation. While we consider it important to include observed climate differences between sites in the interpretation of our results, it was not necessary for there to be extreme climate events at other sites as we consider manipulated water availability to represent changes in precipitation that are expected to occur at these sites with climate change.

      Comment 6) That intraspecific variation in the host plants needs to be explained and interpreted in more detail

      Author response: We thank reviewer #1 for identifying that our current explanations needed development. We have modified the introduction to explore potential mechanisms relating intraspecific diversity to ecosystem function based on recent studies, and have modified the discussion to bring focus to why the effects of intraspecific differ from interspecific.

      Reviewer #1 (Recommendations For The Authors):

      Comment 1) Pare this material down to simpler results. The most significant to me is the intraspecific variation in damage. Were this broken out and reported in some detail it could be quite interesting. I find the results to be a confusing blizzard of multiple factors that differ among sites; after reading the paper twice I could not recall the takeaway lesson beyond that drought wrecks the diversity of herbivores and sometimes even kills the host plant.

      Author response: We agree that the results are complicated given the variation in effects among sites, but this variation and complexity is important – and is in itself is one of the takeaway points. Unfortunately, nature is not simple. We have made several large edits to the results section, including the removal of methodological and otherwise redundant information, to hopefully bring the major takeaways into focus.

      Reviewer #2 (Public Review):

      Comment 1) This is an important and large experimental study examining the effects of plant species richness, plant genotypic richness, and soil water availability on herbivory patterns on Piper species in tropical forests.

      A major strength is the size of the study and the fact that it tackled so many potentially important factors simultaneously. The authors examined both interspecific plant diversity and intraspecific plant diversity. They crossed that with a water availability treatment. And they repeated the experiment across five geographically separated sites.

      The authors find that both water availability and plant diversity, intraspecific and interspecific, influence herbivore diversity and herbivory, but that the effects differ in important ways across sites. I found the study to be solid and the results to be very convincing. The results will help the field grapple with the importance of environmental change and biodiversity loss and how they structure communities and alter species interactions.

      Author response: We thank reviewer #2 for their kind words.

      Reviewer #2 (Recommendations For The Authors):

      Comment 1) I was confused about why the authors measured species diversity/richness as a proportion of the species pool. This means that the metric of richness decreases if species are added to the species pool but not the plot/experiment. I think I understand it, but I suggest the authors explain this choice.

      Author response: We thank reviewer #2 for pointing out that this was confusing. We have clarified the methods (lines 228-232) to explain that this choice was made to allow easier comparison between intra- and interspecific richness.

      Comment 2) One of the stronger estimated relationships was a positive effect of plant species richness on insect richness. I found it a little hard to interpret this relationship. Is this just because there are host species specialists? So, with more host species there are more herbivore species? Or does insect richness increase multiplicatively with increasing plant species richness? One way to look for this would be for the authors to examine the relationship between plant species richness and the average number of herbivore damage types per plant species.

      Author response: We agree that this is important for the reader to understand and have added text to the introduction and discussion sections explaining that this is the expectation based on theory and other empirical studies. We have additionally added text to the discussion (lines 386-388) pointing out that this pattern was not observed at all sites. While we agree that it would be interesting to explore if this effect was additive or multiplicative, we do not believe this is in the scope of the paper due to the methods used to measure insect richness.

      Comment 3) Unless I missed it, some important information about the models was missing. E.g., what distributions were assumed for each of the variables? Any transformations?

      Author response: We thank reviewer #2 for pointing this out, this information has been added to the methods (lines 272-274)

      Comment 4) Why is there no model with water addition affecting insect richness directly but not percent herbivory directly?

      Author response: While we originally decided to not include this model due to lack of theoretical support and low statistical performance, we have added references to this model (now model II) in the methods and results for consistency and to make model performance clearer to the reader. We have additionally moved supplemental table S1 to the main text to make the models and hypotheses tested by each model more accessible.

      Comment 5) Fig. 2. What are the percentages above the figures? Maybe PD values?

      Author response: These values are now clarified in the figure caption

      Comment 6) L364 "can differ dramatically" This is vague and confusing. Differ in what way? From each other? Did the authors really expect plant richness to have the same effect on herbivory and plant survival? What would it mean anyway for plant richness to have the same effect on herbivory and plant survival?

      Author response: We agree that the language here is confusing and thank reviewer #1 for drawing our attention to it. We have modified the discussion (lines 363-365) to clarify that the direction of effect of intraspecific richness can vary from the direction of effect of interspecific richness, rather than the effects on different response variables varying from each other.

      Comment 7) L 375 "only meaningful differences" This statement feels a little overly strong. It seems like there is a good argument for this, but there could be other things going on.

      Author response: We agree that the language here was unnecessarily strong, and have modified the discussion (lines 398-403) to focus on the lack of difference between methodologies at these two sites, and the observed differences in climate and community structure at each site.

    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Kohler et al analyze the impact of miR200c on cell motility in vitro and breast cancer metastasis in mouse models. The they show that miR200c represses metastasis to several different organs and propose that reduced motility is a significant cause of this. The experiments are generally sound and well performed. However, the insight gained with the study does not go much beyond what is already known about miR200c function in breast cancer. The experimental tools used in the study could provide the opportunity to reveal novel insights into the role of miR200c in metastasis. However, the investigators did not take full advantage of this and thus we are left with findings that are rather predictable based on the current literature. Details below.

      Major points:

      1. The primary weakness of this study is limited novelty. miR200c has been shown to regulate migration and invasion of breast cancer cells in several previous studies, and this includes analysis using the same breast cancer cell lines that Kohler et al use in the current study, MCF7 and MDA-MB-231 (Jurmeister et al Mol Cell Bio 2012; Zhang et al Genet Mol Res 2017) and a study by the same group (Ljepoja et al Plos One 2019). Moreover, previous studies have also shown that miR200c represses metastasis in two different claudin low triple negative breast cancer models, MDA-MB-231 and genetically-engineered p53 null transplantable model (Simpson et al Genes 2022, Knezevic et al Oncogene 2016). Of note, Kohler et al do analyze metastases not only in lungs, but also in liver, brain and spleen and this could be a source of novel insights depending on the scientific questions. Is the miR200c mediated repression of metastasis caused by the same mechanisms in all these organs, or is it context dependent? What about molecular mediators downstream of miR200c?
      2. The authors focus primarily on migration issues as the potential cause of miR200c mediated repression of metastasis. However, there is significant literature on the role of miR200c in cancer progression. miR200c has been associated with multiple cellular functions, including regulation of epithelial mesenchymal transition (EMT) by repressing key EMT transcription factors ZEB1 and ZEB2. EMT regulation of course may suggest an effect on cell motility, but also several other functions, such as stem cell activity, plasticity, survival under stress and many more. Indeed, in a clinical setting some may question the importance of migration, considering that breast cancer cells disseminate from the primary tumor early in the process and upon diagnosis the cells are likely already lodged in secondary organs. Therefore, it is probable that cell functions such as survival under stress, proliferation and plasticity would be of even higher importance compared to cell motility. I would think that miR200c functional studies need to go beyond cell motility to generate additional insights into its role in metastasis and reveal potentially actionable targets.
      3. The investigators use a dox inducible system to express miR200c in MDA-MB-231 mammary tumors in mice. The mice were treated with dox to induce miR200c when the tumors reached 200 mm3 in size. This is a rather early induction of miR200c and may not address the ability of miR200c to repress actively growing metastatic lesions. I think these experiments should also be done by waiting longer before miR200c induction. What happens if the tumors are allowed to grow to 500 mm3 or 750 mm3? This would really test the ability of miR200c to inhibit overt metastasis.

      Minor points:

      1. Although in some figures the plots/graphs show individual data points, this is not always the case. All box plots and bar graphs should show individual data points (biological replicates).
      2. Representative histological examples of the metastases in Figure 1C-1D should be shown.
      3. Presentation of the data in Figure 2C-2F is confusing. Statistics are also missing.

      Significance

      Although the study is technically sound, it suffers from limited novelty. Overall conclusions are predictable from previous studies. Of note, this study does provide somewhat more detailed analysis of migratory regulation by miR200c in cancer cells compared to previous reports. However, the study's advance is still quite modest.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this study, the authors aimed to investigate how cells respond to dynamic combinations of two stresses compared to dynamic inputs of a single stress. They applied the two stresses - carbon stress and hyperosmotic stress - either in or out of phase, adding and removing glucose and sorbitol.

      Both a strength and a weakness, as well as the main discovery, is that the cells' hyperosmotic response strongly requires glucose. For in-phase stress, cells are exposed to hyperosmotic shock without glucose, limiting their ability to respond with the well-studied HOG pathway; for anti-phase stress, cells do have glucose when hyperosmotically shocked, but experience a hypo-osmotic shock when both glucose and sorbitol are simultaneously removed. Responding with the HOG pathway and so amassing intracellular glycerol amplifies the impact of this hypo-osmotic shock. Counterintuitively then, it is the presence of glucose rather than the stress of its absence that is deleterious for the cells.

      The bulk of the paper supports these conclusions with clean, compelling time-lapse microscopy, including extensive analysis of gene deletions in the HOG network and measurements of both division and death rates. The methodology the authors develop is powerful and widely applicable.

      Some discussion of the value of applying periodic inputs would be helpful. Cells are unlikely to have previously seen such inputs, and periodic stimuli may reveal behaviours that are rarely relevant to selection.

      We thank the referee for his review. To answer the reviewer’s last comment, our main objective was not to study conditions that are ecologically relevant, but rather to perturb the system in an original way to reveal new mechanisms and properties of the system. The main advantage of periodic inputs over more complex or unpredictible types of temporal fluctuations is that they can be defined with few parameters that are easy to interpret and to integrate in biophysical models. For instance, by using periodic inputs we were able to investigate how changing the phasing of two stresses impacted fitness while keeping other parameters constant (the duration of each stress was kept constant). We added two sentences at the beginning of the discussion to highlight the value of using periodic inputs.

      We do not fully agree with the reviewer’s statement that periodic stimuli may reveal behaviours that are rarely relevant to selection. Indeed, many parameters of natural environments are known to vary periodically, such as light, temperature, predation, tides. Even if the periodic stimuli we use are artificial, they can still be a valuable tool to reveal new molecular processes. For instance, null mutants have been invaluable to understand biological systems despite being unlikely to reveal behaviours relevant to selection.

      The authors' findings demonstrate the tight links that can exist between metabolism and the ability to respond to stress. Their study appears to have parted somewhat from their original aim because of the HOG pathway's reliance on glucose. It would be interesting to see if the cells behaviour is simpler in periodically varying sorbitol and a stress where there is little known connection to the HOG network, such as nitrogen stress.

      The use of periodic nitrogen stress is a very interesting suggestion from both reviewers. However, we think it represents a large amount of work that deserves its own study. In particular, it would require first identifying a relevant period at which nitrogen fluctuations have an impact on division rate similar to what we observed for glucose fluctuations before performing experiments in AS and IPS conditions.

      Nitrogen starvation is known to induce filamentous growth via activation of components of the HOG pathway (Cullen and Sprague, 2012), with potential cross-talk between filamentous growth and hyperosmotic stress response. Therefore, periodic osmotic stress and periodic nitrogen starvation may interact in a complex way.

      Reviewer #2 (Public Review):

      The authors have used microfluidic channels to study the response of budding yeast to variable environments. Namely, they tested the ability of the cells to divide when the medium was repeatedly switched between two different conditions at various frequencies. They first characterized the response to changes in glucose availability or in the presence of hyper-osmotic stress via the addition of sorbitol to the medium. Subsequently, the two stresses were combined by applying the alternatively or simultaneously (in-phase). Interestingly, the observed that the in-phase stress pattern allowed more divisions and low levels of cell mortality compared to the alternating stresses where cells were dividing slowly and many cells died. A number mutants in the HOG pathway were tested in these conditions to evaluate their responses. Moreover, the activation of the MAPK Hog1 and the transcriptional induction of the hyper-osmotic stress promoter STL1 were quantified by fluorescence microscopy.

      Overall, the manuscript is well structured and data are presented in a clear way. The time-lapse experiments were analyzed with high precision. The experiments confirm the importance of performing dynamic analysis of signal transduction pathways. While the experiments reveal some unexpected behavior, I find that the biological insights gained on this system remain relatively modest.

      In the discussion section, the authors mention two important behaviors that their data unveil: resource allocation (between glycolysis and HOG-driven adaptation) and regulation of the HOG-pathway based on the presence of glucose. These behaviors had been already observed in other reports (Sharifan et al. 2015 or Shen et al. 2023, for instance). I find that this manuscript does not provide a lot of additional insights into these processes.

      We thank the referee for his review. We agree with the reviewer that the interaction between glucose availability and osmotic stress response has been investigated in previous studies. However, this interaction was investigated using experimental procedures that differed from our approach in critical ways, and therefore the behaviors observed were not the same. In Sharifian et al. (2015), the authors identified a new negative feedback loop regulating Hog1 basal activity and described underlying molecular mechanisms. This feedback loop is unlikely to explain differences of cell fitness we observed in IPS and AS conditions, because 1) differences of division rate was still observed in hog1 mutant cells and 2) differences of death rate involve glycerol synthesis, which is independent of the feedback loop described in Sharifian et al. (2015). In Shen et al. (2023), the authors observed a stronger expression of Hog-responsive genes at lower glucose concentrations, which seems contradictory with our observation of very low pSTL1-GFP expression in absence of glucose. However, they did not use fluctuating conditions and they did not report expression of stress-response genes when glucose was totally depleted (the lower glucose concentration they used was 0.02%) as we did, which may explain the different outcomes. We added three sentences in the discussion to compare our findings to those of Shen et al. (2023).

      One clear evidence that is presented, however, is the link between glycerol accumulation during the sorbitol treatment and the cell death phenotype upon starvation in alternating stress condition. However, no explanations or hypothesis are formulated to explain the mechanism of resource allocation between glycolysis and HOG response that could explain the poor growth in alternating stresses or the lack of adaptation of Hog1 activity in absence of glucose.

      In the revised version of the manuscript, we included a new result section and a supplementary figure (Figure 4 – figure supplement 2) where we tested three hypotheses to explain the lower division rate observed in AS condition relative to IPS condition. We found no evidence supporting these hypotheses, and the mechanisms responsible for the reduced growth in AS condition therefore remains elusive.

      Another key question is to what extent the findings presented here can be extended to other types of perturbations. Would the use of alternative C-source or nitrogen starvation change the observed behaviors in dynamic stresses? If other types of stresses are used, can we expect a similar growth pattern between alternating versus in-phase stresses?

      As mentioned above in our response to the other reviewer, these are very interesting questions that we think go beyond the scope of our study due to the amount of work involved.

      Recommendations for the authors:

      Reviewer #1

      My comments are only minor.<br /> - More paragraphs would improve legibility.

      To improve legibility, we split the longer section of the Results in three paragraphs (page 12, section entitled “Osmoregulation is impaired under in-phase stresses but not under alternating stresses.” However, we kept it as one section with a single title for global coherency: each section of the results corresponds to one main figure and have one main conclusion.

      • I found AS and IPS confusing because what becomes important is whether sorbitol appears with glucose or not. For me, an acronym that makes that co-occurrence clear would be better or even better still no acronyms at all.

      We tried several alternative names for the two conditions in previous drafts of the manuscript. Based on colleagues feedback, AS and IPS acronyms appeared as a good compromise between concision and clarity. To avoid confusion, the two acronyms are precisely defined when they are first used in the Results section. We think it is more important to emphasize the co-occurrence (or not) of the two stresses, rather than the co-occurrence of glucose and sorbitol. Indeed, standard yeast medium contains glucose but no sorbitol, and therefore we defined the two periodic conditions based on differences from standard medium. Even though we avoided using acronyms as much as possible in the manuscript, the use of these two acronyms to refer to the dual fluctuations of the environment seemed essential for concision. Indeed, IPS and AS acronyms are used many times in the results (16 occurrences on page 12 alone), figures and figure legends.

      • I would consider moving some of Fig S2 to the main text: it helps clarify where Fig 2 is coming from and is referenced multiple times.

      We fully agree with the reviewer and we moved panels A-D from Figure S2 to the main Figure 2.

      • On page 10, "constantly facing a single stress that changes over time" is confusing. Perhaps "repetitively facing a single stress" instead?

      We agree this sentence could be wrongly interpreted the way it was written. We changed it to: “cells grow more slowly when facing periodic alternation of the two stresses (AS) than when facing periodic co-occurrence of these stresses (IPS)”.

      • Is there any knowledge on how cells resist hyperosmotic stress in the absence of glucose? That would help explain the IPS results.

      Based on comments from both reviewers, we surveyed the literature to flesh out the discussion of hypotheses that would help explain observed differences between AS and IPS conditions. We found few studies that investigated cell responses in the absence of glucose, and because of significant differences in the experimental approaches it remains difficult to explain our results from conclusions of these previous studies. For instance, Shen et al., 2023 described and modeled the hyperosmotic stress response at various glucose concentrations. They found that Hog1p relocation to the nucleus after hyperosmotic shock lasted longer at lower glucose concentration, which is consistent with our finding in absence of glucose. However, they did not include the absence of glucose in their experiments or periodic fluctuations of glucose concentration. In addition, their model ignores the impact of cell signaling processes involved in growth arrest in response to hyperosmotic stress or glucose depletion. It is therefore difficult to relate their conclusions to our results. We have developed the discussion of our study to include these hypotheses and to clarify what is explained or not in our IPS and AS results.

      There is knowledge on activation of the hyperosmotic stress pathway in response to glucose fluctuations, but not about the response to hyperosmotic stress in absence of glucose.

      • On page 11, Figure 5a should be Figure 4a.

      Correct.

      • I would explain the components of the HOG pathway in the caption of Fig 1 or in the text when you cite Fig 1a. They are described later, but an early overview would be useful.

      To give more context, we added the following sentences to the caption of Figure 1: “Yeast cells maintain osmotic equilibrium by regulating the intracellular concentration of glycerol. Glycerol synthesis is regulated by the activity of the HOG MAP kinase cascade that acts both in the cytoplasm (fast response) and on the transcription of target genes in the nucleus (long-term response). For simplicity, we only represented on the figure genes and proteins involved in this study.”

      • On page 16, I wasn't sure what "redirect metabolic fluxes against glycerol synthesis" meant.

      For more clarity, we modified this sentence to: “Since glucose is a metabolic precursor of glycerol, the absence of glucose may prevent glycerol synthesis and thereby fast osmoregulation."

      • For Fig 2, having a dot-dash and dash-dash lines rather than both dash-dash would be better.

      We made the proposed change, assuming the reviewer was referring to the gray dashed lines and not the colored ones.

      • In the caption of Fig 3, 2% glucose is 20 g/L.

      We thank the reviewer for catching this typo.

      • In the Materials and Methods Summary, adding how you estimated death rates would be helpful: they are not often reported.

      The calculation of death rates was explained in the Methods section. For more clarity, we modified the names of the parameters in the equation to make more explicit which ones refer to cell death.

      Reviewer #2 (Recommendations For The Authors):

      In Figure 2, it would be interesting to show individual growth rates of the perturbations at various frequencies as shown in Figures 3 c and d.

      We thank the reviewer for this suggestion. We added a new supplementary figure (Figure 2 – figure supplement 2) showing the temporal dynamics of division rates at three different frequencies of osmostress and glucose depletion. We did not include high frequencies (periods below 48 minutes) because the temporal resolution of image acquisition in our experiments (1 image every 6 minutes) was too low. Very interestingly, this new analysis suggests that the positive relationship between the frequency of glucose depletion and division rate is explained by a delay between glucose removal and growth arrest rather than a delay between glucose addition and growth recovery. We therefore added the following conclusion:

      “Under periodic fluctuations of 2% glucose, the division rate was lower during half-periods without glucose than during half-periods with glucose (Figure 2 – figure supplement 2d-f), as expected. However, this difference depended on the frequency of glucose fluctuations: the average division rate during half-periods without glucose was higher at high frequency (small period) than at low frequency (large period) of fluctuations (Figure 2 – figure supplement 2d-f). Therefore, the effect of the frequency of glucose availability on the division rate in 2% glucose is likely due to a delay between glucose removal and growth arrest: cell proliferation never stops when the frequency of glucose depletion is too fast.”

      According to Sharifan et al. 2015, I would have expected that Hog1 would not relocate in the nucleus in 0% glucose. I wonder if this is due to the use of sorbitol as a stressor or the presence of low levels of glucose in the medium. I would suggest performing some control experiments with NaCl as hyperosmotic agent and test the addition of 2-deoxy-glucose to completely block glycolysis.

      After careful reading of Sharifian et al. 2015, we fail to understand why the reviewer think Hog1 would be expected to not relocate to the nucleus after hyperosmotic stress in 0% glucose. In this previous study, the authors never combined glucose depletion with a strong hyperosmotic stress as we did in our study. They report the results of independent experiments where cells were exposed either to a single pulse of hyperosmotic stress (0.4 M NaCl) or to transient glucose starvation, but they did not combine these two stimuli. In this context, it is difficult to compare their results with ours. The fact that Sharifian et al. 2015 did not observe Hog1 nuclear relocation in 0% glucose (consistent with our result in Figure 6 – figure supplement 1a, yellow curve) is not inconsistent with our observation of Hog1 nuclear enrichment in 0% glucose + 1M sorbitol. One potential discrepancy between the two studies is the fact that they observed a small transient peak of Hog1 nuclear localization just after glucose is added back to the medium, while we failed to observe this peak in similar conditions (yellow curve in Figure 6 – figure supplement 1a). However, this could be simply explained by the temporal resolution of our experimental system: we image cells once every 6 minutes and the peak lasts less than 2 minutes in Sharifian et al. 2015. We added a sentence to discuss this minor point in the Results: “Although previous studies observed small transient (less than two minutes) peaks of Hog1-GFP nuclear localization after glucose was added back to the medium following glucose depletion (Sharifian et al., 2015, Piao et al., 2013), the temporal resolution in our experiments (one image every 6 minutes) may have been too low to detect these peaks.”.

      While we agree many additional experiments would be interesting, such as testing the effects of different stress factors or the non-metabolizable glucose analog 2-deoxy-D-glucose, we think this is beyond the scope of this study because such experiments are likely to open broad perspectives and to not be conclusive in a reasonable amount of time.

      When discussing Figure 7, the authors write that the HOG pathway is "overactivated" or "hyperactivated". I would refrain from using these terms because as seen in Figure 6, the Hog1 activity pattern, if anything, decreases as the number of alternative pulses increases. The high level of pSTL1mCitrine measured is mostly due to the long half-life of the fluorescent protein.

      We used the formulation “hyper-activation” of the HOG pathway because Mitchell et al. 2015 used it to refer to the same phenomenon in their seminal study. This "hyper-activation" refers to the fact that both the integral activation of Hog1p (sum of areas under Hog1 nuclear peaks) and the global activation of transcriptional targets is much higher during fast periodic hyperosmotic stress than during constant hyperosmotic stress. That being said, we understand the point made by the reviewer about the decreasing size of Hog1 peaks over time during repeated pulses of osmotic stress. Therefore, we slightly modified the text to refer to hyper-activation of pSTL1-mCitrine transcription or expression instead of hyper-activation of the HOG pathway. For coherency, we replaced all instances of “overactivation” by “hyper-activation”.

      Last but not least, the high level of pSTL1-mCitrine is both due to the long half-life of the protein and to the fact that pSTL1 transcription is never turned off due to high Hog1p activity under fast periodic osmostress.

      Minor comments:

      In the main text, I think it might be more intuitive to refer to doubling time in hours instead of division rates in 1/min which are harder to interpret.

      In an early draft of the manuscript, we made figures with either division rates or with doubling times (ln(2)/division rate) and we received mixed opinions from colleagues on what measure was more intuitive to interpret. Both measures are widely used in the literature, and we decided to use division rates in the final version of the figures because it was more directly related to population growth rate and to fitness. For instance, the population growth rate shown in Figure 5 is simply calculated by subtracting the death rate from the division rate. For coherency, we therefore reported division rates instead of doubling times in figures and results. However, to address the reviewer’s comment we included the doubling times (in addition to the division rates) when mentioning the most important results. For instance, page 12: “Strikingly, cells divided about twice as fast under IPS condition (1.67 x 10-3 division/min, corresponding to an average doubling time of 415 minutes) than under AS condition (9.4 x 10-4 division/min, corresponding to an average doubling time of 737 minutes)”.

      I found various capitalized version of "HOG /Hog pathway"

      We corrected this incoherency and used “HOG pathway” everywhere.

      Page 11. Figure 5a should refer to Figure 4a I believe.

      Correct.

      The methods are generally very thorough and precise. The explanation about the calculation of the division rate seems incomplete. For completeness, it would be good to mention the brand and model of valves used. In addition, it would be interesting to have an idea of the number of cells and microcolonies tracked in the various growth experiments.

      We are not sure why the reviewer found the explanation of the calculation of division rate incomplete. For more clarity, we modified the names of parameters in the equations to make them more explicit. We also added a reference to Supplementary File 1 that contains all R scripts used to calculate division rates and death rates. We included the brand and model of valves used, as requested. As for the number of cells tracked in the various experiments, we mentioned in the Methods: “we selected 25 positions (25 fields of view) of the motorized stage (Prior Scientific ProScan III) that captured 10 to 50 cells in each of the 25 growth chambers of the chip and were focused slightly below the median cell plane based on cell wall contrast.” To address the reviewer’s comment, we also included the range of number of tracked cells for each experiment in corresponding figure legends.

    1. Author Response

      The following is the authors’ response to the original reviews.

      First, we would like to thank you and all the reviewers for acknowledging the meaningful contribution of our manuscript to the field. Your useful comments helped us improve the manuscript's quality. We understood the key issues of the manuscript were the quantification of inference accuracy and applicability to methylome data. We here therefore present a revised version of the manuscript addressing all major comments.

      For each demographic inference we have added the root mean square error as demanded by the reviewers. These results confirm the previous interpretation of the graphs especially in recent times. We also added TMRCA inference analysis as requested by one reviewer as a proof of principle that integrating multiple markers can improve ARG inference.

      The discussion was rewritten to further discuss the challenges of application to empirical methylation data. We clarify that in the case epimutations are well understood and modelled, they can be integrated into a SMC framework to improve the approaches accuracy. When epimutations are not well understood, our approach can help understand the epimutations process through generations at the evolutionary time scale along the genome. Hence, in both cases our approach can be used to unveil marker evolution processes through generations, and/or deepen our understanding of the population past history. We hope our discussion underlies better how our approach is designed and can be used.

      eLife assessment

      This important study advances existing approaches for demographic inference by incorporating rapidly mutating markers such as switches in methylation state. The authors provide a solid comparison of their approach to existing methods, although the work would benefit from some additional consideration of the challenges in the empirical use of methylation data. The work will be of broad interest to population geneticists, both in terms of the novel approach and the statistical inference proposed.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors developed an extension to the pairwise sequentially Markov coalecent model that allows to simultaneously analyse multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

      Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

      Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

      Answer: We thank the reviewer 1 for his positive comments and acknowledging the future promises of our method as better and more reliable data will be available in different species. We appreciate the reviewer noticing the complete set of work undertaken here to integrate local and regional effects of methylation into a model containing as much knowledge of the epigenetics mutational processes as possible. Note that in Figure 2 of the manuscript we observed a gain of accuracy even when the rates are unknown. Our results thus suggests that the accuracy gain of additional marker with unknown rates is also possible, although it is most likely be scenario and rate dependent.

      At last, as noticed and highlighted by the very recent work of the Johannes lab (Yao et al. Science 2023) using phylogenetic methods, knowing the epimutation rate is essential at short time scale to avoid confounding effects of homoplasy. In our estimation of the coalescent trees, the same applies, though our model considers finite site markers. We now provide additional evidence for the potential gain of power to infer the TMRCA (Supplementary Table S7) when knowing or not the epimutation rates and revised the discussion to clarify the potential shortcomings/caveats for the analysis of real data.

      Reviewer #2 (Public Review):

      A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

      Answer: We thank the reviewer 2 for his positive comments. Indeed, surveys of CG methylation in other plant species show that its distribution is clearly bimodal (i.e. binary). This is not the case for non-CG methylation, such as CHG and CHH (where H=C,T,A). However, these later types of methylation contexts are also not heritable across generations and can therefore not be used as heritable molecular markers.

      Reviewer #3 (Public Review):

      I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems sound and reasonable. This is an exciting new avenue for thinking about inference from genomic data. I have a few concerns about the presentation and then also questions about the use of empirical methylation data sets.

      I think a more detailed description of demographic accuracy is warranted. For example, in L245 MSMC2 identifies the bottleneck (albeit smoothed) and only slightly overestimates recent size. In the same analysis the authors' approach with unknown mu infers a nonexistent population increase by an order of magnitude that is not mentioned.

      Answer: We thank the reviewer 3 for his positive comments and refer to our answer to reviewer 1 above. We added RMSE (Root Mean Square Error) analyses to quantify the inference accuracy. We apologize for not mentioning this last point. Thank you for pointing this out and we have now fixed it (line 245-253).

      Similarly, it seems problematic that (L556) the approach requiring estimation of site and region parameters (as would presumably be needed in most empirical systems like endangered nonmodel species mentioned in the introduction) does no better than using only SNPs. Overall, I think a more objective and perhaps quantitative comparison of approaches is warranted.

      Answer : See answer to reviewer 1 above, and more elaborate answers below. We provide now new RMSE analyses to quantify the accuracy of our demographic inference (Supplementary Tables 1,6,7,8,9,10). We also discuss the validity and usefulness of our approach when the epimutation rates are unknown. In short, the discussion was rewritten to further discuss the challenges of application to empirical methylation data. We clarify that in the case epimutations are well known and modelled (as much is known in A. thaliana for example), they can be integrated into a SMC framework to improve the accuracy of the method approach. When epimutations are not well understood and rates unknown, our approach can help understand the epimutational process through generations at the evolutionary time scale. Hence, whether makers are understood or not, our approach can be used to study the marker evolutionary processes through generations and/or to deepen our understanding of the population past history. We hope our discussion underlies better how our approach is designed and can be used.

      The authors simulate methylated markers at 2% (and in some places up to 20%). In many plant genomes a large proportion of cytosines are methylated (e.g. 70% in maize: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496265/). I don't know what % of these may be polymorphic, but this leads to an order of magnitude more methylated cytosines than there are SNPs. Couldn't this mean that any appreciable error in estimating methylation threatens to be of a similar order of magnitude to the SNP data? I would welcome the authors' thoughts here.

      Answer : The reviewer is correct and this is an interesting question. First, studies show that heritable epimutations in plants are restricted to CG dinucleotides that are located well outside of the target regions of de novo methylation pathways in plants. Most of these CGs tend of fall within so-called gene body methylated regions. While it is true that plant species can differ substantially in their proportion of methylation at the genome-wide scale, the number of gene body methylated genes (i.e. genic CG methylation) is relatively similar, and at least well within the same order of magnitude (Takuno et al. Nature Plants 2016, review in Muyle et al. Genome Biol Evol 2022). Moreover, spontaneous CG epimutations in gene body methylated regions has been shown to be neutral (van Der Graaf et al. 2015, Vidali et al. 2016, Yao et al. 2023), which is an ideal property for phylogentic and demographic inference.

      Second, CG methylation calls are sometimes affected by coverage or uncertainty. Stringent filtering for reliable SMP calls typically reduces the total proportion of CG sites that can be used as input for demographic inference. Here we only kept CG sites where the methylation information could be fully trusted after SMP calling (i.e. >99.9% posteriori certainty). Overall, this explains why the percentage of sites with methylation information is so small, and why we have decided to work on simulation with 2% of reliable methylated markers.

      Nevertheless, for the sake of generality, it may be that in some species such as maize a higher percentage of polymorphic methylated sites can be used, and the number of SMPs could be higher than that of SNPs when the effective population size is very small (due to past demographic history and/or life history traits). In this case, any error in the epimutation rate and variance due to the finite site model estimation (and homoplasy) are not corrected by the lack of SNPs and can lead to mis-inference.

      A few points of discussion about the biology of methylation might be worth including. For example, methylation can differ among cell types or cells within a tissue, yet sequencing approaches evaluate a pool of cells. This results in a reasonable fraction of sites having methylation rates not clearly 0 or 1. How does this variation affect the method? Similarly, while the authors cite literature about the stable inheritance of methylation, a sentence or so more about the time scale over which this occurs would be helpful.

      Answer: We thank reviewer 3 for asking those very interesting questions, which we further developed below and mention in the discussion (lines 716-722).

      For Arabidopsis thaliana:

      Following up on our previous comment above, the majority of the CG sites that serve as input to our approach are located in body methylated genes. Previous work has shown that CG methylation in these regions shows essentially no tissue and cellular heterogeneity (e.g. Horvath et al. 2019). This means that bulk methylation measurements only show limited susceptibility to measurement error. That said, to guard against any spurious SMPs call that could arise from residual measurement variation, we applied stringent filtering of CG methylation. We have kept sites where the methylation percentage is close to either 0% or 100% (the rest being removed from the analysis). We have used similar filtering strategies in previous studies of epimutational processes in mutation accumulation lines and long-lived perennials (work of the Johannes lab). In these later studies we found that the SMP calls sufficiently accurate for inferences of phylogenetic parameters in experimental settings (Sharyhary et al. Genome Biology 2021, Yao et al. Science, 2023).

      For other species:

      It is true that currently, evaluating the methylation state of a site from a pool of cells may be problematic for some species for two main reasons: 1) it will add noise to the signal and SMP calling could be erroneous, and 2) the methylation state used in analysis might originate from different tissues at different location of the genome/methylome. Overall, this will lead to spurious SMPs and can render the inference inaccurate (see Sellinger et al 2021 for the effect of spurious SNPs). Hence, caution is advised when calling SMPs in other species and for different tissues.

      Finally, in some species methylated cytosines have mutation rates an order of magnitude higher than other nucleotides. The authors mention they assume independence, but how would violation of this assumption affect their inference?

      Answer: Indeed, we assume the mutation and epimutation process to be independent thus the probability for a SNP to occur does not depend on the local methylation state. If this was the case, the mutation rate use would indeed be wrong to a degree function of the dependency between the processes. We suggest that by ignoring this dependence, we are in the same situation as ignoring the variation of mutation rate along the genome. We have previously documented the effect of ignoring this biological feature of genomes in Strüt et al 2023 and Sellinger et al 2021. The variation in mutation rate along the genome if too extreme and not accounted for can lead to erroneous inference results. However, this problem could be easily solved (modelled) by adapting the emission matrix. To correctly model this dependency, additional knowledge is needed: either the mutation and epimutation rates must be known to quantify the dependency, or the dependency must be known to quantify the resulting rates. As far as we know, these data are at the moment not available, but could maybe be obtained using the MA lines of A. thaliana (used in Yao et al. 2023).

      Recommendations for the authors:

      All three reviewers liked this approach and found it a valuable contribution. I think it is important to address reviewer 1/3 concerns about quantifying the accuracy of inference (the TMRCA approach from reviewer 1 sounds pretty reasonable), and reviewer 1 also highlights an intriguing point about model accuracy being worse when the mutation rate is known. Additionally, I think some discussion is warranted about challenges dealing with empirical methylation data (points from Rev 2 and 3 as well as Rev 1's question about inferred vs published rates of epigenetic mutation).

      Answer : We have added tables containing the root mean square error (RMSE) of every demographic inference in the manuscript to better quantify accuracy. We have below given the explanation on why accuracy in presence of site and region epimutations can in some cases decrease when real rates are known (because methylation state at the region level needs to be first inferred). We added evidence that accounting for methylation can improve the accuracy when recovering the TMRCA along the genome when the rates are known. We also have enhanced the discussion on the challenges of dealing with epimutations data for inference. As is suggested, we hope this study will generate an interest in tackling these challenges by applying the methods to various methylome datasets from different species.

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      • For all of the simulated demographic inference results, only plots are presented. This allowsfor qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

      Answer : We understand the concern of reviewer #1 for a more quantitative approach to compare the inference results. We agree that plots are not sufficient to fully grasp a method performance. To provide better supports to quantity approaches performance, we added Sup tables 1,6,8,9 and 10 containing the RMSE (in log10 for visibility) for all Figures. The root mean-squared error is calculated as in Sellinger 2021 and a description of how the root mean-squared error is calculated and now found in the method section lines 886-893.

      • 434: The discussion downplays the really odd result that inputting the true value of themutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour.

      Answer : There are unfortunately no errors in this plot and those results are perfectly normal and coherent, but we understand they can be confusing at first.

      As described in the method section and in the appendix, when accounting for regionlevel epimutations, our algorithm requires the regional methylation status which needs to be inferred as a first step from the data (real or simulated). Because region and single site epimutation events are occurring at similar rates in our simulated scenario, the methylation state of the region is very hard to correctly recover (e.g. there will be unmethylated site in methylated regions and methylated sites in unmethylated regions). In other words, the accuracy of the region estimation HMM procedure is decreased by the joint action of site and region epimutation processes.

      When subsequently applying the HMM for inference, as described in the appendix, the probabilities of two CG site being in the same or different methylation state depends on the methlylation state of the "region". Hence the mislabelling of the region methylation state is (to some extent) equivalent to spurious SMPs (or inaccurate SMP calling).

      If the true rates for site and region epimutations are given as input, the model forces the demography (and other inferred parameters) to fit the observed distribution of SMPs (given the inputted rates), resulting in the poor accuracy observed in the Figure (Now Supplementary Figure 7).

      Note: The estimated rates from real data in A. thaliana suffer from the same issue as the region and site epimutation rates are independently estimated, and the existence of regions first quantified using an independent HMM method (Denkena et al. 2022).

      However, when rates are freely inferred, they are inferred accordingly to the estimated methylation status of regions and SNPs. Therefore, even if the inferred rates are wrong, they are used by the SMC in a more consistent way.

      Note: When methylation rates violate the infinite site assumption, such as here, we first estimate the tree sequence along the genome using SNPs (i.e. DNA mutations). The algorithm then infers the epimutations rates given the inferred coalescent times and the observed methylation diversity.

      To summarise: when inputting rates to the model, if the model fails to correctly recover the region methylation status there will be conflicting information between SNPs and SMPs leading to accuracy loss. However if the rates are inferred this is realized with the help of SNPs, leading to less conflicting information and potentially smaller loss of accuracy. We apologize that the explanations were missing from the manuscript and have added them lines 449-460 and 702-716.

      A further argument is that if region and site epimutations occur at rates of at least two orders of magnitude difference, the inference results are better (and accurate) when the true rates are given. The reason is that one epimutational process overrides the other (see Supplementary Table 2). In that case one epimutation process is almost negligible and we fall back to results from Figure 5 or Supplementary Figure 6.

      • As noted at 580, all of the added power from integrating SMPs/DMRs should come fromimproved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases.

      Answer : We fully agree with reviewer 1. We have added a comparison in TMRCA inference as proof of principle between using or not using methylation sites. The results are written in Supplementary Table 7 and methodology is inspired by Schiffels 2014 and described at the end of the method section (line 894-907). Those results demonstrate the potential gain in accuracy when using methylation polymorphic. However, TMRCA (or ARG) inference is a very vast and complex subject in its own right. Therefore, we are developing a complete TMRCA/ARG inference investigation and an improve methodology than the one presented in this manuscript. To do so we are currently working on a manuscript focusing on this topic specifically. We hence consider further investigations of TMRCA/ARG inference beyond the scope of this current study.

      • A general remark on the derivations in Section 2 of the supplement: I checked theseformulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

      Answer: We thank the reviewer for this very interesting suggestion! Unfortunately, it is a bit late to re-implement the algorithm and reshape the manuscript according to this suggestion. Speed is not yet an issue but will most likely become one in the future when integrating many different rates or when using a more complex SMC model. Hence, we added reviewer #1 suggestions to the discussion (line 648) and hope to be using it in our future projects.

      • Most (all?) of the SNP-only SMC methods allow for binning together consecutiveobservations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

      Answer: This is a very good question. We do the binning exactly as described in Mailund 2013 & Terhorst 2017, and added this information in the method section (lines 801-809). However, as described in Terhorst 2017, one can only bin observation of the same "type" (to compute the Baum-Welch algorithm). Therefore, the computation time gain by binning is reduced when different markers spread along the genome in high proportion. This is the approach we used throughout the study when facing multiple markers as it had the best speed performance. As for example, when the proportion of site with methylated information is 1% or less, computation time is only slightly affected (i.e. same order of magnitude).

      However, the binning method presented in Mailund 2013 can be extended to observation of different types, but parameters need to be estimated through a full likelihood approach (as presented in Figure 2). In our study this approach did not have the best speed performance. However, as our study is the first of its kind, it remains sub-optimal for now. Hence, we did not further investigate the performance of our approach in presence of many multiple different genomic marker (e.g. 5 different markers each representing ~20% of the genome each). Currently, with SMC approaches a high proportion of sites contain the information "No SNPs", making the Baum welch algorithm described in Terhorst 2017 very efficient. But when further developing our theoretical approach, we expect that most of the sites in a genome analysis will contain some "information", which could render the full likelihood approach computationally more tractable.

      • 486: The assumed site and region (de)methylation rates listed here are several OOMdifferent from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533?

      Answer: We thank the reviewer for asking this question. We believe answering this question is indeed the most interesting aspect of our study. Beyond demographic inference, our study has indeed unveiled a discrepancy between rates inferred through biological experiment and our study through the use of SNPs and branch length. There are several reasons which could explained the discrepancy between both approaches:

      • Firstly, our underlying HMM hypotheses are certainly violated. We ignoredpopulation structure, variation of mutations and recombination rate along the genome as well as the effect of selection. Hence, the branch lengths used for methylation rate estimations are to some extent inaccurate. We note that this is especially likely for the short branches of coalescent tree originating from background selection events in the coding regions and which are especially observable when using the methylation sites with a higher mutation rate than SNPs (Yao et al. 2023) at body methylated genes.

      • Secondly, calling single methylation site polymorphism is not 100 % reliable. If theerror rate is 0.1%, as the study was conducted on ~10 generations a minimum epimutation rate of 10-4 is to be expected. However, because our approach works at the evolutionary time scale, we expect that it suffers less from this bias as the proportion of diversity originating from actual epimutations, and not SMP calling error, should be greater.

      • Thirdly, as mentioned above, recovering the methylation status of a region is veryhard. Hence false region status inference could affect our inference accuracy as shown in Supplementary Figure 4.

      • Lastly and most importantly, the reason behind this discrepancy is the modelling ofepimutation and methylation between sites and regions. As we discuss, the current combination of rates and models is still limited to describe the observed diversity along the genome (as we intend in SMC methods). This is in contrast to the recent study by Yao et al. where very few regions of polymorphic SMPs are chosen, which implicitly avoids the influence of the methylation region effect. A study just published by Biffra et al. (Cell reports 2023) also uses a functional model of methylation modelling using a mix of region and site epimutation, albeit not tuned for evolutionary analyses. Thus we suggest, in line with functional studies, that epimutations are not independent from the local methylation context and may tend to stabilize the methylation state of a region. Therefore, the estimated methylation rates show a discrepancy to the previously measured ones. Indeed, the biological experiment would reveal a fast epimutation rate because epimutations can actually be tracked at sites which can mutate, while region mutation rate is much slower. However, because the methylation state of a region is rather stable through time it would reduce the methylation diversity over long time scale, and these rates would differ between methylated or unmethylated regions (i.e. the methylation rate is higher in methylated regions). Our results are thus in agreement with the observation by Biffra et al. that region methylation modelling is needed to explain patterns of methylation across the genome.

      To solve the discrepancy, one would need to develop a theoretical region + site epimutation model capable of describing the observed diversity at the evolutionary time scale (possibly based on the Biffra et al. model within an underlying population evolution model), and then use this model to reanalyse the sequence data from the biological experiment (i.e. in de Graaf et al. 2015 & Denkena et al. 2022) to re-estimate the methylation region sizes and epimutation rates.

      Minor comments:

      • 189: "SMCtheo" first occurs here, but it's not mentioned until 247 that this is the newmethod being presented.

      Answer : Fixed

      • 199: Are the estimates in this section from a single diploid sequence? Or is it n=5 (diploid) as mentioned in the earlier section?

      Answer : Yes, those results were obtained with 5 diploid individuals. We added it in the Table 1 description.

      • 336: I'm confused by the wording: it sounds like the test rejects the null if there is positivecorrelation in the methylation status across sites. But then, shouldn't 339 read "if the test is significant" (not non-significant)?

      Answer : We apologize for the confusion and rewrote the sentence line 339-348, the choice of word was indeed misleading .

      • Fig. 6: for some reason fewer simulations were run for 10Mb (panels C nad D) than for100Mb (A and B). Since it's very difficult to tell what's happening on average in the 10Mb case, I suggest running the same number of simulations.

      Answer : Yes we understand your concern. Actually, the same number of simulations were run but we plotted only the first 3 runs as it was less visually confusing. We now have added the missing lines to the plot C and D.

      Typos:

      • 104: "or or"

      • 292: build => built

      • 388: fulfil

      • 683: sample => samples

      Answer : Many thanks to reviewer 1 for pointing out the typos. They are all now fixed.

      Reviewer #2 (Recommendations For The Authors):

      The authors may find some valuable information in Pisupati et al (2023) "On the causes of gene-body methylation variation in Arabidopsis thaliana" on interpreting epimutation rates.

      Answer: Many thanks for the recommended manuscript. We add it to the cited literature as it strongly supports our use of heritability or methylation. We also added the recent Biffra et al. paper.

      Reviewer #3 (Recommendations For The Authors):

      There are many places throughout the manuscript with minor grammatical errors. Please review these. A few noted below as I read:

      L104: extra "or"

      L123: built not build

      L 160 "relies" instead of "do rely"

      L161 "events"

      L 336 "from methylation data"

      L 378 "exists"

      L 379 "regions are on average shorter" instead of "there are shorter"

      L 338 "a regional-level"

      L 349 "," instead of "but"

      L 394 DMRs

      Table 1 legend: parentheses not brackets?

      Answer : Many thanks to reviewer #3 for finding those mistakes. They are all now fixed.

      I think a paragraph in the discussion of considerations of when to use this approach might be helpful to readers. Comparison to e.g. increased sample size in MSMC2, while not necessary, might be helpful here. It may often be the case that doubling the number of haplotypes with SNP data may be easier and cheaper estimating methylation accurately.

      Answer : We discuss (lines 691-698) that our approach is always useful by design, but cannot always be used for the same purpose. If the evolutionary properties of the used marker used are not understood, we suggest that our approach can be used to investigate the marker heritability process through generations. This could help to correctly design experiments aiming to study the marker heritability through lineages. And if the properties of the marker are well understood and modelled, it can be integrated into the SMC framework to improve inference accuracy.

      Other minor notes:

      L 486 "known" is a stretch. empirically estimated seems appropriate.

      Answer : Fixed

      L 573 ARG? You are not estimating the full ARG here.

      Answer : We apologize for the wrong choice of word and have rephrased the sentence.

      Fig. 2 is not super useful and could be supplemental.

      Answer : We moved Figure 2 to the appendix (now sup fig 1)

    2. Reviewer #1 (Public Review):

      The authors developed an extension to the pairwise sequentially Markov coalecent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

      Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

      Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

      Major comments:<br /> - For all of the simulated demographic inference results, only plots are presented. This allows for qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

      - 434: The discussion downplays the really odd result that inputting the true value of the mutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour. (Comment addressed in revision. Still, I find the explanation added at 449ff to be somewhat puzzling -- shouldn't the results of the regional HMM scan only improve if the true mutation rate is given?)

      - As noted at 580, all of the added power from integrating SMPs/DMRs should come from improved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases. (Comment addressed in revision via Supp. Table 7.).

      - A general remark on the derivations in Section 2 of the supplement: I checked these formulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

      - Most (all?) of the SNP-only SMC methods allow for binning together consecutive observations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

      - 486: The assumed site and region (de)methylation rates listed here are several OOM different from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533? (Comment addressed in revision.)

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      This study examines the role of host blood meal source, temperature, and photoperiod on the reproductive traits of Cx. quinquefasciatus, an important vector of numerous pathogens of medical importance. The host use pattern of Cx. quinquefasciatus is interesting in that it feeds on birds during spring and shifts to feeding on mammals towards fall. Various hypotheses have been proposed to explain the seasonal shift in host use in this species but have provided limited evidence. This study examines whether the shifting of host classes from birds to mammals towards autumn offers any reproductive advantages to Cx. quinquefasciatus in terms of enhanced fecundity, fertility, and hatchability of the offspring. The authors found no evidence of this, suggesting that alternate mechanisms may drive the seasonal shift in host use in Cx. quinquefasciatus.

      Strengths:

      Host blood meal source, temperature, and photoperiod were all examined together.

      Weaknesses: The study was conducted in laboratory conditions with a local population of Cx. quinquefasciatus from Argentina. I'm not sure if there is any evidence for a seasonal shift in the host use pattern in Cx. quinquefasciatus populations from the southern latitudes.

      We agree on the reviewers observation about the evidence on seasonal shift in the host use pattern in Cx. quinquefasciatus populations from southern latitudes. We include a paragraph in the Introduction section regarding this. Unfortunately, studies conducted in South America to understand host use by Culex mosquitoes are very limited, and there are virtually no studies on the seasonal feeding pattern. In Argentina, there is some evidence (Stein et al., 2013, Beranek, 2019) regarding the seasonal change in host use by Culex species, including Cx. quinquefasciatus, where the inclusion of mammals during the autumn has been observed. As part of a comprehensive study on characterising bridge vectors for SLE and WN viruses, our research group is currently working on the molecular identification of blood meals from engorged females to gain deeper insights into the seasonal feeding pattern of Culex mosquitoes. While the seasonal change in host use by Culex quinquefasciatus has not been reported in Argentina so far, there has been an observed increase in reported cases of SLE virus in humans between summer and fall (Spinsanti et al., 2008). It is based on this evidence that we hypothesise there is a seasonal change in host use by Cx. quinquefasciatus, similar to what occurs in the United States. This is also considering that both countries (Argentina and the United States) have regions with similar climatic conditions (temperate climates with thermal and hydrological seasonality). Since we work on the same species and in a similar temperate climate regimen, we assumed there is a seasonal shift in the host use by this mosquito species.

      Reviewer #1 (Recommendations for the authors):

      Abstract

      Line 23: fed on two different hosts.

      Accepted as suggested.

      I think the concluding statement should be rewritten to say that immediate reproductive outcomes do not explain the shift in host use pattern of Cx. quinquefasciatus mosquitoes from birds to mammals towards autumn.

      Accepted as suggested.

      Introduction

      No comments.

      Materials and Methods

      Please mention sample sizes in the text as well (n = ?) for each treatment.

      Accepted as suggested.

      Page 99: ......C. quinquefasciatus, since C. pipiens and its hybrids are present as well in Cordoba.

      Accepted as suggested.

      Results – Line 146: subsequently instead of posteriorly

      Accepted all changes as suggested.

      Line 148: were counted instead of was counted.

      Accepted all changes as suggested.

      Line 160: Subsequently instead of posteriorly

      Accepted all changes as suggested.

      Line 171: on fertility

      Accepted all changes as suggested.

      Line 174: there was an interaction effect on…

      Accepted all changes as suggested.

      Line 175: there were no differences in the number of eggs

      Accepted all changes as suggested.

      Discussion

      I think the first paragraph in the discussion section is redundant and should be deleted.

      The whole discussion was rewritten to be focused on our aims and results.

      Line 282: this sentence needs to be rewritten.

      Accepted as suggested.

      Line 299: at 28{degree sign}C

      Line 300: at 30{degree sign}C

      Sorry, but we are not sure about your comment here. We checked. Temperatures are written as stated, 28°C and 30°C.

      Line 363: I think the authors need to discuss more about the bigger question they were addressing. I think that the discussion section can be strengthened greatly by elaborating on whether there is evidence for a seasonal shift in host use pattern in Cx. quinquefasciatus in the southern latitudes. If yes, what alternate mechanisms they believe could be driving the seasonal change in host use in this species in the southern latitudes now that they show the 'deriving reproductive advantages' hypothesis to be not true for those populations.

      Thanks for this observation. We agree and so the Discussion section was restructured to align it with our results, as suggested.

      Reviewer #2 (Public Review):

      Summary:

      Conceptually, this study is interesting and is the first attempt to account for the potentially interactive effects of seasonality and blood source on mosquito fitness, which the authors frame as a possible explanation for previously observed host-switching of Culex quinquefasciatus from birds to mammals in the fall. The authors hypothesize that if changes in fitness by blood source change between seasons, higher fitness in birds in the summer and on mammals in the autumn could drive observed host switching. To test this, the authors fed individuals from a colony of Cx. quinquefasciatus on chickens (bird model) and mice (mammal model) and subjected each of these two groups to two different environmental conditions reflecting the high and low temperatures and photoperiod experienced in summer and autumn in Córdoba, Argentina (aka seasonality). They measured fecundity, fertility, and hatchability over two gonotrophic cycles. The authors then used a generalized linear mixed model to evaluate the impact of host species, seasonality, and gonotrophic cycle on fecundity and fertility and a null model analysis via data randomization for hatchability. The authors were trying to test their hypothesis by determining whether there was an interactive effect of season and host species on mosquito fitness. This is an interesting hypothesis; if it had been supported, it would provide support for a new mechanism driving host switching. While the authors did report an interactive impact of seasonality and host species, the directionality of the effect was the opposite of that hypothesized. While this finding is interesting and worth reporting, there are significant issues with the experimental design and the conclusions that are drawn from the results, which are described below. These issues should be addressed to make the findings trustworthy.

      Strengths:

      (1) Using a combination of laboratory feedings and incubators to simulate seasonal environmental conditions is a good, controlled way to assess the potentially interactive impact of host species and seasonality on the fitness of Culex quinquefasciatus in the lab.

      (2) The driving hypothesis is an interesting and creative way to think about a potential driver of host switching observed in the field.

      Weaknesses:

      (1) There is no replication built into this study. Egg lay is a highly variable trait, even within treatments, so it is important to see replication of the effects of treatment across multiple discrete replicates. It is standard practice to replicate mosquito fitness experiments for this reason. Furthermore, the sample size was particularly small for some groups (e.g. 15 egg rafts for the second gonotrophic cycle of mice in the autumn, which was the only group for which a decrease in fecundity and fertility was detected between 1st and 2nd gonotrophic cycles). Replicates also allow investigators to change around other variables that might impact the results for unknown reasons; for example, the incubators used for fall/summer conditions can be swapped, ensuring that the observed effects are not artefacts of other differences between treatments. While most groups had robust sample sizes, I do not trust the replicability of the results without experimental replication within the study.

      We agree egg lay is a variable trait and so we consider high numbers of mosquitoes and egg lay during experiments compared to our studies of the same topics. Evaluating variables such as fecundity, fertility, or other types of variables (collectively referred to as "life tables") is a challenging issue that depends on several intrinsic and extrinsic factors. Because all of this, in some experiments, sample sizes might not be very large, and in several articles, lower sample sizes could be found. For instance, in Richards et al. (2012), for Culex quinquefasciatus, during the second gonotrophic cycle, some experiments had 13 or even 6 egg rafts. For species like Aedes aegypti, the sample size for life table analysis is also usually small. As an example, Muttis et al. (2018) reported between 1 and 4 engorged females (without replicates). In addition, small sample size would be a problem if we would not have obtained any effect, which is not the case due to the fact that we were interested in finding an effect, regardless of the effect size. Because of this, we do find our sample sizes quite robust for our results.

      Regarding the need to repeat the experiments in order to give more robustness to the study we also agree. However, after a review of the literature (articles cited in the original manuscript), it is apparent that similar experiments are not frequently repeated as such. Examples of this are the studies of Richards et al. (2012), Demirci et al. (2014) or Telang & Skinner (2019), which even they manipulate several cages at a time as “replicates”, they are not true replicates because they summarise and manipulate all data together, and do not repeat the experiment several times. We see these “replicates” as a way of getting a greater N.

      As was stated by the reviewer, repetition is a resource and time-consuming activity that we are not able to do. Replicating the experiment poses a significant time and resources challenge. The original experiment took over three months to complete, and it is anticipated that a similar timeframe would be necessary for each replication (6 months in total considering two more replicates). Given our existing commitments and obligations, dedicating such an extensive period solely to this would impede progress on other crucial projects and responsibilities.

      Given the limitations of resources and time and the infrequent use of experimental replication in this type of studies, we performed a simulation-based analysis via a Monte Carlo approach. This approach involved generating synthetic data that mimics the expected characteristics of the original experiment and subsequently subjecting it to the same analysis routine. The main goal of this simulation was to evaluate the potential spuriousness and randomness of the results that might arise due to the experimental conditions. So, evaluating the robustness and confidence of our results and data.

      (2) Considering the hypothesis is driven by the host switching observed in the field, this phenomenon is discussed very little. I do not believe Cx. quinquefasciatus host switching has been observed in Argentina, only in the northern hemisphere, so it is possible that the species could have an entirely different ecology in Argentina. It would have been helpful to conduct a blood meal analysis prior to this experiment to determine whether using an Argentinian population was appropriate to assess this question. If the Argentinian populations don't experience host switching, then an Argentinian colony would not be the appropriate colony to use to assess this question. Given that this experiment has already been conducted with this population, this possibility should at least be acknowledged in the discussion. Or if a study showing host switching in Argentina has been conducted, it would be helpful to highlight this in the introduction and discussion.

      Thanks for this observation. We agree. However, we conducted the experiment beside host use data from Argentina since we used the mosquito species, and the centre region of Argentina (Córdoba) has a similar temperate weather regimen that those observed in the east coast of US.

      We are aware that few studies regarding host shifting in South America are available, some such that those conducted by Stein et al. (2013) and Beranek (2019) reported a moderate host switch for Culex quinquefasciatus in Argentina. We have already performed a study about seasonal host feeding patterns for this species. However, even though there are few studies regarding host shifting, our hypothesis is based mainly in the seasonality of human cases of WNV and SLEV, a pattern that has been demonstrated for our region, see for example the study of Spinsanti et al. (2008).

      We include a new paragraph in the Introduction and Discussion sections. Please see answers Reviewer #1.

      (3) The impacts of certain experimental design decisions are not acknowledged in the manuscript and warrant discussion. For example, the larvae were reared under the same conditions to ensure adults of similar sizes and development timing, but this also prevents mechanisms of action that could occur as a result of seasonality experienced by mothers, eggs, and larvae.

      We understand the confusion that may have arisen due to a lack of further details in the methodology. If we are not mistaken, you are referring to our oversight regarding the consideration of carry-over effects of larvae rearing that could potentially impact reproductive traits. When investigating the effects of temperature or other environmental factors on reproductive traits, it is possible to acclimate either larvae or adults. This is due to the significant phenotypic plasticity that mosquitoes exhibit throughout their entire ontogenetic cycle. In our study, we followed an approach similar to that of other authors where the adults are exposed to experimental conditions (temperature and photoperiod). For a similar approach you can refer to the studies conducted by Ferguson et al. (2018) for Cx. pipiens, Garcia Garcia & Londoño Benavides (2007) for Cx. quinquefasciatus or Christiansen-Jucht et al. (2014, 2015) for Anopheles gambiae.

      (4) There are aspects of the data analysis that are not fully explained and should be further clarified. For example, there is no explanation of how the levels of categorical variables were compared.

      The methodology and statistical analysis were expanded for a better understanding.

      (5) The results show the opposite trend as was predicted by the authors based on observed feeding switches from birds to mammals in the autumn. However, they only state this once at the end of the discussion and never address why they might have observed the opposite trend as was hypothesized.

      The discussion was restructured to focus on our results and our model.

      (6) Generally speaking, the discussion has information that isn't directly related to the results and/or is too detailed in certain parts. Meanwhile, it doesn't dig into the meaning of the results or the ways in which the experimental design could have influenced results.

      As mentioned above, the discussion was restructured to reflect our findings. We also included the effect that our design might have influenced our results. However, as stated above we do not fully agree that the design is inadequate for our analysis, we performed standard protocols followed by other researchers and studies in this research field.

      (7) Beyond the issue of lack of replication limiting trust in the conclusions in general, there is one conclusion reached at the end of the discussion that would not be supported, even if additional replicates are conducted. The results do not show that physiological changes in mosquitoes trigger the selection of new hosts. Host selection is never measured, so this claim cannot be made. The results don't even suggest that fitness might trigger selection because the results show that physiological changes are in the opposite direction as what would be hypothesized to produce observed host switches. Similarly, the last sentence of the abstract is not supported by the results.

      We agree with this observation. However, we did not evaluate the impact of fitness on host selection in this study. Instead, we aimed to investigate the potential influence of seasonality on mosquito fitness as a potential trigger for a shift in host selection. We agree that we have incorrectly used the term “host selection” when we should actually be discussing “host use change”. Our results indicate a seasonal alteration in mosquito fitness in response to temperature and photoperiod changes. Building upon this observation, we re-discussed our hypothesis and theoretical model to explain this seasonal shift in host use.

      (8) Throughout the manuscript, there are grammatical errors that make it difficult to understand certain sentences, especially for the results.

      All English grammar and writing of the manuscript was revised and corrected to be easily understood.

      This study is driven by an interesting question and has the potential to be a valuable contribution to the literature.

      Reviewer #2 (Recommendations for The Authors):

      I hope that the authors will consider the suggested revisions and experimental replication to improve the quality of the study and paper.

      This study tests a very interesting hypothesis. I understand that additional replicates are difficult to conduct, but I do believe that fitness studies absolutely require experimental replicates. Unless you are able to replicate the observed effects, I personally would not trust the results of this study. I hope that you will consider conducting replicates so that this important question can be answered in a more robust manner. Below, I expand upon some additional points in the public review and also provide more specific suggestions. I provided some copy-editing feedback, but was not able to point out all grammatical mistakes. I suggest that you use ChatGPT to help you edit the English. For example, you can feed ChatGPT your MS and ask it to bold the grammatical errors or you can ask it to edit grammatical errors and bold the sections that were edited. I understand that writing in a second language is very difficult (from personal experience!), so I view ChatGPT as a great tool to help even the playing field for publishing. Below are line item suggestions. Apologies that wording is curt, I was trying to be efficient in writing.

      20-21: I suggest that you emphasize that you are investigating the interactive effect.

      Accepted as suggested.

      22: they weren't "reared" (from larvae) in different conditions, they were "maintained" as adults

      Accepted as suggested.

      26-27: increased/decreased is a bit misleading since you did not evaluate these groups sequentially in time. It might be more accurate to describe it as less than/greater than. Also, if you say increased/decreased or less than/greater than, you should always say what you are comparing to. The same applies throughout the MS.

      Accepted as suggested.

      29-30: "finding the" is not correct here; could be "with the lowest..."

      Accepted as suggested.

      34-36: I do not think that your results suggest this, even if you were to replicate the results of this experiment. You haven't shown metabolic changes.

      We understand the point. Accepted as suggested.

      42-44: "one of the main responsible" should be "one of the main species responsible..."

      Accepted as suggested.

      48: I think that "host preference" is better than selection here; -philic denotes preference

      Accepted as suggested.

      50: "Moreover" isn't the correct transition word here

      Accepted as suggested.

      57: "could" isn't correct here; consider saying "... species sometimes feed primarily on mammal hosts, including humans, in certain situations."

      Accepted as suggested.

      58: Different isn't correct word here

      Accepted as suggested.

      60: delete "feeding"

      Accepted as suggested.

      66-68: I am not familiar with any blood meal analysis studies in the southern hemisphere that show host switching for Culex species between summer and autumn. If this hasn't been shown, then this critique of the host migration hypothesis doesn't make sense.

      There are some studies pointing this out (Stein et al., 2013, Beranek 2019), and unpublished data from us). However, our hypothesis has supported by epidemiological data observed in human population which indicate a seasonal activity pattern. It was explained in depth in the Introduction section.

      68: ensures is not the right word; I suggest "suggests"

      Accepted as suggested.

      68-70: this explanation isn't clear to me; please revise

      It will be revised. Accepted as suggested.

      70: change cares to care

      Accepted as suggested.

      76-77: can you explain how they were not supported by the data for the benefit of those who are not familiar with these papers please?

      Accepted as suggested.

      87-89: I suggest the following wording: "In the autumn, we expect a greater number of eggs (fecundity) and larvae (fertility) in mosquitoes after feeding on a mammal host compared to an avian host, and the opposite relationship in the summer."

      Accepted as suggested.

      99: edit for grammar

      Accepted as suggested.

      102: suggest: "...offered a blood meal from a restrained chicken twice a month"

      Accepted as suggested.

      107: powder

      Accepted as suggested.

      108: inbred? Is this the term you meant to use?

      Changed as suggested.

      109: "several" cannot be used to describe 20 generations; suggest using "over twenty generations"; also, it would be good to acknowledge in your discussion that lab adaptation could force evolution, especially since mosquitoes are kept at constant temperatures and fed with certain hosts (with easy access) in the lab. Also, it would be good to know when the experiments were conducted to know the lapse of time between the creation of the colony and the experiments.

      Accepted as suggested.

      110-111: Does humidity vary between summer and fall in Córdoba? If so, I suggest acknowledging in the discussion that if humidity differences are involved in a potential interaction between host species and seasonality, then this would not have been captured by your experimental design.

      Several variables change during seasons. We were interested in capturing the effects of temperature and photoperiod, since humidity is a variable difficult to control.

      113-116: I suggest combining into one sentence to make more concise.

      Accepted as suggested.

      135: You might be obscuring the true impact of seasonality by rearing the larvae under the same conditions. There may be signals that mothers/eggs/larvae receive that influence their behavior (e.g. I believe this is the case for diapause), so this limitation should also be acknowledged. I understand why you decided to do this to control for development time and size, but it is something that should be considered in the discussion.

      As it was explained above, Cx. quinquefasciatus do not suffer diapause in our country. Maintaining mosquitoes from adults was an approach selected by us based on other studies.

      138: edit: "with cotton pads soaked in... on plastic..."; what is plastic glass? Do you mean plastic dishes?

      Accepted as suggested.

      141: here and throughout paragraph, full should be "fully"

      Accepted as suggested.

      144: located should be "placed"

      Accepted as suggested.

      147: suggest editing to "at which point, they were fixed with 1 mL of 96% ethanol and the number of L1 larvae per raft was counted."

      Accepted as suggested.

      154-155: edit for grammar

      Accepted as suggested.

      157: Your GLM explanation doesn't say anything about how you made pairwise comparisons between your levels; did you use emmeans?

      This revised version includes a more detailed methodology and statistical analysis. Accepted as suggested.

      158-160: I don't understand why you took this approach - it seems strange to me to use this analysis, but I am not familiar with it, so it might be that I lack the knowledge to be able to adequately evaluate. Please provide more explanation so that readers can better understand this analysis. A citation for this kind of application of the analysis would be helpful.

      It was changed to be in accordance with the remaining analyses.

      173: replace neither with either

      Accepted as suggested.

      174: this applies throughout; edit to : "An interaction effect was observed..."

      Accepted as suggested.

      175: "it was not found" is grammatically incorrect; instead : "We did not find ..." or "no differences in... were detected", etc

      Accepted as suggested.

      183: "it was detected" is grammatically incorrect

      Accepted as suggested.

      185-186: "being this treatment... in terms of fitness": I do not understand what this means. Please rephrase

      Accepted as suggested.

      170-199: you should provide the effect sizes and p values in text and/or in the figure for the pairwise comparisons

      Accepted as suggested.

      193-196. These two sentences are confusing and I am not sure what you mean, especially in the first sentence.

      It was rewritten. Accepted as suggested.

      Figure 1: This figure is great and easy to read and interpret! Thank you for the comment! 218-219: it is important to state which mosquito species you are referring to here.

      Accepted as suggested.

      226-227: you definitely should acknowledge the small sample size here.

      Considered.

      227: "it was observed" should be "We observed" or "A greater hatching rate.... was observed."

      Accepted as suggested.

      228-229: is the result really comparable even though you took very different approaches to the analysis for these outcomes?

      Changed to be comparable.

      230-278: the discussion of these hypotheses is too long and detailed, especially since the comparison of mouse vs chicken wasn't your main question; you really wanted to understand this in the context of seasonality. I suggest cutting this down a lot and making room to dig into your results more, and also to discuss the potential impacts of your experimental design/limitations on the results.

      Discussion was changed to focus on our results and model. Accepted as suggested.

      281: Hoffman is an old citation; I suggest you cite a modern review.

      Accepted as suggested. We deleted it due to the re-writing of the manuscript.

      282: "It can be recognise".. I am not sure what you are trying to say here

      Accepted as suggested.

      1. After the first time you write a species name, you can abbreviate the genus in all future mentions unless it is at the beginning of a sentence.

      Accepted as suggested.

      303-305: Revise this sentence. E.g "Fewer studies are available regarding photoperiod and show mixed results; Mogi (1992) found that mid and long day lengths induced greater fecundity while Costanzo et al. (2015) did not find differences in fecundity by day length."

      Accepted as suggested.

      315-316: typically, unpublished data shouldn't be referenced; I'm not sure if eLife has a policy on this.

      We will check this with eLife guidelines. However, since the lack of evidence on this pattern we consider important to include this unpublished data.

      316: Aegypti should be lowercase

      Accepted as suggested.

      328-330: This sentence is redundant with the first sentence of the paragraph

      Accepted as suggested.

      321-336: You never reintroduced your hypothesis in your discussion. I suggest that you center your whole discussion more directly around the hypothesis that motivated the study. If you decide not to restructure your discussion, you should at least reintroduce your hypothesis here and discuss how your results do not support the hypothesis.

      Accepted as suggested.

      337-348: This paragraph is a bit confusing as you jump between fertility and hatchability

      Accepted as suggested.

      353: is viral transmission the right word to use here? I think you might mean bridge vector transmission to humans specifically?

      Accepted as suggested.

      357: you say "neither" but never define which traits you are referring to

      Accepted as suggested.

      361: I suggest "two variables previously analyzed separately..."

      Accepted as suggested.

      General: There is no statement about the availability of data; it is eLife policy to require all data to be publicly available. Also, it would be helpful to share your code to help understand how you conducted pairwise comparisons, etc.

      In the submission it was not mentioned anything about data availability. However, all data and scripts will be uploaded with the VOR if it is required.

      Recommendations for the authors:

      I found your study interesting and potentially promising. However, there are some fundamental problems with the study design and the hypothesis, including:

      <(1) Seasonality simulation - Seasonality is strongly associated with time, so it is unusual to simulate seasonal factors without accounting for time. The actual factors associated with seasonal change in reproductive output may be neither a difference in host blood meal nor temperature and photoperiod. It is therefore, odd to reduce seasonality to a difference in photoperiod and temperature in summer and autumn without even mentioning the time of year when the experiment was carried (except for the mention of February as the time the stock samples were collected from the wild).

      The temperature and photoperiod settings are established according to a representative day in both autumn and summer. To determine these settings, we utilized climate data spanning a 3-year period (2020-2022), encompassing the most frequently occurring temperatures and day lengths. The weather conditions remained notably consistent throughout this time frame, which is why the specific year was not mentioned. Moreover, including the year in laboratory experiment details is uncommon, as evident in various papers. This practice can be corroborated by referring to multiple sources (cited in the original manuscript). We mention this in the new version.

      (2) Hypothesis - While the hypothesis alludes to the 'reason' for seasonal host shift, the prediction is on the outcome of the interaction between blood meal type and season.

      It might be nicer to frame your hypothesis to be consistent with the aim, which is, testing the partial contributions of blood meal type, versus photoperiod and temperature to seasonal change in the reproductive output of Culex quinquefasciatus. A hypothesis like that can be accompanied by alternative predictions according to the expected individual and interactive effects of both factors.

      It was rewritten in the revised version to be consistent with our predictions and findings.

      Blood meal type, temperature, and photoperiod are all components of seasonality, so the strength of the study is its potential to decouple the effect of blood meal type from that of temperature and photoperiod on the seasonal reproductive output of Culex quinquefasciatus by comparing the two blood meal types under simulated summer and winter conditions. Ideally, this should have been over a natural summer and winter because a natural time difference captures the effect of other seasonal factors other than temperature and photoperiod.

      Furthermore, the hypothesis stemmed from field observations, while the study itself was conducted under laboratory conditions using a local population of Culex quinquefasciatus from Argentina. It remains uncertain whether there is supporting evidence for a seasonal shift in host usage in Culex quinquefasciatus from the stock population. Discussing the field observations within the stock population would provide valuable insights.

      It was considered in the new version.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This valuable study seeks to disentangle the different selective forces shaping the evolutionary dynamics of transposable elements (TEs) in the wild grass Brachypodium distachyon. Using haplotype-length metrics, and genetic and environmental differentiation tests, the authors present in large parts convincing evidence that positive selection on TE polymorphisms is rare, and that the distribution of TE ages points to purifying selection being the main force acting on TE evolution in this species. A caveat of this study, as of other studies that seek to assess TE insertion polymorphisms with short reads, is that the rates of false negatives and false positives are difficult to estimate, which may have major effects on the interpretation. This study will be relevant for anyone interested in the role of TEs in evolution and adaptation.

      Thank you for considering our manuscript for publication in eLife. We appreciate the constructive comments and suggestions of the reviewers. We have addressed the raised issues by the reviewers. Below, we provide a more detailed response to each of the reviewer comments.

      Public Reviews:

      Reviewer #1:

      The study presented in this manuscript presents very convincing evidence that purifying selection is the main force shaping the landscape of TE polymorphisms in B. distachyon, with only a few putatively adaptive variants detected, even though most conclusions are based on the 10% of polymorphisms contributed by retrotransposons. That first conclusion is not novel, however, as it had already been clearly established in natural A. thaliana strains (Baduel et al. Genome Biol 2021) and in experimental D. simulans lines (Langmüller et al. NAR 2023), two studies that the authors do not mention, or improperly mention. In contrast to the conclusions reached in A. thaliana, however, Horvath et al. report here a seemingly deleterious effect of TE insertions even very far away from genes (>5kb), a striking observation for a genome of relatively similar size. If confirmed, as a caveat of this study is the lack of benchmarking of the TE polymorphisms calls by a pipeline known for a high rate of false positives (see detailed Private Recommendations #1), this set of observations would make an important addition to the knowledge of TE dynamics in the wild and questioning our understanding of the main molecular mechanisms through which TEs can impact fitness.

      Thank you for your positive evaluation of our paper. We have now adjusted the manuscript to include the mentioned studies (Line 330-333) and to address the issue of false positive and false negative calls. The detailed responses to all the raised points are below.

      Reviewer #2:

      Summary:

      Transposable elements are known to have a strong potential to generate diversity and impact gene regulation, and they are thought to play an important role in plant adaptation to changing environments. Nevertheless, very few studies have performed genome-wide analyses to understand the global effect of selection on TEs in natural populations. Horvath et al. used available whole-genome re-sequencing data from a representative panel of B. distachyon accessions to detect TE insertion polymorphisms (TIPs) and estimate their time of origin. Using a thorough combination of population genomics approaches, the authors demonstrate that only a small amount of the TE polymorphisms are targeted by positive selection or potentially involved in adaptation. By comparing the age-adjusted population frequencies of TE polymorphisms and neutral SNPs, the authors found that retrotransposons are affected by purifying selection independently of their distance to genes. Finally, using forward simulations they were able to quantify the strength of selection acting on TE polymorphisms, finding that retrotransposons are mainly under moderate purifying selection, with only a minority of the insertions evolving neutrally.

      Strengths:

      Horvath et al., use a convincing set of strategies, and their conclusions are well supported by the data. I think that incorporating polymorphism's age into the analysis of purifying selection is an interesting way to reduce the possible bias introduced by the fact that SNPs and TEs polymorphisms do not occur at the same pace. The fact that TE polymorphisms far from genes are also under purifying selection is an interesting result that reinforces the idea that the trans-regulatory effect of TE insertions might not be a rare phenomenon, a matter that may be demonstrated in future studies.

      Weaknesses:

      TEs from different classes and orders strongly differ in multiple features such as size, the potential impact of close genes upon insertion, insertion/elimination ratio (ie, MITE/TIR excision, solo-LTR formation), or insertion preference. Given such diversity, it is expected that their survival rates on the genome and the strength of selection acting on them could be different. The authors differentiate DNA transposons and retrotransposons in some of the analyses, the specificities of the most abundant plant TE types (ie, LTR/Gypsy, LTR/Copia, MITE DNA transposons) are not considered.

      The authors used a short-read-based approach to detect TIPs and TAPs. It is known that detecting TE polymorphisms is challenging and can lead to false negatives, depending on the method used and the sequencing coverage. The methodology used here (TEPID) has been previously applied to other species, but it is unclear if the sensitivity of the TIP/TAP caller is equivalent to that of the SNP caller and how these potential differences may affect the results.

      Thank you for your positive evaluation of our paper. We have now adjusted the manuscript and the discussion to include the mentioned points on the different TE superfamilies and the reliability of the TE calls. The detailed responses to all the raised points are below.

      Private Recommendations:

      Reviewer #1:

      (1) TE polymorphisms (presence and absence variants) were called from short-read sequencing data using a pipeline (TEPID, Stuart et al. eLife 2016) that is known to have a low specificity as well as a low sensitivity in its detection of presence variants (Baduel et al. MIMB 2021). An assessment of the rate of false positives and false negatives in the data presented in this study and how it varies across TE superfamilies is therefore of crucial importance as it may bias all downstream analyses, especially if it impacts the identification of polymorphisms contributed by retrotransposons, as these are the basis of most conclusions of the manuscript. Nonetheless, the fact that the PCA of the polymorphisms contributed by DNA transposons is less able to distinguish genetic clades than with those contributed by retrotransposons, suggests the issue of false positives is most preeminent for DNA transposons. However, high rates of false positives may explain why no significant increase in TE frequency is detected within selective sweep regions, a result that runs against the expectation of hitch-hiking of neutral or weakly deleterious polymorphisms which the authors claim is the category of many TE polymorphisms. Furthermore, given that the reference genome belongs to the B_east clade, and the TEPID is better at calling absence than presence it may bias analyses in this clade (where clade-specific insertions will take the form of absence in other clades which are well detected) compared to other clades (where clade-specific insertions will be presence polymorphisms and may be missed). A benchmark of TE polymorphism calls could be done by de novo assembling one genome from each clade or by cross-checking at least the presence variant calls from TEPID with those made with another of the many TE calling pipelines available.

      We agree with this issue raised by both reviewers regarding the effects of false negative and false positive TE calls. We also think that some reasonable follow-ups should be done to check the potential impact of the false negative and false positive TE calls on the presented results, without turning the manuscript in a method comparison paper as this is not the main goal of this study. Therefore, we generated a subsample of our dataset that included only accession with an average genome wide mapping coverages of at least 20x, as the false negative TE call rate is correlated with the mapping coverage and a high mapping coverage is expected to lead to a reduction in the false negative TE call rates. We then used this subsample to check if our results would change if our dataset had a lower false negative TE call rate. However, reducing the rate of false negative calls through the use of only higher coverage samples did not change our results and interpretations.

      Re-running the ANCOVA analyses revealed similar results regarding the accumulation of TEs in selective sweep regions. This was added to the main text Line 143-148: “Similar results were obtained when investigating the number of fixed TE polymorphisms (Additional file 2: Table S1) and the allele frequency of TE polymorphisms (Additional file 2: Table S2) in high iHS regions using a subset of our dataset with an expected lower false negative TE call rate, that only included samples with a genome-wide mapping coverage of at least 20x (see Discussion and Materials and Methods for more details).” and in Additional file 2: Table S1 and S2.

      Further, we re-ran the age-adjusted SFS based on this subset of our dataset and found that the results and conclusions from the age-adjusted SFS were not only driven by false negative TE calls. This was also included in the text Line 338-349: “One caveat of the approach used in this study is that TE calling pipelines based on short-reads tend to have higher false positive and false negative call rates than SNP calling pipelines, which is also the case for the TEPID TE calling pipeline used here [57, 59]. A high false negative TE calling rate however might bias our TE frequency estimates toward lower frequencies, which could drive the observed patterns in the age-adjusted SFS. To assess if the false negative TE calling rate in our study substantially affected our results, we re-run the age-adjusted SFS on a subset of our dataset only including samples with a genome-wide mapping coverage of at least 20x, as higher mapping coverages are expected to reduce the false negative call rate [27, 59]. Using the TE allele frequencies estimated based on this subset of our data to estimate  frequency revealed similar results of the age-adjusted SFS based on the whole dataset (Additional file 1: Fig. S9), indicating that our observation of retrotransposons evolving under purifying selection is not solely driven by a high false negative TE calling rate.” and in Additional file 1: Fig. S9.

      The details of this analyses have been added to the materials and methods Line 493-498: “Mapping coverage is known to influence false discovery rate [27, 59]. To investigate the impact of false positive and false negative TE calls on our results, we down sampled the TE dataset to only include TEs that have been called in samples that had at least an average mapping coverage of 20x. The allele frequencies of TEs present in our high coverage dataset was recalculated only considering samples with at least an average mapping coverage of 20x. This second TE dataset was then used to check if using a dataset with a higher mapping coverage and presumably a lower false TE calling rate impacted our results.”

      (2) If confirmed, the observation that retrotransposons located more than 5kb away from genes appear to be also affected by purifying selection (L209) is indeed surprising. The authors should add a comparison with SNPs at the same distance from genes to strengthen the claim and make sure it is not the result of mapping artifacts, such as alignment quality dropping far away from genes.

      We added a comparison of the age-adjusted SFS of SNPs and retrotransposons more than 5 kb away from genes to evaluate if the observed shape of the age-adjusted SFS of retrotransposons more than 5 kb away from genes were due to artefacts. The results are included on line 383-389: “Finally, we tested whether TE polymorphisms located more than 5 kb away from genes are evolving under purifying selection could be due to mapping or other artefacts by comparing the shape of the age-adjusted SFS of retrotransposons and SNPs more than 5 kb away from genes. However, the age-adjusted SFS of SNPs 5 kb away from genes differs from the one of retrotransposons (Additional file 1: Fig. S10), indicating that the shape of the age-adjusted SFS of retrotransposons more than 5 kb away from genes is not likely to be the result of artefacts in regions of the genome far away from genes.” and Additional file 1: Fig. S10.

      (3) The authors' claim that most TE polymorphisms are under weak to moderate purifying selection (L273) relies on the comparison of the age of polymorphisms in the oldest age bin with forward simulations. However, the conclusions from these comparisons cannot be extrapolated to the fitness effects of all TE polymorphisms as variants in the oldest age bin are de facto a biased sample of the variants of a category, a point the authors highlight.

      We adjusted the mentioned paragraph to better highlight this point. Line 390-397: “To further ascertain the strength of purifying selection, we used forward simulation and showed that simulations assuming a moderately weak selection pressure (S = -5 or S = -8) against TE polymorphisms best fitted our observed data. In theory, no TE polymorphisms under strong purifying selection should be present in a natural population, as such mutations are expected to be quickly lost, especially in a predominantly selfing species where most loci are expected to be homozygous. Therefore, it is not surprising that TE polymorphisms which persist in B. distachyon are under weak to moderate selection, as also shown, for example, for the L1 retrotransposons in humans [27] or the BS retrotransposon family in Drosophila melanogaster [62].”

      L220-228 for high-effect SNPs. Indeed, the most deleterious TE polymorphisms would be purged very quickly and never contribute to variants in the oldest age bin. Unless new arguments can be made to support this claim, this conclusion should be rephrased to claim instead that even the oldest TE polymorphisms are still mostly non-neutral and under weak to moderate purifying.

      This has been adjusted. Line 231-232: “. Hence, even the oldest retrotransposon polymorphisms seem to be mostly non-neutral and are affected by purifying selection.”

      L214: replace smaller with more negative for clarity.

      Done.

      L233: Given the discussion L220-228, the oldest age bin seems to be biased in its composition and thus not useful for comparisons. The sentence should therefore be rephrased to reflect that DNA transposon polymorphisms appear to be actually less deleterious than high-effect SNPs in S9A and B based on the penultimate age bin.

      This has been fixed.

      Reviewer #2:

      • I wonder if false negative detection could artificially increase the evidence for purifying selection by increasing the amount of low-frequency variants. This could be easily checked if long-read data or genome assembly is available for any of the samples in the collection, by comparing the TIP/TAP prediction with the actual sequence.

      We agree with this point from the reviewers that false negative calls can lead to misinterpretations of the observed low-frequencies of the TEs. (But see response to the first comment of reviewer #1). Unfortunately, long-read data from the sample used here are not available to estimate false negative call rates. However, to check if the observed results are manly driven by high false negative rates, we re-run the age-adjusted SFS based on samples with at least 20x mapping coverage, which should result in the reduction the false negative TE calling rate. The results and conclusions from this second analyses were included in the text Line 338-349: “One caveat of the approach used in this study is that TE calling pipelines based on short-reads tend to have higher false positive and false negative call rates than SNP calling pipelines, which is also the case for the TEPID TE calling pipeline used here [57, 59]. A high false negative TE calling rate however might bias our TE frequency estimates toward lower frequencies, which could drive the observed patterns in the age-adjusted SFS. To assess if the false negative TE calling rate in our study substantially affected our results, we re-run the age-adjusted SFS on a subset of our dataset only including samples with a genome-wide mapping coverage of at least 20x, as higher mapping coverages are expected to reduce the false negative call rate [27, 59]. Using the TE allele frequencies estimated based on this subset of our data to estimate  frequency revealed similar results of the age-adjusted SFS based on the whole dataset (Additional file 1: Fig. S9), indicating that our observation of retrotransposons evolving under purifying selection is not solely driven by a high false negative TE calling rate.” and in Additional file 1: Fig. S9.

      • Supplementary Figure S1. DNA transposons are much worse at separating the samples in comparison to LTR-retrotransposons. Doesn´t this suggest that these two classes have very different dynamics in the population and maybe different intensities of the selection forces acting on them? Could this profile be explained as DNA transposons being older and likely more fixed in all the clades, whereas retrotransposons are more recent and more specific to some populations? Another possibility might be that some B. distachyon DNA transposons had an unusually high excision rate. In any case, in my opinion, this reinforces the need to study the different TE orders in more detail.

      Indeed, different TE orders and superfamilies can have different excision rates, age distributions and be under different selective regimes. To investigate the possibility that different TE orders are affected by very different selective regimes, we split our TE dataset into the four different TE types: Copia, Ty3, Helitron and MITE. We than re-run the age-adjusted SFS analyses and added our results to the text Line 422-430: “To further examine our conclusion on purifying selection, we investigated the selective regime affecting different retrotransposons and DNA-transposons superfamilies. Thereby, we generated age-adjusted SFS for the four most common TE superfamilies Copia, Ty3 (also known under the name Gypsy, but we will avoid using this name because of its problematic nature see [71]), Helitron and MITE and found similar deviations of the  frequency from 0 in the four investigated TE superfamilies (Additional file 1: Fig. S12–S15). These results indicate that our conclusion on the broad effect of purifying selection is not driven by a single TE superfamily but is at least common among the four most numerous TE superfamilies.” and in Additional file 1: Fig. S12- S15.

      • Line 112: "most TE polymorphisms in our dataset were young and only a few were very old". Does this change substantially among TE orders/superfamilies?

      Indeed, there are some differences in the age distribution of the TEs depending on the superfamilies, However, the differences are no substantial as the age bins in the age-adjusted SFS of the different TE superfamilies are fairly similar. See Additional file 1: Fig. S12-S15.

      • Figure 2. Is difficult to read, especially lower panels. I think the grey border of the boxplots makes visualization difficult.

      The gray borders have been removed.

    1. Author Response

      Reviewer #1 (Public Review):

      Summary:

      Heer and Sheffield used 2 photon imaging to dissect the functional contributions of convergent dopamine and noradrenaline inputs to the dorsal hippocampus CA1 in head-restrained mice running down a virtual linear path. Mice were trained to collect water rewards at the end of the track and on test days, calcium activity was recorded from dopamine (DA) axons originating in the ventral tegmental area (VTA, n=7) and noradrenaline axons from the locus coeruleus (LC, n=87) under several conditions. When mice ran laps in a familiar environment, VTA DA axons exhibited ramping activity along the track that correlated with distance to reward and velocity to some extent, while LC input activity remained constant across the track, but correlated invariantly with velocity and time to motion onset. A subset of recordings taken when the reward was removed showed diminished ramping activity in VTA DA axons, but no changes in the LC axons, confirming that DA axon activity is locked to reward availability. When mice were subsequently introduced to a new environment, the ramping to reward activity in the DA axons disappeared, while LC axons showed a dramatic increase in activity lasting 90 s (6 laps) following the environment switch. In the final analysis, the authors sought to disentangle LC axon activity induced by novelty vs. behavioral changes induced by novelty by removing periods in which animals were immobile and established that the activity observed in the first 2 laps reflected novelty-induced signal in LC axons.

      Strengths:

      The results presented in this manuscript provide insights into the specific contributions of catecholaminergic input to the dorsal hippocampus CA1 during spatial navigation in a rewarded virtual environment, offering a detailed analysis of the resolution of single axons. The data analysis is thorough and possible confounding variables and data interpretation are carefully considered.

      Weaknesses:

      Aspects of the methodology, data analysis, and interpretation diminish the overall significance of the findings, as detailed below.

      The LC axonal recordings are well-powered, but the DA axonal recordings are severely underpowered, with recordings taken from a mere 7 axons (compared to 87 LC axons). Additionally, 2 different calcium indicators with differential kinetics and sensitivity to calcium changes (GCaMP6S and GCaMP7b) were used (n=3, n=4 respectively) and the data pooled. This makes it very challenging to draw any valid conclusions from the data, particularly in the novelty experiment. The surprising lack of novelty-induced DA axon activity may be a false negative. Indeed, at least 1 axon (axon 2) appears to be showing a novelty-induced rise in activity in Figure 3C. Changes in activity in 4/7 axons are also referred to as a 'majority' occurrence in the manuscript, which again is not an accurate representation of the observed data.

      The reviewer points out a weakness in the analysis of VTA axons in our dataset. The relatively low n (currently 7) comes from the fact that VTA axons in the CA1 region of the hippocampus are very sparse and very difficult to record from (due to their sparsity and the low level of baseline fluorescence inherent in long range axon segments). This is the reason they have not been recorded from in any other lab outside of our lab. LC axons, on the other hand, are more abundant in CA1. In the paper when comparing VTA versus LC axons we deal with the mismatch in n by downsampling the LC axons to match the VTA axons and repeated this 1000 times to create a distribution. However, because the VTA axon n is relatively low, it is possible that we have not sampled the VTA axon population sufficiently and therefore have a biased population in our dataset. The issue is that it takes months for the baseline expression of GCaMP to reach sufficient levels to be able to record from VTA axons, and it is typical to find only a single axon in a FOV per animal. There are additional reasons why mice and/or axon recordings do not reach criteria and cannot be included in the dataset (these exclusion criteria are reported in the Methods section). For instance, out of the 54 DAT-Cre mice injected, images were never conducted in 36 for lack of expression or because mice failed to reach behavioral criteria. Another 11 mice were excluded for heat bubbles that developed during imaging, z-drift of the FOV, or bleaching of the GCaMP signal.

      However, we do have n=2 additional VTA axon recordings that we will add to the dataset to bring the n up from 7 to 9. We plan on re-analyzing the data with n=9 VTA axons and making comparisons to down-sampled LC axons as described above. This boost in n will increase the power of our VTA axon analysis. To more formally test whether this is sufficient for statistical tests, we plan to utilize the G*power power-analysis tool to compute statistical power for each of the different tests we use. We will report this in the next version of the paper. However, the n=2 additional axons were nor recorded in the novel environment, so the next version will remain at n=7 for the novel environment analysis. We agree with the reviewer that the lack of the novelty induced DA axon activity may be a false negative, and so we will adjust the description of our results and discussion accordingly.

      During the data collection of VTA axon activity we tried two variants of GCaMP: 6s and 7b, to see if one would increase the success rate of finding and recording from VTA axons. Given the long time-course of these experiments and the low yield in success, we pooled the GCaMP variants together to increase statistical power. Because the 2 additional VTA DA axons that were recorded from expressed GCaMP6s, the next version of the paper will have n=5 GCaMP6s, and n=4 GCaMP7b VTA DA axons, which will allow us to compare the activity of the two sensors in the familiar environment. The reviewer correctly pointed out that the sensors themselves could confound our results, and so they should not be pooled unless we can show they do not produce different signals in the axons. We will make this comparison and report the findings in the next version of the paper. If we find no significant differences, we will pool the data. If differences are detected, we will keep these axons separate for subsequent analysis and comparisons to LC axons.

      The authors conducted analysis on recording data exclusively from periods of running in the novelty experiment to isolate the effects of novelty from novelty-induced changes in behavior. However, if the goal is to distinguish between changes in locus coeruleus (LC) axon activity induced by novelty and those induced by motion, analyzing LC axon activity during periods of immobility would enhance the robustness of the results.

      This is indeed true, and this suggested analysis could further support our conclusions regarding the LC novelty signal. For the next version of the paper, we will use the periods of immobility to analyze and isolate any novelty induced activity in LC axons. However, following exposure to the novel environment, mice spend much less time immobile, therefore there may not be sufficient periods of immobility close in time to the exposure to the novel environment (which is when the novelty signal occurs). We plan to analyze mouse behavior during the early exposure to the novel environment for immobility and check whether we have enough of this behavior to perform the suggested analysis.

      The authors attribute the ramping activity of the DA axons to the encoding of the animals' position relative to reward. However, given the extensive data implicating the dorsal CA1 in timing, and the remarkable periodicity of the behavior, the fact that DA axons could be signalling temporal information should be considered.

      This is a very good point. We agree that the VTA DA axons could be signaling temporal information, as we have previously shown that these axons also exhibit ramping activity when you average their activity by time to reward (Krishnan et. al., 2022). We will conduct this analysis on this dataset. We have not, however, conducted any experiments designed to separate out time from distance, such as the experiments conducted in Kim et. al., 2020. Therefore, we cannot determine whether this is due to proximity in space to reward or time to reward. We will clarify in our text that by proximity, we mean either place or time, and cannot conclude which feature of the experience drives the VTA axon signal.

      Krishnan, L.S., Heer, C., Cherian, C., Sheffield, M.E. Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal. Nat Commun 13, 6662 (2022).

      Kim, HyungGoo R., Athar N. Malik, John G. Mikhael, Pol Bech, Iku Tsutsui-Kimura, Fangmiao Sun, Yajun Zhang, et al. A Unified Framework for Dopamine Signals across Timescales. Cell 183, no. 6 (2020).

      The authors should explain and justify the use of a longer linear track (3m, as opposed to 2m in the DAT-cre mice) in the LC axon recording experiments.

      LC axon activity was recorded on a 3m track to match the track length from an experiment we recently published (Dong et al., 2021) in which mice were exposed to a novel 3m track while populations of CA1 pyramidal cells were recorded. In that paper we described the time course of place field formation on the novel track. We wanted to test if LC axons signaled novelty (as we hypothesized) and whether the time course of LC axon activity matched the time course of place field formation. We briefly discuss this in the Discussion section of this paper and hypothesize that LC axons in CA1 could open a window of plasticity in which new place fields can form.

      VTA axons were recorded on a 2m track (same VR tracks as LC axons were recorded on) to match another recent paper from our lab in which reward expectation was manipulated (Krishnan et al, 2022). In that study CA1 populations of pyramidal cells were recorded during the reward expectation experiment. To match the experience during recordings of VTA axons in CA1 to test how reward expectation may influence axon signaling along the track, we also used a 2m track. The idea was to check how VTA dopaminergic inputs to CA1 may influence CA1 population dynamics along the track.

      Although the tracks were identical for LC and VTA recordings for both the familiar and novel tracks in terms of visual cues and design, the track lengths are different (simply modulated by gain control of the rotary encoder). To account for this we normalized the lengths for our comparison analysis. This normalization allows for a direct comparison of the patterns of activity across the two types of axons, controlling for the potential confound introduced by the different track lengths. By adjusting the data to a common scale, we could assess the relative changes in activity levels at matched spatial bins, ensuring that any observed differences or similarities are due to the intrinsic properties of the axons rather than differences in track lengths. However, the different lengths do make the animal’s experience slightly different. This is somewhat offset by the observations in our study that none of the LC or VTA axon signals would be expected to be majorly influenced by variations in track length. For instance, LC axons are associated with velocity and a pre-motion initiation signal, neither of which would be influenced by track length. VTA axons are also associated with velocity, which would not influence a direct comparison to LC axon velocity signals as mice reach maximal velocity very rapidly along the track. VTA axons do ramp up in activity as they approach the reward zone, and this signal could be modulated by track length (or maybe not if the signal is encoding time to reward rather than distance). However, LC axons show no ramping to reward signals, so a comparison across axons recorded on different track lengths for this analysis is justified.

      However, to add rigor to comparisons of axon dynamics recorded along 2m and 3m tracks, we plan to plot axon activity of both sets of axons by time to reward, and actual (un-normalized) distance from reward.

      Krishnan, L.S., Heer, C., Cherian, C., Sheffield, M.E. Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal. Nat Commun 13, 6662 (2022).

      Dong, C., Madar, A. D. & Sheffield, M.E. Distinct place cell dynamics in CA1 and CA3 encode experience in new environments. Nat Commun 12, 2977 (2021).

      Reviewer #2 (Public Review):

      Summary:

      The authors used 2-photon Ca2+-imaging to study the activity of ventral tegmental area (VTA) and locus coeruleus (LC) axons in the CA1 region of the dorsal hippocampus in head-fixed male mice moving on linear paths in virtual reality (VR) environments.

      The main findings were as follows:

      • In a familiar environment, the activity of both VTA axons and LC axons increased with the mice's running speed on the Styrofoam wheel, with which they could move along a linear track through a VR environment.
      • VTA, but not LC, axons showed marked reward position-related activity, showing a ramping-up of activity when mice approached a learned reward position.
      • In contrast, the activity of LC axons ramped up before the initiation of movement on the Styrofoam wheel.
      • In addition, exposure to a novel VR environment increased LC axon activity, but not VTA axon activity.

      Overall, the study shows that the activity of catecholaminergic axons from VTA and LC to dorsal hippocampal CA1 can partly reflect distinct environmental, behavioral, and cognitive factors. Whereas both VTA and LC activity reflected running speed, VTA, but not LC axon activity reflected the approach of a learned reward, and LC, but not VTA, axon activity reflected initiation of running and novelty of the VR environment.

      I have no specific expertise with respect to 2-photon imaging, so cannot evaluate the validity of the specific methods used to collect and analyse 2-photon calcium imaging data of axonal activity.

      Strengths:

      (1) Using a state-of-the-art approach to record separately the activity of VTA and LC axons with high temporal resolution in awake mice moving through virtual environments, the authors provide convincing evidence that the activity of VTA and LC axons projecting to dorsal CA1 reflect partly distinct environmental, behavioral and cognitive factors.

      (2) The study will help a) to interpret previous findings on how hippocampal dopamine and norepinephrine or selective manipulations of hippocampal LC or VTA inputs modulate behavior and b) to generate specific hypotheses on the impact of selective manipulations of hippocampal LC or VTA inputs on behavior.

      Weaknesses:

      (1)The findings are correlational and do not allow strong conclusions on how VTA or LC inputs to dorsal CA1 affect cognition and behavior. However, as indicated above under Strengths, the findings will aid the interpretation of previous findings and help to generate new hypotheses as to how VTA or LC inputs to dorsal CA1 affect distinct cognitive and behavioral functions.

      (2) Some aspects of the methodology would benefit from clarification.<br /> First, to help others to better scrutinize, evaluate, and potentially to reproduce the research, the authors may wish to check if their reporting follows the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines for the full and transparent reporting of research involving animals (https://arriveguidelines.org/). For example, I think it would be important to include a sample size justification (e.g., based on previous studies, considerations of statistical power, practical considerations, or a combination of these factors). The authors should also include the provenance of the mice. Moreover, although I am not an expert in 2-photon imaging, I think it would be useful to provide a clearer description of exclusion criteria for imaging data.

      We thank the reviewer for helping us formalize the scientific rigor of our study. There are ten ARRIVE Guidelines and we have addressed most of them in our study already. However, there is an opportunity to add detail. We have listed below all ten points and how we have or will address each one.

      (1) Experimental design - we go into great depth explaining the experimental set-up, how we used the autofluorescent blebs as imaging controls, how we controlled for different sample sizes between the two populations, and the statistical tests used for comparisons. We also carefully accounted for animal behavior when quantifying and describing axon dynamics both in the familiar and novel environments.

      (2)Sample size - We state both the number of ROIs and mice for each analysis. Wherever we state how many axons had a certain kind of activity, we will also state the number of mice we saw this activity in. For the next version of the paper, we plan to conduct a power analysis using G*power to assess the power of our sample sizes for statistical analysis.

      (3) Inclusion/exclusion criteria - Out of the 36 NET-Cre mice injected, 15 were never recorded for either failing to reach behavioral criteria, or a lack of visible expression in axons. Out of the 54 DAT-Cre mice injected, images were never conducted in 36 for lack of expression or failing to reach behavioral criteria. Out of the remaining 21 NET-CRE, 5 were excluded for heat bubbles, z-drift, or bleaching, while 11 DAT-Cre were excluded for the same reasons. This was determined by visually assessing imaging sessions, followed by using the registration metrics output by suite2p. This registration metric conducted a PCA on the motion-corrected ROIs and plotted the first PC. If the PC drifted largely, to the point where no activity was apparent, the video was excluded from analysis.

      (4) Randomization - Already included in the paper is a description of random down sampling of LC axons to make statistical comparisons with VTA axons. LC axons were selected pseudo-randomly (only one axon per imaging session) to match VTA sampling statistics. This randomization was repeated 1000 times and comparisons were made against this random distribution.

      (5) Blinding-masking - no blinding/masking was conducted as no treatments were given that would require this. We will include this statement in the next version.

      (6) Outcomes - We defined all outcomes measured, such as those related to animal behavior and related axon signaling.

      (7) Statistical methods - None of the reviewers had any issues regarding our description of statistical methods, which we described in detail in this version of the paper.

      (8) Experimental animals - We described that DAT- Cre mice were obtained through JAX labs, and NET-Cre mice were obtained from the Tonegawa lab (Wagatsuma et al. 2017)

      (9) Experimental procedure - Already listed in detail in Methods section.

      (10) Results - Rigorously described in detail for behaviors and related axon dynamics.

      Wagatsuma, Akiko, Teruhiro Okuyama, Chen Sun, Lillian M. Smith, Kuniya Abe, and Susumu Tonegawa. “Locus Coeruleus Input to Hippocampal CA3 Drives Single-Trial Learning of a Novel Context.” Proceedings of the National Academy of Sciences 115, no. 2 (January 9, 2018): E310–16. https://doi.org/10.1073/pnas.1714082115.

      Second, why were different linear tracks used for studies of VTA and LC axon activity (from line 362)? Could this potentially contribute to the partly distinct activity correlates that were found for VTA and LC axons?

      A detailed response to this is written above for a similar comment from reviewer 1.

      Third, the authors seem to have used two different criteria for defining immobility. Immobility was defined as moving at <5 cm/s for the behavioral analysis in Figure 3a, but as <0.2 cm/s for the imaging data analysis in Figure 4 (see legends to these figures and also see Methods, from line 447, line 469, line 498)? I do not understand why, and it would be good if the authors explained this.

      This is an error leftover from before we converted velocity from rotational units of the treadmill to cm/s. This will be corrected in the next version of the paper.

      (3) In the Results section (from line 182) the authors convincingly addressed the possibility that less time spent immobile in the novel environment may have contributed to the novelty-induced increase of LC axon activity in dorsal CA1 (Figure 4). In addition, initially (for the first 2-4 laps), the mice also ran more slowly in the novel environment (Figure 3aIII, top panel). Given that LC and VTA axon activity were both increasing with velocity (Figure 1F), reduced velocity in the novel environment may have reduced LC and VTA axon activity, but this possibility was not addressed. Reduced LC axon activity in the novel environment could have blunted the noveltyinduced increase. More importantly, any potential novelty-induced increase in VTA axon activity could have been masked by decreases in VTA axon activity due to reduced velocity. The latter may help to explain the discrepancy between the present study and previous findings that VTA neuron firing was increased by novelty (see Discussion, from line 243). It may be useful for the authors to address these possibilities based on their data in the Results section, or to consider them in their Discussion.

      This is a great point. The decreased velocity in the novel environment could lead to a diminished novelty response in LC axons. We will add a discussion point on this in the next version. This could also be the case for VTA axons, so will add a discussion point that the lack of novelty signaling seen in VTA axons could be due to reduced velocity masking this signal.

      (4) Sensory properties of the water reward, which the mice may be able to detect, could account for reward-related activity of VTA axons (instead of an expectation of reward). Do the authors have evidence that this is not the case? Occasional probe trials, intermixed with rewarded trials, could be used to test for this possibility.

      Mice receive their water reward through a waterspout that is immobile and positioned directly in front of their mouth (which is also immobile as they are head fixed) and water delivery is triggered by a solenoid when the mice reach the end of the virtual track. Therefore, because the waterspout remains in the same place relative to the mouse, and the water reward is not delivered until they reach the end of the virtual track, there is nothing for the mice to detect. We will update the paper to make this clearer.

      Additionally, on the initial laps with no reward, the ramping activity is still present (Krishnan et al, 2022) indicating this activity is not directly related to the presence/absence of water but is instead caused by reward expectation.

      Reviewer #3 (Public Review):

      Summary:

      Heer and Sheffield provide a well-written manuscript that clearly articulates the theoretical motivation to investigate specific catecholaminergic projections to dorsal CA1 of the hippocampus during a reward-based behavior. Using 2-photon calcium imaging in two groups of cre transgenic mice, the authors examine the activity of VTA-CA1 dopamine and LC-CA1 noradrenergic axons during reward seeking in a linear track virtual reality (VR) task. The authors provide a descriptive account of VTA and LC activities during walking, approach to reward, and environment change. Their results demonstrate LC-CA1 axons are activated by walking onset, modulated by walking velocity, and heighten their activity during environment change. In contrast, VTA-CA1 axons were most activated during the approach to reward locations. Together the authors provide a functional dissociation between these catecholamine projections to CA1. A major strength of their approach is the methodological rigor of 2-photon recording, data processing, and analysis approaches. These important systems neuroscience studies provide solid evidence that will contribute to the broader field of learning and memory. The conclusions of this manuscript are mostly well supported by the data, but some additional analysis and/or experiments may be required to fully support the author's conclusions.

      Weaknesses:

      (1) During teleportation between familiar to novel environments the authors report a decrease in the freezing ratio when combining the mice in the two experimental groups (Figure 3aiii). A major conclusion from the manuscript is the difference in VTA and LC activity following environment change, given VTA and LC activity were recorded in separate groups of mice, did the authors observe a similar significant reduction in freezing ratio when analyzing the behavior in LC and VTA groups separately?

      In response to this comment, we will analyze the freezing ratios in DAT-Cre and NET-Cre mice separately. However, other members of the lab have seen the same result in other mouse strains (See Dong et al. 2021), so we do not expect to see a difference (but it is certainly worth checking).

      (2) The authors satisfactorily apply control analyses to account for the unequal axon numbers recorded in the LC and VTA groups (e.g. Figure 1). However, given the heterogeneity of responses observed in Figures 3c, 4b and the relatively low number of VTA axons recorded (compared to LC), there are some possible limitations to the author's conclusions. A conclusion that LC-CA1 axons, as a general principle, heighten their activity during novel environment presentation, would require this activity profile to be observed in some of the axons recorded in most all LC-CA1 mice.

      We agree with the reviewer’s point here. To help avoid this problem, when downsampling LC axons to compare to VTA axons, we matched the sampling statistics of the VTA axons/mice (i.e. only one LC axon was taken from each mouse to match the VTA dataset).

      However, in the next version of the paper we will also report the number of mice that we see a significant novel response in. We will also add the number of mice with significant activity for each of the measures in the familiar environment (e.g. how many mice had axons positively correlated with velocity).

      Additionally, if the general conclusion is that VTA-CA1 axons ramp activity during the approach to reward, it would be expected that this activity profile was recorded in the axons of most all VTA-CA1 mice. Can the authors include an analysis to demonstrate that each LC-CA1 mouse contained axons that were activated during novel environments and that each VTA-CA1 mouse contained axons that ramped during the approach to reward?

      As stated above, we will add the number of mice that had each activity type we reported here.

      (3) A primary claim is that LC axons projecting to CA1 become activated during novel VR environment presentation. However, the experimental design did not control for the presentation of a familiar environment. As I understand, the presentation order of environments was always familiar, then novel. For this reason, it is unknown whether LC axons are responding to novel environments or environmental change. Did the authors re-present the familiar environment after the novel environment while recording LC-CA1 activity?

      This is an important point to address. While we never varied the presentation order of the familiar vs novel environments, we did record the activity of LC axons in some of the mice in a dark environment (no VR cues) prior to exposure to the familiar environment. We will look at these axons to address whether they respond to initial exposure to the familiar environment. This will allow us to check whether they are responding to environmental change or novelty. We will add this analysis to the next version of the paper.

    1. ABSTRACTAs genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results.

      This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.108) as part of our Spatial Omics Methods and Applications series (https://doi.org/10.46471/GIGABYTE_SERIES_0005), and has published the reviews under the same license as follows:

      **Reviewer 1. Chunquan Li **

      1. Page 1, Lines 14-16. The authors indicate that “it is crucial to thoroughly investigate the batch effects in the dataset before integrating and processing the data”. The term “thoroughly” may be not accurate enough. The current method can alleviate the batch effects, but it can’t thoroughly solve the related problems. In addition, this work proposes a batch evaluation tool, such “reasonably evaluate the batch effects” may be more accurate than “thoroughly investigate the batch effects”.
      2. In Figure 1, does the first box is “integrated datasets”?
      3. Page 5, Line 168, and Page 6, Lines 169-175, the content of these two paragraphs is similar, with some redundant descriptions. It is recommended to organize and write them into one paragraph.
      4. There is Table 1 in the table list, but Table 1 is missing in the main text.
      5. Page 8, Discussion section, it is better to discuss the differences between the proposed tool and a similar tool “batchQC”, especially the advantages of the proposed tool.
      6. Some other minor issues: Page 1, Line 22, “to do so” should be “to do it”. Page 3, Line 100, Ref. [13] should be cited when it first appears on Line 97. Page 4, Line 114 and Page 5, Line 146, “UMAP” should be given its full name when it first appears and abbreviated directly in the following text. The variable should be in italics, such as “p” on Page 4, Line 119, “H” on Page 6, Line 184.

      Reviewer 2. W. Evan Johnson and Howard Fan

      Is the source code available, and has an appropriate Open Source Initiative license (https://opensource.org/licenses) been assigned to the code?

      Yes. However, the code could use substantial improvements.

      Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?

      No. The manuscript is missing a section describing the software and its implementation.

      Is there enough clear information in the documentation to install, run and test this tool, including information on where to seek help if required?

      Yes. But it took a while to get it installed.

      Have any claims of performance been sufficiently tested and compared to other commonly-used packages?

      No. I think the most glaring deficiency in the paper is the lack of comparison with other methods. For example, there is no comparison of the tools available in BatchEval compared to other methods, such as BatchQC. Also, they mention that BatchQC might not work on larger datasets, but they perform no performance evaluation for BatchEval, and no comparison with BatchQC to demonstrate improved performance.

      Are there (ideally real world) examples demonstrating use of the software?

      Yes. Missed opportunity--I think the most exciting thing I observed from the paper was that the example data were from spatial transcriptomics data! To my knowledge, existing batch effect methods are not directly adapted to manage these data (although they did mention tools like BatchQC cannot handle large datasets, which may be true). But they don’t mention anything about batch adjustment/evaluation in spatial data in the manuscript. I feel that if the authors address this niche it would increase the value/impact of their work!

      Additional Comments:

      This review was conducted and written by Evan Johnson, who developed the competing BatchQC software.

      The authors provide an interesting toolkit for assessing batch effects in genomics data. The paper was clear and well-written, albeit I had a few concerns (see below). We were also able to download the associated software and test it out (comments below as well).

      I think the most exciting thing I observed from the paper was that the example data were from spatial transcriptomics data! To my knowledge, existing batch effect methods are not directly adapted to manage these data (although they did mention tools like BatchQC cannot handle large datasets, which may be true). But they don’t mention anything about batch adjustment/evaluation in spatial data in the manuscript. I feel that if the authors address this niche it would increase the value/impact of their work!

      In addition, this toolkit is written in Python, while BatchQC and other tools are written in R, so this is an advantage of the method as well—it addresses an audience that uses Python for gene expression analysis (not as big as the R community, but substantial). Their Python toolkit might also be more accessible to implementation in a pipeline workflow (for a core or large project) than R-based tools like BatchQC—this might be important to mention this as well.

      I think the most glaring deficiency in the paper is the lack of comparison with other methods. For example, there is no comparison of the tools available in BatchEval compared to other methods, such as BatchQC. Also, they mention that BatchQC might not work on larger datasets, but they perform no performance evaluation for BatchEval, and no comparison with BatchQC to demonstrate improved performance.

      Similarly, the authors claim: “Manimaran [10] has developed user-friendly software for evaluating batch effects. However, the software does not take into account nonlinear batch effects and may not be able to provide objective conclusions.” I don’t understand what the authors mean by “may not be able to provide objective conclusions” – BatchQC provides – several visual and numerical evaluations of batch effect – more so than even the proposed BatchEval does. Did the authors mean something else, maybe that the lack of non-linear correction may lead to less accurate conclusions?

      A related concern: does BatchEval provide non-linear adjustments? I may have missed this, but it seems that BatchEval is not providing non-linear adjustments either. Also, regarding non-linear adjustments, the authors should show in an example the problems with a lack non-linear adjustments and show that pre-transforming the data before using BatchQC does not perform as well as the non-linear BatchEval adjustments.

      In Equation 10, should “batchScore” be BatchEvalScore?

      Also, in the bottom of Figure on page 15, should the “BatchQCScore” also be BatchEvalScore??

      The manuscript is missing a section describing the software and its implementation.

      I asked my research scientist, who recently graduated with his PhD in Bioinformatics, to assess the software and examples. First of all, much of the software is named “BatchQC”. I think this is confusing, since the method is really named BatchEval and it will be confused with BatchQC which is another existing/competing software. Furthmore, it took him a significant effort to install the BatchEval software and get is working on our cluster. I would recommend the authors make their software more accessible and easier to install.

      The output of the software was a nice .html report diagnosing the batch effects in the data—very useful (attached is a combined .pdfs of the .htmls that we generated). We were also able to generate a report for the harmony adjusted example using their code. One major disadvantage was that these reports are separate files, and this could get very complicated comparing cases using multiple batch effect methods that will all be in separate reports (refer to a recent single cell batch comparison that compared more than a dozen methods – Tran et al. Genome Biology, 2020 – it would be hard to use BatchEval for this comparison).

      Also, it seems that the user is required to conduct the batch correction themselves, BatchEval does not help with the correction except for their example code for Harmony.

      Finally, on comparing the raw and Harmony adjusted datasets, inspection of the visual assessments (e.g. PCA) show some improvement—although not a perfect correction. But must of the numerical assessments are still the sample. The BatchEvalScore in both cases leads to the conclusion “Need to do batch effect removal”. What’s missing is the difference or improvement that Harmony makes on its correction. Maybe this is just because Harmony doesn’t fully remove the batch effects? Or is there something not working in the code? Might be good to see another example where the batch effect correction improves the BatchEvalScore significantly.

      Additional Files: https://gigabyte-review.rivervalleytechnologies.com/journal/gx/download-files?YXJ0aWNsZT00NDImZmlsZT0xNzEmdHlwZT1nZW5lcmljJnZpZXc9dHJ1ZQ~~

      Re-review:

      I find this paper to be much improved in this version. The authors have clearly worked hard to address my concerns and have addressed them in a satisfactory manner. I fully support the publication of this paper, and I believe their tools are a nice addition to the field.

    1. Reviewer #3 (Public Review):

      Summary:<br /> In this study, the authors collected a large set of data on root traits and root-associated microbes in the root endosphere and rhizosphere in order to integrate these important organisms in the root economics spectrum. By sampling a relatively large set of species from the subtropics along an elevation gradient, they tested whether microbial functions covary with root traits and root trait axes and if so, aimed to discuss what this could tell us about the (belowground) functioning of trees and forests.

      Strengths:<br /> The strengths of this study lie mostly in the impressive dataset set the authors compiled: they sampled belowground properties of a relatively large number of tree species from an understudied region: i.e., the subtropics, where species-level root data are notoriously scarce. Secondly, their extensive sampling of associated microbes to integrate them in the root economics space is an important quality, because of the strong associations between roots and fungi and bacteria: soil microbes are directly related to root form (e.g., mycorrhizal fungi and root diameter and SRL), and function (e.g., taking up soil nutrients from various sources). Thirdly, the PCA figures (Figures 2 and 3) look very nice and intuitive and the paper is very well written.

      Weaknesses:<br /> That said, this study also has several methodological weaknesses that make the results, and therefore the impact of this study difficult to evaluate and interpret.

      (1) Design: The design of this study needs further explanation and justification in the Introduction and Methods sections in order to understand the ecological meaning of the results. Root traits and microbial community composition differ with their environment, and therefore (likely) also with elevation. Elevation is included in the redundancy analysis as a main effect, but without further environmental information, its impact is not ecologically meaningful. What is the rationale for including an elevation gradient in the design and as a main effect in the analyses? Do environmental conditions vary across altitudes and how, and if so, how would this impact the data?

      What is the rationale behind sampling endosphere and rhizosphere microbial communities - why do both? And why also include pathogens - what are their expected roles in the RES? What do we know about this already? The introduction needs a more extensive literature review of these additional variables that are included in the analyses.

      (2) Units of replication and analysis in the model: What are the units of replication and analyses, e.g., how many trees were sampled per species, how many species or trees per elevation, and how many plots per elevation? Were all 11 plots at different elevations and if so, which ones? The level of analysis for the redundancy analyses is not entirely clear: L. 404 mentions that the analyses were done 'across the rhizosphere and root tissue samples', but is that then at the individual-tree level? If so, it seems that these analyses should then also account for dependencies between trees from the same species and phylogeny (as (nested) covariates or random factors). With the information provided, I cannot tell whether there was sufficient replication for statistical interpretations.

      (3) PCA: The results of the parallel analyses are not described: which components were retained? Because the authors aim to integrate microbial functions in a root economics space, I recommend first demonstrating the existence of a root economics space across the 52 subtropical species before running a PCA that includes the microbial traits. The PCA shown in this study does not exactly match the RES and this could be because traits of these species covary differently, but may also simply result from including additional traits to the PCA.

      Also, the PCA's shown are carried out at the individual-tree level. I would recommend, however, including the species-level PCA's in the main text, because the individual-level PCA may not only reflect species-inherent ecological strategies (that e.g., the RES by Bergmann et al. 2020 describe) but also plasticity (Figures 2 and 3 both show an elevation effect that may be partly due to plasticity). While the results here are rather similar, intraspecific differences in root traits may follow different ecological principles and therefore not always be appropriate to compare with an interspecific RES (see for example Weemstra & Valverde-Barrantes, 2022, Annals of Botany).

      I could not deduce whether tree species in the "fungal PCA" (Figure 2) were assigned as AM or EcM based on Table 1, or based on their observed fungal community composition. In the former case, the fungal functional guild gradient (from EcM to saprotrophs and AM) is partially an artificial one, because EcM tree species are not AM species (according to Table 1) and therefore, by definition, constitute a tradeoff or autocorrelation. And, as the authors also discuss, AM tree species may host EcM fungal species. Before I can evaluate the ecological meaning of PC1, and whether or not it really represents a mineral/organic nutrient gradient, information is needed on which data are used here.

      I do not agree with the term 'gradient of bacterial guilds' (i.e., PC1 in Figure 3). All but 1 bacterial 'function' positively loaded on PC1 and 'fermentation' was only weakly negatively correlated with PC1. I do not think this constitutes a 'bacterial gradient'.

      (4) Soil samples: Were they collected from the surrounding soil of each tree (L. 341), or from the root zone (L. 110). The former seems to refer to bulk soil samples, but the latter could be interpreted as rhizosphere soils. It is therefore not entirely clear whether these are the same soil samples, and if so, where they were sampled exactly.

      Aims:<br /> The authors aimed to integrate endospheric and rhizospheric microbial and fungal community composition in the root economics space. Owing to statistical concerns (i.e., lacking parallel analysis results and the makeup of the PCs (AM versus EcM classification), I am not sure the authors succeeded in this. Besides that, the interpretation of the axes seems rather oversimplified and needs some consideration.

      Root N is discussed as an important driver of fungal functional composition. Indeed, it was one of the significant variables in the redundancy models predicting microbial community composition, but its contribution to community composition was small (2 - 3 %), and the mechanistic interpretation was rather speculative. Specifically, the role of root N in root (and tree) functioning remains highly uncertain: the link with respiration and exudation is increasingly demonstrated but its actual meaning for nutrient uptake is not well understood (Freschet et al. 2021. New Phytologist). If and how root economics (represented by root N) and the fungal-driven nutrient economy (EcM versus AM, saprotrophs) can indeed be integrated into a unified framework (L. 223 - 224) seems a relevant question that is worth pursuing based on this paper, but in my opinion, this study does not clearly answer it, because the statistical analyses might need further work (or explanation) and underlying mechanisms are not well explained and supported by evidence.

      In addition, the root morphology axis was indeed independent of the "fungal gradient", but this is in itself not an interesting finding. What is interesting, but not discussed is that, generally, AM species are expected to have thicker roots than EcM tree species (Gu et al. 2014 Tree Physiology; Kong et al. 2014 New Phytologist). I am therefore curious to see why this is not the case here? Did the few EcM species sampled just happen to have very thick roots? Or is there a phylogenetic effect that influences both mycorrhizal type and root thickness that is not accounted for here (Baylis, 1975; Guo et al., 2008 New Phytologist; Kubisch et al., 2015 Frontiers in Plant Science; Valverde-Barrantes et al., 2015 Functional Ecology; 2016 Plant and Soil)?

      I also do not agree with the conclusion that this integrated framework 'explained' tree distributions along the elevation gradient. First of all, it is difficult to interpret because the elevation gradient is not well explained (e.g., in terms of environmental variation). Secondly, the framework might coincide with the framework, but the framework does not explain it: an environmental gradient probably underlies the elevation gradient that may be selected for species with certain root traits or mycorrhizal types, but this is not tested nor clearly demonstrated by the data. It thus remains rather speculative, and it should be more thoroughly explained based on the data observed. Similarly, I do not understand from this study how root traits like root N can influence the abundance of EcM and pathogenic fungi (L. 242 - 243). Which data show this causality? It seems a strong statement, but not well supported (or explained).

      Impact:<br /> The data collected for this study are timely, valuable, and relevant. Soilborne microbes (fungi and bacteria; symbionts and pathogens) play important roles in root trait expressions (e.g., root diameter) and below-ground functioning (e.g., resource acquisition). They should therefore not be excluded from studies into the belowground functioning of forests, but they mostly are. This dataset therefore has the potential to improve our understanding of this subject. Making these data publicly available in large-scale datasets that have recently been initiated (e.g., FRED) will also allow further study in comparative (with other biomes) or global (across biomes) studies.

      Technically, the methodology seems sound, although I lack the expertise to judge the Molecular Methods (L. 349 - 397). However, owing to some statistical uncertainties mentioned above (that the authors might well clarify or improve) and the oversimplified discussion, I am hesitant to determine the impact of the contents of this work. Statistical improvements and/or clearer explanation/justification of statistical choices made can make this manuscript highly interesting and impact, however.

      Context:<br /> As motivated above, I am not sure to what extent the EcM - AM/saprotroph presents a true ecological tradeoff. However, if it does, this work would fit very well in the context of the mycorrhizal-associated nutrient economy (Phillips et al. 2013 New Phytology). This theory postulates that EcM trees generally produce low-quality litter (associated with 'slow traits') that can be more readily accessed by EcM but not AM fungi, thereby slowing down nutrient cycling rates at their competitive advantage, and vice versa for AM tree species. This study did not aim to test the MANE, so it was beyond its scope to study litter quality, and the number of EcM and AM species was unbalanced (8 EcM versus 44 AM species): nonetheless, the denser roots of EcM species and higher root N of AM species indicates that the MANE may also apply to this subtropical forest and may be an interesting impetus for future work on this topic. It might also offer one way to bridge the root economics space and the MANE.

      What I also found interesting is the sparse observations of EcM fungal taxa in the root endosphere of species typically identified as AM hosts (L. 212 - 214). While their functionality remains to be tested (fungal structures in the endosphere were not studied here), this observation might call for renewed attention to classifying species as AM, EcM, or both.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We appreciate the positive and constructive comments of the reviewers on our paper. Below please find our point-by-point response to their comments.

      Reviewer #1:

      Main comments:

      1) The expression levels of many genes, including some major TFs (like CEBPa or HNF4) in isolated primary hepatocytes greatly differ from that in normal liver. This is due to the disruption of cell-cell contacts. For this reason, single nuclei sequencing is more reliable and it is the preferred method. It is not indicated how many biological replicates were used and what level of variability was observed between different preparations.

      We thank the reviewer for pointing out the immediate response of hepatocytes to dissociation, including in expression of CEBPa or HNF4 (this reviewer) and stress-related genes (reviewer 3), which we were aware of.

      Unfortunately, however no perfect method exists to explore only hepatocytes in the context of the liver and single nuclei RNA-seq, which was not available at the start of our study, also has its limitations (e.g. substantial ambient RNA contamination, a lower median number of genes detected and potential for biases and higher doublet rates due to increased amplification steps (PMID: 34515767)).

      Importantly, in our current study, we were interested in exploring gene regulatory networks in hepatocytes by the combination of RNA-seq and ATAC-seq. In our hands, data that we obtained from single cell ATAC-seq was far too shallow and noisy to predict gene regulatory networks. Hence, we needed to rely on pure populations of hepatocytes to perform our studies with bulk ATAC-seq, for which we optimized perfusion and subsequent density gradient centrifugation. While we succeeded in obtaining a very pure hepatocyte population, we agree with the reviewer that due to dissociation-associated changes the results that we obtain might not fully reflect the events happening in hepatocytes in the liver.

      To address this issue brought up by reviewer 1 and 3, i) we will better indicate our rationale within the manuscript, and the limitations as indicated by both reviewer 1 and 3; ii) to provide an overview of potential changes that were induced by the perfusion procedure that we applied, we will compare the hepatocyte RNA-seq transcriptomes that we obtained with in vivo liver RNA-seq, with specific attention to transcription factors and stress-related genes (see reviewer 3, point 1); iii) we will better separate in the figures data obtained from hepatocytes versus data obtained from liver (see also point 2 from this reviewer).

      Additionally, we will indicate how many replicated were used, and the level of variability between different preparations (donors).

      2) The regulome studies involved analysis of ENCODE data sets (ChIP-seq), while the RNA-seq data were obtained in the current work. Due to the different source of the data (e.g primary hepatocytes used for ENCODE consortia members and this study) differences are expected. In the present study the cells were FACS-sorted immediately after isolation, while the ones used to produce ENCODE data sets were not subjected to sorting and were also probably cultured. This limits the accuracy of comparisons. Furthermore, the authors should indicate exactly which ENCODE data-sets were used.

      It is also unusual to observe broad distribution of the ATF3, JUND and EGR1 ChIP-seq reads over the PCK1 gene or the Alb gene (Fig S3). Peaks called by MACS should be indicated. Have the authors verified this distribution, e.g by ChIP-PCR or other means? It is quite unlikely that binding motifs are present all over the gene bodies. Is it possible that these factors interact with elongating RNA Pol-II complexes? What is the situation in other actively transcribing gene bodies?

      In the first paragraph of this comment, the reviewer rightfully points out that we use data from different sources in the first part of our study: scRNA-seq and ATAC-seq from perfusion-obtained hepatocytes (this study) and ENCODE ChIP-seq data which, in contrast to what the reviewer seems to assume, is obtained from liver (as profiled by ENCODE).

      We did choose to use ChIP-seq data from liver tissue to corroborate our findings in isolated hepatocytes in the tissue of origin (largely composed of hepatocytes). Indeed, the near perfect co-localization of HNF4A and ATF3/EGR1 in liver tissue and the enrichment of corresponding DNA motifs in our ATAC-seq data strongly suggests interaction between bZIP family members and hepatocyte-specific transcription factors (including HNF4A) and hence support our conclusion.

      To further address this issue, we will better separate the data obtained from hepatocytes versus data obtained from liver in the figures and include additional data for liver if available (see also point 1 from this reviewer). Additionally, we will indicate exactly which ENCODE datasets were used (see table below). Where relevant, we will explicitly mention the limitations/confounding factors of our analysis.

      EGR1-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF389LQC, ENCFF132PDR

      JUND-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF215GBK, ENCFF978CPC

      ATF3-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF522PUA, ENCFF094LXX

      HNF4A-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF302XOK, ENCFF500ZBE

      FOXA1-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF765EAP, ENCFF945VNK

      CTCF-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF002EXB

      RAD21-liver ChIP-seq

      ENCODE Project Consortium

      ENCFF643ZXX, ENCFF171UDL

      EGR1- K562 ChIP-seq

      ENCODE Project Consortium

      ENCFF000PZK, ENCFF000PZP

      JUND- K562 ChIP-seq

      ENCODE Project Consortium

      ENCFF000YSC, ENCFF000YSE

      ATF3- K562 ChIP-seq

      ENCODE Project Consortium

      ENCFF000PWC, ENCFF000PWA

      With respect to the second paragraph: We obtained these liver tissue ChIP-seq profiles from ENCODE, in which these have gone through thorough validation procedures. Furthermore, we do observe very similar patterns with a complementary, but independent approach, ATAC-seq in hepatocytes. Hence, we do not think that further validation by ChIP-qPCR will have much added value.

      We will follow the advice of the reviewer by i) indicating MACS peaks in our examples, ii) check whether ChIP-seq peaks in coding regions are typical for these datasets. If not, we will show better examples. If they are, we will are investigate potential motifs present in gene bodies, iii) investigate literature for a possible link between these factors and elongating RNA Pol-II complexes; and iv) investigate actively transcribing gene bodies

      3) The synergism between AP1 and HNF4 is based on RNA and ChIP data in Primary hepatocytes. The main evidence for the synergism are co-binding of the two factors and the regulome profiles in the individual cells. In ICOs where both factors are expressed at high levels ChIP-seq data are not available and the potential binding distribution is estimated by the presence of binding motifs in ATAC-seq positive areas. Considering the concern described in point 2, it is important to obtain ChIP-seq data in ICOs too.

      We would like to point out that, we make the central observations on overlapping regulatory modules in perfusion-derived hepatocytes, the ChIP-seq data to show co-binding of AP-1 and other factors with HNF4A (Fig 2c-f; Fig S3c-e) is all based on liver tissues. By showing this in the tissue or origin, we feel we provide sufficient evidence for the (potential) interplay between these factors in the liver, making ChIP-seq in ICOs redundant and beyond the scope of this study.

      In addition, more direct experimental evidence for the synergism is needed. For example, demonstrating the synergism between HNF4 and some AP1 factors in specific genes by co-transfection experiments.

      With regards to the potential synergy between HNF4 and AP1 in adult hepatocytes: previous studies have shown an essential role for c-Jun (part of AP1) in normal hematogenesis, with hepatocytes being rounded and detached in c-Jun KO mice (PMID: 8371760). This clearly shows the critical role of c-Jun in liver development and support to a potential interaction with HNF4 factors.

      Yet, we agree with the reviewers that co-transfection (or knock down) experiments would be an elegant means to further support our conclusion. Unfortunately, however, PHHs are refractory to transfection making this experiment nearly impossible. Hence, instead we will tone down our statements about cooperation between these factors, instead referring to overlapping regulatory modules and co-binding as we observe.

      4) Transcriptome comparisons between primary hepatocytes and intrahepatic cholangiocyte organoids (ICO) or ICOs cultured in hepatocyte differentiation medium (DM-ICO) were performed before (Ref. 6). These cells were derived from the same donor. In the current study ICOs were obtained from a biobank, thus they were from different donors. Differences between the expression patterns of primary cells and EM-IOC and DM-IOC organoid cultures are expected even if they derived from the same donor. In Ref.6 it is clearly demonstrated that DM-IOCs closely mimic many, but not all aspects of the liver phenotype. The present paper therefore provides only incremental new knowledge about the usefulness of organoid cultures in general. On the other hand, the scRNA-seq data with cells from the organoids point to the lack of zonation, which is an important new information, not analysed in Ref.6

      We agree with the reviewer that the EM-ICOs and DM-ICOs have been well characterized in the ground-breaking works Reference 6. Indeed, in Figure 5d of Reference 6, it is shown that DM-ICOs display more comparable expression profile to hepatocytes than EM-ICOs. However, there are also clear differences between hepatocytes and DM-ICOs, indicating incomplete differentiation of the later. In our study, we now make the important observation that the differentiation potential of ICOs at least in part depends on the expression of ELF3 (Figure 3B).

      To address this issue, we will put emphasis on the findings in Ref 6, and we will put our observations in better perspective in relation to Ref 6.

      5) In the methods section the description of ICO culture conditions are very epigrammatic. It refers to previously published protocols but also mentions the addition of BMP7 in the first round of culturing without explaining why was this important. It would be useful if the authors describe exactly the culture conditions they used. Were the ICOs from the biobank established under culture conditions described in Ref 6 or by previous protocols?

      We apologize for this being unclear. We will include this information in the revised manuscript.

      6) The results about ELF3 function are interesting and convincing. This is a novel finding and may worth to perform a global transcriptome analysis and some immunostainings with specific markers in siELF3 cells to further strengthen its regulatory role in cholangiocyte-hepatocyte conversion.

      We agree with the reviewer. To follow this up, we will perform RNA-seq during differentiation of ICOs towards hepatocytes, with and without siRNA-mediated ELF3 knockdown. This will further reveal the precise regulatory role of ELF3 in during hepatocyte differentiation.

      Reviewer #2:

      Comments:

      1) Hepatocyte nuclear factors do not form a transcription factor (TF) family, they are from different TF families: the nuclear receptor, homeobox, and forkhead TF (super)families.

      We thank the reviewer for pointing the mistakes in points 1 to 6 with regards to the naming of protein and protein families in our manuscript, we apologize for these inaccuracies. We will correct these naming and references, and check for any further inconsistencies.

      2) AP-1 is not a TF family either. It is basically a heterodimer of FOS and JUN (sub)family members, which are part of the bZIP (super)family such as C/EBPs and ATF3, which latter is related to JDP2.

      We will adapt this.

      3) EGR1 is not a bZIP protein, it is a zinc finger protein from the EGR family. Was the motif of EGRs enriched? Only the motif of C/EBPs is shown on Fig. 2D.

      We will adapt this. We will also analyze whether the motif of EGRs is enriched

      4) RAD21 is not a TF, it is part of the Cohesin ring, which is associated to the insulator-binding CTCF.

      We will adapt this.

      5) EP300 (Fig. 2A) and PPARGC1A (Fig. 3B) are not TFs, they are co-regulators, basically co-activators, which can interact with several TFs. EP300 is otherwise not so specific, its presence in the chromatin is one of the major active enhancer marks.

      We will adapt this.

      6) DNA sequence motifs are typically not specific for a single TF, rather for a TF (sub)family, so based on a motif, it is usually not possible to identify a certain TF (Fig. 3F). Are there other nuclear receptors, SOX or ETS proteins that can bind to the identified motifs? (For example, FLI1 and several other ETS proteins can bind to the motif of ELF3/EHF, or there are several DR1-binding nuclear receptor dimers like HNF4/HNF4 or PPAR/RXR.)

      We agree with the reviewer. We will analyze this and adapt the manuscript according to our findings.

      &) Although the manuscript is easy to follow and understand, it needs to be checked for grammar.

      We have asked a native speaker to proofread and adapt the manuscript.

      Reviewer #3:

      1) It is well known that perfusion of primary hepatic tissues (mice and human) results in immediate genetic responses, which will be captured right away in the performed RNASeq analysis. Stress pathways are upregulated and will normalize when the cells are put in culture for a couple of days. (Not too long, as they then undergo EMT and de-differentiate into non-parenchyma cells.) These responses can influence the expression profiles observed.

      We thank the reviewer for this comment. Please see how we will address this concern in our reply to reviewer 1, issue 1, who raised a very similar point.

      2) Why were the organoid cultures not differentiating properly into hepatocytes using different media cocktails (EM versus DM)? They seem to maintain cholangiocyte features, which questions the culture conditions used.

      We thank the reviewer for the chance to clarify this important point. We like to stress that we do use the standard differentiation protocol as published (which we will also better detail in our material methods) and it does lead to differentiation towards hepatocyte like cells (both morphologically and gene expression-wise). However, what is not highlighted in previous publications, but broadly observed in the field, is that this differentiation is far from being complete and that the extent to which proper differentiation occurs varies between organoids from different donors. In our study, we now make the important observation that the differentiation potential of ICOs at least in part depends on the expression of ELF3 (Figure 3B).

      3) The authors found the up-regulation of the AP-1 family proteins such as ATF3 and EGR1 which are known to induce apoptosis/cell death. Hepatic organoids are often found to have the un-intended necrotic core development which is caused by the oxygen diffusion matter and this issue is highly likely relevant to the size of the organoids. So, it would be advisable to specify the size of hepatic organoids (i.e., diameter) and check the necrosis-related genes.

      To follow-up on this comment of the reviewer: We will measure the size of our organoids. These organoids indeed are typically hollow inside and hence we will check the expression of necrosis related genes and adjust our conclusions accordingly.

      4) The KD approach with ELF3 in the ICOs is a good way forward, however only a minor number of hepatocellular genes are recovered, questioning the central role of ELF3 in driving the hepatocellular program. Functional assays, such as albumin release, bile acid production and CYP450 response should be coupled with the gene expression analysis.

      In line with the response to reviewer 1 (point 6) we will perform RNA-seq to better characterize ELF3 KD-associated genes expression changes including genes typical and functionally relevant for hepatocyte function (e.g. albumin release and bile acid secretion)

      5) The manuscript should be supplemented by adding the statement regarding the specific reason why a different set of donors was selected for two transcriptomics. The authors used three different donors for scRNA-seq and other two donors for the ATAC-seq. It seems better if all five donors were used for both transcriptomics analyses to reduce the inconsistent proportion of primary human hepatocytes (PHHs) from each donor. In addition, the donors which are selected should have identical genetic backgrounds for in-depth analysis of PHHs. The various backgrounds such as age, sex and ethnicity cause the transcriptional and translational heterogeneity. The authors need to explain the criteria on the selection of the donors.

      We do agree with the reviewer that ideally all experiments are performed on the same set of donors. However, PHHs are obtained from surgical margins and hence provide a very limited source, leading to different experiments being performed on different donors. Importantly, the replicates for each experiment type have been obtained from multiple donors enabling us to capture common rather than donor specific expression/chromatin accessibility signatures.

      Within the revised manuscript, we will include a paragraph on the criteria on the selection of the donors, and why a different set of donors was selected for two transcriptomics. Also, we will provide information with respect to the background of the donors.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper, as well as the importance guiding significance to our researches. We have highlighted the changes in yellow within the manuscript.

      *Here is a point-by-point response to the reviewers’ comments and concerns. *

      Comments from Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The provided document, titled "Camel Milk Affects Serum Metabolites by Modulating the Intestinal Microflora," is an extensive research paper. My summary covers the first 44 pages of the total 63 pages. The document begins with a standard review commons manuscript notice and provides contact information for the Review Commons office.

      The research focuses on the effects of camel milk on serum metabolites and the intestinal microflora. It starts with a detailed introduction to the topic, outlining the crucial role of gut microbes in human health and the influence of various factors like diet, genetics, and environment on these microbes. The paper emphasizes the nutritional richness of camel milk and its potential as a functional food, particularly its impact on gut microbiota and host metabolism.

      Initial sections of the paper discuss the research methodologies, including the study's keywords, abstract, and introduction. The abstract highlights the study's significant findings, such as the presence of various beneficial bacteria in sour camel milk, the inter- and intra-species transportation of microbiomes, and the impact of camel milk on the gut microflora and serum metabolites of type 2 diabetic rats.

      The introduction further delves into the composition of the human gut microbiota and the shaping factors of the adult gut microbiome. It also examines the role of diet in modulating gut microbiota and the potential health benefits of dairy products, with a particular focus on camel milk.

      Subsequent sections present detailed research findings, including the results of microbial composition and source analysis in camel milk, the composition and changes of rat gut microbiota under camel milk regulation, and the effects of camel milk-regulated gut microbiota on metabolism in rats. The research also explores the interspecies transfer of microbes using camel milk as a vector and analyzes the gut microbiota in people consuming camel milk.

      The paper further discusses the endophytic flora of camel edible desert plants and their possible influence on the camel's gut microbiota. The discussion section integrates the findings, offering insights into the potential health benefits of camel milk and its probiotic qualities. It also compares the effects of camel milk with other dairy products and discusses its role as a vector for beneficial microbes.

      Materials and methods used in the study are detailed towards the end of the summarized portion, describing sample collection and processing, the experimental setup for rats, and data processing and analysis techniques.

      Reviewer #1 (Significance (Required)):

      The paper continues with detailed research findings, including the microbial composition in camel milk, the impact on the gut microflora of rats and humans, and the serum metabolism effects.

      There's a focus on how camel milk, as a vector, can transfer beneficial microbes between species, influencing gut microbiota and host metabolism.

      The paper compares the effects of camel milk with other dairy products, emphasizing its unique health benefits and its role in transferring beneficial microbes.

      It discusses various bacteria found in camel milk and their potential health benefits.

      The research findings extend to understanding how camel milk affects human gut microbiota, with studies on pastoral herders who consume camel or bovine milk.

      Author response: We thank you for your approval and constructive and valuable feedback from you and other reviewers.

      Comments from Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      summary:

      The authors introduce a study assessing the bacterial flora of sour fermented camel milk and its capability to introduce beneficial species into consumer's gut. They further tested the potential of its nutrients and species for beneficial effects on type 2 diabetic (t2d) rats. They claim that t2d rats fed with high-dose camel whey reveal a microbiota closer to that of healthy rats rather than that of other t2d rats not receiving the camel whey treatment. Further they claim that this effect is due to the presence of Eubacterium limnetica that was exclusively found in the gut microflora of rats taking camel milk and producing MtcB protein. They conclude that camel milk may have the potential to be functional food.

      Overall, I think the approach of looking into camel milk and its microbiota is of broad interest, as it is food consumed traditionally by many tribes and in several countries. However, to me the presentation of the findings, the data and the analysis is often unprecise and confusing.

      For example, the MtcB protein they claim to be the mechanism of reducing the risk for t2d in the abstract is mentioned only once in the whole study and there only as a finding of another study (cited). According to my understanding the abstract should contain the main findings of the study, rather than some side-finding from other studies happens to match with the study results. I assume the authors have plenty of results from their sequencing data and metabolomics that they could mention in the abstract.

      In the text the authors mention the analysis of the microbial composition and source analysis of camel milk, the analysis of the gut microbiota of young camels, the composition, and changes of rat gut microbiota under the regulation of camel milk, the structure and changes of gut microbiota in people taking camel milk and the analysis of the endophytic flora of camel edible desert plants. And this just quoting the headers in the results section. Why is that not represented/mentioned in the abstract? Instead the authors focus on the t2d rats and the MtcB mechanism they fail to present.

      Further the authors are sloppy when it comes to typos and preciseness. For example, in the abstract they talk first about sour camel milk, then whey and then milk again.

      I suggest a major restructuring/rewriting and if necessary partial reanalysing of the results and the conclusions.

      It would be good to have an overview figure combining the work done, also stating the number of samples for each experiment.

      __Author response: __Thank you very much for your nice suggestion on our manuscript, we applied some restructuring to our manuscript and the changes were highlighted in yellow.

      Major comments:

      1) Please make sure all raw data (sequences and filtering/assembly results) are deposited in public databases, like NCBI, ENA or else.

      __Author response: __The corresponding data is available as Mendeley Data, V1, https://doi.org/10. 17632/4w8n8n96tc.1, some datasets with bigger size uploaded failed owing to internet problem. The full version could be offered in other approaches if requested.

      2) Please state briefly for each dataset analysed, which sequencing method was used, how many samples were collected and how many were pooled for the sequencing runs:

      AmpliAeq, whole metagenome HiSeq, MiSeq?

      __Author response: __Sample and dataset information for sequence was supplied in Supplementary Table 9 and 12. Sequencing library was prepared following Illumina library preparation instructions, and sequenced using Illumina Miseq platform at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) with pair-end (PE) 150 bp reads.

      3) Page14 line283:

      F082? What is it? A strain, species or a sample?

      Please state clearly in the text.

      Also please avoid using abbreviations where possible and if you have to use them, please define.

      __Author response: __When applying diversity analysis at the specie level, a species annotated as unclassified_g_norank_f_F082 was found abundant in camel feces in Darbancheng.

      4) Page14 line307:

      "These evidenced that camel milk was a vector transferring microbes from the female camel to their cubs."

      Yes, that may be likely, but 16S amplicon-seq cannot provide evidence. Evidence would be strain similarity confirmed by SNP's or the like. So please state that this is speculative or show appropriate evidence.

      __Author response: __We completely agree that SNP’s is better evidence for this point and thank you. Microbial diversity analysis was a main part of initial design, and our limited sample couldn’t meet the needs of diversity and SNPs in the same time. There also were reports which used 16S based methods to trace the microbes source(Du et al., 2022; El-Mokdad, 2014; Wang et al., 2018).

      5) Page15 line322 ff:

      "Besides, using raw milk was not effective in type 2 diabetic rat model, so we chose camel whey and bovine whey as the diet of type 2 diabetic rats in follow-up experiments"

      Data/evidence? How is it different from whey on a nutrient perspective, as whey was more effective? Any explanation for this difference? And the bovine whey, what species did it contain? Can they be transferred regarding the processing of whey prior to application?

      __Author response: __This is an interesting and valuable question. We prepared raw milk and whey for the pre-test, then directly turned to validate the function of whey. Maybe we will investigate the composition difference in the future. The whey was prepared using the following protocol: Centrifuge fresh milk for 20 mins at 5000 r/min, discard the fat, and precipitate and obtain the middle layer of skim milk. After 20 mins in a 40 ℃ water bath, adjust the pH to 4.6 with 10% glacial acetic acid, and store in a 4°C refrigerator, overnight. Then, the skim milk was centrifuged at 8000 r/min for 20 min, repeated twice, and the middle whey fraction was collected. The centrifuged whey was poured into a petri dish and sealed. It was frozen at -80°C for 12 hours and then pierced with a sterile toothpick on the petri dish and then freeze-dried to get whey powders. A speculation was the preparing progress of whey played an important role in their functional difference. A comprehensive comparison of camel raw milk, camel whey, bovine raw milk, and whey will be an interesting point and we may investigate it shortly.

      6) Page17 line366ff:

      "Taking the number of microbes involved in this pathway, 8001 species were noted in the high-dose camel whey group, 3447 in the positive drug group, and only 1467 in the diabetics." How many species were present in the rats initially? Was species abundance different in the first place, or did they get lost, or came from the camel whey?

      __Author response: __The rats were fed with broad-spectrum antibiotics for 2 weeks, which ensured the same species abundance in the beginning.

      7) Page17 line369 ff:

      "It indicated that these microbes might resist the high glucose environment of the host through the synthesis and metabolism of their amino acids, and the effect of high-dose camel milk was more effective than that of metformin"

      -> How high was the glucose level in the rat gut? Or were there any obvious physiological changes in the t2d model rats that are characteristic for such a high-glucose environment? Please explain.

      __Author response: __This is an interesting and critical question. We didn’t measure the glucose level in the rat gut directly because we had to make sure other related characterizations worked properly. Besides, we thought camel milk could regulate microbial community, and further influence the blood sugar level, which was more representative in our sight. Blood sugar level is supplied in Fig.4O and Supplementary Table 11.

      8) The resolution/quality of the figures is low and the labelling often small. So not all text is readable.

      __Author response: __We adjusted the figures in the manuscript and offered additional independent picture files. Additionally, it seemed caused by the PDF merge progress, please check the pictures in .docx or .png files for details.

      9) Page19 line400 ff:

      What serum metabolites were analysed and why? Please write an intro-sentence to make it easier for the reader.

      Please write more precise what methods were used. Maybe I missed it, but I didn't find it in the methods part as well (Page40/41).

      __Author response: __The rats fed high-dose camel whey or metformin showed similar improvement in serum metabolite imbalance and were closer to normal. Caproylcarnitine, taurodeoxycholic acid, acetylcarnitine, creatinine, linoleic acid, and tridecanoic acid were detected as upregulated; 2-deoxyuridine, cyclohexylamine, L-pipecolic acid, LysoPC(18:0), uracil, caprylic acid, cholesterol sulfate, L-citrulline, pelargonic acid, and phenol downregulated. Carnitine supplementation, due to its key role in lipid metabolism and antioxidant effects, may effectively manage Type 2 Diabetes by addressing fatty acid metabolism dysregulation and oxidative stress(Bene, Hadzsiev, & Melegh, 2018). Studies have shown that taurodeoxycholic acid can enhance the effect of insulin and reduce blood sugar levels by regulating endoplasmic reticulum stress, and have potential in the treatment of diabetes(Xing, Zhou, Wang, & Xu, 2023). Low serum creatinine is associated with the development of T2D(Song, Hong, Sung, & Lee, 2022). Increased linoleic acid consumption was recommended for the prevention of T2D(Henderson, Crofts, & Schofield, 2018). The uridine is phosphorylated into uracil, which is converted to 2-deoxyuridine. Then 2-deoxyuridine is further converted to thymine with thymidine phosphorylase, the expression of thymidine phosphorylase was lost or considerably reduced when the organism suffered nephropathy and the high concentration of thymidine is a cause of DNA impairment, which is related to diabetes and diabetic nephropathy(Spinazzola et al., 2002; Szabo et al.; Xia, Hu, Liang, Zou, Wang, & Luo, 2010). L-Pipecolic acid are associated with higher incidence of T2D(Razquin et al., 2019). A research showed LysoPC(16:0) and (18:0) may mediated a fast progression of diabetic kidney disease(Yoshioka et al., 2022). Cholesterol sulfate is the most abundant known sterol sulfate in human plasma, and it plays a significant role in the control of glucose metabolism, which contribute to the pathogenesis of insulin resistance and the resultant development of diabetes(Shi et al., 2014; Zhang et al., 2022). L-citrulline supplementation might improve glucose homeostasis, some lipid factors and inflammatory markers in overweight and obese patients with T2D(Azizi, Mahdavi, Mobasseri, Aliasgharzadeh, Abbaszadeh, & Ebrahimi-Mameghani, 2021). T2D mellitus is associated with increased total plasma free fatty acid and modulating its concentration is the mechanism of some fibrates and statins drugs(I. S. Sobczak, A. Blindauer, & J. Stewart, 2019). Most of these metabolites have been reported as causes of T2D or consequences of T2D progress, some have been designed as therapeutic target.

      The serum metabolites were carried out using Agilent 1290 Infinity UHPLC system equipped with a HILIC column. The mobile phase of the optimized method consisted of (A) water with 25 mM ammonium acetate and 25 mM ammonia; and (B) acetonitrile (ACN). The following gradient elution was used: 5% A at 0-1min; 5-35% A at 1-14 min; 35-60% A at 14-16 min; 60% A at 16-18 min ; 60-5% A at 18-18.1 min and 5% A at 18.1-23 min. The flow rate was 0.3 mL/min, injection volume 2 μL, and column temperature was 25 ℃. Triple TOF 5600 mass spectrometer was applied for mass spectrometer analysis. The condition was used as following: Ion Source Gas1:60,Ion Source Gas2:60,Curtain gas:30,source temperature:600℃,IonSapary Voltage Floating ± 5500 V. TOF MS scan m/z range:60-1000 Da,product ion scan m/z range:25-1000 Da,TOF MS scan accumulation time 0.20 s/spectra, product ion scan accumulation time 0.05 s/spectra.MS/MS was gathered by information dependent acquisition (IDA) using high sensitivity mode, Declustering potential:±60 V, Collision Energy:35±15 eV, and IDA was set as Exclude isotope within 4 Da, Candidate ions to monito per cycle: 6. The methods part was complemented.

      Minor comments:

      1) Page1, line56-58 ff

      Please phrase more clearly:

      "This study specified that the transportation of microbiome happened both intra- and inter-species and played a principal role in the formation of progeny gut microflora."

      While the content is mostly comprehensible, there is a need for rephrasing and correction of language also in the following text.

      __Author response: __As suggested by the reviewer, we have rephrased and modified the abstract part.

      2) Page14 line300 ff:

      There is no need to show the OTU numbers in the text, please provide your results as a table in the supplements and refer to it in the text.

      Author response: We deleted OTU numbers in the manuscript and added the corresponding table in supplementary file.

      3) Page15 line328: Please check for typos, it is Shannon index, not Shanno.

      __Author response: __The corresponding correction was applied in the manuscript.

      4) Page16 line334:

      Please mention the number, age and sex of the rats used and how many groups you had in your experiments.

      __Author response: __SPF-grade male rats weighing 180-220 g were used for our related experiments. The detailed information is available in Supplementary Material (Supplementary Table 11-13).

      5) The headlines should logically structure the paper:

      For example, the authors have two very similar sections in the results part: "Composition and changes of rat gut microbiota under the regulation of camel milk" and "Analysis of the composition of gut microbiota in rats". Those can be combined or stated more concise.

      Also, other headlines improvement to make it easier for the reader to follow.

      __Author response: __We adjusted this part in the manuscript according to the reviewer’s suggestion.

      Reviewer #2 (Significance (Required)):

      I do think the study is of broad interest and relevance. However, the presentation of the analysis and data needs major revision. Especially it is lacking clarity on what was done for which samples and how the authors draw their conclusions. Also, I think that abstract and main text have a different focus. I would suggest to the authors to concentrate on their findings in abstract and text and state precisely what was done and what they found.

      __Author response: __Thank you very much for your recognition of our manuscript.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors introduce a study assessing the bacterial flora of sour fermented camel milk and its capability to introduce beneficial species into consumer's gut. They further tested the potential of its nutrients and species for beneficial effects on type 2 diabetic (t2d) rats. They claim that t2d rats fed with high-dose camel whey reveal a microbiota closer to that of healthy rats rather than that of other t2d rats not receiving the camel whey treatment. Further they claim that this effect is due to the presence of Eubacterium limnetica that was exclusively found in the gut microflora of rats taking camel milk and producing MtcB protein. They conclude that camel milk may have the potential to be functional food.

      Overall, I think the approach of looking into camel milk and its microbiota is of broad interest, as it is food consumed traditionally by many tribes and in several countries. However, to me the presentation of the findings, the data and the analysis is often unprecise and confusing. For example, the MtcB protein they claim to be the mechanism of reducing the risk for t2d in the abstract is mentioned only once in the whole study and there only as a finding of another study (cited). According to my understanding the abstract should contain the main findings of the study, rather than some side-finding from other studies happens to match with the study results. I assume the authors have plenty of results from their sequencing data and metabolomics that they could mention in the abstract. In the text the authors mention the analysis of the microbial composition and source analysis of camel milk, the analysis of the gut microbiota of young camels, the composition, and changes of rat gut microbiota under the regulation of camel milk, the structure and changes of gut microbiota in people taking camel milk and the analysis of the endophytic flora of camel edible desert plants. And this just quoting the headers in the results section. Why is that not represented/mentioned in the abstract? Instead the authors focus on the t2d rats and the MtcB mechanism they fail to present. Further the authors are sloppy when it comes to typos and preciseness. For example, in the abstract they talk first about sour camel milk, then whey and then milk again.

      I suggest a major restructuring/rewriting and if necessary partial reanalysing of the results and the conclusions.

      It would be good to have an overview figure combining the work done, also stating the number of samples for each experiment.

      Major comments:

      1. Please make sure all raw data (sequences and filtering/assembly results) are deposited in public databases, like NCBI, ENA or else.
      2. Please state briefly for each dataset analysed, which sequencing method was used, how many samples were collected and how many were pooled for the sequencing runs: AmpliAeq, whole metagenome HiSeq, MiSeq?
      3. Page14 line283: F082? What is it? A strain, species or a sample? Please state clearly in the text. Also please avoid using abbreviations where possible and if you have to use them, please define.
      4. Page14 line307: "These evidenced that camel milk was a vector transferring microbes from the female camel to their cubs." Yes, that may be likely, but 16S amplicon-seq cannot provide evidence. Evidence would be strain similarity confirmed by SNP's or the like. So please state that this is speculative or show appropriate evidence.
      5. Page15 line322 ff: "Besides, using raw milk was not effective in type 2 diabetic rat model, so we chose camel whey and bovine whey as the diet of type 2 diabetic rats in follow-up experiments" Data/evidence? How is it different from whey on a nutrient perspective, as whey was more effective? Any explanation for this difference? And the bovine whey, what species did it contain? Can they be transferred regarding the processing of whey prior to application?
      6. Page17 line366ff: "Taking the number of microbes involved in this pathway, 8001 species were noted in the high-dose camel whey group, 3447 in the positive drug group, and only 1467 in the diabetics." How many species were present in the rats initially? Was species abundance different in the first place, or did they get lost, or came from the camel whey?
      7. Page17 line369 ff: "It indicated that these microbes might resist the high glucose environment of the host through the synthesis and metabolism of their amino acids, and the effect of high-dose camel milk was more effective than that of metformin"
      8. How high was the glucose level in the rat gut? Or were there any obvious physiological changes in the t2d model rats that are characteristic for such a high-glucose environment? Please explain.
      9. The resolution/quality of the figures is low and the labelling often small. So not all text is readable.
      10. Page19 line400 ff: What serum metabolites were analysed and why? Please write an intro-sentence to make it easier for the reader. Please write more precise what methods were used. Maybe I missed it, but I didn't find it in the methods part as well (Page40/41).

      Minor comments:

      1. Page1, line56-58 ff Please phrase more clearly: "This study specified that the transportation of microbiome happened both intra- and inter-species and played a principal role in the formation of progeny gut microflora." While the content is mostly comprehensible, there is a need for rephrasing and correction of language also in the following text.
      2. Page14 line300 ff: There is no need to show the OTU numbers in the text, please provide your results as a table in the supplements and refer to it in the text.
      3. Page15 line328: Please check for typos, it is Shannon index, not Shanno.
      4. Page16 line334: Please mention the number, age and sex of the rats used and how many groups you had in your experiments.
      5. The headlines should logically structure the paper: For example, the authors have two very similar sections in the results part: "Composition and changes of rat gut microbiota under the regulation of camel milk" and "Analysis of the composition of gut microbiota in rats". Those can be combined or stated more concise. Also, other headlines improvement to make it easier for the reader to follow.

      Significance

      I do think the the study is of broad interest and relevance. However, the presentation of the analysis and data needs major revision. Especially it is lacking clarity on what was done for which samples and how the authors draw their conclusions. Also I think that abstract and main text have a different focus. I would suggest to the authors to concentrate on their findings in abstract and text and state precisely what was done and what they found.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      DBF4 and DRF1 knockout cells were generated and used to separate DBF4- and CDC7-dependent from DRF1- and CDC7-dependent activities. DBF4- and CDC7-dependent activities at replication forks were independent of DRF1. These include the replication timing pattern, replication fork velocity, DNA damage signaling. DBF4 is required to recruit CDC7 to active replication forks. The study is in large part exceptional.

      The inclusion of quantitation for a modest bandshift on CDC7 in figure 2 (30% vs 50% reduced) is not justified given the abundance of the main band and our knowledge of the lack of linearity of western blot quantitation. This should be removed.

      We thank the reviewer for evaluating our manuscript and for the positive feedback.

      In the revised manuscript we have removed the quantification of the bandshift related to CDC7 autophosphorylation in mitotic cells which was reported in Figure 1E. We recognise that the quantification may not be accurate although performed using semiquantitative near-infrared scanning technology. Importantly the experiment was performed three times with almost identical results.

      The only significant weakness in the paper is the explanation of the replication timing analyses in Figure 3. I don't understand what the differences between the plots equate to in terms of timing. I understand the replication of these regions that diverge is either early or late, but their were only two fractions of cells - 2N-3N and 3N-4N (the cells are "normal"). If this is the case, isn't the readout binary? a sequence either replicates in S phase between 2N and 3N or in S phase between 3N and 4N. Why are the differences so small? Are they only evident in a small population of cells? If that is the case, then what does the difference really mean? I think the description of these data needs to be precise.

      The replication timing experiments were performed with a well-established and reliable protocol (Ryba et al., 2011, https://doi.org/10.1038/nprot.2011.328). Asynchronous cells are labelled with a short pulse of BrdU, and sorted in two fractions, early and late S-phase, as described in Hiratani et al., 2008, Ryba et al., 2010, Hadjadj et al, 2016 and 2020 (https://doi.org/10.1371/journal.pbio.0060245) (https://doi.org/10.1101/gr.099655.109, https://doi.org/10.1016/j.gdata.2016.07.003, https://doi.org/10.1093/nargab/lqaa045).

      This method does not take into account the variation in the DNA copy number (2N vs 4N) between replicated and non-replicated parts of the genome (S/G1 ratio) as in Siefert et al., 2017 (https://doi.org/10.1101/gr.218602.116).

      The profiles depict the average replication timing of a population of 20,000,000 cells; thus, the readout is not binary.

      Replication timing profiles display the log ratio between early and late replicated fractions along the chromosome. Early replicated regions show positive log ratios and late replicated regions show negative ratios. The differential analysis performed with the START-R suite allows the comparison of the profiles (Ctrl vs either CDC7i-treated or DBF4-deficient cells). The genomic regions with altered timing are shown in green or in purple below the profiles, showing advanced and delayed regions, respectively.

      Importantly, the differences in replication timing are expressed with log ratio, that explains why the profiles are varying from -2 (very late replicating regions) and +2 (very early replicating regions). The differences we observed in Figure 3 are representative of two experiments, each composed of two technical replicates that are highly reproducible.

      To better describe the data, we have modified the text in the results section with the words in bold, as below: “These two neo-synthesized DNA fractions were then hybridised on human whole genome microarrays, as previously described. The log ratio between early and late replicated fractions was calculated and visualised for the whole genome.” We also changed the labelling of the replication profiles in Figure 3 and former Figure S3 (now Figure S4) by adding Log2 (Early/Late) to intensity and added two new sentences to the figure legend 3.“____Replication timing profiles display the log ratio between early and late replicated fractions along the chromosome. Positive log ratios correspond to early replicated regions whereas negative ratios correspond to late replicated regions.”

      Reviewer #1 (Significance (Required)):

      I think this paper is a significant advance that should be published. CDC7 is a critical kinase and identifying its co-factor at the replication fork is important both for our understanding of mechanisms of DNA replication and the impact of CDC7 kinase inhibitors in the clinic. I think the majority of the experiments are well designed and the results are unambiguous and precisely described.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      CDC7 is a master cell cycle kinase with essential functions in DNA replication and important roles in the DNA damage response. For its functions, CDC7 relies on a regulatory factor, DBF4, which is essential in many species but not in human cells as a consequence of the presence of a second DBF4-related factor, DRF1. In this work, Göder and colleagues study the relative relevance of these regulatory proteins in CDC7 roles. Their study reveals DBF4 as the major regulatory subunit both in DNA replication, DNA damage checkpoint and fork dynamics. The objective of the study is highly relevant to understand an essential cell cycle kinase with potential applications in cancer therapies, the experiments are well performed and the conclusions are "in principle" sound.

      We thank this reviewer for the time and attention in evaluating the manuscript, for the positive feedback and for indicating key points for improvement and discussion.

      The major handicap of the study is the absence of western blots showing the elimination of DBF4 and DRF1 in the edited cell lines due to the lack of specific antibodies. The authors have generated homozygous mutations that lead to premature stop codons behind critical CDC7 domains. However, as they mention, it is not possible to fully exclude some proteins arising from internal start sites or exon skipping events with residual (functional or altered, and not necessarily residual) activity. This is not unexpected, especially for essential proteins. This would not be a major handicap if the study were focused in a specific factor because it would only question the impact of but not the affected function, but it aims to compare the relative effect of two defective genes. In this case, it is essential to confirm that both genes are eliminated, at least to the same degree.

      We agree with the reviewer that it would be valuable to confirm the effect of the mutations by immunoblotting.

      Over the years we have had multiple attempts at generating sensitive antibodies against both DBF4 and DRF1, using recombinant proteins and synthetic peptides. We also tested several commercially available anti-DBF4 and anti-DRF1 antibodies. While often we were able to detect overexpressed proteins, the detection of endogenous levels has been particularly challenging especially in non-transformed cells, such MCF10A.

      Nevertheless, with an anti-DBF4 serum we obtained from the Diffley lab, which was generated against the C-terminus fragment of hDBF4, we managed to detect endogenous full length DBF4 in parental but not in the DBF4-KO cells (this blot is now included as supplementary Fig S1B). Even with this reagent the detection levels are low and multiple non-specific immunoreactive bands are present, making the detection of DBF4 particularly challenging across the experiments. Interestingly, while DBF4 is no longer detectable in DBF4-11, one the two clones used in this work , we detect a new immunoreactive band of approximately 55kDa in the other clone DBF4-30. We reckon that this may be the result of mRNA translation from the next downstream methionine. In this case this aberrant protein would lack the N domain and most of the M domain, involved in CDC7 binding and activation, and thus this fragment is very likely not functional.

      Importantly, most results in this study were obtained using both DBF4-11 and DBF4-30 clones with indistinguishable results. Only the replication timing experiments were done using a single clone DBF4-11, in which DBF4 protein is not detected.

      We had less success with the direct detection of DRF1. As also suggested by reviewer #3, to screen the clones after genome editing, we originally performed IP-western experiments. We used an anti-DRF1 mAb and unrelated IgG for the immunoprecipitations and an anti-CDC7 antibody as a probe in western blotting. We detected an immunoreactive band above the background at the expected molecular weight for CDC7 when the immunoprecipitation was performed with extracts from parental cells (as well as in a clone obtained with a different sgRNA, targeting DRF1 Exon1 and never used in this study) but not when the immunoprecipitation was performed with extracts from the DRF1- 5 and DRF1-7 clones used in the study. These original co-IPs are credible although not particularly pretty and importantly the result was confirmed in a more convincing experiment in the DRF1-5 clone.

      These new data are now included in the resubmission in Figure S1. So, while the detection of the CDC7 regulatory subunits still remains particularly difficult, we can now provide evidence that their expression is altered in the engineered cell lines used in the study.

      The computational analysis in Figure 1C is consistent with the major conclusion about the primary regulatory role of DBF4 in replication, but it is insufficient to validate the specific phenotypes addressed in the study.

      The figure reports the effects of targeting single genes with multiple sgRNA (4 to 8 according to the library used) on proliferation rate/fitness measured after multiple days in more than 1000 screens across many different human cell types. Loss of fitness can be due either to a direct problem with DNA replication or with other cellular processes.

      We agree with the reviewer that the analysis in Fig 1C is consistent with the phenotypes shown in the study. Particularly it is consistent with the lack of a major defect of DRF1-deficient cells in DNA replication, and it strongly indicates an essential role for CDC7 which was somehow challenged by Suski and co-workers (see also below).

      Indeed, there is a result that is hard to understand if the edited cell lines are defective in the expression of the regulators, specially DRF1. Figure S2D-E shows no synergistic defect in DNA synthesis when the second regulator is knock down with specific siRNAs, not even DRF1 defective cell lines treated with a siDBF4 that reduces its expression 10 times. Also, it is not clear why the defects, specially in DBF4-defective cell lines, are less severe than in cells treated with an inhibitor that causes a partial inhibition of CDC7. If it is due to the expression of DRF4, a siRNA against DRF4 should cause more severe defects.

      Yes, we did not detect synergy or additive effect on the rate of DNA replication when targeting both DBF4 and DRF1 by multiple approaches. This was also for us an unexpected result, that we examined to the best of our capabilities.

      The lack of the expected synergy in the replication assays could be explained in multiple ways and could be of biological or technical nature such as 1) residual low levels of DBF4/DRF1 proteins remaining in the cells upon either CRISPR/Cas9 or siRNA targeting, 2) alternative mechanisms of kinase activation by a different, yet unidentified protein, 3) minimal residual enzymatic activity of hCdc7 kinase not requiring an activating subunit.

      We performed further computational analysis using the dataset of the DepMap project, assessing if the effect of targeting DBF4 on fitness may be dependent on the levels of DRF1 expression. In several instances, when dealing with paralogues the gene effect of knocking out one of the paralogues directly correlates with the expression levels of the second, a phenomenon known as paralogue buffering (De Kegel et al. 2019 https://doi.org/10.1371/journal.pgen.1008466 ).

      In the case of DBF4 and DRF1, this correlation is minimal (plot below: X and Y axes are DRF1 expression levels and DBF4 gene effect respectively, Pearson's correlation = 0.12) so that there are ~ 470 other genes whose expression is more correlated with DBF4 essentiality. Furthermore, by stratifying cell lines according to whether DBF4 was essential or not and then looking at DBF4B (DRF1) expression, we failed to see significant association (graph below).

      Thus, this analysis reinforces the idea that if cooperation between DBF4 and DRF1 exists, it is particularly difficult to demonstrate. To date the interplay between DBF4 and DRF1 is only indicated by the partial impairment on MCM2 phosphorylation and CDC7 autophosphorylation observed in the individual KOs and by the fact that we were unable to obtaining viable double KO mutant clones. We recognise that the latter is a negative result and double KO may be generated in other cellular models or with different strategies.

      We are happy to include the above computational analysis in a revised manuscript and to expand the discussion on the essentiality of CDC7, DBF4 and DRF1.

      The effects of directly inhibiting CDC7 with 10 microM XL413 (concentration used in this study) are indeed stronger than DBF4 KO / depletion on both DNA synthesis (Fig 2A-B) and MCM2 phosphorylation (Fig 4A and Fig 5A).

      We and others have previously shown that CDC7 inhibition by XL413 causes a dose dependent decrease in MCM2 phosphorylation and DNA synthesis. Importantly in the experiments where XL413 was titrated on MCF10A cells from 0.3 microM to 80 microM, we demonstrated that these parameters are uncoupled and that doses that are ~20-fold higher are required to cause a strong impediment of DNA synthesis compared to the dose required to cause full MCM2 dephosphorylation (Rainey et al. 2017 https://doi.org/10.1021/acschembio.7b00117 ).

      DBF4 deficiency only partially affects MCM2 phosphorylation thus it is comparable to very low doses of XL413, that we can estimate to be in the range between 1 and 2 microM.

      Minor points

      • Title in Pag 12. "DBF4 mediates the majority of CDC7 functions in the replication stress response". In this section the authors address only the role of CDC7 in checkpoint signalling but not in other processes related to the replication stress response.

      We agree and we have modified the title of this section accordingly.

      • Figure 2. "EdU incorporation in late S-phase/ per cell" is clearer

      We have modified the label of this figure.

      • Right panels in Figures 3A and 3B are duplicated

      We sincerely apologise for the mistake occurred while assembling the figure. The figure has been corrected, and shows that the changes in the replication timing with the CDC7i or with DBF4-KO are indeed similar but not identical.

      **Referees cross-commenting**

      I am aware of the difficulty to sort out the detection problem, a major handicap of the work. Immunoprecipitation as suggested by rev. 3 might be an interesting possibility. The results should be published, in any case, as they are well performed and try to answer a relevant question. But, if finally the authors fail to detect the proteins, they should make clear in the paper the limitation of their conclusions by the possibility that the expression of the regulators is not completely eliminated or could be altered. Indeed, the apparent contradiction with Suski's results raised by Rev 3 might be discussed in this context.

      We appreciate the reviewer’s recognition of the technical problems we have encountered. We are glad that we now are in a position to provide evidence of impairment of DBF4 and DRF1 expression in the engineered cells (discussed above and reported in new Figure S1 and S2).

      Also, it is important to explain the lack of synergism when combining the edited mutations with siRNAs.

      In a revised manuscript we will explain the potential reasons why lack of synergism either doesn’t exist or is not observed, as discussed above.

      Reviewer #2 (Significance (Required)):

      In summary, the work is relevant and interesting, but the lack of controls about the effect of the edition rises important concerns about the conclusions. It is evident from the acknowledgment section that the authors have tried without success to generate specific antibodies. An alternative possibility would be 1) to get similar results with at least two clones addressing different exons (actually, only one clone was used for DRF1 in most cases) and 2) show synergistic effects for the more important phenotypes in edited cells transfected with efficient siRNAs. This is particularly important for DRF1-defective cells, which show no phenotypes except for an increase in micronuclei. If DBF4 is not essential because the complementary activity of DRF1, impairment of DBF4 expression with siRNAs in DRF1 deficient cells should cause synergistic defects at least in DNA replication and cell viability.

      We hope we have satisfactory addressed this reviewer’s comments, by providing experimental evidence of the impairment of DBF4 and DRF1 expression/function in the engineered cells and several points for discussion addressing the lack of obvious synergy between DBF4 and DRF1.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary Assembly of the CMG helicase during DNA replication initiation is regulated by the DBF-Dependent Kinase known as CDC7 (or DDK), which also plays roles at DNA replication forks during elongation. In vertebrates, DDK has two regulatory subunits called DBF4 and DRF1. Until now, the division of labour between these two activators of CDC7 was poorly understood in mammalian cells. To address this issue, the authors used CRISPR-Cas9 to edit the DBF4 and DRF1 genes in immortalised human breast cells (MCF10A), thereby truncating key domains of the DBF4 and DRF1 proteins. The DBF4-deficient and DRF1-deficient lines are viable, whereas the double mutant was unobtainable and likely inviable, as reported previously by the authors for knockout of CDC7 in MCF10A cells. The authors compare the DBF4-deficient and DRF1-deficient lines with the CDC7 inhibitor XL413, providing evidence that DBF4 has the major role in supporting CDC7 activity in MCF10A cells compared to DRF1, in terms of DNA replication, origin firing, fork progression, and checkpoint activation. Curiously, DRF1 appears to be more important in preventing the formation of micronuclei - another phenotype seen upon inhibition of CDC7 kinase activity.

      Major comments: The data are of high quality and the key conclusions are convincing, although it is unfortunate that the authors were not able to monitor the level of DBF4 and DRF1 by immunoblotting to validate their edited cell lines. The authors previously reported using immunoprecipitation of CDC7, DBF4 and DRF1 (Tenca et al, 2007, 10.1074/jbc.M604457200) to monitor DDK subunits in HeLa cells, which would presumably have been helpful here in MCF10A cells. Nevertheless, the DNA sequence of the edited clones indicates frameshift mutations that lead to premature STOP codons, and the various phenotypes reported in this manuscript are consistent with loss of DBF4 / DRF1 function as described.

      We thank the reviewer the time an effort in carefully assessing the manuscript, and with his/her positive assessment.

      We have now included experimental evidence indicating that DBF4 expression is deficient in the DBF4 KO cells used in this study and that the interaction with DRF1 and CDC7 is deficient in the DRF1-KO cells using the same Co-IP strategy previously reported in Hela cells. Please see also the response to reviewer #2 to the same point.

      Minor comments: 1. The authors should discuss their data in the context of the recent study by Suski et al (https://doi.org/10.1038/s41586-022-04698). The latter study reported that knockout of DBF4 in mouse fibroblasts impairs proliferation but is not lethal, in agreement with the present manuscript, but Suski et al also argue that CDC7 is dispensable for DNA replication in mammalian cells due to redundancy with CDK1.

      The requirement for CDC7 kinase activity for genome duplication in mammalian cells has become a contentious point of debate. CRISPR screens in more than 1000 cell lines indicate that CDC7 is a core essential gene required for proliferation (DepMap.org). Clearly human cells can clearly withstand reduced CDC7 activity, and several proteins contribute both positively and negatively to the effectiveness of CDC7 inhibition in DNA replication and cell proliferation e.g. RIF1 depletion, ATR inhibition, PTBP1 mutation. (Hiraga et al. 2017 https://doi.org/10.15252/embr.201641983 ; Rainey et al. 2020 https://doi.org/10.1016/j.celrep.2020.108096 : Jones et al. 2021 https://doi.org/10.1016/j.molcel.2021.01.004 ; Göder et al. 2023 https://doi.org/10.1016/j.isci.2023.106951).

      Specifically CDK1-phosphporylatyon of RIF1 was shown to disrupt RIF1/PP1 interaction and PP1’s ability to counteract CDC7-dependnet phosphorylation of the MCM complex (Moiseeva et al. 2019 https://doi.org/10.1073/pnas.1903418116 ; Jones et al. 2021 https://doi.org/10.1016/j.molcel.2021.01.004). Thus increased CDK1 activity can be helpful in dealing with low levels of CDC7 kinase.

      Suski et al argue that CDC7 is dispensable for DNA replication in human cells based on acute degradation of CDC7 or by its inhibition using an “Shokat type” analogue sensitive CDC7 allele. However, another study showed that DNA replication is not completed using the same approach and the same analogue sensitive allele (Jones et al. 2021 https://doi.org/10.1016/j.molcel.2021.01.004). In mouse embryonic stem cells, the Masai group had previously shown that CRE-Lox mediated inactivation of mDBF4 leads to a strong decrease of DNA synthesis and that mDBF4, like mCDC7 is essential for cell ES cells viability (Kim et al, 2002 https://doi.org/10.1093/emboj/21.9.2168 and Yamashita 2005 https://doi.org/10.1111/j.1365-2443.2005.00857.x ). Intriguingly mDRF1 has yet not been identified nor characterised. In our opinion, the simplest explanation to reconciliate the different reports is that human and mouse CDC7 are indeed required for DNA replication and for cell proliferation, but the phenotype of the most severe effects of its inhibition requires the complete loss of function of the kinase and may be delayed in time. We are happy to add these considerations in the discussion section of the revised manuscript.

      1. Some discussion of the increased frequency of micronuclei in DRF1-deficient cells compared to DBF4-deficient lines would be useful (c.f. Figure 1F-G).

      In the discussion we have suggested that the increase of micronucleated cells in the DRF1 deficient clones “could be consistent with a (DRF1) specific but not yet identified function in chromosome segregation, in the fine-tuning of DNA replication or the DNA repair process”. Of interest, CDC7 kinase was recently involved in modulating ATR function in cytokinetic abscission, and impairment of this process can lead to increase frequency of micro nucleated cells (Luessing et al. 2023 https://doi.org/10.1016/j.isci.2022.104536 ). It is possible that this new role of CDC7 could be dependent on DRF1, an hypothesis at present purely speculative, that we will be testing in the future. We are happy to add these considerations to the discussion section of the revised manuscript.

      1. It would be helpful to present actual p values in Figure 2, rather than asterisks.

      Asterisks report the range in which the p values fall into, which currently is specified in the legend. These can be substituted with actual numbers in the figures, and we will comply with the requirement of the journal in which the manuscript will be accepted.

      Reviewer #3 (Significance (Required)):

      The main strength of this manuscript is the exploration of the division of labour between DBF4 and DRF1 in human cells, regarding the roles of CDC7 kinase during DNA replication initiation, fork progression and checkpoint control. A limitation would be the failure to monitor the level of DBF4 and DRF1 in the CRISPR-edited cell lines, whilst it is also possible that the relative roles of DBF4 and DRF1 might vary in different cell types.

      Previous studies of DNA replication in Xenopus egg extracts (e.g. Takahashi et al, 2005: doi: 10.1101/gad.1339805) indicated that DRF1 is the dominant activator of CDC7. In contrast, past work from the current authors (Tenca et al, 2007, 10.1074/jbc.M604457200) indicated that DBF4 is the major partner of CDC7 in human HeLa cells, at least at the level of promoting MCM2 phosphorylation (the only parameter monitored in the previous study, whereas the present manuscript goes much deeper into the various roles of CDC7 in DNA replication control and focusses on the role of CDC7 at replication forks and in checkpoint control).

      This study should be of interest to those studying chromosome replication, checkpoints and genome integrity. It should also interest those with a more clinical perspective, due to the potential importance of CDC7 kinase inhibitors as anti-cancer agents.

      My own expertise is in the field of chromosome replication.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study assesses anatomical, behavioral, physiological, and neurochemical effects of early-life seizures in rats, describing a striking astrogliosis and deficits in cognition and electrophysiological parameters. The convincing aspects of the paper are the wide range of convergent techniques used to understand the effects of early-life seizures on behavior as well as hippocampal prefrontal cortical dynamics. While reviewers thought that the scope was impressive, there was criticism of the statistical robustness and number of animals used per study arm, as well as the lack of causal manipulations to determine cause-and-effect relationships. This paper will be of interest to neurobiologists, epileptologists, and behavioral scientists.

      We thank Joseph Gleeson as the Reviewing Editor and Laura Colgin as the Senior Editor for considering this revision of our manuscript for publication in eLife. We appreciate the positive acknowledgment of the study and the critical points raised by the reviewers. We have addressed all the excellent comments of the two reviewers, providing a detailed response for each comment. We believe that these revisions have significantly improved the quality and rigor of our study.

      We want to assure you that our experimental design was meticulously crafted, incorporating adequate control groups, and is grounded in prominent studies in systems neurophysiology focusing into early-life seizures effects, especially for capturing mild effects. We conducted statistical tests adhering to established norms and recommendations, ensuring a thorough and transparent description of the employed statistical methods. We welcome any specific suggestions to further improve this aspect.

      In fact, the concerns raised by the reviewers regarding statistical robustness may stem from a misunderstanding of the rat cohorts used in each experiment. Criticism was directed at the use of only 5 animals without a control group for acute electrophysiological recording. It is essential to clarify that this group served the sole purpose of confirming that the injection of lithium-pilocarpine would induce both behavioral and electrographic seizures. Importantly, this was a descriptive result, and no statistical test or further analysis was conducted with these data. In the revised manuscript, we have made adjustments to this description, aiming to eliminate any ambiguity, particularly addressing the issue of sample size in each experiment.

      Regarding the lack of causal manipulations, we fully agree that this approach would provide a deeper mechanistic understanding of our findings and is an essential next step. Still, developmental brain disturbances are linked to manifold intricate outcomes, so an initial observational exploration would offer insights about particular and nuanced relationships for following studies aimed at targeted interventions. In this context, our objective was to provide a comprehensive characterization of ELS effects to serve as a foundation for future research. While recognizing the relevance of causal manipulations, only a more sophisticated data analyses were able to reveal more complex aspects like specific multivariate associations and non-linear relationships that would not have been revealed by causally perturbing one or another factor at first. In the revised manuscript, we emphasized the limitation of lacking causal manipulations as well as the advantages of our approach. Also, we mentioned some possible targets for following perturbational investigations based on our findings.

      For a more detailed discussion on these matters, we invite you to review our response to reviewers.

      Reviewer 1

      In this paper, Ruggiero, Leite, and colleagues assess the effects of early-life seizures on a large number of anatomical, physiological, behavioral, and neurochemical measures. They find that prolonged early-life seizures do not lead to obvious cell loss, but lead to astrogliosis, working memory deficits on the radial arm maze, increased startle response, decreased paired pulse inhibition, and increased hippocampal-PFC LTP. There was a U-shape relationship between LTP and cognitive deficits. There is increased theta power during the awake state in ELS animals but reduced PFC theta-gamma coupling and reduced theta HPC-PFC coherence. Theta coherence seems to be similar in ACT and REM states in ELS animals while in decreases in active relative REM in controls.

      Strengths:

      The main strength of the paper is the number of convergent techniques used to understand how hippocampal PFC neural dynamics and behavior change after early-life seizures. The sheer scale, breadth, and reach of the experiments are praiseworthy. It is clear that the paper is a major contribution to the field as far as understanding the impact of early-life seizures. The LTP findings are robust and provide an important avenue for future study. The experiments are performed carefully and the analysis is appropriate. The paper is well-written and the figures are clear.

      We express our gratitude to Reviewer #1 for conducting a thoughtful and comprehensive review of our manuscript. We sincerely value both the constructive criticisms provided and your acknowledgment of the manuscript's strengths.

      Weaknesses:

      The main weakness of the paper is the lack of causal manipulations to determine whether prevention or augmentation of any of the findings has any impact on behavior or cognition. Alternatively, if other manipulations would enhance working memory in ELS animals, it would be interesting to see the effects on any of these parameters measured in the paper.

      We sincerely appreciate the insightful comments from Reviewer #1 regarding the potential benefits of including causal manipulations in our study. We wholeheartedly agree that such manipulations can provide a deeper understanding of the mechanistic underpinnings of the observed relationships and represent a crucial next step in our research trajectory.

      Our primary objective in this study was to establish a comprehensive framework through observational examinations, exploring intricate relationships across various neurobiological and behavioral variables in the aftermath of early-life seizures (ELS). By identifying these associations, our work aims to provide a foundation for future investigations that can delve into targeted interventions.

      While we acknowledge the importance of causal manipulations, we would like to underscore the advantages of our initial multivariate correlational study. Importantly, developmental brain disturbances have lasting impacts affecting multiple biological outcomes that may have intricate relationships between themselves. Firstly, although some neurobiological variables stood out from the comparisons of group means, this did not reveal some nuanced relationships within the data. The complexity of the relationships we uncovered, involving behavior, cognition, immunohistochemistry, plasticity, neurochemistry, and network dynamics, required a more elaborate analytical approach. Only through sophisticated data analysis techniques, we were able to dissect important peculiarities, such as the robust multivariate association between brain-wide astrogliosis and sensorimotor impairments, as well as non-linear relationships, such as the inverted-U relationship between plasticity and working memory. These nuances might not have been fully revealed through causal manipulations, since several variables are strongly related and consequently can affect several outcomes, leading to a false conclusion of direct causality.

      Nevertheless, we acknowledge the understatement of the limitation of lacking causal manipulations in our manuscript. To address this, we have included a dedicated section in the discussion highlighting this limitation. We emphasize the advantages of this exploratory phase, supported by a review of the literature on cause-and-effect studies that align with our findings. Additionally, we speculate on promising targets for future cause-and-effect studies based on our findings. For instance, we hypothesize that enhancing plasticity may improve working memory in control subjects, while attenuating plasticity might have a similar effect in ELS subjects. Furthermore, we propose that reactive astrogliosis and concurrent neuroinflammatory processes likely underlie sensorimotor changes in the ELS group. Lastly, we suggest that dopaminergic antagonism in the ELS group could normalize behavioral deficits, prevent the exaggerated LTP induction of the HPC-PFC pathway, reestablish the state-dependent network dynamics, and desensitize the dopaminergic response.

      [...]Also, I find the sections where correlations and dimensionality reduction techniques are used to compare all possible variables to each other less compelling than the rest of the paper (with the exception of the findings of U-shaped relationship of cognition to LTP). In fact, I think these sections take away from the impact of the actual findings.

      We appreciate the reviewer's feedback and would like to emphasize the significance of the multivariate analysis conducted in our study. Multivariate analysis extends beyond bivariate correlations and is the only type of analysis capable of comprehending the relation of data in a multidimensional way, offering a comprehensive approach to understanding complex relationships among multiple variables. By employing techniques such as principal component analysis (PCA), generalized linear models (GLM), and canonical correlation analysis (CCA), we aimed to unravel intricate patterns of covariance that explore how different variables collectively contribute to the observed outcomes and assess the impact of each independent variable (predictor) on the dependent variable (the variable to be predicted or explained). Importantly, it enables us to control for potential confounding factors by keeping all other variables constant.

      While we acknowledge that these sections may appear intricate, their inclusion is indispensable for a comprehensive understanding of the diverse variables associated with SE outcomes. We believe that these analyses offer valuable insights into the intricate dynamics of our study, providing a more holistic perspective on the altered spectrum induced by early-life seizures (ELS).

      Regarding the reviewer's observations about the impact of the U-shaped relationship between cognition and LTP, we have made graphical and textual adjustments to emphasize the significance of these findings, aiming to enhance their clarity and impact within the broader context of our research. We trust that these modifications contribute to a more compelling presentation of our results.

      […]Finally, the apomorphine section seemed to hang separately from the rest of the paper and did not seem to fit well.

      We appreciate the Reviewer #1 feedback on the apomorphine section. In order to address this point, we carefully rewrote our rationale before the results to clarify our hypothesis and chosen methodology. In our work, we performed the apomorphine experiment as a logical next step of previous data. We showed that ELS rats display REM-like oscillatory dynamics during active behavior, similar to genetically and pharmacologically hyperdopaminergic mice (Dzirasa et al., 2006). Furthermore, other results also indicated possible dopamine neurotransmission alterations, such as working memory deficits, hyperlocomotion, PPI deficits, aberrant HPC-PFC LTP, and abnormal PFC gamma coordination. Therefore, we hypothesized that ELS animals would present a state of hyperdopaminergic activity. Among the possible methodologies to investigate the hyperdopaminergic state, we choose the apomorphine sensitivity test, which is classically used and induces unambiguous behavior and neurochemical alterations in hyperdopaminergic rodents (Duval, 2023; Ellenbroek & Cools, 2002).

      Reviewer 1 (Recommendations For The Authors):

      (1) It would be useful to stain for other GABAergic interneuron markers such as somatostatin, VIP, CCK.

      (2) The authors refer to neuroinflammation but they are really referring to reactive astrogliosis. I would also suggest staining for microglial markers.

      (3) The duration of chronic electrographic seizures in ELS animals should also be calculated and presented.

      (4) Word usage: the authors frequently use the word "presents" when "demonstrates" would be more appropriate

      (1) We appreciate your insight into staining for other GABAergic interneuron markers such as somatostatin, VIP, CCK. While investigating additional interneuron types is indeed relevant, it was not the primary focus of this study for several reasons: 1) The overall neuron density, assessed through NeuN immunostaining, revealed no differences between controls and early life seizure (ELS) groups, even in brain regions susceptible to neuron death after SE (i.e., CA1). Therefore, differences in interneurons, which are more resistant to death in SE and constitute approximately 20% of the cells, are unlikely. 2) Among all interneuron subtypes, Parvalbumin-positive (PV+) interneurons represent a substantial population and are susceptible to various stressors. In the hippocampus, 24% of GABAergic neurons are PV+, whereas 14% are SST+, 10% are CCK+, and VIP+ are less than 10% (Freund and Buzsaki, 1996). Consequently, we considered PV+ interneurons to be a more sensitive subpopulation for evaluating the effects of SE. As they showed no significant difference, we do not believe that assessing smaller subtypes, such as VIP+ or CCK+ cells, would yield significant differences.

      (2) While we often see activated microglia in hippocampal sclerosis, these cells are only slightly increased in cases without hippocampal sclerosis (which are similar to our animals), as we previously published (Peixoto-Santos et al., 2012). Astrocytes are a better marker for the epileptogenic zone, as are increased in epileptogenic zones without neuron loss and are also important for controlling neuronal activity by neurotransmitter recycling and ion buffering. In fact, our present model is very similar to the mesial temporal lobe epilepsy patients with gliosis-only, which are characterized by only presenting increased reactive astrogliosis in the hippocampus, without cell loss, and also present changes in innate inflammatory response related to the presence of reactive astrocytes (Grote et al., 2023).

      (3) We have performed these calculations and added this information to the revised manuscript.

      (4) We thank the reviewer for the word usage recommendation. Indeed, we frequently used “present” throughout the manuscript to describe the observations and patterns the groups “exhibited” or “showed”. However, we believe this is truly not the most appropriate usage in the Discussion when we describe the multivariate latent factors, as we did not “present” them, but rather, we “demonstrated” their existence and significance through our analysis. We rewrote these sentences and hope this is the point the reviewer was referring to.

      References:

      Duval F. Systematic review of the apomorphine challenge test in the assessment of dopaminergic activity in schizophrenia. Healthcare. 2023 11 (1487): 1-11. doi: 10.3390/healthcare11101487.

      Dzirasa K, Ribeiro S, Costa R, Santos LM, Lin SC, Grosmark A, Sotnikova TD, Gainetdinov RR, Caron MG, Nicolelis MAL. Dopaminergic control of sleep-wake states. Journal of Neuroscience. 2006 26:10577–10589. doi:10.1523/JNEUROSCI.1767-06.2006.

      Freund TF, Buzsáki G. Interneurons of the hippocampus. Hippocampus. 1996;6(4):347-470. doi: 10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-I. PMID: 8915675.

      Ellenbroek BA & Cools AR. Apomorphine susceptibility and animal models for psychopathology: genes and environment. Behavior Genetics. 2002 32 (5): 349-361. doi: 10.1023/a:1020214322065.

      Grote A, Heiland DH, Taube J, Helmstaedter C, Ravi VM, Will P, Hattingen E, Schüre JR, Witt JA, Reimers A, Elger C, Schramm J, Becker AJ, Delev D. 'Hippocampal innate inflammatory gliosis only' in pharmacoresistant temporal lobe epilepsy. Brain. 2023 Feb 13;146(2):549-560. doi: 10.1093/brain/awac293. PMID: 35978480; PMCID: PMC9924906.

      Peixoto-Santos JE, Galvis-Alonso OY, Velasco TR, Kandratavicius L, Assirati JA, Carlotti CG, Scandiuzzi RC, Serafini LN, Leite JP. Increased metallothionein I/II expression in patients with temporal lobe epilepsy. PLoS One. 2012;7(9):e44709. doi: 10.1371/journal.pone.0044709. Epub 2012 Sep 18. Erratum in: PLoS One. 2016;11(7):e0159122. PMID: 23028585; PMCID: PMC3445538.

      Reviewer 2

      In this manuscript, the authors employ a multilevel approach to investigate the relationship between the hippocampal-prefrontal (HPC-PFC) network and long-term phenotypes resulting from early-life seizures (ELS). Their research begins by establishing an ELS rat model and conducting behavioral and neuropathological studies in adulthood. Subsequently, the manuscript delves into testing hypotheses concerning HPC-PFC network dysfunction. While the results are intriguing, my enthusiasm is tempered by concerns related to the logical flow

      We thank the reviewer for bringing attention to the logical flow of the manuscript. Given the diverse array of behavioral and neurobiological variables examined in our study obtained through various methods and measures, we utterly recognize the utmost importance of a clear and coherent logical flow to provide a comprehensive understanding of the overall narrative.

      Our goal was to articulate the neurobiological findings in a manner that underscores their convergence of mechanisms, revealing a cohesive relationship between early-life seizure, cognitive deficits, sensorimotor impairments, abnormal network dynamics, aberrant plasticity, neuroinflammation and dysfunctional dopaminergic transmission.

      Briefly, an outline of our narrative could be summarized in the highlights:

      (1) ELS induces sensorimotor alterations and working memory deficits.

      (2) ELS does not induce neuronal loss, so neurobiological underpinnings may be molecular and functional.

      (3) ELS induces brain-wide astrogliosis and exaggerated HPC-PFC long-term plasticity.

      (4) ELS does not induce neuronal loss, so neurobiological underpinnings may be molecular and functional.

      (5) Sensorimotor alterations are more correlated to astrogliosis, while cognitive deficits to altered HPC-PFC plasticity.

      (6) ELS-induced functional alterations may also be observable in freely moving subjects. ELS induces state-dependent alterations in the HPC-PFC network dynamics, such as increased hippocampal theta and abnormal PFC gamma coordination during behavioral activity.

      (7) ELS leads to REM-ACT similarity, previously reported in hyperdopaminergic mice, indicating dopaminergic dysfunction.

      (8) ELS exhibits altered dopaminergic transmission and behavioral sensitivity that mirror the initial sensorimotor findings.

      (9) The literature establishes an inverted-U relationship between dopamine and cognition and PFC plasticity, which may explain our finding of an inverted-U relationship between working memory and HPC-PFC LTP across CTRL and ELS rats.

      To address this concern, we have made revisions to enhance the logical flow, ensuring a more seamless transition between the different sections of the Results by presenting clearer links between observations and following investigations. We hope these changes contribute to a more straightforward rationale and easily understandable presentation of our hypotheses and results.

      Focus on Correlations: The manuscript primarily highlights correlations as the most significant findings. For instance, it demonstrates that ELS induces cognitive and sensorimotor impairments. However, it falls short of elucidating why these deficits are specifically linked to HPC-PFC synaptic plasticity/network. Furthermore, the manuscript mentions the involvement of other brain regions like the thalamus in the long-term outcomes of ELS based on immunohistochemistry data.

      Thank you for your insightful comments, which allowed us to provide further clarification on our study's focus and findings. Our primary goal was to delve into the electrophysiological alterations within the HPC-PFC pathway. The rationale behind this choice lies in the hypothesis that, even in the absence of significant neuronal loss, functional changes in circuits closely linked to the cognitive and behavioral aspects under investigation could be identified.

      While we concentrated our electrophysiological investigation on the HPC-PFC pathway due to its well-established functional correlates in existing literature, it is essential to highlight that our data reveal broader alterations in neural circuitry. Notably, we observed an increase in GFAP in the entorhinal cortex and thalamic reticular nucleus, along with changes in the dopaminergic release within the VTA-NAc pathway. These findings suggest that the impact of early-life seizures extends beyond the HPC-PFC circuit.

      While we recognize the relevance of other brain circuits in the outcomes of ELS, we argue for a specific role of the HPC-PFC circuit in the outcomes of ELS. We will detail the supporting evidence and arguments that specifically link the HPC-PFC function to our ELS-related observations in a later comment regarding the "overinterpretation" of the HPC-PFC role. To better convey these important nuances, we have made specific modifications to the results and in the discussion section to underscore the broader implications of our findings, providing a more comprehensive understanding of the study's scope and outcomes.

      […]This raises questions about the subjective nature and persuasiveness of the statistical studies presented.

      All statistical analyses were carefully applied based on the literature and following well-established precepts and precautions. Specifically, we constructed the experimental design for univariate inferential statistics for the data related to behavioral tests, synaptic plasticity, immunohistochemistry, oscillatory activity, and dopaminergic sensitization. However, we also submitted our data to multivariate statistical analysis, which is recommended in cases with a considerable amount of data, and intend to investigate possible hidden effects. In this situation, multivariate analyses are inherently exploratory due to the possibility of using multiple measurements for each phenomenon investigated. Nevertheless, their application is not subjective and follows the same statistical rigor as univariate analyses. We firmly believe that abstaining from exploring these data, would not reach the full potential of this analytical method in dissecting the multidimensional associations within our dataset. In order to eliminate any doubt regarding the objectivity in the choice and application of statistics, we carefully rewrote the methods, highlighting the details of statistical rigor even more.

      Sample Size Concerns: The manuscript raises concerns about the adequacy of sample sizes in the study. The initial cohort for acute electrophysiology during ELS induction comprised only 5 rats, without a control group. Moreover, the behavioral tests involved 11 control and 14 ELS rats, but these same cohorts were used for over four different experiments. Subsequent electrophysiology and immunohistochemistry experiments used varying numbers of rats (7 to 11). Clarification is needed regarding whether these experiments utilized the same cohort and why the sample sizes differed. A power analysis should have been performed to justify sample sizes, especially given the complexity of the statistical analyses conducted.

      We appreciate the reviewer's thoroughness and considerations regarding the sample sizes used in our study. The concerns raised about statistical robustness seem to stem from a lack of clarity in delineating the rat cohorts used in each experiment. It is encouraging to note that several studies in the field of neurophysiology, employing similar analyses, utilize a sample size similar to what was used in our research. The choice of the sample size was based on a thorough analysis of the existing literature, considering specific experimental demands, the complexity of employed techniques, and the need to achieve statistically robust results. In response to these concerns and to enhance clarity on the sample sizes, we have made several modifications (highlighted in red) in the text. Below, we provide details for each animal cohort utilized:

      Cohort 1 - Acute Electrophysiology

      The decision to use only 5 animals without a control group for acute electrophysiological recording aimed specifically to confirm that the injection of lithium-pilocarpine would induce both behavioral and electrographic seizures. It is crucial to note that this was a descriptive result and a methodological control of the ELS model. Besides, no statistical test or further analysis was conducted on these data. We maintain the belief that a group of 5 animals is sufficient to demonstrate that the protocol induces electrographic seizures, and introducing a control group was considered unnecessary to show that saline injection does not induce electrographic seizures.

      Cohort 2 - Behavior, LTP Recording, and Immunohistochemistry

      Initially, 14 (ELS) and 11 (CTRL) rats were used for behavior assessment. The reduction in sample size for LTP and immunohistochemistry experiments was influenced by practical challenges, including mortality during LTP surgery and issues with immunohistochemical staining that hindered a proper analysis for some animals.

      Cohort 3 - Chronic Freely-Moving Electrophysiology

      A new cohort of animals (n=6 and 9 for CTRL and ELS, respectively) was used specifically for freely-moving electrophysiological data.

      Cohort 4 - Behavioral Sensitization to Psychostimulants

      A fourth cohort was utilized for assessing behavioral sensitization to psychostimulants (CTRL n=15 and ELS n=14). The reduced sample size for neurotransmitter analysis (CTRL n=8 and ELS n=9) was a deliberate selection of a subsample to ensure a sufficient sample for quantification while maintaining statistical validity

      Overinterpretation of HPC-PFC Network Dysfunction: The manuscript potentially overinterprets the role of HPC-PFC network dysfunction based on the results.

      We appreciate the insight from Reviewer #2 regarding the potential overinterpretation of the role of the hippocampal-prefrontal cortex (HPC-PFC) network dysfunction in the various alterations observed after ELS.

      The significance of HPC-PFC plasticity and network function has been extensively documented concerning cognitive, affective, and sensorimotor functions, as well as in models of neuropsychiatric diseases. Our recent review (Ruggiero et al., 2021) compiles these findings. Specifically, the HPC-PFC network has been linked to spatial working memory through a series of causal and correlational studies conducted by Floresco et al. and Gordon et al. These findings make the HPC-PFC pathway a plausible candidate for underlying alterations associated with working memory, consistent with our observation of exaggerated HPC-PFC LTP associated with poorer performance in the ELS group. Regarding the immunohistochemical observations, we concur with Reviewer #2 that these findings suggest broader-scale brain alterations related to sensorimotor dysfunction beyond the HPC-PFC circuitry. Surely, we acknowledge that these large-scale alterations may underlie brain-wide network functional changes.

      In our network dynamics study arm, we investigated HPC-PFC oscillatory activity, allowing us to discuss potential relationships between abnormal plasticity (verified in the second study arm) and network dynamics. It is important to note that while there is some anatomical specificity to the LFPs recorded in the HPC and PFC, these activities may represent larger-scale limbic-cortical dynamics. The intermediate HPC exhibits a significant influence from both dorsal and ventral HPC, and the prelimbic PFC is intricately related to both hippocampal and thalamic oscillations exhibiting under-demand state-dependent synchrony. Additionally, the state maps used in our study were initially described to distinguish states at a global forebrain network level. Even in our past studies, we have described HPC-PFC patterns of network activity (Marques et al., 2022a) that later were found to represent a part of a brain-wide synchrony pattern (Marques et al., 2022b). However, most of our findings on oscillatory dynamics were centered around theta oscillations, a well-established brain-wide activity that originates and spreads from the hippocampus and are present in the HPC-PFC circuit during activity.

      In conclusion, we believe the correlations between HPC-PFC LTP and working memory, as well as the specific alterations of theta coordinated activity, support a particular role of the HPC-PFC network dysfunction in the effects of ELS. However, the brain-wide immunochemical alterations are plausible indications of larger-scale dysfunctional networks. To address this issue, we emphasized in the discussion of network findings that the immunohistochemical and neurochemical findings endorse the need to investigate ELS effects on larger networks.

      Notably, cognitive deficits are described as subtle, with no evidence of learning deficits and only faint working memory impairments. However, sensorimotor deficits show promise. Consequently, it's essential to justify the emphasis on the HPC-PFC network as the primary mechanism underlying ELS-associated outcomes, especially when enhanced LTP is observed. Additionally, the manuscript seems to sideline neuropathological changes in the thalamus and the thalamus-to-PFC connection. The analysis lacks a direct assessment of the causal relationship between HPC-PFC dysfunction and ELS-associated outcomes, leaving a multitude of multilevel analyses yielding potential correlations without easily interpretable results.

      We thank Reviewer #2 for the thorough review and insightful comments. To better grasp the context, it is crucial to consider this characterization within the scope of our experimental design and expected outcomes. Unlike epilepsy models involving adult animals or interventions causing pronounced neuronal loss and structural modifications, our study was intentionally designed to explore moderate behavioral alterations. In fact, the mild behavioral alterations observed in ELS models and the lack of neuronal loss guided our focus on investigating changes in HPC-PFC communication.

      While our observed cognitive deficits may be milder compared to certain models, it is imperative to underscore their robustness and clinical relevance. These findings have been consistently replicated globally across various experimental models, encompassing ELS induced by hyperthermia (Chang et al., 2003; Kloc et al., 2022), kainic acid (Statsfrom et al. 1993), flurothyl (Karnam et al., 2009a; 2009b), and hypoxia (Najafian et al., 2021; Hajipour et al., 2023). Mild cognitive deficits were also evident by other research groups using the pilocarpine model in P12 (Mikulecká et al., 2019; Kubová et al., 2013; Kubová et al., 2002). Furthermore, our group replicated the working memory deficit results using an alternative paradigm (the T-maze) and a different rat strain (Sprague Dawley), enhancing the reliability of our observations (D’Agosta et al., 2023).

      The clinical perspective gains importance, considering that cognitive effects of ELS may be less severe than those in patients with long-term epilepsy. In fact, the majority of patients with childhood epilepsy exhibit mild cognitive impairment as the most common grade of severity - more than two times the rate of severe cognitive impairment (Sorg et al., 2022). Investigating the mechanisms underlying these mild cognitive changes is crucial for shedding light on neurobiological aspects not fully understood, thereby expanding our comprehension of the consequences of ELS.

      We recognize the challenges associated with conducting causal experiments in neuroscience, especially in long-term and chronic alterations as seen in our model. Isolating modifications of specific activities is indeed intricate. However, it's essential to acknowledge that neuroscience progress has not solely relied on causal experiments but has significantly advanced through correlational observations. Our findings serve as a foundational step in comprehending the repercussions of ELS, proposing mechanisms and circuits that necessitate further in-depth dissection and study in the future. We have integrated these considerations into the discussion section of the manuscript to enhance clarity.

      Overall, while the manuscript presents intriguing findings related to the HPC-PFC network and ELS outcomes, it requires a more rigorous experimental design[…]

      We thank the reviewer for acknowledging our intriguing findings. Regarding the experimental design, we are confident that all the manuscript hypotheses, design, and execution of experiments were rigorously based on the literature and carried out with all necessary controls. As stated earlier, we constructed the experimental design for univariate inferential statistics and explored associations between variables using multivariate statistics. Specifically, we achieved a rigorously experimental design following a series of guidelines. First, the planning of the sample size in each experiment and their respective controls were based on mild effects from the ELS literature. As previously indicated, the only experiment with one group was just the description of the behavioral effects and electrographic seizures after the acute injection of lithium-pilocarpine. Given the exhaustive replication of these data in the ELS literature, this result was presented descriptively as a methodological control. Second, detailed descriptions of statistics were made in both methods and results, always indicating positive and negative results. Notably, the experimental designs used in the work do not correspond to any novelty or radicalization, strictly following the literature of the field. However, new indications and references about the experimental accuracy were added to the manuscript to resolve any doubts regarding objectivity.

      References:

      Chang YC, Huang AM, Kuo YM, Wang ST, Chang YY, Huang CC. Febrile seizures impair memory and cAMP response-element binding protein activation. Ann Neurol. 2003 Dec;54(6):706-18. doi: 10.1002/ana.10789. PMID: 14681880.

      D'Agosta R, Prizon T, Zacharias LR, Marques DB, Leite JP, Ruggiero RN. Alterations in hippocampal-prefrontal cortex connectivity are associated with working memory impairments in rats subjected to early-life status epilepticus. In: NEWROSCIENCE INTERNATIONAL SYMPOSIUM, 2023, Ribeirão Preto. Poster.

      Hajipour S, Khombi Shooshtari M, Farbood Y, Ali Mard S, Sarkaki A, Moradi Chameh H, Sistani Karampour N, Ghafouri S. Fingolimod Administration Following Hypoxia Induced Neonatal Seizure Can Restore Impaired Long-term Potentiation and Memory Performance in Adult Rats. Neuroscience. 2023 May 21;519:107-119. doi: 10.1016/j.neuroscience.2023.03.023. Epub 2023 Mar 28. PMID: 36990271.

      Karnam HB, Zhou JL, Huang LT, Zhao Q, Shatskikh T, Holmes GL. Early life seizures cause long-standing impairment of the hippocampal map. Exp Neurol. 2009 Jun;217(2):378-87. doi: 10.1016/j.expneurol.2009.03.028. Epub 2009 Apr 2. PMID: 19345685; PMCID: PMC2791529.

      Karnam HB, Zhao Q, Shatskikh T, Holmes GL. Effect of age on cognitive sequelae following early life seizures in rats. Epilepsy Res. 2009 Aug;85(2-3):221-30. doi: 10.1016/j.eplepsyres.2009.03.008. Epub 2009 Apr 22. PMID: 19395239; PMCID: PMC2795326.

      Kubová H, Mareš P. Are morphologic and functional consequences of status epilepticus in infant rats progressive? Neuroscience. 2013 Apr 3;235:232-49. doi: 10.1016/j.neuroscience.2012.12.055. Epub 2013 Jan 7. PMID: 23305765.

      Kloc ML, Marchand DH, Holmes GL, Pressman RD, Barry JM. Cognitive impairment following experimental febrile seizures is determined by sex and seizure duration. Epilepsy Behav. 2022 Jan;126:108430. doi: 10.1016/j.yebeh.2021.108430. Epub 2021 Dec 10. PMID: 34902661; PMCID: PMC8748413.

      Kubová H, Mares P, Suchomelová L, Brozek G, Druga R, Pitkänen A. Status epilepticus in immature rats leads to behavioural and cognitive impairment and epileptogenesis. Eur J Neurosci. 2004 Jun;19(12):3255-65. doi: 10.1111/j.0953-816X.2004.03410.x. PMID: 15217382.

      Marques DB, Ruggiero RN, Bueno-Junior LS, Rossignoli MT, and Leite JP. Prediction of Learned Resistance or Helplessness by Hippocampal-Prefrontal Cortical Network Activity during Stress. The Journal of Neuroscience. 2022a 42 (1): 81-96.. https://doi.org/10.1523/jneurosci.0128-21.2021.

      Marques DB, Rossignoli MT, Mesquita BDA, Prizon T, Zacharias LR, Ruggiero RN and Leite JP. Decoding fear or safety and approach or avoidance by brain-wide network dynamics abbreviated. bioRxiv. 2022b https://doi.org/10.1101/2022.10.13.511989.

      Mikulecká A, Druga R, Stuchlík A, Mareš P, Kubová H. Comorbidities of early-onset temporal epilepsy: Cognitive, social, emotional, and morphologic dimensions. Exp Neurol. 2019 Oct;320:113005. doi: 10.1016/j.expneurol.2019.113005. Epub 2019 Jul 3. PMID: 31278943.

      Najafian SA, Farbood Y, Sarkaki A, Ghafouri S. FTY720 administration following hypoxia-induced neonatal seizure reverse cognitive impairments and severity of seizures in male and female adult rats: The role of inflammation. Neurosci Lett. 2021 Mar 23;748:135675. doi: 10.1016/j.neulet.2021.135675. Epub 2021 Jan 28. PMID: 33516800.

      Ruggiero RN, Rossignoli MT, Marques DB, de Sousa BM, Romcy-Pereira RN, Lopes-Aguiar C and Leite JP. Neuromodulation of Hippocampal-Prefrontal Cortical Synaptic Plasticity and Functional Connectivity: Implications for Neuropsychiatric Disorders. Frontiers in Cellular Neuroscience. 2021 15 (October): 1–23. https://doi.org/10.3389/fncel.2021.732360.

      Sorg AL, von Kries R, Borggraefe I. Cognitive disorders in childhood epilepsy: a comparative longitudinal study using administrative healthcare data. J Neurol. 2022 Jul;269(7):3789-3799. doi: 10.1007/s00415-022-11008-y. Epub 2022 Feb 15. PMID: 35166927; PMCID: PMC9217877.

      Stafstrom CE, Chronopoulos A, Thurber S, Thompson JL, Holmes GL. Age-dependent cognitive and behavioral deficits after kainic acid seizures. Epilepsia. 1993 May-Jun;34(3):420-32. doi: 10.1111/j.1528-1157.1993.tb02582.x. PMID: 8504777.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1

      This is a short but important study. Basically, the authors show that α-synuclein overexpression's negative impact on synaptic vesicle recycling is mediated by its interaction with E-domain containing synapsins. This finding is highly relevant for synuclein function as well as for the pathophysiology of synucleinopathies. While the data is clear, functional analysis is somewhat incomplete.

      (1) The authors should present a clearer dissociation of endocytosis and exocytosis under the various conditions they study. They should quantify the rate of rise and decay of pHluorin signals. 2. In addition, I strongly recommend a few additional experiments with and without a vATPase inhibitor such as bafilomycin to estimate the relative effects on exo- vs. endocytosis. As the authors are aware bafilomycin will mask the re-acidification /endocytosis component, thus revealing pure exocytosis and thus enabling quantification of endocytosis with minimal contamination from exocytosis.

      In the revised version, we analyzed and quantified exocytosis and endocytosis separately, with bafilomycin experiments, as the reviewer suggested (new data, Fig. 1- Fig. Supp. 1A-B). Overexpression of human alpha-synuclein only attenuated exocytosis in neurons that also expressed synapsins (WT neurons and synapsin TKO neurons transduced with synapsin Ia). In parallel, we also examined endocytosis by calculating the time-constant of the decay in the fluorescence of sypHy during the endocytotic phase (Fig. 1- Fig. Supp. 1C-E). Previous studies have shown that after brief stimulus-trains – like those used in our study (20Hz/300AP) – most endocytosis occurs after the cessation of stimulation 1. Expression of human alpha-synuclein did not alter the endocytosis time-constant in any of our experiments. To summarize, the interaction of alpha-synuclein with the synapsin E domain was required for alpha-synuclein induced attenuation of exocytosis, but not endocytosis.

      Reviewer #2

      ...The paper will be improved significantly if additional experiments are added to expand and provide a more mechanistic understanding of the effect of α-syn and the intricate interplay between synapsin, α-syn, and the SV. For an enthusiastic reader, the manuscript as it looks now with only 3 figures, ends prematurely. Some of the experiments above or others could complement, expand and strengthen the current manuscript, moving it from a short communication describing the phenomenon to a coherent textbook topic. Nevertheless, this work provides new and exciting evidence for the regulation of neurotransmitter release and its regulation by synapsin and α-syn.

      (1) Did the authors try to attach E-domain for example to synapsin Ib and restore α-syn inhibition with synapsin Ib-E?

      This is an interesting idea, but in previous studies, we found that synapsin Ib does not associate with synaptic vesicles2, so it will not be present at the right location to be able to restore alpha-synuclein induced synaptic attenuation. We have also seen that this mis-localization alters synaptic properties (unpublished).

      (2) Was the expression level of Synapsin-IaScrE examined and compared to WT Synapsin-Ia in Fig 3?

      Yes, this data is now shown in Fig. 3-Fig. Supp. 1.

      (3) Were SVs dispersed in α-syn overexpression as predicted?

      We interpret the reviewer’s question and reasoning as follows. If alpha-synuclein binds to the E-domain of synapsin, a prediction in the alpha-synuclein over-expression scenario is that the overabundance of alpha-synuclein molecules would bind to and sequester the E-domain synapsins away from synaptic vesicles. In the absence of E-domain synapsins, the synaptic-vesicle clustering effects of synapsins would be lost, and there would be dispersion of synaptic vesicles. We tested this prediction, which is now shown in an additional figure (new data, Fig. 4). Indeed, the AAV-mediated over-expression of alpha-synuclein leads to a dispersion of synaptic vesicles, and this dispersion is dependent on synapsins Ia and Ib, but not IIa and IIb (please see Fig. 4D-E in the revised manuscript). Appropriate text is also added, starting with “Previous studies have shown that loss of all synapsins...” presents this data and interprets it.

      (4) How does this study coincide with the effects of α-syn on fusion pore and endocytosis? This should be at least discussed. It is also possible that the effects of α-syn on endocytosis might affect the results as if endocytosis is affected, SVs number and distribution will be also affected.

      It is difficult to reconcile our data with the idea that alpha-synuclein facilitates fusion-pore opening, as proposed by the Edwards lab 3. In fact, its difficult to reconcile this concept with their own previous data, showing that alpha-synuclein over-expression attenuates SV-recycling 4. As mentioned above, modulation of endocytosis does not seem to be a major factor in our experiments, though this does not rule out a physiologic role for alpha-synuclein in endocytosis, since all our experiments are based on over-expression paradigms. Future experiments looking at phenotypes after acute alpha-synuclein knockdown may provide more clarity. In any case, there are many purported roles of alpha-synuclein, and this is now mentioned in the last paragraph (starting with Additionally, -syn has been implicated…”

      (5) What happened after stimulation when synapsin is detached from SV, does α-syn continues to be linked to it?

      The fate of alpha-synuclein after stimulation is unclear in our experiments. Previous experiments suggest that while both synapsin and alpha-synuclein detach from the SV cluster during stimulation, synapsin returns to synapses while alpha-synuclein does not 5. However, our more recent experiments (unpublished) suggest that the activity-induced dispersion of alpha-synuclein might be phosphorylation-dependent, and that over-expression of alpha-synuclein may not be the best setting to evaluate protein dispersion. We hope to answer this question more rigorously using alpha-synuclein knock-in constructs.

      (6) The experiment with E-domain fused to syPhy assumes that α-syn will still be bound to the SV. So how does α-syn inhibit ST?

      The goal of this experiment was to force the synapsin E-domain to be in a location where it would normally be present – i.e. surface of the synaptic vesicle – by tagging it to sypHy (sypHy-E), and ask if this forced-retention would be sufficient to reinstate the alpha-synuclein mediated attenuation of SV-recycling (as shown in Fig. 3F, it does). Please note that the sypHy-E in these experiments does target to the synapses (new data, Fig. 3-Fig. Supp. 2D). In this context, we are not sure what the reviewer means by “So how does a-syn inhibit synaptic transmission?” We don’t think that alpha-synuclein needs to unbind from the SVs in order to inhibit synaptic transmission. Overall, we think that alpha-synuclein needs to cooperate with synapsins to perform its function, but as mentioned above and in the manuscript, the precise role of alpha-synuclein in this process is still unclear.

      (7) An interesting experiment will be the expression of the isolated E-domain and examining blockage of α-syn inhibition and disruption of synapsin- α-syn interaction. Have the authors examined it as was done in other models?

      We did do the experiment where we only over-expressed the isolated synapsin E-domain in neurons. We were thinking that perhaps the E-domain would have a dominant-negative effect on SV-clustering, as it did in the lamprey and other model-systems, where the E-peptide was directly injected into the axon. However, we found that in cultured hippocampal neurons, the over-expressed E-domain behaves like a soluble protein and is not enriched in synapses (see new data, Fig. 3-Fig. Supp. 2B). Also, the over-expressed E-domain cannot reinstate the synaptic attenuation induced by alpha-synuclein (new data, Fig. 3-Fig. Supp. 2C), likely because the E-domain does not target to synapses. Actually, this is why we did the syPhy-E domain experiment in the first place, to ensure that the E-domain was in the right location to have an effect.

      (8) A schematic model/scheme providing a mechanistic view of the interplay between the proteins is essential and can improve the paper.

      The only model we can confidently make right now would be stick-figures showing the site where alpha-synuclein C-terminus binds to synapsin, which is obviously not very insightful. As noted above (and in the revised version), several different functions have been attributed to alpha-synuclein, and the precise role of alpha-synuclein/synapsin interactions in regulating the SV-cycle is unclear. We hope to create a better model after getting some more data from us and our colleagues working on this challenging problem.

      References

      (1) Kononenko NL & Haucke V. (2015) Molecular mechanisms of presynaptic membrane retrieval and synaptic vesicle reformation. Neuron 85, 484-496.

      (2) Gitler D, Xu Y, Kao H-T, Lin D, Lim S, Feng J, Greengard P & Augustine GJ. (2004) Molecular Determinants of Synapsin Targeting to Presynaptic Terminals. J. Neurosci. 24, 3711-3720.

      (3) Logan T, Bendor J, Toupin C, Thorn K & Edwards RH. (2017) α-Synuclein promotes dilation of the exocytotic fusion pore. Nat Neurosci 20, 681-689.

      (4) Nemani VM, Lu W, Berge V, Nakamura K, Onoa B, Lee MK, Chaudhry FA, Nicoll RA & Edwards RH. (2010) Increased expression of alpha-synuclein reduces neurotransmitter release by inhibiting synaptic vesicle reclustering after endocytosis. Neuron 65, 66-79.

      (5) Fortin DL, Nemani VM, Voglmaier SM, Anthony MD, Ryan TA & Edwards RH. (2005) Neural activity controls the synaptic accumulation of alpha-synuclein. J Neurosci 25, 10913-10921.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors conducted two tasks at 300 days of separation. First, a social perception task, where Ps responded whether a pictured person either deserved or needed help. Second, an altruism task, where Ps are offered monetary allocations for themselves and a partner. Ps decide whether to accept, or a default allocation of 20 dollars each. The partners differed in perceived merit, such that they were highly deserving, undeserving, or unknown. This categorisation was decided on the basis of a prisoner's dilemma game the partner played beforehand. "Need" was also manipulated, by altering the probability that the partner must have their hand in cold water at the end of the experiment and this partner can use the money to buy themselves out. These two tasks were conducted to assess the perception of need/merit in the first instance, and how this relates to social behaviour in the second. fMRI data were collected alongside behavioural.

      The authors present many analyses of behaviour (including DDM results) and fMRI. E.g., they demonstrate that they could decode across the mentalising network whether someone was making a need or deserving judgement vs control judgement but couldn't decode need vs deserving. And that brain responses during merit inferences (merit - control) systematically covaried with participants' merit sensitivity scores in the rTPJ. They also found relationships between behaviour and rTPJ in the altruism task. And that merit sensitivity in the perception task predicted the influence of merit on social behaviour in the altruism task.

      Strengths:

      This manuscript represents a sensible model to predict social perceptions and behaviours, and a tidy study design with interesting findings. The introduction introduced the field especially brilliantly for a general audience.

      Response: We are pleased that the reviewer found the model sensible and the findings interesting! Below, we respond to each of the reviewer’s comments/critiques.

      Weaknesses: (1) The authors do acknowledge right at the end that these are small samples. This is especially the case for the correlational questions. While the limitation is acknowledged at the end, it is not truly acknowledged in the way that the data are interpreted. I.e. much is concluded from absent relationships, where the likelihood of Type II error is high in this scenario. I suggest that throughout the manuscript, authors play down their conclusions about absence of effects.

      Response: We agree with the reviewer that the limitation of small samples should be adequately reflected in the interpretation of the data. We have therefore added cautionary language to the interpretation of the correlational effects in several places of the revised manuscript. For example, we now state: “However, this absence of effects for need ought to be interpreted with caution, given the comparatively small sample size.” (pg. 33) and “As mentioned above, we cannot rule out the possibility that null findings may be due to the comparatively small sample size and should be interpreted cautiously (also see discussion)” (pg. 34-35).

      (2) I found the results section quite a marathon, and due to its length I started to lose the thread concerning the overarching aims - which had been established so neatly in the introduction. I am unsure whether all of these analyses were necessary for addressing the key questions or whether some were more exploratory. E.g. it's unclear to me what one would have predicted upfront about the decoding analyses.

      Response: We acknowledge and share the reviewer’s concern about the length of the results section and potential loss of clarity. Regarding the decoding analyses, we want to clarify that they were conducted as a sanity check to compare against the results of the univariate analysis. We didn’t have apriori hypotheses regarding these supplemental decoding analysis. We have clarified this issue in the revised version of the manuscript and moved the decoding analyses fully to the supplemental material to streamline the main text. The remaining results reported in the manuscript are indeed all based on apriori, key questions (unless specified otherwise, for example, supplemental analyses for other regions of interest for the sake of completeness). The only exception is the final set of results (Neural markers of merit sensitivity predict merit-related behavioral changes during altruistic choice) which represent posthoc tests to clarify the role of activation in the right temporoparietal junction (rTPJ) in merit-related changes in other-regard in altruistic decisions. While we acknowledge that this is a complex paper, after careful consideration we couldn’t identify any other parts of the results section to remove or report in the supplemental material.

      (3) More specifically, the decoding analyses were intriguing to me. If I understand the authors, they are decoding need vs merit, and need+merit vs control, not the content of these inferences. Do they consider that there is a distributed representation of merit that does not relate to its content but is an abstracted version that applies to all merit judgements? I certainly would not have predicted this and think the analyses raise many questions.

      Response: We thank the reviewer for sharing their thoughts on the decoding analyses and agree that this set of analyses are intriguing, yet raise additional questions, such as the neural computations required to assess content. However, we wish to clarify that the way we view our current results is very much analogous to results obtained from studies of perception in other fields. For example, in the face perception literature, it is often observed that the fusiform face area is uniformly more active, not only when a face (as opposed to an object) is on the screen, but when a compound stimulus consistent of features of a face and other features (e.g. of objects) is on the screen, but participants are instructed to attend to and identify solely the face. Moreover, multivariate activity in the FFA (but not univariate activity) is sufficient to decode the identity of the face. We view the results we report in the manuscript as more akin to the former types of analyses, where any region that is involved in the computation is uniformly more active when attention is directed to judgment-specific features. Unfortunately, the present data are not sufficient to properly answer the latter questions, about which areas enable decoding of specific intensity or identity of merit-related content. Follow-up experiments with a more optimized design are needed. Although interesting, we thus refrain from further discussing the decoding analyses in the manuscript to avoid distracting from the main findings based on the univariate comparison of brain responses observed while participants make merit or need inferences in the social perception task.

      Reviewer #2 (Public Review):

      When people help others is an important psychological and neuroscientific question. It has received much attention from the psychological side, but comparatively less from neuroscience. The paper translates some ideas from a social Psychology domain to neuroscience using a neuroeconomically oriented computational approach. In particular, the paper is concerned with the idea that people help others based on perceptions of merit/deservingness, but also because they require/need help. To this end, the authors conduct two experiments with an overlapping participant pool:

      (1) A social perception task in which people see images of people that have previously been rated on merit and need scales by other participants. In a blockwise fashion, people decide whether the depicted person a) deserves help, b) needs help, and c) whether the person uses both hands (== control condition).

      (2) In an altruism task, people make costly helping decisions by deciding between giving a certain amount of money to themselves or another person. How much the other person needs and deserves the money is manipulated.

      The authors use a sound and robust computational modelling approach for both tasks using evidence accumulation models. They analyse behavioural data for both tasks, showing that the behaviour is indeed influenced, as expected, by the deservingness and the need of the shown people. Neurally, the authors use a block-wise analysis approach to find differences in activity levels across conditions of the social perception task (there is no fMRI data for the other task). The authors do find large activation clusters in areas related to the theory of mind. Interestingly, they also find that activity in TPJ that relates to the deservingness condition correlates with people's deservingness ratings while they do the task, but also with computational parameters related to helping others in the second task, the one that was conducted many months later. Also, some behavioural parameters correlate across the two tasks, suggesting that how deserving of help others are perceived reflects a relatively stable feature that translates into concrete helping decisions later-on.

      The conclusions of the paper are overall well supported by the data.

      Response: We thank the reviewer for the positive evaluation of our study and the comprehensive summary of our main findings. We would like to clarify, though, that we did originally collect fMRI data for the independent altruism task. Unfortunately, due to COVID-19-related interruptions, only 25 participants from the sample that performed the social perception task also completed the fMRI altruism task (see pg. 18). Given the limited sample size and noise level of fMRI data, we moved anything related to the neuroimaging data of the altruism task to the supplemental material (see Note S7) and decided to focus solely on the behavior of the altruism task to address our research objectives. We apologize for any confusion.

      (1) I found that the modelling was done very thoroughly for both tasks. Overall, I had the impression that the methods are very solid with many supplementary analyses. The computational modelling is done very well.

      Response: We are pleased that the reviewer found the computational model sensible.

      (2) A slight caveat, however, regarding this aspect, is that, in my view, the tasks are relatively simplistic, so even the complex computational models do not do as much as they can in the case of more complex paradigms. For example, the bias term in the model seems to correspond to the mean response rate in a very direct way (please correct me if I am wrong).

      Response. We agree that the Bias term relates to mean responding (although it is not the sole possibility: thresholds and starting default biases can also produce changes in mean levels of responding that, without the computational model, are not possible to dissociate). However, we think that the primary value of this parameter comes not from the analysis of the social judgment task (where the reviewer is correct that the bias relates in a quite straightforward way to the mean response rate), but in the relationship of this parameter to the un-contextual generosity response in the altruism task. Here, we find that this general bias term relates not to overall generosity, but rather to the overall weight given to others’ outcomes, a finding that makes sense if the tendency to perceive others as deserving overall yields an increase in overall attention/valuation of their outcomes. Thus, a simple finding in one task relates to a more nuanced finding in another. However, we agree it is important to acknowledge the point raised by the reviewer, and now do so on pg. 20: “It is worth noting that the Bias parameters are strongly associated with (though not the sole determinant of) the mean response rate.”

      (3) Related to the simple tasks: The fMRI data is analysed in a simple block-fashion. This is in my view not appropriate to discern the more subtle neural substrates of merit/need-based decision-making or person perception. Correspondingly, the neural activation patterns (merit > control, need > control) are relatively broad and unspecific. They do not seem to differ in the classic theory of mind regions, which are the focus of the analyses.

      Response: The social perception task is modified from a well-established social inference task (Spunt & Adolphs, 2014; 2015) designed to reliably localize the mentalizing network in the brain. As such, we acknowledge that it is not optimally designed to discern the intrinsic complexities of social perception, or the specific appraisals or computations that yield more or less perception (of need or merit) in a given context. Instead, it was designed to highlight regions that are more generally recruited for performing these social perceptions/inferences.

      We heartily agree with the reviewer that it would be interesting and informative to analyze this task in a trial-wise way, with parametric variation in evidence for each image predicting parametric variation in brain activity. Unfortunately, the timing of this task is not optimal for this kind of an analysis, since trials were presented in rapid and blocked fashion. We were also limited in the amount of time we could devote to this task, since it was collected in conjunction with a number of other tasks as part of a larger effort to detail the neural correlates of social inference (reported elsewhere). Thus, we were not able to introduce the kind of jittered spacing between trials that would have enabled such analysis, despite our own wish to do so. We hope that this work will thus be a motivator for future work designed more specifically to address this interesting question, and now include a statement to this effect on pgs. 2223: “Future research may reveal additional distinctions between merit and need appraisals in trial-wise (compared to our block-wise) fMRI designs.”

      References:

      Spunt, R. P. & Adolphs, R. Validating the Why/How contrast for functional MRI studies of Theory of Mind. Neuroimage 99, 301-311, doi:10.1016/j.neuroimage.2014.05.023 (2014).

      Spunt, R. P. & Adolphs, R. Folk explanations of behavior: a specialized use of a domain-general mechanism. Psychological Science 26, 724-736, doi:10.1177/0956797615569002 (2015).

      (4) However, the relationship between neural signal and behavioural merit sensitivity in TPJ is noteworthy.

      Response: We agree with this assessment and thank the reviewer for their positive assessment; we feel that linking individual differences in merit sensitivity with variance in TPJ activity during merit judgments is one of the key findings of the study.

      (5) The latter is even more the case, as the neural signal and aspects of the behaviour are correlated across subjects with the second task that is conducted much later. Such a correlation is very impressive and suggests that the tasks are sensitive for important individual differences in helping perception/behaviour.

      Response: Again, we share the reviewer’s impression that this finding is more noteworthy for appearing in tasks separated both by considerable conceptual/paradigmatic differences, and by such a long temporal distance. These findings make us particularly excited to follow up on these results in future research.

      (6) That being said, the number of participants in the latter analyses are at the lower end of the number of participants that are these days used for across-participant correlations.

      Response: We fully agree with this assessment. Unfortunately, COVID-related disruptions in data collection, as well as the expiration of grant funds due to the delay, severely limited our ability to complete assessments in a larger sample. Future research needs to replicate these results in a larger sample. We comment on this issue in the discussion on pg. 40. If the editor or reviewer has suggestions for other ways in which we could more fully acknowledge this, we would be happy to include them.

      Reviewer #3 (Public Review):

      Summary:

      The paper aims to provide a neurocomputational account of how social perception translates into prosocial behaviors. Participants first completed a novel social perception task during fMRI scanning, in which they were asked to judge the merit or need of people depicted in different situations. Secondly, a separate altruistic choice task was used to examine how the perception of merit and need influences the weights people place on themselves, others, and fairness when deciding to provide help. Finally, a link between perception and action was drawn in those participants who completed both tasks.

      Strengths:

      The paper is overall very well written and presented, leaving the reader at ease when describing complex methods and results. The approach used by the author is very compelling, as it combines computational modeling of behavior and neuroimaging data analyses. Despite not being able to comment on the computational model, I find the approach used (to disentangle sensitivity and biases, for merit and need) very well described and derived from previous theoretical work. Results are also clearly described and interpreted.

      Response: We thank the reviewer for their positive comments regarding presentation, approach, and content.

      Weaknesses:

      My main concern relates to the selection of the social perception task, which to me is the weakest point. Such weakness has been also addressed by the same authors in the limitation section, and related to the fact that merit and need are evaluated by means of very different cues that rely on different cognitive processes (more abstract thinking for merit than need). I wonder whether and how such difference can bias the overall computational model and interpretation of the results (e.g. ideal you vary merit and need to leave all other aspects invariant).

      Response: We agree with the reviewer on the importance of future research to more fully unpack the differences in this task, and develop better ways to manipulate need and merit in more comparable fashion. However, we point out that the issue of differences in abstractness of cues for need and merit does not actually seem to have a strong influence on the parameters retrieved by the computational model. Participants seem to be equally sensitive to BOTH merit and need information, despite that information deriving from different sources, as evidenced by the fact that the magnitude of the sensitivity parameters for need and merit in the social judgment task were nearly identical, and not statistically distinguishable. Nor were other parameters related to non-decision time or threshold statistically different (see Supplemental Table S2). If our results were driven purely by differences in the difficulty or abstractness of these judgments, we would have expected to see some evidence of this in the computational model, in the form of longer non-decision times, higher thresholds, or both. We do not. Likewise, the neural underpinnings evoked by both need and merit perceptions in this task (in the mentalizing brain network) were comparable. This is not to say that there aren’t real differences in the cues that might signal these quantities in our social perception task - just that there is little direct evidence for this difference in computational parameters or evoked brain responses, and thus it is unlikely that our results (which rely on an analysis of computational parameters) are driven solely by computational model biases, or the inability of the model to adequately assess participant sensitivity to need as opposed to merit.

      A second weakness is related to the sample size which is quite small for study 2. I wonder, given that study 2 fRMI data are not analyzed, whether is possible to recover some of the participants' behavioral results, at least the ones excluded because of bad MR image quality.

      Response: We fully agree with the reviewer that increasing the sample size for the cross-task correlations would be desirable. Unfortunately, the current sample size already presents the maximum of ‘usable’ data; the approach suggested by the reviewer won’t affect the sample size. We used all participants whose behavioral data in the altruism task suggested they were performing the task in good faith and conscientiously.

      Finally, on a theoretical note, I would elaborate more on the distinction of merit and need. These concepts tap into very specific aspects of morality, which I suspect have been widely explored. At the moment I am missing a more elaborate account of this.

      Response: Need and merit are predominantly studied in separate lines of research (Molouki & Bartels, 2020) so there is relatively little theoretical research on the distinction between the two. Consequently, Siemoneit (2023) states that the relation between the concepts of need and merit in allocative distributions remains diffuse. To emphasize the distinct concepts of morality in the introduction we have now added to pg. 3: “Need and deservingness (merit) are two distinct principles of morality. The need principle involves distributing resources to those who require them, irrespective of whether they have earned them, while the "merit principle" focuses on allocating resources based on individuals' deservingness, regardless of their actual need (Wilson, 2003).”

      One of the added values of our paper to the research literature is in adding to the clarification of computational and neural underpinnings of broad concepts like merit and need. To highlight the latter point, we have added the following statement on pg. 5 to the manuscript: “Examining need and merit concurrently in this task will also help clarify the computational and neural underpinnings of related, but distinct concepts, distinguishing between them more effectively.”

      References:

      Molouki, S., & Bartels, D. M. (2020). Are future selves treated like others? Comparing determinants and levels of intrapersonal and interpersonal allocations. Cognition, 196, 104150.

      Siemoneit, A. (2023). Merit first, need and equality second: hierarchies of justice. International Review of Economics, 70(4), 537-567.

      Wilson, C. (2003). The role of a merit principle in distributive justice. The Journal of ethics, 7, 277-314.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I acknowledge the difficulty with respect to recruitment, especially in the age of covid, but is it possible for the authors to collect larger samples for their behavioural questions via online testing? Admittedly, I'm sure they don't want to wait 300 days to have the complete dataset, but I would be in favour of collecting a sample in the hundreds on these behavioural tasks, completed at a much shorter separation (if any). I believe this would strengthen the authors' conclusions considerably if they could both replicate the effects they have and check these null effects in a sample where they could draw conclusions from them. Indeed, Bayesian stats to provide evidence for the null would also help here.

      Response: We share the reviewer’s desire to see these results replicated (ideally in a sample of hundreds of participants). We have seriously considered the possibility of trying to replicate our results online, even before submitting the first version of the paper. However, it is difficult to fully replicate this paradigm online, given the elaborate story and context we engaged in to convince participants that they were playing with real others, as well as the usage of physical pain (Cold Pressor Task) for the need manipulation in the altruism task. Moreover, given comments by this reviewer that the results are already a little long, adding a new, behavioral replication would likely only add to the memory burden for the reader. We have thus opted not to include a replication study in the current work. However, we are actively working on a replication that can be completed online, using a modified experimental paradigm and different ways to manipulate need and merit. Because of the differences between that paradigm and the one described here, which would require considerable additional exposition, we have opted not to include the results of this work in the current paper. We hope to be able to publish this work as a separate, replication attempt in the future.

      Given the difficulty of wading through the results section while keeping track of the key question being answered, I would suggest moving any analyses that are less central to the supplementary. And perhaps adding some more guiding sentences at the start and end of each section to remind the reader how each informs the core question.

      Response: We deliberated for quite some time about what results could be removed, but in the end, felt that nearly all results that we already described need to be included in the paper, since each piece of the puzzle contributes to the central finding (relating parameters and behavior to neural and choice data across two separate tasks). However, we did move the decoding analysis results to the supplemental (see point below). We also take the reviewers point that the results can be made clearer. We thus have worked to include some guiding sentences at the start and end of sections to remind the readers how each analysis informs the core questions.

      I think it needs unpacking more for the reader what they should conclude from the significant need+merit vs control decoding analyses, and what they would have expected in terms of cortical representation from the decoding analyses in general.

      Response: We agree with the reviewer that given the decoding results position in the main manuscript it would need unpacking. After considering the reviewer's prior suggestion, we have reevaluated the placement of these supplemental results. Consequently, we have relocated it to the supplemental materials, as it was deemed less relevant to directly addressing the core research questions in the main manuscript. On pg. 23, the main manuscript now only states “We also employed supplemental multivariate decoding analyses (searchlight analysis 85-87), as commonly used in social perception and neuroscience research 7,58,82,88,89, corroborating our univariate findings (see Supplemental Note S6, Supplemental Table S10).”

      Reviewer #2 (Recommendations For The Authors):

      (1) I would suggest moving information on how the computational models were fitted to the main text.

      Response: The computational models are a key element of the paper and we deliberated about the more central exposure of the description of how the models were fitted in the main manuscript. However, we are concerned about the complexity and length of the article, which requires quite a lot from readers to keep in mind (as also commented on by reviewer 1). Those readers who are particularly interested in details of model fitting can still find an extensive discussion of the procedures we followed in the supplements. We thus have opted to retain the streamlined presentation in the main manuscript. However, if the editor feels that including the full and extensive description of model fitting in the main paper would significantly improve the flow and exposition of ideas, we are happy to do so.

      (2) For the fMRI analyses: Could it be worth analysing the choices in the different conditions? They could be modelled as a binary regressor (yes/no) and this one might be different across conditions (merit/need/hands). Maybe this won't work because of the tight trial timeline, but it could be another avenue to discern differences across fMRI conditions.

      Response: We thank the reviewer for this interesting suggestion! Unfortunately, the block design and rapid presentation of stimuli within each condition make it challenging to distinguish the different choices (within or across conditions). While we see the merit in the suggested analytical approach (in fact, we discussed it before the initial submission of the article), it would require some modifications of the task structure (e.g., longer inter-trial-intervals between individual stimuli) and an independent replication fMRI study. We were not able to have such a long inter-trial interval in the original design due to practical constraints on the inclusion of this paradigm in a larger effort to examine a wide variety of social judgment and inference tasks. We hope to investigate this kind of question in greater detail in future fMRI work.

      (3) The merit effects seem to be more stable across time than the need conditions. Would it be worthwhile to test if the tasks entailed a similar amount of merit and need variation? Maybe one variable varied more than the other in the task design, and that is why one type of effect might be stronger than the other?

      Response: We thank the reviewer for drawing attention to this important point. We used extensive pilot testing to select the stimuli for the social perception task, ensuring an overall similar amount of need and merit variation. For example, the social perception ratings of the independent, normative sample suggest that the social perception task entails a similar amount of need and merit variation (normative participant-specific percentage of yes responses for merit (mean ± standard deviation: 53.95 ± 13.87) and need (45.65 ± 11.07)). The results of a supplemental paired t-test (p = 0.122) indicate comparable SD for need and merit judgments. Moreover, regarding the actual fMRI participant sample, Figure S3 illustrates comparable levels of variations in need and merit perceptions (participant-specific percentage of yes responses for merit (56.70 ± 11.91) and need (48.69 ± 10.81) in the social perception task). Matching the results for the normative sample, the results of a paired t-test (p = 0.705) suggest no significant difference in variation between need and merit judgments. With respect to the altruism task, we manipulated the levels of merit and need externally (high vs. low).

      Reviewer #3 (Recommendations For The Authors):

      (1) It would be good to provide the demographics of each remaining sample.

      Response: We appreciate the attention to detail and agree with the reviewer’s suggestion. We have now added the demographics for each remaining sample to the revised manuscript.

      (2) The time range from study 1 to study 2, is quite diverse. Did you use it as a regressor of no interest?

      Response: We thank the reviewer for this interesting suggestion. We have examined this in detail in the context of our cross-task analyses (i.e., via regressions and partial correlations). Interestingly, variance in the temporal delay between both tasks does not account for any meaningful variation, and results don’t qualitatively change controlling for this factor.

      For example, when we controlled for the delay between both separate tasks (partial correlation analysis), we confirmed that variance in merit sensitivity (social perception task) still reflected meritinduced changes in overall generosity (altruism task; p = 0.020). Moreover, we confirmed that variance in merit sensitivity reflected individuals’ other-regard (p = 0.035) and self-regard (p = 0.040), but not fairness considerations (p = 0.764) guiding altruistic choices. Regarding people’s general tendency to perceive others as deserving, we found that the link between merit bias (social perception task) and overall other-regard (p = 0.008) and fairness consideration (p = 0.014) (altruism task) holds when controlling for the time range (no significant relationship between merit bias and self-regard, p = 0.191, matching results of the main paper).

      We refer to these supplemental analyses in the revised manuscript on ps. 33 and 35: “Results were qualitatively similar when statistically controlling for the delay between both tasks (partial correlations).”

      (3) Why in study 1 a dichotomous answer has been used? Would not have been better (also for modeling) a continuous variable (VAS)?

      Response: We appreciate the reviewer's thoughtful feedback. In Study 1, opting for a dichotomous response format in the social perception task (Figure 1a) was a deliberate methodological choice. This decision, driven by the study's model requirements, aligns with the common use of a computational model employing two-alternative forced choices ("yes" and "no") as decision boundaries. While drift– diffusion models for multiple-alternative forced-choice designs exist, our study's novel research questions were effectively addressed without their complexity. Finally, our model cannot accept continuous response variables as input unless they are transformed into categorical variables.

      (4) In the fMRI analyses, when you assess changes in brain activity as a function of merit, I would control for need (and the other way round), to see whether such association is specific.

      Response: Regarding the reviewer’s suggestion on controlling for need when assessing changes in brain activity as a function of merit, and vice versa, we would like to clarify the nature of our fMRI analyses in the social perception task. Our focus is on block-wise assessments (need vs. control, merit vs. control, need vs. merit blocks, following the fMRI task design from which our social perception task was modified from). We don’t assess changes in brain activity as a function of the level of perceived merit or need (i.e., “yes” vs. “no” trials within or across task blocks). Blocks are clearly defined by the task instruction given to participants prior to each block (i.e., need, merit, or control judgments). Thus, unfortunately, given the short inter-stimulus-intervals of each block, the task design is not optimal to implement the suggested approach.

    1. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity): Summary:

      This research article describes genetic identification and expression analyses of six Ephrin type-B receptor 4 (EPHB4) variants identified in patients with dilated cardiomyopathy (DCM). Variants were identified Variants were identified in a cohort of 573 patients enrolled through the multicenter DZHK-TORCH (TranslatiOnal Registry for CardiomyopatHies) study and the Institute for Cardiomyopathies Heidelberg registry. Expression of downstream molecules, CAV1 and CD36, was assessed in human cardiac tissues by immunohistochemistry. EPHB4 cardiac expression was assessed using recently published single-cell/nucleus RNA sequencing data (Nicin et al 2022) incorporating siRNA-seq data from two other studies (healthy cardiac tissue, Litvinukova et al 2020) and (hypertrophic/aortic stenosis, Nicin et al. 2020).

      We thank the reviewer for the recommendations that have improved our manuscript.

      Major Comments:

      1. Details of identified truncating RBM20 and TTN variants must be provided. These should be integrated into Table 1 alongside each co-occurring EPHB4 variant. List whether the TTN truncating variant is located in the A-band and whether these variants would be adjudicated as pathogenic/likely pathogenic, variant of uncertain significance by ACMG and/or similarly refined DCM criteria (Morales et al. 2020, Circ-Genom Precis Med).

      Details of the truncating RNM20 and TTN have been provided in the new supplementary table 1. As indicated in the table both mutations are pathogenic, and thus, most probable the cause for the disease in these patients. In case of TTN this is a truncating variant and is located in the M-band in exon 358, which is annotated with a PSI in DCM of 100% in cardiodb.org. The fact that these mutations are most probably the cause for DCM in these patients has been included in the discussion section and reads as follows:

      Although it is most probable that in the case of the patients carrying TNN and RNM20 variants this would be the cause of the disease, this study further supports, the importance of EPHB4 regulating CD36 caveolar trafficking to the membrane, whether this happens in endothelial cells or cardiomyocytes, maintaining cardiac homeostasis in humans and its implication on DCM

      1. Discuss co-occurrence of multiple EPHB4 variants in two patients (DCM1, DCM3) and identification of 2 EPHB4 variants in more than one proband.

      As shown in Figure 1A, the detected variants are found in multiple domains of the protein, hence no clear hotspot is detected. We did not yet investigate on the exact mechanisms of action, however, when we compare the two patients with multiple EPHB4 variants, the average LVEF (echo) is 17.5 compared to 38,67 for the remaining 4 patients with only one EPHB4 variant and 35,17 for the six non-EPHB4 variant-carriers. Although the sample number only allows for a semi-quantitively analysis, it still hints at a possible EPHB4-variant effect, which certainly needs verification in a larger cohort.

      Since we do not postulate the detected variants as independently disease-causing, and we also did not explicitly filter for very rare variants, it is not surprising that we find two variants in multiple patients. As stated above, we did not investigate this further, but evidence is growing that compound heterozygosity is playing a role in heritable diseases. It will be interesting to analyze e.g. phasing (Hofmeister et al., Nature Genetics, 2023) or additive (biallelic) effects, which have come to attention also in cardiomyopathies recently (Lipov et al., Nature Cardiovascular Research, 2023).

      This fact has now been included in the manuscript, both in the results and in the discussion. It reads as follows:

      Interestingly, two of the analysed patients present more than one variant of EPHB4 and we could identify the same variant in more than one patient (Table 1)

      (…)

      Nevertheless, two of the patients carrying one benign or likely benign also carry another variant classified as likely pathogenic or of uncertain significance (Table 1) and interestingly, the average LVEF of the two patients with multiple EPHB4 variants is 17.5 compared to 38,67 for the remaining 4 patients with only one EPHB4 variant and 35,17 for the six non-EPHB4 variant-carriers. Although the sample number only allows for a semi-quantitively analysis, it still hints at a possible EPHB4-variant effect, which certainly needs verification in a larger cohort.

      1. Three of the six variants (p.Lys635Asn, Val113Ile, Glu890Asp) are classified as Clinvar Benign/Likely Benign. Additionally, p.Glu890Asp has been identified in 50 homozygotes in gnomAD non-Finnish European population. These data cast doubt on the pathogenicity of these variants. These classifications, as well as VUS classification of p.Pro79Leu, should be listed in Table 1. The authors should reconcile the benign/likely benign Clinvar classifications with their presented evidence for pathogenicity in the discussion.

      We have now included the ACMG classification in Table 1. Similar to the Clinvar classification, some of the variants are classified benign or likely benign. Still, the fact that the patients that carry them also carry another variant and that the histological findings are similar among the patients carrying an EPHB4 variant and different to those that don’t and the enriched presence of EPHB4 variants in the DCM population support our hypothesis that the Eph-ephrin signalling pathway plays a role in the development of DCM.

      Nevertheless, we agree with the reviewer that the fact that some of these variants have been classified as benign, and the presence of mutations in other genes already related to DCM like TNN or RMM20 may suggest that the EPHB4 mutations may not be the only cause for the disease but rather have an additive effect. As a consequence, we have toned down our conclusions and the discussion reads now as follows:

      Finally, although not as the main disease cause, this study not only supports the role of EPHB4 in the heart, but it also corroborates the importance of CD36 and CAV1 for the cardiac health, and has the potential to improve diagnosis and risk stratification tools for DCM. In addition, as other genes crucial for fatty acid transport may be involved in cardiac disease, this study may help identify new diagnostic or therapeutic targets.

      1. CD36 and CAV1 expression are not quantified. Qualitatively, it is difficult to confirm CD36 reduction in DCM and disruption in EPHB4 variant samples as imaging parameters are not specified and do not appear to be standardized across treatments. Clearly state (either in the figure legend or in the methods) whether identical imaging parameters were used across panels 1C-1E. Note any differences in these parameters.

      We have now quantified the two IHC. It is very clear that the total CD36 is significantly reduced in both groups when compared to the healthy donor (Figure for the reviewer 1A). In case of CAV1 this is not so evident, although the signal seems reduced this is not significant (Figure for the reviewer 1B). These new data have been included in the figure of the manuscript.

      Figure for the reviewer 1. Quantification of (A) CD36 and (B) CAV1 in the immunohistochemistry analysis of patients biopsies. Data shown as mean ± SEM. (A) P value was calculated using one sample one sample Wilcoxon test for DCM and one sample t test for DCM EPHB4. Both cohorts where compared to the mean of HD. P value < 0.05 was considered significant. (B) P value was calculated with one sample t test for both cohorts. P value < 0.05 was considered significant.

      All the images have been taken in the same conditions. The observed difference in the background is due to the disease conditions of the DCM samples. Furthermore, the apparent reduced number of capillaries observed in the DCM patients are caused by the hypertrophic state of the cardiomyocytes in the diseased state. These are bigger and thus, less cells and capillaries appear per picture. The parameters have been included in the methods and read as follow:

      Immunohistochemistry was imaged in a Leica Stellaris confocal microscope. All images were obtained with 63x magnification and the same laser and gain intensities. Images were acquired using the software LAS X (Leica, version 4.4) and quantified using the Volocity Software (Quorum Technologies, version 6.5.1)

      1. Why was EPHB4 membrane localization not assessed or reported?

      We agree with the reviewer that this would be a very interesting point. Unfortunately, we had very limited amount of material and we did not have a proper working antibody.

      1. A key finding of the manuscript is that all six variants produce similar histological impacts on CAV1 and CD36 expression, denoting downstream impacts of EPHB4 genetic disruption. There is no granular data presented to support this claim. Additional discussion is also required to address how the authors anticipate variants in functionally distinct domains on either side of the plasma membrane to similarly impact downstream expression of CAV1/CD36. Mapping to available crystal structures in the Protein Data Bank (PDB) may be insightful to determine which variants may be most likely to have an impact on heterotetramer formation or to exert dominant negative effects on receptor function.

      As an appendix to this revision we have included a figure with representation images of all biopsies analysed to support our claim.

      The whole protein structure is not solved and only some individual domains are present in the Protein Data Bank making difficult to analyse the effect on the tetramer without crystallising the whole protein.

      1. Study limitations are not discussed and are significant. 5 of the 6 samples were from male patients, there are limitations to analyses of non-diverse patient ancestry, there is uncertainty regarding pathogenic contributions of variants in established DCM genes in 2/6 patients, data is limited to expression-only analyses highlighting need for additional functional modeling in cell or animal based systems.

      We have now included a limitation sections that includes all the points raised by the reviewer. It reads as follows:

      Although this study offers valuable insights to the potential implication of the Eph-ephrin signalling pathway in the development of DCM it has some limitations that need to be discussed. Despite finding increased presence of EPHB4 variants in the DCM population when compared to the healthy population, analysis of the identified variants in using different classifications (CADD and ACMG) not always predicted pathogenicity for these variants. For this reason, further experiments should be performed to determine the effect of every variant.

      It is also important to note that given the lower number of patients analysed these are not age and gender matched. The EPHB4 carrying DCM patients were younger than the DCM patients carrying a wild type EPHB4 sequence and mainly male. Finally, no biomaterial nor genetic testing from family related patients is available.

      1. Language used in conclusions overstates study findings ["our results confirm a crucial role of the Eph-ephrin signaling pathway in DCM" (page 3), "this study not only confirms the crucial role of EPHB4 in the heart..." (page 8)]. Change to "suggest" or "support".

      We have revised our discussion according to the limitations discussed in the previous remark and these words have been corrected.

      Major Methods Comments:

      1. DCM diagnostic criteria (clinical and imaging) for inclusion in the DZHK-TORCH study and the Institute for Cardiomyopathies Heidelberg registry should be stated or referenced. Likewise, describe and/or reference DCM exclusion criteria. State any relevant differences in DCM enrolment criteria for the two registries.

      We have now included our inclusion criteria in the methods and include two references to support this. The paragraph reads as follows:

      The criteria to be included in the study was reduced left ventricular ejection fraction (LVEF) <50% validated either with two independent image techniques or at two different time points with the same imaging technique. Furthermore, patients should include left ventricular dilation (LVEDD) >117% corrected with age and body surface according to the Henry-Formel formula (LVEDD= 45,3 * BSA1/3 – 0,03*Age –7,2). In both cases the heart were analysed either by echocardiography or magnetic resonance tomography (MRT)

      1. Describe how the final cohort of 573 DCM patients was reached. (All patients with DCM in the DZHK-TORCH study/Heidelberg registry? All patients with available exome data meeting QC standards and having available cardiac tissue?).

      From the 573 DCM patients, 100 have been recruited as part of the DZHK-TORCH registry and have been genome sequenced. Further 62 genomes and 411 exomes have been sequenced from patients of the cohort from the Institute for Cardiomyopathies (ICH) at the Heidelberg University Hospital.

      From this cohort, we selected 6 patients with and 6 without an EPHB4 variant and received heart tissue slides from the pathology department.

      1. State whether any family/segregation data is available for these patients.

      DCM4 has a mother and aunt (mother’s sister) who are also affected by CMP. In case of DCM6, the mother was also diagnosed with CMP. Unfortunately, no further biomaterial nor genetic testing of those individuals is available. This has been included in the new limitation sections as described above.

      1. Description of genetic testing methods are inadequate. Describe how genetic analyses were completed for each study/registry and how results were filtered/quality controlled. If sequencing methods were different across registries, state which patients were tested by which methods. If any testing was gene-targeted rather than whole exome/genome, list the specific DCM genes tested.

      All data has been sequenced using Illumina paired-end technology with either 2x100bp or 2x150bp. Exome enrichment was achieved using SureSelect Human All Exon V6 Target Enrichment (Agilent Genomics) was used. Bioinformatics analysis pipeline was based on “Best Practices Guideline” from the Genome Analysis Toolkit (GATK) (https://gatk.broadinstitute.org/hc/en-us). Besides the analysis for EPHB4, we assessed further genes associated with cardiomyopathies (ACTC1, ACTN2, ALPK3, BAG3, CRYAB, CSRP3, DES, DMD, DSC2, DSG2, DSP, FLNC, GLA, HCN4, HRAS, JPH2, JUP, KRAS, LAMP2, LDB3, LMNA, MIB1, MYBPC3, MYH7, MYL2, MYL3, MYPN, NEXN, PKP2, PLN, PRDM16, PRKAG2, PTPN11, RAF1, RBM20, RYR2, SCN5A, SHOC2, TAZ, TMEM43, TNNC1, TNNI3, TNNT2, TPM1,TTN, TTR, VCL).

      This is information has been included in the methods section.

      1. Provide additional detail for human cardiac biopsies. Was the same chamber/tissue biopsied in all samples? Is an endomyocardial biopsy available for all 573 patients included in this study? If not, were additional EPHB4 variants identified in patients without biopsy samples?

      All biopsies investigated are from left-ventricular tissue, accessed during cardiac catheterization.

      We did find additional, mainly non-coding variants in the cohort. However, as the focus on the study was on the histological analysis of the CD36 and CAV1 expression, we did restrict our analysis to our selected samples as described in the response to comment 2.

      1. Describe the source of the healthy control biopsy, alongside brief clinical detail establishing suitability as a control. Did DCM controls carry variants in known DCM genes (including truncating variants in RBM20 or TTN)? How were DCM controls selected?

      The healthy control biopsy was kindly donated by Prof. Dettmeyer from the University Gießen. This is a postmortem sample with unrelated cause of death. Cardiac biopsy was examined to discard any pathological alterations. This sample originates from a 27 years old female, and thus ideal as a healthy sample. This information has been included in the methods.

      1. List statistical analyses and associated experiments. (Page 5).

      Statistical tests have been included in the figure legend of each experiment. This reads as follows:

      (B) EPHB4 variant allelle frequency analysis. Each variant is compared in a paired wise manner between the two population. P value was calculated with a paired one-tailed Student’s t test comparing the frequencies of the different variants in the two populations.

      And

      (F) Quantification of CD36 and CAV1 expression in the immunohistochemistry analysis of patients biopsies. Data shown as mean ± SEM. In the case of CD36, P value was calculated using one sample one sample Wilcoxon test for DCM and one sample t test for DCM EPHB4. P value < 0.05 was considered significant. In the case of CAV1, P value was calculated with one sample t test for both cohorts. P value < 0.05 was considered significant. In both cases, the cohorts where compared to the mean of HD.

      1. List microscopes/equipment and software used to complete immunohistochemistry experiments. Describe imaging parameters to facilitate comparisons between treatments in Figure 1C-E.

      Immunohistochemistry was imaged in a Leica Stellaris confocal microscope. All images were obtained with 63x magnification and the same laser and gain intensities. Images were acquired using the software LAS X (Leica, version 4.4) and quantified using the Volocity Software (Quorum Technologies, version 6.5.1)

      This paragraph has now been included in the methods section.

      1. Please reword the following passage, which is almost verbatim to the same passage in Nicin et al. 2022.

      Page 4

      **"In brief, a combination of two human snRNA-seq datasets was used. Data from healthy cardiac tissue from the septum of 14 individuals in the Litvinukova et al. study and data from location-matched hypertrophic cardiac tissues from five patients with aortic stenosis."

      Nicin et al. 2022 (https://doi.org/10.1038/s44161-022-00019-7)**

      "Two human snRNA-seq datasets were used: data from healthy cardiac tissue from the septum of 14 individuals in the Litvinukova et al. study and data from location-matched hypertrophic cardiac tissues from five patients with aortic stenosis."

      We have reworded the paragraph in the methods sections. Now it reads as follows:

      Healthy cardiac tissue data was derived from the cardiac septum of 14 individuals 15. Subsequently, it was integrated with data from the septum of hypertrophc cardiac tissue from 5 patients with aortic stenosis 16.

      Minor Comments:

      1. Results: List source for Non-Finnish European Control cohort (gnomAD) (Page 5).

      The Non-Finnish European Control cohort (gnomAD) was obtained from https://gnomad.broadinstitute.org/. This information has been included in the methods section.

      1. Discussion: "all DCM patients" (page 6) requires clarification.

      We have made clear that this refers to the patients analysed in this study. The new sentence reads as follows:

      Furthermore, our results stress the importance of the endothelial CD36 in the onset of cardiac disease as all DCM patients analysed by immunohistochemistry show a downregulation of CD36 in the endothelium and warrant a more detailed assessment of genes involved in vascular function20

      1. Discussion: Define acronyms. CSF, IL4, LPS (Page 7)

      We have defined the acronyms in the discussion. The new sentence reads as follows:

      CD36 expression is upregulated by the nuclear hormone transcription factor Peroxisome Proliferator-Activated Receptor-Gamma (PPAR-ɣ), cerebrospinal fluid (CSF) cytokines and Interleukin-4 (IL4). In the other hand, lipopolysaccharides (LPS) and dexamethasone downregulate its expression In microvascular endothelial cells, CD36 is downregulated by lysophosphatidic acid.

      1. Table 1. Table is confusingly arranged. It would make more sense to organize the table by cDNA/AAchange to better correspond to Figure 1A. List the impacted protein domain for each variant in a separate column. It is also unclear how DCM allele frequencies were calculated as the reported number of patients (DCM1-6) carrying each variant do not universally correspond to the listed allele frequencies (see AFs of 0.0052 and 0.0208). Clarification should be added to the legend so it is clear to the reader how these frequencies were determined

      In case of the EPHB4 variants table, we agree with the reviewer and to make the table more understandable we have removed the first three columns, which are the same for all variants. This information has been included in the table legend. Nevertheless, this information has been kept in the new Supplementary table 1 that contains the variants on the other DCM causing genes.

      Regarding the calculation of the allele frequency we made by dividing the number of alleles found in the population by the total number of alleles in the population. This information has been included in the methods.

      We want to note that we performed a mistake in the original table. We had calculated the frequencies by dividing the number of alleles by the number of individuals in the population. We have now corrected both Table 1 and Figure 1B.

      1. Figure 1B. Add variant labels. Indicate relevant p-values for each variant. It is unclear to which comparison the p = 0.024 belongs. State in legend that 2 variants were omitted (presumably due to absence from gnomAD)

      No variants were omitted in the representation of Figure 1B. Some of them have the same allele frequency in the DCM population and thus, the individual data points appear overlapping. The variants that were not detected in the genomAD population were considered as 0 for the representation and for the analysis.

      For the comparison with P=0.024 (now corrected to 0.0011) between the two groups we have performed a one tail paired t test comparing the frequencies in both populations. The information regarding the test has been included in the figure legend and included in the methods section as indicated above.

      1. Figure 1E. Add label to indicate which EPHB4 variant is depicted.

      The DCM sample from which the images originates is now indicated in Figure 1E.

      <br /> Referees cross-commenting****

      As is, this manuscript is not ready for publication. Our comments are in complete alignment. Like the other reviewer, I also emphasize the need for other DCM genes tested to be listed. I also reiterate that any similarly worded passages to other published material must be corrected

      Reviewer #1 (Significance): This study presents genetic and expression data on a novel DCM gene candidate (EPHB4) from a European cohort of 573 DCM patients. This work is of interest as much of genetic DCM remains unexplained and identification of novel genes and pathways will be critical to advance understanding of the disease and to develop novel treatments. Reported data will be of greatest interest to cardiovascular practitioners and translational/basic researchers working with genetic heart disease/DCM. The fact that cardiac tissue was available for histological analyses for all six patients is an asset. There are considerable weaknesses to the paper, as written. There is a lack of detail in the included genetic methods and results. While the premise of the study is intriguing, additional detail is required for identified TTN and RBM20 truncating variants and additional discussion is needed to resolve confusion regarding reported allele frequencies and benign/likely benign Clinvar classifications. Because study design is restricted to genetic and expression analyses, reported data do not address possible pathogenic mechanisms. Overall, there is insufficient data presented to confirm a role for EPHB4 in causing DCM. Manuscript-specific (as-opposed to study specific) weaknesses include insufficient methods detail, a lack of clarity in the presented genetic and expression data (particularly Figure 1), insufficiently described study limitations, and overstated study conclusions. These scientific and manuscript issues will need to be addressed for the manuscript to be suitable for publication.

      Reviewer fields of expertise: cardiovascular genetics, DCM.

      Insufficient expertise to evaluate statistical methods.

      Reviewer #2 (Evidence, reproducibility and clarity):<br /> I reviewed a paper by Luxan et al. describing EPHB4 variants as a novel disease gene for dilated cardiomyopathy (DCM).

      The short report is interesting, however, not enough evidence is given to convince me EPHB4 is indeed a novel disease gene for DCM. More work is needed before this can be published.

      Major points:

      1. Genetics: two individuals have EPHB4 variants together with DCM causing TTN tv or RBM20 variants. Which other DCM genes were excluded for the remaining four cases? GnomAD MAF of 0.008748404 suspiciously high.

      So overall the small case number makes it hard to judge whether these are truly pathogenic variants.<br /> Could the authors attempt co-segregation of DCM with EPHB4 variant in families?

      Unfortunately we do not have family information from these patients. We have included this in the new limitation sections in the discussion that reads as follows:

      Although this study offers valuable insights to the potential implication of the Eph-ephrin signalling pathway in the development of DCM it has some limitations that need to be discussed. Despite finding increased presence of EPHB4 variants in the DCM population when compared to the healthy population, analysis of the identified variants in using different classifications (CADD and ACMG) not always predicted pathogenicity for these variants. For this reason, further experiments should be performed to determine the effect of every variant.

      It is also important to note that given the lower number of patients analysed these are not age and gender matched. The EPHB4 carrying DCM patients were younger than the DCM patients carrying a wild type EPHB4 sequence and mainly male. Finally, no biomaterial nor genetic testing from family related patients is available.

      1. Only CADD tools was used for pathogenicity, several tools should be used. Is the structure solved? Structural predictions on the consequences of the variants should be done.

      We have now included the ACMG classification in Table 1. As discussed above in the comments of Reviewer 1, some of the variants are classified as benign or likely benign. For this reason we have now toned down our conclusion suggesting that the EPHB4 may not be sufficient to trigger DCM but act as modifiers. This is supported by the fact that the histological analysis revealed that the patients carrying EPHB4 variants are similar among themselves and different to the other patients. Furthermore, our hypothesis is also supported by the fact that those patients carrying benign or potentially benign variants also carry another variant and the fact that they even have lower LVEF. The new classification has been included in the results and discussion sections and it reads as follows:

      Nevertheless, the classification of the variants according to the American College of Medical Genetics (ACMG) 25 suggests that two of the variants are benign, two likely benign, one likely pathogenic and one variant of uncertain significance (Table1). Nevertheless, two of the patients carrying one benign or likely benign also carry another variant classified as likely pathogenic or of uncertain significance (Table 1) and interestingly, the average LVEF of the two patients with multiple EPHB4 variants is 17.5 compared to 38,67 for the remaining 4 patients with only one EPHB4 variant and 35,17 for the six non-EPHB4 variant-carriers. Although the sample number only allows for a semi-quantitively analysis, it still hints at a possible EPHB4-variant effect, which certainly needs verification in a larger cohort.

      And

      Our analysis identified several variants in EPHB4 enriched in a cohort of DCM patients. According to the CADD score prediction, all these variants have a deleterious potential. Nevertheless, the ACMG classified some of the variants as benign or potentially benign. Also the fact, that one variant has identified in two non-related patients suggests that this variant may be benign for the protein. Nevertheless, two of the patients carrying a benign or potentially benign variant also carried another potentially pathogenic or of uncertain significance. During Eph-ephrin signalling, the binding of the ligand induces Eph receptor heterotetramers to initiate the signalling via Eph–Eph cis interactions30. Thus, variant EPHB4 molecules could have a dominant negative effect on these heterotetramers, and while maybe not completely abrogating its function, reducing the functionality of the heterotetramers. This observation could explain why the presence of one variant copy in the DCM patients of our cohort would be sufficient to reduce the activity of the Eph-ephrin signalling pathway. Although this shows that some of the variants may indeed not be the sole cause for DCM it shows that the Eph-ephrin signalling pathway, and in particular EPHB4 may be important for the development of DCM.

      Only parts of the protein have been resolved and present in the Protein Data Base.

      1. The microscopy Figure 1C-E is not convicing. Only one sample shown while 6 were available/investigated. I would not be comfortable to identify cardiomyocytes/endothelial cells from these sections

      As an appendix to this document, we included figures with images obtained from all the analysed patients. These were not included on the original figure for space reasons.

      These sections are perfect to identify cardiomyocytes and endothelial cells in cardiac tissue. First, endothelial cells, that form the microvasculature are labelled with ULEX, a well known marker of endothelial cells. Secondly, cardiomyocytes are really big cells easy to score for their size and location between the capillaries in the heart. Other cells present in the heart, like fibroblasts, macrophages, or pericytes would also be located in the space left in between cardiomyocytes but would need to be labelled for visualization. We believe that our interpretation of the immunohistochemistry pictures is correct.

      1. Functional work is needed to understand the interplay between EPHB4, CAV1 and CD36. Such as transfecting mutant EPHB4 into cells and probing for altered localisation/attachment of binding partners, most likely in endothelial - cardiomyocyte co-culture systems.

      Our study is based in our previous murine study in which we showed that the deletion of EphB4 or its ligand ephrinB2 would induce a phenotype similar to DCM in mice. At the molecular level, defects in the Ephb4 are linked to compromised caveolar function and reduced CAV1 phosphorylation, which involves the kinase Src, a known mediator of Eph receptor signalling. In the healthy heart, caveolar transport is required for the membrane translocation and correct function of fatty acid translocase FAT/CD36, which mediates the uptake of fatty acids. The objective of this follow up study was to study whether we could identify EPHB4 mutations in DCM patients. As seen in the results we have observed that there is an enrichment of EPHB4 variants in the DCM population. We think that the previous study supports our conclusions and hope that the reviewer agrees with us. Nevertheless, we agree with the reviewer that functional assays could be performed with every variant. We have included this in the new limitation sections of the manuscript described above.

      Minor points:

      1. Figure 1B does not make sense

      Figure 1B confirms the enrichment of EPHB4 mutations in the DCM population. We have corrected the labelling to make this clearer. We have now labelled the figure “EPHB4 variant allele frequency in control and DCM population”.

      1. Statistics: Which tests were performed, if normality tests were applied, which one was used?

      The tests used for every comparison are included in the figure legend. In case of EPHB4 variant allele frequency, we performed a paired one-tailed Student’s t test comparing the frequencies of the different variants in the two populations. In case of the CD36 and CAV1 quantifications, we performed a two-tailed one sample t test. In this case, we compare the expression of CD36 and CAV1 to an hypothetical healthy population with mean equal 1 as que have used this value for normalization.

      1. Please do not use contractions, e.g. 'can't' in discussion section

      Contractions have been removed from the manuscript.

      <br /> Referees cross-commenting****

      Overall I agree with the other reviewer on the points raised.

      Reviewer #2 (Significance): Description of EPHB4 as a novel DCM gene is of interest, but the current data are not convincing enough to make this statement.

      Mechanistic work on the interplay of endothelial cells and cardiomyocytes and consequences of EPHB4 variants would make it a very compelling story.

      Reviewer #3 (Evidence, reproducibility and clarity): Summary:

      The authors of this manuscript studied the prevalence of a population of Ephrin type-B receptor 4 (EPHB4) in a cohort of 573 DCM patients and found six new EPHB4 variants, possibly pathogenic based on the Combined Annotation Dependent Depletion (CADD) score and population frequency. Moreover, the authors perform immunofluorescence (IF) and histologic analysis on 6 EPHB4 variant carrying DCM patients, 6 DCM patients with wild type EPHB4 and one healthy control biopsy and found dysregulation of Caveolin 1 (CAV1) and CD36 (which are implicated in fatty acid transport in endothelial cells and cardiomyocytes) in both groups of DCM patients.

      Major comments:

      • Additional experiments are necessary to prove the hypothesis: for example, co-IF staining with endothelial markers should be provided. IF should be supported by western blots and qPCR.

      The objective of this study was to explore whether we could identify EPHB4 mutants in a DCM cohort. Interestingly we have shown that EPHB4 mutations are enriched in the DCM population when compared to the general population. Nevertheless, we agree with the reviewer that a more in depth mechanistic study would improve the significance of the study. We have included a limitations section that reads as follows:

      Although this study offers valuable insights to the potential implication of the Eph-ephrin signalling pathway in the development of DCM it has some limitations that need to be discussed. Despite finding increased presence of EPHB4 variants in the DCM population when compared to the healthy population, analysis of the identified variants in using different classifications (CADD and ACMG) not always predicted pathogenicity for these variants. For this reason, further experiments should be performed to determine the effect of every variant.

      It is also important to note that given the lower number of patients analysed these are not age and gender matched. The EPHB4 carrying DCM patients were younger than the DCM patients carrying a wild type EPHB4 sequence and mainly male. Finally, no biomaterial nor genetic testing from family related patients is available.

      • The DCM samples with wild type EPHB4, have no CD36: the mechanism by which a mutation in another gene could affect the Eph-ephrin signaling pathway should be at least discussed.

      These patients do not have any mutation on EPHB4. Based in the literature and the previous murine study show that the Eph-ephrin signaling pathway is upstream of CD36. For these reasons we believe that our observation that shows that CD36 expression is reduced in all DCM patients confirms the important role of CD36 in cardiac homeostasis and the development of DCM. We further, as indicated in the discussion, other genes crucial for fatty acid transport may be involved in cardiac disease and thus, this study may help identify new diagnostic or therapeutic targets.

      • The authors should discuss and possibly prove the correlation between mutant EPHB4 and CD36 and CAV1 expression and localization in endothelial cells vs cardiomyocytes and explain the mechanistic implications of co-localization of CAV1 with CD36.

      In a previous study we showed that the deletion of EphB4 or its ligand ephrinB2 would induce a phenotype similar to DCM in mice. At the molecular level, defects in the Ephb4 are linked to compromised caveolar function and reduced CAV1 phosphorylation, which involves the kinase Src, a known mediator of Eph receptor signalling. In the healthy heart, caveolar transport is required for the membrane translocation and correct function of fatty acid translocase FAT/CD36, which mediates the uptake of fatty acids. We have expanded the introduction to explain the relationship between these molecules. It reads as follows:

      Mechanistically, EPHB4 deficient endothelial cells are characterized by compromised caveolar function and reduced Caveolin 1 (CAV1) phosphorylation. EPHB4 is required for the phosphorylation of CAV1 at Tyr-149. The phosphorylation of CAV1 promotes the release of caveolae from the plasma membrane10. Caveolae are required for the correct membrane translocation of the fatty acid translocase FAT/CD3611 and fatty acids are used by cardiomyocytes to obtain about 50% to 70% of their energy12. Absence of CD36 in cardiomyocytes reduces fatty acid uptake by the cardiac muscle cells13 and accelerates the progression from compensated hypertrophy to heart failure14. Finally, some cardiomyopathies a causally related to defects in the synthesis of the proteins required for fatty acid uptake in the heart15.

      • The available snRNAseq raw data are from normal subjects and aortic stenosis patients who are different from DCM patients. A better dataset would be the one from Reichart D, et al. Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. Science 2022.

      The single nucleus RNA sequencing data was used in an exploratory manner to study whether EPHB4 would also be expressed in cardiomyocytes. We did not perform any study on gene expression comparing the two groups. We believe that the use of this dataset is justified. We hope that the reviewer agrees with us.

      • Furthermore, the link between the analysis done on the published snRNA seq datasets and the authors' own data is not clearly explained.

      As we stated above and in the methods, we have used the single nucleus RNA sequencing to explore whether cardiomyocytes express EPHB4. The sentence in the methods reads as follows:

      The single-nucleus-RNA-sequencing data set generated in the paper by Nicin et al.14 was used to explore EPHB4 expression in human cardiac cells

      • DCM1 and DCM 3 carry 2 EPHB4 variants: please describe if the phenotype was more severe.

      As discussed above in the response to reviewer 1, the two patients with multiple EPHB4 variants present an average LVEF (echo) of 17.5 compared to 38,67 for the remaining 4 patients with only one EPHB4 variant and 35,17 for the six non-EPHB4 variant-carriers. Although the sample number only allows for a semi-quantitively analysis, it still hints at a possible EPHB4-variant effect, which certainly needs verification in a larger cohort.

      This information has been included in the manuscript and reads as follows:

      and interestingly, the average LVEF of the two patients with multiple EPHB4 variants is 17.5 compared to 38,67 for the remaining 4 patients with only one EPHB4 variant and 35,17 for the six non-EPHB4 variant-carriers. Although the sample number only allows for a semi-quantitively analysis, it still hints at a possible EPHB4-variant effect, which certainly needs verification in a larger cohort.

      • Provide p values on suppl table 1. The 2 groups are not matched by age and maybe gender, and this could affect the histological findings.

      We have not performed any comparison between the two groups in the characteristics shown in supplementary table 1. Nevertheless, we agree with the reviewers that the fact that the patients are not matched in age and gender is a limitation to our study. We have acknowledged this in the new included limitations section that is mentioned above.

      • Please discuss why in the DCM population the EPHB4 variant is enriched as compared with controls. Causal role? Modifiers?

      The deletion of EphB4 and its ligand ephrin-B2 induce DCM in mouse. The objective of this study was to determine whether there would be mutations in EPHB4 associated to DCM. We agree with the reviewer that in depth mechanistic studies both in vivo and in vitro would be required to determine the exact role of the here identified mutations in the development of DCM. This has been acknowledged in the new limitations sections and indicated in the discussion of the results as follows:

      Finally, this study not only supports the crucial role of EPHB4 in the heart, but it also corroborates the importance of CD36 and CAV1 for the cardiac health, and has the potential to improve diagnosis and risk stratification tools for DCM. Nevertheless, whether mutations in EPHB4 are causative or modifiers of the disease should be further studied. In addition, as other genes crucial for fatty acid transport may be involved in cardiac disease, this study may help identify new diagnostic or therapeutic targets.

      • The data and the methods are presented in such a way that they could be reproduced however,

      We thank the reviewer for the positive comment on our methods section.

      • At least 2 more healthy controls should be included, and the DCM groups should be matched by gender and age.

      Healthy donor biopsies are very rare and difficult to obtain. Although we agree with the reviewer that this could strengthen our study, we cannot add more healthy biopsies. We hope the reviewer understands this.

      As stated above, we have included a limitation section in the manuscript discussing the issue with the gender and age.

      • The causal mutation of the DCM patients should be provided.

      Only 35% of DCM cases have been related to mutations in genes encoding cytoskeletal, sarcomere or nuclear envelope proteins. In our case, the DCM patients that we use do not carry a variant in any of the DCM known genes. We have now expanded the methods sections explaining the inclusion criteria for the DCM patients including this issue:

      The criteria to be included in the study was reduced left ventricular ejection fraction (LVEF) <50% validated either with two independent image techniques or at two different time points with the same imaging technique. Furthermore, patients should include left ventricular dilation (LVEDD) >117% corrected with age and body surface according to the Henry-Formel formula (LVEDD= 45,3 * BSA1/3 – 0,03*Age –7,2). In both cases the heart were analysed either by echocardiography or magnetic resonance tomography (MRT).

      Minor comments:

      • I would explain in more detail the interactions among EPHB4, CD36 and CAV1 in the introduction, as the readers may not be familiar with this pathway.

      We have completed the introduction expanding the paragraph where the relationship between EPHB4, CD36 and CAV1 is presented. It now reads as follows:

      Mechanistically, EPHB4 deficient endothelial cells are characterized by compromised caveolar function and reduced Caveolin 1 (CAV1) phosphorylation. EPHB4 is required for the phosphorylation of CAV1 at Tyr-149. The phosphorylation of CAV1 promotes the release of caveolae from the plasma membrane10. Caveolae are required for the correct membrane translocation of the fatty acid translocase FAT/CD3611 and fatty acids are used by cardiomyocytes to obtain about 50% to 70% of their energy12. Absence of CD36 in cardiomyocytes reduces fatty acid uptake by the cardiac muscle cells13 and accelerates the progression from compensated hypertrophy to heart failure14. Finally, some cardiomyopathies a causally related to defects in the synthesis of the proteins required for fatty acid uptake in the heart15.

      • Panel B in Fig 1 shows 4 variants and not 6.

      All variants are shown in the panel As stated in the response to reviewer 1, it the fact that some variants have the same value that induces to think that only four are shown. The variants that do not appear in the genomAD have been considered 0 for this analysis.

      • IF in Fig 1: make sure that control and DCM are at the same magnification.

      Both control and DCM are at the same magnification. The reason why it looks different is the DCM phenotype. Cardiomyocytes are hypertrophic in the in the disease samples giving the impression that they are shown in a higher magnification.

      • The authors analyze snRNA seq data from available datasets and not from their own patients: so, the paragraph title in the method section should be changed as it is misleading.

      We have changed the title of this section of the methods. We have labelled it now “Analysis of single-nucleus-RNA-sequencing”.

      Reviewer #3 (Significance): Despite the main focus of the manuscript is EPHB4, dysregulation of CD36 and its interaction with CAV1 seem to be a common mechanism in the pathogenesis of all DCM. The significance of these findings is higher than the role of EPHB4 alone and should be improved.<br /> Metabolic abnormalities, mainly affecting the fatty acid metabolism, have been described as causes or modifiers of DCM pathogenesis but in my knowledge the role of EPHDB4, CD36 and CAV 1 have not been studied in human tissues. The discovery of the mechanisms through which dysregulation of metabolism is induced by DCM genetic mutations would be an advance in the field. However, the paper in the present form is not going to have a significant impact. There is no clear connection between the sets of experiments and more mechanistic experiments should be provided to prove causality. This may take months or even years depending on the availability of human tissues and resources.

      The type of audience interested in this research are mainly translational scientists mainly in the field of genetic cardiomyopathies. Furthermore, the elucidation of the metabolic effects of genetic mutations on DCM evolution may be of interest in the field of heart failure in general.

      The focus of my research is genetic and molecular pathogenesis of cardiomyopathies.

    1. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Revision summary.

      Additional new data.

      • CYPA expression levels in Scrm Vs KO Vs R55A isogenic cell lines as new Fig 1C.
      • ATR signaling: western blot analysis of HU-induced p-CHK1 (S345) in Scrm, KO and R55A isogenic cell lines as new Suppl Fig 1B.
      • MRN expression: western blot analysis of expression of NBS1, MRE11, RAD50 and MCM2 is Scrm, KO and R55A isogenic cell lines as new Suppl Fig 7A.
      • NBS1 subcellular fractionation: western blot analysis of NBS1 from whole cell extract Vs cytoplasmic extract Vs nuclear extract comparing expression/distribution in Scrm, KO and R55A isogenic cell lines, as new Suppl Fig 7B.
      • CYPA immunofluorescence (IF) staining on untreated and HU treated U2OS, as new Suppl Fig 7C.
      • CYPA immunofluorescence (IF) staining on untreated and HU treated U2OS following pre-extraction, as new Suppl Fig 7D.
      • DepMap Project Score Cancer Gene Dependency cell survival (“fitness”) following PPIA/CYPA-KO in breast carcinoma cell lines mapped against BRCA2 status, as a new Suppl Table 5.
      • DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Neuroblastoma cell lines, as a new Suppl Spreadsheet 4.
      • DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Multiple Myeloma cell lines, as a new Suppl Spreadsheet 4.
      • DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Chronic Myelogenous Leukaemia cell lines, as a new Suppl Spreadsheet 4.

      Revised and/or additional text.

      The Abstract, Introduction, Materials & Methods, Results and Discussion have been amended as necessary, to facilitate the issues raised by the Reviewers.

      Reviewer #1: We thank this reviewer for their understanding and appreciation of our CYPA study as espoused by their comprehensive summary of the content, importance, and potential implications of our work; “The manuscript presents clear and comprehensive data, demonstrating the profound impact of CYPA on DNA repair.” Furthermore, we very much appreciate their robust and complementary words regarding the significance of our work and its wide appeal; “The significance of this study is twofold: it adds a new layer to our understanding of DNA repair mechanisms and, importantly, it could point the way to novel therapeutic strategies for cancer. It will spark interest from molecular biologists to clinicians and pharmaceutical researchers.”

      Query:

      It's surprising to find that the loss of CYPA abolished HU-induced NBS1 foci, as the MRE11 interactive domain of NBS1 should remain intact in CYPA deficient conditions and the N-terminus of NBS1 is dispensable for ATM activation (Kim et al., 2017; Stracker and Petrini, 2011). A more detailed mechanistic explanation of this phenotype would be appreciated. The authors should check the subcellular localization of NBS1 and the stability of MRN in wildtype and CYPA KO cells. Additionally, including the kinetics of NBS1 foci formation using multiple timepoints in wildtype and CYPA KO cells after damage will further support the observation.

      RESPONSE:

      Regarding NBS1 foci formation, we note that rather than abolish HU-induced NBS1 foci formation, CYPA loss (through KO) and/or inhibition (through p.R55A) in fact results in a “…spontaneously elevated yet unresponsive amount of NBS1 foci/cells when compared to scrambled” (see original Fig 9A legend and associated Results section text). We have reinforced this observation in the revised Results section entitled ‘CYPA influences NBS1 and MDC1 foci formation’ and in the Discussion section. We do describe a kinetic impairment of RAD51 foci formation in the CYPA-engineered lines up to 16hrs post HU-treatment (Fig 6D). Our mechanistic working model is that CYPA interacts directly with NBS1 via a Pro residue within the short linking peptide between the FHA and BRCT1, and that this likely influences the relative dynamic positioning of the FHA with BRCA1-BRCT2, at least following acute HU treatment; replication fork stalling, likely biased towards ATR-dependent signaling initially, rather than that of ATM. The relative positioning of these functional domains can impact MRN function, and we discuss this possible mechanism in the section entitled ‘CYPA and the MRN complex’, with reference to the detailed structure-function analyses and complementary DDR activation models described by<br /> - Williams, R.S., et al., Nbs1 flexibly tethers Ctp1 and Mre11-Rad50 to coordinate DNA double-strand break processing and repair. Cell, 2009. 139(1): p. 87-99.<br /> and<br /> - Lloyd, J., et al., A supramodular FHA/BRCT-repeat architecture mediates Nbs1 adaptor function in response to DNA damage. Cell, 2009. 139(1): p. 100-11.<br /> and<br /> - Rotheneder, M., et al., Cryo-EM structure of the Mre11-Rad50-Nbs1 complex reveals the molecular mechanism of scaffolding functions. Mol Cell, 2023. 83(2): p. 167-185.e9.

      The N-terminal FHA-BRCT region of NBS1 does indeed influence MRN recruitment and HRR execution, a point we highlight in the section entitled ‘CYPA influences NBS1 and MDC1 foci formation’, with reference to the seminal original observations of<br /> - Sakamoto, S., et al., Homologous recombination repair is regulated by domains at the N-<br /> and C-terminus of NBS1 and is dissociated with ATM functions. Oncogene, 2007. 26(41): p.6002-6009<br /> and<br /> - Tauchi, H., et al., The forkhead-associated domain of NBS1 is essential for nuclear foci formation after irradiation but not essential for hRAD50-hMRE11-NBS1 complex<br /> DNA repair activity. J Biol Chem, 2001. 276(1): p. 12-15.<br /> and<br /> - Zhao, S., W. Renthal, and E.Y. Lee, Functional analysis of FHA and BRCT domains of NBS1 in chromatin association and DNA damage responses. Nucleic Acids Res, 2002. 30(22): p. 4815-22.<br /> and<br /> - Cerosaletti, K.M. and P. Concannon, Nibrin forkhead-associated domain and breast cancer C-terminal domain are both required for nuclear focus formation and phosphorylation. J Biol Chem, 2003.<br /> 278(24): p. 21944-21951.

      HU-unresponsive NBS foci (indicative of MRN dysfunction) and MDC1 foci formation are consistent with the DNA-R (i.e., DR-GFP reporter systems: Fig 3A-C and impaired RAD51 foci formation: Fig 6D) and resection-related phenotypes (Fig 6A-B) we report here and are also consistent with the relative resistance to HU-induced killing we report for CYPA-KO and CYPA-R55A cells (Fig 11A and as reported by Manthey, K.C., et al., NBS1 mediates ATR-dependent RPA hyperphosphorylation following replication-fork stall and collapse. J Cell Sci, 2007. 120(Pt 23): p. 4221-9).

      At the reviewer’s request we include additional novel experimental data showing that MRN expression is stable and equivalent in control, CYPA-KO and CYPA-R55A cells (Suppl Fig 7A). We also provide evidence that NBS1 subcellular distribution (via extract fractionation) is not altered upon CYPA loss and/or inhibition (Suppl Fig 7B).

      Query:

      The authors showed that the interaction between CYPA and MRN didn't change after HU treatment. The authors should also include co-localization analysis of CYPA and NBS1 after HU.

      RESPONSE:

      At the reviewer’s suggestion we undertook a series of IF analyses concerning endogenous CYPA (i.e., +/- HU, +/- pre-extraction). We found that endogenous CYPA failed to form foci following HU thereby precluding CYPA-NBS1 foci co-localization analysis (Suppl Fig 7C-D).

      Query:

      The paper demonstrated that BRCA2 knockdown cells were sensitive to CsA. The authors should also examine CsA sensitivity in BRCA2 deficient cancer cells. In addition, the authors could elaborate more on their criteria for selecting cancers for CYPA inhibition, whether it is based on high genomic instability or an addiction to HRR for survival.

      RESPONSE:

      Despite repeated attempts we have been unable to successfully routinely culture the TNBC suspension line HCC1599 (BRCA2 c.4154_5572del1419 and p.K1517fs*23), consistent with its reported ~5 days population doubling time. Although not a tumour line per se, we also failed to effectively culture the FANC-D1 patient FB line HSC62 (BRCA2 c.8488-1 G>A (IVS19-1G>A)) to enable survival analysis. We provide new quantification analysis of the CsA survival on the H1299 conditional shBRCA2 line (Fig 11E). Additionally, we include a comprehensive new analysis of cell survival (“fitness”) of a range of breast carcinoma cell lines following PPIA/CYPA-KO, extracted from DepMap Project Score Cancer Gene Dependency portal (https://score.depmap.sanger.ac.uk/), and also specify the BRCA2 status of each line. Interestingly, we find that reduced BRCA2 copy number is more commonly associated with loss of fitness following PPIA/CYPA loss (Suppl Table 5). We also include similar cell line fitness datasets for each of the cancers for whom we demonstrate elevated sensitivity to CYPAi (i.e., Neuroblastoma, Multiple Myeloma and CML) (Suppl Spreadsheet 4). Fascinatingly, PPIA/CYPA loss clearly results in loss of fitness in most of these cancer cell lines. Collectively, these new independent comprehensive datasets support our argument that targeting CYPA in select cancer scenarios shows impact in the preclinical setting and may represent an effective new strategy.

      The unifying features of the cancers showing elevated sensitivity to CYPAi are indeed high genomic instability, denoted by elevated RS and hence a dependency upon replication fork protection machinery. This would be consistent with the observed lethality of our CYPA-panel to shBRCA2, siXRCC3 and siRAD51C. The cancers are additionally characterised by aberrantly elevated HRR (i.e. an addiction to/dependency on HRR). This would be consistent with the observed lethality of our CYPA-panel to siCtIP, siRAD52, siXRCC3, and siRAD51C. At the Reviewer’s request we have reinforced and better clarified this point in the section Potential rational applications of CYPA inhibition in select cancers and in the Discussion.

      Reviewer #2:

      We thank this reviewer for their positive and supportive comments concerning our work; “Authors have quite conclusively explored the interaction between NBS1 and cyclophilinA as well as the putative proline residue important for this interaction.” We appreciate the constructive feedback concerning the range of consequences/impacts of CYPA impairment and we concur with their contention that “This manuscript will have broad interest from groups working on genomic stability, immunology as well as cancer therapy.”; a general view also voiced by Reviewer #1.

      We do stress that whilst other prolyl isomerases have previously been linked to DNA repair (e.g., most notably the Parvulin family member PIN1), this is the first time that CYPA has been directly implicated in DNA repair, and the first time CYPA has been shown to directly interact with a known DNA-R protein (i.e. NBS1).

      We believe that the comprehensive CYPA-BioID we describe is worthy of report and should serve as a very useful starting point for additional studies concerning CYPA biology, which is undoubtedly complex. The interactome will also function as a useful tool in helping dissect the clinically significant wider biological consequences of CYPA inhibition. Our interactome findings demonstrate that CYPA may influence DNA-R via multiple, and not necessarily mutually exclusive, routes. We do not argue that CYPA’s role in DNA-R is exclusively via NBS1/MRN. This is clearly demonstrated by our validation of CYPA interactions via co-IP with endogenous CYPA with proteins including PCNA, 53BP1, CHAMP1 and ILF2-3 complex (Fig 5). These are completely novel observations that furthermore reinforce the validity and efficacy of our experimental approach in leveraging the CYPA-BioID to provide new biological insight into this druggable prolyl cis-trans isomerase.

      Query:

      Authors show delayed S-phase transit along with reduced replication speed indicating replication stall. However, authors have not discussed how cyclophilinA might regulate replication (other than hypothesizing regarding altered dynamism of FHA-BRCT). It is conceivable that it could be an indirect effect on cellular metabolism or if authors believe it could be due to direct disruption to core replication machinery or signaling. In this regard, it will be helpful to see if there is shortening of (premature entry) G1 phase and comment on the status of the associated G1/S checkpoint.

      RESPONSE:

      The reviewer makes a very interesting and astute observation concerning the DNA replication phenotypes we report following CYPA loss and/or inhibition. The bases of these phenotypes are likely multifactorial, and we have revised the associated Discussion text to reflect this. Specifically, we highlight the elevated and unresponsive NBS1 and MDC1 foci seen in the CYPA-KO lines (Fig 9. i.e., persistent protein-DNA complexes) and dependence upon fork protection factors (XRCC3, RAD51C, BRCA2: Fig 11). We also report that a range of DNA replication factors are found in the CYPA-BioID (Fig 5A). Untangling the functional significance of these putative interactions would involve further study. Are they direct/indirect interactors? If direct, are they prolyl isomerase substrates or chaperone clients or regulated by liquid-liquid phase separation (LLPS)? Similarly, the CYPA-BioID throws-up an extensive set of RNA binding factors (Suppl Table 2), many of whom may conceivably contribute to the replication–transcription fork conflicts/collisions under conditions of CYPA-dysfunction. As this is the first comprehensive report of the cellular impacts of CYPA loss and inhibition, we thought it worth reporting the DNA replication associated phenotypes specifically to demonstrate the pleiotropic impact of loss and inhibition of this particular prolyl isomerase, to underscore its significance/importance. Although we have indeed found cell cycle phase transition impairments in our CYPA-KO and CYPA-R55A cells (for both G1-S and G2-M), these constitute additional studies requiring more thorough molecular-mechanistic characterization. We chose to focus on DNA repair for this first manuscript, as the CYPA-NBS1 interaction was the physical relationship for which we have assembled the most detailed and interconnected datasets, to-date. We do intend to pursue the cell cycle work as it too is derived from our CYPA-BioID (Suppl Spreadsheet 1), and we have already validated some of those relevant interactions by CYPA co-IP, but this is very much a work-in-progress. With this manuscript we’re endeavoring to tread a fine line by showcasing a wide range of cellular phenotypes resultant from CYPA loss and inhibition, but then also showing a deeper level of characterisation with at least one relevant interactor known to function in a range of DNA-R pathways wherein we’ve found impairments and dependencies.

      Query:

      In connection to this, it will also be interesting to see if the ATR/Chk1 signaling axis is intact in CYPA KO cells with or without additional DNA damage compared to WT.

      RESPONSE:

      At the reviewer’s request we include new data showing that HU-induced ATR-dependent CHK1 phosphorylation is normal in CYPA-KO and CYPA-R55A cells, and that ATR does not appear to be spontaneously activated in the absence of replication stress in these cells (Suppl Fig 1B).

      Query:

      Authors show that the P112 residue of NBS1 is important for the binding of cyclophilinA. What is the status of interaction among components of the MRN complex in CYPAKO cells and P112G NBS1? Further, what are the authors' thoughts on rescue experiments and whether P112G containing NBS1 to perform resection function.

      RESPONSE:

      We include new data showing normal expression of MRN components and normal subcellular localisation of NBS1 in the CYPA-KO and CYPA-R55A cells (Suppl Fig 7A-B). Regarding the interaction status of P112G, we show that this fails to co-IP endogenous CYPA when transiently expressed in HEK293 cells, in marked contrast to WT-NBS1 (Fig 8A). Furthermore, we show that ablation of another FHA Pro residue (P64) does not impair co-IP with endogenous CYPA under similar conditions, suggesting P112G is unique in this regard. Our recombinant protein interaction work demonstrates that CYPA-Step directly interacts with a HIS-(FHA-BRCT1) peptide and that P112G abolishes this interaction (Fig 8B). Regarding rescue experiments, we’ve found that stable overexpression of NBS1 can be neomorphic, resulting in resistance to certain DNA damaging agents, thereby complicating cell-based rescue analyses. We stress that along with our engineered KO and R55A (isomerase-dead) lines we have employed the well-known CYPAi Cyclosporin A (CsA) to reproduce several of the DNA-R related phenotypes (e.g., Fig 1, Fig 3, Fig 6, Fig 10, Fig 11). To further examine impacts upon resection specifically, a logical approach would be to engineer P112G into a full-length recombinant (baculoviral produced) human MRN complex for in vitro kinetic assessment using various labelled DNA substrates. But we think that this specialist and not insignificant undertaking is outside the scope of our report of the extensive cellular consequences of CYPA loss and dysfunction and it’s potential (pre)clinical significance with regards CYPAi repurposing.

      Query:

      What are the protein levels of MRN, RAD51 etc. in CYPAKO cells? It will be important control to delineate the effects of CYPA on global transcription and translation vs specific and direct effect on end-resection. Can overexpression of NBS1 rescue the observed resection and focus phenotypes?

      RESPONSE:

      Basal levels of RAD51 foci/cell are comparable between Scrm and both CYPA-KO and R55A cells (Fig 6D). We also find comparable levels of MRN components between these lines (Suppl Fig 7A). Importantly, we observe the pRPA/resection defect following an acute (up to 3hrs) treatment with CsA; conditions unlikely to grossly impair translation to an extent that would result in reduced expression of the relevant DNA-R proteins. Furthermore, microarray based transcriptomic analyses of these isogenic lines did not show evidence of a global impact upon transcription following CYPA-KO or R55A, nor was there evidence of reduced expression of any genome stability/DNA-R genes. We did not include this negative data so as to maintain the focus on the functional link with DNA repair.

      Reviewer #3: This critically negative review is myopic, unbalanced, self-contradictory and frustratingly mis-represents some of our key findings. The dismissive tone of the text unnecessarily and unprofessionally crosses into the pejorative (“Either evidence is lacking or experiments were not performed in a convincing way”). The stark contrast between this review and the summations of Reviewer #1 and Reviewer #2 serve to highlight this hyper-negative approach.

      It is very frustrating that this reviewer describes our findings as “…an interesting story…”, that “…the identification of NBS1 as a novel substrate of CYPA is significant” , that the “..manuscript may provide new insight…”, and that “…the role of CYPA in DNA repair is fairly well described using its inhibitor or KO cells”, and yet then concludes by stating “… the current manuscript suffers lack of evidence to support the main conclusion”. This is self-contradictory and unbalanced. Again, the contrast with Reviewer #1 and Reviewer #2 in this regard is stark.

      Major critical theme no. 1.

      Expression of CYPA-R55A: “…vastly different…”

      RESPONSE.

      This reviewer dismisses the entirety of the R55A model cell line work based upon the apparent “…vastly different…” expression levels of the reconstituted lines. This is an overstatement of the situation and notably not an issue for either Reviewer #1 or Reviewer #2. Nonetheless, we have replaced the original CYPA blot in Fig 1C with a clearer and more representative depiction of expression levels between the engineered lines and control. Importantly, the pRPA/resection work, siRAD52 and siXRCC3 dependency work were all corroborated/reproduced using the CYPA PPI inhibitor Cyclopsorine A (CsA). The plurality of our complementary approaches showing the influence of CYPA upon DNA-R is minimised and/or ignored by this Reviewer. Although not shown in this study, we find that the R55A cells are selectively sensitive to DNA cross-linker melphalan, in contrast to the CYPA-KO cells. Although we don’t yet understand the basis of this observation, this clearly indicates that R55A expression is a valid model in our hands and is not a like-for-like mimic of CYPA-KO simply because of reduced expression. We appreciate the reviewer could not know this.

      Major critical theme no. 2.

      CYPA-NBS1 work: “Another major concern is that the evidence to support that NBS1 is the major substrate of CYPA is lacking since all the experiments were performed with the CYPA mutant or CsA treatment.”

      RESPONSE:

      We do not claim that NBS1 is ”… the major substrate of CYPA.” . We do not claim that all the DNA-R deficits we have identified are specifically a consequence of impaired NBS1 function. These are misrepresentations of our findings and how we’ve presented and discussed them. This Reviewer ignores our comprehensive CYPA-BioID, and specifically our discussion pertaining to the DNA-R and Replication factors found therein (section entitled ‘CYPA Interacting protein partners’ and Fig 5A). We explicitly discuss the fact that “A recurring theme amongst these CYPA interactors is that all are involved in end-resection” whilst also demonstrating CYPA co-IP with 53BP1, CHAMP1 and ILF2-3 (Fig 5C-E). In the ‘Discussion’ section we describe a “homesostatic role for CYPA in genome stability”, including possible contributions to controlling LLPS of well-known DNA-R factors and the fact that several mitotic, kinetochore, centrosomal and spindle proteins are found in the CYPA-BioID.

      Major critical theme no. 3.

      A major repeated criticism levelled by this reviewer as a basis for dismissing the entirety our findings is that we have failed to demonstrate that the catalytic activity of CYPA is required for DSB repair.

      • Their conclusion should be supported by additional key experiments to prove that the catalytic activity of CYPA is indeed required for DSB repair…

      • Another major concern is that the evidence to support that NBS1 is the major substrate of CYPA is lacking since all the experiments were performed with the CYPA mutant or CsA treatment.

      • One major weakness of this study is that it focuses on characterizing the interaction between CYPA and NBS1, then jumps into a conclusion that the catalytic activity of CYPA is required for DSB repair based on its direct interaction with NBS1

      RESPONSE:

      As this criticism is repeated, the impression created, and no doubt intended, is that the manuscript is irreparably flawed (“…major weakness…”). This is an over-simplification and a misdirection. It’s notable that this critique isn’t raised in such a manner by either Reviewer #1 or Reviewer #2. This is likely because any modest inferences we made concerning the possible role of CYPA catalytic isomerase activity were based on a combination of differing but complementary approaches. Firstly, we routinely used the p.R55A engineered CYPA variant, although this Reviewer regards our use of this as invalid. This longstanding peptidyl prolyl isomerase (PPI)-dead mutant model has frequently been employed to invoke the catalytic function of CYPA. The mutant was originally proposed and characterized as catalytically-dead using the in vitro chymotrypsin-coupled prolyl isomerase assay using N-succinyl-AAPF-p-nitroanilide as a substrate as far back as 1992 (Zydowsky, L.D., et al., Active site mutants of human cyclophilin A separate peptidyl-prolyl isomerase activity from cyclosporin A binding and calcineurin inhibition. Protein Science, 1992. 1(9): p.1092-1099). In addition, we routinely use Cyclopsorin A (CsA), the longstanding clinically relevant CYPA PPI inhibitor, and we also use a different and more potent CYPA PPI inhibitor, namely NIM811 (N-methyl-4-isoleucine-cyclosporine) for the DR-GFP reporter assays of individual DNA-R pathway function (i.e.’ NHEJ, HRR and SSA).

      With regards to our findings concerning CYPA-NBS1 interaction, in the Discussion section we clearly state that mechanistic analyses of prolyl isomerase on the dynamism of NBS1 FHA-BRCT would require specialist approaches outside the scope of this manuscript, as the manuscript is firmly within the realm of cellular biology. This is ignored by this Reviewer. Specifically, we state that “A regulated cis-trans isomerisation of the E111-P112 peptide bond could conceivably dynamically alter the relative positioning of the FHA domain with the tandem BRCTs of NBS1 (Fig 7C-D). This may then impact on these domains’ abilities to dynamically interact with their respective phospho-threonine (for FHA) and phospho-serine (BRCT) containing targets, consequently likely shaping/impacting NBS1 recruitment dynamics and/or plasticity of its interactome [120-122]. Demonstrating this hypothesis would require additional structural analysis using techniques such as 2D-NMR which is outside the scope of this manuscript.”

      Minor comments: 1.

      Fig. 1E; is the survival between KO and R55A statistically significant? If so, do the authors have an explanation? Why is the reconstitution of R55A more toxic than KO alone?

      RESPONSE:

      Yes, R55A is slightly more sensitive compared to KO for this endpoint. The irony that this observation runs contrary to the Reviewer’s dismissal of the R55A model line is not lost on us (Major critical theme no. 1). As is well-known for PARP1, inhibition is not equivalent to absence. A possible speculative explanation is that the R55A isomerase-dead could have additional dominant impacts compared to the KO situation. Nonetheless, we suspect this Reviewer would object to such speculation in the absence of ever more data.

      Minor comments: 2.

      In Fig. 3D, the NHEJ activity of CsA- or NIM811-treated cells is significantly downregulated in comparison to control, which raises the possibility of the pleiotropic effect of CYPA inhibition. The authors should discuss this issue.

      RESPONSE:

      Not necessarily indicative of a pleiotropic effect if one accepts that absence of a protein is not always biologically equivalent to the presence of an inhibited version the same protein. Of note, we do see somewhat reduced NHEJ following siCYPA (Fig 3A), something not mentioned by this Reviewer. Furthermore, we explicitly discuss and show interaction between CYPA and 53BP1, CHAMP1 and ILF2-3 complex, all players in NHEJ and in the intricate balance between NHEJ and resection-mediated recombination directed repair pathways.

      Minor comments: 3.

      In Figure 8A, since the expressions of Flag-NBS1 WT, P112G, and P64G are very different, the conclusion that the isomerization of CYPA is essential for NBS1 cannot be supported. Given the variation of input levels, it appears that the P64G mutation actually enhances the interaction with endogenous CYPA. Is this reproducible? This co-IP result may need to be quantified from independent sets for statistical analysis.

      RESPONSE:

      We do not claim that “…isomerization of CYPA is essential for NBS1…”. Fig 8A data is derived from a transient transfection. Whilst there is some variation in expression, we do not make any precise quantitative conclusions from these co-IPs. Nonetheless, FLAG-NBS1-P112G clearly interacts less with endogenous CYPA in this system. Importantly, and ignored by this Reviewer, the associated recombinant protein work shown in Fig 8B clearly confirms that NBS1-P112G is profoundly compromised in its ability to interact with CYPA.

      Minor comments: 4.

      A defect in DSB repair generally hypersensitizes cells to DNA replication stress, including HU. In this regard, resistance of the CYPA KO (or R55A cells) to HU is interesting, but it may be due to the nonspecific effect of the CYPA loss in multiple DNA damage signaling and repair processes. Alternatively, cell cycle may be affected nonspecifically, rendering cells resistant to replication-associated genotoxic stress. This needs to be addressed further. Analysis of overall cell cycle profile may be required.

      RESPONSE:

      Resistance to HU is likely multifactorial and cell cycle transition kinetics may be relevant here. That is why we linked the DNA replications phenotypes to this discussion in the section entitled “Impaired CYPA function reveals novel genetic dependencies/vulnerabilities”. A comprehensive analysis of cell cycle profile and phase transits is outside the scope of the current manuscript (see response to Reviewer #2).<br /> Impaired HU-induced pRPA has been linked to HU-resistance via NBS1 previously: Manthey, K.C., et al., NBS1 mediates ATR-dependent RPA hyperphosphorylation following replication-fork stall and collapse. J Cell Sci, 2007. 120(Pt 23): p. 4221-9.

      Minor comments: 5.

      Text not to mention Abstract is too dense. The manuscript will benefit a lot from extensive editing and rearrangement of figures to make the story more succinct for journal submission.

      RESPONSE:

      The Reviewer’s view concerning a lack of succinctness is not shared by Reviewer #1 and Reviewer #2. We have endeavored to draft a concise and accessible manuscript, the main body of which comes in at just over 23x sides of A4 (including Materials & Methods). Considering we guide the reader through 12x multipart figures, 5x supplementary tables and 8x supplementary figure, we believe we have achieved succinctness. Nonetheless, we will of course take direction from the appropriate journal editorial team regarding house style and format.

    1. Authors’ response (3 January 2024)

      GENERAL ASSESSMENT

      The TMEM16 protein family is composed of ten members in mammals, and fewer in lower eukaryotes. Members within this protein family play remarkably different roles: some serve as Ca<sup>2+</sup>-activated ion channels, others work as lipid scramblases in a Ca<sup>2+</sup>-dependent manner, and some combine the two functions. The molecular determinants responsible for lipid transport in TMEM16 scramblases are not fully defined. The current view of lipid scrambling is that, in presence of Ca<sup>2+</sup>, TMEM16 scramblases change their conformation to expose a hydrophilic ‘groove’ to the membrane. This destabilizes the lipid bilayer, enabling translocation of lipids (e.g. phosphatidylserine) from the inner to outer leaflet of the membrane. However, recent evidence suggests that scrambling can occur even when the hydrophilic groove is closed.

      The new study by Feng and colleagues aims to investigate the molecular basis of closed-groove scrambling using the fungal scramblase, nhTMEM16. This protein was previously reported to maintain closed groove conformations even in the presence of Ca<sup>2+</sup>. The authors resolved a series of WT nhTMEM16 structures in two different nanodisc scaffolds, as well as several mutants with impaired scrambling. Strikingly, the conformational landscape of nhTMEM16 was found to rely on the lipid composition and scaffold used: the smaller E3D1 scaffold favored closed groove states and the larger 2N2 scaffold permitted intermediate and open-groove conformations. A high-resolution closed-groove structure obtained in E3D1 allowed the identification of a continuous file of lipid molecules around the catalytic groove region, providing a structural basis for lipid interaction with the closed groove. This complements prior work from this group involving a closely-related homolog, afTMEM16, in which the authors were able to visualize lipid molecules around the open groove. Furthermore, the authors succeeded in capturing three novel states of nhTMEM16 (Ca<sup>2+</sup>-free closed, Ca<sup>2+</sup>-bound intermediate-open and Ca<sup>2+</sup>-bound wider open states), completing the picture of conformational transitions that this protein undergoes upon activation.

      Mutation of key residues interacting with outer leaflet lipids selectively impaired scrambling in the absence of Ca<sup>2+</sup>. Residues involved in groove opening (E313-R432) were also identified and a mutation at this site (R432A) locked the nhTMEM16 scramblase in a closed-groove conformation, providing new insights into residues critical for groove opening. Furthermore, the authors tested the activity of nhTMEM16 mutants in several lipid compositions and reported striking differences, clarifying discrepancies from the authors’ prior work on nhTMEM16 using different lipid compositions and consolidating some of the observations from other TMEM16 homologs. It is noteworthy that the authors probed the effect of nanodisc size and lipid composition on nhTMEM16 conformation, providing thought-provoking insights for the membrane protein field. This approach is particularly valuable for closed-groove mutant structures, to ensure that the observed conformation is not dictated by scaffold size.

      Overall, this is a piece of carefully executed experimental work. The results are interpreted carefully in the context of the published literature, and the work provides important insight into plasma membrane lipid homeostasis. While the study does not have technical weaknesses, it could be improved in its presentation in order to make it more accessible to readers who are not experts in the TMEM16 field.

      We wish to thank the Colab editor and reviewers for their insightful comments, helpful suggestions, and appreciation of our work. We have extensively revised our manuscript to address their comments and suggestions. Below is a detailed point-by-point response to their suggestions.

      RECOMMENDATIONS

      Essential revisions:

      1. For readers not familiar with the field, some technical details might need to be explained in greater detail. For example:

      - In the section “Residues coordinating outer leaflet lipids are important in closed groove scrambling”, please indicate the method of measuring scrambling (liposome-based activity assay etc.) and refer to some of your prior work where the method is described for readers not familiar with the TMEM16 field. Additionally, it needs to be stated clearly what is considered a significant change in scrambling, as liposome assays are usually quite variable.

      We thank the reviewers for this suggestion. We edited the text to indicate the use of the well-established in vitro assay and added the relevant references (Lines 236-238).

      We illustrate the reproducibility of the experimental results by reporting in the bar charts the mean ± St. Dev of the scrambling rate constants, and by showing the values obtained from individual experiments (red dots superimposed to the bar charts). Additionally, we evaluated the statistical significance of the reported changes using Student’s t-test with Bonferroni correction. Finally, we added text discussing the limitations of our assay in lines 318-322.

      - Since prior work done by the group indicates that membrane thinning is a determinant of scrambling, and an open groove further thins the membrane to potentiate scrambling, it is not intuitive why the R432A mutant scrambles with WT-like rates in the presence of Ca<sup>2+</sup>. If this is due to the limitation of the assay (e.g. rate of NBD lipids bleaching), this should be stated more explicitly. Do the authors have insights from their structures regarding membrane thinning by R432A with/without Ca<sup>2+</sup> and how that compares to WT protein?

      We thank the reviewers for raising this important point. In the presence of Ca2+ the fluorescence decay of N-NBD-PE in nhTMEM16 vesicles occurs with kinetics that are slightly slower than those of the chemical reduction step by dithionite. Therefore, while we can resolve two exponential components, it is possible we are underestimating the scrambling rate constants α and β. However, we note that a large slowing effect would be well resolved in our experimental conditions. In contrast, in the absence of Ca2+, which is the focus of our current analyses, scrambling is much slower than the chemical step and is well resolved. Finally, we note that the triple mutant Y327A/F330A/Y439A alone has no effect on scrambling in 0.5 mM Ca2+ but induces a ~8 fold reduction in the scrambling rate constants in 0 Ca2+. When this mutant is combined with R432A, which favors the closed groove conformation, we now see in the presence of Ca2+ the same ~8-fold reduction in the scrambling rate constants. This suggests that our assay can resolve effects even in the presence of Ca2+. This is discussed in Lines 318-322.

      We only determined the structure of R432A in the presence of Ca2+, therefore we cannot evaluate how Ca2+ binding affects membrane thinning in this mutant.

      - It is difficult to follow the reasoning for the R432A+Y327A/F330A/Y439A mutant phenotype. Is the assumption that Y327A/F330A/Y439A is in the open conformation with Ca<sup>2+</sup>, and therefore adding a mutation stabilizing the closed groove impairs scrambling in presence of Ca<sup>2+</sup>?

      We have expanded the rationale for this experiment in lines 307-315.

      - What the authors believe about the lipid pathway when the groove is open should be discussed in more detail and with reference to Alvadia et al 2019.

      We thank the reviewers for this important suggestion. We now explicitly state that: “With a closed groove, thinning is less pronounced, and scrambling is slower than when the groove is open, rationalizing the Ca2+ dependence of this process (Extended Data Fig. 10d-f).” (Lines 432-434) Since the present work is focused on the mechanism of closed groove scrambling, we prefer to refrain from adding more speculations on what happens when the groove is open, especially since this topic was the focus of a paper we recently published (Falzone, Feng et al., Nat Comms, 2022).

      2. A more detailed account of the physiological significance of the findings should be presented in the Discussion to offer reader the authors’ view on the broader implications of the work. Relevant points include:

      - Do the authors believe that conformational bias in nhTMEM16 in various cryo-EM conditions may be reflective of physiological regulation? Is it likely to happen in cells in vivo?

      This is an excellent point. We do hypothesize that the various observed conformation are physiological and indeed we explicitly state “…that the 7 observed conformations represent intermediates along the transition from apo closed to Ca2+ bound open” (Lines 444-445). Beyond this, we cannot speculate on whether the environmental dependent bias on nhTMEM16 can happen in a physiological context. We imagine that subtle changes in membrane composition can affect TMEM16 function, and indeed we see quite dramatic effects of lipid composition of scrambling activity, however whether these changes are reflective of shifts in the conformational landscape of groove opening, of effects of membrane properties, or both, it remains to be seen. Gaining definitive insights into this would require extensive additional structural experiments in unbounded membranes (i.e., from reconstituted liposomes of different composition or native vesicles, cell membranes) that are outside of the scope of the present work.

      - Do the authors believe that such regulation may also apply to mammalian TMEM16 scramblases or even channels?

      We consider this is a definite possibility, and now added a sentence stating that “This raises the possibility that unbounded membranes, such as those of liposomes, might perturb less the conformational landscape of the imaged proteins.” (Lines 499-501) However, without direct evidence we prefer to avoid speculating on this fascinating topic.

      - What implications do these findings have for our understanding of lipid scrambling mechanisms by TMEM16 scramblases that work in intracellular (thinner) membranes (such as TMEM16K)?

      We agree this is an important point. We now added a sentence stating “The strong dependence of closed groove scrambling on membrane properties could provide a mode of regulation of TMEM16 activity in cellular membranes, such as the cholesterol rich plasma membrane or the thinner ER membrane.” (Lines 434-436)

      - What implications might the knowledge of residues involved in lipid scrambling of closed scramblases potentially have for medicine and therapy? Can the authors speculate as to whether the identified residues have the potential to be tackled pharmacologically and what use could this have?

      We do not know whether the residues we identified as important for closed groove scrambling could provide a pathway to pharmacological manipulation of TMEM16 scramblase activity. This is a fascinating topic, especially in light of the very poor availability of pharmacological tools to manipulate TMEM16 scramblase activity. However, at present it remains speculative and outside the scope of the present manuscript.

      More generally, what is the physiological role of lipid transport in the absence of Ca<sup>2+</sup>? Does this constitute a lipid "leak”?

      This is an excellent question. One possibility is that scramblases have a basal activity, that in cellular homeostasis is counteracted by the activity of flippases and floppases. Alternatively (or complementarily), it is possible that in the context of an unperturbed native membrane the basal activity is negligible. However, we do not have data addressing the present point and therefore our hypotheses remain limited to pure speculations, therefore we prefer to maintain the focus of the present manuscript on the mechanism of closed groove scrambling and on the potential effects that the environment can have on the interpretation cryoEM imaging experiments.

      Optional suggestions:

      1. Regarding residues involved in groove opening (E313-R432), it would be very interesting to expand the work by studying additional mutants and investigating more fully the role of E313 in DOPC:DOPG lipids, since at present only a mutation in R432 was tested experimentally in this lipid composition.

      We agree with the reviewers that expanding the analysis to other residues, such as E313, would be interesting. However, initial functional experiments suggested this mutant behaves similarly to R432A, and thus we did not think it would provide much additional mechanistically insights to what we already have.

      2. Measurements of ion transport in nhTMEM16 would also be useful to further validate the closed groove conformation of R432A. This could shed new light onto whether ion transport and lipid transport are coupled in TMEM16 proteins.

      This is an excellent suggestion, one that indeed we considered at length during this project. Ultimately, we decided not to pursue this avenue of investigations because of the limitations of the flux assay for non-specific ion channels. While flux assays can provide quantitative measures of effects for anion or cation selective channels, for non-selective channels these assays only provide very coarse yes/no answers (i.e., whether the construct mediates any channel activity or not). Since we expected these mutants might have intermediate phenotypes, rather than completely ablating channel activity, we were concerned that the experiments would be inconclusive at best or, at worst, misleading. These limitations are extensively discussed in our previous manuscripts (Lee et al., Nat Comms, 2018; Falzone and Accardi, Methods Mol Biol, 2020).

      3. Since the authors found significant differences in their new structures with previously reported, how do Ca<sup>2+</sup>-bound closed structures of nhTMEM16 in POPC/POPG (previously published) and DOPC/DOPG (obtained in this study) compare to each other?

      We thank the reviewers for this suggestion. In Lines 167-168 we now state: “The Ca2+ bound closed conformations in MSP1E3 DOPC/DOPG (PDBID: 6QMB) and MSP2N2 POPC/POPG are nearly identical (Cα r.m.s.d ~0.50 Å).”

      4. The purpose of creating composite symmetric maps from symmetry expanded monomers is questionable – if it is not possible to isolate this symmetric state by classification approaches, it is probably very transient, or not present at all. However, there are no strict guidelines, and it is acceptable as long as everything is described in MM and all the maps deposited. Are composite and monomer E3D1 apo maps deposited alongside the main map as EMD-41477?

      We agree with the reviewers that depositing the maps of the unexpanded dimers is appropriate and opportune, and indeed we did so

      i. the combined dimer map which was primarily used for model building is deposited as EMDB: 41453 and the model as PDB: 8TOI;

      ii. the local refined monomer map was deposited as EMDB: 41458

      iii. the dimer consensus map used for map combination was deposited as EMDB: 41457

      The rationale to generate a combined dimer map is that this allows for a better visualization of the protein-bilayer interface and the ensuing distortions. When viewing the map of a single monomer it is difficult to appreciate these effects.

      5. The authors show that Ca<sup>2+</sup>-dependent α6 straightening is important for closed-groove scrambling. This is directly relevant for TMEM16F, for which this is the only conformational change observed. The authors note that extracellular α4 is more mobile in R432A mutant, is this in any way similar to the conformations reported for more active TMEM16F mutants (Arndt et al., 2022)?

      What we see is that the density for the top of TM4 becomes very weak. This is quite different from what Arndt et al. reported, where they see a significant and defined movement of both TM4 and TM3. While we think many of the basic mechanisms of closed-groove scrambling we and many others are beginning to unravel are likely conserved across TMEM16 homologues, it is very likely that differences will exist between homologues. We now make this important point in Lines 432-434.

      (This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

    1. Authors’ response (11 February 2024)

      GENERAL ASSESSMENT

      Ionotropic glutamate receptors mediate the large majority of excitatory synaptic transmission in the brain. These receptors consist of four classes: AMPA, kainate, NMDA and delta receptors. NMDA receptors are obligate tetramers composed of two GluN1 and two GluN2 (or GluN3) subunits. Compared to other iGluRs, they have the particularity of requiring two different agonists for their channel to open: glycine binding on GluN1 and glutamate on GluN2.

      Seljeset et al. investigate the molecular determinants controlling ligand potency and NMDAR activity at the level of the ligand-binding domains (LBDs), where the agonists bind. They identify a specific position, D732, whose mutation to either leucine or phenylalanine leads to a constitutively active GluN1 subunit, and thus to NMDARs activated solely by glutamate. This aspartate is well known in the field, since it is a highly conserved, signature residue in iGluRs that binds amino acid ligands, together with an arginine in the LBD upper lobe. Surprisingly, although glycine cannot further activate GluN1-D732L/GluN2Awt receptors, glycine site antagonists like 5,7-DCKA or CGP-78608 can still bind and inhibit NMDAR activity. This study is therefore very intriguing, as it raises new questions about something that was previously thought to be understood. By using a combination of unnatural amino acids and conventional mutagenesis, the authors propose that D732 contributes to glycine-mediated effects by changing local interactions with nearby residues. In addition, they show that this behavior is specific for the GluN1 subunit, since mutation of the equivalent aspartate in the GluN2 subunit does not yield constitutively activated GluN2 subunits. Finally, the authors identify a homomeric iGluR from the placozoan Trichoplax adhaerens, Trichoplax AKDF<sup>19383</sup>, in which this conserved aspartate is replaced by a tyrosine. When expressed in Xenopus oocytes, the channel shows constitutive activity. Mutation of the tyrosine into an aspartate, to convert Trichoplax AKDF<sup>19383</sup> into a “classical” iGluR, decreases Trichoplax AKDF<sup>19383</sup> constitutive current and allows this channel to be activated by glycine and D-serine. Interestingly, an adjacent residue that is a serine in most mammalian subunits is also a tyrosine in Trichoplax AKDF<sup>19383</sup>, and mutation of both tyrosines yields a glutamate-gated ion channel comparable to mammalian receptors. All of this suggests that the nature of the residue at position 732 influences not only ligand binding but also channel gating.

      The study is technically sound, with appropriate controls, and uncovers intriguing properties of a position in GluN1 LBD at which specific side chain mutations can lock the subunit in an active state. Investigation of Trichoplast iGluR further reinforces these findings. This study should lead to a better understanding of how LBDs prime channel opening in iGluRs in the absence of agonists. In addition, co-agonist insensitive GluN1-D732L containing NMDARs could be used as tools to investigate the physiological consequences of NMDAR regulation by their co-agonist site. In contrast to previously engineered NMDARs activated solely by glutamate, which rely on the LBD being locked in its active state by cysteine bridges (Blanke and VanDongen, J Biol Chem 2008), GluN1-D37L/GluN2A NMDARs remain druggable (i.e. they can still be inhibited by glycine-site competitive antagonists). This is a great advantage when investigating the function of these receptors in a native context. The study identifies a few gaps that remain in our mechanistic understanding of D732’s role in channel gating. Particularly, it is unclear how subtle modification of residue side chains at position D732 lead to such drastic changes in function and why these effects are specific to GluN1 LBD. Also, why does mutation of D732 into isoleucine lead to a constitutively active GluN1 subunit, while mutation of a closely related leucine residue prevents activation of the receptor by glycine? The idea of a “hydrophobic plug” formed by D732L or D732F sidechains leading to constitutive activation would benefit from further validation since other hydrophobic substitutions (A, V, I, Y, and W) do not produce similar effects. Finally, it would be interesting to carry out further investigations of the role of the interaction between D732 and Q536 in open conformation stability. Thus, this paper puts forth interesting questions that could be addressed by future studies, for example molecular dynamics simulations and exploration of the LBD free energy landscapes (as in Yao et al., Structure 2013), to understand the impact of the GluN1-D732L mutation on GluN1 LBD conformational mobility.

      RECOMMENDATIONS

      Essential revisions:

      1. Page 2, “These data show that essentially all substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions also remove the requirement for glycine co-agonism in GluN1/GluN2A NMDA receptors”: One other hypothesis for the lack of glycine dependence of GluN1-D732I and D732Y + GluN2A receptors could be that the mutated receptors have a glycine potency so high that GluN1 LBD is already saturated by contaminating, ambient glycine. At this point in the paper, the authors cannot distinguish between one hypothesis or the other, therefore we suggest that this sentence be rephrased. Later in the text, control experiments with GluN1-R523K mutations that kill glycine binding and competition with 5,7-DCKA show that glycine-independent activation of GluN1-D732L/GluN2A mutants is not due to constitutive occupancy of GluN1 LBD by contaminating glycine.

      ER1) We have now changed this to (page 4): “These data show that most substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions alter GluN1 activity in such a way that leads to single-mutant NMDA receptors activated solely by glutamate.”

      1. Does glycine insensitivity in GluN1-D732L/GluN2A NMDARs reflect a constitutively active GluN1 subunit or is this subunit locked in another conformational state that cannot be further modified by glycine? This could be answered by estimating the maximum open probability of GluN1-D732L/GluN2A NMDARs compared to their wt counterparts. To estimate Po, the authors could measure the kinetics of NMDA receptor current inhibition by MK801 (the slower MK801 inhibition, the lower the Po; see Chen et al., J. Neurosci 1999; Blanke and VanDongen, JBC 2008) in the presence of saturating agonist concentrations (100 μM Glu, 100 μM Gly for wt and only 100 μM Glu for mutant).

      ER2) We have now assessed the rate of MK-801 block in glutamate-gated mutant and glycine + glutamate-gated WT receptors, and reshuffled text/figures, as this ties in well with ER4) below. MK-801 results now in Figure 3 on page 6, and main text on page 5: “In order to understand whether the glycine-insensitive GluN1-D732L subunit is in a constantly activated state or occupies a different conformation that may reflect an alternative to typical channel gating, we compared the kinetics of WT receptor and GluN1-D732L-containing receptor inhibition by the open-channel blocker MK-801, which can be used to evaluate maximum open probability of NMDARs <sup>26,30</sup>. We observed very similar kinetics of inhibition of WT and mutant receptors (Fig. 3A), indicating similar open probability in solely glutamate-gated GluN1-D732L-containing receptors and glutamate and glycine-gated WT receptors. This reflects unchanged maximum open probability in solely glutamate-gated NMDARs with disulfide-locked GluN1 LBDs assayed by single channel recordings <sup>27</sup>. This suggests that the GluN1-D732L subunit is in a constantly activated state.”

      When viewed alongside high sensitivity of mutant subunits to DCKA - OS1) below - it’s difficult to conclude what sort of active state the mutant subunit adopts. We’ve assessed the best we can at the moment, and in this paper we’ll have to leave it at “here is the observation; here is some evidence ruling out various possibilities; and here is a receptor from another family that shows something remarkably consistent”. Future studies will have to establish exactly what state the mutant subunit adopts.

      1. Page 4: The term “hydrophobic plug” is not fully justified since other hydrophobic residues do not lock GluN1 LBD in its active state.

      ER3) We have replaced nearly all use of this term, in the title and in the main text, to e.g. “certain hydrophobic substitutions” or “L/F substitutions”.

      1. Figure 2, redox sensitivity of GluN1-D732L/GluN2Awt: It would be helpful to explain the point of this experiment – maybe to investigate if the D732L mutation has an impact on the receptor gate rather than on the LBD? In any case, the authors should investigate the effect of DTT on the activity of wt GluN1/GluN2A receptors to determine whether there is an absence of an effect of the D732L mutant on redox sensitivity.

      ER4) Indeed we were curious if D732L affected the gate via this allosteric route, rather than by just altering LBD conformation. And we have now shown the effect of DTT on WT receptors.

      In addition to re-writing to better explain the point, as suggested, we have also re-written to follow on from new data/text on the whether the D732L mutation affects LBD, gating, etc: “We next questioned if D732L/F substitutions affect channel gating, rather than simply altering the LBD conformation. The gating machinery is complex, but it includes the peptide segment linking the C-terminal end of the LBD to membrane-spanning helix 4 (LBD-M4 linker, (11)). The LBD and LBD-M4 linker are confined by a C744—C798 disulfide, just four helical turns after D732, whose disruption by reduction enhances channel gating (28)). We considered that if the D732L/F substitution is coupled to channel gating via this route, then removal of the C744—C798 disulfide via the C744A mutation might alter glutamate-gated currents in GluN1-D732L-containing receptors. Alternatively, the typical enhancement by the reducing agent dithiothreitol (DTT) might differ in GluN1-D732L compared to WT receptors.”

      And new Figure 3 now includes DTT effects on WT receptors.

      1. Page 6: The authors find that mutation of Q536 decreases glycine potency and conclude there is an interaction between D732 and Q536. However, the effects of D732 and Q536 mutations could be independent, therefore the authors should consider mutating both residues together to look at the additive/non-additive effects of the mutations. Or perhaps, note in the Discussion that some sort of mutant cycle analysis or molecular dynamics simulation would be needed to rigorously test these ideas.

      ER5) We have now made and tested a double mutant combining D732E and Q536N and performed mutant cycle analysis.

      (We also tried to do this for Q536 side chain (regular mutations) and A734 main chain (non-canonical substitutions), but double mutants involving non-canonical amino acids at A734 were not successful – Figure S1.)

      As is now shown in Figure 4D, the effects of the mutations are decidedly non-additive, yielding an Ω value of 0.05, corresponding to a reasonably high energetic coupling of ~7 kJ/mol. We have now added to the relevant section of the Results on page 8: “If an interaction between Q536 and D732 were energetically important for receptor activation, the effects of their mutations should be non-additive <sup>31</sup>. We therefore tested glycine potency at double-mutant GluN1-Q536N/D732E-containing receptors and observed non-additive changes in EC<sub>50</sub>, with a strong coupling value, Ω, of 0.05 (Fig. 4D). This deviation of Ω from unity, corresponding to an interaction energy of 7.4 kJ/mol is relatively high <sup>31</sup>, confirming that Q536 and D732 are energetically coupled. We tried to analyse energetic coupling between Q536 and A734 via double mutants incorporating nonsense suppression at the A734 position, but unfortunately, attempts to incorporate Aah into such double mutants via nonsense suppression were unsuccessful (Fig. S1B).”

      1. Page 6, “A hydrophobic plug does not cause constitutive activity in all NMDA receptor subtypes”: This title is misleading as it raises the expectation that the effect of GluN1-D732L has been investigated in the context of GluN1/GluN2A, GluN1/GluN2B, etc NMDARs. Instead, the equivalent mutation is made in the GluN2 subunit. We suggest using the word “subunit” rather than “subtype”.

      ER6) We have changed this Results section title (page 8) to: “L/F substitutions do not cause constitutive activity in all NMDA receptor subunits”

      1. Page 7, effect of GluN1-D732L in the context of GluN1/GluN3 NMDARs: We would not expect current to be observed with GluN1-D732L/GluN3 NMDARs, since locking GluN1 LBD in its active state desensitizes the receptors. The effect of the D732L mutation seems therefore conserved between GluN1/GluN2 and GluN1/GluN3 NMDARs. In addition, when using CGP, please cite Grand et al., Nat. Commun. 2018 since they were the first to use CGP as a tool to record GluN1/GluN3 currents.

      ER7) We have now cited that paper specifically here (page 8) and inserted the following (page 8/9): “While this seems like inactivity of the mutant GluN1 subunit in GluN1(4a)/GluN3A, it could yet reflect the activity of constitutively active mutant GluN1 subunits in GluN1/GluN2A receptors, as GluN1 activity in GluN1/GluN3A receptors is known to cause more desensitization than activation (Grand et al 2018).”

      1. Figure 5C: It is stated in the text that the aspartate position is “highly” conserved. However, no actual number or percentages are given for this statement. How does it compare to the residues in the highly conserved SYTANLAAF motif or other conserved positions? This sort of analysis does not need to be done for the entire receptor, but perhaps for glycine and glutamate binding residues and SYTANLAAF motif, to give a quantitative feel for statements about conservation. In addition, what other types of residues occupy this position in other species? And what was the number of species/subunits included in the analysis?

      ER8) To clarify the level of conservation, we have added Table 1 (page 10) listing the % conservation of amino acids at selected positions.

      In analyzing % conservation, we noticed that several iGluR sequences with gaps in the ligand-binding domain or channel-forming helices had escaped our filtering out incomplete sequences in our phylogenetic analysis. We therefore revisited our phylogenetic analysis, removed several incomplete sequences, and replaced Crassostrea gigas (a mollusc spiralian) iGluR sequences with Schmidtea mediterranea (a flatworm spiralian) sequences. This (1) means less sequences with gaps in the ligand-binding domain in our alignment/tree and (2) better covers the diversity of the lineage Spiralia now that we have sequences of Lingula anatina and Schmidtea mediterranea, which are more distantly related than Lingula anatina and Crassosttrea gigas (Laumer et al 2019, PMID:31690235; Marlétaz et al., 2019, PMID:30639106).

      The result is a phylogenetic and amino acid sequence analysis of 204 iGluR genes (previous version had 212 genes) with the same overall topology as the previous version, including lambda, NDMA, epsilon, and AKDF iGluR families (Fig. 5B, page 9).

      The number of subunits/genes used is stated in the Figure legend. The number of and reasoning behind the number of species used is described under Methods, Bioinformatic analyses: in exploring the conservation of the D732 residue, we have not tried to use as many iGluR sequences as possible; rather we have tried to assess this residue in a broad sample covering all (animal) iGluR families and from a careful selection of different animal lineages, while also avoiding fast-evolving species like Drosophila, which complicate tree topology. Hence our description of “two ctenophores, one poriferan, etc” under Methods, Bioinformatic analyses. In the main text (Results, page 9), we retain our original description: “We assembled diverse iGluR sequences, covering all animal lineages and animal iGluR families (Fig. 6A,B)…”

      1. Figure 5, panel F: From what we understand, the authors created dose-response curves for wt Trichoplast AKDF<sup>193863</sup> based on steady-state currents and for Y742D/Y743S mutants based on peak currents. If this is the case, one cannot compare the two dose-response curves since peak current potentiation and steady-state inhibition likely reflect different conformational transitions.

      ER9) We acknowledge this issue and that we can’t really say that ligand-activated D742 channels bind D-serine better than ligand-deactivated Y742 channels. But we think it’s fair to point out that mutant D742 channels react (by conducting current) to micromolar ligand concentrations whereas wildtype Y742 channels react (with decreased current) only to millimolar concentrations, and we have re-written to acknowledge the issue raised for this comparison (page 11): “Finally, we tried to assess whether position 742 determines ligand potency in addition to channel activity in AKDF<sup>19383</sup> receptors. For these experiments we employed D-serine, as recovery from glycine-induced deactivation (Fig. 6C, far-left) and activation/desensitization (Fig. 6C, far-right) was very slow. Substantial deactivation of WT receptors was only induced by millimolar D-serine concentrations, whereas Y742D-containing mutants were activated by micromolar concentrations (Fig. 6D,E), with an EC<sub>50</sub> of 490 ± 120 µM at Y742D/Y743S (n = 4; Y742D EC<sub>50</sub> not assessed due to slow recovery from desensitization). Our measure of potency is confounded by the fact that deactivation (in WT channels) and activation (in mutant channels) are presumably coupled to D-serine binding via different conformational transitions. Nonetheless, we observe that a naturally occurring large hydrophobic side chain at the top of the β-strand preceding the αI helix leads to an AKDF homo-tetramer that shows constitutive activity and responds only to millimolar concentrations of D-serine. In contrast, “re-introducing” an aspartate to this position reinstates more typical ligand-dependent activation and sensitivity to micromolar concentrations of D-serine.”

      Optional suggestions:

      1. Figure 2, glycine/DCKA competition: It is difficult to understand how a GluN1 LBD-locked closed (active state) could still bind DCKA. If the open-to-close equilibrium of GluN1 LBD is displaced towards its closed state, then DCKA Ki should be shifted to the right compared to wt receptors. Additionally, DCKA inhibition kinetics should be slower if DCKA needs to “wait” for rare resting-like conformational changes to bind. Did the authors investigate DCKA potency and inhibition kinetics?

      OS1) We have now investigated DCKA potency. DCKA capably inhibits GluN1-D732L/GluN2A-WT activity, and perhaps surprisingly, potency of DCKA at the mutant is greater than at wildtype. We suspect this is due to (1) the introduction of a hydrophobic leucine residue right next to an aryl group of DCKA, increasing DCKA affinity directly, (2) the absence of glycine binding to this site, so no need for competition, and (3) potentially other mechanisms such as cooperativity between subunits. Again, establishing the precise nature of our mutant LBD conformation here is for future structural and molecular dynamics studies. But we have described the results, along with our following interpretation, (page 4): “Whether increased DCKA potency in GluN1-D732L subunits derives from the now non-competitive nature of the inhibition in mutant receptors or from the introduction of a favourable hydrophobic interaction with the dichlorobenzene moiety of the inhibitor is unclear. But the high DCKA potency would suggest that the constitutively active GluN1-D732L subunit is, unexpectedly, not due to a permanently clamshell-closed LBD in the mutant. This may reflect the fact that extent of LBD closure is poorly correlated with agonist efficacy in GluN1 subunits, in contrast to AMPA receptor GluA2 subunits <sup>21</sup>.”

      1. The authors show in many panels that GluN1/GluN2A currents desensitize (e.g. Fig.1B, 3C, 4A). In Xenopus oocytes, NMDAR currents do not normally desensitize. We fear this desensitization might stem from contamination of the NMDA current by calcium-activated chloride channels, which can be activated by high quantities of barium when large NMDAR currents are measured. To avoid this problem, we advise that NMDA currents above 2 µA are avoided.

      OS2) We have moved forward presuming that potential changes in current amplitude due to a small chloride flux doesn’t affect our measures of potency or ligand-selectivity. But in our new experiments, we’ve especially tried to avoid large currents.

      1. Page 5, investigation of D732 state-dependent interactions: Mutation of residues near D732 to unnatural amino acids to replace the peptidic NH do not bring much information about the mechanisms of D732 action. The fact that the 734Aah and 735Vah cannot mimic the effect of the D732L mutation could be due to many factors, including the fact that changing the peptide bond probably changes the local structure of the LBD. Perhaps mention this in the discussion.

      OS3) We have now acknowledged this possibility in the Results, right after we describe the decrease in glycine potency caused by the 734Aah mutation (page 7): “Although this may be due to local conformational changes due to altered main chain structure,…”

      1. It is intriguing that the D732L mutation locks an active conformation of the GluN1 subunit but not the GluN2 subunit, suggesting two different mechanisms of LBD closure by glutamate and glycine. It would be interesting to look at the effect of the equivalent mutation on the GluN3 subunit to investigate if this locking effect is specific to glycine-binding LBDs or just to the GluN1 subunit.

      OS4) We have now made and tested mutant GluN3A subunits D485L and D485F. Simply decreases glycine activity altogether (reflecting the effects of the mutations in GluN2A). Described on page 9: “Similarly, at oocytes injected with GluN1(4a)-WT and GluN3A-D845L or -D845F mRNAs, we saw no response to glycine alone or glycine in the presence of CGP 78608 (Fig 5D). Together, these results indicate that the induction of a constitutively active state by the D732L/F substitution is an exclusive feature of the GluN1 subunit, and the only conserved feature of the mutation in different subunits is a decrease in agonist potency.”

      1. Page 9: Discussing the position of residue side chains from structures with 4 Å resolution does not seem relevant and would benefit from a caveat.

      OS5) We want to retain our comparison of experiments with available structural data, so we have kept this but re-written to more openly acknowledge the caveat (page 12): “Indeed, in a cryo-electron microscopy (cryo-EM) study of GluN1/GluN2B receptors, D732 has only swung toward the ligand and away from A734 in a second of two putative pre-gating step structural models, although this is speculative considering the poor resolution of D732 side chains in those cryo-EM maps (12).”

      1. Page 10: We don’t understand the point that the authors want to make with the activation of Aplysia californica. Please clarify.

      OS6) He we were trying to say that “not much is required to change NMDARs from requisite co-agonism to single-ligand agonism”, either (a) in the lab via the D732L mutation or (b) naturally, as invertebrate NMDA receptors apparently show single-ligand agonism (results on invertebrate NDMARs in the literature). Further, we want to say that “by extension, we wonder if (c) in certain physiological situations, vertebrate NMDARs might indeed need only a single ligand.” We acknowledge this was unclear and – although it’s still speculative – we have now changed to (page 13): “Our work shows that only small changes in the GluN1 LBD are required for solely glutamate-gated currents in vertebrate GluN1/GluN2 receptors, and previous work suggests that invertebrate Drosophila melanogaster and Aplysia californica GluN1/GluN2 receptors can be activated by single ligands <sup>50,51</sup>. This suggests that NMDA receptors’ requirement of co-agonism is easily alleviated by certain mutations or conditions. As iGluR-modulatory proteins vary across cell types or even across neuronal compartments <sup>52,53</sup> and NMDA receptor sequence varies across animals, it is foreseeable that in certain physiological settings, certain NMDA receptors might be activated by glutamate alone. But in most settings, certainly in vertebrates, it seems that glutamate-induced activation of NMDA receptors relies on a system of ambient glycine or D-serine <sup>54,55</sup>.”

      1. In iGluRs, constitutive currents are often induced by mutations in the gate region, near the SYTANLAAF motif (e.g. lurcher mutations). Does the sequence around the gate of Trichoplast AKDF<sup>193863</sup> predict channel constitutive activity?

      OS7) Our results with WT, single mutant Y742D, and double mutant Y742D/Y743S Trichoplax AKDF<sup>19383</sup> receptors already show convincing evidence that the constitutive activity is via the Y742 and Y743 position: the tyrosine residues are unique to this leaky channel, and their mutation to more typical residues removes the leak current (Fig. 7B, page 11, revised manuscript).

      But a look at upper M3 is warranted. As shown in Fig. 6C, AKDF<sup>19383</sup> (YTANMAAFL) is quite similar to typical iGluRs (e.g. GluA2 YTANLAAFL). But one might ask about the single M/L difference in that motif, and we have therefore made and tested the M657L AKDF<sup>19383</sup> mutant, comparing it with WT. Results show that this small M3 difference has little effect on channel activity. We have added this data in new Figure 7D and described it (page 11): “As channel activity of iGluRs also relies on the upper segment of the third membrane-spanning helix (M3, (34)), we also examined this segment in AKDF<sup>19383</sup>. AKDF<sup>19383</sup> differs only subtly from most iGluRs with a methionine residue (M657) instead of leucine here (Fig. 6C), but we tested potential effects of this difference by mutating M657 to leucine. M657L activity was much like WT (Fig. 7D), however, confirming that divergence at Y742/Y743 and not the upper M3 segment determines the unique activity of AKDF<sup>19383</sup>.”

      1. D-serine is another co-agonist that binds the GluN1 subunit. Compared to glycine, D-serine can make additional interactions with the lower lobe of GluN1 LBD. It would be interesting to look at D-serine dose-response curves in GluN1-D732L/GluN2A receptors: are these receptors also D-serine insensitive or can they be further activated by D-serine?

      OS8) We have now measured the effects of D-serine on GluN1-D732L/GluN2A-WT receptors. As we now show in Figure 1B (green symbols), D-serine at increasing concentrations (100 nM through 100 μM) activates no additional current on top of the glutamate-gated current in mutant receptors. We have added to the end of the first Results paragraph (page 3): “Similarly, large currents were activated in mutant GluN1-D732L/GluN2A-WT receptors when 100 nM through 100 μM D-Serine was applied the presence of 100 µM glutamate (green in Fig. 1B).”

      (This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

    1. Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

      Strengths:

      The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

      Weaknesses:

      The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

      We agree with the reviewer in their assessment that more independent stem cell clones and an equal gender balance would be preferable. We will mention these considerations as limitations of our study and encourage a larger-scale follow-up.

      Regarding the estimated copy numbers, we would like to highlight that they have been extensively in the field, with direct validation of the differences in copy numbers with orthogonal methods like FACS2-4,7,10. Furthermore, the original paper directly compared the copy numbers estimated using the “proteomic ruler” to spike-in protein epitope signature tags and found remarkable concordance. This was performed with a much older generation mass spectrometer with reduced peptide coverage, and the author predicted that higher coverage would increase the quantitative performance.

      Reviewer #2 (Public Review):

      Summary:

      Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine.

      A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs.

      This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

      We thank the reviewer for the positive assessment.

      Strengths:

      The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

      Weaknesses:

      While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

      We agree with the Reviewer that our study does not provide a mechanistic reason for the quantitative differences between the two cell types. However, we will include an expanded section in the discussion where we discuss the potential causes.<br /> We also agree studying hiPSCs reprogrammed from different cell types, such as blood lymphocytes, would be of great interest and will include a section about this within the discussion to encourage further research into the area.

      Reviewer #3 (Public Review):

      Summary:

      In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

      Strengths:

      The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

      Weaknesses:

      It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

      We respectfully disagree with the reviewer on this point. Our data shows that hESCs and hiPSCs show significant differences in protein mass and cell size, validated by the EZQ assay and FACS, while having no significant differences in their cell cycle profiles. Thus increased size and protein content would require higher growth rates to sustain the increased mass, which is what we show.

      The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.<br /> We apologise for this omission; the details will be included in the revised version of the document.

      Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

      We agree with the reviewer on this. The hiPSC lines were generated by the HipSci consortium in the Wellcome Sanger Centre as described in the flagship HipSci paper13. We cite the flagship paper which specifies in great detail the reprogramming protocols and quality control measures, including looking at copy number variations13. However, we agree that we did not make this information easily accessible for readers. We also believe it is relevant to also explicitly include this information on our manuscript instead of expecting readers to look at the flagship paper. These details will be added to the revised version.

      The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

      We agree with the reviewer on this. FACS is indeed much more quantitative and a better method to study heterogeneity. However, we did not have protocols to study these markers using FACS.

      TEM analysis should ideally be quantified.

      We agree with the reviewer that it would be nice to have a quantitative measure.

      All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

      We agree with the reviewer completely. These points will be remediated in the revised version of the manuscript.

      Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

      We will include these details within the updated manuscript.

      The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

      We agree with the reviewer and will add a clear sentence in the discussion making this point explicitly.

      Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

      Regarding H1 histones, our study of the literature as well as interaction with chromatin and histone experts both within our institute and externally have not shed light into what the differences could imply. We think this is an interesting result that merits further study, but we don’t have a clear hypothesis on the consequences.

      In summary, we thank the reviewers for their comments and will prepare a revised version that addresses their suggestions.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This study uses a multi-pronged empirical and theoretical approach to advance our understanding of how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats. The work makes an important contribution and parts of the data and analyses are solid, although several of the main claims are only partially supported or overstated and require additional support.

      Public Reviews:

      We thank the Editor and both Reviewers for their time and for their constructive evaluation of our manuscript. We worked to address each comment and suggestion offered by the Reviewers in our revision—please see our point-by-point responses below.

      Reviewer #1 (Public Review):

      Summary:

      In this highly ambitious paper, Breen and Deffner used a multi-pronged approach to generate novel insights on how differences between male and female birds in their learning strategies might relate to patterns of invasion and spread into new geographic and urban areas.

      The empirical results, drawn from data available in online archives, showed that while males and females are similar in their initial efficiency of learning a standard color-food association (e.g., color X = food; color Y = no food) scenario when the associations are switched (now, color Y = food, X= no food), males are more efficient than females at adjusting to the new situation (i.e., faster at 'reversal learning'). Clearly, if animals live in an unstable world, where associations between cues (e.g., color) and what is good versus bad might change unpredictably, it is important to be good at reversal learning. In these grackles, males tend to disperse into new areas before females. It is thus fascinating that males appear to be better than females at reversal learning. Importantly, to gain a better understanding of underlying learning mechanisms, the authors use a Bayesian learning model to assess the relative role of two mechanisms (each governed by a single parameter) that might contribute to differences in learning. They find that what they term 'risk sensitive' learning is the key to explaining the differences in reversal learning. Males tend to exhibit higher risk sensitivity which explains their faster reversal learning. The authors then tested the validity of their empirical results by running agent-based simulations where 10,000 computersimulated 'birds' were asked to make feeding choices using the learning parameters estimated from real birds. Perhaps not surprisingly, the computer birds exhibited learning patterns that were strikingly similar to the real birds. Finally, the authors ran evolutionary algorithms that simulate evolution by natural selection where the key traits that can evolve are the two learning parameters. They find that under conditions that might be common in urban environments, high-risk sensitivity is indeed favored.

      Strengths:

      The paper addresses a critically important issue in the modern world. Clearly, some organisms (some species, some individuals) are adjusting well and thriving in the modern, human-altered world, while others are doing poorly. Understanding how organisms cope with human-induced environmental change, and why some are particularly good at adjusting to change is thus an important question.

      The comparison of male versus female reversal learning across three populations that differ in years since they were first invaded by grackles is one of few, perhaps the first in any species, to address this important issue experimentally.

      Using a combination of experimental results, statistical simulations, and evolutionary modeling is a powerful method for elucidating novel insights.

      Thank you—we are delighted to receive this positive feedback, especially regarding the inferential power of our analytical approach.

      Weaknesses:

      The match between the broader conceptual background involving range expansion, urbanization, and sex-biased dispersal and learning, and the actual comparison of three urban populations along a range expansion gradient was somewhat confusing. The fact that three populations were compared along a range expansion gradient implies an expectation that they might differ because they are at very different points in a range expansion. Indeed, the predicted differences between males and females are largely couched in terms of population differences based on their 'location' along the rangeexpansion gradient. However, the fact that they are all urban areas suggests that one might not expect the populations to differ. In addition, the evolutionary model suggests that all animals, male or female, living in urban environments (that the authors suggest are stable but unpredictable) should exhibit high-risk sensitivity. Given that all grackles, male and female, in all populations, are both living in urban environments and likely come from an urban background, should males and females differ in their learning behavior? Clarification would be useful.

      Thank you for highlighting a gap in clarity in our conceptual framework. To answer the Reviewer’s question—yes, even with this shared urban ‘history’, it seems plausible that males and females could differ in their learning. For example, irrespective of population membership, such sex differences could come about via differential reliance on learning strategies mediated by an interaction between grackles’ polygynous mating system and malebiased dispersal system, as we discuss in L254–265 (now L295–306). Population membership might, in turn, differentially moderate the magnitude of any such sex-effect since an edge population, even though urban, could still pose novel challenges—for example, by requiring grackles to learn novel daily temporal foraging patterns such as when and where garbage is collected (grackles appear to track this food resource: Rodrigo et al. 2021 [DOI: 10.1101/2021.06.14.448443]). We now introduce this important conceptual information— please see L89–96.

      Reinforcement learning mechanisms:

      Although the authors' title, abstract, and conclusions emphasize the importance of variation in 'risk sensitivity', most readers in this field will very possibly misunderstand what this means biologically. Both the authors' use of the term 'risk sensitivity' and their statistical methods for measuring this concept have potential problems.

      Please see our below responses concerning our risk-sensitivity term.

      First, most behavioral ecologists think of risk as predation risk which is not considered in this paper. Secondarily, some might think of risk as uncertainty. Here, as discussed in more detail below, the 'risk sensitivity' parameter basically influences how strongly an option's attractiveness affects the animal's choice of that option. They say that this is in line with foraging theory (Stephens and Krebs 2019) where sensitivity means seeking higher expected payoffs based on prior experience. To me, this sounds like 'reward sensitivity', but not what most think of as 'risk sensitivity'. This problem can be easily fixed by changing the name of the term.

      We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. We further apologise for not clearly explaining how our lambda parameter estimates such risk-sensitive foraging. To do so here, we need to consider our Bayesian reinforcement learning model in full. This model uses observed choice-behaviour during reinforcement learning to infer our phi (information-updating) and lambda (risksensitivity) learning parameters. Thus, payoffs incurred through choice simultaneously influence estimation of each learning parameter—that is, in a sense, they are both sensitive to rewards. But phi and lambda differentially direct any reward sensitivity back on choicebehaviour due to their distinct definitions. Glossing over the mathematics, for phi, stronger reward sensitivity (bigger phi values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For lambda, stronger reward sensitivity (bigger lambda values) means stronger internal determinism about seeking the non-risk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’. We hope this information, which we have incorporated into our revised manuscript (please see L153–161), clarifies the rationale and mechanics of our reinforcement learning model, and why lamba measures risk-sensitivity.

      In addition, however, the parameter does not measure sensitivity to rewards per se - rewards are not in equation 2. As noted above, instead, equation 2 addresses the sensitivity of choice to the attraction score which can be sensitive to rewards, though in complex ways depending on the updating parameter. Second, equations 1 and 2 involve one specific assumption about how sensitivity to rewards vs. to attraction influences the probability of choosing an option. In essence, the authors split the translation from rewards to behavioral choices into 2 steps. Step 1 is how strongly rewards influence an option's attractiveness and step 2 is how strongly attractiveness influences the actual choice to use that option. The equation for step 1 is linear whereas the equation for step 2 has an exponential component. Whether a relationship is linear or exponential can clearly have a major effect on how parameter values influence outcomes. Is there a justification for the form of these equations? The analyses suggest that the exponential component provides a better explanation than the linear component for the difference between males and females in the sequence of choices made by birds, but translating that to the concepts of information updating versus reward sensitivity is unclear. As noted above, the authors' equation for reward sensitivity does not actually include rewards explicitly, but instead only responds to rewards if the rewards influence attraction scores. The more strongly recent rewards drive an update of attraction scores, the more strongly they also influence food choices. While this is intuitively reasonable, I am skeptical about the authors' biological/cognitive conclusions that are couched in terms of words (updating rate and risk sensitivity) that readers will likely interpret as concepts that, in my view, do not actually concur with what the models and analyses address.

      To answer the Reviewer’s question—yes, these equations are very much standard and the canonical way of analysing individual reinforcement learning (see: Ch. 15.2 in Computational Modeling of Cognition and Behavior by Farrell & Lewandowsky 2018 [DOI: 10.1017/CBO9781316272503]; McElreath et al. 2008 [DOI: 10.1098/rstb/2008/0131]; Reinforcement Learning by Sutton & Barto 2018). To provide a “justification for the form of these equations'', equation 1 describes a convex combination of previous values and recent payoffs. Latent values are updated as a linear combination of both factors, there is no simple linear mapping between payoffs and behaviour as suggested by the reviewer. Equation 2 describes the standard softmax link function. It converts a vector of real numbers (here latent values) into a simplex vector (i.e., a vector summing to 1) which represents the probabilities of different outcomes. Similar to the logit link in logistic regression, the softmax simply maps the model space of latent values onto the outcome space of choice probabilities which enter the categorial likelihood distribution. We can appreciate how we did not make this clear in our manuscript by not highlighting the standard nature of our analytical approach—we now do so in our revised manuscript (please see L148–149). As far as what our reinforcement learning model measures, and how it relates cognition and behaviour, please see our previous response.

      To emphasize, while the authors imply that their analyses separate the updating rate from 'risk sensitivity', both the 'updating parameter' and the 'risk sensitivity' parameter influence both the strength of updating and the sensitivity to reward payoffs in the sense of altering the tendency to prefer an option based on recent experience with payoffs. As noted in the previous paragraph, the main difference between the two parameters is whether they relate to behaviour linearly versus with an exponential component.

      Please see our two earlier responses on the mechanics of our reinforcement learning model.

      Overall, while the statistical analyses based on equations (1) and (2) seem to have identified something interesting about two steps underlying learning patterns, to maximize the valuable conceptual impact that these analyses have for the field, more thinking is required to better understand the biological meaning of how these two parameters relate to observed behaviours, and the 'risk sensitivity' parameter needs to be re-named.

      Please see our earlier response to these suggestions.

      Agent-based simulations:

      The authors estimated two learning parameters based on the behaviour of real birds, and then ran simulations to see whether computer 'birds' that base their choices on those learning parameters return behaviours that, on average, mirror the behaviour of the real birds. This exercise is clearly circular. In old-style, statistical terms, I suppose this means that the R-square of the statistical model is good. A more insightful use of the simulations would be to identify situations where the simulation does not do as well in mirroring behaviour that it is designed to mirror.

      Based on the Reviewer’s summary of agent-based forward simulation, we can see we did a poor job explaining the inferential value of this method—we apologise. Agent-based forward simulations are posterior predictions, and they provide insight into the implied model dynamics and overall usefulness of our reinforcement learning model. R-squared calculations are retrodictive, and they say nothing about the causal dynamics of a model. Specifically, agent-based forward simulation allows us to ask—what would a ‘new’ grackle ‘do’, given our reinforcement learning model parameter estimates? It is important to ask this question because, in parameterising our model, we may have overlooked a critical contributing mechanism to grackles’ reinforcement learning. Such an omission is invisible in the raw parameter estimates; it is only betrayed by the parameters in actu. Agent-based forward simulation is ‘designed’ to facilitate this call to action—not to mirror behavioural results. The simulation has no apriori ‘opinion’ about computer ‘birds’ behavioural outcomes; rather, it simply assigns these agents random phi and lambda draws (whilst maintaining their correlation structure), and tracks their reinforcement learning. The exercise only appears circular if no critical contributing mechanism(s) went overlooked—in this case computer ‘birds’ should behave similar to real birds. A disparate mapping between computer ‘birds’ and real birds, however, would mean more work is needed with respect to model parameterisation that captures the causal, mechanistic dynamics behind real birds’ reinforcement learning (for an example of this happening in the human reinforcement learning literature, see Deffner et al. 2020 [DOI: 10.1098/rsos.200734]). In sum, agent-based forward simulation does not access goodness-of-fit—we assessed the fit of our model apriori in our preregistration (https://osf.io/v3wxb)—but it does assess whether one did a comprehensive job of uncovering the mechanistic basis of target behaviour(s). We have worked to make the above points on the method and the insight afforded by agent-based forward simulation explicitly clear in our revision—please see L192–207 and L534–537.

      Reviewer #2 (Public Review):

      Summary:

      The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find moderate support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as those observed in the Grackle data. In the third part, the authors present an evolutionary model, in which they show that learning strategies as observed in male Grackles do evolve in what they regard as conditions present in urban environments.

      Strengths:

      The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show moderately better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency, especially regarding data analysis.

      Thank you—we are pleased to receive this positive evaluation, particularly concerning our efforts to improve scientific transparency via our study’s preregistration (https://osf.io/v3wxb).

      Weaknesses:

      One major weakness of this manuscript is the fact that the authors are working with quite low sample sizes when we look at the different populations of edge (11 males & 8 females), middle (4 males & 4 females), and core (17 males & 5 females) expansion range. Although I think that when all populations are pooled together, the sample size is sufficient to answer the questions regarding sex differences in learning performance and which learning processes might be used by grackles but insufficient when taking the different populations into account.

      In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results (it might; so might muchneeded population replicates—see L310), but our Bayesian models still allow us to learn a lot from our current data. We now explain this in our revised manuscript—please see L452–457.

      Another weakness of this manuscript is that it does not set up the background well in the introduction. Firstly, are grackles urban dwellers in their natural range and expand by colonising urban habitats because they are adapted to it? The introduction also fails to mention why urban habitats are special and why we expect them to be more challenging for animals to inhabit. If we consider that one of their main questions is related to how learning processes might help individuals deal with a challenging urban habitat, then this should be properly introduced.

      In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question. We have worked towards flushing out how urban-imposed challenges faced by grackles, such as the wildlife management efforts introduced in L64–65 (now L85–86), may apply to animals inhabiting urban environments more broadly; for example, we now include an entire paragraph in our Introduction detailing how urban environments may be characterised differently to nonurban environments, and thus why they are perhaps more challenging for animals to inhabit— please see L56–71.

      Also, the authors provide a single example of how learning can differ between populations from more urban and more natural habitats. The authors also label the urban dwellers as the invaders, which might be the case for grackles but is not necessarily true for other species, such as the Indian rock agama in the example which are native to the area of study. Also, the authors need to be aware that only male lizards were tested in this study. I suggest being a bit more clear about what has been found across different studies looking at: (1) differences across individuals from invasive and native populations of invasive species and (2) differences across individuals from natural and urban populations.

      We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49), and we are careful to call attention to the fact that these data cover both resident urban and non-urban species as well as urban invasive species (please see L49–50). We also revised our labelling of the lizard species (please see L44). We are aware only male lizards were tested but this information is not relevant to substantiating our use of this study; that is, to highlight that learning can differ between urbandwelling and non-urban counterparts. We hope the changes we did make to our manuscript satisfy the Reviewer’s general suggestion to add biological clarity.

      Finally, the introduction is very much written with regard to the interaction between learning and dispersal, i.e. the 'invasion front' theme. The authors lay out four predictions, the most important of which is No. 4: "Such sex-mediated differences in learning to be more pronounced in grackles living at the edge, rather than the intermediate and/or core region of their range." The authors, however, never return to this prediction, at least not in a transparent way that clearly pronounces this pattern not being found. The model looking at the evolution of risk-sensitive learning in urban environments is based on the assumption that urban and natural environments "differ along two key ecological axes: environmental stability 𝑢 (How often does optimal behaviour change?) and environmental stochasticity 𝑠 (How often does optimal behaviour fail to pay off?). Urban environments are generally characterised as both stable (lower 𝑢) and stochastic (higher 𝑠)". Even though it is generally assumed that urban environments differ from natural environments the authors' assumption is just one way of looking at the differences which have generally not been confirmed and are highly debated. Additionally, it is not clear how this result relates to the rest of the paper: The three populations are distinguished according to their relation to the invasion front, not with respect to a gradient of urbanization, and further do not show a meaningful difference in learning behaviour possibly due to low sample sizes as mentioned above.

      Thank you for highlighting a gap in our reporting clarity. We now take care to transparently report our null result regarding our fourth prediction; more specifically, that we did not detect credible population-level differences in grackles’ learning (please see L130). Regarding our evolutionary model, we agree with the Reviewer that this analysis is only one way of looking at the interaction between learning phenotype and apparent urban environmental characteristics. Indeed, in L282–288 (now L325–329) we state: “Admittedly, our evolutionary model is not a complete representation of urban ecology dynamics. Relevant factors—e.g., spatial dynamics and realistic life histories—are missed out. These omissions are tactical ones. Our evolutionary model solely focuses on the response of reinforcement learning parameters to two core urban-like (or not) environmental statistics, providing a baseline for future study to build on”. But we can see now that ‘core’ is too strong a word, and instead ‘supposed’, ‘purported’ or ‘theorised’ would be more accurate—we have revised our wording throughout our manuscript to say as much (please see, for example, L24; L56; L328). We also further highlight the preliminary nature of our evolutionary model, in terms of allowing a narrow but useful first-look at urban eco-evolutionary dynamics—please see L228–232. Finally, we now detail the theorised characteristics of urban environments in our Introduction (rather than in our Results; please see L56–71), and we hope that by doing so, how our evolutionary results relate to the rest of our paper is now better set up and clear.

      In conclusion, the manuscript was well written and for the most part easy to follow. The format of eLife having the results before the methods makes it a bit harder to follow because the reader is not fully aware of the methods at the time the results are presented. It would, therefore, be important to more clearly delineate the different parts and purposes. Is this article about the interaction between urban invasion, dispersal, and learning? Or about the correct identification of learning mechanisms? Or about how learning mechanisms evolve in urban and natural environments? Maybe this article can harbor all three, but the borders need to be clear. The authors need to be transparent about what has and especially what has not been found, and be careful to not overstate their case.

      Thank you, we are pleased to read that the Reviewer found our manuscript to be generally digestible. We have worked to add further clarity, and to tempter our tone (please see our above and below responses).

      Reviewer #1 (Recommendations For The Authors):

      Several of the results are based on CIs that overlap zero. Tone these down somewhat.

      We apologise for overstating our results, which we have worked to tone down in our revision. For instance, in L185–186 we now differentiate between estimates that did or did not overlap zero (please also see our response to Reviewer 2 on this tonal change). We note we do not report confidence intervals (i.e., the range of values expected to contain the true estimate if one redoes the study/analysis many times). Rather, we report 89% highest posterior density intervals (i.e., the most likely values of our parameters over this range). We have added this definition in L459, to improve clarity.

      The literature review suggesting that urban environments are more unpredictable is not convincing. Yes, they have more noise and light pollution and more cars and planes, but does this actually relate to the unpredictability of getting a food reward when you choose an option that usually yields rewards?

      To answer the Reviewer’s question—yes. But we can see that by not including empirical examples from the literature, we did a poor job of arguing such links. In L43–49 we now give three empirical examples; more specifically, we state: “[...] experimental data show the more variable are traffic noise and pedestrian presence, the more negative are such human-driven effects on birds' sleep (Grunst et al., 2021), mating (Blickley et al., 2012), and foraging behaviour (Fernández-Juricic, 2000).” We note we now detail such apparently stable but stochastic urban environmental characteristics in our Introduction rather than our Results section, to hopefully improve the clarity of our manuscript (please see L56–71). We further note that we cite three literature reviews—not one—suggesting urban environments are stable in certain characteristics and more unpredictable in others (please see L59–60). Finally, we appreciate such characterisation is not certain, and so in our revision we have qualified all writing about this potential dynamic with words such as “apparent”, “supposed”, “theorised”, “hypothesised” etc.

      It would be interesting to see if other individual traits besides sex affect their learning/reversal learning ability and/or their learning parameters. Do you have data on age, size, condition, or personality? Or, the habitat where they were captured?

      We do not have these data. But we agree with the Reviewer that examining the potential influence of such covariates on grackles’ reinforcement learning would be interesting in future study, especially habitat characteristics (please see L306–309).

      For most levels of environmental noise, there appears to be an intermediate maximum for the relationship between environmental stability and the risk sensitivity parameter. What does this mean?

      There is indeed an intermediate maximum for certain values of environmental stochasticity (although the differences are rather small). The most plausible reason for this is that for very stable environments, simulated birds essentially always “know” the rewarded solution and never need to “relearn” behaviour. In this case, differences in latent values will tend to be large (because they consistently get rewarded for the same option), and different lambda values (in the upper range) will produce the same choice behaviour, which results in very weak selection. While in very unstable environments, optimal choice behaviour should be more exploratory, allowing learners to track frequently-changing environments. We now note this pattern in L240–248.

      Reviewer #2 (Recommendations For The Authors):

      L2: I'd encourage the authors to reconsider the term "risk-sensitive learning", at least in the title. It's not apparent to me how 'risk' relates to the investigated foraging behaviour. Elsewhere, risk-reward sensitivity is used which may be a better term.

      We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. In explaining our reinforcement model, we also now detail how risk relates to foraging behaviour. Specifically, in L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies why lamba measures risk-sensitivity, and why we continue to use this term.

      L1-3: The title is a bit misleading with regard to the empirical data. From the data, all that can be said is that male grackles relearn faster than females. Any difference between populations actually runs the other way, with the core population exhibiting a larger difference between males and females than the mid and edge populations.

      It is customary for a manuscript title to describe the full scope of the study. In our study, we have empirical data, cognitive modelling, and evolutionary simulations of the background theory all together. And together these analytical approaches show: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; and (3) risk-sensitive learning should be favoured in statistical environments characterised by purported urban dynamics. So, we do not feel our title “Leading an urban invasion: risk-sensitive learning is a winning strategy” is misleading with regard to our empirical data; it just doesn’t summarise only our empirical data. Finally, as we now state in L312–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences.

      L13: "Assayed", is that correctly put, given that the authors did not collect the data?

      Merrian-Webster defines assay as “to analyse” or “examination or determination as to characteristics”, and so to answer the Reviewer’s question—yes, we feel this is correctly put. We note we explicitly introduce in L102–103 that we did not collect the data, and we have an explicit “Data provenance” section in our methods (please see L342–347).

      L42-46: The authors provide a single example of how learning can differ between populations from more urban and more natural habitats. I would like to point out that many of these studies do not directly confirm that the ability in question has indeed led to the success of the species tested (e.g. show fitness consequences). Then the authors could combine these insights to form a solid prediction for the grackles. As of now, this looks like cherry-picking supportive literature without considering negative results.

      Here are some references that might be helpful in identifying relevant literature to cite:

      Szabo, B., Damas-Moreira, I., & Whiting, M. J. (2020). Can cognitive ability give invasive species the means to succeed? A review of the evidence. Frontiers in Ecology and Evolution, 8, 187.

      Griffin AS, Tebbich S, Bugnyar T, 2017. Animal cognition in a human-dominated world. Anim Cogn 20(1):1-6.

      Kark, S., Iwaniuk, A., Schalimtzek, A., & Banker, E. (2007). Living in the city: Can anyone become an "urban exploiter"? Journal of Biogeography, 34(4), 638-651.

      We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49). We are aware that direct evidence of fitness consequences is entirely lacking in the scientific literature on cognition and successful urban invasion; hence why such data is not present in our paper. But we now explicitly point out a role for likely fitness-affecting anthropogenic disturbances on sleep, mate, and foraging behaviour on animals inhabiting urban environments (please see L63–68). We hope these new data bolster our predictions for our grackles. Finally, the Reviewer paints a (in our view) inaccurate picture of our use of available literature. Nevertheless, to address their comment, we now highlight a recent meta-analysis advocating for further research to confirm apparent ‘positive’ trends between animal ‘smarts’ and successful ‘city living’ (please see L43).

      L64: Is their niche historically urban, or have they recently moved into urban areas?

      In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question.

      L66-67: This is an important point that is however altogether missing from the discussion.

      We thank the Reviewer for highlighting a gap in our discussion regarding populationlevel differences in grackles’ reinforcement learning. In L310–312 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

      L68-71: The paper focuses on cognitive ability. The whole paragraph sets up the prediction of why male grackles should be better learners due to their dispersal behaviour. This example, however, focuses on aggression, not cognition. Here is a study showing differences in learning in male and female mynas that might be better suited:

      Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS, 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn 20(1):65-74.

      We thank the Reviewer for suggesting this paper. We feel it is better suited to substantiating our point in the Discussion about reversal learning not being indicative of cognitive ability—please see L276–277.

      L73: Generally, I suggest not writing "for the first time" as this is not a valid argument for why a study should be conducted. Furthermore, except for replication studies, most studies investigate questions that are novel and have not been investigated before.

      The Reviewer makes a fair point—we have removed this statement.

      L80-81: Here again, this is left undiscussed later on.

      By ‘this’ we assume the Reviewer is referring to our hypothesis, which is that sex differences in dispersal are related to sex differences in learning in an urban invader— grackles. At the beginning of our Discussion, we state how we found support for this hypothesis (please see L250–261); and in our ‘Ideas and speculation’ section, we discuss how these hypothesis-supporting data fit into the literature more broadly (please see L294–331). We feel this is therefore sufficiently discussed.

      L77-81: This sentence is very long and therefore hard to read. I suggest trying to split it into at least 2 separate sentences which would improve readability.

      Per the Reviewer’s useful suggestion, we have split this sentence into two separate sentences—please see L97–115.

      L83: Please explain choice-option switches. I am not aware of what that is and it should be explained at first mention.

      We apologise for this operational oversight. We now include a working definition of speed and choice-option switches at first mention. Specifically, in L107–108 we state: “[...] we expect male and female grackles to differ across at least two reinforcement learning behaviours: speed (trials to criterion) and choice-option switches (times alternating between available stimuli)”.

      L83-87: Again, a very long sentence. Please split.

      We thank the Reviewer for their suggestion. In this case we feel it is important to not change our sentence structure because we want our prediction statements to match between our manuscript and our preregistration.

      L96-97: Important to not overstate this. It merely demonstrates the potential of the proposed (not detected) mechanism to generate the observed data.

      As in any empirical analysis, our drawn conclusions depend on causal assumptions about the mechanisms generating behaviour (Pearl, J. (2009). Causality). Therefore, we “detected” specific learning mechanisms assuming a certain generative model, namely reinforcement learning. As there is overwhelming evidence for the widespread importance of value-based decision making and Rescorla-Wagner updating rules across numerous different animals (Sutton & Barto (2018) Reinforcement Learning), we would argue that this assumed model is highly plausible in our case. Still, we changed the text to “inferred” instead of “detected” learning mechanisms to account for this concern—please see L123–124.

      L99: "urban-like settings" again a bit confusing. The authors talk about invasion fronts, but now also about an urbanisation gradient. Is the main difference between the size and the date of establishment, or is there additionally a gradient in urbanisation to be considered?

      We now include a paragraph in our Introduction detailing apparent urban environmental characteristics (please see 56–71), and we now refer to this dynamic specifically when we define urban-like settings (please see L126–127). To answer the Reviewer’s question—we consider both differences. Specifically, we consider the time since population establishment in our paper (with respect to our behavioural and mechanistic modelling), as well as how statistical environments that vary in how similar they are to apparently characteristically urban-like environments, might favour particular learning phenotypes (with respect to our evolutionary modelling). We hope the edits to our Introduction as a whole now make both of the aims clear.

      L11-112: Above the authors talk about a comparable number of switches (10.5/15=0.7), and here of fewer number of switches (25/35=0.71), even though the magnitude of the difference is almost identical and actually runs the other way. The authors are probably misled by their conservative priors, which makes the difference appear greater in the second case than in the first. Using flat priors would avoid this particular issue.

      Mathematically, the number of trials-to-finish and the number of choice-optionswitches are both a Poisson distributed outcome with rate λ (we note lambda here is not our risk-sensitivity parameter; just standard notation). As such, our Poisson models infer the rate of these outcomes by sex and phase—not the ratio of these outcomes by sex and phase. So comparing the magnitude of divided medians of choice-option-switches between the sexes by phase is not a meaningful metric with respect to the distribution of our data, as the Reviewer does above. For perspective, 1 vs. 2 switches provides much less information about the difference in rates of a Poisson distribution than 50 vs 100 (for the former, no difference would be inferred; for the latter, it would), but both exhibit a 1:2 ratio. To hopefully prevent any such further confusion, and to focus on the fact that our Poisson models estimate the expected value i.e., the mean, we now report and graph (please see Fig. 2) mean and not median trialsto-finish and total-switch-counts. Finally, we can see that our use of the word “conservative” to describe our weakly informative priors is confusing, because conservative could mean either strong priors with respect to expected effect size (not our parameterisation) or weak priors with respect to such assumptions (our parameterisation). To address this lack of clarity, we now state that we use “weakly informative priors” in L457–458.

      L126: It is not clear what risk sensitivity means in the context of these experiments.

      Thank you for pointing out our lack of clarity. In L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies what risk sensitivity means and measures, with respect to our behavioural experiments.

      L128-129: I find this statement too strong. A plethora of other mechanisms could produce similar patterns, and you cannot exclude these by way of your method. All you can show is whether the mechanism is capable of producing broadly similar outcomes as observed

      In describing the inferential value of our reinforcement learning model, we now qualify that the insight provided is of course conditional on the model, which is tonally accurate. Please see L161.

      L144: As I have already mentioned above, here is the first time we hear about unpredictability related to urban environments. I suggest clearly explaining in the introduction how urban and natural environments are assumed to be different which leads to animals needing different cognitive abilities to survive in them which should explain why some species thrive and some species die out in urbanised habitats.

      Thank you for this suggestion. We now include a paragraph in our Introduction detailing as much—please see L56–71.

      L162: "almost entirely above zero" again, this is worded too strongly.

      In reporting our lambda across-population 89% HPDI contrasts in L185–186, we now state: “[...] across-population contrasts that lie mostly above zero in initial learning, and entirely above zero in reversal learning”. Our previous wording stated: ““[...] across-population contrasts that lie almost entirely above zero”. The Reviewer was correct to point out that this previous wording was too strong if we considered the contrasts together, as, indeed, we find the range of the contrast in initial learning does minimally overlap zero (L: -0.77; U: 5.61), while the range of the contrast in reversal learning does not (L: 0.14; U: 4.26). This rephrasing is thus tonally accurate.

      L178-179: I think it should be said instead that the model accounts well for the observed data.

      We have rephrased in line with the Reviewer’s suggestion, now stating in L217–218 that “Such quantitative replication confirms our reinforcement learning model results sufficiently explain our behavioural sex-difference data.”

      L188-190: I am not convinced this is a general pattern. It is quite a bold claim that I don't find to be supported by the citations. Why should biotic and abiotic factors differ in how they affect behavioural outcomes? Also, events in urban environments such as weekend/weekday could lead to highly regular optimal behaviour changes.

      Please see our response to Reviewer 1 on this point. We note we now touch on such regular events in L94–96.

      L209-211: The first sentence is misleading. The authors have found that males and females differ in 'risk sensitivity', that their learning model can fit the data rather well, and that under certain, not necessarily realistic assumptions, the male learning type is favoured by natural selection in urban environments. A difference between core, middle, and edge habitats however is barely found, and in fact seems to run the other way than expected.

      In our study, we found: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; (3) we are sufficiently certain risk-sensitive learning generates our sex-difference data, as our agentbased forward simulations replicate our behavioural results (not because our model ‘fits’ the data, but because we inferred meaningful mechanistic differences—see our response to Reviewer 1 on this point); and (4) under theorised dynamics of urban environments, natural selection should favour risk-sensitive learning. We therefore do not feel it is misleading to say that we mapped a full pathway from behaviour to mechanisms through to selection and adaptation. Again, as we now state in L311–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences. And we note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale—please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324.

      L216: "indeed explain" again worded too strongly.

      We have tempered our wording. Specifically, we now state in L218: “sufficiently explain”. This wording is tonally accurate with respect to the inferential value of agent-based forward simulations—please see L192–207 on this point.

      L234: "reward-payoff sensitivity" might be a better term than risk-sensitivity?

      Please see our earlier response to this suggestion. We note we have changed this text to state “risk-sensitive learning” rather than “reward-payoff sensitivity”, to hopefully prevent the reader from concluding only our lambda term is sensitive to rewards—a point we now include in L153–154.

      L234-237: I think these points may be valuable, but come too much out of the blue. Many readers will not have a detailed knowledge of the experimental assays. It therefore also does not become clear how they measure the wrong thing, what this study does to demonstrate this, or whether a better alternative is presented herein. It almost seems like this should be a separate paper by itself.

      We apologise for this lack of context. We now explicitly state in L275 that we are discussing reversal learning assays, to give all readers this knowledge. In doing so, we hope the logic of our argument is now clear: reversal learning assays do not measure behavioural flexibility, whatever that even is. The Reviewer’s suggestion of a separate paper focused on what reversal learning assays actually measure, in terms of mechanism(s), is an interesting one, and we would welcome this discussion. But any such paper should build on the points we make here.

      L270-288: Somewhere here the authors have to explain how they have not found differences between populations, or that in so far as they found them, they run against the originally stated hypothesis.

      We thank the Reviewer for these suggestions. In L310—313 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

      L284: should be "missing" not "missed out"

      We have made this change.

      L290-291: It is unclear what "robust interactive links" were found. A pattern of sexbiased learning was found, which can potentially be attributed to evolutionary pressures in urban environments. An interaction e.g. between learning, dispersal, and sex can only be tentatively suggested (no differences between populations). Also "fully replicable" is a bit misleading. The analysis may be replicable, but the more relevant question of whether the findings are replicable we cannot presently answer.

      We apologise for our lack of clarity. By “robust” we mean “across population”, which we now state in L333. We again note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale— please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324. Finally, the Reviewer makes a good point about our analyses but not our findings being replicable. In L334 we now make this distinction by stating “analytically replicable”.

      L306-315: I think you have a bit of a sample size issue not so much when populations are pooled but when separated. This might also factor in the fact that you do not really find differences across the populations in your analysis. When we look at the results presented in Figure 2 (and table d), we can see a trend towards males having better risk sensitivity in core (HPDI above 0) and middle populations (HPDI barely crossing 0) but the difference is very small. Especially the results on females are based on the performance of only 8 and 4 females respectively. I suggest making this clear in the manuscript.

      In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results; it might; so might muchneeded population replicates—see L310. But our Bayesian models still allow us to learn a lot from our current data, and, at present, we infer no meaningful population-level behavioural or mechanistic differences in grackles’ behaviour. To make clear the inferential sufficiency of our analytical approach, we now include some of the above points in our Statistical analyses section in L452–457. Finally, we caution against speculating on any between-population variation, as we now highlight in L311—313 of our Discussion.

      Figure 2: I think the authors should rethink their usage of colour in this graph. It is not colour-blind friendly or well-readable when printed in black and white.

      We used the yellow (hex code: #fde725) and green (hex code: #5ec962) colours from the viridis package. As outlined in the viridis package vignette (https://cran.rproject.org/web/packages/viridis/index.html), this colour package is “designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing”.

      Figure 3B: Could the authors turn around the x-axis and the colour code? It would be easier to read this way.

      We appreciate that aesthetic preferences may vary. In this case, we prefer to have the numbers on the x-axis run the standard way i.e., from small to large. We note we did remove the word ‘Key’ from this Figure, in line with the Reviewer’s point about these characteristics not being totally certain.

      I also had a look at the preregistration. I do think that there are parts in the preregistration that would be worth adding to the manuscript:

      L36-40: This is much easier to read here than in the manuscript.

      We changed this text generally in the Introduction in our revision, so we hope the Reviewer will again find this easier to read.

      L49-56: This is important information that I would also like to see in the manuscript.

      We no longer have confidence in these findings, as our cleaning of only one part of these data revealed considerable experimenter oversight (see ‘Learning criterion’).

      L176: Why did you remove the random effect study site from the model? It is not part of the model in the manuscript anymore.

      The population variable is part of the RL_Comp_Full.stan model that we used in our manuscript to assess population differences in grackles’ reinforcement learning, the estimates from which we report in Table C and D (please note we never coded this variable as “study cite”). But rather than being specified as a random effect, in our RL_Comp_Full.stan model we index phi and lambda by population as a predictor variable, to explicitly model population-level effects. Please see our code:

      https://github.com/alexisbreen/Sex-differences-in-grackles- learning/blob/main/Models/Reinforcement%20learning/RL_Comp_Full.stan

      L190-228: I am wondering if the model validation should also be part of the manuscript as well, rather than just being in the preregistration?

      We are not sure how the files were presented to the Reviewer for review, but our study preregistration, which includes our model validation, should be part of our manuscript as a supplementary file.

    1. how do we educate our children so they have a sense of cultural identity and so that we can pass on the cultural genes of our 00:00:53 communities while being part of the process of globalization how do you square that circle the problem is they're trying to meet the future by doing what they did in the past

      I think it is important to know your culture. In my opinion it's important because it's where I came from and where my family comes from. It also may provide reasoning as to why some things are the way they are in your life.

    1. Let’s face it, very few people read the “terms and conditions,” or the “terms of use” agreements prior to installing an application (app). These agreements are legally binding, and clicking “I agree” may permit apps (the companies that own them) to access your: calendar, camera, contacts, location, microphone, phone, or storage, as well as details and information about your friends.

      This is so important it's not something I ever considered or worried about when thinking of privacy and security. I never read the "terms and conditions" when getting on to new apps or websites. I didn't think about how I could be agreeing to things that I would never agree to if someone asked me directly. This could not only be harmful to me, but to family and friends too because their information could be embedded into what I allowed access to. This caused me to think about how I can say something or see something and an ad for that specific thing pops up on my social media or google account right after. I'm agreeing for social media accounts to listen and look at everything on my device and share it with people. This is scary to think about, especially for young children. Anyone could hack into these accounts and get information about everything dealing with a child's life. As educators, we need to be cautious about reading the terms and conditions and we need to teach our students to be cautious of them too for their safety.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      (1.1) This work introduces a new method of imaging the reaction forces generated by small crawling organisms and applies this method to understanding locomotion of Drosophila larva, an important model organism. The force and displacement data generated by this method are a qualitative improvement on what was previously available for studying the larva, improving simultaneously the spatial, temporal, and force resolution, in many cases by an order of magnitude. The resulting images and movies are quite impressive.

      We thank the reviewer for their recognition of the achievements our work presents and for their feedback with regard to what they consider our most important findings and the points raised in their review. We will address these points individually below.

      (1.2) As it shows the novel application of recent technological innovations, the work would benefit from more detail in the explanation of the new technologies, of the rationales underlying the choice of technology and certain idiosyncratic experimental details, and of the limitations of the various techniques. In the methods, the authors need to be sure to provide sufficient detail that the work can be understood and replicated. The description of the results and the theory of motion developed here focus only on forces generated when the larva pushes against the substrate and ignores the equally strong adhesive forces pulling the larva onto the substrate.

      As the reviewer correctly points out, our present work adapts a recently developed set of methods (namely, ERISM and WARP) for use with small soft-bodied animals. The foundational methods have been described in detail in previous publications (refs, 23 and 26). However, upon reflection, we agree that more information can be provided to ensure our work is more accessible and reproducible. We also agree that some additional clarifying information on our approach could be helpful. We have addressed this in the following ways:

      (1) We have included a detailed Key Resources table in the methods section to allow for maximum transparency on equipment and reagent sourcing. This can now be found on Pages 16-19.

      (2) We have modified the ‘Freely behaving animals force imaging’ section of the Materials and Methods section to include more detailed information on practical aspects of conducting experiments. These changes can be found on page 23-24 (lines 566–567, 571-577).

      (3) We have re-ordered the Materials and Methods section, such that microcavity fabrication and microcavity characterisation occur prior to the description of ERISM and WARP experiments - this change should hopefully aid replication. Details regarding the application of a silicone well to the surface of microcavities have also been added (lines 472-474).

      (4) We have added additional text in the Introduction and Results (Pages 3-4 and 7, lines 56-86, and 152-153) to explain our rationale for using ERISM/WARP and additional text in the discussion that discusses the potential role(s) of adhesive forces in larval locomotion (Page 12, lines 301307).

      (1.3) The substrate applies upward, downward, and horizontal forces on the larva, but only upward and downward forces are measured, and only upward forces are considered in the discussions of "Ground Reactive Forces." An apparent weakness of the WARP technique for the study of locomotion is that it only measures forces perpendicular to the substrate surface ("vertical forces" in Meek et al.), while locomotion requires the generation of forces parallel to the substrate ("horizontal forces"). It should be clarified that only vertical forces are studied and that no direct information is provided about the forces that actually move the larva forward (or about the forces which impede this motion and are also generated by the substrate). Along with this clarification, it would be helpful to include a discussion of other techniques, especially micropillar arrays and traction force microscopy, that directly measure horizontal forces and of why these techniques are inappropriate for the motions studied here.

      We attempted to provide a streamlined Introduction in our initial submission and then compared ERISM/WARP to other methods in our discussion. We are happy to provide a brief overview of substrate force measurement methods in the introduction to help set the stage for readers. The Introduction section of our revised manuscript now contains the following comparison of different mechanobiological imaging techniques on pages 3-4 lines 56-86:

      ‘However, in the field of cellular mechanobiology, many new force measuring techniques have been developed which allow measurement of comparatively small forces from soft structures exhibiting low inertia (15–17) often with relatively high spatial-resolution. Early methods such as atomic force microscopy required the use of laser-entrained silicon probes to make contact with a cell of interest (15). This approach is problematic for studying animal behaviour due to the risk of the laser and probe influencing behaviour. Subsequently, techniques have been developed which allow indirect measurement of substrate interactions. One such approach is Traction Force Microscopy (TFM) in which the displacement of fluorescent markers suspended in a material with known mechanical properties relative to a zero-force reference allows for indirect measurement of horizontally aligned traction forces (17–19). This technique allows for probe-free measurement of forces, but the need to obtain a precise zero-force reference would make time-lapse measurements on behaving animals challenging; further, depending on the version used, it has insufficient temporal resolution for the measurement of forces produced by many behaving animals, despite recent improvements (20). A second approach revolves around the use of micropillar arrays; in this technique, horizontally-aligned traction forces are measured by observing the deflection of pillars made of an elastic material with known mechanical properties. This approach can be limited in spatial resolution and introduces a non-physiological substrate that may influence animal behavior (21,22).

      Recently we have introduced a technique named Elastic Resonator Interference Stress Microscopy (ERISM) which allows for the optical mapping of vertically aligned GRFs in the pico and nanonewton ranges with micrometre spatial resolution by monitoring local changes in optical resonances of soft and deformable microcavities. This technique allows reference-free mapping of substrate deformations and calculation of vertically directed GRFs; it has been used to study a range of questions related to exertion of cellular forces (23–25). Until recently, this technique was limited by its low temporal resolution (~10s), making it unsuitable for recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26). Given ERISM/WARP allows for probe-free measurement of vertical ground reaction forces with high spatial and temporal resolution, it becomes an attractive method for animal-scale mechanobiology.’

      (1.4) The larvae studied are about 1 mm long and 0.1 mm in cross-section. Their volumes are therefore on order 0.01 microliter, their masses about 0.01 mg, and their weights in the range of 0.1 micronewton. This contrasts with the force reported for a single protpodium of 1 - 7 micronewtons. This is not to say that the force measurements are incorrect. Larvae crawl easily on an inverted surface, showing gravitational forces are smaller than other forces binding the larva to the substrate. The forces measured in this work are also of the same magnitude as the horizontal forces reported by Khare et al. (ref 32) using micropillar arrays.

      I suspect that the forces adhering the larva to the substrate are due to the surface tension of a water layer. This would be consistent with the ring of upward stress around the perimeter of the larva visible in S4D, E and in video SV3. The authors remark that upward deflection of the substrate may be due to the Poisson's ratio of the elastomer, but the calibration figure S5 shows that these upward deflections and forces are much smaller than the applied downward force. In any case, there must be a downward force on the larva to balance the measured upward forces and this force must be due to interaction with the substrate. It should be verified that the sum of downward minus upward forces on the gel equals the larva's weight (given the weight is neglible compared to the forces involved, this implies that the upward and downward forces should sum to 0).

      We have carefully calculated the forces exerted by protopodia and are confident in the accuracy of our measurements as reported. We further agree with the reviewer’s suggestion that gravitational forces can be largely neglected.

      As the reviewer points out, one would expect forces due to upward and downward deflections to cancel when considering the entire system. However, we see indications that the counteracting / balancing force often acts over a much larger area than the acting force, e.g. a sharp indentation by a protopodium might be counteracted by an upward deflection over a 10-20 fold larger radius and hence 100 to 400-fold larger area, thereby reducing the absolute value of the upward deflection at any given pixel surrounding the indentation. This in turn increases error in determining the integrated upward deformation, making it difficult to perform an absolute comparison of acting and counteracting force. Further, recording the entire counteracting force induced deformation would require acquiring data with a prohibitively large field of view.

      We agree that in some situations, water surface tension may be adhering animals to the substrate. Importantly, this is a challenge that the animal faces outside the lab in its natural environment of moist rotting fruit and yeast. The intricate force patterns seen in our study in the presence of water surface tension are therefore ecologically relevant. In other situations (e.g. preparing for pupation), larvae are able to stick to dry surfaces, suggesting that other adhesive forces such as mucoid adhesion can also come into play in certain behavioural contexts. A full characterization of the effects of water tension and mucoid adhesion are beyond the scope of this study. However, we have now added a sentence on pages 8 and 12 commenting on these other biomechanical forces at play:

      ‘We also observed that the animals travel surrounded by a relatively large water droplet (lines 189-190).’

      ‘We observed that larvae travel surrounded by moisture from a water droplet, which produces a relatively large upwardly directed force in a ring around the animal. The surface tension produced by such a water droplet likely serves a role in adhering the animal to the substrate. However, during forward waves, we found that protopodia detached completely during SwP, suggesting this surface tensionrelated adhesion force can be easily overcome by the behaving animal. (lines 301-307) .’

      (1.5) Much of the discussion and the model imply that the sites where the larva exerts downward force on the gel are the sites where horizontal propulsion is generated. This assumption should be justified. Can the authors rule out that the larva 'pulls' itself forward using surface tension instead of 'pushing' itself forward using protopodia?

      Determining the exact ‘sites’ where horizontal propulsion is generated is challenging. In our conceptual model, movement is not initiated by protopodia per se, but rather by a constellation of muscle contractions, which act upon the hydrostatic skeleton, which in turn causes visceral pistoning that heaves larvae forward. This is based on previous findings in Ref 31. While there are indeed downward protopodial ‘vaulting’ forces prior to initiation of swing, we propose that the main function of protopodia is not to push the larvae forward, but rather to provide anchoring to counteract opposing forces generated by muscles. We agree that water surface tension could also be sculpting biomechanical interactions; however, a full characterization of how water surface tension shapes larval locomotion is beyond the scope of this study.

      Since we have observed larvae move over dry terrain (e.g. glass) without an encasing water bubble, we do not believe that an encasing water bubble is strictly required for locomotion. We have also seen no obvious locomotion related modulations in the pulling forces created by water bubbles encasing larva, which would be expected if animals were somehow using water tension to pull themselves forward. Overall, the most likely explanation is that larvae use a mixture of biomechanical tactics to suit the moment in a given environment. This represents a challenge but also an opportunity for future research.

      We have now added additional text in the ‘Functional subdivisions within protopodia’ subsection to discuss these nuances (page 14, lines 382-387):

      ‘This increased force transmitted into the substrate is unexpected as the forces generated for the initiation of movement should arise from the contraction of the somatic muscles. We propose that the contraction of the musculature responsible for sequestration acts to move haemolymph into the protopodia thus exerting an increased pressure onto the substrate while the contact area decreases as a consequence of the initiation of sequestration.’

      and (page 15, lines 398-399):

      ‘Water surface films appear to facilitate larval locomotion in general but the biomechanical mechanisms by which they do this remain unclear.’

      (1.6) More detail should be provided about the methods, their limitations, and the rationale behind certain experimental choices.

      We thank the reviewer for this comment. As this significantly overlaps with a point raised earlier, we kindly direct them to our answer to comment #1.2 above.

      (1.7) Three techniques are introduced here to study how a crawling larva interacts with the substrate: standard brightfield microscopy of a larva crawling in an agarose capillary, ERISM imaging of an immobilized larva, and WARP imaging of a crawling larva. The authors should make clear why each technique was chosen for a particular study - e.g. could the measurements using brightfield microscopy also be accomplished using WARP? They should also clarify how these techniques relate to and possibly improve on existing techniques for measuring forces organisms exert on a substrate, particularly micropillar arrays and Traction Force Microscopy.

      Indeed, each of the three methods used has a specific merit. The brightfield microscopy was selected to track features on the animal’s body and to provide a basic control for the later measurements. However, this technique cannot directly measure the substrate interaction, it only allows inferences to be made from tracked features at the substrate interface. ERISM provides high resolution maps of the indentation induced by the larva; it is also extensively validated for mapping cell forces and the data analysis is robust against defects on the substrate (refs 23, 24 and 25). However, as we explain in the manuscript, ERISM lacks the temporal resolution needed to monitor mechanical activity of behaving larva. Its use was therefore limited to the study of anaesthetised animals. For mapping forces exerted by behaving larva, we used WARP which is a further development of ERISM that offers higher frame rates but at the cost of requiring more extensive calibration (Supplementary Figure S4). The streamlined introduction of the different methods in our original manuscript originates from our attempt to be as concise as possible. However, as state in response to comment #1.2, we agree that additional explanation and discussion will be helpful for readers and that it will helpful to briefly refer to other methods for force mapping. We have now added references to a variety of techniques in the Introduction (Page 3-4, lines 56-86) as stated in a prior response.

      (1.8) As written, "(ERISM) (19) and a variant, Wavelength Alternating Resonance Pressure microscopy (WARP) (20) enable optical mapping of GRFs in the nanonewton range with micrometre and millisecond precision..." (lines 53-55) may generate confusion. ERISM as described in this work has a much lower temporal resolution (requires the animal to be still for 5 seconds - lines 474-5); In this work, WARP does not appear to have nanonewton precision (judging by noise on calibration figures) and it is not clear that it has millisecond precision (the camera used and its frame rate should be specified in the methods).

      Previous studies have demonstrated the capabilities and limitations of ERISM and WARP. Upon reflection, we agree that our wording here could be more precise. To clarify our claim, we now separate the statements on ERISM and WARP in the introduction as follows (page 4, lines 78-83):

      “Until recently, this technique was limited by its low temporal resolution (~10s) making it unsuitable for use in recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26)”

      While WARP can achieve comparable force resolution as ERISM when used in a cellular context (c.f. Ref 26), we agree that for the present study, the resolution was in the 10s of nanonewton range, due to the need to use stiffer substrates and larger fields of view.

      The camera used in our work was specified in the appropriate subsection of the Materials and Methods (“All WARP and ERISM images were acquired using an Andor Zyla 4.2 sCMOS camera (Andor Technology, Belfast, UK)”). We apologise that the exact frame rate used in our current work was not mentioned in our original manuscript; this has now been added to the ‘Freely behaving animals force imaging’ section of the Materials and Methods (page 23, lines 574-577).

      (1.9) It would be helpful to have a discussion of the limits of the techniques presented and tradeoffs that might be involved in overcoming them. For instance, what is the field of view of the WARP microscope, and could it be increased by choosing a lower power objective? What would be required to allow WARP microscopy to measure horizontal forces? Can a crawling larva be imaged over many strides by recentering it in the field of view, or are there only particular regions of the elastomer where a measurement may be made?

      We agree with the reviewer that some discussion of the limitations of our technique will allow readers to have a more informed appreciation of what we are capable of measuring using WARP. However, as this is the first work to ever demonstrate such measurements, the limitations and tradeoffs cannot all be known with certainty at the present stage.

      To answer your individual questions:

      (1) There is a trade-off between numerical aperture and the ability to resolve individual interference fringes. Since our approach to calculate displacement from reflection maps relies upon counting of individual fringe transitions, going to a lower powered objective risks having these fringes blend and thus the identification of the individual transitions becoming impossible. The minimum numerical aperture of the objective will therefore generally depend on the steepness of indentations produced by the animals; the steeper an indentation, the closer the neighbouring fringes and thus the higher the required magnification to resolve them.

      (2) From WARP and ERISM data, one can make inferences about horizontal forces, as is described in detail in our earlier publications about ERISM (ref, 23). However, quantitation of horizontal forces at sufficient temporal resolution to allow the investigation of behaving Drosophila larva is currently not possible.

      (3) Many strides can indeed be imaged using our technique, however, this comes with additional technical challenges. Whether or not the animal itself can be recentred is an ongoing challenge. We have found that the animals are amenable to recentring themselves within the field of view if chasing an attractive odorant. However, manual recentering using a paintbrush risks destroying the top surface of the soft elastic resonator and recentering the microscope stage would require real-time object tracking which has been outside the scope of this original work, given the other challenging requirements on hardware and optics for obtaining high quality force maps.

      To provide more information on limitations of our technique, we have added the following text into the discussion (pages 13-14, lines 356-370).

      ‘Despite the substantial advances they have provided, the use of WARP and ERISM also brings challenges and has several technical limitations. For example, fabrication of resonators is much more challenging than preparation of the agarose substrates conventionally used for studying locomotion of Drosophila. This problem is compounded by the fragility of the devices owing to the fragility of the thin gold top mirror. This becomes problematic when placing animals onto the microcavities, as often the area local to the initial placement of the animal is damaged by the paintbrush used to move the animals. Further, as a result of the combining of the two wavelengths, the effective framerate of the resultant displacement and stress maps is equal to half of the recorded framerate of the interference maps. To be able to monitor fast movements, recording at very high framerates is therefore necessary which, depending on hardware, might require imaging at reduced image size, but this in turn reduces the number of peristaltic waves that can be recorded before the animal escapes the field of view. A further limitation is that WARP and ERISM are sensitive mainly to forces in the vertical direction; this is complementary to TFM, which is sensitive to forces in horizontal directions. Using WARP in conjunction with high speed TFM (possibly using the tuneable elastomers presented here) could provide a fully integrated picture of underlying vertical and horizontal traction forces during larval locomotion.’ And further on page 13, lines 337-341:

      ‘More detailed characterisation of this behaviour remains a challenge owing to the changing position of the mouth hooks. Due to their rigid structure and the relatively large forces produced in planting, mouth hooks produce substrate interaction patterns which our technique struggles to map accurately due to overlapping interference fringes ambiguating the fringe transitions.’

      We trust that the above discussion and our modifications to our manuscript resulting from these will address the reviewer’s concerns.

      Reviewer #2 (Public Review):

      (2.1) With a much higher spatiotemporal resolution of ground dynamics than any previous study, the authors uncover new "rules" of locomotory motor sequences during peristalsis and turning behaviors. These new motor sequences will interest the broad neuroscience community that is interested in the mechanisms of locomotion in this highly tractable model. The authors uncover new and intricate patterns of denticle movements and planting that seem to solve the problem of net motion under conditions of force-balance. Simply put, the denticulated "feet" or tail of the Drosophila larva are able to form transient and dynamic anchors that allow other movements to occur.

      We thank the reviewer for their feedback and the information regarding which of our results is likely to resonate most impactfully with readers from a biological background.

      The biology and dynamics are well-described. The physics is elementary and becomes distracting when occasionally overblown. For example, one doesn't need to invoke Newton's third law, per se, to understand why anchors are needed so that peristalsis can generate forward displacements. This is intuitively obvious.

      We are sorry to hear that the reviewer found some of the physics details distracting. To address this concern, we have simplified some of the language while still attempting to keep the core arguments intact. For context and analogy, we still believe that including a brief reference to the laws of motion is helpful for some readers to explain some of our results and highlight their general implications, especially with regard to anchoring against reaction forces.

      One of our objectives is to make this article accessible and interesting for biologists and physicists at all levels. We feel it is important to reach out to both communities and try to be inclusive as possible in our writing. Newton’s 3rd law is clearly relevant for our study and it is a common point of reference for anyone with a highschool education, and so we feel it is appropriate to mention it as a way to help readers across disciplines understand the biophysical challenges faced by the animals we study.

      (2.2) Another distracting allusion to "physics" is correlating deformation areas with displaced volume, finding that "volume is a consequence of mass in a 2nd order polynomial relationship". I have no idea what this "physics" means or what relevance this relationship has to the biology of locomotion.

      Upon reflection, we agree that this language may be overly complex and distracts from what is, at its core, a simple, but important principle governing how Drosophila larvae interact with their substrates. The point we are trying to make is that our data show that forces exerted by an animal are proportional in a non-linear way to contact area. This suggests that to increase force exerted on the substrate, an animal must increase contact area. We do not observe contact area remaining constant while force increases, or vice versa. To make this result more clear, we have made several changes in our revised manuscript. Figure 5B no longer shows the relationship between the protopodial contact area and the displaced volume of the elastic resonator, but instead now shows the protopodial contact area and recorded force transmitted into the substrate. This then shows that in order to increase force transmitted into the substrate, these animals must increase their contact area. We have made changes to the figure legend of Figure 5 and the statements in the Results section accordingly (Page 9, lines 220-222).

      2.3 The ERISM and WARP methods are state-of-the-art, but aside from generally estimating force magnitudes, the detailed force maps are not used. The most important new information is the highly accurate and detailed maps of displacement itself, not their estimates of applied force using finite element calculations. In fact, comparing displacements to stress maps, they are pretty similar (e.g., Fig 4), suggesting that all experiments are performed in a largely linear regime. It should also be noted that the stress maps are assumed to be normal stresses (perpendicular to the plane), not the horizontal stresses that are the ones that actually balance forces in the plane of animal locomotion.

      We largely agree with the statement made by the reviewer here. However, we have found that in many contexts, audiences appreciate having the absolute number of the forces and stresses involved reported. Therefore, where possible, we have used stress maps, rather than displacement maps. We also observe that while stress and displacement maps show similar patterns, features sometimes appear sharper in the stress map, which is a result of the finite element algorithm being able to attribute a broad indentation to a somewhat more localised downward force. We have thus opted to keep to original stress maps. We have been more explicit about WARP and ERISM being more tuned to recording vertically directed forces throughout the revised manuscript (lines 75, 78, 86, 162, 301, 305, 336).

      We have also modified our Discussion section to encourage further investigation of our proposed model using a technique more tuned to horizontal stresses (pages 12-13, lines 324-328):

      ‘However, WARP microscopy is best suited to measurements of forces in the vertical direction, and though we can make inferences such as this as they are a consequence of fundamental laws of physics, we present this conclusion as a testable prediction which could be confirmed using a force measurement technique more tuned to horizontally directed forces relative to the substrate.’

      (2.4) But none of this matters. The real achievements are the new locomotory dynamics uncovered with these amazing displacement measurements. I'm only asking the authors to be precise and down-to-earth about the nature of their measurements.

      We thank the reviewer for their perceptiveness in finding that though the forces are interesting, the interactions themselves are the most noteworthy result here. We trust that with the changes made in our revised manuscript, the description is now more “down-to-earth”, more concise where appropriate, and accurate as to which results are particularly important and novel.

      (2.5) It would be good to highlight the strength of the paper -- the discovery of new locomotion dynamics with high-resolution microscopy -- by describing it in simple qualitative language. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles.

      We thank the reviewer for this recommendation. We agree that including a more explicit statement of some of our findings, especially with regards to these new posterior tripod structures and the whole-abdomen preparatory anchoring prior to head sweeps, would make the paper more impactful. As a result, we have modified the discussion section to include a statement for each new result and have also amended our abstract as a result (lines 407-416):

      “Here we have provided new insights into the behaviour of Drosophila larval locomotion. We have provided new quantitative details regarding the GRFs produced by locomoting larvae with high spatiotemporal resolution. This mapping allowed the first detailed observations of how these animals mitigate friction at the substrate interface and thus provide new rules by which locomotion is achieved. Further, we have ascribed new locomotor function to appendages not previously implicated in locomotion in the form of tripod papillae, providing a new working hypothesis of how these animals initiate movement. These new principles underlying the locomotion outlined here may serve as useful biomechanical constraints as called for by the wider modelling community (39).”

      (2.6) As far as I know, these anchoring behaviors are new. It is intuitively obvious that anchoring has to occur, but this paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

      We agree with the reviewer on this. We think it is best to let our colleagues reflect on our findings and then decide how best to include them in future models.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Please be sure to describe in a figure caption or in the methods the details of the optical setup, especially the focal lengths of all the lenses, including the objective, and part numbers of the LEDs and filters. It would be helpful to have a figure in the main paper explaining the principles of ERISM/WARP microscopy along with the calibration measurements and computational pipeline (this would mainly combine elements already in the supplement). Such a figure should also include details of the setup that are alluded to in the methods but not fully explained (for instance, a "silicone well" is referred to in the methods but never described). The calibration of elastomer stiffness that now appears in the main text could be made a supplementary figure, unless there is some new art in the fabrication of the elastomers that should be highlighted as an advance in the main text.

      We appreciate the importance of explaining our methods to readers.

      In response to the public comments, we have added further details in our methods section to clarify practical aspects and ensure that readers will be able to reproduce our work.

      In Supplemental Figure 2, we show the full optical light path for ERISM and WARP along with named components. In addition, the principles of ERISM and WARP microscopy have already been extensively described in previous publications (See Refs 23-26). In light of this, we feel that the best approach in this paper is to direct readers to those publications.

      We feel that it is appropriate to present the calibration of elastomer stiffness in the main text because this is indeed a new innovation that is not just about making the elastomers but making force sensors based on these different materials. This is really important because it shows how researchers can tune the stiffness of an ERISM/WARP elastomer to match the type of tissue or organism under study. This is really the key technical advance that enables whole animal biomechanics across a range of animal sizes, so we think it is appropriate to keep it in the main text.

      We want to make sure that we do not oversell this point, and we feel that we make it sufficiently clear in the main text of our manuscript that making elastomer based force sensors of appropriate stiffness is important, when we state

      “First, we developed optical microcavities with mechanical stiffnesses in the range found in hydrogel substrates commonly used for studying Drosophila larval behaviour, i.e. Young’s modulus (E) of 10-30kPa (36–38).” (p. 5, ll. 124) and later

      “Here we used Drosophila larvae as a test case, but our methods now allow elastic optical resonators to be tuned to a wide range of animal sizes and thus create new possibilities for studying principles of neuro-biomechanics across an array of animals.” (p. 12, ll. 337)

      I would appreciate a description of the "why" behind some experimental choices, as understanding the motivation would be helpful for other researchers looking to adopt these techniques.

      We have now added additional text in the introduction and discussion that explains the rationale behind our experimental choices. in more detail. Please see our response to Reviewer 1’s public comments on the same point.

      (1) The WARP and ERISM experiments were conducted on a collagen coated gold surface rather than agarose. Why? EG does agarose not adhere to the gold, or would its thickness interfere with the measurement?

      The gold layer is applied above the elastomer and the collagen on top of the gold layer makes the gold a more natural biological surface for the animals. Agarose is unsuitable as an elastomer because it would dry during the vacuum based deposition of the gold. It is also unsuitable as a surface coating on top of the gold as the coating on the gold needs to very thin to preserve the spatial and mechanical resolution of our sensors. Further, processing of agarose generally requires temperatures of 60°C and higher which we find can damage the elastomer / gold films.

      (2) The ERISM measurements are made on a cold anesthetized animal right as it starts to wake up (visible mouth-hooks movement), which presents some difficulty. Why not start imaging while the animal is still completely immobile? Or why not use a dead larva?

      This approach allowed us to get measurements of forces exerted by denticles that are physiologically and biomechanically accurate. In dead or fully anesthetized animals, one cannot be sure that the forces exerted by denticles and denticle bands are representative of the forces exerted by an animal with active hydrostatic control.

      (3) In the ERISM setup the monochromator is spatially filtered by focusing through pinhole, while in the WARP setup, the LEDs are not.

      Yes that’s correct. The LED light sources used in WARP have better spatial homogeneity than the tungsten filament used in ERISM and so a pinhole is not required in WARP.

      (4) SV4 shows the interference image of a turning larva (presumably from one illumination wavelength) rather than a reconstruction of the displacement or stresses. Why?

      We felt that in this particular case the interference images provided a clearer representation of the behavioural sequence, showing both the small indentations generated by individual denticles and the larger indentations of the animal overall.

      Lines 49-50 "a lack of methods with sufficient spatiotemporal resolution for measuring GRFs in freely behaving animals has limited progress." This needs a discussion of what sufficient spatial and temporal resolutions would be and how existing methods fall short of these goals.

      We have now rewritten the introduction to include an overview of other alternative approaches and of what we see as the requirements here. See our response to the public comments.

      Figure caption 1B (line 789) refers to "concave areas of naked cuticle (black line) which generally do not interact with the substrate" While I think this might be supported by later WARP images, it's not clear how the technique of figure 1 measures interaction, which could e.g. be mediated by surface tension of a transparent fluid.

      The technique of Figure 1 provides qualitative information which as the reviewer points out is validated by WARP measurements later.

      Lines 184-189 "However, unexpectedly, we observed an additional force on the substrate when protopodia leave the substrate (SI) and when they are replanted (ST). To investigate whether this force was due to an active behaviour or due to shifting body mass, we plotted integrated displacement (i.e. displaced volume) against the contact area for each protopodium, combining data from multiple forwards waves (Figure 5B). Area is correlated with displaced volume for most time points, indicating that volume is a consequence of mass in a 2nd order polynomial relationship." I couldn't follow this argument at all.

      We have now reworded this section and explained our rationale. Also see our response to a similar critique in Reviewer 2’s public comments.

      Generally the authors might reconsider their use of acronyms. e.g. (244-246) "SI latencies were much more strongly correlated with wave duration across most segments than ST latencies. SIs scale with SwP and this could be mediated by proprioceptor activity in the periphery" is made more difficult to parse by the abbreviations.

      As we need to refer to these terms multiple times throughout the manuscript, we feel the use of acronyms is appropriate here.

      The video captions are inadequate. Please expand on them to explain clearly what is shown, and also describe in the methods how the data were acquired and processed. For instance, it seems that in SV3 a motion correction algorithm is applied so that the larva appears stationary even as it crawls forward. I think "fourier filtered" means that the images were processed with a spatial high pass filter - this should be explained and the parameters noted.

      We have revisited the video captions provided in the supplementary information document and conclude that these contain the important information. The mode of acquisition are described in the methods, e.g. Video 1 and 2 see section in Methods on “Denticle band kinematic imaging” and Videos 3 and 4 see section in Methods on WARP. Supplementary Video 3 does not make use of motion correction; indeed, one can see the larvae moving upwards/forwards in the field of view. We apologize for not explaining the Fourier filtering process for Video 3. We have now modified the video caption to read as follows:

      Video SV3. WARP imaging during forwards peristalses.

      Video showing high frame rate displacement maps produced by a freely behaving Drosophila larva. Displacement maps were Fourier filtered to make denticulated cuticle more readily visible and projected in 3D to show the effects of substrate interaction. Details of the Fourier filtering procedure were described elsewhere [Kronenberg et al, Nat Cell Biol 19, 864–872 (2017)].

      What were the reflectances of the bottom (10 nm Au/Cr) and top (15nm Au) metal layers at the wavelengths used? I imagine the bottom layer should be less than 38%, the top layer higher, and the product of the square of the bottom transmission and the top reflectance coefficients equal to the bottom reflectance (to make the two paths of the interferometer contribute equal intensity), but none of this is stated.

      The reflectance of the gold mirrors was studied in detail in prior work on ERISM. See Kronenberg et al, Nat Cell Biol 19, 864–872 (2017). We therefore refrained from adding a complete optical characterization of the ERISM sensors again here. In brief, we found that a reflectance >13% at each Au mirror is required for reliable ERISM measurements.

      The description of the gold coated elastomer as a microcavity is confusing to me. Does the light really make multiple round trips between the plates before returning to the detector? The loss of light on each round trip would depend on the reflectance and parallelism of the top and bottom mirrors. From the WARP calculation it's appears that there is only one round trip - a pi/2 phase shift results from the calculation for one round trip: 2pi*2nL 5nm/(630nm)^2, with n = 1.4 and L = 8 microns - if there were two round trips, the phase shift would be pi etc. Would this better be described as a mostly common path interferometer?

      The physics of our devices is best described within the framework of thin film interference and (weak) microcavity optics. Indeed, light can make multiple roundtrips, though it gets attenuated with each reflection. The complete calculation of the multiple roundtrips is only required to obtain quantitative information on the amount of light that is reflected. The spectral position of minima in reflectance can also be obtained from assuming one roundtrip which is what is done in the description of the WARP calculations.

      Figure 2 e,f: the line fits appear to be dominated by the data points at 2 s. If these are removed, do the fits change? To support the argument that 2e shows a correlation and 2f does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

      If we remove the data points at 2 s, then R^2’s for swing initiation latencies change as follows: A2: 0.35 to 0.005; A4: 0.78 to 0.31; A6: 0.61 to 0.01. The data in 2e,f are the averages from 3 waves in each animal and so the data points at 2 s are not simply the result of single ‘rogue’ waves but rather averages of several trials. Further, if all individual waves are plotted, we can see that the overall trends are still visible.

      We don’t think it is appropriate to remove the data at 2 s from our analysis, but we take the point regarding statements about presence or absence of correlation in a formal sense. We have therefore changed the wording in the description of 2e,f to refer simply to the fact that wave duration can ‘largely determine' latencies in some instances, but is less able to in other instances, as is suggested by the R^2 (coefficient of determination) data. In discussion, we have also adjusted our wording.

      Figure 4 - please provide in the main figure or as a supplement the full images (i.e. not cropped to the assumed shape of the larva)

      We do not feel that it is necessary or helpful to provide the full images given that the focus of the analysis is on dynamics of protopodia movements.

      Figure 5e top: single data points around wave duration 0.6s appear to dominate fit lines. Does removing these points alter the fits? To support the argument that 5e top shows a correlation and 5e bottom does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

      In Figure 5e, we are showing all waves analysed across animals. If we remove the datapoints at 0.6 s, A2 R^2 changes from 0.24 to 0.05, A4 R^2 changes from 0.48 to 0.11, A6 R^2 changes from 0.69 to 0.34; however we don’t feel it is appropriate to remove these data from our analysis. We take the point about needing to be cautious about making claims about correlation versus no correlation and have now reworded description of these results along same lines as Figure 4.

      It appears from the methods (467-489) that animals were kept wet for warp imaging but not for ERISM imaging. Please confirm or explain further the presence or absence of a water layer in these two sets of measurements, as this could affect the adhesion forces.

      In each case, the animals were transferred onto experimental substrates with a moistened paintbrush. We have added text explicitly stating this in the methods section.

      Kim et al. Nature Methods 2017 (10.1038/nmeth.4429) describes recording two images separated by less than 60 microseconds using a scientific CMOS camera with a frame rate of 200 Hz. This is accomplished by triggering a pulsed LED once at the end of one frame's capture window and then a second time at the beginning of the next frame's window (see Supplementary Figure 10). I'm not sure if this trick is widely known, but it's worth considering if the authors are running into a problem with movement between the two wavelength exposures in their WARP setup.

      Thank you for this tip. We will take this under consideration for future work.

      Is the setup compatible with optogenetics? (EG is the red light dim enough that it wouldn't activate CsChrimson, or could a longer wavelength led be used for interferometry?) If so, activation of mooncrawler descending neuron (MDN) could be used to study backward crawling (or thermogenetic activation of MDN), e.g. to contrast the sites and order of "anchoring" between the two directions of crawling.

      The set-up is potentially compatible with optogenetics. We are in the process of exploring this in current ongoing work.

      Reviewer #2 (Recommendations For The Authors):

      Simplify/reduce the commentary about force measurements, and highlight the clear, qualitative descriptions of the novel locomotion patterns that they have observed. The microscopy and movements seem to matter more than the ground force estimations.

      We have addressed these issues in our responses to Reviewer 2’s public comments.

    1. Author Response

      The following is the authors’ response to the current reviews.

      We thank the reviewers for their valuable feedback which has improved this work greatly from its original form, and are elated to have such glowing reviews of the revised work published alongside the revised preprint. Reviewer 3 raises some final salient points, which deserve a brief address here.

      Teeth: We thank the reviewer for clarifying their points. We do make the assumption that the ecological parameter space of toothed and beaked organisms will be comparable. Both are governed by the same set of physical principles and have the jaw bone as the most likely point of failure (teeth are harder than bone, and keratinous rhamphothecae are malleable and can be regrown with relative ease when deformed). Differences in stress/strain distribution between toothed and beaked organisms will occur but are already accounted for in our methods as we model both the teeth and rhamphotheca and will observe these different effects. We have added an explicit statement of this hypothesis to the Methods section of the manuscript.

      Cranial kinesis: In our opinion, it is a safe assumption that the lower jaws of extant birds and enantiornithines are comparable. We do not see why the acquisition of kinesis in the upper jaw would generally affect the functional role of or constraints on the lower jaw. One possibility we discussed is that a quickly-moving kinetic premaxilla could let the lower jaw move a shorter distance during effective prey capture and lower the selection for speed (i.e. allow jaw-closing MA to remain higher). While we have added this possibility to our call for the investigation of cranial kinesis, we consider it too speculative to begin altering interpretations of fossil taxa. All raw measurement data remains available so that, if evidence is found for cranial kinesis having predictable effects on our measured parameters, future researchers can re-analyse our data and update any ecological predictions accordingly.

      Organization: To our knowledge eLife format incorporates what one would think of as a Conclusions section into the Discussion. Our Discussion section currently contains 18 subheadings which should guide a reader to any specific topic of interest. The Discussion also progresses from a more narrow to broad focus which we and several colleagues find intuitive.

      We thank all three reviewers once again for their feedback that has improved this work and their kind words throughout the process.


      The following is the authors’ response to the original reviews.

      We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

      • Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

      • Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

      • Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

      • Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

      As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

      • Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

      Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

      • Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

      • Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

      As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

      We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

      References

      Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

      Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

      Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

      Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

      Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

      Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

      Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

      Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The current work by Kulich et al. examines the dynamic relocalization of NGR1 (LAZY2) a member of the LAZY protein family which is key for auxin redistribution during gravitropic responses. After gravistimulation of the triple mutant ngr123 (lazy234), the PIN3 activating kinase D6PK is not polarized in the columella cells.

      Strengths:

      The authors show a thorough characterization of NGR1 relocalization dynamics after gravistimulation.

      Weaknesses:

      Genetically the relocalization of D6PK depends on the LAZY protein family, but some essential details are missing in this study. On the one hand, NGR1-GFP does not associate with the BFA compartments and maintains its association with the PM and amyloplasts. On the other hand, D6PK relies on GNOM, via vesicle trafficking sensitive to BFA, suggesting that D6PK follows a different relocalization route than NGR1 which is BFA-insensitive. Based on these observations, D6PK relocalization requires the LAZY proteins, but D6PK and NGR1 relocalize through independent routes. How can this be interpreted or reconciled?

      Response: Since we demonstrated that D6PK does not relocalize in the absence of NGR proteins, we conclude that NGR1 acts upstream of D6PK. The molecular mechanism driving this interaction is not fully understood; however, it is evident that NGR1 triggers the mobilization of D6PK. Despite previous investigations into D6PK mobility, the underlying mechanisms remain elusive. Notably, despite its sensitivity to BFA, D6PK does not localize to BFA bodies and does not undergo conventional endocytosis (https://doi.org/10.1016/j.devcel.2014.05.006). We fully acknowledge the importance and interest in gaining a better understanding of these processes, and it will be a focal point of our future research.

      Two other works (now published) provide valuable and fundamental findings related to the mechanism examined in the current manuscript and display complementary and similar results to the ones shown in the current manuscript. Given the similarities in the examined mechanisms, these preprints should be referenced, recognized, and discussed in the manuscript under review. It is assumed that the three projects were independently developed, but the results of these previous works should be addressed and taken into account at least during the discussion and when drawing any conclusions. This does not mean that this work is less relevant. On the contrary, some of the observations that seem to be redundant are more solid, and firm conclusions can now be drawn from them.

      Response: We have included and discussed these works in the revised discussion

      Reviewer #2 (Public Review):

      Summary:

      This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

      The current manuscript complements and extends Nishimura et al., Science, 2023. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

      The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180-degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and an ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

      Strengths:

      This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well-written and accessible to a general readership. The figures are clear and of high quality, and the methods are sufficiently explained for reproduction of the experiments.

      Weaknesses:

      Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends.

      Response: We added this information to the figure legends

      The title claims a bit more than what is actually shown in the manuscript: While auxin response reporter alterations are monitored, "rapid redirection of auxin fluxes" are not really directly addressed and, while D6PK can activate PIN proteins in other contexts, it is not explicitly shown in the manuscript that PIN3 is a target in the context of columella cells in vivo. A title such as "Rapid redirection of D6 protein kinase during Arabidopsis root gravitropism relies on plasma membrane translocation of NGR proteins" would reflect the results better.

      Response: We modified the title to Rapid translocation of NGR proteins driving polarization of PIN-activating D6 protein kinase during root gravitropism

      Fig. 4: The point that D6PK is transcytosed cannot be made here based on the data of these authors. They should have used a photoswitchable version of NGR1 to show that the same molecules observed at the upper PM are translocated to the lower PM. Nishimura and colleagues actually did that for NGR4. However, this is a lot of work and maybe for NGR1 that fusion would have too low fluorescence intensity (as it was the case for NGR3). So, I think a rewording would be sufficient such as NGR-dependent reorientation of D6PK plasma membrane localization" as this does not say, from where it comes to the lower PM. Theoretically, the signal could also be amyloplast-derived or newly synthesized (or just folded) NGR1-GFP.

      Response: We fully agree and rephrased the text using translocation instead of transcytosis

      The authors make a model in which D6PK AGCVIII kinase-dependent on NGRs activates PIN3 to drive auxin fluxes. However, alterations in auxin responses are observed prior to PIN3 reorientation. They should explain this discrepancy better and clearly describe that this is a working hypothesis for the future rather than explicitly proven, yet.

      Reviewer #3 (Public Review):

      The mechanism controlling plant gravity sensing has fascinated researchers for centuries. It has been clear for at least the past decade that starch-filled plastids (termed statoliths) in specialised gravity-sensing columella cells sense changes in root orientation, triggering an asymmetric auxin gradient that alters root growth direction. Nevertheless, exactly how statolith movement triggers PIN auxin efflux carrier activation and auxin gradient formation has remained unclear until very recently. A series of new papers (in Science and Cell) and this manuscript report how LAZY proteins (also referred to as NEGATIVE GRAVITROPIC 50 RESPONSE OF ROOTS; NGR) play a pivotal role in regulating root gravitropism. In terms of their overall significance, their collective findings provide seminal insights into the very earliest steps for how plant roots sense gravity which are arguably the most important papers about root gravitropism in the past decade.

      In the current manuscript, Kulich et al initially report (through creating a functional NGR1-GFP reporter) that "NGR1-GFP displayed a highly specific columella expression, which was most prominent at the PM and the statolith periphery." Is NGR1-GFP expressed in shoot tissues? If yes, is it in starch sheath (the gravity-sensing equivalent of root columella cells)? The authors also note "NGR1-GFP signal from the PM was not evenly distributed, but rather polarized to the lower side of the columella cells in the vicinity of the sedimented statoliths (Fig. 1A)." and (when overexpressing NGR-GFP) "chloroplasts in the vicinity of the PM strongly correlated with NGR1 accumulating at the PM nearby, similar to the scenario in columella" suggesting that NGR1 does not require additional tissue-specific factors (i.e. trafficking proteins or lipids) to assist in its intracellular movement from plastid to PM.

      Response: Yes, NGR1, also called LAZY2 is expressed in the inner hypocotyl tissues, according to https://doi.org/10.1104/pp.17.00942. Unfortunately, we saw very little signal with our NGR-GFP construct, possibly due to NGR1-GFP weak signal and/or NGR1 being expressed only exclusively in the inner tissues.

      Next, the authors study the spatiotemporal dynamics of NGR1-GFP re-localisation with other early gravitropic signals and/or components Calcium, auxin, and PIN3. The temporal data presented in Figure 1 illustrates how the GCaMP calcium reporter (in panel E) revealed "the first signaling event in the root gravitropic bending is the statolith removal from the top membrane, rather than its arrival at the bottom" It appeared that the auxin DII-VENUS reporter was also changing rapidly (panel G) - was this detectable BEFORE statolith re-sedimentation?

      Response: In our data (Figure 1G), we observe that the increase in signal at the top side begins prior to starch sedimentation, in contrast to the bottom side, where the decrease starts only after starch grains land on the bottom membrane. While this observation aligns with our hypothesis and other data, we refrained from commenting on it due to the small differences between the first 2-3 timepoints, which are obscured by noise. This phenomenon arises because the DII response relies on protein degradation and is relatively slow. Hence, for rapid tracking of the auxin response, we utilized auxin-induced calcium as a proxy, with NPA treatment serving as a negative control.

      Please can the authors explain their NPA result in Fig 1E? Why would treatment with the auxin transport inhibitor NPA block Ca signalling (unless the latter was dependent on the former)?

      Response: Auxin induces rapid calcium transients (e.g., http://dx.doi.org/10.1016/j.cub.2015.10.025). Consequently, when auxin reaches the bottom elongation zone approximately 5-6 minutes after rotation, we observe an increased GCaMP signal at this location. Notably, when we inhibit PIN function using NPA, the GCaMP signal persists, but the difference between the top and bottom diminishes. This validates that the calcium transients at the bottom side can be interpreted as monitoring increase in auxin accumulation as a result of auxin transport.

      They go on to note "This initial auxin asymmetry is mediated by PIN-dependent auxin transport, despite visible polarization of PIN3 can be detected only later" which suggests that PIN activity was being modified prior to PIN polarisation.

      In contrast to other proteins involved in gravity response like RLDs and PINs, NGR1 localization and gravity-induced polarization does not undergo BFA-sensitive endocytic recycling by ARF-GEF GNOM. This makes sense given NGR1 is initially targeted to plastids, THEN the PM. Does NGR1 contain a cleavable plastid targeting signal? The authors go on to elegantly demonstrate that NGR1 PM targeting relies on palmitoylation through imaging and mutagenesis-based transgenic ngr rescue assays.

      Response: Yes, there is weakly conserved plastid targeting signal on NGR1. Although we also started researching in this direction, we quickly realized, that two other groups showed very comprehensive data regarding NGR plastid localization.

      Finally, the authors demonstrate that gravitropic-induced auxin gradient formation is initially dependent on PIN3 auxin efflux activation (prior to PIN3 re-localisation). This early PIN3 activation process is dependent on NGR1 re-targeting D6PK (a PIN3 activating kinase). This elegant molecular mechanism integrates all the regulatory components described in the paper into a comprehensive root gravity sensing model.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Minor comments:

      Line 83: This construct fully rescued the agravitropic bending phenotype of the ngr1/2/3 triple mutant (see further).

      What does it mean the see further in this context?

      Response: It is a reference to the second part of the manuscript (Fig. 3, Supplementary Fig S3, Fig S4), where we extensively address the complementation with wild type and point mutated versions of NGR. There we show that the construct we are using is functional. This does not prove, but strongly imply that the GFP signal we obtain is relevant. We updated the text to point this out.

      Line 101: Timing of events during the gravitropic response

      When describing the equipment employed and the rotation applied to the samples, "the vertical stage microscope and minimized the time required for rotating the sample. 180{degree sign} rotation..."

      The authors mentioned a travel time of 5 minutes first and later of 15 minutes for the relocalization of NGR1. Are these two different experiments? Were there two different rotation angles or degrees applied? Could the authors please rephrase this part of the description to answer these questions and help the reader understand how the assay performed?

      Response: We added this explanation to the text.

      Figure 1 E, F, and G.

      Could the authors please provide pictures and/or videos for the PIN3 localization dynamics, intracellular calcium transients, and auxin reporter DII-Venus? In other words, show the complementing images for Figure 1E, 1F, and 1G as the authors did for Figure 2D where authors presented the pictures and the corresponding quantification plots.

      Response: We wanted to avoid overcrowding the figure, but we would also love to show the videos. Therefore, we did additional supplementary movie 3, where we put all the additional observations.

      Line 194: This implies the existence of posttranslational modifications such as S-acylation to associate with PM.

      Why is this specific modification suggested/examined and no other modification? What is the criteria to select this kind of modification? Based on what premises? Could the authors elaborate on that? Could the authors please include references?

      Response: Thank you for this comment. We of course first checked the prediction tools which have shown very strongly conserved S-acylation side. We now clarified this in the text and added other modifications as an example. Later on, we rule out myristoylation (that happens on the glycins) and prenylation (it happens only at the C-terminus CAAX box).

      Line 255: NGR1 PM localization is synergistically mediated by polybasic regions and a palmitoylation site

      Similarly to the previous commentary, How and why are these regions examined/analyzed? Likewise, why is the palmitoylation site selected? Please provide some background, criteria, and references.

      Response: Here, we clearly state that the prediction of the palmitoylation site is made based on the GPS lipid prediction tool.

      As for the polybasic region, these can be seen upon manual inspection of the primary protein sequence. We simply looked at the protein and saw it there. We rephrased the text so that it is more clear.

      Reviewer #2 (Recommendations For The Authors):

      Please, proofread the manuscript for style and minor language errors.

      Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends. Where it has been performed it is not given what "n" number of roots, cells, or plasma membranes were analyzed NGR1-GFP and no information is given whether the data is derived from a representative experiment or several or pooled data from several experiments. This certainly requires revision in Fig. 1D-G, Fig. 2B-D, Fig. S2 B,E, Fig. 3B,D, F-H, Fig. S.3 B,D, Fig. S. 4 ,E-H, Fig. 4 D.

      Response: Thank you, we added this information to the figure legends.

    1. Author Response

      The following is the authors’ response to the original reviews.

      We appreciate the care and the detail shown by the Reviewers. Their comments have made our article more focused and more accessible to a general audience.

      We would like to begin with a comment about the last sentence of the “eLife assessment”. The evolution of metamorphosis in insects was a major triumph in animal evolution that subsequently impacted almost every aspect of plant and animal evolution in the terrestrial and freshwater aquatic biospheres. Unlike the metamorphoses of most other groups, whose evolutions are lost in time, insect evolution arose relatively recently (~400 mya) and insect orders have branched off at various points in this evolution and have persisted to modern times. Although these “relic” groups also have undergone millions of years of evolution and specialization, they still provide us with windows into how this progression may have come about. The study of these groups provides a unique opportunity to explore the mechanisms that underlie major life history shifts and should be of interest to anyone interested in evolution – not just entomologists.

      Reviewer #1 (Public Review):

      Summary:

      This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:

      • JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.

      • JH redirects ecdysone-induced molting to direct formation of a more mature cuticle

      • Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects

      • The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

      Strengths:

      Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

      Weaknesses:

      This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

      We appreciate the comments about the strengths and weaknesses of the paper. To deal with the weaknesses, we have condensed some of the Results to make them less cumbersome and the Discussion has been completely revised, keeping a sharp focus on the actions of JH in Thermobia embryos and how these actions relate to the status quo functions of JH in insects with metamorphosis. As part of the revision of the Discussion, we have replaced Figures 10 and 11.

      Reviewer #1 (Recommendations For The Authors):

      In keeping with my public review, this paper is very strong and I have very few suggestions for improvement. They are:

      (1) Thermobia are extant insects and are not ancestral insects. It is likely that they retain features found in an insect ancestor. However, these insects have been evolving for a very long time, and for any one feature, many changes may have occurred, both gain and loss of gene function and morphology. Further, even for morphological features present in an extant species that are the same as an ancestor, genetic pathways regulating this feature may have changed over time (see for examples papers from the Haag and Pick labs). Although I realize this is a small, possibly almost semantic point, I feel it is important to be precise here. For example, in the title, "before" is speculative as there could have been a different role in the ancestor with the role in embryogenesis arising in lineages leading to Thermobia; similarly in the abstract, "this ancestral role of JH' is an overstatement since we cannot actually measure the ancestral role.

      Since the title has already been cited in a Perspectives review, we decided to keep the title as is.

      (2) I don't understand the results in Met and myo in Fig. 3B. Perhaps include them in the explanation of Fig.3 and not after the description of Fig. 4 and explain them in more detail (or perhaps not include them at all?). I don't really understand the statistical analysis of these panels either.

      We have revised the figure legends to explain the statistics.

      (3) Another point regarding language - talking about the embryo being "able" to go through a developmental stage implies decision-making. I would suggest dropping that wording (e.g, in the description of Fig. 5C). Similarly, in explaining Fig. 6B, it would be more correct to say "JH treatment no longer inhibited" than as written "could no longer inhibit" (implying 'no matter how hard it tried, it still couldn't do it')

      We have removed the “can’t” wording. Figure 6 has been revised

      Reviewer #2 (Public Review):

      The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

      This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

      The unique and intriguing aspect of juvenile hormone is its status quo action in the control of metamorphosis. Our reason for dealing with an insect group that branched off from the line of insects that eventually evolved metamorphosis, was to gain insight into the ancestral functions of this hormone. Our data from Thermobia as well as that from grasshoppers and crickets indicate that the developmental actions of JH were originally confined to embryogenesis where it promoted the terminal differentiation of the embryo. Its actions in promoting differentiation also included suppressing morphogenesis. This latter function was not pronounced during embryogenesis because JH only appeared after morphogenesis was essentially completed. However, it was a preadaptation that proved useful in more derived insects that delayed aspects of morphogenesis into the postembryonic realm. JH was then used postembryonically to inhibit morphogenesis until late in juvenile growth when JH disappears, and this inhibition is released.

      Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

      While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

      We disagree with the reviewer. An analogous situation would be in exploring gene function in which both gain-of-function and loss-of-function experiments often provide complementary insights into how a gene functions. We see JH effects only when its receptor, Met, is present and JH can induce its main effector protein, Kr-h1. The latter gives us confidence that we are looking at bona fide JH effects. We have also kept in mind, though, that the nature of the responding tissues is changing through time. Nevertheless, we see a consistent pattern of responses in the embryo and these can be related to its postembryonic effects in metamorphic insects.

      Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

      We have tried to dial back the speculation.

      Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

      Our Figure 12 should put this into perspective.

      Reviewer #2 (Recommendations For The Authors):

      Main points

      (1) Please, reduce the level of overinterpretation of ectopic treatment experiments with JHm, since the resulting observations represent effects, but not necessarily functions of JH.

      We have revised this section to indicate that the “effects” of ectopic treatments provide insights into the function of JH. Using a genetic analogy, both “loss-of-function” and “gain-of-function” experiments provide insights into a given gene. (see response to Public Comments)

      (2) Especially in the sections "JH and the progressive nature of embryonic molts" and "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories", and the entire "Discussion", please keep the level of speculation within reasonable limits, avoiding especially the inference of conclusions on the basis of speculation, itself based on previous speculation.

      We have toned down some of the speculation and provided reasons why it is worth suggesting.

      (3) Please revisit the argued roles of myoglianin in the story, in light of its effects as an inhibitor of JH production, repressing the expression of JHAMT, as has been reliably demonstrated in hemimetabolan species (DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R).

      Our appreciation to the reviewer. We are more explicit about the relationship between JH and myo.

      Minor points

      (4) Please keep the consistency of the scientific binomial nomenclature for the species mentioned. For example, read "Manduca sexta" (in italics) at the first mention, and then "M. sexta" (in italics) in successive mentions (instead of reading "Manduca" on page 17, and then "Manduca sexta" on page 18, for example). The same for "Drosophila" ("Drosophila melanogaster" first, and then "D. melanogaster"), "Thermobia" ("Thermobia domestica" first, and then "T. domestica"), etc. In the figure legends, I recommend using the complete name: Thermobia domestica, in the main heading.

      Where there is no possibility of confusion, we intend to use Thermobia, rather than T. domestica, etc. We think that it is easier for a non-specialist to read and it is commonly done in endocrine papers.

      (5) There is no purpose in evolution and biological processes. Thus, I suggest avoiding expressions that have a teleological aftertaste. For example (capitals are mine), on p. 3 "appears to have been extended into postembryonic life where it acts TO antagonize morphogenic and allow the maintenance of a juvenile state".

      We have tried to avoid teleological wording.

      (6) The title "The embryonic role of juvenile hormone in the firebrat, Thermobia domestica, reveals its function before its involvement in metamorphosis" contains a redundancy ("role" and "function"), and an apparent obviousness ("before its involvement in metamorphosis"). I suggest a more straightforward title. Something like "Juvenile hormone plays developmental functions in the embryo of the firebrat Thermobia domestica, which predate its status quo action in metamorphosis".

      As noted above, we are retaining the title since it has already been cited.

      (7) Page 2. "The transition from larva to adult then occurred through a transitional stage, the pupa, thereby providing the three-part life history diagnostic of the "complete metamorphosis" exhibited by holometabolous insects (reviews: Jindra, 2019; Truman & Riddiford, 2002, 2019)". I suggest adding the reference ISBN: 9780128130209 9 7 8 - 0 - 1 2 - 8 1 3 0 2 0 - 9, as the most comprehensive and recent review on complete metamorphosis.

      Done

      (8) Page 3. "These severe developmental effects suggest that the developmental role of JH in insects was initially CONFINED to the embryonic domain" (capitals are mine). This appears contradictory with the observations of Watson, 1967, on the relationships between the apparition of scales and JH, mentioned shortly before by the authors.

      This is explained in the Discussion. Although JH can suppress scale appearance in the J4 stage, we have not been able to show that scales appearance is caused by changes in the juvenile JH titer.

      (9) Page 4. "we measured JH III levels during Thermobia embryogenesis at daily intervals starting at 5 d AEL". Why not before, like in the case of ecdysteroids? The authors might perhaps argue that the levels of Kr-h1 expression are consistently low from the very beginning, according to Fernandez-Nicolas et al, 2022 (reference cited later in the manuscript).

      (10) Page 4. "Ecdysteroid titers through embryogenesis and the early juvenile instars were measured using the enzyme immunoassay method (Porcheron et al., 1989) that is optimized for detecting 20-hydroxyecdysone (20E)". The antibody generated by Porcheron (and now sold by Cayman) recognizes ecdysone and 20-hydroxyecdysone alike. But that's not relevant here. I would refer to "ecdysteroids" when mentioning measurements. Also in figure 2B (and "juvenile hormone III" without the formula, in Panel A, for harmonization). And I would not expand on specifications, like those at the beginning of page 5, or towards the end of page

      We thank the reviewer for this important correction.

      (12) ("the fact that we detected only a slight rise in ecdysteroids at this time (Fig 2B) is likely due to the assay that we used being designed to detect 20E rather than ecdysone").

      Omitted.

      (11) Page 5. "Low levels of Kr-h1 transcripts were present at 12 hr after egg deposition, but then were not detected until about 6 d AEL when JH-III first appeared". There is a very precise Kr-h1 pattern in Fernandez-Nicolas et al. 2023 (reference mentioned later in the manuscript).

      (12) Page 5. "notably myoglianin (myo), have become prominent as agents that promote the competence and execution of metamorphosis in holometabolous and hemimetabolous insects (He et al., 2020; Awasaki et al., 2011)". See my note 3 above.

      The myoglianin issue has been revised.

      (13) Page 5. "a drug that suppresses JH production". Rather, "a drug that destroys the JH producing tissues". Why the way, do the authors know when the CA are formed in T. domestica embryo development?

      We prefer to keep our original wording. There have been some cases in which precocene has blocked JH production but did not kill the CA cells. We do not have observations that show that 7EP kills the CA cells in Thermobia embryos.

      (14) Page 5. "subsequent treatment with a JHm". I would say here that the JHm is pyriproxyfen, not on page 6 or page 7. Thus, to be consistent, after the first mention of "pyriproxyfen (JHm)" on page 5, I'd consistently use the abbreviation "JHm".

      (15) Page 9. "Limb loss in such embryos was often STOCHASTIC, i.e., in a given embryo some limbs were completely lost while others were maintained in a reduced state" (capitals are mine). The meaning of "stochastic" is random, involving a random variable; it is a concept usually associated to probability theory and related fields. I suggest using the less specialized word "variable", since to ascertain that the values are really stochastic would require specific mathematical approaches.

      We are still using stochastic because the loss is random.

      (16) Page 10. "9E). Indeed, the JH treatment redirects the molt to be more like that to the J2 stage, rather than to the E2 (= J1) stage". Probably too assertive given the evidence available (see my points 1 and 2 above).

      We do not see a problem with our conclusion. In response to the JHm treatment, the embryo produced a smooth, rather than a “pebbly” cuticle, failed to make the J1-specific egg tooth, and attempted to make cuticular lenses (a J2 feature). This ability of premature JH exposure to cause embryos to “skip” a stage is also seen in locusts (Truman & Riddiford, 1999) and crickets (Erezyilmaz et al., 2004). The JHm treatment resulted in the production of smooth cuticle, lack of a hatching tooth, and an attempt to make cuticular lenses.

      (17) Page 11. "early JHM treatment", read "early JHm treatment".

      Corrected

      (18) Page 11. "likely. A target of JH, and likely Kr-h1, in Thermobia is myoglianin...". Please see my notes 1, 2, and especially 3, above.

      This has been revised

      (19) Page 13. "the locust, Locusta americana (Aboulafia-Baginshy et al.,1984)". Please read "the locust, Locusta migratoria (Aboulafia-Baginshy et al.,1984)".

      Corrected

      (20) Page 13 "Acheta domesticus" three times. The correct name now is "Acheta domestica", after harmonizing the declension of the specific name with the generic one. See additionally my note 4 above.

      Acheta domesticus has been used in hundreds (thousands?) of papers since it was originally named by Linnaeus. We will continue to use it.

      (21) Page 15, "(also called the vermiform larva (Bernays, 1971) redirects embryonic development to form an embryo with proportions, cuticular pigmentation, cuticular sculpturing and bristles characteristic of a nymph, while pronymph modifications, such as the cuticular surface sculpturing (Bernays, 1971)". The reference "Bernays, 1971" is indeed "Bergot et al., 1971".

      There was a mistake in the references. The Bernays reference was omitted from the revised Discussion

      (22) Page 16. "Since JH also induces Kr-h1 in embryos of many insects, including Thermobia". I'm not sure that this has been studied in many insects. In any case, any reference would be useful.

      (23) Page 17. "Tribolium casteneum". Please read "Tribolium castaneum".

      Changed

      (24) Page 17. "...results in a permanent larva that continues to molt well after it has surpassed its critical weight (He et al., 2019)". The paper of He et al., 2019 is preceded by two key papers that previously demonstrate (and in hemimetabolan insects) that myoglianin is a determining factor in the preparation for metamorphosis: DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R). See my note 3 above.

      Corrected in revision

      (25) Page 18. "These persisting embryonic primordia join the wing primordia in delaying their morphogenesis into postembryonic life". This reader does not understand this sentence.

      Made clearer in the revision.

      (26) Page 18. "is first possible in the commercial silkworm (Daimon et al., 2015)". Please mention the scientific Latin name of the species, Bombyx mori.

      (27) Page 19. "The functioning of farnesol derivatives in growth versus differentiation control extends deep into the eukaryotes.../... this capacity was eventually exploited by the insects to provide the hormonal system that regulates their metamorphosis". This information appears quite out of place.

      We have retained this point.

      (28) Page 21. Heading "Hormones". I suggest using the heading "Bioactive compounds", as neither pyriproxyfen nor 7-ethoxyprecocene are hormones.

      Done

      (29) Page 29, legend of figure 1. "Photomicrographs" is somewhat redundant. The technical word is "micrographs". "Thermobia domestica" appears in the explanation of panel C, but this is not necessary, as the name appears in the main heading of the legend.

      Done

      (30) Page 30, legend of figure 2. Panel B, see my comment 10 above. Why embryonic age is expressed in % embryo development in panel C (and in days in panels A and B)?

      All have been converted to days AEL

      (31) Page 35, legend of figure 5. "Photomicrograph" see my note 28 above.

      Done

      (32) Page 40, figure 10. In panel A, the indication of the properties of JH is misleading. The arrow going to promoting differentiation and maturation is OK, but the repression sign that indicates suppression of morphogenetic growth and cell determination seems to suggest that JH has retroactive effects. In panel B, I suggest to label "Flies" instead of "Higher Diptera", which is an old-fashioned term. In any case, see my general comments 1 and 2, above, about speculation.

      Figure has been completely revised

      (33) Figure 11. See my general comments 1 and 2, above, about speculation.

      Figure has been revised

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

      Strengths:

      The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.

      The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.

      The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

      Weaknesses:

      The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

      We agree completely and should be a feature of future work.

      The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

      We have tried to be less all-encompassing in the Discussion. The strongest comparisons can be made between ametabolous and hemimetabolous insects and we have focused most of the Discussion on the role of JH in that transition. We still include some discussion of holometabolous insects because the ancestral embryonic functions of JH may be somehow related to the unusual reappearance of JH in the prepupal period. We have reduced this discussion to only a few sentences.

      Reviewer #3 (Recommendations For The Authors):

      (1) The overall manuscript is very long (especially the discussion), and the main messages of the manuscript get lost in some of the details. I would suggest that the authors move some of the results to the supplementary material (e.g. it might be possible to put a lot of the detail of Thermobia embryogenesis into the supplementary text if the authors feel it is appropriate). The discussion contains a lot of speculation and I suggest the authors make this more concise. One example: At the moment there is a large section on the modification in JH function during the evolution of holo and hemi-metabolous life history strategies. There are some interesting ideas in this section and the authors do a good job of integrating their findings with the literature - but I would encourage the authors to limit the bulk of their discussion to the specific things that their results demonstrate. E.g. The first half of p17 contains too much detail, and the focus should be on the relationship with Thermobia (as at the bottom of p17).

      Section has been revised and is more focused

      (2) I would also suggest a thorough proofread of the manuscript, I have highlighted some of the errors/points of confusion that I found in the list below - but this list is unlikely to be exhaustive . We appreciate catching the errors. Hopefully the final version is better proofed.

      (3) It might be me, but I found the wording in the second half of the abstract a bit confusing. Particularly the statement about the redeployment of morphogen systems - could this be stated more clearly?

      Abstract has been revised.

      (4) Introduction

      a. "powered flight" rather than 'power flight'

      Done

      b. 'brought about a hemimetabolous lifecycle' implies causality which hasn't been shown and directionality to evolution - suggest 'facilitated the evolution of a hemi...". Similar comment for 'subsequent step to complete metamorphosis'.

      c. Bottom of p2 - unclear whether you are referring to hemi- holo- or both

      d. Suggest removing sentence beginning "besides its effects..." as the relevance of the role of JH in caste isn't clear.

      Kept sentence but removed initial clause

      e. State that Thermoia is a Zygentoma.

      Done

      f. Throughout - full species names on first usage only, T. domestica on subsequent usages.

      We will continue to use genus names for the reason given above.

      Gene names e.g. kr-h1 in italics.

      g. 'antagonise morphogens"? rather than 'antagonise morphoentic'.

      Done

      (5) Results

      a. Unclear why drawings are provided rather than embryonic images in Fig. 1A

      We think that the points can be made better with diagrams.

      b. Top of p4, is 'slot' the correct word?

      Corrected

      c. Unclear why the measurements of JHIII weren't measured before 5 days AEL, especially given that many of the manipulative experiments are at earlier time points than this. I appreciate that, based on kr-h1, levels that JHIII is also likely to be low.

      d. Reference for the late embryonic peak of 20E being responsible for the J2 cuticle?

      Clarified that this is an assumption

      e. Clarify "some endocrine related transcripts" why were these ones in particular picked? Kr-h1 is a good transcriptional proxy for JH and Met is the JH-receptor, why myoglianin and not some of the other transcriptional proxies of neuroendocrine signalling?

      Hopefully, the choice is clearer.

      f. Fig 2C rather than % embryo development for the gene expression data please represent this in days (to be consistent with your other figures).

      It is now consistent with other parts of figure.

      g. In Fig. 3 the authors do t-tests, because there are three groups there needs to be some correction for multiple testing (e.g. Bonferroni) can the authors add this to the relevant methods section?

      We think that pair-wise comparisons are appropriate.

      h. Fig. 3 legend: you note that you treat stage 2 juveniles with 7EP - I couldn't tell what AEL this corresponded to.

      This is after hatching so AEL does not apply.

      i. Top of p7 'deformities' rather than 'derangements'?

      Done

      j. Regarding the dosage effects of embryonic abnormalities - it would be good to include these in the supp material, as it convinces the reader that the effects you have seen aren't just due to toxicity.

      It is not clear what the objection is.

      k. Bottom of p7 'problematic' not 'problematical'

      Done

      l. P8 Why are the clusters of Its important? - provide a bit more interpretation for the reader here.

      This is clear in the revised version.

      m. P9 Why is the modulation of transcription of kr-h1, met, and myo important in this context

      Explained

      n. P9 'fig. 7F'? there is no Fig. 5F

      Thanks for catching the typo.

      o. Fig. 7B add to the legend which treatment the dark and light points correspond to.

      We think it is obvious from the labeling on Fig 7B.

      (6) Discussion:

      a. What do we know about how terminal differentiation is controlled in non-insect arthropods? Most of the discussion is focused on insects (which makes sense as JH is an insect-specific molecule), but if the authors are arguing the ancestral role of JH it would be useful to know how their findings relate to non-insect arthropods.

      We have not been able to find any information about systemic signals being involved in non-insect arthropods.

      b. There is no Fig. 5E (are they referring to 7E?)

      Yes, it should have been Fig. 7E.

      c. Is myoglianin a direct target of JH in other species?

      Other reports are in postembryonic stages and show that myoglianin suppresses JH production. Our paper is the first examination in embryos and we find that the opposite is true – i.e., that JH treatment suppresses myoglianin production. We suspect that these two signaling systems are mutually inhibitory. It would be interesting to see whether treatment of a post-critical weight larva with JH (which would induce a supernumerary larval molt) would also suppress myoglianin production (as we see in Thermobia embryos).

      d. P12 What is the evidence that JH interacts with the first 20E peak to alter the embryonic cuticle?

      We are not sure what the issue is. The experimental fact is that treatment with JH before the E1 ecdysteroid peak causes the production of an altered E1 cuticle. We are faced with the question of why is this molt sensitive to JH when the latter will not appear until 3 or 4 days later? A possible answer is that the ecdysone response pathway has a component that has inherent JH sensitivity. The mosquito data suggest that Taiman provides another link between JH and ecdysone action

      e. Top of p13 - this paragraph can be cut down substantially. Although this is evidence that JH can alter ecdysteriods - it is in a species that is 400 my derived from the target species. Is it likely to be the exact same mechanism? I would encourage the authors to distil and retain the most important points.

      This paragraph has been shortened and focused.

      f. Bottom of p13 - what does this study add to this knowledge?

      The response of Thermobia embryos to JH treatment is qualitatively the same as seen in other short germband embryos. This similarity supports the assumption that the same responses would have been seen in their last common ancestor.

      g. P19 the last paragraph in the conclusions is really peripherally relevant to the paper and is a bit of a stretch, I would encourage the authors to leave this section out.

      We agree that it is a stretch. JH and its precursor MF are the only sesquiterpene hormones. How did they come about to acquire this function? We think it is worth pointing out the farnesol metabolites have been associated with promoting differentiation in various eukaryotes. An ancient feature of these molecules in promoting (maintaining?) differentiation may have been exploited by the insects to develop a unique class of hormones. It is worth putting the idea out to be considered.

      h. P19 "conclusions" rather than 'concluding speculations'.

      Changed as suggested.

      Methods:

      It is standard practice to include at least two genes as reference genes for RT-qPCR analysis (https://doi.org/10.1186/gb-2002-3-7-research0034, https://doi.org/10.1373/clinchem.2008.112797) If there are large-scale differences in the tissues being compared (e.g. as there are here during development) then more than two reference genes may be required and a reference gene study (such as https://doi.org/10.3390%2Fgenes12010021) is appropriate. Have the authors confirmed that rp49 is stably expressed during the stages of Thermobia development that they assay here?

      We have explained our choice in the Methods.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This work describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

      Strengths:

      The main strength and novelty of the proposed approach lies in the idea of combining stateof-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with recent developments in the field of protein language models (the ESM2 model was used). The authors show that features extracted from high-dimensional, information-rich ESM2 sequence embeddings can be suitable for efficient use with the aforementioned tools.

      The reduced features take the form of amino acid occurrence probability matrices estimated from ESM2 masked-token predictions, or structural descriptors predicted by a modified variant of the ESM2 model. However, we believe that these should not be called "embeddings" or "representations". This is because they don't come directly from any layer of these networks, but rather from their final predictions.

      We agree that there is some room for discussion about whether the amino acid probabilities returned by pre-trained ESM-2 and the 3Di sequences returned by ESM-2 3B 3Di can be properly referred to as “embeddings”. The term “embedding” doesn’t have a formal definition, other than some kind of alternative vector representation of the input data which, preferably, makes the input data more suitable for some downstream task. In that simple sense of the word “embedding”, amino acid probabilities and 3Di sequences output by our models are, indeed, types of embeddings. We posed the question on Twitter (https://twitter.com/TrichomeDoctor/status/1715051012162220340) and nobody responded, so we are left to conclude that the community is largely ambivalent about the precise definition of “embedding”.

      We’ve added language in our introduction to make it more clear that this is our working definition of an “embedding”, and why that definition can apply to profile HMMs and 3Di sequences.

      The benchmarks presented suggest that the approach improves sensitivity even at very low sequence identities <20%. The method is also expected to be faster because it does not require the computation of multiple sequence alignments (MSAs) for profile calculation or structure prediction.

      Weaknesses:

      The benchmarking of the method is very limited and lacks comparison with other methods. Without additional benchmarks, it is impossible to say whether the proposed approach really allows remote homology detection and how much improvement the discussed method brings over tools that are currently considered state-of-the-art.

      We thank the reviewer for the comment. To address the question, we’ve expanded the results by adding a new benchmark and added a new figure, Figure 4. In this new content, we use the SCOPe40 benchmark, originally proposed in the Foldseek paper (van Kempen et al., 2023), to compare our best method, ESM-2 3B 3Di coupled to Foldseek, with several other recent methods. We find our method to be competitive with the other methods.

      We are hesitant to claim that any of our proposed methods are state-of-the-art because of the lack of a widely accepted standard benchmark for remote homology detection, and because of the rapid pace of advancement of the field in recent years, with many groups finding innovative uses of pLMs and other neural-network models for protein annotation and homology detection.

      Reviewer #2 (Public Review):

      Summary:

      The authors present a number of exploratory applications of current protein representations for remote homology search. They first fine-tune a language model to predict structural alphabets from sequence and demonstrate using these predicted structural alphabets for fast remote homology search both on their own and by building HMM profiles from them. They also demonstrate the use of residue-level language model amino acid predicted probabilities to build HMM profiles. These three implementations are compared to traditional profile-based remote homology search.

      Strengths:

      • Predicting structural alphabets from a sequence is novel and valuable, with another approach (ProstT5) also released in the same time frame further demonstrating its application for the remote homology search task.

      • Using these new representations in established and battle-tested workflows such as MMSeqs, HMMER, and HHBlits is a great way to allow researchers to have access to the state-of-the-art methods for their task.

      • Given the exponential growth of data in a number of protein resources, approaches that allow for the preparation of searchable datasets and enable fast search is of high relevance.

      Weaknesses:

      • The authors fine-tuned ESM-2 3B to predict 3Di sequences and presented the fine-tuned model ESM-2 3B 3Di with a claimed accuracy of 64% compared to a test set of 3Di sequences derived from AlphaFold2 predicted structures. However, the description of this test set is missing, and I would expect repeating some of the benchmarking efforts described in the Foldseek manuscript as this accuracy value is hard to interpret on its own.

      The preparation of training and test sets are described in the methods under the heading “Fine tuning ESM-2 3B to convert amino acid sequences into 3Di sequences”. Furthermore, there is code in our github repository to reproduce the splits, and the entire model training process: https://github.com/seanrjohnson/esmologs#train-esm-2-3b-3di-starting-from-the-esm-2-3bpre-trained-weights

      We didn’t include the training/validation/test splits in the Zenodo repository because they are very large: train 33,924,764; validation 1,884,709; test 1,884,710 sequences, times 2 because there are both amino acid and 3Di sequences. It comes out to about 30 Gb total, and is easily rebuilt from the same sources we built it from.

      We’ve added the following sentence to the main text to clarify:

      “Training and test sets were derived from a random split of the Foldseek AlphaFold2 UniProt50 dataset (Jumper et al., 2021; van Kempen et al., 2023; Varadi et al., 2022), a reducedredundancy subset of the UniProt AlphaFold2 structures (see Methods for details).”

      To address the concern about comparing to Foldseek using the same benchmark, we’ve expanded the results section and added a new figure, Figure 4 using the SCOPe40 benchmark originally presented in the Foldseek paper, and subsequently in the ProstT5 paper to compare Foldseek with ESM-2 3B 3Di to Foldseek with ProstT5, AlphaFold2, and experimental structures.

      • Given the availability of predicted structure data in AFDB, I would expect to see a comparison between the searches of predicted 3Di sequences and the "true" 3Di sequences derived from these predicted structures. This comparison would substantiate the innovation claimed in the manuscript, demonstrating the potential of conducting new searches solely based on sequence data on a structural database.

      See response above. We’ve now benchmarked against both ProstT5 and AF2.

      • The profile HMMs built from predicted 3Di appear to perform sub-optimally, and those from the ESM-2 3B predicted probabilities also don't seem to improve traditional HMM results significantly. The HHBlits results depicted in lines 5 and 6 in the figure are not discussed at all, and a comparison with traditional HHBlits is missing. With these results and presentation, the advantages of pLM profile-based searches are not clear, and more justification over traditional methods is needed.

      We thank the reviewer for pointing out the lack of clarity in the discussion of lines 5 and 6.

      We’ve re-written that section of the discussion, and reformatted Figure 3 to enhance clarity.

      We agree, a comparison to traditional HHBlits could be interesting, but we don’t expect to see stronger performance from the pLM-predicted profiles than from traditional HHBlits, just as we don’t see stronger performance from pLM-hmmscan or pLM-Foldseek than from the traditional variants. We think that the advantages of pLM based amino acid hmm searches are primarily speed. There are many variables that can influence speed of generating an MSA and HMM profile, but in general we expect that it will be much slower than generating an HMM profile from a pLM.

      We don’t know why making profiles of 3Di sequences doesn’t improve search sensitivity, we just think it’s an interesting result that is worth presenting to the community. Perhaps someone can figure out how to make it work better.

      • Figure 3 and its associated text are hard to follow due to the abundance of colors and abbreviations used. One figure attempting to explain multiple distinct points adds to the confusion. Suggestion: Splitting the figure into two panels comparing (A) Foldseek-derived searches (lines 7-10) and (B) language-model derived searches (line 3-6) to traditional methods could enhance clarity. Different scatter markers could also help follow the plots more easily.

      We thank the reviewer for this helpful comment. We’ve reformatted Figure 3 as suggested, and we think it is much easier to read now.

      • The justification for using Foldseek without amino acids (3Di-only mode) is not clear. Its utility should be described, or it should be omitted for clarity.

      To us, the use of 3Di-only mode is of great theoretical interest. From our perspective, this is one of our most significant results. Previous methods, such as pLM-BLAST and related methods, have made use of very large positional embeddings to achieve sensitive remote homology search. We show that with the right embedding, you don’t need very many bits per position to get dramatically improved search sensitivity from Smith-Waterman, compared to amino acid searches. We also doubt that predicted 3Di sequences are the optimal small encoding for remote homology detection. This result and observation opens up an exciting avenue for future research in developing small, learned positional embeddings that are optimal for remote homology detection and amenable to SIMD-optimized pre-filtering and Smith-Waterman alignment steps.

      We’ve expanded the discussion, explaining why we are excited about this result.

      • Figure 2 is not described, unclear what to read from it.

      It's just showing that ESM-2-derived amino acid probabilities closely resemble amino acid frequencies in MSAs. We think it gives readers some visual intuition about why predicted profile HMMs perform as well as they do. We’ve added some additional explanation of it in the text.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The paper would mainly benefit from a more comprehensive benchmark:

      We suggest that the authors extend the benchmark by including the reference methods (HHpred and Foldseek) run with their original representations, i.e., MSAs obtained with 2-3 iterations of hhblits (for HHpred) and experimental or predicted structures (for Foldseek). HHpred profile-profile comparisons and Foldseek structure-structure comparisons would be important reference points for assessing the applicability of the proposed approach in distant homology detection. It is also essential to compare the method with other emerging tools such as EBA (DOI: 10.1101/2022.12.13.520313), pLM-BLAST (DOI: 10.1101/2022.11.24.517862), DEDAL (DOI: 10.1038/s41592-022-01700-2), etc.

      We also suggest using an evolutionary-oriented database for the benchmark, such as ECOD or CATH (these databases classify protein domains with known structures, which is important in the context of including Foldseek in the benchmark). We ran a cursory benchmark using the ECOD database and generated HH-suite .hhm files (using the single_seq_to_hmm.py and hhsearch_multiple.py scripts). Precision and recall appear to be significantly lower compared to "vanilla" hhsearch runs with MSA-derived profiles. It would also be interesting to see benchmarks for speed and alignment quality.

      The pLM-based methods for homology detection are an emerging field, and it would be important to evaluate them in the context of distinguishing between homology and analogy. In particular, the predicted Foldseek representations may be more likely to capture structural similarity than homology. This could be investigated, for example, using the ECOD classification (do structurally similar proteins from different homology groups produce significant matches?) and/or resources such as MALISAM that catalog examples of analogy.

      We’ve added the SCOPe40 benchmark, which we think at least partially addresses these comments, adding a comparison to pLM-BLAST, ProstT5, and AF2 followed by Foldseek. The question of Analogy vs homology is an interesting one. It could be argued that the SCOPe40 benchmark addresses this in the difference between Superfamily (distant homology) and Fold (analogy, or very distant homology).

      Our focus is on remote homology detection applications rather than alignment quality, so we don’t benchmark alignment quality, although we agree that those benchmarks would be interesting.

      Page 2, lines 60-67. This paragraph would benefit from additional citations and explanations to support the superiority of the proposed approach. The fact that flattened embeddings are not suitable for annotating multidomain proteins seems obvious. Also, the claim that "current search implementations are slow compared to other methods" should be supported (tools such as EBA or pLM-BLAST have been shown to be faster than standard MSA-based methods). Also, as we mentioned in the main review, we believe that the generated pseudo-profiles and fine-tuned ESM2 predictions should not be called "smaller positional embeddings".

      Discriminating subdomains was a major limitation of the influential and widely-cited PfamN paper (Bileschi et al., 2022), we’ve added a citation to that paper in that paragraph for readers interested in diving deeper.

      To address the question of speed, we’ve included data preparation and search benchmarks as part of our presentation of the SCOPe40 benchmark.

      Finally, we were not sure why exactly every 7th residue is masked in a single forward pass. Traditionally, pseudo-log likelihoods are generated by masking every single token and predicting probabilities from logits given the full context - e.g. https://arxiv.org/pdf/1910.14659.pdf. Since this procedure is crucial in the next steps of the pipeline, it would be important to either experiment with this hyperparameter or explain the logic used to choose the mask spacing.

      We’ve added discussion of the masking distance to the Methods section.

      Reviewer #2 (Recommendations For The Authors):

      • While the code and data for the benchmark are available, the generation of searchable databases using the methods described for a popular resource such as Pfam, AFDB, SCOP/CATH which can be used by the community would greatly boost the impact of this work.

      3Di sequences predicted by ESM-2 3B 3Di can easily be used as queries against any Foldseek database, such as PDB, AFDB, etc. We’ve added Figure 4E to demonstrate this possibility, and added some related discussion.

      • Minor: In line 114, the text should likely read "compare lines 7 and 8" instead of "compare lines 6 and 7."

      We’ve clarified the discussion of Figure 3.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Redhardt and colleagues describe a structure of the voltage and Ca-activated Slo1 channel in complex with an auxiliary subunit, γ1. In complex with γ1, Slo1 adopts an open state that closely resembles previous open state structures. Of γ1, only the single membrane-spanning helix, which binds to the periphery of the Slo1 VSD, is resolved. There, it establishes several interactions with Slo1 that authors propose may favor adoption of the open state, potentially explaining how γ1 can shift I-V profile of Slo1 to be activated at more negative membrane potentials. The interactions described fit well with existing mutagenesis analyses.

      While this report provides a first glimpse of how γ1 can bind to Slo1, its impact will be minimal. It describes a single structural snapshot and there are no functional analyses presented. Additional analyses would be helpful in understanding of how γ1 can regulate Slo1 channels.

      We thank the reviewer for their honest judgment. We agree that validating the structure by biochemical and/or functional data would have significantly strengthened the manuscript. However, we are convinced that our structural data alone already provides significant novel understanding of the assembly of the Slo1-γ1 complex and regulation of Slo1 by γ1. Thus, we feel that publication of this manuscript is justified by the high importance of Slo channels and our data will have an impact in the field.

      __Major comments: __ 1. The authors propose several models for how γ1 regulates Slo1, yet none of them are experimentally evaluated. For example, on page 8, it is written that "we propose that the combination of three different principles, namely shape complementarity, covalent anchoring and lowering the resting state potential by a positively charged intracellular stretch, act in concert to stabilize an active VSD conformation in the Slo1-γ1 complex." This is a testable hypothesis and one that should be experimentally evaluated to better understand regulation by γ1.

      We agree with the reviewer that experimental validation of this hypothesis would have been an asset. Nevertheless, we think that our structural data in context of previous functional data e.g. from Li et al. 2015,2016) and also in comparison with the other two manuscripts on the same topic which have been published while this manuscript was under review, allows us to draw conclusions about the mechanism of γ1-mediated activation of Slo1. We have now, however, toned down some of the earlier statements and changed parts of our interpretations in light of the novel findings by Yamanouchi et al. and Kallure et al.

      The authors analysis of the extracellular domain of γ1 is incomplete. The only presented structure was performed with C4 symmetry imposed, in which extracellular domains were largely lost. The authors propose that these domains are dynamic and that their dynamism would enable simultaneous binding of both γ and b subunits, as occurs in cells. A more thorough analysis of the dynamics and well as potential asymmetric conformations should be performed to better understand how these domains interact with Slo1.

      We completely agree with the reviewer that a thorough analysis of the extracellular domain is important and thank the reviewer for their valuable suggestions. We had attempted such analysis already from the beginning, but were not successful. More specifically, we have attempted reconstructions with lower symmetry (C2 and C1) from the beginning or by symmetry relaxation after initial C4 reconstruction. Also, we tested different masking and signal subtraction strategies in combination with different global and local refinements, as well as symmetry expansion and 3D classification. Unfortunately, none of these strategies led to a better resolved LRR module.

      We now think that in comparison with Kallure et al. and Yamanouchi et al., the ice in our sample was thinner, which allowed us to reach higher resolution in the core particle (Slo1 and γ1 TM helix), but at the cost of the γ1 LRRs being denatured or at least distorted by the air-water interface.

      The refinement statistics suggest that the model was incompletely refined. This reviewer was not provided with the map or models, but the validation report lists a clashscore of 9 and 5.7% of the rotamers as being outliers, both of which are high for the reported resolution of the structure. It is also strange that the Q-score varied between different γ1 protomers. Why are the four protomers not identical when the map is 4-fold symmetric? The authors should carefully inspect their model to insure that it is as correct as possible.

      We thank the reviewer for pointing this out, and while the values for clashscores and rotamers were not outside the range of values typically found in many other cryo-EM structures, we agree that there was still some room for improvement. We have worked on this and could lower the values to a clashscore of 7.0 and 1.8 % rotamer outliers.

      The difference in Q-score is also something not too uncommon since, while the map is indeed C4-symmetric, during model refinement the NCS restraints are not completely preventing small deviations between the protomers. We have now also successfully attempted to minimize these differences further.

      Reviewer #1 (Significance (Required)):

      The impact of this report is limited. Functional analyses will be necessary to uncover precisely how gamma subunits regulate Slo1 channels.

      We thank the reviewer for this honest statement, but respectfully disagree. While additional functional analyses would have certainly boosted the impact, we are certain that our structural data and their interpretation will be very valuable for the field, because they provide (as stated by Reviewer 3) new insights into the regulation of Slo channel activity by the γ subunits and suggest (as stated by reviewer 2) a novel mechanism of activation of voltage-gated ion channels..

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary This study presents a high resolution cryo-EM study of a voltage-gated Ca++-dependent K+ channel in the presence of a gamma1 subunit. Analysis of the structure and sequence alignments suggest a novel mechanism of activation of voltage-gated ion channels.

      __Major comments __ The major issue in this paper is that it is only a structural biology paper. There is no structure-function relationship study, no functional studies of mutants that could validate -or not- the inferred underlying mechanism. Even though the authors have identified good candidates for mutations (e.g. p. 6) they have not attempted to validate their importance experimentally. As a result, reading their discussion is somewhat frustrating and full of assumptions, as indicated by sentences (p.7) like

      "a possible mechanism... might be... which would make... more likely".

      "... which might act ... seems important... might indicate... might lower... likely most pronounced... could be responsible..."

      "... might play an important role... does not allow a certain conclusion..."

      We completely agree with the reviewer that the paper would have been much stronger if we would have been able to perform biochemical or functional assays testing mutations in the binding interface. However, this would have unfortunately been beyond the scope of the project. We are nevertheless confident that our structural data will be of value for the field, also in context of the two structure-function papers that have been published since which confirm and validate our data and provide the link to function.

      __Minor comments which could be confidently addressed __ The Introduction contains no description of the state-of-the-art in the field concerning the available structures in the same system or similar ones. Hence, it is difficult to judge for people outside the field if the novelty. is incremental or significant.

      We have adjusted the introduction to explicitly mention previously published structural data on the Slo channels.

      References 10 and 42 (eLife) lacj some details.

      We have adjusted said references accordingly.

      __Reviewer #2 (Significance (Required)): ______


      Significance general assessment As it turns out, at least two papers in exactly the same field just appeared: -one in Molecular Cell by a Japanese group, which is much more developed and contains functional tests and structure-function relationships, in addition to beautiful structures (available on-line early December) https://www.sciencedirect.com/science/article/pii/S1097276523009218

      -one in biorxiv, deposited yesterday https://www.biorxiv.org/content/biorxiv/early/2023/12/20/2023.12.20.572542.full.pdf

      Advances wrt known results See above. As a result of these new papers in Mol Cell and biorxiv, I think the authors should reconsider submitting their article elsewhere, perhaps for a more specialized audience.

      We agree with the reviewer that in light of the other two publications which both were published a while after we deposited our preprint on biorxiv and while the manuscript was under review, the uniqueness of our data is somewhat lowered. However, since our data is overall in large agreement with these two other publications, but we report a structure at significantly higher resolution and from a different species (indeed the first Slo1 structure from rabbit, a model organism of BK channel characterization in the last decades), we are confident that our data are still very valuable for the field and qualify for publication in one of the affiliate journals of Review Commons. After all, the fact that three papers reporting very similar data were published within a few weeks (plus another preprint reporting structures of a Slo channel, but unrelated to γ subunits) illustrates the importance for understanding the regulation of this essential ion channel and the impact of all structural data enhancing this understanding, and independent confirmation by three different labs is something very valuable to the community.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      "This manuscript by Redhardt et al. presents the cryo-EM structure of the Slo K+ channel from rabbits in conjunction with its auxiliary subunit, γ1, and proposes a mechanistic model for regulating channel activation. "This manuscript by Redhardt et al. presents the cryo-EM structure of the Slo K+ channel from rabbits in conjunction with its auxiliary subunit, γ1, and proposes a mechanistic model for regulating channel activation. The Slo channel, also known as the large-conductance calcium-activated potassium channel or BK channel, is an ion channel type found in various cell membranes, including neurons, muscle cells, and other tissue types. Its key features encompass Ca2+ activation, voltage dependence, and regulation by auxiliary subunits. Different auxiliary subunits have been shown to modulate channel functions distinctly; notably, the γ1 subunit enables channel activation at lower voltages compared to the wild-type channel. This manuscript offers a structural-functional framework that enhances our comprehension of how Slo channels are regulated by auxiliary subunits, such as gamma and beta subunits. While the structure of Slo channels in complex with the beta subunit is understood, the binding and interaction of the gamma subunit with the channels remain elusive due to the absence of corresponding structures. Along these lines, the presented structure here indeed provides new insights into the regulation of Slo channel activity by the gamma subunit. However, there are some important questions below that should be addressed."

      1. In Figure 1D panel, the calcium ions appear to be indistinct, likely due to the figure's low resolution. The authors are recommended to enhance the figure quality and consider a better positioning to effectively illustrate the ions.

      We have adjusted the coloring of calcium ions Fig. 1D to increase their visibility.

      It would be beneficial for the readers if the authors provided detailed methodology explaining how they arrived at the 7% and 11% coexpression, aiding in the complex formation. Additionally, it would be informative to know the observed shift in the size exclusion chromatography (SEC) profile of Slo1-Y1 compared to apo Slo1.

      We have arrived at these concentrations of the respective viruses by empirically testing ranges between 3 % and 15 %. We have now added a sentence to the manuscript to explain this.

      Is there any rationale behind initially purifying using strep affinity followed by His affinity?

      The idea behind using a dual-affinity protocol is to ensure that all purified complexes contain at least one copy of Slo1 and one copy of γ1. Using the Strep tag first allows to remove most contaminants already in the first step, due to its higher specificity compared to the His tag. We have added a sentence to the methods section to explain this.

      Regarding the Slo1 tetramer with gamma subunit binding, are there other classes where one, two, or three gamma subunits are bound to Slo1? Or is there only one class where all protomers of Slo1 are occupied by the gamma subunit? How do these classes appear when refined in C1 symmetry? Are there classes displaying C1 or C2 symmetry, or is the four-fold symmetry preserved across all refined classes?"

      We exclusively observe complexes with four γ1 subunits. This is also in agreement with the other two recent publications reporting Slo1-γ1 complex structures, but could in principle be an artifact of artificial overexpression. Also when we refine the particles in C1, we retain C4 symmetry and do not observe any classes with C2 or C1 symmetry.

      The authors utilized nearly 1.9 million particles to reconstruct the final class, resulting in a high resolution. Is such a large number of particles truly necessary to achieve high resolution in this context?

      The large number of particles is not strictly necessary, i.e. we could obtain similar quality by using fewer particles. In the end, we have now further classified down to ~827k particles, which very slightly improved the resolution and quality of the map.

      Authros mentioned that F273 of γ1 forms pi-stacking interactions, it remains unclear with which components of the channel these interactions occur.

      F273 forms (slightly distorted) T stacking interactions with F164 in S2 and F187 in S3. We now changed the sentence in the manuscript to mention the residues that line the hydrophobic pocket to make it more clear which elements contribute to the interaction with F273.

      The authors propose that the disulfide bond between the γ subunit and Slo1 could play a crucial role in their interaction. Was there any observation of a covalent linkage in SDS page analysis? Furthermore, how would this interaction be affected if either cysteine C253 of gamma1 or C141 on the channel were mutated or neutralized?"

      We have run all our SDS-PAGE experiments under reducing conditions, thus destroying any disulfide bridges that might have been present in the complex. We have now, however obtained a slightly better defined reconstruction (as pointed out in our answer to point 5 raised by this reviewer) where we do not see as clear continuous density anymore between the two cysteine side chains. Thus, we have removed the cystine bond from the final model and have adjusted text and figures accordingly. We still think that it might be more than coincidence that those two side chains come into such close proximity, though, and still discuss the possibility of a cystine bridge in the manuscript.

      Author's state that "The presence of several immobile positive charges on the intracellular side in close proximity to the VSD as in the case of the Slo1-γ1 complex is likely to locally lower the resting state potential and repulse the gating charges, thereby reducing the energy to overcome for the VSD to transition to the active conformation." Authors need to be little more elaborative here as it is not clear what authors mean repulse of gating charges.

      We have expanded our description of the proposed repulsive effect of the positive charges in the manuscript and in addition also discuss the additional role of the charges in stabilizing the Ca2+-bound conformation of the gating ring as proposed by Yamanouchi et al.

      Probably beyond this study but I was wondering whether it is possible that Beta and gamma subunit can together assemble as heteromers to form a cage-like structure with contribution from both.

      We agree with the reviewer that this is an interesting question which we have also thought about and one which should be tested, but as the reviewer already mentioned, this would go beyond the present study and should be subject to an independent follow-up investigation.

      Are there any specific lipids observed within the structure that could potentially contribute to the functional conformation or stability of the complex?"

      Given the high resolution of our structure, we observe a number of ordered lipid and detergent molecules, most of which were located at similar positions as in previous structures of Slo channels. Besides those molecules clustering in the deep cleft between neighboring voltage-sensor domains, we also observe lipid densities close to the binding site of γ1 on the distal side of the VSD. However, as their relevance for γ1 binding is unclear, we don’t discuss them in the manuscript. In general, of course, we agree with the reviewer that lipids can have a large impact on the function of membrane proteins.

      It would be interesting to see if the kink in the gamma subunit is entirely neutralized through mutations of proline and glycine, how these alteration might impact the assembly of the mutated gamma subunit with the channel. The authors should provide insights into whether this mutated form of the gamma subunit assembles effectively with the channel and whether there are functional consequences associated with this alteration.

      As shown by Kallure et al., substituting P270 in the kink by serine (the native residue at this position in γ3) strongly diminished the ability of γ1 to associate with Slo1 in vitro, demonstrating the importance of the kink and providing a rationale for the observed differences in the potency of the TM helices of γ1 and γ3 in Slo1 activation.

      It would be generally beneficial for the authors to provide functional insights that can support the physiological relevance of this kink in the gamma subunit. Understanding the potential consequences of this mutation and its implications for the assembly and function of the channel complex will offer valuable insights into the physiological role of the kink.

      We absolutely agree with the reviewer that functional insights on the relevance of the kink would be very valuable, but we think that the available experimental data together with the natural sequence differences in γ1-γ4 and the correlation with their physiological activity are very clear indications that the kink is relevant. However, future follow-up studies that prove this beyond any doubt would be valuable.

      Is it known that binding of beta or gamma subunit can impact the subsequent binding of beta and gamma to channels. If it is, it need to be discussed briefly in the discussion part.

      This is, to the best of our knowledge, not known. The only existing data that suggests co-presence of beta and gamma subunits on Slo1, reported in Gonzalez-Perez et al., 2015, stems from electrophysiological experiments and does not reveal anything about hierarchy and temporal order of binding events.

      Reviewer #3 (Significance (Required)):

      The Slo channel, also known as the large-conductance calcium-activated potassium channel or BK channel, is an ion channel type found in various cell membranes, including neurons, muscle cells, and other tissue types. Its key features encompass Ca2+ activation, voltage dependence, and regulation by auxiliary subunits. Different auxiliary subunits have been shown to modulate channel functions distinctly; notably, the γ1 subunit enables channel activation at lower voltages compared to the wild-type channel. This manuscript offers a structural-functional framework that enhances our comprehension of how Slo channels are regulated by auxiliary subunits, such as gamma and beta subunits. While the structure of Slo channels in complex with the beta subunit is understood, the binding and interaction of the gamma subunit with the channels remain elusive due to the absence of corresponding structures. Along these lines, the presented structure here indeed provides new insights into the regulation of Slo channel activity by the gamma subunit.

      We thank the reviewer for this positive assessment of the data and agree that our structural data, also when regarded together with the complementary manuscripts by Kallure et al. and Yamanouchi et al., provides significant new insight into the assembly and activity of γ subunits.

    1. We may characterize this process with reference to thechanges which it brings about in the familiar instinctual dispositions of human beings, to satisfy which is,after all, the economic task of our lives. A few of these instincts are used up in such a manner thatsomething appears in their place which, in an individual, we describe as a character-trait.

      You can see this within different generations. For example, how boomers and gen z do not think the same as society has changed and so has different norms. It is more normal for gen Z to stay with their parents as long as they can because of factors that have changed from the boomer generation.

    1. Before we talk about public criticism and shaming and adults, let’s look at the role of shame in childhood. In at least some views about shame and childhood1, shame and guilt hold different roles in childhood development: Shame is the feeling that “I am bad,” and the natural response to shame is for the individual to hide, or the community to ostracize the person. Guilt is the feeling that “This specific action I did was bad.” The natural response to feeling guilt is for the guilty person to want to repair the harm of their action. In this view, a good parent might see their child doing something bad or dangerous, and tell them to stop. The child may feel shame (they might not be developmentally able to separate their identity from the momentary rejection). The parent may then comfort the child to let the child know that they are not being rejected as a person, it was just their action that was a problem. The child’s relationship with the parent is repaired, and over time the child will learn to feel guilt instead of shame and seek to repair harm instead of hide.

      I think whats important is supporting children's emotional well-being, social growth, and moral knowledge during childhood requires an awareness of and appropriate response to the subtle differences between shame and guilt. It makes it possible for adults to help kids respond constructively to errors and setbacks, establishing the foundation for them to grow into emotionally strong, socially proficient, and morally aware adults.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The coupling between cell polarity and cell cycle progression is an important aspect of symmetric and asymmetric cell division. Although there are several examples of cell cycle kinases phosphorylating polarity proteins, it has been difficult to assess the importance of these on cell division due to the strong and pleiotropic effects of manipulating these kinases. Here, the authors generate an analogue-sensitive allele of cdk1 in flies to tackle this question in neuroblasts (NBs) and sensory organ precursors (SOPs), two well characterised examples of asymmetric cell divisions. They show that partial Cdk1 inhibition (which still allows cell cycle progression) does not block Bazooka (PARD3 in mammals) polarization in NBs, but prevents coalescence of the Baz crescent, which has previously been shown to be an actomyosin-based process. They further identify a Cdk1 consensus site on Baz (S180) for which they generate a phospho-specific antibody, allowing them to show that this site is specifically phosphorylated in dividing NBs and SOPs. Although mutations at this site do not recapitulate the effect of Cdk1 on Baz coalescence, they do delay Miranda polarization in NBs and affect lateral inhibition and asymmetric cell division of SOPs. Finally, the authors show that human PARD3 can also be phosphorylated by Cyclin B/Cdk1 in vitro.

      Major comments:

      • Figure 2A: it would be good to show that polarization of Baz::GFP in consecutive divisions is maintained in cdk1as2 animals in the absence of 1-NA-PP1. We now show in Fig S2B a panel with two consecutive divisions of a cdk1as2 neuroblast in the absence of 1-NAP-PP1, followed by a third division in the presence of 1-NAP-PP1. The neuroblast shows high levels of Baz polarization in the two first divisions.

      • The interpretation of the observed SOP phenotypes is complicated by the uneven expression of the pnr-GAL4 driver and the fact that it is expressed in epithelial cells rather than just SOPs. The authors could express their control and mutant Baz constructs under the control of neurP72-GAL4. It is not likely they would be able to deplete endogenous Baz as they have done in NBs, as neurP72-GAL4 is expressed too late to deplete most proteins before SOP division, but they could at least look at localization of the mutants and any possible gain-of-function phenotypes.

      Following this suggestion, we have recombined Neur-GAL4 with UAS-delta RNAi to attempt to deplete both endogenous Baz::mScarlet and Delta while expressing our Baz::GFP constructs specifically in SOPs. Baz::mScarlet depletion was surprisingly efficient considering, as the reviewer points out, the late timing of Neur-GAL4 expression. However, the adult flies did not present any sensory organs transformations, perhaps because Delta might not be as efficiently depleted. We can at least rule out dominant-negative effects.

      We thank the reviewer for his constructive feedback and as suggested, we now extensively analysed the localisation of the Baz-S180 mutants in SOPs and found significant defects. We describe these observations in a new Figure 6. Briefly, we observed that the Baz phosphomutants have localisation defects during the pIIa cell division but not the pI cell division. We also observed a very surprising mosaicism of expression of our UASz-driven constructs within the SOP lineage that allowed us to make a few interesting observations which should be of interest to SOP specialists. Briefly, mosaic expression of Baz::GFP within the SOP lineage allowed to analyse the relative contributions of pIIa and pIIb/pIIIb to different Baz cortical pools and revealed an unexpected cell non-autonomous mechanism controlling pIIb division orientation. We describe these findings in a new associated supplemental figure.

      The authors speculate that Baz phosphorylation during lateral inhibition may be the reason for the observed excess specification of SOPs in the S180 mutants. This could easily be tested by looking at their antibody staining at earlier stages in the notum. Following this suggestion (also coming from Reviewer #2), we have stained nota between around 8h APF. We observed that patches of cells of the early notum display a strong Baz-pS180 phospho-signal. These patches partially overlap with the Delta-positive stripes in which lateral inhibition occurs (as described for example in (Corson et al., 2017), consistent with the possibility that Baz-S180 phosphorylation does somehow regulate lateral inhibition.

      These new experiments clearly show that Baz can be phosphorylated on S180 in cells that do not divide asymmetrically. This led us to change the title.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Cell polarization in dividing cells, including stem cells, is typically coupled such that polarity can inform the architecture, orientation, and/or asymmetry during cell division. In Drosophila neural stem cells (neuroblasts/SOP), Par polarity is coupled to the cell cycle, but the nature of this coupling remains unclear. In this work, Loyer and colleagues report on impacts of CDK1 inhibition on Bazooka/Par3 localization and basal fate determinant localization. They provide evidence for a novel phosphorylation site that appears unique to asymmetrically dividing cells and may be involved in regulation of asymmetric division. Finally, they show that CDK1 can, at least in principle, phosphorylate human Par3 in vitro.

      Overall, the major claims of the abstract appear supported by the experimental work; however, we think the title overstates the overall conclusions that can be drawn from the work.

      Major comments:

      • The major claim of the paper is the role of specific phosphorylation of S180 in asymmetrically dividing cells in polarization and sensory organ formation, which relies heavily on interpretation of S180A/D phosphomutants. The experiments are carefully performed and quantified, and are consistent with the conclusions drawn. However, we wondered if it possible that the phenotypes are not linked to phosphorylation (the authors acknowledge this in the Discussion)? In other words could the A/D mutants simply be weak Baz mutants? This could this potentially explain the extra-SOP phenotype if Baz function is generally altered, especially given that it is difficult to rationalise a role for SOP-specific phosphorylation in the processes that specify SOP cells in the precursor epithelial cells. The authors speculate that these early precursors may exhibit also phosphorylation, but this isn't examined. Chasing this down seems key to support the core titular claim of the paper. Following this suggestion (also coming from Reviewer #1), we have stained nota around 8h APF. We observed that patches of cells of the early notum display a strong Baz-pS180 phospho-signal. These patches partially overlap with the Delta-positive stripes in which lateral inhibition occurs (as described for example in Corson et al., 2017). This result is presented in Fig. 5H. As would be the case for any phosphomutant, this does not strictly rule out that the S180A and S180D could simply be weak Baz mutants, but it strongly supports the possibility that the lateral inhibition defects observed in these mutants result from defective Baz-S180 phosphorylation.

      • Implicit in the core message of the paper is the elucidation of CDK1 regulation of polarity and specifically Baz. However, the connection between CDK1 and S180 (and Baz regulation overall) is relatively tenuous in this work. First, the S180A mutant does not phenocopy CDK1 inhibition with respect to basal determinant phenotypes, though obviously CDK1 may be more pleiotropic. Second, whether the CDK1 inhibition phenotype is linked to any effect on Baz/PAR behaviour is not really explored. Third, they do not test whether S180 phosphorylation is CDK1-dependent. We fully agree with these comments. We cannot think of any way of addressing the first two points, which would require fully inhibiting CDK1 and somehow maintaining neuroblasts in mitosis to examine how it impacts Baz localisation. We tried to arrest neuroblasts in mitosis and block the proteasome as this at least in HeLa cells led to persistence of mitosis when CDK1 was inhibited (Skoufias et al., 2007). However, neuroblasts arrested in mitosis by proteasome inhibition slipped out of mitosis.

      However, concerning the third point, we now provide evidence showing that, at least in vitro, Drosophila BazS180 is phosphorylated by CDK1 (see below).

      The method for quantifying domain signal only references prior work and should be described in this work. From our search of the cited reference, it appears to be peak signal intensity at a user specified point on the cortex. While this does not undermine the core findings as presented, it may not capture additional features that may be informative (domain size, fluorescence distribution, total signal etc.). For example domain coalescence would imply smaller, brighter domains, but similar total protein amounts, which appears to be the case from images, but isn't quantified per se. We now describe our method for quantifying average signal intensity in the middle of the Baz crescents. We agree that quantifying additional features to check whether they are affected by partial CDK1 inhibition would be interesting. However, doing so requires determining exactly where Baz crescents start and end. As Baz crescent edges in neuroblasts often end in a gradient rather than a sharp edge (Hannaford et al., 2018), we are not sure to be able to confidently do so in every case with the image quality of our dataset: we prioritised limiting photobleaching to accurately quantify the levels of endogenously expressed Baz rather than obtaining very sharp and high contrast images. This is further complicated by the fact that, depending on the depth of neuroblasts within the tissue and the orientation of their division relative to the imaging plane, the signal intensity of Baz crescents is quite variable, preventing a simple thresholding approach to arbitrarily determine the limits of crescents based on signal intensity. In short, accurately determining the size of crescents is very challenging.

      The phosphospecific antibody signal is relatively weak, leading to relatively low signal to noise, which could compromise the ability to detect phospho-S180 in non-asymmetrically dividing cells or generally in cells in which Baz is not polarised and thus signal would be diffused around the cell rather than concentrated. Similar caveats could also apply to the lack of signal in interphase cells, where Baz may be less enriched at the cortex and not polarized. We are inclined to believe the authors conclusions, particularly given their examination of multiple cell types and tissues. However, it is a potential caveat as it may be most visible in polarised cells where it is asymmetrically enriched. We thank the reviewer for pointing this out. Given the fact that Baz levels at the neuroepithelial cells adherens junctions are similar, we are confident that Baz-S180 is phosphorylated in dividing neuroblasts but not in non-mitotic epithelial cells, which is at least consistent with our new finding that CDK1 phosphorylates Baz-S180 in vitro. However, we agree that we cannot strictly rule out that Baz-S180 is phosphorylated but below a detection threshold in mitotic neuroepithelial cells as cortical Baz levels decrease in these cells.

      We have also gathered new data showing that, in the early notum, Baz-S180 is detected in epithelial cells that are not dividing asymmetrically, definitely ruling out the notion that Baz-S180 is strictly ACD-specific. We have changed the title of the paper accordingly, toned down the mention of apparently ACD-specific Baz-S180 phosphorylation in the abstract and now describe and discuss the fact that the apparent ACD-specificity of Baz-S180 phosphorylation is context-specific.

      Examination of in vitro phosphorylation of human Par3D (Figure 6) seems out of place and does not add much. It is human, not Bazooka. They reveal 30 sites, 18 of which in both replicates, but most are not obvious CDK sites and the S180 equivalent site is missing. None of these sites is validated in vivo, at least in this work.

      We fully agree with these comments. We initially attempted to purify both full length Baz and human PARD3 but only managed to purify small amounts of PARD3, which is why our analysis was limited to human PARD3. To circumvent these difficulties, we instead purified a smaller N-terminal fragment of Baz and PARD3, which was successful for both proteins and gave us much higher quantities of sample for analysis. Using two different approaches (Western blot with our phospho-specific antibody on Baz and targeted mass spectrometry on Baz and PARD3), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro.

      Minor comments: Figure 1: Uses metaphase arrested cells, presumably colcemid, but colcemid is only noted in Figure 2. We now mention Colcemid in the legend of Figure 1. - Figure 2A: Scale bar is truncated. We have corrected this. - Figure 2A: Example images of control neuroblasts could be useful to readers. We now show control neuroblasts in Figure 2A. - Figure 2G' vs H': Because G' has two panels and H' has only one, we often confused the PKC and Mira box plots when comparing to Numb. Perhaps Mira could be in a separate sub panel or be more closely juxtaposed with Numb? The quantification of the Mira signal is now right next to Numb. - Whereas both Numb/Mira were examined in CDK1(as), only Mira is reported for the S180A/D experiments. Is there a Numb phenotype as well?

      We actually co-stained Numb and Miranda in the dataset that we analysed in the S180A/D experiments shown in Fig 4E, F. We did not analyse Numb localisation in the first version we submitted because of a penetration issue of the Numb antibody: the Numb signal fades extremely fast as we image deeper in the tissue, causing large difference of signal intensity even within a single cell. This prevents us from performing any meaningful quantitative measurement of the Numb signal like the one we did in Fig. 2H, K, for which we did not encounter this issue. All our further immunostaining experiments with this antibody have had the same problem since then, even after using Triton concentrations up to 4% for permeabilization.

      Nonetheless, following the reviewer’s question, we have at least performed a simple qualitative analysis of Numb localisation in this experiment. We observed that Numb localised to the basal pole in most cases in controls and Baz phosphomutants, but localised uniformly at the cortex in half the cases where Miranda showed very low levels of polarisation in metaphase in BazS180D mutants. This Numb localisation defect suggests a loss of function of the PAR complex whereas, intriguingly, the Miranda localisation defect suggests a gain of function of the PAR complex. These new observations are described in Fig. 4G-H’.

      • The discussion of the notch / Baz phenotypes (Figure 5) is rather complicated and a bit difficult to follow. We agree with this, we have rewritten this part. This is further simplified by our new observation that Baz-S180 is phosphorylated in the early notum during lateral inhibition.

      • Figure 5A: captions should indicate that RFP RNAi is depleting Baz. We have modified the figure accordingly.

      • Box plots are used, but not described. i.e. outliers seem to be marked, but criteria unclear. Mean vs median, etc. We now describe boxplots in the legend in the first instance they are used (Fig 2A’), and in the material and methods
      • Some grammatical mistakes:
      • Title: neuroblast (no 's'),
      • Page 1: Cell fate difference(s?) in the resulting daughter cells
      • Page 4: (As) CDK1 inhibition with 10 μM 1-NA-PP1 prevents neuroblasts from cycling and causes metaphase- arrested neuroblasts to slip out of mitosis. (Reword)
      • Page 6: increased levels of basal fate(no 's') determinants

      We have corrected these mistakes.

      Reviewer #2 (Significance (Required)):

      The links between cell cycle and cell polarity are clearly important and remain poorly understood. Hence, the work addresses key conceptual/mechanistic questions relevant to our fundamental understanding of stem cell biology and regulation of polarity and asymmetric cell division. In our opinion, there are clearly some interesting observations in the manuscript, the experiments are performed carefully, and the data are generally well described. That said, overall, the work seems somewhat premature.

      The direct impact of CDK1 on Baz behaviour remains somewhat unclear. The authors do a good job of limiting the concentration of inhibitor to decouple effects of cell cycle progression from CDK1 levels per se, but this does potentially impact the strength of the phenotypes they can detect and hence the observed phenotypes are relatively minor. Note that driving cells out of mitosis with stronger CDK1 inhibition clearly impacts Baz localization, so the 'real' effect of CDK1 inhibition on Baz could be stronger than reported here. It is also unclear whether the phenotypes observed are directly linked to CDK1 regulation of PAR polarity or an indirect effect of cell cycle control of other processes. The authors' suggestion that it could be related to defects in cortical actin organization, which is known to be cell cycle controlled, seems most likely, but neither this or other models are explored further. We agree but are not aware of any experiment that would allow testing full inhibition of CDK1 on membrane-bound Baz in mitotic neuroblasts. As mentioned above in our response to reviewer #1 we tried to arrest neuroblasts in mitosis and block the proteasome as this at least in HeLa cells led to persistence of mitosis when CDK1 was inhibited (Skoufias et al., 2007). However, neuroblasts arrested in mitosis by proteasome or Colcemid or both slipped out of mitosis upon inhibition of CDK1.

      We agree it would be interesting to study how CDK1 affects the actomyosin network in neuroblasts but feel that this is somewhat beyond the scope of the manuscript.

      Using phosphospecific antibodies, they report on a novel putative CDK1 phosphorylation site, but aside from looking like a consensus CDK1 site, whether this site is CDK1 dependent is not examined. Notably, the corresponding phosphomutants have modest effects and don't obviously account for the CDK1 inhibition phenotype, leaving it somewhat unclear whether it is under cell cycle regulation. We now provide a new figure 7 to address this point. As mentioned already above, using two different approaches (Western blot with our phospho-specific antibody on Baz and targeted mass spectrometry on Baz and PARD3 using), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro. Again, we cannot identify any experiment that would allow us testing whether S180 Baz is a direct target of CDK1 in vivo. The fact that we now report significant defects on Baz localisation in pIIa divisions, strongly suggests functional relevance and CDK1 seems a plausible kinase based on the new in vitro results.

      The observation that S180 phosphorylation appears unique to asymmetrically dividing cells is very curious, but this observation is not followed up extensively. Again phenotypes of phosphomutants are quite modest, and while one can propose models to rationalise the phenotypes observed, these models are not fully explored. As mentioned above, we now show that Baz-S180 phoshorylation is not strictly ACD-specific and changed the title accordingly. We also have new data showing that the S180 phosphomutants of Baz have localisation defects in mitotic pIIa divisions (new figure 6). Therefore, this phosphorylation event on Baz can be linked to Baz’s cortical localisation and interestingly shows context dependency.

      The findings that human Par3D can be phosphorylated by CDK1 in vitro do not add much particularly as they obtain a very large number of putative sites raising questions of specificity, the sites are not validated, and an S180 equivalent site was not identified. We agree that this has been a weakness which we feel we have addressed. We paste here the answer already provided above when replying to reviewer #1.

      We initially attempted to purify both full length Baz and human PARD3 but only managed to purify small amounts of PARD3, which is why our phospho-proteomics analysis was limited to human PARD3. To circumvent these difficulties, we instead purified a smaller N-terminal fragment of Baz and PARD3, which was successful for both proteins and gave us much higher quantities of sample for analysis. Using two different approaches (Western blot with our phospho-specific antibody on Baz and phosphor proteomics on Baz and PARD3 using mass spectrometry), we now show in a new Figure 7 that CDK1 phosphorylates Baz-S180 and PARD3-S187 in vitro.

      References

      CORSON, F., COUTURIER, L., ROUAULT, H., MAZOUNI, K. & SCHWEISGUTH, F. 2017. Self-organized Notch dynamics generate stereotyped sensory organ patterns in Drosophila. Science, 356.

      HANNAFORD, M. R., RAMAT, A., LOYER, N. & JANUSCHKE, J. 2018. aPKC-mediated displacement and actomyosin-mediated retention polarize Miranda inDrosophilaneuroblasts. eLife, 7__,__ 166.

      SKOUFIAS, D. A., INDORATO, R. L., LACROIX, F., PANOPOULOS, A. & MARGOLIS, R. L. 2007. Mitosis persists in the absence of Cdk1 activity when proteolysis or protein phosphatase activity is suppressed. J Cell Biol, 179__,__ 671-85.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Cell polarization in dividing cells, including stem cells, is typically coupled such that polarity can inform the architecture, orientation, and/or asymmetry during cell division. In Drosophila neural stem cells (neuroblasts/SOP), Par polarity is coupled to the cell cycle, but the nature of this coupling remains unclear. In this work, Loyer and colleagues report on impacts of CDK1 inhibition on Bazooka/Par3 localization and basal fate determinant localization. They provide evidence for a novel phosphorylation site that appears unique to asymmetrically dividing cells and may be involved in regulation of asymmetric division. Finally, they show that CDK1 can, at least in principle, phosphorylate human Par3 in vitro.

      Overall, the major claims of the abstract appear supported by the experimental work; however, we think the title overstates the overall conclusions that can be drawn from the work.

      Major comments:

      1. The major claim of the paper is the role of specific phosphorylation of S180 in asymmetrically dividing cells in polarization and sensory organ formation, which relies heavily on interpretation of S180A/D phosphomutants. The experiments are carefully performed and quantified, and are consistent with the conclusions drawn. However, we wondered if it possible that the phenotypes are not linked to phosphorylation (the authors acknowledge this in the Discussion)? In other words could the A/D mutants simply be weak Baz mutants? This could this potentially explain the extra-SOP phenotype if Baz function is generally altered, especially given that it is difficult to rationalise a role for SOP-specific phosphorylation in the processes that specify SOP cells in the precursor epithelial cells. The authors speculate that these early precursors may exhibit also phosphorylation, but this isn't examined. Chasing this down seems key to support the core titular claim of the paper.
      2. Implicit in the core message of the paper is the elucidation of CDK1 regulation of polarity and specifically Baz. However, the connection between CDK1 and S180 (and Baz regulation overall) is relatively tenuous in this work. First, the S180A mutant does not phenocopy CDK1 inhibition with respect to basal determinant phenotypes, though obviously CDK1 may be more pleiotropic. Second, whether the CDK1 inhibition phenotype is linked to any effect on Baz/PAR behaviour is not really explored. Third, they do not test whether S180 phosphorylation is CDK1-dependent.
      3. The method for quantifying domain signal only references prior work and should be described in this work. From our search of the cited reference, it appears to be peak signal intensity at a user specified point on the cortex. While this does not undermine the core findings as presented, it may not capture additional features that may be informative (domain size, fluorescence distribution, total signal etc.). For example domain coalescence would imply smaller, brighter domains, but similar total protein amounts, which appears to be the case from images, but isn't quantified per se.
      4. The phosphospecific antibody signal is relatively weak, leading to relatively low signal to noise, which could compromise the ability to detect phospho-S180 in non-asymmetrically dividing cells or generally in cells in which Baz is not polarised and thus signal would be diffused around the cell rather than concentrated. Similar caveats could also apply to the lack of signal in interphase cells, where Baz may be less enriched at the cortex and not polarized. We are inclined to believe the authors conclusions, particularly given their examination of multiple cell types and tissues. However, it is a potential caveat as it may be most visible in polarised cells where it is asymmetrically enriched.
      5. Examination of in vitro phosphorylation of human Par3D (Figure 6) seems out of place and does not add much. It is human, not Bazooka. They reveal 30 sites, 18 of which in both replicates, but most are not obvious CDK sites and the S180 equivalent site is missing. None of these sites is validated in vivo, at least in this work.

      Minor comments:

      • Figure 1: Uses metaphase arrested cells, presumably colcemid, but colcemid is only noted in Figure 2.
      • Figure 2A: Scale bar is truncated
      • Figure 2A: Example images of control neuroblasts could be useful to readers.
      • Figure 2G' vs H': Because G' has two panels and H' has only one, we often confused the PKC and Mira box plots when comparing to Numb. Perhaps Mira could be in a separate sub panel or be more closely juxtaposed with Numb?
      • Whereas both Numb/Mira were examined in CDK1(as), only Mira is reported for the S180A/D experiments. Is there a Numb phenotype as well?
      • The discussion of the notch / Baz phenotypes (Figure 5) is rather complicated and a bit difficult to follow.
      • Figure 5A: captions should indicate that RFP RNAi is depleting Baz.
      • Box plots are used, but not described. i.e. outliers seem to be marked, but criteria unclear. Mean vs median, etc.

      Some grammatical mistakes:

      • Title: neuroblast (no 's'),
      • Page 1: Cell fate difference(s?) in the resulting daughter cells
      • Page 4: (As) CDK1 inhibition with 10 μM 1-NA-PP1 prevents neuroblasts from cycling and causes metaphase- arrested neuroblasts to slip out of mitosis. (Reword)
      • Page 6: increased levels of basal fate(no 's') determinants

      Significance

      The links between cell cycle and cell polarity are clearly important and remain poorly understood. Hence, the work addresses key conceptual/mechanistic questions relevant to our fundamental understanding of stem cell biology and regulation of polarity and asymmetric cell division. In our opinion, there are clearly some interesting observations in the manuscript, the experiments are performed carefully, and the data are generally well described. That said, overall, the work seems somewhat premature.

      1. The direct impact of CDK1 on Baz behaviour remains somewhat unclear. The authors do a good job of limiting the concentration of inhibitor to decouple effects of cell cycle progression from CDK1 levels per se, but this does potentially impact the strength of the phenotypes they can detect and hence the observed phenotypes are relatively minor. Note that driving cells out of mitosis with stronger CDK1 inhibition clearly impacts Baz localization, so the 'real' effect of CDK1 inhibition on Baz could be stronger than reported here. It is also unclear whether the phenotypes observed are directly linked to CDK1 regulation of PAR polarity or an indirect effect of cell cycle control of other processes. The authors' suggestion that it could be related to defects in cortical actin organization, which is known to be cell cycle controlled, seems most likely, but neither this or other models are explored further.
      2. Using phosphospecific antibodies, they report on a novel putative CDK1 phosphorylation site, but aside from looking like a consensus CDK1 site, whether this site is CDK1 dependent is not examined. Notably, the corresponding phosphomutants have modest effects and don't obviously account for the CDK1 inhibition phenotype, leaving it somewhat unclear whether it is under cell cycle regulation.
      3. The observation that S180 phosphorylation appears unique to asymmetrically dividing cells is very curious, but this observation is not followed up extensively. Again phenotypes of phosphomutants are quite modest, and while one can propose models to rationalise the phenotypes observed, these models are not fully explored.
      4. The findings that human Par3D can be phosphorylated by CDK1 in vitro do not add much paritcularly as they obtain a very large number of putative sites raising questions of specificity, the sites are not validated, and an S180 equivalent site was not identified.

      In summary, the individual findings of this work are interesting and generally solid. Each could be followed up to provide mechanistic insight into cell cycle- or cell type-dependent regulation of Par polarity. However, in their current state, the results seem more like a loosely connected set of observations.

      Expertise: Cell polarity and asymmetric cell division

    1. Author Response

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their thorough reading and helpful comments which has allowed us to further improve the manuscript. Following the suggestions of the reviewers we have run a number of new simulations including mutations of the PIP binding residues and with an elastic network allowing more mobility of the linker. Together these excellent ideas have allowed us to strengthen the conclusions of the study. Below, we provide point-by-point responses to their suggestions.

      Reviewer #1 (Public Review):

      Summary:

      Here, the authors were attempting to use molecular simulation or probe the nature of how lipids, especially PIP lipids, bind to a medically-important ion channel. In particular, they look at how this binding impact the function of the channel.

      Strengths:

      The study is very well written and composed. The techniques are used appropriately, with plenty of sampling and analysis. The findings are compelling and provide clear insights into the biology of the system.

      Weaknesses:

      A few of the analyses are hard to understand/follow, and rely on "in house" scripts. This is particularly the case for the lipid binding events, which can be difficult to compute accurately. Additionally, a lack of experimental validation, or coupling to existing experimental data, limits the study.

      Our analysis scripts have now been made publicly accessible as a Jupyter notebook on Github https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project

      It is my view that the authors have achieved their aims, and their findings are compelling and believable. Their findings should have impacts on how researchers understand the functioning of the Nav1.4 channel, as well as on the study of other ion channels and how they interact with membrane lipids.

      Reviewer #2 (Public Review):

      Summary:

      Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete and lacks the expected confirmatory studies which are relevant to such proposals.

      Strengths:

      The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation.

      Weaknesses:

      However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

      Following the reviewers suggestion we have conducted new simulations demonstrating that there are many fewer protein-PIP interactions after mutating the positive residues as shown in the new Supplementary Fig S6.

      The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However, as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

      Following the reviewer’s suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7, PIP binding to VSDI-III can impair activation of the channel.

      Major concern:

      (1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

      Done as described above

      (2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it.

      Done as described above

      Minor points:

      There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

      We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP bound the channel is expected to recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

      Reviewer #3 (Public Review):

      Summary:

      This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

      Strengths:

      Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipidprotein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

      Weaknesses:

      Although the set of current simulations and analysis supports the conclusions drawn nicely, there are some limitations imposed by the authors on the course-grained simulations. If those were not imposed, it would have allowed for an even richer set and more thorough exploration of the protein-lipid interactions. The Martini 2 force field indeed cannot change secondary structure but if run with a properly tuned elastic network instead of backbone restraints, the change in protein configuration can be sampled and/or some adaptation of the protein to the specific protein environment can be observed. Additionally, with the 4to1 heavy atoms to a bead mapping some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored.

      We thank the reviewer for their excellent suggestions and have run new simulations with an elastic network instead of backbone restraints which have generated new insights. Indeed, as shown in the new panel Fig 4E, the new data allows us to demonstrate that the presence of PIP in the proposed binding site stabilises binding of the DIII-DIV linker to the inactivation receptor site, strengthening the conclusions of the paper.

      We thank the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 line 16 to reflect this. We have not run additional CG simulations with each of these parameters but use the all-atom simulations to examine the interactions of phosphates at specific positions.

      In our atomistic simulations, we backmapped both PI(4,5)P2 and PI(4)P in the binding site to study their specific interactions. We chose to focus on PI(4,5)P2 given its physiological significance. However, we agree that differences in binding with PI(3,4)P2 would be interesting and warrants future investigation. We also note that the newer Martini3 forcefield would be useful in further work to differentiate between PIP subspecies interactions.

      Detailed Comments

      We thank the reviewers for their thorough reading and helpful comments which has allowed us to further strengthen the manuscript. Below, we provide point-by-point responses to their suggestions.

      Reviewer #1 (Recommendations For The Authors):

      I don't have many suggestions for the manuscript, just a few text edits. Of course, experimental analysis would bolster the claims made in the text, but I don't believe that this is necessary, given the quality of the data.

      I understand the focus on the PIP lipids, but it's a shame that the high binding likelihood of glycosphingolipid isn't considered or analysed in any way. This is an especially interesting lipid from the point-of-view of raftlike membrane domains. Given the potential role of raft-like domains in sodium channel function, I feel this would be worth a paragraph or two in the discussion.

      We thank the reviewer for bringing our attention to this interesting point. Glycolipids accumulate around Nav1.4 in our complex membrane simulations, however, given reports that carbohydrates tend to interact too strongly in the Martini2.2 forcefield (Grünewald et al. 2022, Schmalhorst et al. 2017) and there are no specific residues on Nav1.4 that interact preferentially with glycolipid species, we chose not to focus on this. However, we have noted that interactions with other lipids deserve further attention in our revised discussion.

      The analyses have been run using Martini 2. I don't suggest the authors repeat using the Martini 3 force field, but some mention of this in the discussion would be good.

      We have added the following statement to the discussion: “Our coarse grain simulations were carried out using the Martini2.2 forcefield, for which lipid parameters for many plasma membrane lipids have been developed. We expect that future investigations of lipid-protein interactions will benefit from use of the newer, refined Martini 3 forcefield (Souza et al. 2021) as parameters become available for more lipid types.

      This might just be an oversight, but no mention is made of an elastic network applied to the backbone beads.

      Lack of a network has been known to cause the protein to collapse, so if this is missing, I'd like to see an RMSD to show that the protein dynamics are not compromised.

      While no elastic network was used in our original CG simulations, weak protein backbone restraints (10 kJ mol-1 nm-2) used in our simulations allowed us to maintain the structure while allowing some protein movement. However, following the suggestion of reviewer 3, we conducted additional simulations with an elastic instead of backbone restraints as described in the results on page 9 line 30-37 (and in Fig 4E) of the revised manuscript.

      Minor

      •In Fig 3B, are these lipids binding to the channel at the same time? And therefore do the authors see cooperativity?

      The Fig 3B caption has been amended in the revised manuscript to read “Representative snapshots from the five longest binding events from different replicates, showing the three different PIP species (PIP1 in blue, PIP2 in purple and PIP3 in pink) binding to VSD-IV and the DIII-IV linker.” We cannot comment on PIP cooperativity based on these simulations shown in Fig 3, due to the artificially high concentrations used here; however, in model complex membrane simulations we see co-binding of PIPs at the binding site. This is likely due to PIP’s ability to accumulate together and the high density of positively charged residues in the region, attracting and supporting multiple PIP bindings.

      •What charges were used for the atomistic PIP lipids? Does this match the CG lipids?

      We used the CHARMM-GUI PIP parameters for the atomistic simulations. SAPI24 (PIP2) has a headgroup charge of –4e which is one less negative charge than the CG PIP2; whereas SAPI14 (PIP1) has a charge of –3e which is the same as the CG PIP1. We have explicitly included this charge information in the updated Methods of the manuscript (on page 15-16).

      •Line 259-260: "we performed embedded three structures"

      Corrected in the revised manuscript.

      •Line 272: "us" should be "µs"

      Corrected in the revised manuscript.

      •Line 434: kJ/mol should probably also have 'nm-2' included

      Corrected in the revised manuscript.

      •What charge state titratable residues were set to, and were pKa analyses done to decide this?

      Charge states were assigned to default values at neutral pH. We appreciate that future studies could examine this more carefully using constant pH simulations or similar.

      •It's stated that anisotropic scaling is used the AT sims - is this correct? If so, is there a reason this was chosen over semi-isotropic scaling?

      Anisotropic scaling was used for the atomistic simulations allowing all box dimensions to change independently.

      •I would recommend in-house analysis scripts are made available on GitHub or similar, just so the details can be seen.

      Per the reviewer’s request, the Jupyter notebooks used for analysis has been made available on GitHub (https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project ).<br /> -One coarse grained notebook:

      • Lipid DE

      • Contact occupancy + outlier plots

      • Binding duration plots

      • Minimum distance plots

      • Number of ARG/LYS plots

      • PIP Occupancy, binding duration, gating charge residues

      • One atomistic notebook:

      • RMSD, RMSF and distance between IFM and its binding pocket (using MDAnalysis)

      • Atomistic PIP headgroup interaction analyses and plots (using ProLIF)

      As a final note, I am NOT saying this needs to be done for the current study, but I recommend the authors try the PyLipID package (https://github.com/wlsong/PyLipID) if they haven't yet, as it might be useful for similar projects they run in the future (i.e. for binding site identification, accurate binding kinetics calculations, lipid pose generation etc.).

      We thank the reviewer for this suggestion and will keep this in mind for future projects.

      Reviewer #2 (Recommendations For The Authors):

      Lin Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete lacks the expected confirmatory studies which are relevant to such proposals.

      The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation. However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

      Major concern:

      (1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

      (2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it. Minor points:

      Following the reviewer’s suggestion we have conducted new simulations demonstrating that there are notably fewer protein-PIP interactions after performing charge neutralizing and charge reversal mutations to the positive residues as shown in the new Fig S6.

      The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

      Following the reviewers suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7 PIP binding to VSDI-III can impair activation of the channel.

      There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

      We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP present the channel will recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

      Reviewer #3 (Recommendations For The Authors):

      As mentioned in the public review, overall, I am impressed with the manuscript and do think the conclusions are supported. There are, however, quite a few mistakes, mostly minor (listed below). Additionally, I do have a few questions and several extensions that could be done and I mention a few but fully realize many of those could be outside of the scope of the current manuscript.

      We greatly appreciate the time taken by Reviewer 3 to carefully review our manuscript and provide detailed comments. We believe their suggestions have helped to improve our manuscript.

      First comments are in general about the PIP subtype.

      • In the paper you claim:

      L196, "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding"

      L367, "it does not distinguish between phosphate positions within each charge state (e.g. PI(3,4)P2 vs PI(4,5)P2)."

      This is not true the PIP2 most commonly used in Martini 2 is from dx.doi.org/10.1021/ct3009655 and is a PI(3,4)P2 subtype. Also other extensions and alternative parameters exist for PIPs in Martini 2 e.g. http://cgmartini.nl/index.php/tools2/other-tools - Martini lipid .itp generator has all three main variants of both PIP1 and PIP2.

      As described in the response to the public review we are grateful for the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 to reflect this, and clarified the parameters chosen in the methods section (page 16 line 2-3). We have not run additional CG simulations with each of these parameters in the current work but use the all-atom simulations to examine the interactions of phosphates at specific positions.

      • One detail that is missing in the manuscript is some mention of the charge state of the PIPs e.g. Fig.1D does not specify and Fig.4D PIP2 looks like -2 on position 5 and -1 on position 4. Which I think fits the used SAPI24, please specify. Also, what if you use SAPI25 with the flipped charges would that significantly alter the results?

      The charge state of PIP2 is -2e on the 5’ phosphate and -1e on the 4’ phosphate, using the SAPI24 CHARMM lipid parameters. We have ensured that this charge information is stated clearly in the revised manuscript in the methods section on page 16 (line 21). We considered looking at SAPI25, however we expected that it would behave quite similarly, given that the PIP headgroup can adopt slightly different poses and orientations within the binding site across replicates and does fluctuate over simulations (Fig S8). We have noted this in the revised discussion on page 14 line 15-17.

      • I was very intrigued and puzzled by the lower binding of PIP3 vs PIP2 in the Martini simulations. Could it be that PIP3 has a harder time fully entering the binding site, or maybe just sampling? i.e. and its lower number of binding events is a sampling issue.

      We agree with the reviewer that PIP3 is less able to access the binding site than PIP2, likely because of its larger size. This might also be why we see PIP1 binding at the location via a more buried route (since it has the smallest headgroup size). However, PIP1 does not have enough negative charge to keep it in the binding site. It seems to be a Goldilocks-like situation where PIP2 has the optimal size and charge to allow access and stable binding at the site. We also see that when PIP3 enters the binding site it leaves before the end of the simulations. While it is hard to prove statistical significance given the number of binding and dissociation events even with the high and equal concentrations of all three PIP species in the enriched PIP membrane CG simulations, the data strongly suggests preferential binding of PIP2 over PIP3.

      Also the same L196 sentence as above "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding". The later part is also wrong, there are no conformational changes due to the restraints on the protein backbone, from methods "backbone beads were weakly restrained to their starting coordinates using a force constant of 10 kJ mol−1nm−2". Martini in general might have a hard time with some conformational changes and definitely cannot sample changes in secondary structure, but conformational changes can, and have on many occasions, been successfully sampled (even full ion channel opening and closing).

      On a similar note, in L179 you mention "owing to the flexibility of the linker." Hose does this fit with simulation with position restraints on all backbone atoms?

      We applied fairly weak restraints to the backbone only – therefore we still observe some flexibility in the highly flexible loop portion of the linker, where sidechains are able to flip between membrane-facing and cytosol-facing orientations.

      However, after reading the comments from the reviewer we have run additional simulations with an elastic network rather than backbone restraints on the DIII-DIV linker which have given further insight. As seen in Fig 4E and described in the results paragraph on page 9 line 30-37 of the revised manuscript, we can see that the presence of PIP does stabilise the linker in its receptor site. To accentuate this effect, we also ran simulation of the ‘IQM’ mutant known to have a less stable fast inactivated state due to weaker binding to the receptor. Without backbone restraints we can see partial dissociation of the DIII-DIV linker from the receptor that is partially rescued by the presence of PIP.

      I know the paper focuses on PIPs, also very nicely in Fig.2B and Fig. S1-2 the lipid enrichment is shown for other lipids, but why show all lipid classes except cholesterol? And, for the left-hand panels in Fig. S1-2 those really should be leaflet specific - as both the membrane and protein are asymmetric.

      The depletion/enrichment of Cholesterol is shown in Fig 2B and as are the Lipid Z-Density maps and contact occupancy structures a (in row 5 of Fig S2, labeled as CL in yellow). The Z-density maps are meant to provide an overall summary of lipid distribution. The contact occupancy structures showing the transverse views and intracellular/ extracellular views provide a better indication of the occupancy across the different leaflets.

      In L237 for the comparison of Cav2.2 and Kv7.1 bound to PI(4,5)P2 structures: They do agree well with the PIP1 simulations but not as much for the main PIP2 binding site. If you look in the CG simulations, is there another (not the main) PIP2 binding site at that same location (which might also be stable in AA simulations)?

      In some replicates of the CG simulations, we identify stable PIP1 binding via the other orientation (i.e. the one that overlaps with the Cav2.2 and Kv7.1 structures). Since we did not directly observe any PIP2 binding events from the other orientation, we did not run any backmapped atomistic simulations with PIP2 at this position. However, the binding site residues that the PIP1/2 headgroup binds to are the same regardless of which side PIP1/2 approaches from. We would expect that PIP2 bound from the alterative position is also stable.

      Two references I want to put for consideration to the authors, for potential inclusion if the authors find their inclusion would strengthen the manuscript. This one gives a good demonstration of using the same PM mixture to define lipid protein fingerprints with Martini:

      https://pubs.acs.org/doi/10.1021/acscentsci.8b00143.

      And this one https://pubmed.ncbi.nlm.nih.gov/33836525/ shows how Nav1.4 function could also be affected by general changes in bilayer properties (in addition to the specific lipid interactions explored here).

      We thank the reviewer for bringing to our attention these two relevant references that will help to respectively substantiate the use Martini to study membrane protein-lipid interactions, as well as, why Nav channels are interesting to study in the context of their membrane environment (and also the potential implications with drugs that can bind from within the membrane). We have added these citations to the introduction and discussion.

      Minor comments and fixes:

      L2, Title: A binding site for phosphoinositide modulation of voltage-gated sodium channels described by multiscale simulations

      The title reads very strangely to me, should it be "A binding site for phosphoinositide" ; "modulation". We thank the reviewer for this comment - title has been updated to: A binding site for phosphoinositides described by multiscale simulations explains their modulation of voltage gated sodium channels.

      L25, Abstract, "The phosphoinositide PI(4,5)P2 decreases Nav1.4 activity by increasing the difficulty of channel opening, accelerating fast activation and slowing recovery from fast inactivation." Assuming this is referring to results from Gada et al JGP, 2023 should this not be "accelerating fast inactivation"?

      Corrected in the revised manuscript.

      L71 maybe good to write the longer version of IFM on first use e.g. Ile-Phe-Met (IFM), as to not mistake it for some random three letter acronym.

      Corrected in the revised manuscript.

      L109, Fig.2. Maybe change the upper and lower leaflet to intracellular and cytoplasmic leaflets (or outer / inner). In D "(D) Distribution of PIP binding occupancies (left)" something missing can I assume, for/over all lipids exposed residues. Also, for D I am a little confused how occupancy is defined as the total occupancy per residue dose not add up to 100.

      The figure has been updated with intracellular and cytoplasmic leaflet labels. The binding occupancy distribution boxplot shows binding occupancies for all lipid exposed residues. In our analysis, we define contact occupancy as the proportion of simulation time in which a lipid type is within 0.7 nm of a given residue. It is possible for more than one lipid to be within this cut in any given frame – that is, both a PIP and PE can be simultaneously bound.

      L160 "occurring the identified site" in the

      Corrected in the revised manuscript.

      L170 "PIP3 (headgroup charge: -7e) has interacts similarly to PIP1," - remove has Corrected in the revised manuscript.

      L194, "reducing system size" the size does not change, I am assuming you want to say reducing the number of particles?

      Corrected in the revised manuscript.

      L252, Fig.6 "(B) Occupancy of all PIPs (PIP1, PIP2, PIP3) at binding site residues in the three systems" A little confusing, initially was expecting 3x3 data points per residue, maybe change to, Combined occupancy of all PIPs...

      Corrected in the revised manuscript.

      L253, Fig.6 D, I don't really have a good suggestion for improvement here, so this is just a FYI that this panel was very confusing for me and took some time to figure out what is shown.

      We have added to the caption of Fig. 6D to try to clarify this panel.

      L257, Fig.6 (F) not in bold

      Corrected in the revised manuscript.

      L259 "PIP binding, we performed embedded three structures of Nav1.7" something missing?

      Corrected in the revised manuscript.

      L272, "In triplicate 50 us coarse-grained simulations" us instead of (micro_greek)s

      Corrected in the revised manuscript.

      L272, that paragraph how long/many simulations only reported for the inactivated Nav1.7 system not the Nav1.7-NavPas chimera, which I am assuming is the same?

      Corrected in the revised manuscript.

      L297, "marked by both shortened inactivation times", can I assume this is: shortened times to inactivation (i.e. to get inactivated not times in the inactivated states)?

      Corrected in the revised manuscript.

      L331, "are conserved in Nav1.1-1.9 (Fig. 5D)," Fig.5C Corrected in the revised manuscript.

      L353, "channel opening []" [] maybe a missing reference?

      Thank you for pointing out this oversight - Goldschen-Ohm et al. has been cited here.

      L394, "The composition of the complex mammalian membrane is as reported in Ingólfsson, et al. (38)." Ref 38 is the "Computational lipidomics of the neuronal plasma membrane" which indeed uses the 63 component PM but the original reference for the average 63 lipid mixture PM is dx.doi.org/10.1021/ja507832e.

      Corrected in the revised manuscript.

      L404, "Additionally, a model Nav1.7 with all four VSDs in the deactivated state using Modeller (40)." Something missing, e.g. was also built and simulated for ...

      Corrected in the revised manuscript.

      Table S1 "Disease information", I am guessing this should be Disease information; mechanism? Of the x5 entries two have mechanism, one has "; unknown significance ", one has "; unknown" maybe clarify in title and make same if unknown.

      Corrected in the revised manuscript.

      Table S1 and S2 have different styles.

      The tables have been amended to have the same style.

      Fig. S3 "for all 12 lipid types in the mammalian membrane " there are many more lipid types in a typical PM (hundreds) and 63 in the PM mixture simulated here, so maybe write: 12 lipid classes?

      Corrected in the revised manuscript.

      Fig.S6 PIP headgroup, can I assume that is for the bound PIP only, please specify.

      Only a single PIP at the identified binding site was backmapped into all cases of atomistic simulations. We have now clarified this point in the methods, results and the FigS6 caption.

      Writing of PI(4,5)P2 and PI(4)P1 most of the time use 1 and 2 as subscripts but not always (at least not in SI), also the same with Nav vs Na_v (v subscript) and even NAV (in Table S1).

      Subscripts have been implemented in the updated Supplementary Information (as well as within various figures and throughout the manuscript).

    1. O now in danger tri'd, now known in Armes Not to be overpowerd, Companions deare, Found worthy not of Libertie alone, [ 420 ] Too mean pretense, but what we more affect, Honour, Dominion, Glorie, and renowne, Who have sustaind one day in doubtful fight (And if one day, why not Eternal dayes?) What Heavens Lord had powerfullest to send [ 425 ] Against us from about his Throne, and judg'd Sufficient to subdue us to his will, But proves not so: then fallible, it seems, Of future we may deem him, though till now Omniscient thought. True is, less firmly arm'd, [ 430 ] Some disadvantage we endur'd and paine, Till now not known, but known as soon contemnd, Since now we find this our Empyreal form Incapable of mortal injurie Imperishable, and though pierc'd with wound, [ 435 ] Soon closing, and by native vigour heal'd. Of evil then so small as easie think The remedie; perhaps more valid Armes, Weapons more violent, when next we meet, May serve to better us, and worse our foes, [ 440 ] Or equal what between us made the odds, In Nature none: if other hidden cause Left them Superiour, while we can preserve Unhurt our mindes, and understanding sound, Due search and consultation will disclose. [ 445 ]

      In this section Satan is attempting to boost the confidence of his council. The speaker says that they will gain more than they have suffered, that it's not all for loss (ll. 429-45). The speaker goes on to say that the council must prepare with "weapons more violent" (6.439) than before. The speaker seems to be describing his plans for revenge in hopes to boost the council's confidence. Sadly, knowing that one cannot win fighting fire with fire, this could foreshadow Satan and all of Hell's population to continue to suffer eternally.

    2. Whom the grand foe with scornful eye askance Thus answerd. Ill for thee, but in wisht houre [ 150 ] Of my revenge, first sought for thou returnst From flight, seditious Angel, to receave Thy merited reward, the first assay Of this right hand provok't, since first that tongue Inspir'd with contradiction durst oppose [ 155 ] A third part of the Gods, in Synod met Thir Deities to assert, who while they feel Vigour Divine within them, can allow Omnipotence to none. But well thou comst Before thy fellows, ambitious to win [ 160 ] From me som Plume, that thy success may show Destruction to the rest: this pause between (Unanswerd least thou boast) to let thee know; At first I thought that Libertie and Heav'n To heav'nly Soules had bin all one; but now [ 165 ] I see that most through sloth had rather serve, Ministring Spirits, traind up in Feast and Song; Such hast thou arm'd, the Minstrelsie of Heav'n, Servilitie with freedom to contend, As both thir deeds compar'd this day shall prove. [ 170 ]

      Lines 149-170 are Satan speaking to the seraph Abdiel. The passage begins with Satan telling Abdiel that he will be the first to face his wrath, saying he will be victim to "the first assay / of this right hand provok'd" (6.153-154). As we discussed earlier in class, before Christ, Satan was God's right hand, so the use of "right hand provok'd" works both physically with the image of an attack, as well as in the sense of his former title. Satan then goes on to reference a group or "Synod" of gods who agreed that "while they feel / Vigor divine within them, can allow / Omnipotence to none" (6.158-159). This is an important part of Satan's speech, both because of his rejection of singular omnipotence, as well as his assertion of multiple gods. This reminded me of a key pillar of Christianity: a singular God. It is important to note, though, the biblical quote "Thou shalt have no other gods before me" (Exodus 20:3), which can be interpreted as there being multiple gods, but a more powerful, singular God. So, when Satan mentions other gods rejecting omnipotence and implies that God embraced it, his portrayal of a more sinister, power-hungry God can be understood.

      Further in this section, Satan says that Abdiel has only confronted him out of a desire to be praised by God. He states, "But well thou com'st / Before thy fellows, ambitious to win / From me some Plume, that thy success may show / Destruction to the rest" (6.159-162). According to the OED, "Plume" in this instance refers to an adornment received for an accomplishment or merit (as opposed to the modern definition of colourful feathers). Following his assertion about Abdiel's motivations, Satan explains that he used to think that freedom could exist in Heaven, but that he realized that servitude to God removes the possibility for true freedom. He mocks those who have remained faithful to God, saying "Minist'ring Spirits, train'd up in Feast and Song; / Such hast thou arm'd, the Minstrelsy of Heav'n, / Servility with freedom to contend" (6.167-169). These lines serve the dual purpose of mocking the power of God's army, and making Satan's followers represent the positive attribute of freedom, as opposed to the [generally] negative attribute of servitude.

    1. To whom the Angel. Therefore what he gives (Whose praise be ever sung) to man in part [ 405 ] Spiritual, may of purest Spirits be found No ingrateful food: and food alike those pure Intelligential substances require As doth your Rational; and both contain Within them every lower facultie [ 410 ] Of sense, whereby they hear, see, smell, touch, taste, Tasting concoct, digest, assimilate, And corporeal to incorporeal turn. For know, whatever was created, needs To be sustaind and fed; of Elements [ 415 ] The grosser feeds the purer, Earth the Sea, Earth and the Sea feed Air, the Air those Fires Ethereal, and as lowest first the Moon; Whence in her visage round those spots, unpurg'd Vapours not yet into her substance turnd. [ 420 ] Nor doth the Moon no nourishment exhale From her moist Continent to higher Orbes. The Sun that light imparts to all, receives From all his alimental recompence In humid exhalations, and at Even [ 425 ] Sups with the Ocean: though in Heav'n the Trees Of life ambrosial frutage bear, and vines Yield Nectar, though from off the boughs each Morn We brush mellifluous Dewes, and find the ground Cover'd with pearly grain: yet God hath here [ 430 ] Varied his bounty so with new delights, As may compare with Heaven; and to taste Think not I shall be nice. So down they sat,

      the speaker is describing the hierarchy of Earth, starting with humans, then animals, and then slowly moving down the line to the inanimate, "Corporeal to incorporeal" (413) as the poem says. The speaker goes on to say that there needs to be some type of food chain in the world and relates the chain to the elements as it lists which element feeds another as the cycle continues. They then compare the moon and the sun and state how the moon does not give or take from anything, "unpurg'd / Vapors not yet into her substance turn'd. / Nor doth the Moon no nourishment exhale" (419-421), while the sun both gives and takes from the world, "The Sun that light imparts to all, receives / From all his alimental recompense / In humid exhalations" (423-425). In the last bit of the passage the speaker describes the food in heaven and is comparing them with the new foods that God has created on earth.

    1. He urges that men of science should then turn to the massive task of making more accessible our bewildering store of knowledge. For years inventions have extended man's physical powers rather than the powers of his mind.

      Interesting to read this after just watching Oppenheimer on the plane to London.

    2. an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility.

      Definitely my activity page!

    1. 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state, where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset. 13.1.1. Digital Detox?# Some people view internet-based social media (and other online activities) as inherently toxic and therefore encourage a digital detox, where people take some form of a break from social media platforms and digital devices. While taking a break from parts or all of social media can be good for someone’s mental health (e.g., doomscrolling is making them feel more anxious, or they are currently getting harassed online), viewing internet-based social media as inherently toxic and trying to return to an idyllic time from before the Internet is not a realistic or honest view of the matter. In her essay “The Great Offline,” Lauren Collee argues that this is just a repeat of earlier views of city living and the “wilderness.” As white Americans were colonizing the American continent, they began idealizing “wilderness” as being uninhabited land (ignoring the Indigenous people who already lived there, or kicking them out or killing them). In the 19th century, as wilderness tourism was taking off as an industry, natural landscapes were figured as an antidote to the social pressures of urban living, offering truth in place of artifice, interiority in place of exteriority, solitude in place of small talk. Similarly, advocates for digital detox build an idealized “offline” separate from the complications of modern life: Sherry Turkle, author of Alone Together, characterizes the offline world as a physical place, a kind of Edenic paradise. “Not too long ago,” she writes, “people walked with their heads up, looking at the water, the sky, the sand” — now, “they often walk with their heads down, typing.” […] Gone are the happy days when families would gather around a weekly televised program like our ancestors around the campfire! But Lauren Collee argues that by placing the blame on the use of technology itself and making not using technology (a digital detox) the solution, we lose our ability to deal with the nuances of how we use technology and how it is designed: I’m no stranger to apps that help me curb my screen time, and I’ll admit I’ve often felt better for using them. But on a more communal level, I suspect that cultures of digital detox — in suggesting that the online world is inherently corrupting and cannot be improved — discourage us from seeking alternative models for what the internet could look like. I don’t want to be trapped in cycles of connection and disconnection, deleting my social media profiles for weeks at a time, feeling calmer but isolated, re-downloading them, feeling worse but connected again. For as long as we keep dumping our hopes into the conceptual pit of “the offline world,” those hopes will cease to exist as forces that might generate change in the worlds we actually live in together. So in this chapter, we will not consider internet-based social media as inherently toxic or beneficial for mental health. We will be looking for more nuance and where things go well, where they do not, and why. { requestKernel: true, binderOptions: { repo: "binder-examples/jupyter-stacks-datascience", ref: "master", }, codeMirrorConfig: { theme: "abcdef", mode: "python" }, kernelOptions: { kernelName: "python3", path: "./ch13_mental_health" }, predefinedOutput: true } kernelName = 'python3' previous 13. Mental Health next 13.2. Unhealthy Activities on Social Media By Kyle Thayer and Susan Notess © Copyright 2022.

      This paragraph talks about how social media, especially Instagram, might affect mental health, especially for teenage girls. It mentions different opinions, a study by Meta, anecdotes from cosmetic surgeons, and thoughts from comedian Bo Burnham. The passage acknowledges the complexity of measuring social media's impact and mentions a "digital detox." It doesn't label social media as entirely good or bad, aiming for a more nuanced view. I find it interesting, but the whole social media and mental health issue is really complicated.

    2. 13.1. Social Media Influence on Mental Health# In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state, where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset.

      The examination of social media's impact, particularly its detrimental effects on the mental health of teenage girls, highlights a critical area of concern within the digital age's societal framework. The revelation from Meta's internal study about Instagram underscores the ethical dilemma faced by social media corporations: the responsibility to safeguard the mental health of their users while balancing the inherent drive for engagement and growth. This conundrum is further complicated by the rising trend of individuals seeking to emulate digitally altered self-images, which distorts perceptions of body image and exacerbates mental health issues.

    3. 13.1. Social Media Influence on Mental Health# In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state, where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset. 13.1.1. Digital Detox?# Some people view internet-based social media (and other online activities) as inherently toxic and therefore encourage a digital detox, where people take some form of a break from social media platforms and digital devices. While taking a break from parts or all of social media can be good for someone’s mental health (e.g., doomscrolling is making them feel more anxious, or they are currently getting harassed online), viewing internet-based social media as inherently toxic and trying to return to an idyllic time from before the Internet is not a realistic or honest view of the matter. In her essay “The Great Offline,” Lauren Collee argues that this is just a repeat of earlier views of city living and the “wilderness.” As white Americans were colonizing the American continent, they began idealizing “wilderness” as being uninhabited land (ignoring the Indigenous people who already lived there, or kicking them out or killing them). In the 19th century, as wilderness tourism was taking off as an industry, natural landscapes were figured as an antidote to the social pressures of urban living, offering truth in place of artifice, interiority in place of exteriority, solitude in place of small talk. Similarly, advocates for digital detox build an idealized “offline” separate from the complications of modern life: Sherry Turkle, author of Alone Together, characterizes the offline world as a physical place, a kind of Edenic paradise. “Not too long ago,” she writes, “people walked with their heads up, looking at the water, the sky, the sand” — now, “they often walk with their heads down, typing.” […] Gone are the happy days when families would gather around a weekly televised program like our ancestors around the campfire! But Lauren Collee argues that by placing the blame on the use of technology itself and making not using technology (a digital detox) the solution, we lose our ability to deal with the nuances of how we use technology and how it is designed: I’m no stranger to apps that help me curb my screen time, and I’ll admit I’ve often felt better for using them. But on a more communal level, I suspect that cultures of digital detox — in suggesting that the online world is inherently corrupting and cannot be improved — discourage us from seeking alternative models for what the internet could look like. I don’t want to be trapped in cycles of connection and disconnection, deleting my social media profiles for weeks at a time, feeling calmer but isolated, re-downloading them, feeling worse but connected again. For as long as we keep dumping our hopes into the conceptual pit of “the offline world,” those hopes will cease to exist as forces that might generate change in the worlds we actually live in together. So in this chapter, we will not consider internet-based social media as inherently toxic or beneficial for mental health. We will be looking for more nuance and where things go well, where they do not, and why.

      I think the discussion concerning social media and mental health is not about deciding between blatant rejection and unquestioning acceptance. It is about achieving a balanced relationship with digital technology, in which the advantages are maximized while the hazards are actively addressed through informed use, supporting communities, and responsible platform administration.

    1. Facial expressions can help bring a speech to life when used by a speaker to communicate emotions and demonstrate enthusiasm for the speech. As with vocal variety, we tend to use facial expressions naturally and without conscious effort when engaging in day-to-day conversations. Yet I see many speakers’ expressive faces turn “deadpan” when they stand in front of an audience. Some people naturally have more expressive faces than others—think about the actor Jim Carey’s ability to contort his face as an example.

      When you have an animated face, it conveys that you take interest in what you are saying, even if that may not be the case. It also makes it easier to connect with your audience. Jim Carey is a good exaggerated example of this. In a speech, it's easy to get so caught up in what you have to say. How you present yourself is just as important in getting your message across.

    1. Author Response

      Public reviews:

      Reviewer 1:

      Weaknesses:

      While I generally agree with the author's interpretations, the idea of Saccorhytida as a divergent, simplified off-shot is slightly contradictory with a probably non-vermiform ecdysozoan ancestor. The author's analyses do not discard the possibility of a vermiform ecdysozoan ancestor (importantly, Supplementary Table 4 does not reconstruct that character),

      Reply: Thanks for the comments. Saccorhytids are only known from the early Cambrian and their unique morphology has no equivalent among any extinct or extant ecdysozoan groups. This prompted us to consider them as a possible dead-end evolutionary off-shot. The nature of the last common ancestor of ecdysozoan (i.e. a vermiform or non-vermiform animal with capacities to renew its cuticle by molting) remains hypothetical. At present, palaeontological data do not allow us to resolve this question. The animal in Fig. 4b at the base of the tree is supposed to represent an ancestral soft-bodied form with no cuticle from which ecdysozoan evolved via major innovations (cuticular secretion and ecdysis). Its shape is hypothetical as indicated by a question mark. Our evolutionary model is clearly intended to be tested by further studies and hopefully new fossil discoveries.

      and outgroup comparison with Spiralia (and even Deuterostomia for Protostomia as a whole) indicates that a more or less anteroposteriorly elongated (i.e., vermiform) body is likely common and ancestral to all major bilaterian groups, including Ecdysozoa. Indeed, Figure 4b depicts the potential ancestor as a "worm". The authors argue that the simplification of Saccorhytida from a vermiform ancestor is unlikely "because it would involve considerable anatomical transformations such as the loss of vermiform organization, introvert, and pharynx in addition to that of the digestive system". However, their data support the introvert as a specialisation of Scalidophora (Figure 4a and Supplementary Table 4), and a pharyngeal structure cannot be ruled out in Saccorhytida. Likewise, loss of an anus is not uncommon in Bilateria. Moreover, this can easily become a semantics discussion (to what extent can an animal be defined as "vermiform"? Where is the limit?).

      Reply: We agree with you that “vermiform” is an ill-defined term that should be avoided. “Elongated” might be a better term to designate the elongation of the body along the antero-posterior axis. Changes have been made in the text to solve this semantic problem. Priapulid worms or annelids are examples of extremely elongated, tubular animals. In saccorhytids, the antero-posterior elongation is present (as it is in the vast majority of bilaterians) but extremely reduced, Saccorhytus and Beretella having a sac-like or beret-shape, respectively. That such forms may have derived from elongated, tubular ancestors (e.g. comparable with scalidophoran worms) would require major anatomical transformations that have no equivalent among modern animals. We agree that further speculation about the nature of these transformations is unnecessary and should be deleted simply because the nature of these ancestors is purely hypothetical. We also agree that the loss of anus and the extreme simplification of the digestive system is common among extant bilaterians. The single opening seen in Saccorhytus and possibly Beretella may result from a comparable simplification process. In Figure 4b, the hypothetical pre-ecdysozoan animal is slightly elongated (antero-posterior axis and polarity) but in no way comparable with a very elongated and cylindrical ecdysozoan worm (e.g. extant or extinct priapulid).

      Therefore, I suggest to leave the evolutionary scenario more open. Supporting Saccorhytida as a true group at the early steps of Ecdysozoa evolution is important and demonstrates that animal body plans are more plastic than previously appreciated. However, with the current data, it is unlikely that Saccorhytida represents the ancestral state for Ecdysozoa (as the authors admit), and a vermiform nature is not ruled out (and even likely) in this animal group. Suggesting that the ancestral Ecdysozoan might have been small and meiobenthic is perhaps more interesting and supported by the current data (phylogeny and outgroup comparison with Spiralia).

      Reply: We agree the evolutionary scenario should be more open, especially the evolutionary process that gave rise to Saccorhytida. Again, we know nothing about the morphology of the ancestral ecdysozoan (typically the degree of body elongation, whether it had a differentiated introvert or not, whether it had a through gut or not). Simplification appears as one possible option, but which assumes that the ancestral ecdysozoan was an elongated animal with a through gut. Changes will be made in Fig.4A accordingly. Alternatively, the ancestral ecdysozoan might have been small and meiobenthic.

      Reviewer 2:

      Weaknesses:

      The preservations of the specimens, in particular on the putative ventral side, are not good, and the interpretation of the anatomical features needs to be tested with additional specimens in the future. The monophyly of Cycloneuralia (Nematoida + Scalidophora) was not necessarily well-supported by cladistic analyses, and the evolutionary scenario (Figure 4) also needs to be tested in future works.

      Reply: Yes, we agree that our MS is the first report on an enigmatic ecdysozoan. Whereas the dorsal side of the animal is well documented (sclerites), uncertainties remain concerning its ventral anatomy (typically the mouth location and shape). Additional better-preserved specimens will hopefully provide the missing information. Concerning Cycloneuralia, their monophyly is generally better supported by analyses based on morphological characters than in molecular phylogenies. I

      Reviewer 3:

      Weaknesses: I, as a paleontology non-expert, experienced several difficulties in reading the manuscript. This should be taken into consideration when assuming a wide range of readers including non-experts.

      Reply: We have ensured that the text is comprehensible to biologists. Our main results are summarized in relatively simple diagrams (e.g. Fig. 4). We are aware that technical descriptive terms may appear obscure to non-specialists. However, we think that our text-figures help the reader to understand the morphology of these ancient animals.

    1. Author Response

      eLife assessment

      The manuscript explores the ways in which the genetic code evolves, specifically how stop codons are reassigned to become sense codons. The authors present phylogenetic data showing that mutations at position 67 of the termination factor are present in organisms that nevertheless use the UGA codon as a stop codon, thereby questioning the importance of this position in the reassignment of stop codons. Alternative models on the role of eRF1 would reflect a more balanced view of the data. Overall, the data are solid and these findings will be valuable to the genomic/evolution fields.

      Public Reviews:

      Reviewer #1 (Public Review):

      The issue:

      The ciliates are a zoo of genetic codes, where there have been many reassignments of stop codons, sometimes with conditional meanings which include retention of termination function, and thus > 1 meaning. Thus ciliate coding provides a hotspot for the study of genetic code reassignments.

      The particular issue here is the suggestion that translation of a stop (UGA) in Blastocritihidia has been attributed to a joint change in the protein release factor that reads UGA's and also breaking a base pair at the top of the anticodon stem of tRNATrp (Nature 613, 751, 2023).

      The work:

      However, Swart, et al have looked into this suggestion, and find that the recently suggested mechanism is overly complicated.

      The broken pairing at the top of the anticodon stem of tRNATrp indeed accompanies the reading of UGA as Trp as previously suggested. It changes the codon translated even though the anticodon remains CCA, complementary to UGG. A compelling point is that this misreading matches previous mutational studies of E coli tRNA's, in which breaking the same base pair in a mutant tRNATrp suppressor tRNA stimulated the same kind of miscoding.

      This is a fair characterization, and we would also note the additional positive aspect: that we observed there is consistency in the presence of 4 bp tRNA-Trp anticodon stems in those ciliates which translate UGA as tryptophan, and generally 5 bp anticodon stems in those that do not (including Euplotes with UGA=Cys).

      But the amino acid change in release factor eRF1, the protein that catalyzes termination of protein biosynthesis at UGA is broadly distributed. There are about 9 organisms where this mutation can be compared with the meaning of UGA, and the changes are not highly correlated with a change in the meaning of the codon. Therefore, because UGA can be translated as Trp with or without the eRF1 mutation, Swart et al suggest that the tRNA anticodon stem change is the principal cause of the coding change.

      We do think multiple lines of evidence support the shorter tRNA anticodon stem promoting UGA translation, but also think other changes in the translation system may be important. For instance, structural studies suggest interaction of ribosomal RNA with extended stop codons (particularly the base downstream of the triplet) during translation termination (Brown et al. 2015, Nature). As we noted, previous studies have sought to correlate individual eRF1 substitutions with genetic code changes, but the proposed correlations have invariably disappeared once new tranches of eRF1 sequences and alternative genetic codes for different species became available. This is why we concluded that there needs to be more focus on obtaining and understanding molecular structures during translation termination, particularly in the organisms with alternative codes.

      The review:

      Swart et al have a good argument. I would only add that eRF1 participation is not ruled out, because finding that UGA encodes Trp does not distinguish between encoding Trp 90% of the time and encoding it 99% of the time. The release factor could still play a measurable quantitative role, but the major inference here seems convincing.

      We agree that eRF1 may participate and compete with the tRNA, but we question the hypothesis that the particular amino acid position/substitution proposed by Kachale et al. 2023 is the key. There is experimental evidence in the form of Ribo-seq for the ciliate Condylostoma magnum (A67), which does appear to efficiently translate UGA sense codons (Swart et al. 2016, Figure S3: https://doi.org/10.1016/j.cell.2016.06.020): we observed no dip in ribosome footprints downstream of these codons, as there would be in the case of classical translational readthrough in standard genetic code organisms (which is usually relatively inefficient - certainly well below 50% of upstream translation from our reading of the literature). Ribo-seq also supports efficient termination at those Condylostoma UGA codons that are stops.

      Of course, the entire translation system may have evolved to be as efficient as what we currently observe, and it is not unreasonable to consider that it may have been less efficient in the past. However, not so inefficient that the error rate incurred would have been strongly deleterious. Importantly also, we believe the role of multiple eRF1 paralogs in translation termination in the ciliates really needs to be investigated, given that translation is inherently probabilistic with any of these proteins potentially being incorporated into the ribosome.

      Reviewer #2 (Public Review):

      The manuscript raises interesting observations about the potential evolution of release factors and tRNA to readdress the meaning of stop codons. The manuscript is divided into two parts: The first consists of revealing that the presence of a trp tRNA with an AS of 5bp in Condylostoma magnum is probably linked to contamination in the databases by sequences from bacteria. This is an interesting point which seems to be well supported by the data provided. It highlights the difficulty of identifying active tRNA genes from poorly annotated or incompletely assembled genomes.

      We will consider adding subheadings in revising the manuscript to make the structure more explicit, as it really has three parts to it, with the third largely in the supplement. The “good” was that there is a range of support for the 4 bp AS stem, with new evidence we supplied from ciliates and older studies with E. coli tRNAs. The “bad” is that scrutiny of eRF1 sequences, with the addition of ones we provided, contradicts the hypothesis by Kachale et al. that a S67A/G substitution is necessary for genetic code evolution in Blastocrithidia and certain ciliates. The “ugly” is that a tRNA shown in a main figure in Kachale et al. 2023, and which was investigated in a number of subsequent experiments, is almost certainly a bacterial contaminant.

      Proper scrutiny of the bacterial tRNA should have led to its immediate recognition and rejection, as one of us did years ago in searches of tRNAs in a preliminary Condylostoma genome assembly (only predicted 4 bp AS tRNA secondary structures were shown in Swart et al. 2016, Fig S4B and C). Evidence for the bacterial nature of this tRNA was placed in the supplement of the present manuscript, as the meat of the critique was the consideration of the evidence for and against its good and bad aspects. The bacterial tRNA secondary structure has been removed from the main figure by Kachale et al. 2023, and downstream experiments based on synthetic constructs for this tRNA have also been revised (https://www.nature.com/articles/s41586-024-07065-0).

      Much of the rest of the supplement served to correct multiple errors in genetic codes in public sequence databases that led to additional errors and difficulties in interpreting the eRF1 substitutions in Kachale et al. 2023. It is important that these codes get corrected. If not they create multiple headaches for users besides those investigating genetic codes, as we found out in communications with authors and a colleague of Kachale et al. 2023 (in particular, leading to thousands of missing genes in the macronuclear genome of the standard code ciliate Stentor coeruleus that were removed in automated GenBank processing due to incorrectly having an alternative genetic code specified).

      Recently the NCBI Genetic Codes curators reinstated a genetic code incorrectly attributed to the ciliate Blepharisma (“Blepharisma nuclear genetic code”) (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi#SG15), despite us requesting a reasonable fix years ago. This would be very confusing for those that are not in the know. We have explained this confusion in our supplement too. Thus we also hope that this paper will aid in communication with the genetic code database curators and in correcting such issues.

      The second part criticises the fact that a mutation at position S67 of eRF1 is required to allow the UGA codon to be reassigned as a sense codon. As supporting evidence, they provide a phylogenetic study of the eRF1 factor showing that there are numerous ciliates in which this position is mutated, whereas the organism shows no trace of the reassignment of the UGA codon into a sense codon. While this criticism seems valid at first glance, it suffers from the lack of information on the level of translation of UGA codons in the organisms considered.

      Firstly, we not only showed that there are organisms with the S67 substitution but no UGA reassignment, but also provided evidence for the converse: organisms with a UGA=Trp reassignment but without the S67 substitution (both ciliates and a non-ciliate). So, two related lines of substitutions were not consistent with the eRF1 substitution hypothesis proposed.

      Secondly, we disagree that there is a “lack of information about UGA translation in the organisms considered”. Evolution has already supplied information as to whether UGA codons are translated at an appreciable level in the organisms of interest, in the form of codon frequencies within their protein-coding sequences and those ending them. If UGA was translated at appreciable levels, it would be found at a corresponding frequency in coding sequences. In genomes with thousands of genes, if not predicted as amino acids, they likely primarily serve as stops. Low levels of potential readthrough of actual stops would not change the arguments. With the exception of selenocysteine translation (which is restricted to a limited number of genes by the condition of requiring a specific mRNA secondary structure) there is no expectation of meaningful levels of UGA translation when this codon is missing from the bulk of coding sequences (CDSs).

      This is well illustrated by the heterotrichs, a clade of ciliates that use a variety of genetic codes. In heterotrichs that use the standard code, UGA is virtually absent from coding sequences, only appearing at the 3’ end of transcripts in the predicted stop codon and 3’-UTR (Seah et al. 2022, Figure 5). This contrasts notably with other genera like Blepharisma where appreciable levels of UGA codons occur throughout coding sequences, upstream of the predicted UAA and UAG stops (Seah et al. 2022, Figure 5: https://www.biorxiv.org/content/biorxiv/early/2022/07/12/2022.04.12.488043/F5.large.jpg). The difference in the UGA, UAG and UAA codon frequencies in 3’ UTRs compared to the upstream frequencies in CDSs of standard genetic code heterotrichs is stark. Frequencies of all three codons are elevated in the 3’ UTRs of all heterotrich ciliates, irrespective of their genetic codes (Seah et al. 2022, Figure 5), according with these codons not being deleterious in this region and strongly selected against upstream, within CDSs.

      The reviewer raises the possibility that UGA may appear to be a stop codon but still have biologically significant translational readthrough. We think that this is unlikely in the heterotrich ciliate species discussed here, which have extremely short (median 21-26 bp) and AU-rich 3’-UTRs compared to yeast and animals (Seah et al. 2022). Therefore, in heterotrichs where UGA is predicted to be a stop, translational readthrough would lead to extensions of only a few amino acids and be relatively inconsequential, as there are plenty of secondary UAA, UAG and UGA codons downstream of the typical stop.

      If one were to consistently pursue the reviewer’s line of argumentation, one would also have to argue against the very reasoning used in Kachale et al. 2023 about all the stop codon predictions/reassignments in protists for which experiments were not conducted in S. cerevisiae or other translation systems, as well as decades of prior work using sequence conservation in multiple sequence alignments to infer alternative genetic codes.

      Furthermore, experimental information for UGA translation levels is available for the ciliate Condylostoma magnum, predominantly in the form of Ribo-seq (Swart et al. 2016). Similarly to Condylostoma’s UAA and UAG codons, Ribo-seq shows that the UGA codons are generally either efficiently translated when present in the bodies of CDSs or terminate translation as actual stops close to mRNA 3’ termini/poly(A) tails (Swart et al. 2016). Thus, irrespective of the presence of the hypothesized eRF1 substitution there is an example of relatively discrete reading of UGA codons in ciliates as either stops or amino acids. This contrasts with Kachale et al 2023’s experiments in yeast with yeast eRF1 S67G or Blastocrithida eRF1 which also has glycine at the equivalent position that appear to lead to modest readthrough. In addition, efficient reading of codons in either of two ways also occurs in the ciliate genus Euplotes in which “stop” codons can either serve as frameshift sites during translation within coding sequences or be actual stops when they are close to 3’ mRNA termini (Lobanov et al. 2017), as verified by Ribo-seq and protein mass spectrometry.

      It has been clearly shown that S67G or S67A mutations allow a strong increase in the reading of UGA codons by tRNAs, so this point is not in doubt. However, this has been demonstrated in model organisms, and we now need to determine whether other changes in the translational apparatus could accompany this mutation by modifying its impact on the UGA codon. This is a point partly raised at the end of the manuscript.

      There is no doubt that S67G or S67A mutations lead to increased translational readthrough, but this is restricted to experiments with or in baker’s yeast or other standard genetic code surrogate model organisms. Experiments introducing eRF1 sequences from alternative genetic code eukaryotes into translation systems of such standard genetic code eukaryotes are not compelling because the rest of the associated translation system has also evolved tremendously. As far as we are aware, no in vivo experiments with ciliate eRF1s have been conducted to determine if position 67 or other substitutions have any effect. These considerations are critical given the vast evolutionary distances between yeasts, Blastocrithidia, the ciliates and Amoebophrya sp. ex Karlodinium veneficum. On the other hand, the evolutionary information presented contradicts the importance of this substitution in the Amoebophyra species and ciliates. We will consider how to incorporate these ideas in the revised version of the manuscript.

      Indeed, it is quite possible that in these organisms the UGA codon is both used to complete translation and is subject to a high level of readthrough. Actually, in the presence of a mutation at position 67 (or elsewhere), the reading of the UGA can be tolerated under specific stress conditions (nutrient deficiency, oxidative stress, etc.), so the presence of this mutation could allow translational control of the expression of certain genes.

      As explained a couple replies above, it is not constructive to invoke the additional complexity of conditional translation or any other kinds of factors that lead to enhanced readthrough, because the translation of UGA sense codons in the ciliate Condylostoma, where we have supporting experimental evidence, does not resemble translational readthrough. These codons occur in constitutively expressed single-copy genes, like a tryptophan tRNA synthetase and an eRF1 protein (Swart et al. 2016), not ones that might be expected to be conditionally translated.

      On the other hand, it seems obvious to me that there are other ways of reading through a stop codon without mutating eRF1 at position S67. So the absence of a mutation at this position is not really indicative of a level of reading of the UGA codon.

      It may seem obvious to the reviewer, but that is neither what Kachale et al. originally proposed nor what we questioned. Kachale et al. hypothesized that mutation of S67 to A or G is necessary for UGA=Trp translation, but we provided evidence that it is not: multiple organisms with S67 or C67 that translate UGA as tryptophan. Kachale et al. also originally suggested that the S67 to A/G substitution is also necessary in Condylostoma for UGA translation as tryptophan by weakening its recognition of this codon as a stop (from their abstract: “Virtually the same strategy has been adopted by the ciliate Condylostoma magnum.”). However, as we have stated, Condylostoma (A67) is both able to efficiently terminate at UGA stop codons and to efficiently translate (other) UGA sense codons, which does not fit this hypothesis.

      Before writing such a strong assertion as that found on page 3, experiments should be carried out. The authors should therefore moderate their assertion.

      Experiments should be carried out in the organisms in which stop codon reassignments have readily occurred and their close relatives that have not, not distantly related ones where they rarely, if ever, occur, like yeasts. We made this point in the conclusion. There is too much emphasis on models for investigation of genetic code evolution via stop codon reassignments in questionable models and too little investigation in the really good ones, particularly the ciliates. This clade has genera that are amenable to molecular experiments including Paramecium, Tetrahymena and Oxytricha. We plan to add some text about these considerations in revision.

      To make a definitive conclusion, we would need to be able to measure the level of termination and readthrough in these organisms. So, from my point of view, all the arguments seem rather weak.

      We reiterate: there is experimental information about translation and termination in two ciliate species worth considering, including one that translates UGA codons depending on their context. If one chooses to ignore the evolutionary information presented, this not only ignores all prior approaches to infer genetic codes, but also the fact that there is experimental verification and other lines of evidence supporting these approaches.

      Moreover, the authors themselves indicate that the conjunction between a Trp tRNA that is efficient at reading the UGA codon and an eRF1 factor that is not efficient at recognising this stop codon could be the key to reassignment.

      This does not convey well what we wrote, since the main consideration was overall eRF1 structure, rather than individual amino acid substitutions. Here are the key sentences:

      “Instead, in a transitional evolutionary phase, codons may be interpreted in two ways, with potential eRF1-tRNA competition. With time, beneficial mutations or modifications in either the tRNA or eRF1 (or other components of translation) that reduce competition may be selected.

      Instead of focusing on individual eRF1 substitutions, we propose future investigations should more generally explore the structure of non-standard genetic code eRF1’s captured in translation termination in the context of their own ribosomes.”

    1. When social media users work together, we can consider what problem they are solving. For example, for some of the Tiktok Duet videos from the virality chapter, the “problem” would be something like “how do we create music out of this source video” and the different musicians contribute their own piece to the solution.

      I think this collaborative problem-solving dynamic not only fosters a sense of community but also demonstrates the versatility of social media platforms as spaces for collective creativity. It goes beyond mere individual expression and taps into the collective intelligence of the user base. By identifying a problem or challenge, users can come together to contribute unique perspectives, skills, or talents, ultimately leading to the co-creation of content that may not have been possible without the collaborative efforts of the community.

    1. Author Response

      The authors' responses to the public reviews can be found here


      The following is the authors’ response to the most recent recommendations.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I appreciate the effort that the authors have put into this revised version of the manuscript. Before going into details, I would suggest that, in the future, the authors include enough information in their response to allow reviewers to follow the changes made. Not simply "Fixed", but instead "we have modified the description of these results and now state on lines XXX to XXX (revised text)".

      We greatly apologize, we certainly did not wish to cause more work for the reviewer to find the necessary changes. We will list the line number and our changes in the following response.

      The authors' response to my comments was confined to the minor points, with no attention to more important questions regarding speculations about mechanism which were (and still are) presented as factual conclusions. I do not consider the responses adequate.

      We responded to each of your comments and where we disagree, we have explained in detail.

      With respect to the meaning of "above" and "below" in the context of an intracellular organelle, I think that referring to up and down in a figure is fine, provided that the cytoplasmic and luminal sides are indicated in that figure. I think that labeling to that effect in each figure would be immensely helpful for the reader.

      We agree with this point and have updated all the figures to include these labels.

      The statement on lines 333-335 about non-competitive inhibition is a bit naïve. The only thing ruled out by this type of inhibition is that substrate and TBZ binding do not share the same binding process, in which case they would compete. It doesn't show that TBZ gets to its binding site from the lumen or from the bilayer, or by any other process that isn't shared with substrate. It also doesn't rule out kinetic effects, such as slow inhibitor dissociation, that result in non-competitive kinetics. Please rewrite this sentence to indicate that one explanation of the non-competitive nature of TBZ inhibition would be that TBZ diffuses into the vesicle and binds from the lumen. It's not the only explanation.

      We have changed this sentence lines 334-336 to be more speculative and not include any statement about non-competitive inhibition. Please see, “Studies have proposed that TBZ first enters VMAT2 from the lumenal side, binding to a lumenal-open conformation.”

      The revised version integrates the MD simulations into a plausible mechanism for luminal release of substrate. A key element in this mechanism is the protonation of D33, E312 and D399, which allows substrate to leave following water entry into the binding site. The acidic interior of synaptic vesicles should facilitate such protonation, but the fate of those protons needs to be considered. Are any of them predicted to dissociate prior to the return to a cytoplasm-facing conformation? If so, are all 3 released in that conformation? Postulating protonation events at one point in the reaction cycle requires some accounting for those protons - or at least recognition of the problem of reconciling their binding with the known stoichiometry of VMAT.

      We completely agree with this point and while we cannot account for all protons with a single structure and simulation of neurotransmitter release, some discussion of the fate of the protons is warranted. We have included a highly speculative statement in the discussion on this point, see lines 462-465, “Given the known transport stoichiometry of two protons per neurotransmitter, we speculate that two protons may dissociate back into the lumen, perhaps driven by the formation of salt bridges between D33 and K138 or R189 and E312 for example in an cytosol-facing state.”

      Reviewer #3 (Recommendations For The Authors):

      On page 13, line 238, the statement "The protonation states of titratable residues D33, E312, D399, D426, K138 and R189, which are in close proximity to TBZ, also impact its binding stability (Table 4)" is misleading. Table 4 only shows that D426 is charged and what the pKa values are. This should be rephrased to separate out which residues are in close proximity from what is known about how their protonation states affect TBZ stability.

      We agree with this statement and have rephrased this on line 290-294 on page 13 to read, “Several titratable residues, including D33, E312, D399, D426, K138, and R189, line the central cavity of VMAT2 and impact TBZ binding stability (Table 4). We found that maintaining an overall neutral charge within the TBZ binding pocket, as observed in system TBZ_1, most effectively preserves the TBZ-bound occluded state of VMAT2. Residues R189 and E312 in particular are within close proximity of TBZ and participate directly in binding.” We note that given the acidic pH of the vesicle lumen (5.5), it is likely all four residues may be protonated to a significant degree in this state.

      Typos:

      • luminal is another name for the drug generically known as phenobarbital, lumenal means in the lumen. (This typo seems to have crept into the published literature now too).

      Thank you for pointing this out. Indeed, we had considered carefully whether to use ‘lumenal’ or ‘luminal’ in our revised text. In fact, both are used interchangeably throughout the scientific literature and luminal is the more commonly used term. Please also see: https://www.merriam-webster.com/medical/luminal we do agree that there may be confusion because ‘Luminal’ is a trademark of phenobarbital. Therefore, we have changed the text to read ‘lumenal’ throughout.


      The following is the authors’ response to the original recommendations.

      Reviewer #1 (Recommendations For The Authors):

      I congratulate the authors on this study, which I enjoyed reading. Overall, the study reports a novel and exciting new structure for a member of the SLC18 family of vesicular monoamine transporters. Associated MD, binding and transport assays provide support for the hypothesis and firm up the modelled pose for the TBZ drug. The main strengths of the study largely sit with the structure, which, as the authors say, provides additional and essential insights above those available from AF2. The structures also reveal several potentially interesting observations concerning the mechanism of gating and proton-driven transport. The main weakness lies in the limited mutational data and studies into the role of pH in regulating ligand binding. As detailed below, my main comment would be to spend a little extra time expanding the mutational data (perhaps already done during the review?) to enable more evidence-based conclusions to be drawn.

      We thank reviewer #1 for their helpful comments and suggestions. We agree that mutational analysis specifically of neurotransmitter transport would strengthen the mechanistic conclusions of the work. We also agree with reviewer #1 and #3 that the role of pH and the protonation state of charged residues was a weakness in the first version of the manuscript. Therefore, we have expanded our mutational and computational data as detailed below and we believe that this has further solidified our findings.

      Specific comments & suggestions:

      It is an interesting strategy to fuse the mVenus and anti-GFP nanobody to the N-/C-termini. The authors should also include in SI Fig. 1 a full model for the features observed in these maps and deposit this in the PDB.

      Great point, we have made a main text panel describing the construct. Figure S1 includes a full description of the construct. The reviewer will note that the PDB entry contains the entire amino acid sequence of the construct and while the GFP and GFP-Nb cannot be well modeled into the density, we have included all of the relevant information for the reader.

      Difficult to make out the ligand in Fig. 2b, I would suggest changing the color of the carbon atoms.

      Fixed.

      It is difficult to make out the side chains in ED Fig. 5d.

      This is now its own supplemental figure and is presented larger.

      ED Figures are called out of order in the manuscript. For example, in line 143 ED Fig.6 is called before ED Fig. 5d (line 152), and then ED 5d is called before ED 5a. This makes it rather confusing to follow the description, analysis, and data when reading the paper. Although there are other examples. I would suggest trying to order the figure callouts to flow with the narrative of the study.

      Agreed. Fixed.

      It wasn't clear to me what the result was produced by just imaging the ligand-free chimaera protein. It would be useful to say whether this resulted in low-resolution maps and whether the presence of the TBZ compound was essential for high-resolution structure determination.

      The ligand is likely required for structure determination. We have not, however, made such a statement largely because we have yet to determine an apo reconstruction.

      The role of E127 and W318 on EL1 in gating the luminal side of the transporter is very intriguing. As the authors suggest, this may represent an atypical gating mechanism for the MFS (line 182). I did wonder if the authors had considered providing more insight into this potentially novel mechanism. Additional experiments would be further mutations of W318 to F, Y, V, and I to see if they can identify a non-dead variant that could be analysed kinetically. They may have more luck with variants of E127, as they suggest this stabilises W318. If these side chains are important for gating and transport regulation, one might expect to see interesting effects on the transport kinetics.

      This is a fantastic suggestion. We have done this, and we think that the reviewer will find the results to be quite interesting. Some VMAT2 sequences have an R or an H at position 318 while VPAT has an F at the equivalent position. We have made these mutants including the E127A mutant and analyzed them using TBZ binding and transport experiments. Interestingly the W318R, H, and F mutants preserve activity in varying degrees with the R mutant closely resembling wild type. W318A has no transport activity. Only the W318F mutant retains some TBZ binding. The E127A mutant also has little transport activity but nearly wild type like TBZ binding which we believe suggests a role for this residue also in stabilizing W318.

      The authors identify an interesting polar network, which is described in detail and shown in Fig. 2d. However, the authors present no experimental data to shed further mechanistic insight into how these side chains contribute to monoamine transport or ligand binding. Additional experiments that would be helpful here might include repeating the binding and competition assays shown in Fig. 1c under different pH conditions for the WT and different mutations of this polar network. At present, this section of the manuscript is very descriptive without providing much novel insight into the mechanism of VMAT transport. I did wonder whether a similar analysis of pH effects on DTBZ binding might also provide insight into the role of E312 and the role of protons in the mechanism.

      Thank you, we have addressed this point in several different ways. The first is that many of these residues have already been characterized in several earlier studies, see refs 31, 32, and 42 and we have incorporated this into our discussion where appropriate. With respect to E312, the reviewers’ comments are again very appropriate. We have addressed this using computational experiments exploring the protonation status of E312 and other residues as well as TBZ. Our simulations and Propka calculations clearly show that E312 must be protonated and TBZ must be deprotonated to maintain TBZ binding. We have also extended these computational studies toward understanding the protonation status of residues which orchestrate dopamine binding and release.

      The authors then describe the binding pose for TBZ. This section also provides some biochemical characterisation of the binding site, in the form of the binding assay introduced in Fig. 1. However, the insights are again somewhat reduced as the mutants were chosen to show reduced binding. Could the authors return to this assay and try more conservative mutations of the key side chains to illuminate more detail? For example, does an R189K mutant still show binding but not transport? Similarly, what properties does an E312D have? The authors speculate that K138 might play a role in coupling ligand binding/transport to the protonation, possibly through an interaction with D426 and D33 (line 236). Given the presence of D33 in the polar network described previously, I was left wondering how this might occur. I feel that some of the experiments with pH and conservative mutants might shed some light on this important aspect. Please label the data points in Fig. 3d.

      Indeed, alanine mutants at these positions while valuable do not provide the level of detailed insight into mechanism that we also would have liked to obtain. Thus, we have made more conservative and targeted mutants like the R189K mutant and various mutants at N34 for example and tested them in both transport and binding assays. We have also made a mutant at K138 and found that it is not transport competent or able to bind TBZ to a significant degree. With respect to labels and color codes, we have made the color codes consistent between the bar graphs and the curves. We have also labeled the data points in the figure legends.

      The manuscript currently doesn't present a hypothesis for how TBZ induces the 'dead-end' complex compared to physiological ligands. Does the MD shed any light on this aspect of the study? If the authors place the physiological ligand in the same location as the TBZ and run the simulation for 500ns, what do they observe? 100ns is also a very short time window. I appreciate the comment about N34 in line 303, but is this really the answer? It would be very interesting to provide more evidence on this important aspect of VMAT pharmacology.

      MD with a natural ligand (dopamine) provides substantial insight into why TBZ is a dead-end complex. Since water cannot penetrate into the binding site in the TBZ bound complex, this does not allow for substantial luminal release. In contrast, simulations conducted in the presence of DA bound to the occluded VMAT2 show the propensity of that structure to accommodate an influx of water molecules that promote the release of DA to the lumen. The new results are illustrated in Figure 5 (main text) as well as supplemental figure 8 panels d-h. The new simulations further emphasized the importance of the protonation state of acidic residues near the substrate-binding pocket.

      Reviewer #2 (Recommendations For The Authors):

      Line 68, "both sides of the membrane" -> "alternately to either side of the membrane".

      Fixed. Thanks.

      Transmembrane proteins in intracellular organelles present unique issues of nomenclature. I suggest the authors refer to cytoplasmic and luminal faces of the protein (not intracellular or extracellular (line 124)) and adhere to these names to avoid confusion. This creates problems for loops called IL and EL, but they could be defined on first use.

      We agree with this point and had initially gone with the conventional definitions used in the literature. We have now changed this throughout the text to be luminal and cytosolic.

      Lines 135-6, are these residue numbers correct? The pdb file lists 126 as Asp and 333 as Ala.

      Thank you. This is fixed.

      ED Fig. 6 is not clear. A higher-resolution figure is needed.

      We have updated this figure and hope that the reviewer will find it to be much clearer.

      Lines 158-9, Is there any data to support effects on dynamics or folding? If not, please indicate that this is speculation.

      Fixed.

      Line 174, Should "I315" be "L315"?

      Fixed.

      Line 179, Please indicate what is meant by "inner" and "below" (also lines 183 and 258).

      We have added Figure calls here where needed.

      Line 192, S197 is listed as part of polar network 1, but not discussed further. Is it actually involved, or just in the neighborhood?

      It is part of the network, but we did not discuss in further detail because we do not have data indicating its precise function and thus have left this as a description.

      Line 199, E312, and N388 are fairly distant from each other. Do you want to clarify why they represent a network?

      While they are not within hydrogen bonding distance, we nevertheless include them as part of the same network because they may come into closer proximity in a different conformational state.

      Line 206, Protonation of all 3? VMAT2 doesn't transport 3 protons per cycle. Please clarify.

      We believe that these residues may be protonated, but they may not necessarily all be involved in proton transport.

      Line 219, Do you mean the aspartate unique to DAT, NET, and SERT? This is Gly in all the amino acid transporters in the NSS family. Please be specific.

      Fixed. Thank you.

      Line 224, "mutation of E312 to Q" or "mutation of Glu312 to Gln".

      Fixed. Thank you.

      Fig. 3d, Normally, one would expect full saturation curves for each mutant. How can a reader distinguish between low affinity or a decrease in the number of binding sites? Would full binding curves be prohibitive for the mutants because of the cost or availability of the ligand? These points should be addressed. A couple of the curves are not visible. Would an expanded scale inset show them more clearly? Also, would it be possible to include chemical structures for all ligands discussed?

      Many if not most of these mutants bind TBZ with such low affinity that it is not possible to measure a full saturation curve either because of ligand availability (radioactive ligand concentration is only in µM) or due to technical issues with being able to measure such low affinity binding. We have changed the presentation of the curves and have split the gating and binding site mutants into their own figures. We feel this improves the readability of these curves. We have also included a table with the respective Kd values determined for each of the mutants where possible.

      Line 235, The distances are long for a direct interaction between K138 and the TBZ methoxy groups. The unusual distances should be mentioned if an interaction is being proposed.

      We do not think that K138 is directly involved in TBZ binding, however this was written in a confusing way and has been now changed.

      Line 243, Please give a quantitative estimate of the affinity difference. "modestly" is vague.

      It is an approximately 2-fold difference. Fixed in the text.

      Line 248, 150 nM is, at best, a Kd, not an affinity.

      Agreed, this is changed.

      Reviewer #3 (Recommendations For The Authors):

      The (3 x ~100ns-long) molecular dynamics simulations provided suggest some instability of the pose identified by cryo-EM. While it is not unreasonable that ligands shift around and adopt multiple conformations within a single binding site (in a reversible manner), the present results do raise questions about the assumptions made when starting the simulations, in particular (1) the protonation states of charged residues in the TBZ binding sites; (2) the parameters used for tetrabenazine; (3) the conformations of acidic side chains that are notoriously difficult to resolve in cryoEM maps; and (4) any contributions of the truncated regions truncated in the simulated structure, namely the cysteine cross-linked loop and the terminal domains. The authors should examine and/or discuss these contributions before attributing mechanistic insights into the newly observed binding orientation.

      In order to estimate the effects of protonation states on TBZ binding, we now added three new systems with altered protonation on TBZ and binding pocket lining residues (see Table 3 in the revised vision); and for each system, we performed multiple MD runs to address the question and concerns raised by reviewer.

      Regarding the protonation states: Propka3.0 was used to determine the protonation states, finding that E312 and D399 should be protonated. If I am not mistaken, this version of ProPka cannot account for non-protein ligands (https://github.com/jensengroup/propka). Given their proximity to the binding site, these protonation states will be critical factors for the stability of the simulations. The authors could test their assumption by repeating the calculations with Propka 3.1 or higher, to establish sensitivity to the ligand. Beyond this, showing the resultant hydrogen bond networks will help to reassure the reader that the dynamics in the lumenal gates do not arise from an artifact.

      We thank the reviewer for suggestion of using higher version of Propka. We used the most recent Propka3.5 and carried out protonation calculations in the presence and absence of TBZ. The new calculations are presented in Table 4 and SI Figure 8c of the revised version.

      It should be possible to assess whether waters penetrate the ligand binding site during the simulations if that is of concern.

      We now added the number of waters within the ligand binding pockets for all MD simulations we performed, which are presented in Table 3 and Table 5 of the revised version.

      Finally, I didn't fully understand the conclusion based on the simulations and the "binding affinity" calculations: do they imply that the pose identified in the EM map is not stable? What is the value of the binding affinity histogram?

      We apologize for this confusion. For each MD snapshot, we calculated TBZ binding affinity using PRODIGY-LIG (Vangone et al., Bioinformatics 2019), which is a contact-based tool for computing ligand binding affinity. The binding affinity histogram shown in the original submission was the histogram of those binding affinities calculated for MD snapshots. In the revision, we replaced binding affinity histogram by time evolution of binding affinity changes (SI Fig 6c in the revision). The simulations confirmed that the pose identified in the EM map is stable, with a flattened binding affinity of -9.4 ± 0.3 kcal/mol in all three runs.

      Recommendations regarding writing/presentation:

      The authors use active tense terminology in attributing forces to elements of structure (cinching, packing tightly, locking). While appealing and commonplace in structural biology, this style frequently overstates the understanding obtained from a static structure and can give a rather misleading picture, so I encourage rephrasing.

      We appreciate this point; the use of these words is not meant to overstate or provide a misleading picture but rather to aid the reader in mechanistic understanding of the proposed processes.

      I would also recommend replacing the terms "above" and "below" for identifying aspects of the structure; the protein's location in the vesicular membrane makes these terms particularly difficult to follow.

      These terms refer specifically to the Figures themselves which we have always oriented with the luminal side at the top of the page and the cytosolic on the bottom. We have indicated in Figure 1 the orientation of VMAT2. The Figures are the point of reference which we refer to, and the ‘above’ and ‘below’ terms have been used to assist the reader to make the manuscript easier for a more casual or non-expert reader to follow.

      Minor corrections:

      • the legend in Figure 2 lacks details, e.g. how many simulation frames are shown, how were the electrostatic maps calculated?

      We revised Figure 2 and moved simulation frames to SI figure 6e. A total of 503 simulation frames are shown.

      • how were the TBZ RMSDs calculated? using all atoms or just the non-hydrogen atoms?

      For TBZ RMSDs, we used non-hydrogen atoms. This information is presented in the Methods section.

      MD simulation snapshots and input files can be provided via zenodo or another website.

      We will upload snapshots and input files to Zenodo upon acceptance of the manuscript.

      Reviewing editor specific points:

      Specific points

      L.97: Remove "readily available"

      Fixed.

      L.99: The authors are not measuring competition binding. It is well known that reserpine and substrates inhibit TBZ binding only at concentrations 100 times higher than their respective KD and KM values. It is, therefore, surprising that the authors use this isotherm and refrain from commenting on the significance of the finding. Moreover, the presentation of results as "Normalized Counts" does not provide any information about the fraction of VMAT molecules binding the ligand. At least, the authors should provide the specific activity of the ligand, and the number of moles bound per mole of protein should be calculated.

      The point was not to infer any details about the conformations that TBZ and reserpine bind but merely to point out that both constructs have a similar behavior with respect to their Ki for reserpine. We have added a sentence to say that reserpine binding stabilizes cytoplasmic-open so the reader is aware of the significance of this competition experiment.

      L.102: The characterization of serotonin transport activity needs to be more satisfactory. The Km in rVMAT2 is 100-200 nM, so why are the experiments done at 1 and 10 micromolar? Is the Km of this construct very different? The results provided (counts per minute at the steady state) need to give more information.

      The Km of human VMAT2 varies somewhat according to the source but has generally been reported to be between 0.6 to 1.4 µM for serotonin according to these references.

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019297/ https://www.cell.com/cell/pdf/0092-8674(92)90425-C.pdf https://www.pnas.org/doi/abs/10.1073/pnas.93.10.5166

      Fig 1B could be more informative. I suggest adding a cartoon model with TMs labeled, similar to ED Fig6a.

      This panel is to aid the reader in accessing the overall map quality and thus we do not wish to add additional labels/fits which would distract from that point. Instead, we have added overall views of the model in Figs 2,3.

      L.179: The authors claim that the inner gate is located "below" (whatever this could mean) the TBZ ligand. In L.214, they claim that TBZ adopts a pose.....just "below" the location of the luminal gating residues. Please clarify and use appropriate terminology.

      This refers to the position of these residues in the Figures themselves. We have added figure calls where appropriate here.

      Fig. 4: The cartoon could be more informative.

      We have added more information to the mechanism cartoon which is now Figure 6. This incorporates some of our new data and we believe it will be more informative.

      L. 213: The paragraph describes residues involved in TBZ binding. Mutagenesis is used to validate the structural information. However, the results (ED fig. 5B) must be corrected for protein expression levels. In the Methods section, the authors state (L.444), "Mutants were evaluated similarly from cell lysates of transfected cells." Without normalization of protein expression levels, the results are meaningless even if they agree with predictions.

      In fact, we have normalized the concentrations of protein in our binding experiments. This was noted in the methods section. And to account for these differences, experiments were conducted using 2.5 nM of VMAT2 protein as assessed by FSEC.

      L.220: The referral to ED Fig.7 is not appropriate here. The figure shows docking-predicted poses of dopamine and serotonin.

      Figure call has been changed.

      L.226: The referral to Fig. 3b needs to be corrected. The figure shows TBZ and not the neurotransmitter.

      This has been corrected.

      L. 337: "The neurotransmitter substrate is bound at the central site." What do the authors mean in this cartoon? Do they have evidence for this? Tetrabenazine is not a substrate.

      This cartoon drawing is meant to illustrate the elements of structure. Similar drawings are presented throughout the literature such as here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940252/ Figure 3 and here: https://pubs.acs.org/doi/10.1021/acs.chemrev.0c00983 Figure 2.

      The same compound is mentioned with different names: 3H-dihydrotetrabenazine and 3H-labeled DTBZ.

      Fixed.

      ED fig 1d is illegible.

      The high-resolution figure is completely legible. We will provide this to the journal upon publication.

      Figure 2d: A side view would be more visual.

      We have updated this figure and believe that it is much easier to understand now.

      L. 179: The inner gate is located 'below' the TBZ ligand

      Please see above response, this refers to the figures themselves. The figures are our point of reference.

      L. 213-215: Tetrabenazine binding site just 'below' the location of the luminal gating residues.

      See above.

      Throughout the paper, results are given as cpm or counts. The reader can only estimate the magnitude of the binding/transport by knowing the specific activity of the radiolabel. I recommend switching to nano/picomoles or supplying enough information to understand what the given cpm values could mean.

      Binding experiments were done using scintillation proximity assays and therefore converting the CPMs to values in pmol of bound ligand is simply not possible. For the transport experiments (now Fig 1d) the point was to show that the wild type was similar in activity to the chimera. In our new transport experiments we have presented for the mutants, many experiments were combined together and therefore, we have normalized the counts to the relative activity of wild type VMAT2.

    1. When the focus is shifted from leadership as individual direction to leadership as freely chosen collective work, the shared moral purpose of that work becomes prominent, and it is work to which all may contribute regardless of role or positional status.

      I believe my very first school held true to this. We had our principal and five teachers. That was the whole staff and we truly worked as a team. Not once did my principal ever feel like a "boss" and the team of teachers I was a part of really steered the school. And I like to think that that model was for the better. The students were at the heart of every decision made and many of the things we engaged in came from a collective decision we made as a team rather than an order given from the top down. I felt as if being a first year teacher, I had just as much respect from my principal and colleagues as the veteran teacher of twenty-five years had. Unfortunately, I have since learned that this is not the culture of many schools and is very hard to achieve. Why? I'm not quite sure. I think the small nature of the school is what kept the team so close and respectful.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      The manuscript from Richter et al. is a very thorough anatomical description of the external sensory organs in Drosophila larvae. It represents an important tool for investigating the relationship between the structure and function of sensory organs. Using improved electron microscopy analysis and digital modeling, the authors provide compelling evidence offering the basis for molecular and functional studies to decipher the sensory strategies of larvae to navigate through their environment.

      Public Reviews:

      Summary

      This is a very meticulous and precise anatomical description of the external sensory organs (sensillia) in Drosophila larvae. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Improved serial electron microscopy and digital modeling are used to the fullest to provide a definitive and clear picture of the sensory organs, the sensillia, and adjacent ganglia, providing an integral and accurate map, which is dearly needed in the field. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments and construct a complete structural and neuronal map of the external larval sensilla.

      Strengths

      It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely use it as a reference paper. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. It will allow for investigating sensory processing in depth. Serial electron microscopy and digital modeling are used to the fullest to provide a comprehensive, definitive, and clear picture of the sensory organs. The discussion places the anatomical data into a functional and developmental frame. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

      Weaknesses

      There are no apparent weaknesses, although it is not a complete novel anatomical study. It revisits many data that already existed, adding new information. However, the repetitiveness of some data and prior studies may be avoided for easy readability.

      We would like to thank the reviewers for their respective reviews. The detailed comments and efforts have helped us to improve our manuscript. In the following, we have listed the comments one by one and provide the respective information on how we addressed the concerns.

      Recommendations for the authors:

      We have tried to address every single comment as far as possible. In order to structure our response a little better, we have listed the relevant page number and the original comments once again. Directly following this you will find our response and a description of what we have changed in the manuscript.

      REVIEWER #1 (Recommendations For The Authors):

      I have a few comments that will help the reader navigate this long and detailed paper.

      REVIEWER 1.1. page 4

      The final section of "the Structural organization of Drosophila larvae" needs some reorganization.

      Specifically:

      "The DO and the TO are prominently located on the tip of the head lobes" Can the authors rewrite the sentence in a way that it is clear that there is one DO and one VO on each side of the head? Check at the beginning of each section, please. There is a mention about hemi-segments but it is still confusing.

      Done – replaced with “The largest sense organs of Drosophila larvae are arranged in pairs on the right and left side of the head.”

      REVIEWER 1.2. page 5

      "The sequence of sensilla is always similar for and different between T1, T2-T3, and A1-A7" This sentence is not clear, please break it into two sentences.

      Done – replaced with: “We noticed varying arrangements for T1, T2-T3, and A1-A7, with a consistent sequence of sensilla in each configuration.”

      REVIEWER 1.3. figures page 4

      Double hair can't be found in Figure 1B or C (is it h3, h4?) - please clarify.

      Done - changed to double hair organ in page 11, included double hair sketch in legend in figure 1B. We changed the name of the structure to double hair organ, to clarify that this is a compound sensillum consisting of two individual sensilla.

      REVIEWER 1.4. page 5

      The authors go back and forth in their descriptions of the different sensory organs. Knob sensilla and then papilla sensilla are discussed and then a few lines later a further description is done. Please unify the description of each separately.

      Done – we restructured the whole section.

      REVIEWER 1.5. figures page 6

      "We found three hair sensilla on T1-T3, and "two" on A1-A7" - in the figure there seem to be "four" on A1-A7.

      Done – we included the two hair sensilla of the double hair organ

      REVIEWER 1.6. figures page 6

      DORSAL ORGAN:

      Can the authors explain the colour map meaning in Figure 2A? It is explained in 2C but the image already has colours. Add your sentence "Color code in A applies to all micrographs in this Figure".

      Done – we added a sentence to explain that the color code in A applies to the whole figure.

      REVIEWER 1.7. page 6

      Page 10: which comprises seven olfactory sensilla "composing" three dendrites each: replace this with"with". At the end, we want to think 7 X 3= 21 ORNs.

      Done – replaced.

      REVIEWER 1.8. page 9

      CHORDOTONAL ORGANS:

      "We find these these DO associated ChO (doChO).. .". Please remove one "these"

      Done – removed.

      REVIEWER 1.9. page 8

      Is the DO associated ChO part of the dorsal ganglion???? It does not look like it. Could you clarify?

      Done – we added a sentence that clarifies that the ChO neuron is not iside the DOG.

      REVIEWER 1.10. page 9 VENTRAL ORGAN: A figures page 12

      Please add to the Figure 8 legend the description of 8c' and 8c'?

      Done – added description in figure legend.

      B page 9

      8H, what are the *, arrows? Please clarify - it is hard to interpret the figure.

      Done – we added parentheses in the figure legend that state which structures the asterisks and arrows indicate.

      C page 9

      "Three of them are innervated by a single neuron () and one by two neurons () (Figure 8F-I). Please add which are innervated by 1 (VO1, VO2-VO4) and which by 2 (VO3).

      Done – we added parentheses that clarify which sensilla are innervated by 1 or 2 neurons.

      REVIEWER 1.11. page 9

      Can you add something (or speculate) about the difference in sensory processing of the different types of sensilla?

      Done – new sentence in discussion:

      ‘Their different size and microtubule organization likely correlate with processing of different stimulus intesities applied to the mechanotransduction apparatus (Bechstedt et al. 2010).’

      REVIEWER 1.12. figures page 16

      PAPILLA AND HAIR SENSILLA:

      FIGURE 10a, please add the name of each sensillum from p1, p2, px py, etc... (if not we have to go back to figure 1 when you describe specific ps.)

      Thanks for the comment, it really makes it a lot easier for the reader.

      REVIEWER 1.13. figures page 18 Figure 11, can you add the name of each hair, please?

      Done – updated figure.

      REVIEWER 1.14. figures pages 16, 18, 20

      In Figures 10, 11, and 12 you clearly draw an area on the internal side that I assume is what you call the "electron-dense sheath". It is wider in papilla sensilla than in hair sensilla, most likely due to the difference in stimuli sensed that you explain in detail in the discussion. Can you say in the figure what this "internal" thing is? Can you add this difference to your list "Apart from the difference in outer appearance and structure of the tubular body"?

      This is the basal septum, but it is not certain that it is wider in the papillae sensillae, at least we could not observe this in our data sets. The impression could have been created by different scales in the 3D reconstructions and a perspective view. Therefore, we do not want to list this as a difference here, as we are not sure.

      However, we have now specified the socket septum in the figure legends and in Figures 10A, 11A and 12A.

      REVIEWER 1.15. page 11

      KNOB SENSILLA:

      Page 25;" Knob sensilla have been described under "vaious" names such as": add various.

      Done

      REVIEWER 1.16. page 12

      "reveals that the three hair and the two papilla sensilla are associated with a single dendrite." Can you write that "reveals THAT EACH OF the three hair and the two papilla sensilla" if not it seems that there is only one dendrite.

      Done

      REVIEWER 1.17. figures page 25 TERMINAL SENSORY CONES:

      Please name the t1-t7 cones in Figure 15A.

      Done – we updated the figure.

      REVIEWER 1.18. page 13

      The spiracle sense organ deserves a new paragraph. As does the papilla sensillum of the anal plate.

      Done – we added subtitles before the prargraphs.

      Discussion:

      REVIEWER 1.19. page 15

      Page 38: "v'entral" correct typo

      PAGE 15

      Done – we have updated the nomenclature  ventral 1 (v), ventral 2 (v’) and ventral 3 (v’’)

      REVIEWER #2 (Recommendations For The Authors):

      I have only a few comments:

      REVIEWER 2.1. page 5

      p.5, right column, middle: the use of trichoid, campaniform, and basiconical (sensilla) in previous works were based on even older papers and reviews that attempted to link EM architecture to function (e.g., KEIL, T. A. & STEINBRECHT, R. A. (1986). Mechanosensitive and olfactory sensilla of insects. In Insect Ultrastructure, vol. 2. (ed. R. C. King & H. Akai), pp. 477-516. New York/London: Plenum Press). Trichoid sensilla can be mechano-sensitive, olfactory, or gustatory; trichoid simply refers to the shape (hair). The same applies to basiconical sensilla. The use of "campaniform", which Ghysen et al called "papilla sensilla", was the only really problematic case, because these (Drosophila larval) sensilla did not really resemble closely the classical campaniform sensilla (e.g., adult haltere). The only reason we called them campaniform is because they were not more similar to any other type of (previously named) sensillum.

      Thank you for the explanation. The nomenclature of structures is generally always a complex topic with often different approaches and principles. We are aware of this and have therefore tried to be as careful as possible. We were not sure from this comment whether you were suggesting to change the text or whether you wanted to explain how these names were assigned to the sensilla in the past. However, we hope that the current version is in line with your understanding, but could of course make changes if necessary (see also comments of reviewer 1).

      REVIEWER 2.2. page 9

      p.21, Labial Organ: the ventral lip is the labium; the dorsal one is the labrum.

      Done – replaced labrum with labium.

      REVIEWER 2.3. page 9

      p.20/21, Ventral organ and labial organ: here, the projection of the axons could be mentioned as an ordering principle. In the previous literature, for larva and embryo, a labial organ (lbo) was described that most likely corresponds to the labial organ presented here. This (previously mentioned) lbo characteristically projects along the labial nerve to the labial segment (hence the name). It fasciculates with axons of another sensory complex, also generated by the labial segment, namely the ventral pharyngeal sensory organ (VPS). Does the labial organ described here share this axonal path?

      Yes, it has the same axonal pathway and is the same organ as the lbo. We have tried to standardise the nomenclature for all important external head organs (DO, TO, VO, LO) and have therefore used abbreviations with two letters. However, to avoid confusion, we have now added that the LO was also called lbo in the past.

      For the ventral organ, the segmental origin (to my knowledge) was never clarified. The axons of the ventral organ project along the maxillary nerve (which carries axons of the terminal=maxillary organ). This nerve, closely before entering the VNC, splits into a main branch to the maxillary segment (TO axons) and a thinner branch that appears to target the mandibular segment. This branch could contain the axons of the ventral organ (as described previously and in this paper). Could the authors confirm this axonal projection of the VO?

      In this work, we did not focus on the axonal projections into the SEZ. This is also not a simple and fast process, as in the entire larval dataset, the large head nerves unfortunately exhibit a highly variable quality of representation. Therefore, the reconstruction of nerves and individual neurons within it is often challenging and very time-consuming. The research question is, of course, very intriguing, and one could also attempt to match each sensory neuron of the periphery with the existing map of the brain connectome. However, this is a project in itself, exceeding the scope of this work, and is therefore more feasible as a subsequent project.

      REVIEWER #3 (Recommendations For The Authors):

      Minor suggestions that the authors might consider:

      REVIEWER 3.1. figures all

      Recheck the scale bar in figures and figure legends. Missing in a few places.

      Done – we replaced or added some (missing) scale bars in figures and figure legends (see annotated figure document).

      REVIEWER 3.2. figures page 4

      The color schematic in Figure 1 can be improved for readability.

      Done – we changed the color schematic, especially for the head region to improve readability.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. The authors find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

      Strengths & Weaknesses:

      Various analyses nicely support the authors' claims. Accordingly, I have only one significant comment and several minor comments that focus on wordsmithing - e.g., clarifying the interpretation of statistical results and requesting additional citations to support claims in the introduction.

      We are pleased the reviewer found the analyses to support the claims. We have addressed comments related to clarifying interpretations as suggested in the Recommendations to the Authors. For example, we have added discussion and clarification on the other parameters that could affect the strength of ERC correlations.

      Reviewer #2 (Public Review):

      Summary:

      The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction types that all fall under the umbrella explanation of 'co-functional interactions'.

      Strengths:

      Very smart experimental design that leverages individual protein domains for increased resolution.

      Weaknesses:

      Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

      We appreciate the reviewer’s acknowledgement of the experimental design. We have addressed the nuance of the results by changing the title and clarifying other statements throughout the manuscript as suggested in the reviewer’s recommendations. We have also addressed reviewer comments asking for further explanation on using Fisher transformations when normalizing the Pearson correlations for branch counts.

      Reviewer #3 (Public Review):

      Summary:

      The paper makes a convincing argument that physical interactions of proteins do not cause substantial evolutionary co-variation.

      Strengths:

      The presented analyses are reasonable and look correct and the conclusions make sense.

      Weaknesses:

      The overall problem of the analysis is that nobody who has followed the literature on evolutionary rate variation over the last 20 years would think that physical interactions are a major cause of evolutionary rate variation. First, there have been probably hundreds of studies showing that gene expression level is the primary driver of evolutionary rate variation (see, for example, [1]). The present study doesn't mention this once. People can argue the causes or the strength of the effect, but entirely ignoring this body of literature is a serious lack of scholarship. Second, interacting proteins will likely be co-expressed, so the obvious null hypothesis would be to ask whether their observed rates are higher or lower than expected given their respective gene expression levels. Third, protein-protein interfaces exert a relatively weak selection pressure so I wouldn't expect them to play much role in the overall evolutionary rate of a protein.

      We thank the reviewer for their comments and suggestions. A point to immediately clarify is that the methods studied in this manuscript deal with rate variation of individual proteins over time, and if that variation correlates with that of another protein.. The numerous studies the reviewer refers to deal with explaining the differences in average rate between proteins. These are different sources of variation. It has not, to our knowledge, been shown that variation in the expression level of a single protein over time is responsible for its variation in evolutionary rate over time, let alone to a degree that allows its variation to correlate with that of a functionally related protein. That question interests us, but it is not the focus of this study.

      In our study, we sought to test for a contribution of physical interaction to the correlation of evolutionary rate changes as they vary over time, i.e. between branches. We made many changes to clarify this distinction in our revisions.

      We agree that the manuscript would be more clear to define the forces proposed to lead to difference in rate in general, which includes expression levels. We had generally considered expression level as one of the many potential non-physical forces, but failed to make that explicit and instead focused on selection pressure. In our revision we describe expression level as another potential driver of evolutionary rate variation over time. References to previous literature have been made in the introduction. We also added a more explicit explanation of the rate covariation over time that we are measuring in contrast with the association between expression level and rate differences between proteins that was studied in previous literature.

      On point 3, the authors seem confused though, as they claim a co-evolving interface would evolve faster than the rest of the protein (Figure 1, caption). Instead, the observation is they evolve slower (see, for example, [2]). This makes sense: A binding interface adds additional constraint that reduces the rate at which mutations accumulate. However, the effect is rather weak.

      The values in Fig 1B are a measure of correlation, specifically a Fisher transformed correlation coefficient. They are not evolutionary rates, so they are not reflecting faster or slower evolution, rather more or less covariation of evolutionary rates over time. We are not predicting that physically interacting interfaces evolve faster than the rest of the protein, but rather that if physical interaction drives covariation in evolutionary rates over time, their correlation would be stronger between pairs of physically interacting domains. In response, we have used clearer language in the figure caption and reorganized labels in Figure 1B to clearly show that the values are correlations. Revised Figure 1 Legend:

      “Overview of experimental schema and hypotheses. Proteins that share functional/physical relationships have similar relative rates of evolution across the phylogeny, as shown in (A) with SMC5 and SMC6. The color scale along the bottom indicates the relative evolutionary rate (RER) of the specific protein for that species compared to the genome-wide average. A higher (red) RER indicates that the protein is evolving at a faster rate than the genome average for that branch. Conversely, a lower (blue) RER indicates that protein is evolving at a slower rate than the genome average. The ERC (right) is a Pearson correlation of the RERs for each shared branch of the gene pair. (B) Suppose the correlation in relative evolutionary rates between two proteins is due to compensatory coevolution and physical interactions. In that case, the correlation of their rates (ie. ERC value) would be higher for just the amino acids in the physically interacting domain. (C) Outline of experimental design. Created with Biorender.com

      All in all, I'm fine with the analysis the authors perform, and I think the conclusions make sense, but the authors have to put some serious effort into reading the relevant literature and then reassess whether they are actually asking a meaningful question and, if so, whether they're doing the best analysis they could do or whether alternative hypotheses or analyses would make more sense.

      [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523088/

      [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854464/

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major comments

      (1) Numerous parameters influence ECR calculation. The authors note that their use of a large dataset of budding yeast provides sufficient statistical power to calculate ECR. I agree with that. However, a discussion of other parameters needs to be improved, especially when comparing the present study to others like Kann et al., Hakes et al., and Jothi et al.. For example, what is the evolutionary breadth and depth used in the Kann, Hakes, Jothi and other studies? How does that compare to the present study? Budding yeast evolve rapidly with gene presence/absence polymorphisms observed in genes otherwise considered universally conserved. Is there any reason to expect different results in a younger, slower-evolving clade such as mammals? There is potential to acknowledge and discuss other parameters that may influence ECR, such as codon optimization and gene/complex "essentiality," among others.

      More discussion of these parameters is a good idea. We have added the number and phylogeny of species used in the previous studies in the discussion paragraph starting with “Previous studies attributed varying degrees of evolutionary rate covariation signal to physical interactions between proteins.” We also like the idea of studying the effect of younger and more slowly evolving clades as opposed to the contrary, but currently we lack the required number of datasets to do this.

      We have also added more discussion and clarification of potential non-physical forces leading to ERC correlations in the introduction.

      Minor comments

      (1) It would be good to add a citation to the second sentence of the first paragraph, which reads, "It has been observed that some genes have rates that covary with those of other genes and that they tend to be functionally related."

      Added citation to Clark et al. 2012

      (2) In the last sentence of the first paragraph of the introduction, ERC is discussed in the context of only amino acid divergence, however, there is no reason that DNA sequences can't be used, especially if ERC is being calculated among species that are less ancient than, for example, Saccharomycotina yeasts. Thus, it may be more accurate to suggest that ERC measures how correlated branch-specific rates of sequence divergence are with those of another gene.

      Nice suggestion to generalize. We have made this change.

      (3) ERC was not calculated in reference #2. For the sentence "Protein pairs that have high ERC values (i.e., high rate covariation) are often found to participate in shared cellular functions, such as in a metabolic pathway2 or meiosis3 or being in a protein complex together," I think more appropriate citations (including inspiring work by the corresponding author) would be

      a) Coevolution of Interacting Fertilization Proteins (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000570)

      b) Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007720)

      c) An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function (https://www.science.org/doi/10.1126/sciadv.abn0105)

      d) PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data (https://academic.oup.com/bioinformatics/article/37/16/2325/6131675)

      Thank you for pointing out these works. We agree that there are more appropriate citations and we have referenced your suggested b-d.

      (4) The dataset of 343 yeast species also includes outgroup taxa. Therefore, indicating that 332 species are Saccharomycotina yeast and 11 are closely related outgroup taxa may be more accurate.

      Thank you for the suggestion, the following sentence has been added, citing the Shen et. al 2018 paper that the dataset was derived from:

      “To investigate the discrepancy between contributions to ERC signal from co-function and physical interaction, we used a dataset of 343 evolutionarily distant yeast species. 332 of the species are Saccharomycotina with 11 closely related outgroup species providing as much evolutionary divergence as humans to roundworms3”

      (5) Are there statistics/figures to support the claim that "Almost all complexes and pathways had mean ERC values significantly greater than a null distribution consisting of random protein pairs"?

      This is shown in supplementary figure 1. A reference to this figure was added as well as quantification within the text.

      (6)Similar to the previous comment, can quantitative values be added to the statement "While protein complexes appear to have higher mean ERC scores than the pathways..."?

      The median of the mean ERC scores for protein complexes is 5.366 while the median for the mean ERC score in pathways is 4.597. This quantification has been included in the text: “While protein complexes have higher mean ERC scores (median 5.366) than the pathways (median 4.597), the members of a given complex are also co-functional, making interpretation of the relative contribution of physical interactions to the average ERC score difficult”

      These quantifications are were also added to the figure caption for figure 2A

      (7) A semantic point: In the sentence "The lack of significance in the global permutation test shows that the...", I recommend saying that the analysis suggests, not shows, because there is potential for a type II error.

      Good suggestion, we have made this change.

      (8) The authors suggest that shared evolutionary pressures, "and hence shared levels of constraint," drive signatures of coevolution. The manuscript does not delve into selection measures (e.g., dN/dS). Perhaps it would be more representative to remove any implication of selection.

      We have added better language to clarify that discussion of selection is purely a hypothesis and that selection is not probed in our analyses.

      “Previous work finds evidence that relaxation of selective constraint can lead to drastic rate variation and hence covariation6. Rather, the greater and consistent contribution comes from non-physical interaction drivers that could include variation in essentiality, expression level, codon adaptation, and network connectivity. These non-physical forces would be under shared selective pressures and hence shared levels of constraint, the result of which was elevated ERC between non-interacting proteins, as visible in our study of genetic pathways that do not physically interact (Figure 2).”

      Reviewer #2 (Recommendations For The Authors):

      Major comments:

      -Title: In my opinion, the title of the manuscript is a somewhat misleading summary of the results of this paper. In the majority of the analyses in this paper, physical interactions do account for a significantly outsized portion of the ERC signature. The current title downplays the consistent (although sometimes small effect-sized) result that physically interacting domains do show higher ERC than non-physically interacting domains by every statistical measure employed in this paper to compare physical vs non-physical interactions. The authors' interpretation of their results within the manuscript body is that the effect of physical interactions is an inconsistent, weak, and non-generalizable driver of ERC. I generally agree with the authors' interpretations, but the nuance of these interpretations is lost in the title of the paper. I would suggest rewording the title to try to capture the nuance or at least be subjectively accurate. For example, stating that "...physical interactions are not the sole driving force.." is inarguably accurate based on these results.

      As an alternative title, I would suggest focusing on an important takeaway from the paper: ERC is a reliable predictor of co-functional interactions but not necessarily physical interactions. I agree with the statement that "there is not a strong enough signal to confidently call an interaction physical or not and would be of little value to an experimentalist wanting to infer interacting domains" and I think that a title that emphasizes this idea would be more accurate and impactful.

      Great suggestion. We agree that the current title is downplaying the minutiae of the method and the signal we capture with it, we have used your suggested title.

      There are an outsized number of complexes that had ROC-AUCs greater than 0.5 which is why we performed the permutation tests to determine how significant each of the individual ROC-AUCs were given the differing number of protein/domain pairs in each complex. Between the statistical methods used only 3 of the 17 complexes ranked physical interactions significantly higher than non-physically interacting domains in every analysis. Even among the 3 that were statistically significant some of the physically interacting domains still fell among the bottom portion of the ERC scores for that complex (Figure 5: MCM and CUL8 complexes) This is why we concluded that physical interactions are not the sole driving force of the signal captured by ERC.

      -Abstract: related to my preceding comment, the word "negligible" in the abstract is misleading. If physical interactions were truly entirely negligible, the comparisons of physically interacting vs non-physically interacting domains would yield 0.5. Instead, these comparisons always yielded results greater than 0.5. Consider rewording.

      Thank you for the suggestion this phrasing has been changed to “Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC”

      We agree that “negligible” may be too strong of a word, however, the comparisons do not always yield results greater than 0.5.

      5 of the 17 complexes do not reach the 0.5 threshold for the initial ROC analysis and even among those that do, only 4 had significantly high ROC-AUCs. You are correct that the signal is not completely negligible which is why we continued by determining if the physical interaction was driving high ERC only within proteins (Figure 5)

      -Figure 3: I think there may be an error in the domain labeling in Figure 3. The comparison between OKP1_2 and AME1_3 is the highest ERC value in the matrix. From the complex structure, it appears that OKP1_2 and AME1_3 are two helix domains that appear to physically interact. However, in the ERC matrix, they are not shaded to indicate they are a physical interaction pair. Please double-check that the interacting domains are properly annotated, since mis-annotation would have a large impact on the interpretation of this figure with respect to the overall question the paper addresses.

      Thank you for catching this - fixed.

      Minor comments:

      -Methods: "The full ERC pipeline can be found at (Github)." Provide github URL here? Thanks for the catch, fixed

      -Discussion: "Evidence for physical coevolution however was tempered by a global permutation test, which did not reach significance, indicating that this inference is sensitive to approach and further underlines the relatively weak contribution of physical coevolution." The word "relatively" may not be a good choice of words. In comparison to what? As is, the phrasing could be interpreted as implying "in comparison to non-physical interactions". This would not be accurate, because the results show that in general, physical interactions are a stronger contributor to ERC (consistent trend but varied significance, depending on methodology) than non-physical interactions.

      Thank you for your help with clarification. The word relatively was removed.

      However, we do not agree that in general physical interactions are a stronger contributor to ERC than non-physical interactions (such as gene expression, codon adaptation, etc.). In all of our statistical tests a maximum of 5 of the 17 complexes ranked physical interactions significantly higher than non-physical interactions. While the ROC-AUC is greater than 0.5 for 12 of the 17 complexes only 4 of those were significant.

      -I have not seen Fisher-transformed correlation coefficients used in the context of ERC. I understand that it's helpful in normalizing the results so that they are comparable between ERC comparisons with differing numbers of overlapping branches (i.e. points on a linear correlation plot). A reference of where the authors got this idea or a little more verbiage to describe the rationale would be helpful. On a related note, I would expect that using linear correlation p-value instead of R-squared would account for differences in overlapping branches, eliminating the need to apply fisher-transformation. It would be helpful for the authors to outline their rationale for using a correlation coefficient rather than a P-value.

      We agree that this method could be made clearer. We made a methodological choice to use Fisher transformation over linear correlation p-value. Both methods should achieve the same end result by taking the number of branches into consideration. We have added additional explanation to the results section “Both protein pathways and complexes have elevated ERC”:

      “ERC was calculated for all pairs of the 12,552 genes. For each pair the correlation is Fisher transformed to normalize for the number of shared branches that contribute to the correlation. This normalization is necessary to reduce false positives that have high correlation solely due to a small number of data points. This normalization also allows for direct comparison of ERC between gene pairs that have differing numbers of branches contributing to the score.”

      We also added additional explanation in the methods section including the formula used to calculate the Fisher transformation

      -Did the authors use Pearson or Spearman correlation coefficient?

      Pearson. We clarified this in the methods section, “Calculating evolutionary rate covariation” : “Evolutionary rate covariation is calculated by correlating relative evolutionary rates (RERs) between two gene trees using a Pearson correlation.”

      -Did the authors explore ERC between domains within a single protein? Do domains within a protein exhibit ERC? I would expect that they do. If they do, this could likely be attributed to linkage/genetic hitchhiking, representing a new angle/factor beyond physical interaction that could lead to ERC. This is just an idea for a future analysis, not necessarily a request within the scope of the present paper.

      We did calculate the ERC between domains of a single protein but did not include them in the analysis since they didn’t address the specific question we posed. As expected they are highly correlated, and past unpublished studies in the lab do find a very weak, but detectable genome-wide, signature of rate covariation between neighboring colinear genes on a chromosome. That signal was however so weak as to be eclipsed by true functional relationships, when present.

      Reviewer #3 (Recommendations For The Authors):

      Please read the literature and revise accordingly.

      We understand the confusion surrounding previous literature on the relationship between expression levels and evolutionary rates when comparing between different proteins. Those studies clearly showed how expression level is highly predictive of a given protein’s average evolutionary rate. However, we are studying the change in evolutionary rate over branches for single proteins. This is inherently different because we’re following rate fluctuations in the same protein over time. To our knowledge it has not yet been shown that expression level commonly varies enough over time to produce large rate variations over time in the same protein, and if it is responsible for the correlations of rate we observe between co-functional proteins. It is however reasonable to expect that what governs between-protein differences in rate could also contribute to between-branch differences (over time for a single protein). In fact, our earlier study approached this (Clark et al. Genome Research 2012). We expect expression level could influence rate over time and lump its effect together with general non-physical forces, such as selection pressures. We recognize we could do better in defining more of the non-physical forces and the past literature. We added the following section to the introduction and many other clarifying statements throughout the manuscript:

      “For the purposes of this study, the forces that contribute to correlated evolutionary rates are grouped into two bins, physical and non-physical. The physical force is coevolution occurring at physical interaction interfaces. Non-physical forces include gene co-expression, codon adaptation, selective pressures, and gene essentiality. There is a well accepted negative relationship between gene expression and rate of protein evolution where genes that are highly expressed generally have slower rates of evolution14,15. However, Cope et al.16 found that there is a weak relationship between both gene expression and the number of interactions a protein has with the coevolution of expression level. Conversely, they found a strong relationship between proteins that physically interact and the coevolution of gene expression. These findings illuminate the difference between the strong relationship of gene expression level on the average evolutionary rate of a protein and the weak contribution of gene expression level to correlated evolutionary rates of proteins across branches. The finding that physically interacting proteins have strong expression level coevolution brings to question how much coevolution of physically interacting proteins contributes to overall covariation in protein evolutionary rates.”

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1:

      Summary:

      In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

      The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

      Strengths:

      (1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

      (2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

      Weaknesses:

      The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

      (1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

      We are very grateful that Reviewer #1 proposed the possibility that MTA and MTB form a receptor-ligand complex in which one acting as the ligand and the other as the receptor. We considered this hypothesis when asking how dose MTRC function, too. However, our current results do not support this idea. For instance, if MTA were a ligand and MTB a receptor, we would expect a mating signal upon treatment with MTAxc protein, but not with MTBxc. Contrary to this expectation, our experiments revealed that both MTAxc and MTBxc exhibit very similar effects (Figure 5, green and blue), and their combined treatment produces a stronger effect (Figure 5, teal). This suggests a mixed function for both proteins. (We incorporated this discussion into the revised version [line 120-121, 240-244].) It is pity that our current knowledge does not provide a detailed molecular mechanism for this intricate system. We are actively investigating the protein structures of MTA, MTB, and the entire MTRC, hoping to gain deeper insights into the molecular functions of MTA and MTB.

      Additionally, we also realized that the expression we used in the previous version, “simple receptor-ligand model”, is not clearly defined. As Reviewer #1 pointed out, in this section, we examined whether the individual proteins of MTA and MTB act as a couple of receptor and ligand. We think this is the simplest possibility as a null hypothesis for Tetrahymena mating-type recognition. We have clarified it in the revised version (line 90-91, 104-106). According to this section, we proposed that MTA and MTB may form a complex that serves as a recognizer (functioning as both ligand and receptor) (line 117-118).

      (2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

      Several factors could contribute to the inability of MTA/Bxc to induce costimulation. It is highly likely that additional components are necessary, given that MTA/B form a protein complex with other proteins. Moreover, the expression of MTA/Bxc in insect cells, compared with Tetrahymena, might result in differences in post-translational modifications. Additionally, there are variations in protein conditions; on the Tetrahymena membrane, these proteins are arranged regularly and concentrated in a small area, while MTA/Bxc is randomly dispersed in the medium. The former condition could be more efficient. If there is a threshold required to stimulate a costimulation marker, MTA/Bxc may fail to meet this requirement. Much more studies are needed to fully answer this question. We acknowledged this limitation in the revised version (line 244-248).

      Reviewer #2:

      This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

      Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

      Thanks for the valuable comments and suggestions. We are currently in the process of constructing deletion strains for these genes. As of now, we have successfully obtained ΔMRC1-3 and MRC4-6 knockdown strains. Our preliminary observations indicate that ΔMRC1-3 strains are unable to undergo mating. However, we prefer not to include these results in the current manuscript, as we believe that more comprehensive studies are still needed.

      Reviewer #3:

      The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

      The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

      We express our gratitude to Reviewer #3 for proposing the occupation model and have incorporated this possibility into the manuscript. We believe it is possible that occupation may serve as the molecular mechanism through which self-recognition negatively regulates mating. If there is a physical interaction between mating-type proteins of the same type, but this interaction blocks the recognition machinery rather than initiating mating, it can be considered a form of self-recognition. This aligns with the observation that strains expressing MTA/B6 and MTB2 mate normally with WT cells of all mating types except for VI and II (line 203-204). A concise discussion on this topic is included in the manuscript (line 288-293, 659-661). We are actively investigating the downstream aspects of mating-type recognition, and we hope to provide further insights into this question soon.

      The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

      We are very grateful that Reviewer #3 provide this insightful view and relevant papers. In response to the feedback, we removed the sentences regarding “multiple mating types greatly complicate the process” in the revised version. Instead, we have introduced a discussion section comparing the mating systems of yeasts and Tetrahymena (line 279-286).

      The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013.

      In T. thermophila, the MIC (diploid) contains several mating-type gene pairs (mtGP, i.e., MTA and MTB) organized in a tandem array at the mat locus on each chromosome. In sexual reproduction, the new MAC of the progeny develops from the fertilized MIC through a series of genome editing events, and its ploidy increases to ~90 by endoreduplication. During this process, mtGP loss occurs, resulting in only one mtGP remaining on the MAC chromosome. The mating-type specificity of mtGPs on each chromosome within one nucleus becomes relatively pure through intranuclear coordination. After multiple assortments (possibly caused by MAC amitosis during cell fission), only mtGPs of one mating-type specificity exist in each cell, determining the cell’s mating type.

      It is pity that the exact mechanisms involved in this complicated process remain a black box. The loss of mating-type gene pairs is hypothesized to involve a series of homologous recombination events, but this has not been completely proven. Furthermore, there is no clear understanding of how intranuclear coordination and assortment are achieved. While we have made observations confirming these events, a breakthrough in understanding the molecular mechanism is yet to be achieved.

      We included more information in the revised version (line 672-683). Given the complexity of these unusual processes, we recommend an excellent review by Prof. Eduardo Orias (PMID: 28715961), which offers detailed explanations of the process and related concepts (line 685-686).

      Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

      Sorry that we didn’t realize the term “contact” is not precise enough. In Tetrahymena, physical contact is indeed necessary, but it can refer to temporary interactions. Unlike yeast, Tetrahymena cells exhibit rapid movement, swimming randomly in the medium. Occasionally, two cells may come into contact, but they quickly separate instead of sticking together. Even newly formed loose pairs often become separated. As a result, one cell can come into contact with numerous others and stimulate them. We have clarified this aspect in the revised version (line 50-51, 57).

      Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

      We have clarified the expression in the revised version (line 20, 38, 40, 45).

      Recommendations for the authors:

      Reviewer #1:

      I really enjoyed reading this manuscript and I think a few tweaks in the writing/data presentation could greatly improve the experience for the reader:

      (1) The information about your previous work in identifying downstream proteins CDK19, CYC9, and CIP1 (lines 170-173) could be directly presented in the introduction.

      We have moved this information in the introduction in the revised version (line 74-77).

      (2) For a reader who is not familiar with Tetrahymena, a few more details on how reporter and knock-out lines are generated would be beneficial.

      We introduced the knock-out method in Figure 2 – figure supplement 1B, HA-tag method in Figure 3A, and MTB2-eGFP construction method in Figure 4E. In addition, we introduced how co-stimulation markers observed in Materials and Methods (line 404-410)

      (3) Figures 5 and 6: clarify the types of pairing and treatments that were done directly in the figure (eg. adding additional labels). As of now, it is necessary to go through the text and legend to try and understand in detail what was done.

      Cell types and treatments were directly introduced in the revised figure (Figure 5 and 6).

      (4) The logical transition in lines 136-142 is hard to follow.

      We rewrote this paragraph in the revised version (lines 143-156). Additionally, we added a figure to illustrate the theoretical mating-type recognition model between WT cells and ΔCDK19, ΔCYC9 cells, MTAxc, MTBxc proteins, and ΔMTA, ΔMTB cells (Figure 2 – figure supplement 1D-G).

      (5) Lines 191-196: the fact that cells expressing multiple mating types can self goes against an active self-rejection system - if this is the case there should be self-rejection among all expressed mating types. Unless non-self recognition is an active process and self-recognition is simply the absence of non-self recognition. The authors briefly mention this in lines 263-265, but it would be interesting to expand and clarify this.

      We appreciate that Reviewer #1 notice the interesting selfing phenotype of the MTB2-eGFP (MTVI background) strain. We further discussed it in the revised manuscript (line 298-306).

      (6) The authors briefly mention the possibility of different mating types using different recognition mechanisms (lines 255-260), based on the big differences in the size of the mating-specific region of MT proteins. Following this and the weakness nr. 2, I think it would be pertinent to gather and present more information on the properties and structures of the mating-type specific regions of MT proteins. Simple in silico analysis of motifs, structure, etc. could help clarify the role of these regions. It seems more parsimonious that MT proteins would have variable mating type specific regions that account for the recognition of the different mating types, and conserved cytoplasmic functions that could trigger a single downstream signaling cascade. It would be interesting to know the authors' opinion on this.

      We are very grateful for this suggestion. Actually, we are currently working on determining the 3D structure of MTRC. The Alphafold2 prediction indicates that the MT-specific region is comprised of seven global β-sheets, resembling the structure of immunoglobulins (Ig). Our most recent cryo-EM results have revealed a ~15Å structure, aligning well with the prediction. However, the main challenge lies in the low expression levels, both in Tetrahymena and insect/mammal cells. We anticipate obtaining more detailed results soon. Therefore, we prefer to present the MT recognition model with robust experimental evidence in the future, and didn’t discuss too much on this aspect in the current manuscript.

      (7) Adding a figure including a proposed model, as well as expanding the discussion on the points presented as "weaknesses" would help clarify the ideas/hypothesis on how the mating recognition works. I think this would really elevate the paper and help highlight the results.

      We added a figure to introduce the model and the weaknesses in the revised version (Figure 7, line 656-665).

      (8) Line 202-203: It is far-fetched to infer subcellular localization based on the data presented here, couterstaining with other dyes and antibodies specific to certain cell components, as well as negative control images, are required.

      Thanks for the suggestion. We attempted to stain cell components using various dyes and antibodies. Unfortunately, we found that cell surface and cilia (especially oral cilia) is very easy to give a false positive signal. We think this issue seriously affects the credibility of the results. It may seem like splitting hairs, but we are trying to be precise.

      Meanwhile, we still believe the mating-type proteins localizes to cell surface because MTA-HA is identified in the isolated cell surface proteins.

      Regarding negative control, as shown in Fig. 4G, where a MTB2-eGFP cell is pairing with a WT cell, no GFP signal is observed in the WT cell.

      (9) Lines 131: clarify the sentence - expression of Con-A receptors requires both MTA and MTB (MTA to receive the signal).

      We modified the sentence in the revised version (line 139-140).

      Reviewer #2:

      Minor points.

      (1) Line 194-196. Why are these cells able to self?

      These cells able to self may because the MTRC contain heterotypic mating-type proteins (MTA6 and MTB2), which activate mating when they interact with another heterotypic MTRC (line 207-208).

      (2) Line 232. What do the authors mean by the term synergistic effect here? Definition and statistics?

      Sorry about the confusion. The synergistic effect refers to the effect of MTAxc and MTBxc become stronger when using together. We clarified it in the revised version (line 232).

      (3) For Figure 4 panel D, are there antibodies that are available as a control for cilia? If so, then blotting this membrane would show that cilia-associated proteins are in the cilia preparation, which is a standard control for sub-cellular fractionation.

      Thanks for the suggestion. Unfortunately, we didn’t find a suitable cilia-specific antibody yet. Instead, we employed MS analysis to confirm the presence of cilia proteins in this sample (line 195-196, Figure 4–Source data 1). We also observed the sample under the microscope, which directly revealed the presence of cilia (Figure 4C).

      (4) At least one reference cited in the text was not present in the reference list. The authors should go through the references cited to ensure that all have made it into the reference list.

      We have checked all the references.

      Some minor edits:

      (1) MTA and MTB are presented in both roman and italics (e.g. line 209) in the manuscript. Maybe all should be in italics? Or is this a distinction between the gene and the protein?

      The italics word (MTA) refers to gene, and non-italics word (MTA) refers to protein.

      (2) Line 251. Change "achieving" to "achieve".

      We have corrected this word (line 266).

      Reviewer #3:

      Line 101. It would help to explain this expectation earlier in this paragraph.

      We explained the expectation in the revised version (line 92-97, 104-106).

      Line 109. How is a co-receptor different from the MTRC complex?

      We have rewritten the relevant sentences to enhance clarity (line 116-119). The molecular function of the MTRC complex could involve acting as a co-receptor or recognizer (functioning as both ligand and receptor). Based on the results presented in this section, we propose that MTA and MTB may function as a complex, but the confirmation of this hypothesis (MTRC) is provided in a later section. Therefore, we did not use the term “MTRC” here. These sentences briefly discuss the molecular function of this complex and explain why MTRC does not appear to function as a co-receptor.

      Line 251: which "dual approach" is referred to?

      Dual approach is referred to both self and non-self recognition. We explained it in the revised version (line 265-266).

      Line 258: what "different mechanisms" do the authors have in mind? Why would a different mechanism be expected? The different sizes could have evolved for (coevolutionary?) selection on the same mechanism.

      Sorry about the confusion. We clarified it in the revised version (line 269-278).

      What we intended to express is that we are uncertain whether the mating-type recognition model we discovered in T. thermophila is applicable to all Tetrahymena species due to significant differences in the length of the mating-type-specific region. We believe it is important to highlight this distinction to avoid potential misinterpretations in future studies involving other Tetrahymena species. At the same time, we look forward to future research that may provide insights into this question.

      Fig 2 C&D. Is it correct that these figures show the strains only after 'preincubation'? This is not apparent from the caption of the text. Additionally, the order of the images is very confusing. Write in the figures (so not just in the caption) what the sub-script means.

      These panels are re-organized in the revised version (Fig. 2C&D). There are three kinds of pictures: “not incubated”, “WT pre-incubated by mutant” and “mutant pre-incubated by WT”.

      The methods used to generate Figure 5 are not clearly described. I understand that the obtained xc proteins were added to the cells, and then washed, after which a test was performed mixing WT-VI and WT-VII cells. Were both cells treated? Or only one of the strains? The explanation for the reused washing medium is not clear and the method is not indicated.

      Both cells are treated. More details are provided in the revised manuscript (line 230-231, 633-634, 637-639, Fig. 5). To prepare the starvation medium containing mating-essential factors, cells were starved in fresh starvation medium for ~16 hours. Subsequently, cells were removed by three rounds of centrifugation (1000 g, 3 min) (line 330-332).

      In general, the figures are difficult to understand without repeated inquiries in the captions. Give more information in the figures themselves.

      More information is introduced in the figure (Fig. 2C, Fig. 3B, Fig. 4A, B, D, Fig. 5 and Fig. 6).

  4. mail-attachment.googleusercontent.com mail-attachment.googleusercontent.com
    1. Similarly, it is a constitutive featureof the concept 'correct' that, if you judge that it is correct for you to disbelieveq and not correct for you to believe q, you are thereby committed to not believ?ing q

      Given the author's thesis, I was wondering if it is a reasonable task to determine/ define the correctness of a belief in such a sense, since many people hold beliefs to be deeply personal and private. Not all beliefs that a person has may necessarily be true, and this knowledge may be possessed by the person themselves as well, but that does not make the belied any less of one. A belief is not fact, and facts are not beliefs--normatively too, when we speak of beliefs, there is an aspect of the personal attached to it. We do not use "believes" as a placeholder for "knows", nor is it a moniker for fact--and i think that inherently suggests that the nature of the correctness of a belief is more subjective, and thus cannot really be said to be determined through the truth value of its propositions. A belief may seem irrational on the surface, but if it holds great value for the person who upholds said belief, it seems uncharitable to suspend judgement on the correctness of it--especially if it a belief that does not have to do with how things are in the world.

    1. Activity: Value statements in what goes viral# 12.7.1. Choose three scenarios# When content goes viral there may be many people with a stake in it’s going viral, such as: The person (or people) whose content or actions are going viral, who might want attention, or get financial gain, or might be embarrassed or might get criticism or harassment, etc. Different people involved might have different interests. Some may not have awareness of it happening at all (like a video of an infant). Different audiences might have interests such as curiosity or desire to bring justice to a situation or desire to get attention for themselves or their ideas based on engaging the viral content, or desire to troll or harass others. Social networking platforms might have interests such as increased attention to their platform or increased advertising, or increased or decreased reputation (in views of different audiences). List at least three different scenarios of content going viral and list out the interests of different groups and people in the content going viral. 12.7.2. Create value statements# Social media platforms have some ability to influence what goes viral and how (e.g., recommendation algorithms, what actions are available, what data is displayed, etc.), though they only have partial control, since human interaction and organization also play a large role. Still, regardless of whether we can force any particular outcome, we can still consider of what you think would be best for what content should go viral, how much, and in what ways. Create a set of value statements for when and how you ideally would want content to go viral. Try to come up with at least 10 value statements. We encourage you to consider different ethics frameworks as you try to come up with ideas.

      As we engage with viral content, whether as creators, participants, or observers, these value statements require us to reflect on the broader implications of online interactions. Behind every viral phenomenon lies a complex web of human stories, aspirations and responsibilities that deserve our thoughtful consideration.

    1. What does the individual badger‘hear’ as a result of the changing pressures on its tympanum that we choose to calla sound?

      Its interesting how he put the word hear in quotes because it shows that we have no way of knowing if badgers perceive sound the way that we do and process it in the way that we can. What we think of as hearing may not be the same process for animals. Even though we can scientifically know the hearing levels of a badger, we can't ever really know what hearing is like for them

    Annotators

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: #RC-2023-02281

      Corresponding author(s): Maurizio Molinari

      Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      • *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In this manuscript from Fasana et al., the authors present data that investigates potential compensatory degradation pathways for misfolded glycoproteins in the ER - postulating that the ER-to-lysosome associated degradation (ERLAD) pathway becomes employed in the absence of a path for substrates to reach the ER-associated degradation (ERAD) mechanism. Using the classic ERAD substrate alpha1-antitrypsin NHK variant (NHK), the authors first demonstrate that pharmacologically preventing access of NHK to ERAD either with KIF (early) or PS-341 (late) elevates the number of LAMP-1 positive endolysosomes also immunoreactive for NHK (via HA), similar to what is observed for the ATZ variant that forms polymers in the ER (Fig 2). The authors next use shRNAs that silence essential ERAD factors (EDEM1, OS-9) involved in glycan recognition to demonstrate comparable enrichment of NHK in endolysosomes through genetic disruption (Fig 3). Next, the authors employ FAM134B-deficient MEFs to demonstrate the requirement for this ER-phagy receptor when ERAD is unavailable (Fig 4). Reconstituting FAM134B-/- MEFs treated with KIF/PS-341 + Baf, with a full length FAM134B rescue plasmid restores endolysosomal accumulation of NHK while a FAM134B-∆LIR does not, providing supporting evidence for substrate rerouting to ERLAD. Finally, the authors use knockouts of Atg7 and Atg13 to demonstrate dependence on LC3 lipidation and independence from macro-ERphagy (Fig 6), that points towards a pathway that is like that used to remove ATZ polymers. From these data, the authors conclude that ERLAD is increasingly engaged for substrate degradation when ERAD is impaired.

      MAJOR COMMENTS 1. All assays rely on quantification of the NHK-HA substrates by microscopy. Would it be possible for the authors to also include biochemical analysis of NHK - potentially including data assessing its changing abundance and glycosylation state?

      To consider this, and other comments, the new submission includes biochemical data (pulse-chase analyses) on NHK (new panels A-D in Fig. 2) and on BACE457delta, an additional ERAD substrate (new Fig. 6). Please also refer to Comment 3.

      In Figure 3D, the knockdown of OS-9.1/2 is modest compared to that of EDEM1 (Fig 3A). Moreover, there is only data from single shRNAs presented. Could the authors please at least include another shRNA to confirm and demonstrate whether the targeting to ERLAD is accordingly scaled to loss of access to ERAD (based on the degree of OS-9 or EDEM1 remaining)?

      __The reviewer is right. The phenotype (i.e., lysosomal delivery of NHK, Figs. 3B, 3C) is quite modest upon EDEM1 silencing. However, one has to consider that in contrast to OS9 lectins, EDEM1 is an enzyme, and residual protein may partially facilitate NHK de-mannosylation and access to the ERAD pathways and therefore reduce the ERLAD contribution for NHK clearance in these cells. Moreover, cells also express EDEM2 and 3 that may partially compensate the loss of EDEM1. __

      While degradation is implied, it is not specifically demonstrated at any point in the manuscript. Perhaps the authors might include some demonstration of NHK stabilization in one of the figures via a translational shutoff or pulse-chase assay.


      __In the new submission, we show biochemical analyses (pulse-chase) that reveal the decay of radiolabeled NHK (Fig. 2A, lanes 1-3) and BACE457delta (Fig. 6A, lanes 1-3), the inhibition by PS341 (lanes 4, 5) and by KIF (lanes 8, 9), and the intervention of lysosomal enzymes when ERAD is inhibited (lanes 6, 7 and 10, 11). Moreover, we confirm that the protein delivered to the endolysosome is eventually degraded by performing a Bafilomycin washout experiment (new Fig. 2J-2O). __

      10-30% of NHK-HA positive endolysosomes are detected even with Baf alone (e.g. Fig 2E)? Does this mean that Baf impairs ERAD to some extent since or is it evidence for continuous ERLAD involvement when ERAD is intact? If so, how much is its contribution?

      Pulse-chase analyses (new Fig. 2D) and published data show that BafA1 or chloroquine do not inhibit clearance of the ERAD substrates NHK and BACE457delta (e.g., Liu et al 1999, Molinari et al 2002, references in the manuscript). A basal level of endolysosomal delivery between the 20 and 30% as quantified with LysoQuant is observed in all experiments (Figs. 2I, 2O, 3C, 3F, 4C, 4K, 5H, 6G, 6O), which have been performed in 3 different cell lines (3T3, HEK293, MEF). We measure similar basal levels also when ER-phagy is monitored on quantification of lysosomal delivery of endogenous ER marker proteins (e.g., CNX), possibly to be ascribed to constitutive ER phagy that controls physiologic ER turnover.


      An accounting of how much ERLAD is contributing to NHK degradation with or without ERAD impairment is not really present.. Effectively, how much degradation capacity is ERLAD making up? These would be interesting data to include if possible as they would speak to the "division of labour" for ER substrate degradation its potentially dynamic nature.

      The biochemical analyses show the contribution of ERLAD on NHK (new Figs 2B, 2C, grey zones) and BACE457delta (new Figs. 6B,C, grey zones) clearance, when ERAD is dysfunctional.

      MINOR COMMENTS 1. In Figure 4, an increase is observed for the rescue of FAM134B-/-MEFs with WT FAM134B that is 50% greater that of WT MEFs, suggesting that its availability might be rate limiting. Could the authors compare the relative levels of FAM134B for the WT and KO-rescue MEFs to address this possibility?

      __The referee is right in assuming that FAM134B, expressed at low levels in these cells, is limiting. We now show the levels of endogenous FAM134B and of recombinant FAM134B in WB (new Fig. 4A). __

      In Figures 1 and 6, the terms siOS9 and siEDEM1 are used but Figure 3 shows data from shRNAs and not siRNAs.

      We apologize for the mistake. We have corrected this in the new Figures 1 and 7.

      Samples from Figure 3 treated with Baf but this is not indicated in the figure or figure legend.


      We have corrected this, thank you.

      VCP/p97 inhibitors typically stabilize ERAD glycoprotein substrates better than proteasome inhibitors do. Is the same degree of endolysosomal targeting present ?


      __For the convenience of the reviewer (we did not put these data in the new manuscript). In our experiments, the p97 inhibitor DBeQ is less efficient in deviating NHK to the endolysosomal degradative compartments, if compared with KIF (see below). At higher doses, DBeQ also inhibits other AAA-ATPases (e.g., VPS4, which plays a role in certain types of autophagy). This, or other cross-reactivities of DBeQ could explain the moderate capacity to activate ERLAD pathways as a response of ERAD inhibition, if compared with the phenotypes observed when ERAD is inhibited with KIF or PS341. __

      Reviewer #1 (Significance (Required)):

      Deconvolution of the different pathways taken by misfolded proteins to escape the ER is of great interest not only to the ER community but also represents consequences to consider for those interested in therapeutics involving UPS inhibition. While concise, this manuscript does a good job of trying to demonstrate the principal of substrate rerouting and the prioritisation of degradation pathways. Overall, the manuscript is well written, the experiments presented are performed to a sufficient standard, the data are lean but of good quality, and the appropriate statistical analyses have mostly been included where necessary and are described. The Methods and Materials is brief but describes the experiments that have been performed. The manuscript is brief in its results and would obviously benefit from additional complementary assays that would strengthen and broaden the authors arguments for rerouting. But too their credit, the authors do not grossly overstate their findings and merely present the culmination of a set of experiments to answer a single question - what happens to a misfolded glycoprotein substrate when ERAD is impaired. This is a key question with broad implications.

      While their limited data clearly demonstrates an acquired dependence on ERLAD, one can't help but wonder how broadly these findings hold true, as only a single glycoprotein substrate example is used.

      We have now added a complete set of experiments (imaging + biochemical to monitor clearance of the model polypeptides by pulse-chase analyses) performed with a second ERAD substrate (BACE457delta, Fig. 6). These data fully recapitulate the results obtained with NHK.


      Moreover, it is not clear what percentage ERLAD contributes to overall NHK degradation (with or without ERAD) as the total NHK amount remaining is not assessed or measured.


      Pulse-chase analyses (new Fig. 2D) and published data (e.g., Liu et al 1999, Molinari et al 2002, references in the manuscript) show that BafA1 or chloroquine do not inhibit clearance of the ERAD substrates NHK and BACE457delta. The biochemical analyses now show the contribution of ERLAD on NHK (new Figs 2B, 2C, grey zones) and BACE457delta (new Figs. 6B,C, grey zones) clearance, when ERAD is dysfunctional.

      Nevertheless, the manuscript is an advancement of understanding of the fate of substrates unable to access ERAD and raises many future questions of interdependency between the ERAD and ERLAD pathways. The data just need a bit of shoring up.

      Expertise - ERAD, UPS, protein quality control

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The endoplasmic reticulum (ER) is a crucial site for protein synthesis and folding within the cell, and strict protein quality control is essential for maintaining ER homeostasis. In this context, ER-associated degradation (ERAD) and the unfolded protein response (UPR) play pivotal roles. Recent researches have highlighted the significance of ER-phagy in protein quality control. In this manuscript, the authors demonstrate the role of FAM134B in degrading misfolded proteins such as ATZ through the ER-phagy pathway when the ERAD pathway is obstructed. This work partially addresses a prominent issue in the field, unveiling the interconnections between different regulatory pathways in maintaining ER homeostasis.

      Major issues: 1: In a multitude of experiments, the authors employed Bafilomycin A1 (BafA1) to block the fusion between autophagosomes and lysosomes, attempting to demonstrate that the clearance of misfolded proteins mediated by FAM134B is independent of autolysosomes. However, in Figure 4, the lack of rescue of FAM134B knockout by overexpressing FAM134B△LIR suggests a dependence on the interaction between FAM134B and LC3. The conclusions drawn before and after appear contradictory.

      We apologize if our explanations were unclear. We have now modified the text and performed new experiments to clarify these issues.

      __The inhibitor of the V-ATPase BafA1 is used here to inhibit the activity of lysosomal hydrolases and to accumulate undegraded material in the LAMP1-endolysosomes (note that these endolysosomes also display RAB7 at their limiting membrane) (Fregno et al 2018, Forrester et al 2019, Fregno et al 2021, …). __

      __In Figs. 2A-2D, we now monitor the lack of NHK stabilization by cell exposure to BafA1 (Fig. 2D), which correlates with lack of accumulation of NHK in the LAMP1-positive compartment (e.g., Fig. 2F, 2J, and quantifications in 2I and 2O). The biochemical data also show that BafA1 stabilizes NHK in cells where ERAD has been inactivated with PS341 or KIF (Fig. 2A, lanes 6, 7, 10, 11 and grey zones in Figs. 2B and 2C), which correlates with accumulation of NHK in LAMP1-positive organelles (Figs. 2G, 2H, 2I, 2K, 2M, 2O). __

      __In Figs. 2J-2O, we have now added panels showing that NHK clearance from the LAMP1-positive endolysosome lumen is restored upon BafA1 washout. __

      Importantly, the involvement of the lipidation machinery, of the ER-phagy receptor FAM134B and of the LC3-binding function of FAM134B (the LIR), does not necessarily imply the involvement of autophagosomes in the process under investigation, as the comment by the referee seems to suggest. For example, both the clearance from the ER of ATZ polymers and of mutant forms of procollagen rely on the LC3 lipidation machinery and on the LC3-binding function of FAM134B, but ERLAD of ATZ polymers does not rely on autophagosomes intervention (new Fig. 1B, arrow 1 and Fregno et al 2018), whereas ERLAD of procollagen relies on intervention of autophagosomes (new Fig. 1B, arrow 2 and Forrester et al 2019).

      2: Some Western blot data are insufficient to substantiate the author's conclusions. For instance, in Figure 5D, the ATG7 KO line is inadequately supported

      The WB show____s the absence of ATG7 in the ATG7-KO cells (a well-established cell line generated in the lab of Masaaki Komatsu (____Komatsu M, et al. J Cell Biol 169: 425-434_) and used in many_ laboratories, including our lab in Fumagalli et al 2016, Fregno et al 2018, Fregno et al 2021, Loi et al 2019, Kucinska et al 2023). We agree with the reviewer that the anti-Atg7 shows cross-reactions. We have now added a WB showing the lack of LC3 lipidation in the Atg7-KO cells exposed to nutrient deprivation (new Fig. 5D).

      3: The author employed Lamp1 antibody for lysosomal staining in cells and observed a significant abundance of lysosomes in some experiments, as depicted in Figure 2C, 2D, 4I, etc. Is the phenomenon of lysosomes extensively filling the entire cell a common occurrence? Is it indicative of a normal physiological state?

      There may be variations depending on the cell type used for the experiments. In the new version of the manuscript, we now present imaging data for 3 cell lines (NIH 3T3 with stable expression of NHK and ATZ (Figs. 2E-2H), MEF (Figs. 2J-2N, 4, 5, 6) and HEK293 with transient expression of ERAD clients (Figs. 3).

      Minor issues: 1: Some immunofluorescence experimental data are unclear. Please request the authors to replace these with more distinct images, as seen in Figure 3B and 3E.


      We hope that the quality of the new images will be considered sufficient for publication.

      2: Some expressions appear to be questionable. For instance, the necessity of utilizing endolysosomes requires clarification.

      For the use of endolysosomes (lysosome would be incorrect in our opinion to indicate these LAMP1/RAB7-positive degradative organelles), we now refer to the papers by Bright et al ____Endolysosomes Are the Principal Intracellular Sites of Acid Hydrolase Activity_ Curr Biol 2016, and the original definition by Huotari and Helenius _Endosome maturation EMBO J 2011 (Introduction, page 2).

      3: Some writing lacks precision, such as referring to FAM134B as FAM134.

      __Corrected, thank you____ __ Reviewer #2 (Significance (Required)):

      o General assessment: o Advance: provide an meaningful evidence that how two degradative pathways are coordinated in maintaining ER homeostasis. o Audience: cell biologist o Reviewer's expertise: autophagy, vesicle trafficking, organelle biolgy Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In their study, Fasana and colleagues investigate protein quality control in the ER. Specifically, they test whether folding-incompetent proteins that are normally cleared by ER-associated degradation (ERAD) can also be targeted for degradation by direct vesicular transport from the ER to lysosomes in case ERAD is blocked. They show that blocking ERAD pharamacologically or genetically indeed leads to re-rerouting of an ERAD model substrate (the NHK variant of alpha-antitrypsin) to lysosomes and that this pathway requires the reticulon-like protein FAM134B, the ability of FAM134B to interact with the ubiquitin-like protein LC3 and the machinery for LC3 lipidation.

      The paper is, for the most part, easy to follow. There are, however, a few minor issues and I think the authors could do more to connect their work with similar studies in the literature. Accordingly, I have some general and specific suggestions to make the manuscript more accessible for the reader.

      General suggestions

      1. To avoid confusion, it would be helpful to more clearly distinguish between vesicular transport to endolysosomes and autophagy. Previous work by the authors has defined a trafficking pathway from the ER to endolysosomes that appears to rely on conventional vesicle-mediated transport (Fregno et al, EMBO J 2018). This pathway delivers material from the ER lumen to the lumen of endolysosomes, which are both topologically equivalent to the extracellular space. Hence, this pathway is distinct from autophagy, which is the transport of cytoplasmic components to endolysosomes and thus the transport of material from intracellular to extracellular space. This distinction is particularly important as both vesicular ER-to-lysosome transport and autophagy of the ER involve LC3 and FAM134B, which is typically referred to as an ER-phagy receptor. To make this less confusing, it may be helpful to explain that FAM134B appears to be a multifunctional molecule that can function as a receptor for macroautophagy but also in the vesicular transport pathway studied here. In addition, it would be helpful to point out that LC3 appears to also have roles unrelated to autophagosome formation.

      The reviewer is referring to the original definition of ERLAD to describe the mechanisms of clearance of ATZ polymers (Fregno et al 2018). The definition of ERLAD has now been expanded and is given, for example, in Klionsky DJ, et al (2021) Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy 17: 1-382 and is explained in detail in our recent review Rudinskiy M, Molinari M (2023) ER-to-lysosome-associated degradation in a nutshell: mammalian, yeast, and plant ER-phagy as induced by misfolded proteins. Febs Letters: 1928-1945.

      __Notably, the acronym ERAD for ER-associated degradation has originally been used to describe ____the proteasomal clearance from the ER of misfolded pro-alpha factor in a reconstituted yeast system in McCracken AA, Brodsky JL (1996) Assembly of ER-associated protein degradation in vitro: dependence on cytosol, calnexin, and ATP. The Journal of cell biology 132: 291-298. Only later on, the acronym has been used as an umbrella term that now covers all the pathways that control proteasomal clearance of misfolded proteins from the ER. A short historical excursus is presented in the new introduction to better explain these issues. __

      It is well established that LC3 and the LC3 lipidation machinery have functions that go beyond macroautophagy (which involves double membrane autophagosomes). Micro-autophagy (or micro-ER-phagy to remain on the topic of our paper) is an example of autophagic pathway relying on ER-phagy receptor that engage LC3, on the LC3 lipidation machinery, without involving autophagosomes. This is schematically represented in the new Fig. 1B.

      Several recent papers that appear relevant to the present study are not mentioned. In particular, Sun et al., Dev Cell 2023 (PMID: 37922908) appears worthy of discussion, as does Gonzalez et al., Nature 2023 (PMID: 37225996).

      Thank you. Both papers are not directly linked to our study addressing the intervention of ERLAD pathways when ERAD activity is impaired. In particular the work of Gonzales et al describes post-translational modification of ER-phagy receptors for their activation. The Sun et al paper is not really related to the topic covered in our manuscript, but we cite it as an alternative pathway that removes ATZ from the ER (page 8).

      Specific suggestions

      1. Abstract: The abstract begins with "About 40% of the eukaryotic cell's proteome is synthesized ... in the ER." Similar statements can be found in many papers and purportedly reflect common knowledge. However, it is unclear where the figure of 'about 40%' comes from. It would be proper to provide a reference and demonstrate that giving such a fairly precise estimate is supported by experimental data. Alternatively, the statement could be modified to avoid being precise than is justified.

      No reference is allowed in the abstract. We therefore modified the sentence as suggested by the reviewer.

      1. p2: "The ER is site of gene expression in nucleated cells and ... native proteins to be delivered at their site of activity ...". There is something missing at the beginning of this sentence. Also, it should be 'delivered to their site of activity', not 'delivered at'.

      Thank you

      1. p2: "... by mechanistically distinct ER-phagy pathways collectively defined as ER-to-lysosome-associated degradation ERLAD." This statement suggests that all pathways subsumed under the term ERLAD are ER-phagy pathways, which I believe is misleading (see comment above on the distinction between autophagy and vesicular transport pathway).

      See point 1.

      1. p2: "KIF selectively ...". Please spell out KIF and explain what kind of compound it is.

      Thank you, we changed to “_The alkaloid kifunensine (KIF) is a cell permeable selective inhibitor of the members of the glycosyl hydrolase 47 family of a____1,2-mannosidases_”____ __ 5. p3: "Notably, ERAD inhibition delays, rather than blocking degradation of ERAD clients ...". Please correct, for example: Notably, ERAD inhibition delays rather than blocks degradation of ERAD clients ...

      Thank you

      Figures 2 - 5: The number of quantified cells is given but it is not clear if experiments were done once or in biological replicates. Please indicate this in the figure legends.

      __N is now given for all panels in the corresponding figure legends.____ __ 7. p4: "To verify if ERAD inactivation ..." sounds odd. Less ambiguous would be 'To test whether' or 'To ask if'.

      Thank you

      1. p7, beginning of discussion: Please correct "delivered at" to 'delivered to'.

      Thank you

      Reviewer #3 (Significance (Required)):

      This is a concise and convincing manuscript with a clear message. The idea that proteins that cannot be processed by ERAD can be eliminated by other means, for instance by autophagy, is not new. Similarly, the FAM134B- and LC3-dependent pathway for ER-to-lysosome transport has been described by the authors before (Fregno et al, EMBO J 2018). Furthermore, the study exclusively relies on microscopy and does not attempt to tackle new mechanistic questions. Still, this study presents a definite functional advance in our understanding of the interplay of various ER quality control pathways.

      The findings presented here will be of interest mainly to molecular cell biologists working on protein quality control and organelle homeostasis. However, given the disease-relevance of misfolded proteins, and alpha-antitrypsin in particular, the impact of this study may eventually go beyond basic research and may also interest translational researchers.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      This is significant work, and you should certainly make the best case you can on the weaknesses discussed.

      We thank reviewer for this positive comment on the significance of our work. The referee indicates as weaknesses (i) that the force involving the bent or straight αI-helix is not readily apparent, (ii) the residue types were not varied in the helix mutations, and (iii) that the chemical shift perturbations are indirect observations.

      We think we have tried to address a large part of these questions by being very careful in our analysis and by the discussion in the manuscript. The following remarks may help to clarify this further:

      (i) The force emanating from the helix is e.g. visualized in the PC2 loadings in Figure 6E of the PCA carried on all observed SH3-SH2-KD resonances for all apo forms of the helix mutants. The SH2 residues identified by these loadings are in direct vicinity to the αI-helix. The respective PC2 scores correlate to 98% with the vmax of the catalytic reaction and to 94 % with the PC1 scores found for imatinib-induced opening. Importantly, the structure of the KD with the straight αI-helix indicates that mostly residues F516, Q517, S520, and I521 would clash with the SH2 domain in a closed core (Figure 6F). Thus, the expected clashes are in direct vicinity of the SH2 residues identified by the PC2 loadings as correlated to vmax and imatinib-induced opening. These data are completely orthogonal and show that most of the force is coming from residues F516, Q517, S520, and I521 in the αI-αI’ turn.

      (ii) We agree that we mainly used truncations of the αI-helix to study its involvement in activation. Point (i) makes it clear that a larger part of the αI-helix effects is caused by steric clashes of the residues in the αI-αI’ turn. In the latter region, we don’t expect strong amino acid type-specific effects besides excluded volume. Due to expression problems, we could not vary the helix length between residues 519 and 534. However, in this region we introduced the amino acid type mutation E528K. The latter showed a clear specific effect. Further amino acid type-specific effects may be possible in this region. However, we expect that the identified electrostatic E528-R479 interaction is one of the most important interactions in this region.

      (iii) We agree that chemical shift changes of individual resonances are often hard to interpret. However, we want to stress that our conclusions are all drawn from principal component analyses, which in all cases had as input well over 100 if not over 200 1H-15H resonances. The first two principal components of these analyses are robust averages over many residues, which reveal general correlated structural trends.

      We assume that chemical shift deposition etc will be pursued.

      We are currently depositing a larger collection of our Abl data to the “Biological Magnetic Resonance Data Bank (BMRB)”, which includes the NMR chemical shift data of the present work. A ‘collection’ will be a new feature of the BMRB, and we are in discussion with their staff. We will provide the accession codes as soon as possible (probably within the next month) to be included into the final version of the manuscript. We have amended the Data Availability Section accordingly.

      Reviewer #2 (Recommendations For The Authors):

      1) The overall discussion of the implications of the described allostery on kinase activation is provided through lenses of imatinib binding, which is used as an experimental trigger to disassemble the autoinhibited core. Can the authors elaborate in the Discussion on what event would play this role in the kinase catalytic cycle, communicating to helix I? Would dissociation of the myristate from the active site be hypothesized to be the first step in kinase activation? While I understand that certainty may be challenging to attain, it would be good to introduce some ideas into the Discussion.

      We appreciate the reviewer’s suggestions for the discussion and added the following text to the Conclusion section:

      "We have used here imatinib binding to the ATP-pocket as an experimental tool to disassemble the Abl regulatory core. Our previous analysis (Sonti et al., 2018) of the high-resolution Abl transition-state structure (Levinson et al., 2006) indicated that due to the extremely tight packing of the catalytic pocket, binding and release of the ATP and tyrosine peptide substrates is only possible if the P-loop and thereby the N-lobe move towards the SH3 domain by about 1–2 Å. This motion is of similar size and direction as the motion of the N-lobe observed in complexes with imatinib and other type II inhibitors (Sonti et al., 2018). From this we concluded that substrate binding opens the Abl core in a similar way as imatinib. The present NMR and activity data now clearly establish the essential role of the αI-helix both in the imatinib- and substrate-induced opening of the core, thereby further corroborating the similarity of both disassembly processes.

      Notably, the used regulatory core construct Abl83-534 lacks the myristoylated N-cap. Although we have previously demonstrated that the latter construct is predominantly assembled (Skora et al., 2013), the addition of the myristoyl moiety is expected to further stabilize the assembled conformation in a similar way as asciminib.

      Considering this mechanism, dissociation of myristoyl from the native Abl 1b core may be a first step during activation. However, it should be kept in mind that the Abl 1a isoform lacks the N-terminal myristoylation, and it is presently unclear whether other moieties bind to the myristoyl pocket of Abl 1a during cellular processes."

      2) Can the authors comment more on the differentiation between assembled conformations induced by type I inhibitor binding vs apo forms (or AMP-PNP and allosteric inhibitor) reported in Figure 3B? The differences are clearly identified by PCA but not sufficiently discussed.

      As indicated in the text, we think two structural effects are intermingled within PC2. Due to this admixture, it is hard to draw strong conclusions and we don’t want to expand on this too much. We have slightly modified the respective paragraph (p.7) as follows):

      "As the affected residues react differently to perturbations by type I inhibitors and truncation of the αI’-helix (Figure 3A, right), we attribute this behavior to two effects intermixed into the PC2 detection: (i) a minor rearrangement of the SH3/KD N-lobe interface caused by filling of the ATP pocket with type I inhibitors, which in contrast to the stronger N-lobe motion induced by type II inhibitors does not yet lead to core disassembly and (ii) a small rearrangement of the SH2/KD C-lobe interface caused by shortening and mutations of the αI-helix."

      3) The allosteric connection between active site inhibitor binding and the myristate/allosteric inhibitor binding has been observed in the past and noted before, in papers such as Zhang et al, Nature 2010. While the authors reference this paper, they do not acknowledge its specific findings or engage in a broader discussion of how their conclusions relate to this work.

      We have modified the beginning of the Conclusion section:

      "The allosteric connection between Abl ATP site and myristate site inhibitor binding has been noted before, albeit specific settings such as construct boundaries and the control of phosphorylation vary in published experiments. Positive and negative binding cooperativity of certain ATP-pocket and allosteric inhibitors has been observed in cellular assays and in vitro (Kim et al., 2023; Zhang et al., 2010). Furthermore, hydrogen exchange mass spectrometry has indicated changes around the unliganded ATP pocket upon binding of the allosteric inhibitor GNF-5 (Zhang et al., 2010). Here, we present a detailed high-resolution explanation of these allosteric effects via a mechanical connection between the kinase domain N- and C-lobes that is mediated by the regulatory SH2 and SH3 domains and involves the αI helix as a crucial element.

      Specifically, we have established a firm correlation between the kinase activity of the Abl regulatory core, the imatinib (type II inhibitor)-induced disassembly of the core, which is caused by a force FKD–N,SH3 between the KD N-lobe and the SH3 domain, and a force FαI,SH2 exerted by the αI-helix towards the SH2 domain. The FαI,SH2 force is mainly caused by a clash of the αI-αI’ loop with the SH2 domain. Both the FKD–N,SH3 and FαI,SH2 force act on the KD/SH2SH3 interface and may lead to the disassembly of the core, which is in a delicate equilibrium between assembled and disassembled forms. As disassembly is required for kinase activity, the modulation of both forces constitutes a very sensitive regulation mechanism. Allosteric inhibitors such as asciminib and also myristoyl, the natural allosteric pocket binder, pull the αI-αI’ loop away from the SH2 interface, and thereby reduce the FαI,SH2 force and activity. Notably, all observations described here were obtained under nonphosphorylated conditions, as phosphorylation will lead to additional strong activating effects."

      4) Figure 6 could do a better job of providing an illustration of steric clashes.

      We have revised Figure 6, panel F, in order to better illustrate the steric clashes, and modified the legend accordingly.

      5) There is a typo in line 5 from the top on page 11 (dash missing from "83534" superscript).

      Thank you. This was fixed.

    1. Reviewer #1 (Public Review):

      Summary:<br /> The authors utilize fluid-structure interaction analyses to simulation fluid flow within and around the Cambrian cnidarian Quadrapyrgites to reconstruct feeding/respiration dynamics. Based on vorticity and velocity flow patterns, the authors suggest that the polyp expansion and contraction ultimately develop vortices around the organism that are like what modern jellyfish employ for movement and feeding. Lastly, the authors suggest that this behavior is likely a prerequisite transitional form to swimming medusae.

      Strengths:<br /> While fluid-structure-interaction analyses are common in engineering, physics, and biomedical fields, they are underutilized in the biological and paleobiological sciences. Zhang et al. provide a strong approach to integrating active feeding dynamics into fluid flow simulations of ancient life. Based on their data, it is entirely likely the described vortices would have been produced by benthic cnidarians feeding/respiring under similar mechanisms. However, some of the broader conclusions require additional justification.

      Weaknesses:

      1. The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.<br /> 2. While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.<br /> 3. There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

      Despite these weaknesses the authors dynamic fluid simulations convincingly reconstruct the feeding/respiration dynamics of the Cambrian Quadrapyrgites, though the large claims of transitionary stages for this behavior are not adequately justified. Regardless, the approach the authors use will be informative for future studies attempting to simulate similar feeding and respiration dynamics.

      The following text is directly in response to the revised version of the manuscript.<br /> Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps

      Revision 1

      I think this manuscript has been improved by the authors, and I appreciate their time and effort in considering my earlier comments. While most of my line by line comments have been incorporated, I do feel that some of my larger points have been insufficiently addressed. Those are repeated with additional clarifications below.

      Original comment: The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

      Author response: Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

      Reviewer response: This response does not sufficiently address my earlier comment. While the authors are correct that individual Ediacaran affinities are an area of active research and that Olivooids can reasonably be considered cnidarians, this doesn't address the actual critique in my comment. Most (not all) Ediacaran soft-bodied fossils are considered to have been benthic, but pelagic cnidarian life is widely acknowledged to at least be present during later White Sea and Nama assemblages (and earlier depending on molecular clock interpretations). The authors have certainly provided support for the mechanics of this type of feeding being co-opted for eventual jet-propulsion swimming in Olivooids. They have not provided sufficient justifications within the manuscript for this to be broadened beyond this group.

      Original comment: There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

      Author response: Thanks for your suggestion. We revised the section "Perspectives for future work and improvements" (lines 396-404 in our revised version of MS). Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a "breakthrough" in simulation gregarious feeding. However, we modified the corresponding description in section "Perspectives for future work and improvements" to make it more appropriate.

      Reviewer response: I think I understand where the authors are trying to take this next step. If the authors were to follow up on this study with the proposed implementation of inhalant/exhalent velocities profiles (or more preferably velocity/pressure fields), then that study would be a breakthrough in simulating such gregarious feeding. Based on what has been done within the present study, I think the term "breakthrough" is instead overly emphatic.<br /> An additional note on this. The authors are correct that incorporating additional models could be used to simulation a population (as has been successfully done for several Ediacaran taxa despite computational limitations), but it's not the only way. The authors might explore using periodic boundary conditions on the external faces of the flow domain. This could require only a single Olivooid model to assess gregarious impacts - see the abundant literature of modeling flow through solar array fields.

      Original comment: L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

      Author response: Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

      Reviewer response: As the authors point out in the main text, these organisms are small (millimeters in scale) and certainly lived within the boundary layer range of the ocean. While the boundary layer is not the main point, it still needs to be accurately resolved as it should certainly affect the flow further towards the far field at this scale. I'm not suggesting the authors need to perfectly resolve the boundary layer or focus on using turbulence models more tailored to boundary layer flows (such as k-w), but the flow field still needs sufficient realism for a boundary bounded flow. The authors really should consider quantitatively assessing the number of hexahedral elements within their mesh refinement study.

    1. Reviewer #1 (Public Review):

      Questions and concerns:

      The abstract is hard to follow. The authors there refer to a previous experiment showing that "overnight fasting diminishes excessive avoidance and speeds up fear extinction by decreasing subjective relief during threat omissions" (L26). They go on to say that "relief tracks the reward prediction error signal that governs safety learning" (L28). This is puzzling. While getting less relief/safety from avoidance actions will surely diminish avoidance (because avoidance actions are less reinforced), getting less relief/safety from omissions of an unconditioned stimulus (US) in fear extinction should slow down (not speed up) fear extinction. In the same vein, why are "lower activations [in fMRI] in the ventromedial prefrontal cortex and nucleus accumbens in response to threat omissions signaled by a safe cue" (L34) associated with "increased effective avoidance and sped up fear extinction" (L33)? This clearly goes against the existing literature on reward prediction errors (PEs) in fear learning paradigms, where these PEs in the mesolimbic dopamine system drive extinction, that is, they are associated with better extinction (and should therefore also be associated with more avoidance). For instance, in the rodent, Luo et al., 2018 (DOI: 10.1038/s41467-018-04784-7) and Salinas-Hernandez et al., 2018 (DOI: https://doi.org/10.7554/eLife.388181 of 25RESEARCH ARTICLE) and 2023 (https://doi.org/10.1016/j.neuron.2023.08.025ll) have in various constellations optogenetically enhanced and diminished, respectively, the PE signal at the time of US omission in extinction in either VTA or nucleus accumbens and thereby sped up and slowed down, respectively, extinction learning. If the results of the current experiment contradict established knowledge, the reader must be clearly informed about this. By contrast, the abstract gives the impressions as if the current results were to be expected and in line with the literature ("since relief tracks the reward prediction error signal ..., we hypothesized ...").

      It would also help the reader if it was clarified that the finding of "increased effective avoidance" (L33) went counter to the hypothesis, e.g., by saying "Contrary to our hypothesis, we observed ...".

      Introduction:

      L51: The presentation of exposure therapy is a bit misleading and may create confusion. While it is probably correct that exposure works by "promoting safety learning", this is generally thought to be the case only for Pavlovian associations (CS-US), that is, for extinction (where safety learning creates the new association of CS and "no US"). It is, however, not generally considered to be the case for the instrumental action-outcome associations that underlie avoidance learning ("I do this or that, then I do not have to experience the feared object or situation"). Therapists try to prevent this type of learning from happening, exactly by promoting the confrontation with fear objects or situations in the absence of any avoidance action.

      Generally, I think the introduction suffers from the absence of a short explanation of what avoidance and extinction learning are, behaviorally, and what types of mechanisms are believed to drive them, and that the one (avoidance) is thought to contribute to the maintenance of fears whereas the other (extinction) reduces fear. The non-specialist reader is somehow left in the dark.

      In the same vein, on L63, presenting the results of their previous fasting study that serves as a discovery study for the present experiment, the authors make a distinction between "unnecessary avoidance during a signal of safety" and "effective avoidance during a signal of upcoming threat". It is really expecting too much from the reader that they will understand at this stage that a CS can become a signal of safety through extinction or that a CS not paired with a US during conditioning (a "CS-") is a safety signal and that it is not necessary to avoid such a signal, whereas a non-extinguished CS (signaling threat) may well be avoided. (At least, this is how I understood the distinction.)

      I was then really confused by the following statement (L65) that "the decrease in unnecessary avoidance was mediated by lower levels of relief ... during omissions of threat". If a CS is already extinguished (has no remaining or only little threat value, that is, is a safety stimulus), there is no longer threat omission when the US does not occur, and no relief. There should also be no relief to US omission after a CS-. More importantly even, if fasted participants reported lower levels of relief from threat omission, why did they not also show less effective avoidance (which is driven by the reinforcement provided by the relief that occurs when a successful avoidance action has prevented a US from occurring after or during the CS)?

      L69: Also the statement "a faster decline in relief ... ratings during ... extinction, suggesting faster decrease of threat expectancies" can only be understood by the reader if they already know what a PE is and by what rules PE-driven learning is governed (that is, essentially, if they know Rescorla-Wagner). I think the authors must explain, in order to allow a non-specialist reader to follow their text, that the PE (supposed to be indexed by the relief rating) reflects the discrepancy between the magnitude of an outcome expectation (e.g., here, expectation of the US) and the obtained outcome (here, US or not); that, therefore, a PE is generated when a subject expects a US (as a result of prior conditioning) but does not get it; that this leads to a proportional update (reduction) of the US expectation in the next trial; and that this in turn leads to a diminished PE when the US again does not occur. Notably, the reader must be made aware that the higher the PE, the higher the reduction and the faster the extinction (proportionality).

      The reader must also be made aware that the update is additionally determined by some multiplicatory "transmission" function or constant (e.g., learning rate in Rescorla-Wagner) that defines the size of the relationship between the magnitude of the PE and the magnitude of the update (reduction). Hence, in two individuals, even if the magnitude of the PE is identical, the magnitude of the update may differ because of individual differences in the learning rate (to take the Rescorla-Wagner implementation). The authors, however, seem to ignore the possibility that fasting changes the learning rate.

      Both the dynamics of the PE and the learning rate, of course, add complexity to the interpretation of the past and present data. But I think the authors cannot avoid this when they want to make sense of a treatment (fasting) that they believe affects safety learning. Speaking of "lower levels of relief" (L66) must be qualified by whether these lower ratings were observed initially (when the first PEs were registered at initial threat omissions, meaning that safety learning should be relatively slowed down by fasting) or on average or later during a safety learning experiment (which could indicate that learning under fasting was relatively quicker/more successful).

      Following upon this, in L74, the conclusion from observations of lower levels of relief during avoidance and faster decline in relief during extinction in the previous study that "overnight fasting decreased the reward value of safety (less relief pleasantness)" may be wrong if the faster decline and the resulting lower average levels of relief were the consequence of a higher initial PE in the fasting group, as would be expected from the Rescorla-Wagner rule. If the latter were the case, this would suggest that subjects actually registered more safety (a higher discrepancy to their threat expectation) in early trials. This could also explain why fasting sped up extinction in that study (see Abstract). It might also explain why "effective avoidance" (L64) was at least maintained (although it should actually also be sped up). It might make less parsimonious explanations ("fasting biases .. to focus on food at the expense of safety", L79), requiring the presence of a food source and a utility function of accepting a threat in the obtainment of food, unnecessary.

      All this, however, rests on whether I think I have understood what the authors want to say about their relief measurements and the way the operationalized avoidance in the previous study.

      More unclarities due to not giving full information: L91: "... extinction and avoidance learning. Accordingly, human fMRI studies have found ... activations in the ventral striatum and the VTA during threat omissions that might contribute to establishing a new safety CS-->noUS memory that reduces the initial fear response." However, in avoidance, it is an action that is reinforced by the US omission and hence an action-->noUS memory that is being formed. The CS keeps its threat value acquired during the preceding conditioning phase, and the reduction of fear during CS presentations is contingent upon the exertion of the avoidance action.

      L99: "Because overnight fasting decreased relief rating particularly during omissions after safety signals". Again, if a US is omitted after a safety signal (an extinguished CS or a CS-), there should be no PE and no relief. If there were still relief ratings at US omission after a safety signal, this would suggest extinction did not fully work or differential conditioning was not successful. In any case, it is not clear at all why relief was specifically decreased during omissions after safety signals and not (and much more so) during omissions after threat signals, where there is clearly a PE. If this was not the case, one has to wonder if something went wrong in the discovery study.

      The paragraph starting L103 and the associated figure 1 could be a bit more precise and give a bit more information in order to provide the reader a proper understanding of key experimental manipulations, in particular the ART task. Please define abbreviations "CS+unav", "CS+av". L108 ff.: One gets the impression there is only one CS+, whereas there are two. Say explicitly that one CS+ remains unavoidable during the Avoidance phase (CS+unav). What is the purpose of this stimulus? Do participants learn during the Avoidance phase that the CS+unav is unavoidable and the CS+av is avoidable or is this instructed? Do participants have to press the button within a certain time after CS+unav onset in order to avoid the US, or with a certain force? Is avoidance in case of successful button pressing deterministic or probabilistic? Say that the frame with the non-lit lamp is the ITI.

      Relief ratings (Figure 1b): The rating says "How pleasant was the relief that you felt?". That is, the experimenter insinuates that the participant will have felt relief and only wants to know how pleasant that relief was. The subjects has no chance to indicate there was no relief. This may be the reason why, in the discovery study, subjects indicated relief to safe stimuli, see above. Why did the authors not simply ask about the degree of relief felt, which would give a subject the chance to say there was no relief? I think this is a major flaw.

      L119: "We previously found that overnight fasting reduces avoidance and relief mostly to a safe CS-." If this is really the only thing that the authors found, then the fasting manipulation in their previous study failed to modulate avoidance of CS+s and the PE signaling at the time of US omissions after CS+s, that is, after actual threat stimuli. The procedure then clearly is not suited to study influences of fasting on avoidance learning. Whatever it does manipulate, it is not relief-based avoidance learning.

      L130: It makes absolutely no sense to hypothesize that a manipulation reducing relief in extinction learning will decrease activation in the neural PE circuitry at the time of US omission more after the CS- than after the CS+. Of course, the PE is highest when the US is not given after the CS+, and this is where any relief manipulation should have an effect. As said above, the authors must also specify their hypothesis with respect of timing (early or late extinction? See the animal papers cited above.)

    1. Author Response

      We would like to thank the editor and the reviewers for their constructive comments and the chance to revise the manuscript. The suggestions have allowed us to improve our manuscript. We have been able to fulfil all reviewer comments and added new statistical analyses to examine associations for subsets of data. Whilst suggested by a reviewer, we did not perform large-scale experiments to confirm the viability of low sporozoite densities at different time-points post salivary gland colonization. For these assays there are currently no satisfactory in vitro models for sporozoites harvested from single mosquitoes and setting up and validating such experiments could be a PhD project in itself. We do consider this suggestion very relevant but beyond the scope of the current work.

      Relevantly, during the time the manuscript was under review at eLife, we have been able to examine the multiplicity of infection in our field experiments. This was, as written in the original manuscript, a key reason to also perform experiments in the field where there is a greater diversity of parasite lines. We have successfully performed AMA-1 amplicon deep sequencing on infected mosquito salivary glands and infected skins. Although this does not change the key messages of the manuscript and is secondary to our main hypothesis, we do consider it a relevant addition since we were able to demonstrate that for some infected mosquitoes from the Burkina Faso study, multiple clones were expelled by mosquitoes during probing on a single piece of artificial skin. We have added a short paragraph to our revised manuscript and updated the acknowledgement section to include the supporting researcher who conducted those experiments.

      Reviewer #1 (Public Review):

      Summary: There is a long-believed dogma in the malaria field; a mosquito infected with a single oocyst is equally infectious to humans as another mosquito with many oocysts. This belief has been used for goal setting (and modelling) of malaria transmission-blocking interventions. While recent studies using rodent malaria suggest that the dogma may not be true, there was no such study with human P. falciparum parasites. In this study, the numbers of oocysts and sporozoite in the mosquitoes and the number of expelled sporozoites into artificial skin from the infected mosquito was quantified individually. There was a significant correlation between sporozoite burden in the mosquitoes and expelled sporozoites. In addition, this study showed that highly infected mosquitoes expelled sporozoites sooner.

      Strengths:

      • The study was conducted using two different parasite-mosquito combinations; one was lab-adapted parasites with Anopheles stephensi and the other was parasites, which were circulated in infected patients, with An. coluzzii. Both combinations showed statistically significant correlations between sporozoite burden in mosquitoes and the number of expelled sporozoites.

      • Usually, this type of study has been done in group bases (e.g., count oocysts and sporozoites at different time points using different mosquitoes from the same group). However, this study determined the numbers in individual bases after multiple optimization and validation of the approach. This individual approach significantly increases the power of correlation analysis.

      Weaknesses:

      • In a natural setting, most mosquitoes have less than 5 oocysts. Thus, the conclusion is more convincing if the authors perform additional analysis for the key correlations (Fig 3C and 4D) excluding mosquitoes with very high total sporozoite load (e.g., more than 5-oocyst equivalent load).

      In the revised manuscript, we have also performed our analysis including only the subset of mosquitoes with low oocyst burden. In our Burkina Faso experiments, where we could not control oocyst density, 48% (15/31) of skins were from mosquitoes with <5 oocyst sheets. Whilst low oocyst densities were thus not very uncommon, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities.

      • As written as the second limitation of the study, this study did not investigate whether all expelled sporozoites were equally infectious. For example, Day 9 expelled sporozoites may be less infectious than Day 11 sporozoites, or expelled sporozoites from high-burden mosquitoes may be less infectious because they experience low nutrient conditions in a mosquito. Ideally, it is nice to test the infectivity by ex vivo assays, such as hepatocyte invasion assay, and gliding assay at least for salivary sporozoites. But are there any preceding studies where the infectivity of sporozoites from different conditions was evaluated? Citing such studies would strengthen the argument.

      We appreciate this thought and can see the value of these experiments. We are not aware of any studies that examined sporozoite viability in relation to the day of salivary gland colonization or sporozoite density.

      One previous study assessed the NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (https://doi.org/10.1111/cmi.12745), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

      • Since correlation analyses are the main points of this paper, it is important to show 95% CI of Spearman rank coefficient (not only p-value). By doing so, readers will understand the strengths/weaknesses of the correlations. The p-value only shows whether the observed correlation is significantly different from no correlation or not. In other words, if there are many data points, the p-value could be very small even if the correlation is weak.

      We appreciate this comment and agree that this is indeed insightful. We have added the 95% confidence intervals to all figure legends and main text. We also provide them below.

      Fig 3b: 95% CI: 0.74, 0.85

      Fig 3c: 95% CI: 0.17, 0.50

      Fig 4c: 95% CI: 0.80, 0.95

      Fig 4d: 95% CI: 0.52, 0.82

      Supp Fig 5a: 95% CI: 0.74, 0.85

      Supp Fig 5b: 95% CI: 0.73, 0.93

      Supp Fig 6: 95% CI: 0.11, 0.48

      Supp Fig 7: 95% CI: -0.12, 0.16

      Reviewer #2 (Public Review):

      Summary: The malaria parasite Plasmodium develops into oocysts and sporozoites inside Anopheles mosquitoes, in a process called sporogony. Sporozoites invade the insect salivary glands in order to be transmitted during a blood meal. An important question regarding malaria transmission is whether all mosquitoes harbouring Plasmodium parasites are equally infectious. In this paper, the authors investigated the progression of P. falciparum sporozoite development in Anopheles mosquitoes, using a sensitive qPCR method to quantify sporozoites and an artificial skin system to probe for parasite expelling. They assessed the association between oocyst burden, salivary gland infection intensity, and sporozoites expelled.

      The data show that higher sporozoite loads are associated with earlier colonization of salivary glands and a higher prevalence of sporozoite-positive salivary glands and that higher salivary gland sporozoite burdens are associated with higher numbers of expelled sporozoites. Intriguingly, there is no clear association between salivary gland burdens and the prevalence of expelling, suggesting that most infections reach a sufficient threshold to allow parasite expelling during a mosquito bite. This important observation suggests that low-density gametocyte carriers, although less likely to infect mosquitoes, could nevertheless contribute to malaria transmission.

      Strengths: The paper is well written and the work is well conducted. The authors used two experimental models, one using cultured P. falciparum gametocytes and An. stephensi mosquitoes, and the other one using natural gametocyte infections in a field setup with An. coluzzii mosquitoes. Both studies gave similar results, reinforcing the validity of the observations. Parasite quantification relies on a robust and sensitive qPCR method, and parasite expelling was assessed using an innovative experimental setup based on artificial skin.

      Weaknesses: There is no clear association between the prevalence of sporozoite expelling and the parasite burden. However, high total sporozoite burdens are associated with earlier and more efficient colonization of the salivary glands, and higher salivary gland burdens are associated with higher numbers of expelled sporozoites. While these observations suggest that highly infected mosquitoes could transmit/expel parasites earlier, this is not directly addressed in the study. In addition, whether all expelled sporozoites are equally infectious is unknown. The central question, i.e. whether all infected mosquitoes are equally infectious, therefore remains open.

      We agree that the manuscript provides important steps forward in our understanding of what makes an infectious mosquito but does not conclusively demonstrate that highly infected mosquitoes are more likely to initiate a secondary infection. We consider this to be beyond the scope of the current work although the current work lays the foundation for these important future studies. For human Plasmodium infections the most satisfactory answer on the infectiousness of low versus high infected mosquitoes comes from controlled human infection models. In response to reviewer comments, we have extended our Discussion section to highlight this importance. To accommodate the (very fair) reviewer comments, we have avoided any phrasings that suggest that our findings demonstrate differences in transmission.

      Reviewer #3 (Public Review):

      Summary: This study uses a state-of-the-art artificial skin assay to determine the quantity of P. falciparum sporozoites expelled during feeding using mosquito infection (by standardised membrane feeding assay SMFA) using both cultured gametocytes and natural infection. Sporozoite densities in salivary glands and expelled into the skin are quantified using a well-validated molecular assay. These studies show clear positive correlations between mosquito infection levels (as determined by oocyst numbers), sporozoite numbers in salivary glands, and sporozoites expelled during feeding. This indicates potentially significant heterogeneity in infectiousness between mosquitoes with different infection loads and thus challenges the often-made assumption that all infected mosquitoes are equally infectious.

      Strengths: Very rigorously designed studies using very well validated, state-of-the-art methods for studying malaria infections in the mosquito and quantifying load of expelled sporozoites. This resulted in very high-quality data that was well-analyzed and presented. Both sources of gametocytes (cultures vs. natural infection) show consistent results further strengthening the quality of the results obtained.

      Weaknesses: As is generally the case when using SMFAs, the mosquito infections levels are often relatively high compared to wild-caught mosquitoes (e.g. Bombard et al 2020 IJP: median 3-4 ), and the strength of the observed correlations between oocyst sheet and salivary gland sporozoite load even more so between salivary gland sporozoite load and expelled sporozoite number may be dominated by results from mosquitoes with infection levels rarely observed in wild-caught mosquitoes. This could result in an overestimation of the importance of these well-observed positive relationships under natural transmission conditions. The results obtained from these excellently designed and executed studies very well supported their conclusion - with a slight caveat regarding their application to natural transmission scenarios

      For efficiency and financial reasons, we have worked with an approach to enhance mosquito infection rates. If we had worked with gametocytes at physiological concentrations and a small number of donors, we probably have had considerably lower mosquito infection rates. Whilst this would indeed result in lower infection burdens in the sparse infected mosquitoes, addressing the reviewer concern, it would have made the experiments highly inefficient and expensive. The skin mimic was initially provided free of charge when the matrix was close to the expiry date but for the experiments in Burkina Faso we had to purchase the product at market value. Whilst we consider the biological question sufficiently important to justify this investment – and think our findings prove us right – it remained important to avoid using skins for uninfected mosquitoes. Since oocyst prevalence and density are strongly correlated (doi: 10.1016/j.ijpara.2012.09.002; doi: 10.7554/eLife.34463), a low oocyst density in natural infections typically coincides with a high proportion of negative mosquitoes.

      Of note, our approach did result in the inclusion of 15 skins from infected mosquitoes with 1-4 oocysts. This number may be modest but we did include observations from this low oocyst range which is, we agree, highly important for better understanding malaria epidemiology.

      This work very convincingly highlights the potential for significant heterogeneity in the infectiousness between individual P. falciparum-infected mosquitoes. Such heterogeneity needs to be further investigated and if again confirmed taken into account both when modelling malaria transmission and when evaluating the importance of low-density infections in sustaining malaria transmission.

      Reviewer #4 (Public Review):

      Summary: The study compares the number of sporozoites expelled by mosquitoes with different Plasmodium infection burden. To my knowledge this is the first report comparing the number of expelled P. falciparum sporozoites and their relation to oocyst burden (intact and ruptured) and residual sporozoites in salivary glands. The study provides important evidence on malaria transmission biology although conclusions cannot be drawn on direct impact on transmission.

      Strengths: Although there is some evidence from malaria challenge studies that the burden of sporozoites injected into a host is directly correlated with the likelihood of infection, this has been done using experimental infection models which administer sporozoites intravenously. It is unclear whether the same correlation occurs with natural infections and what the actual threshold for infection may be. Host immunity and other host related factors also play a critical role in transmission and need to be taken into consideration; these have not been mentioned by the authors. This is of particular importance as host immunity is decreasing with reduction in transmission intensity.

      Weaknesses: The natural infections reported in the study were not natural as the authors described. Gametocyte enrichment was done to attain high oocyst infection numbers. Studying natural infections would have been better without the enrichment step. The infected mosquitoes have much larger infection burden than what occurs in the wild.

      Nevertheless, the findings support the same results as in the experiments conducted in the Netherlands and therefore are of interest. I suggest the authors change the wording. Rather than calling these "natural" infections, they could be called, for example, "experimental infections with wild parasite strains".

      We have addressed these concerns and, in the process, also changed our manuscript title. The following sentences have been changed:

      “It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and natural infections in Burkina Faso”.

      Now reads: “It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and experimental infections with naturally circulating parasite strains in Burkina Faso”. 226-228 “Experimental infections with naturally circulating parasite strains show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

      Has now replaced the original phrasing: “Natural infected mosquitoes by gametocyte carriers in Burkina Faso show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

      I do not believe the study results generate sufficient evidence to conclude that lower infection burden in mosquitoes is likely to result in changes to transmission potential in the field. In study limitations section, the authors say "In addition, our quantification of sporozoite inoculum size is informative for comparisons between groups of high and low-infected mosquitoes but does not provide conclusive evidence on the likelihood of achieving secondary infections. Given striking differences in sporozoite burden between different Plasmodium species - low sporozoite densities appear considerably more common in mosquitoes infected with P. yoelii and P. berghei the association between sporozoite inoculum and the likelihood of achieving secondary infections may be best examined in controlled human infection studies. However, in the abstract conclusion the authors state "Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites and may need to be considered in estimations of transmission potential." Kindly consider ending the sentence at "expelled sporozoites." Future studies on CHMI can be recommended as a conclusion if authors feel fit.

      We agree that we need to be very cautious with conclusions on the impact of our findings for the infectious reservoir. We have rephrased parts of our abstract and have updated the Discussion section following the reviewer suggestions. We agree with the reviewer that CHMI studies are recommended and have expanded the Discussion section to make this clearer. The sentence in the abstract now ends as:

      "Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites. Future work is required to determine the direct implications of these findings for transmission potential."

      Reviewer #1 (Recommendations For The Authors):

      • Prevalence data shown in Fig 2A and Table S1 are different. For example, >50K at Day 11, Fig 2A shows ~85% prevalence, but Table S1 says 100%. If the prevalence in Table S1 shows a proportion of observations with positive expelled sporozoites (instead of a proportion of positive mosquitoes shown in Fig 2A), then the prevalence for <1K at Day 11 cannot be 6.7% (either 0 or 20% as there were a total of 5 observations). So in either case, it is not clear why the numbers shown in Fig 2A and Table S1 are different.

      Figure 2A and Table S2 are estimated prevalence and odds ratios from an additive logistic regression model (i.e. excluding the interaction between day and sporozoite categories). Table S1 includes this interaction when estimating prevalence and odds ratios and as we can see some categories in the interaction were extremely small resulting in blown up confidence intervals especially in day 11. So Table S1 and Fig 2A are the results from two different models. Whilst our results are thus correct, we can understand the confusion and have added a sentence to explain the model used in the figure/table legends.

      Figure. 2 Extrinsic Incubation Period in high versus low infected mosquitoes. A. Total sporozoites (SPZ) per mosquito in body plus salivary glands (x-axis) were binned by infection load <1k; 1k-10k; 10k-50k; >50k and plotted against the proportion of mosquitoes (%) that were sporozoite positive (y-axis) as estimated from an additive logistic regression model with factors day and SPZ categories. Supplementary Table S1. The extrinsic incubation period of P. falciparum in An. stephensi estimated by quantification of sporozoites on day 9, 10, 11 by qPCR. Based on infection intensity mosquitoes were binned into four categories (<1k, 1k-10k, 10k-50k, >50) that was assessed by combining sporozoite densities in the mosquito body and salivary gland. Prevalences and odds ratios were estimated from a logistic regression model with factors day, SPZ category and their interaction.

      There are 3 typos in the paper. Please fix them.

      Line 464; ...were counted using a using an incident....

      Line 473; Supplementary Figure 7 should be Fig S8.

      Line 508: ...between days 9 and 10 using a (t=-2.0467)....

      We appreciate the rigour in reviewing our text and have corrected all typos.

      Reviewer #2 (Recommendations For The Authors):

      High infection burdens may result in earlier expelling capacity in mosquitoes, which would reflect more accurately the EIP. The fact that earlier colonization of SG and correlation between SG burden and numbers expelled suggest it could be the case, but it would be interesting to directly measure the prevalence of expelling over time to directly assess the effect of the sporozoite burden (not just at day 15 but before). This could reveal how the parasite burden in mosquitoes is a determinant of transmission.

      We appreciate this suggestion and will consider this for future experiments. It adds another variable that is highly relevant but will also complicate comparisons where sporozoite expelling is related to both time since infectious blood meal and salivary gland sporozoite density (that is also dependent on time since infectious bloodmeal). Moreover, we then consider it important to measure this over the entire duration of sporozoite expelling, including late time-points post infectious bloodmeal. This may form part of a follow-up study.

      Another question is whether all sporozoites (among expelled parasites) are equally infective, i.e. susceptible to induce secondary infection. If not, this could reconcile the data of this study and previous results in the rodent model where high burdens were associated with an increased probability to transmit.

      As also indicated above, we are aware of a single study that assessed NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (ref), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

      The authors evaluated oocyst rupture at day 18, i.e. 3 days after feeding experiments (performed at day 15). Did they check in control experiments that the prevalence of rupture oocysts does not vary between day 15 and day 18?

      We did not do this and consider it very unlikely that there is a noticeable increase in the number of ruptured oocysts between days 15 and 18. We observe that salivary gland invasion plateaus around day 12 and the provision of a second bloodmeal that is known to accelerate oocyst maturation and rupture (doi: 10.1371/journal.ppat.1009131) makes it even less likely that a relevant fraction of oocysts ruptures very late. Perhaps most compellingly, the time of oocyst rupture will depend on nutrient availability and rupture could thus occur later for oocysts from a heavily infected gut compared to oocysts from mosquitoes with a low infection burden. We observe a very strong association between salivary gland sporozoite density (day 15) and oocyst density (assessed at day 18) without any evidence for change in the number of sporozoites per oocyst for different oocyst densities. In our revised manuscript we have also assessed correlations for different ranges of oocyst intensities and see highly consistent correlation coefficients and find no evidence for a change in ‘slope’. If oocyst rupture would regularly happen between days 15 and 18 and this late rupture would be more common in heavily infected mosquitoes, we would expect this to affect the associations presented in figures 3B and 4C This is not the case.

      The authors report higher sporozoite numbers per oocyst and a higher proportion of SG invasion as compared to previous studies (30-50% rather than 20%). How do they explain these differences? Is it due to the detection method and/or second blood meal? Or parasite species?

      We were also intrigued by these findings in light of existing literature. To address potential discrepancies, it is indeed possible that the 2nd bloodmeal made a difference. In addition, NF54 is known to be a highly efficient parasite in terms of gametocyte formation and transmission. And there are marked differences in these performances between NF54 isolates and definitely between NF54 and its clone 3D7 that is regularly used. We also used a molecular assay to detect and quantify sporozoites but consider it less likely that this is a major factor in terms of explaining SG invasion since sporozoite densities were typically within the range that would be detected by microscopy. We can only hypothesize that the 2nd bloodmeal may have contributed to these findings and acknowledge this in the revised Discussion section.

      The median numbers of expelled sporozoites seem to be higher in the natural gametocyte infection experiments as compared to the cultures. Is it due to the mosquito species (An. coluzzii versus An. stephensi?).

      The added value of our field experiments, a more relevant mosquito species and more relevant parasite isolates, is also a weakness in terms of understanding possible differences between in vitro experiments and field experiments with naturally circulating parasite strains. We only conclude that our in vitro experiments do not over-estimate sporozoite expelling by using a highly receptive mosquito source and artificially high gametocyte densities. We have clarified this in the revised Discussion.

      39% of sporozoite-positive mosquitoes failed to expel, irrespective of infection densities. Could the authors discuss possible explanations for this observation?

      In paragraph 304-307 we now write that:” This finding broadly aligns with an earlier study of Medica and Sinnis that reported that 22% of P. yoelii infected mosquitoes failed to expel sporozoites. For highly infected mosquitoes, this inefficient expelling has been related to a decrease of apyrase in the mosquito saliva”.

      In Figure 3, it would be interesting to zoom in the 0-1k window, below the apparent threshold for successful expelling.

      We have generated correlation estimates for different ranges of oocyst and sporozoite densities and added these in Supplementary Table 5. We agree that this helps the reader to appreciate the contribution of different ranges of parasite burden to the observed associations.

      In Fig S8. Did they observe intact oocysts with fixed samples? These could be shown as well in the figure.

      We have incorporated this comment. An intact oocyst from fixed samples was now added to Fig S10.

      Minor points

      -line 119: LOD and LOQ could be defined here.

      We agree that this should have been defined. We changed line 119 to explain LOD and LOQ to: …“the limit of detection (LOD) and limit of quantification (LOQ)”….

      • line 126: the title does not reflect the content of this paragraph.

      We have changed the title: “Immunolabeling allows quantification of ruptured oocysts ”into: A comparative analysis of oocyst densities using mercurochrome staining and anti-CSP immunostaining.

      -line 269: infectivity is not appropriate. The data show colonization of SG.

      Line 269: infectivity has been changed with colonization of salivary glands.

      There seems to be a problem with Fig S6. The graph seems to be the same as Fig 3C. Please check whether the graph and legends are correct.

      Supplementary Figure 6 shows the sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites while Fig 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1qPCR without any threshold density. We have explained this in more detail in the revised supplemental figure where we now state

      “Of note, this figure differs from Figure 3C in the main text in the following manner. This figure presents sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites to conclude sporozoite positivity while Figure 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1 qPCR without any threshold density and thus includes all observations with a qPCR signal”

      Reviewer #3 (Recommendations For The Authors):

      Congratulations to the authors for the really excellently designed and rigorously conducted studies.

      My main concern is in regards to the relatively high oocyst numbers in their experimental mosquitoes (from both sources of gametocytes) compared to what has been reported from wild-caught mosquitoes in previous studies in Burkina Faso.

      We have addressed this concern above. For completeness, we include the main points here again. We enriched gametocytes for efficiency reasons, experiments on gametocytes at physiological concentrations would have resulted in a lower oocyst density (and thus more ‘natural’ although a minority of individuals achieves very high oocyst densities in all studies that included a broad range of oocyst densities (e.g. doi: 10.1016/j.exppara.2014.12.010; doi: 10.1016/S1473-3099(18)30044-6). Of note, we did include 15 skins from low oocyst densities (1-4 oocysts). Whilst low oocyst densities were thus not very uncommon in our sample set, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities. In the revised manuscript we have also performed our analysis including only the subset of mosquitoes with low oocyst burden.

      The best way to address this would be to do comparable artificial skin-feeding experiments on such wild-caught mosquitoes, but I appreciate that this is very difficult to do.

      This would indeed by difficult to do. Mostly because infection status can only be examined post-hoc and it is likely that >95% of mosquitoes are sporozoite negative at the moment experiments are conducted (in many settings this will even be >99%). Importantly, also in wild-caught mosquitoes very high oocyst burdens are observed in a small but relevant subset of mosquitoes (doi: 10.1016/j.ijpara.2020.05.012).

      Instead, I would suggest the authors conduct addition analysis of their data using different cut-offs for maximum oocyst numbers (e.g. <5, <10, <20) to determine if these correlations hold across the entire range of observed oocyst sheets and salivary gland sporozoite load.

      We have provided these calculations for the proposed range of oocyst numbers. In addition, we also provided them for a range of sporozoite densities. These findings are now provided in

      Entire range of observed oocyst sheets and salivary gland sporozoite load. A minor point on the regression lines in Figures 3 & 4: both variables in these plots have inherent variation (measurement & natural), but regression techniques such as reduced major exit regression (MAR) that allow error in both x and y variables may be preferable to a standard lines regression. Also, as it is implausible that mosquitoes with zero sporozoite in salivary glands expel several hundred sporozoites at feeding, the regression should probably also be constrained to pass through the 0,0 point.

      Since the main priority of the analyses is the correlation, and not the fit of the regression line – which is only for indication, and also because of the availability of software, we did not change the type of regression. We have however added a disclaimer to the legend, and we have also forced the intercept to 0 – which does indeed better reflect the biological association. Additionally we added 95% confidence intervals to all Spearman’s correlation coefficients in the legends.

    1. Author Response

      Responses to public reviews

      Reviewer 1

      We thank the reviewer for the valuable and constructive comments and are pleased that the re-viewer finds our study timely and our behavioral results clear.

      1) The RSA basically asks on the lowest level, whether neural activation patterns (as measured by EEG) are more similar between linked events compared to non-linked events. At least this is the first question that should be asked. However, on page 11 the authors state: "We ex-amined insight-induced effects on neural representations for linked events [...]". Hence, the critical analysis reported in the manuscript fully ignores the non-linked events and their neu-ral activation patterns. However, the non-linked events are a critical control. If the reported effects do not differ between linked and non-linked events, there is no way to claim that the effects are due to experimental manipulation - neither imagination nor observation. Hence, instead of immediately reporting on group differences (sham vs. control) in a two-way in-teraction (pre vs. post X imagination vs. observation), the authors should check (and re-port) first, whether the critical experimental manipulation had any effect on the similarity of neural activation patterns in the first place.

      We completely agree that the non-link items are a critical control. Therefore, we had reported not only the results for linked but also for non-linked events on page 15, lines 336-350. We clarified this important point now on page 12 lines 283-286:

      “Subsequently, we examined insight-induced effects on neural representations for linked (vs. non-linked) events by comparing the change from pre- to post-insight (post-pre) and the difference between imagination and observation (imagination - observation) between cTBS and sham groups using an independent cluster-based permutation t-test.”

      Moreover, to directly compare linked and non-linked events we performed a four-way in-teraction including link vs. non-link. This analysis yielded a significant four-way interaction, showing that the interaction of time (pre vs. post), mode of insight (imagination vs. obser-vation) and cTBS differed for linked vs. non-linked items. We then report the follow-up analyses, separately for linked and non-linked events. Please see pages 12-13, lines 287-294:

      “First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

      2) Overall, the focus on the targeted three-way interaction is poorly motivated. Also, a func-tional interpretation is largely missing.

      In order to better explain our motivation for the three-way interaction, we em-phasized in the introduction the importance of disentangling potential differences due to the mode of insight, given the known role of the angular gyrus in imagination on pages 4-5, lines 107-115:

      “Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked event.”

      As discussed on page 21 (starting from line 478; see also the intro on page 4), we expected that the angular gyrus would be particularly implicated in imagination-based insight, given its known role in imagination (e.g.: Thakral et al., 2017). Moreover, given the angular gyrus’s strong connectivity with other regions, the results observed may not be driven by this re-gion alone but also by interconnected regions, such as the hippocampus. We clarified these important points at the very end of the discussion on pages 23-24, lines 543-560:

      “Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014).”

      3) "Interestingly, we observed a different pattern of insight-related representational pattern changes for non-linked events." It is not sufficient to demonstrate that a given effect is pre-sent in one condition (linked events) but not the other (non-linked events). To claim that there are actually different patterns, the authors would need to compare the critical condi-tions directly (Nieuwenhuis et al., 2011).

      We completely agree and now compared the two conditions directly. Specifical-ly, we now report the significant four-way interaction, including the factor link vs. non-link, before delving into separate analyses for linked and non-linked events on pages 12-13, lines 287-294:

      “First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

      4) "This analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B)." (p. 11). The authors report results with specificity for certain topographical locations. However, this is in stark contrast to the fact that the authors derived time X time RSA maps.

      We did derive time × time similarity maps for each electrode within each partic-ipant, which allowed us to find a cluster consisting of specific electrodes. We apologize for not making this aspect clear enough and have, therefore, modified the respective part of our methods section on page 38, lines 951-952:

      “In total, this analysis produced eight Representational Dissimilarity Matrices (RDMs) for each electrode and each participant.”

      5) "These theta power values were then combined to create representational feature vectors, which consisted of the power values for four frequencies (4-7 Hz) × 41 time points (0-2 sec-onds) × 64 electrodes. We then calculated Pearson's correlations to compare the power pat-terns across theta frequency between the time points of linked events (A with B), as well as between the time points of non-linked events (A with X) for the pre- and the post-phase separately, separately for stories linked via imagination and via observation. To ensure un-biased results, we took precautions not to correlate the same combination of stories twice, which prevented potential inflation of the data. To facilitate statistical comparisons, we ap-plied a Fisher z-transform to the Pearson's rho values at each time point. This yielded a global measure of similarity on each electrode site. We, thus, obtained time × time similarity maps for the linked events (A and B) and the non-linked events (A and X) in the pre- and post-phases, separately for the insight gained through imagination and observation." (p. 34+35).

      If RSA values were calculated at each time point and electrode, the Pearson correlations would have been computed effectively between four samples only, which is by far not enough to derive reliable estimates (Schönbrodt & Perugini, 2013). The problem is aggra-vated by the fact that due to the time and frequency smoothing inherent in the time-frequency decomposition of the EEG data, nearby power values across neighboring theta frequencies are highly similar to start with. (e.g., Schönauer et al., 2017; Sommer et al., 2022).

      Alternative approaches would be to run the correlations across time for each electrode (re-sulting in the elimination of the time dimension) or to run the correlations at each time point across electrodes (resulting in the elimination of topographic specificity).

      At least, the authors should show raw RSA maps for linked and non-linked events in the pre- and post-phases separately for the insight gained through imagination and observa-tion in each group, to allow for assessing the suitability of the input data (in the supple-ments?) before progressing to reporting the results of three-way interactions.

      Although we do see the reviewer’s point, we think that an RSA specific to the theta range yielding electrode specific time × time similarity maps must be run this way, otherwise, as you pointed out, one or the other dimension is compromised. Running an RSA across time for each electrode will lead to computing a similarity measure between the events without information on when these stimuli become more or less similar, thereby ig-noring the temporal dynamics crucial to EEG data and not taking advantage of the high temporal resolution. Conversely, conducting an RSA across electrodes might result in an overall similarity measure per participant, disregarding the spatial distribution and potential variations among electrodes. Although EEG has limited spatial resolution, different elec-trodes can capture differences that may aid in understanding neural processing. However, as suggested by the reviewer, we included the raw RSA maps for linked and non-linked events separately for pre- and post-phases, imagination and observation and link and non-link in the supplement and refer to these data in the results section on pages 12-13, lines 293-295:

      “For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

      And on page 15, lines 339-341:

      “This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

      Reviewer 2

      We thank the reviewer for the very helpful and constructive comments and appreciate that the reviewer finds our study relevant to all areas of cognitive research.

      1) While the observed memory reconfiguration/changes are attributed to the angular gyrus in this study, it remains unclear whether these effects are solely a result of the AG's role in re-configuration processes or to what extent the hippocampus might also mediate these memory effects (e.g., Tambini et al., 2018; Hermiller et al., 2019).

      We agree that, in addition to the critical role of the angular gyrus, there may be an involvement of the hippocampus. We point now explicitly to the modulatory capacities of angular gyrus stimulation on the hippocampus. Please see page 4, lines 81-88:

      “One promising candidate that may contribute to insight-driven memory reconfiguration is the angular gyrus. The angular gyrus has extensive structural and functional connections to many other brain regions (Petit et al., 2023), including the hippocampus (Coughlan et al., 2023; Uddin et al., 2010). Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014). Furthermore, the angular gyrus has been implicated in a myriad of cognitive func-tions, including mental arithmetic, visuospatial processing, inhibitory control, and theory-of-mind (Cattaneo et al., 2009; Grabner et al., 2009; Lewis et al., 2019; Schurz et al., 2014).”

      We further added a new paragraph to the discussion pointing at the possibility that not solely the angular gyrus but another brain region, such as the hippocampus, may have me-diated the changes observed in our study on pages 23-24, lines 546-562:

      “Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

      2) Another weakness in this manuscript is the use of different groups of participants for the key TMS intervention, along with underspecified or incomplete hypotheses/predictions.

      In our view, the chosen between-subjects design is to be preferred over a crossover design for several reasons. First, our choice aimed to eliminate potential se-quence effects that may have adversely affected performance in the narrative-insight task (NIT). Second, this approach ensured consistency in expectations regarding the story links while also mitigating potential differences induced by fatigue. Additionally, we accounted for the potential advantage of a within-subject design – the stimulation of the same brain – by utilizing neuro-navigated TMS for targeting the stimulation coordinate. Finally, it is im-portant to note that we measured the event representations pre- and post-insight and that also the mode of insight was manipulated within-subject. Thus, our design did include a within-subject component and we are convinced that the chosen paradigm balances the different strengths and weaknesses of within-subject and between-subjects designs in the best possible manner. We specified our rationale for choosing a between-subjects ap-proach in the introduction on page 5, lines 122-126:

      “We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

      Moreover, to provide a comprehensive portrayal of the two groups, we incorporated de-scriptions concerning trait and state variables alongside age and motor thresholds and in-cluded t-test comparisons between these variables on page 7, lines 157-160:

      “Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

      And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-404:

      “Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

      For greater precision in outlining our hypotheses, we specified these at the end of the in-troduction on pages 4-55, lines 107-118:

      “Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures.”

      3) Furthermore, in some instances, the types of analyses used do not appear to be suitable for addressing the questions posed by the current study, and there is limited explanation pro-vided for the choice of analyses and questionnaires.

      We addressed this concern by inserting a new section “control variables” in the methods explaining our rationale for employing the different questionnaires as control var-iables on pages 40-41, lines 1003-1019:

      “Control variables In order to ensure that the observed effects were solely attributable to the TMS manipula-tion and not influenced by other factors, we comprehensively evaluated several trait and state variables. To account for potential variations in anxiety levels that could impact our re-sults, we specifically measured state and trait anxiety using STAI-S and STAI-T (Laux et al., 1981), thus minimizing the potential confounding effects of anxiety on our findings (Char-pentier et al., 2021). Additionally, we evaluated participants’ chronic stress levels using the TICS (Schulz & Schlotz, 1999) to exclude any group variations that might explain the effect on memory, cosidering the well-established impact of stress on memory (Sandi & Pinelo-Nava, 2007; Schwabe et al., 2012). Moreover, we assessed participants’ depressive symp-toms employing the BDI (Hautzinger et al., 2006), to guarantee group comparability on this clinical measure. We further assessed fundamental personality dimensions using the BFI-2 (Danner et al., 2016) to exclude any potential group discrepancies that could account for dif-ferences observed. Lastly, we assessed participants’ imaginative capacities using the FFIS (Zabelina & Condon, 2019), to ensure uniformity across groups regarding this central varia-ble, considering the significant role of imagination in relation to the cTBS-targeted angular gyrus (Thakral et al., 2017).”

      We further specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-85:

      “For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

      Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

      “This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

      To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

      “Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

      Reviewer 3

      We thank the reviewer for the constructive and very helpful comments. We are pleased that the reviewer considered our experimental design to be strong and our behavioral results to be striking.

      1) My major criticism relates to the main claim of the paper regarding causality between the angular gyrus and the authors' behavior of interest. Specifically, I am not convinced by the evidence that the effects of stimulation noted in the paper are attributable specifically to the angular gyrus, and not other regions/networks.

      While our results showed specific changes after cTBS over the angular gyrus, demonstrating a causal involvement of the angular gyrus in these effects, we completely agree that this does not rule out an involvement of additional areas. In particular, there is evidence suggesting that cTBS over parietal regions, such as the angular gyrus, could poten-tially influence hippocampal functioning. We address this issue now in a new paragraph that we have added to the discussion, on pages 23-24, lines 546-564:

      “Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations.”

      Responses to reviewer recommendations

      Reviewer 1

      1) On page 26, the authors write: "[...] different video events (A, B, and X) were recalled from day one [...]". I may have missed this point, but I had the impression that the task was con-ducted within one day.

      Indeed, this study was conducted within a single day. We rephrased the respec-tive statement accordingly. Please see page 7, lines 149-153:

      “To test this hypothesis and the causal role of the angular gyrus in insight-related memory reconfigurations, we combined the life-like video-based narrative-insight task (NIT) with representational similarity analysis of EEG data and (double-blind) neuro-navigated TMS over the left angular gyrus in a comprehensive investigation within a single day.”

      We further included this information in the methods section on page 27, lines 634-635:

      “In total, the experiment took about 4.5 hours per participant and was completed within a single day. ”

      Reviewer 2

      1) There is a substantial disconnection between the introduction and the methods/results sec-tion. One reason is that there is not sufficient detail regarding the hypotheses/predictions and the specific types of analyses chosen to test these hypotheses/predictions. Additionally, it is not explained what comparisons and outcomes would be informative/expected. This should be made clear. Second and related to the above, the rationale for conducting certain types of analyses (correlation, coherence, see below) sometimes is not specified.

      To address this concern, we elaborated on our hypotheses incorporating specif-ic predictions for the free recall, given its higher variability than the other memory measures, and for imagination vs. observation at the end of the introduction on pages 4-5, lines 107-122:

      “Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures. Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connec-tivity analysis building upon prior findings concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational ap-proach to relate observations from these two domains.”

      Moreover, we made our rationale for the employed analyses more explicit and specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

      “For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

      Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

      “This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

      To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

      “Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

      2) The authors suggest that besides Branzi et al. (2021), this is one of the first studies showing that memory update is linked to the AG. I suggest having a look at work from Tambini, Nee, & D'Esposito, 2018, JoCN, and other papers from Joel Voss' group that target a similar re-gion of AG/Inferior parietal cortex. Many studies, using multiple TMS protocols, have now shown this brain region is causally involved in episodic and associative memory encoding.

      As mentioned above, further consideration of this literature is important as it delves into the region's hippocampal connectivity (and other network properties), and how that mediates the memory effects. Indeed because of the nature of the methods employed in this study, we do not know if the memory-related behavioural effects are due to TMS-changes induced at the AG's versus the hippocampal' s level, or both. How do the current findings square with the existing TMS effects from this region? Can the connectivity profile of the target re-gion highlighted by previous studies provide further insight into how the current behaviour-al effect arises? Some comments on this could be added to the discussion.

      We completely agree that the other studies showing enhanced associative memory after TMS to parietal regions need to be addressed. Therefore, we updated the discussion on page 20, lines 449-453:

      “Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

      Moreover, we included a section specifically addressing the possibility that the effects ob-served may pertain to having modulated other regions via the targeted region and updated the discussion on pages 23-24, lines 543-562:

      “Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

      3) Another comment I have regards the results observed for the observation vs imagination insight conditions. The authors mention that the 'changes in representational similarity for the observation condition should be interpreted with caution, as these seemingly opposite changes appeared to be at least in part driven by group differences already in the pre-phase before participants gained insight.' I wonder what these group differences are and whether the authors have any hypothesis about what factors determined them.

      We could only speculate about the basis of the observed pre-insight phase dif-ferences. However, we provide now the raw RSA data as supplemental material to make the pattern of the (raw) RSA findings in the pre- and post-insight phases more transparent. We refer the interested reader to this material on pages 12-13, lines 293 to 295:

      “For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

      And on page 15, lines 339-341:

      “This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

      Furthermore, the age of participants is not reported separately for the two groups (cTBS to AG vs Sham), I think. This should be reported including a t-test showing that the two groups have the same age.

      We agree and report now explicitly that groups did not significantly differ in rel-evant control variables including age. Please see page 7, lines 157-160:

      “Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

      And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-412:

      “Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

      The fact this study is not a within-subject design makes difficult the interpretation of the results and this should be recognised as an important limitation of the study.

      As outlined above, a within-subject design would in our view come with several disadvantages, such as significant sequence/carry-over effects. Moreover, the neural rep-resentation change was measured in a pre-post design, enabling us to measure the insight-driven neural reconfiguration at the individual level.

      We clarify our rationale for the between-subjects factor TMS in the introduction on page 5, lines 122-126:

      “We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

      Furthermore, we included our rationale for choosing a between-subjects approach for the crucial TMS manipulation in the methods section on page 25, lines 601-604:

      “We implemented a mixed-design including the within-subject factors link (linked vs. non-linked events), session (pre- vs. post-link), and mode (imagination vs. observation) as well as the between-subjects factor group (cTBS to the angular gyrus vs. sham) to mitigate the risk of carry-over effects and sequence biases of the crucial cTBS manipulation.”

      4) The angular gyrus is a heterogeneous region with multiple graded subregions. The one tar-geted in the present study is the ventral AG which has strong connections with the episodic-hippocampal memory system. I was wondering if this might explain why the AG TMS ef-fects on representational changes have been observed for events linked via imagination but not direct observation. Perhaps the stimulation of a more 'visual' AG subregion (see Hum-phreys et al., 2020, Cerebral Cortex) would have resulted in a different (opposite) pattern of results. It would be good to add some comments on this in the discussion.

      We appreciate this interesting perspective offered regarding the potential out-comes of our study, particularly in relation to the activation of a more ventral sub region of the angular gyrus. We incorporated this idea into our discussion, alongside considerations regarding the potential effects of a more dorsal angular gyrus stimulation on observation-based linking. However, caution is warranted recognizing the inherent limitations posed by the precision of TMS manipulations, which is further underscored by our electric field simu-lations, utilizing a 10 mm radius. We included this section in the discussion on pages 23-24, lines 546-569:

      “Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations. Yet, while acknowledging the functional heterogeneity within the angular gy-rus (Humphreys et al., 2020), pinpointing specific sub regions via TMS remains challenging due to its limited focal precision at the millimeter level (Deng et al., 2013; Thielscher & Kammer, 2004), as reinforced by our electric field simulations utilizing a 10 mm radius. Hence, drawing definitive conclusions regarding distinct angular gyrus sub regions requires future research employing rigorous checks to assess the focality of their stimulation.”

      5) Regarding the methods section, I have the following specific queries. It is unclear what is the purpose of the coherence and correlation analyses (pages 35, 36). Could the authors pro-vide further clarification on this? These analyses seem not to be mentioned anywhere in the introduction. This should be clarified briefly in the introduction and then in the methods sec-tion. The same for the questionnaires (anxiety, stress, etc): It is unclear the reason for col-lecting this type of data. This should be clarified in the introduction as well.

      We agree, and have updated the introduction as follows on page 5, lines 118-122:

      “Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connectivity analysis building upon findings from the RSA anal-yses concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational approach to relate observations from these two domains.”

      We additionally provided an explanation for including these questionnaires in the introduc-tion on page 5, lines 126-129:

      “To control for any group differences beyond the TMS manipulation, we gathered various control variables through questionnaires, including trait- and state-anxiety, depressive symptoms, chronic stress levels, personality dimensions, and imaginative capacities.”

      Moreover, we elaborated on the underlying rationale guiding our chosen analytical ap-proaches. Therefore, we specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

      “For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

      Furthermore, we added an explanation on why we opted for the RSA approach in the methods section on page 37, lines 920-923:

      “This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

      To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

      “Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

      6) The preregistration webpage is in German. This is not ideal as it means that the information is available only to German speakers.

      This webpage can easily be switched to English by changing the settings in the top right corner:

      To address this issue, we included a description of how to set the webpage to English in the methods section on page 25, lines 581-582:

      “For translation to English, please adjust the page settings located in the top right corner.”

      7) Page 18. 'NIT' and 'MAT' - avoid abbreviations when possible.

      We included the full name for the narrative-insight task (NIT) on page 7, line 151, line 153, and line 165, page 8 lines 177-178 and line 187, page 19 on line 427, page 26 on line 615, line 629 and line 632, page 27, line 653, page 30, lines 730-731, page 31, line 754, page 35, line 870, line 873, and page 36 and line 885.

      We further included the full name for the multi-arrangements task (MAT) on page 19, lines 428-429.

      8) Line 21....we further observed DECREASED...should be replaced with INCREASED, if I am not wrong.

      We checked the sentence again and it looks correct to us, since it describes the change for observation-based insight, not imagination-based insight. We clarified that this finding pertains to observation-based linking by modifying the sentence on page 23, lines 525-528, as follows:

      “Following cTBS to the angular gyrus, we further observed decreased pattern similarity for non-linked events in the observation-based condition, resembling the pattern change ob-served in the sham group for linked events, which may highlight the role of the angular gy-rus in representational separation during observation-based linking”

      Reviewer 3

      1) The major claim of the paper is that the angular gyrus is causally involved in insight-driven memory reconfiguration. To the authors' credit, they localized stimulation to the angular gyrus using an anatomical scan, the strength of the estimated electromagnetic field in the angular gyrus correlated with their behavioral results, and there were also brain-behavior correlations involving sensors located in the parietal lobe. However, the minimum evidence needed to claim causality is 1) evidence of a behavioral change (which the authors found) and 2) evidence of target engagement in the angular gyrus. It is also important to show brain-behavior correlations between target engagement and behavior. Although the au-thors stimulated the angular gyrus, that does not mean that rTMS specifically affected this region or that the behavioral results can be attributed to rTMS effects on the angular gyrus. As the authors point out, the angular gyrus has dense connections with other regions such as the hippocampus. In fact, several studies have shown that angular gyrus (or near AG) stimulation affects the hippocampal network (Wang et al., 2014, Science; Freedberg et al. 2019, eNeuro; Thakral et al., 2020, PNAS). EEG also has a poor spatial resolution, so even though the results were attributable to parieto-temporal sensors, this is not sufficient evi-dence to claim that the angular gyrus was modulated. Source localization would be re-quired to reconstruct the signal specifically from the AG. Thus, with the manuscript written as is, the authors can claim that "cTBS to the angular gyrus modulates insight-driven memory reconfiguration," but the current claim is not sufficiently substantiated.

      While acknowledging the potential role of the angular gyrus in driving the ob-served changes, we recognize that the available evidence may not be sufficient. Conse-quently, we have introduced several modifications within our manuscript to address this concern.

      In the revised Introduction, we now explicitly address the possibility of a stimulation of the hippocampus via the angular gyrus on page 4, lines 84-85:

      “Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014).”

      Additionally, we included relevant evidence demonstrating previous instances of targeted stimulation of the angular gyrus, which led to alterations in hippocampal connectivity and associative memory. These insights have been included in the discussion on page 20, lines 449-453:

      “Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

      Next, we have integrated crucial modifications essential for establishing a conclusive infer-ence of causality in our study. Moreover, we now explore the potential mediation of the effects observed from angular gyrus stimulation through other brain regions, like the hip-pocampus. In addition, we have highlighted prior work where such stimulation coincided with alterations in associative memory. For the updated discussion section, please see pag-es 23-24, lines 538-562:

      “Although our study provided evidence suggesting a causal role of the angular gyrus in in-sight-driven memory reconfigurations – highlighted by behavioral changes after cTBS to the angular gyrus, neural changes in left parietal regions, and relevant brain-behavior associa-tions – it is important to acknowledge the limitations imposed by the spatial resolution of EEG. Consequently, the precise source of the observed signal changes in the parietal re-gions remains uncertain, potentially tempering the definitive nature of these findings. Fur-thermore, the differential impact of cTBS to the angular gyrus on neural reconfigurations between events linked via imagination and those linked via observation may be attributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). An-other intriguing aspect to consider is that the stimulated site was situated in the more ven-tral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

      We further replaced terms that imply inhibition of the angular gyrus with a more operation-ally descriptive phrase:

      “cTBS to the angular gyrus”

      2) The authors frequently claim that cTBS is "inhibitory stimulation" and that inhibition of the angular gyrus caused their effects. There is a common misconception within the cognitive neuroscience literature that stimulation is either "inhibitory" or "excitatory," but there is no such thing as either. The effects of rTMS are dependent on many physiological, state, and trait-specific variables and the location of stimulation. For example, while cTBS does repro-ducibly inhibit behavior supported by the motor cortex (Wilkinson et al., 2010, Cortex; Rosenthal et al., 2009, J Neurosci), cTBS of the posterior parietal cortex reproducibly en-hances hippocampal network functional connectivity and episodic memory (Hermiller et al., 2019, Hippocampus; Hermiller et al., 2020, J Neurosci). The authors reference the Huang et al. (2005) paper as evidence of its inhibitory effects but work in this paper is not sufficient to broadly categorize cTBS as inhibitory. First, Huang et al. stimulated the motor cortex and measured the effects on corticospinal excitability, which is significantly different from what the current authors are measuring. Furthermore, this oft-cited study only included 9 sub-jects. Other studies have found that the effects of theta-burst are significantly more varia-ble when more subjects are used. For example, intermittent theta-burst, which is assumed to be excitatory based on the Huang paper, was found to produce unreliable excitatory ef-fects when more subjects were examined (Lopez-Alonso, 2014, Brain Stimulation). Thus, the a priori assumption that stimulation would be inhibitory is weak and cTBS should not be dis-cussed as "inhibitory."

      We agree and included now a statement in the methods section that explicitly states that cTBS effects may be region-specific on page 33, lines 817-819:

      “Nonetheless, the effects of cTBS appear to vary based on the targeted region, with cTBS to parietal regions demonstrating the capability to enhance hippocampal connectivity (Hermiller et al., 2019, 2020).”

      We further substituted all terminology suggestive of an inhibitory effect with the phrase:

      “cTBS to the angular gyrus”.

      However, it is important to note, that while other studies (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014) found increased hippocampal connectivity after rTMS to a parie-tal region as well as enhanced associative memory, we observed impaired memory for the linked events. We included this clarification in the discussion on page 24, lines 558-562:

      “In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

      3) The hypothesis at the end of the introduction did not strike me as entirely clear. From this hypothesis, it seems that the authors are just comparing the differences in memory and re-configuration during imagination-based insight links. However, the authors also include ob-servation-based links and a non-linking condition, which seem ancillary to the main hy-pothesis. Thus, I am confused about why these extra factors were included and exactly what statistical results would confirm the authors' hypothesis.

      We agree, and have clarified our hypotheses on pages 4-5, lines 107-115:

      “Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to reduce the increase in neural similarity for linked events and in-crease of neural dissimilarity for non-linked events.”

      4) Many of the distributions throughout the paper do not look normal. Was normality checked? Are non-parametric stats warranted?

      We evaluated and reported the normality assumption in our behavioral anal-yses. Despite the non-normal distribution of our data, we chose to utilize linear-mixed models due to their robust performance even in case of deviations from normal distribu-tions. This update in our methods section can be found on page 36, lines 890-896:

      “After outlier correction, we identified non-normality in our data using a Shapiro-Wilk test (narrative-insight task: W = 0.92, p < 0.001; multi-arrangements task: W = 0.94, p < 0.001; forced-choice recognition: W = 0.50, p < 0.001; free recall details: W = 0.85, p < 0.001; free recall naming of linking events: W = 0.94, p < 0.001). However, we mitigated this by employ-ing linear-mixed models (LMMs), recognized for their robustness even with non-normally distributed data (Schielzeth et al., 2020).”

      We recalculated the correlational analysis between the RSA data and the behavioral recall of linking events by using the Spearman method on page 13, lines 306-308:

      “Furthermore, to address a deviation from the normality assumption, the correlational analysis was repeated using the Spearman method, which indicated an even stronger cor-relation (r(59) = 0.32, p = 0.012).”

      We further recalculated the correlation between the change in coherence for linked events and the recall of details for events linked via imagination on page 16, lines 376-378:

      “Please note that for addressing a deviation from the normality assumption, the correla-tional analysis was repeated using the Spearman method, which yielded a significant corre-lation of similar strength (r(59) = 0.31, p = 0.015).”

      Our EEG analyses , including RSA and coherence analyses, utilized a cluster-based permuta-tion test (Fieldtrip; Oostenveld et al., 2011). These tests do not assume a normal distribu-tion by utilizing empirical sampling for statistical inference. This approach ensures robust-ness without constraints imposed by specific distributional assumptions. Subsequent t-tests, stemming from significant clusters identified in the initial non-parametric analyses, were extensions of the robust non-parametric approach and did not require additional normality testing.

      5) Can the authors include more detail about the sham coil? Was it subthreshold? Did the EMF cross the skull?

      The sham coil, also obtained from MAG & More GmbH, München, Germany, provided a similar sensory experience; however, the company did not specify any field strength (n.a.) as this coil was purposefully designed to prevent the induction of an elec-tromagnetic field (EMF) capable of penetrating the skull, thereby ensuring it had no impact on the brain. We clarified on this point in the methods section on pages 31-32, lines 772-778:

      “Two identically looking but different 70 mm figure-of-eight-shaped coils were used de-pending on the TMS condition: The PMD70-pCool coil (MAG & More GmbH, München, Germany) with a 2T maximum field strength was used for cTBS, while the PMD70-pCool-SHAM coil (MAG & More GmbH, München, Germany), with minimal magnetic field strength, was employed for sham, providing a similar sensory experience, with stimulation pulses being scattered over the scalp and not penetrating the skull.”

      6) There are differences between exclusion criteria in pre-registration and report. For example, BMI is an exclusion factor in the report, but not in the pre-registration. Can the authors provide a reason for this deviation?

      This discrepancy is due to (partial) participant recruitment from previous fMRI studies conducted in our lab that involved a stress induction protocol (as a structural MRI image was needed for the ‘neuronavigated’ TMS). Owing to the distinct cortisol stress reac-tivity observed in individuals with varying body mass indices (BMIs), participants with a BMI below 19 or above 26 kg/m² were excluded from these studies. To maintain consistency within our sample, only participants meeting these criteria were included. We elaborated on this point in the methods section on page 25, lines 586-592:

      “Participants were screened using a standardized interview for exclusion criteria that com-prised a history of neurological and psychiatric disease, medication use and substance abuse, cardiovascular, thyroid, or renal disease, evidence of COVID-19 infection or expo-sure, and any contraindications to MRI examination or TMS. Additionally, participants with a body mass index (BMI) below 19 or above 26 kg/m² were excluded. This decision stemmed from recruiting some participants from prior studies that incorporated stress induction pro-tocols, which imposed this specific criterion (Herhaus & Petrowski, 2018; Schmalbach et al., 2020).”

      7) Were impedances monitored and minimized during EEG?

      Yes, they were monitored. We clarified this point in the methods section on page 34, lines 845-847:

      “We maintained impedances within a range of ± 20 μV using the common mode sense (CMS) and driven right leg (DRL) electrodes, serving as active reference and ground, re-spectively”

      8) I think there may be a typo related to the Thakral coordinates. I believe Thakral used MNI coordinates -48,-64, 30, whereas the authors stated they used -48,-67,30. Is this a mistake?

      Upon reevaluation of our study coordinates, we identified a slight deviation in our stimulation coordinates compared to those reported by Thakral et al. (2017; +3mm on the y-axis). This variance resulted from the required MNI to Talairach (TAL) transformations necessary for utilizing the neuronavigation software Powermag View! (MAG & More GmbH, München, Germany). Notably, this deviation was consistent across all participants in our study. While TMS is more precise than tDCS, its focality is not as fine-grained down to the millimeter level. Despite this, our electric field simulations, adopting a 10mm radius, ef-fectively encompassed the original coordinates specified by Thakral et al. (2017). This radius ensured coverage over the intended target area, mitigating the impact of this minor devia-tion on the overall study outcomes. We updated the methods section accordingly on page 33, lines 800-806:

      “Based on the individual T1 MR images, we created 3D reconstructions of the participants' heads, allowing us to precisely locate the left angular gyrus coordinate (MNI: -48, -67, 30), initially derived from previous work (Thakral et al., 2017), for TMS stimulation. Despite a mi-nor deviation in coordinates due to necessary MNI to Talairach transformations for soft-ware compatibility (Powermag View! by MAG & More GmbH, München, Germany), our methodology ensured precise localization of the angular gyrus target area.”

      9) How was the tail of the coil positioned during stimulation? Was it individualized so that the lobes of the coil are perpendicular to the nearest gyrus, as is commonly done?

      The coil handle always pointed upwards to maintain optimal positioning with the coil holder. We followed the positioning procedure in the neuronavigation software Powermag View!, which did not indicate any positioning of the coil handle but specified the position and angle of the coil itself. To incorporate this aspect, we updated the legend of figure 2 on page 11, lines 260-261:

      “Please note that in the study, the coil handle was oriented upwards; however, in this illus-tration, it has been intentionally depicted as pointing downwards for better visibility pur-poses.”

      We further updated the method section on page 33, lines 723-824:

      “The coil was positioned tangentially on the head and mechanically fixed in a coil holder, with its handle pointing upwards to maintain its position”

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      This work describes a new and powerful approach to a central question in ecology: what are the relative contributions of resource utilisation vs interactions between individuals in the shaping of an ecosystem? This approach relies on a very original quantitative experimental set-up whose power lies in its simplicity, allowing an exceptional level of control over ecological parameters and of measurement accuracy.

      In this experimental system, the shared resource corresponds to 10^12 copies of a fixed single-stranded target DNA molecule to which 10^15 random single-stranded DNA molecules (the individuals populating the ecosystem) can bind. The binding process is cycled, with a 1000x-PCR amplification step between successive binding steps. The composition of the population is monitored via high-throughput DNA sequencing. Sequence data analysis describes the change in population diversity over cycles. The results are interpreted using estimated binding interactions of individuals with the target resource, as well as estimated binding interactions between individuals and also self-interactions (that can all be directly predicted as they correspond to DNA-DNA interactions). A simple model provides a framework to account for ecosystem dynamics over cycles. Finally, the trajectory of some individuals with high frequency in late cycles is traced back to the earliest cycles at which they are detected by sequencing. Their propensities to bind the resource, to form hairpins, or to form homodimers suggest how different interaction modes shape the composition of the population over cycles.

      The authors report a shift from selection for binding to the resource to interactions between individuals and self-interactions over the course of cycles as the main drivers of their ecosystem. The outcome of the experiment is far from trivial as the individual resource binding energy initially determines the relative enrichment of individuals, and then seems to saturate. The richness of the population dynamics observed with this simple system is thus comparable to that found in some natural ecosystems. The findings obtained with this new approach will likely guide the exploration of natural ecosystems in which parameters and observables are much less accessible.

      My review focuses mainly on the experimental aspects of this work given my own expertise. The introduction exposes very convincingly the scientific context of this work, justifying the need for such an approach to address questions pertaining to ecology. The manuscript describes very clearly and rigorously the experimental setup. The main strengths of this work are (i) the outstanding originality of the experimental approach and (ii) its simplicity. With this setup, central questions in ecology can be addressed in a quantitative manner, including the possibility of running trajectories in parallel to generalize the findings, as reported here. Technical aspects have been carefully implemented, from the design of random individuals bearing flanking regions for PCR amplification, binding selection and (low error) amplification protocols, and sequencing read-out whose depth is sufficient to capture the relevant dynamics.<br /> :<br /> We thank the reviewer for summarizing our work and the main findings in a very clear and effective manner.

      One missing aspect in the data analysis is the quantification of the effect of PCR amplification steps in shaping the ecosystem (to be modeled if significant). In addition, as it stands the current work does not fully harness the power of the approach. For instance, with this setup, one can tune the relative contributions of binding selection vs amplification for instance (to disentangle forces that shape the ecosystem). One can also run cycles with new DNA individuals, designed with arbitrarily chosen resource binding vs self-binding, that are predicted to dominate depending on chosen ecological parameters. I have three main recommendations to the authors:

      1) PCR amplification steps (and not only binding selection steps) should be taken into account when interpreting the outcome of experiments.

      2) More generally, a systematic analysis of the possible modes of propagation of a DNA molecule from one cycle to the next, including those considered as experimental noise, would help with interpreting the results.

      3) Testing experimentally the predictions from the analysis and the modelling of results would strengthen the case for this approach.

      Despite its conceptual simplicity, our approach has indeed a few experimental handles that enable exploring a relevant variety of conditions much beyond those described in this paper, of which we are very aware. These involve selection vs. amplification or set the stage to explore competition, parasitism or cooperation among specific species, as the reviewer points out, but also introduce mutations and explore the kinetics of evolution in static or dynamic environments. Ongoing experiments are considering some of these conditions. We modified the text to mention more explicitly these possibilities, which are now mentioned in p11 lines 376-378 and lines 416-417. The three points raised by the reviewer helped us to further improve and clarify strengths and limitations of our work, as detailed below.

      Regarding the first point, here are my suggestions :

      • Run one cycle of just amplification vs 'binding + amplification', or simply increase the number of PCR cycles (and subsample the product) to check whether it impacts the population composition, in particular for sequences with predictions derived from the current analysis.

      The point raised by the reviewer is indeed very relevant and not discussed in our manuscript. Prompted by the reviewer’s comment, we performed two new experiments to distinguish resource-binding selection from PCR amplification effects.

      First, we performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

      After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

      This indicates that the drift due to non-specific binding + PCR amplification is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.

      This results are now cited in p14 lines 468-470, and described in Appendix 1, Experimental controls.

      Second, we tested the effect of PCR amplification on the selection process. We exploited the fact that we have aliquots for each generation of our evolution experiment, which we sampled and saved after PCR and before sequencing. We thus chose a specific generation - specifically generation 9 from Oligo 1 experiments - and performed another PCR round we proceeded directly to sequencing with no beadsselection step. We then compared the ensemble of oligos obtained in this way, which we named Oligo 1 “cycle 9 replica”, with both the original Oligo1 cycle 9, and with Oligo1 cycle 10.

      We sampled 20 times 4 x 10^5 sequences from the cycle 9 dataset, from cycle 9 replica and from cycle 10 with a bootstrap approach. To compare the three systems we extracted the fraction of the population of each covered by the 10 most abundant individuals. The results are shown in Figure 2 - Figure Supplement 4. In the figure caption further details on the analysis can be found. The similarity between cycle 9 and cycle 9 replica and the marked difference between cycle 9 replica and cycle 10

      indicates that the relevant part of the selection is indeed performed by the resourcebinding mechanism, while drifts induced by PCR play a secondary role.

      As a further check, we compared the specific sequences across the 20 samples in cycle 9 and cycle 9 replica datasets and found that the 10 most abundant sequences are almost always the same. In particular, the first 8/9 are always the same, possibly shuffled.

      These new pieces of evidence are now cited in p14 lines 483-484 and described in Appendix 1, Experimental controls.

      • Sequencing read-out includes the same PCR protocol as the one used for amplification steps, so read-out potentially has an effect on the composition of the ecosystem. Again, varying the number of PCR cycles is a direct way to test this.

      The PCR amplification involved in the read-out might have a minor effect on the sequencing outcome but not on the composition of the ecosystem. In fact, the sample that undergoes sequencing is taken from the pool at each cycle, and not inserted back into it. Thus, it does not participate in the following selection steps. This is specified in the text at p3 line 104

      • Could self-interactions (hairpins of homodimers) benefit individuals during amplification steps? The role of self-interactions during binding selection steps could also be tested directly over one cycle (again varying the relative weight of the binding vs amplification to disentangle both).

      Our choice of conditions for PCR amplification were thought to minimize effects of this type. PCR amplification is carried out at 68 C, a temperature at which, given the level of self and mutual complementarity in the sequences analyzed in the text, hairpins or homodimers should be melted and thus have no effect. This is specified in the text at p. 14 lines 479-480 However, if an effect is present, it gives a disadvantage (rather than an advantage) to self-interacting individuals. For the amplification step we used Q5® Hot Start HighFidelity DNA Polymerase, which does not possess strand displacement activity. Therefore, in theory, if during amplification the polymerase encounters a double strand portion, it stops and synthesizes only a truncated product, which will be then lost during the purification step. In other words, sequences with secondary and/or tertiary structures are less likely to be amplified during the polymerization step. As a consequence, a DNAi that is characterized by this kind of structures, will be negatively selected even in the case of optimal binding to the resource, and will be underrepresented in the pool.

      About the second point:

      • Regarding the effect of sampling (sequencing read-out), PCR amplification errors: explicitly check the consistency of observations with the expected outcome, in the methods section (right now these aspects are only briefly mentioned in the main text), which would highlight again the level of control and accuracy of the system.

      Hoping to have well interpreted the request, we performed a technical replicate sequencing Oligo 1 cycle 9 again and analyzed the sequences that have at least 100 reads (corresponding to 27.42% of the total reads). We find that among the 800 DNA species that have at least 100 reads, 93.6% are found in both replicates. All the nonoverlapping sequences have very low abundance, close to 100.

      Moreover, we compare the population size of each DNA species between the two replicas, after having equalized the database sizes. The results are now cited in p14 lines 509-510, In Appendix 1, Experimental Controls and shown in Figure 2-figure supplement 3, where we plot the ratio of the number of reads in the two replicates for each sequence as a function of the number of reads in one. We found an average of 0.965 with a standard deviation of 0.119. High fluctuations are found in the most rare species, as expected.

      We think this evaluation indeed strengthens the solidity of our results.

      • I have a small concern about target resource accessibility: is there any spacer between the ssDNA and the bead? The methods section does not mention any, and I would expect such a proximity between the target DNA and the bead to yield steric repulsion that impedes interactions with random DNA individuals.

      Yes, there is a 12-carbon spacer between the bead and the resource, which was inserted exactly to make the resource more accessible. This information is now available in Table 1 of Supplementary Information detailing the sequences used in the experiment. However, as now described in the text (p8 lines 284-286), we observe that the interaction with the resource is always shifted to the 3', the terminal furthest from the bead, indicating some residual issue of accessibility to the resource sections closest to the bead.

      • Regardless of the existence of a spacer, binding of random DNA molecules to beads instead of the target DNA constitutes a potential source of noise (described for now as '1-x' in the IBEE model), which can be probed by swapping targets, selecting without target etc.

      This issue is addressed by the test with bare beads described above, in which we found little effects, corresponding to small 1−𝑥 value.

      • Is there any recombination potentially occurring during amplification steps? This could be tested with a set of known molecules amplified over 24 amplification steps in a row (no binding step).

      It is possible for recombination to occur during the amplification steps. In Appendix 2, the section "By-Product Formation from PCR Amplification", discusses PCR byproducts as aberrant forms of amplification, such as recombination events. We adopted several strategies to limit by-product formation, such as: i) use of “blockers” characterized by a phosphate group at 3’ end (thus inhibiting their usage during the amplification and allowing a better control of the reaction conditions over the PCR cycles), ii) a high annealing temperature (to limit the possibility of a spurious primer annealing to the random region), iii) fewer PCR cycles, iv) a high primer concentration, v) a very short elongation step (all these strategies have been implemented to avoid a possible mispriming event between different DNAi, and the formation of concatemers). However, the formation of by-products is a problem inherent to the technique: in fact, it is a known issue for classical SELEX technology (Tolle et al. 2014), mainly due to the random region within the DNAi. Q5® Hot Start High-Fidelity DNA Polymerase (New England Biolabs, Ipswich, MA, USA) has an error rate of <0.44 x 10-6/base.

      In classic SELEX technology, the average number of selection cycles is 10. This limitation is partly due to the increase in PCR by-products. As we can see from Figure 2 Supplementary Figure 1, the percentage of PCR by-products is less than 20% at cycle 12, and then increases dramatically in the following cycles. We are performing a series of experiments with known and limited sequences to verify and better understand the phenomenon for future applications of the SEDES platform. On this issue we decided not to modify the manuscript since we think it is already well discussed in Appendix 1.

      And the third point:

      • Perform one cycle (or a few cycles) with random DNA individuals, the most frequent individuals at the end of the current experiment, newly designed individuals with higher binding affinity to the target than currently dominating individuals, newly designed individuals with higher propensity to form hairpins or to form homodimers. Such experimental testing of predictions from the data analysis/modeling, typical of a physics approach, would illustrate the level of understanding one can reach with a simple yet powerful experimental setup.

      We perfectly agree that the approach we propose and the set of results we obtained call for further investigations that could strengthen analysis and modeling. The final aim we envisage is the understanding, within this simplified approach, of key evolutionary factors such as fitness. Indeed, becoming able to write an explicit fitness function would be a significant new contribution to the understanding of evolutionary processes, even within the limited settings of the ADSE approach, as discussed in the conclusions of the manuscript.

      However, undergoing such an analysis is a long and expensive job, which we have started and will be completed in a not immediate future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population and discuss in a future contribution the behavior of smaller designed sets of competing, collaborating or parasitic individuals.

      Looking ahead, additional stages of investigations will also include mutations - to investigate the kinetics of speciation, and, in an even further stage, the interplay between evolution kinetics and dynamical mutation of resources.

      I have a few smaller points:

      • It would be very useful to provide the expected dynamic range of binding free energies (in terms of DeltaG and omega): what is the maximum binding free energy for the perfect complement?

      The NUPACK-computed binding free energy of a 20 basis-long oligomer complementary to the resource (𝜔=20) is -24.36 Kcal/mol for Oligo1 and -23.08 Kcal/mol for oligo 2. This is the best answer we can offer to the reviewer’s request, since the maximum binding free energy of DNAi individuals (much longer than the target strand) would include contributions from the unpaired bases. Indeed, the values give above are approached by the left tail of the distribution of Fig. 3a, which however includes DNAi self-energies.<br /> The perfect complement binding free energy is now cited in the text as a reference for the dynamical range of DeltaG (p4 lines 151-152).

      • How is the number of captured DNA molecules quantified? Is 10^12 measured, estimated, or hypothesized?

      The number of sequences was calculated from data obtained from 260 nm absorbance quantification. We have now added this information in the Methods, Selection Phase” section.

      Reviewer #2:

      Summary:

      In this manuscript, the authors introduced ADSE, a SELEX-based protocol to explore the mechanism of emergency of species. They used DNA hybridization (to the bait pool, "resources") as the driving force for selection and quantitatively investigated the factors that may contribute to the survival during generation evolution (progress of SELEX cycle), revealing that besides individual-resource binding, the inter- and intra-individual interactions were also important features along with mutualism and parasitism.

      Strengths:

      The design of using pure biochemical affinity assay to study eco-evolution is interesting, providing an important viewpoint to partly explain the molecular mechanism of evolution.

      Weaknesses:

      Though the evidence of the study is somewhat convincing, some aspects still need to be improved, mostly technical issues.

      Major:

      1) There are a few technical issues that the authors should clarify in the manuscript to make the analysis more transparent:

      1.1) To my understanding, it is difficult to guarantee the even distribution of different species (individuals) in the initial individual pool. Even though the authors have shown in Fig. 2a that the top 10 sequences take up ~ 0% in the pool, it remains unclear how abundant these top and bottom representative sequences are, given the huge number of the pool (10E15). Can the author show the absolute number of these sequences in different quantiles? Please show both Oligo sets.<br /> : First, we thank the reviewer for both positive and critical comments that have guided us in reformulating or clarifying some messages of our work.

      As for this specific point: 10E15 is a small number compared to 4^50 = 10E30, the number of possible sequences of length 30. Thus, we don’t expect more than one individual per sequence in the initial pool. However, sequencing requires a preparation amplification, which may lead to detecting a few sequences with more than one individual.

      Specifically, in the initial pool of Oligo 1, the most abundant individual (of sequence GAACTAAAGGGGCGGTGTCCACTTGCCTGTAGTGGTTATCAGTCCGGTTG)has 3 copies. The 0.7% of the sequences has 2 copies, while the vast majority of strings (99.3% on a sample of about 1.5 x 10E6 sequenced DNAi) is present in one copy only. A similar situation holds for Oligo 2, with 4 DNAi present in 3 copies and the 0.8% of the sequences (in a pool of 2 x 10E6 DNAi) in 2 copies.

      It is worth noticing that none of the 10 most abundant species in the last cycle is present in the sample. Indeed, the fraction of the pool which is sequenced is removed from the population that undergoes evolution (as now specified in p2, line 104). We specified in the text (p2, lines 69-70, p3 lines 94-96) the fact that in the initial pool no sequence is expected to be present in more than one individual.

      1.2) The author claimed that they used two different oligo sets (Oligo1 and Oligo2) in this study. It is unclear which data was used in the presentation. How reproducible are they? Similar to this concern, how reproducible if the same oligo set was used to repeat the experiment?

      The oligo used in the main text was declared in Methods, Replica section. It is now declared also in the main text (p3 lines 106-108 and in the captions of Figure 2, Figure 3 and Figure 4). Reproducibility is addressed in: Figure 2-figure supplement 5; Figure 2-figure supplement 6; Appendix 2: Results of the experimental replica.

      It should also be noted that two starting pools of random 50mers are necessarily disjoint sets for the same reason discussed in the previous answer: the probability of common sequences in two 10E15 selections from a 10E30 is negligibly small. Thus, it is expected that each time a new evolution experiment is started, different dominant sequences are found. However, the statistical properties of the DNAi pool during the evolution process of Oligo1 and Oligo2 are similar as discussed in Appendix 2 of the paper.

      1.3) PCR and illumina sequencing itself introduced selection bias. How would the analysis eliminate them? The authors only discussed the errors created during PCR cycles (page 3, lines 115-122). However the PCR itself would prefer to amplify some sequences over the others (e.g. with high GC content). Similarly, the illumina sequencing would be difficult to sequence the low complexity sequences. How would this be circumvented?

      Yes, both PCR and Illumina sequencing have some known biases in the amplification process (e.g. sequencing of homopolymers or amplification of GC-rich sequences) that are intrinsic to the used techniques. Regarding PCR, we implemented a thermal protocol optimized for our chosen experimental setup, characterized by very short denaturation, annealing and amplification steps performed at high temperatures. Regarding Illumina sequencing, we can’t rule out a bias against specific sequences (e.g, homopolymers), which however should not be captured during the selection step, due to the design of the resource. Also, the libraries subjected to sequencing are characterized by a low complexity: according to the experimental design, the first and last 25 nucleotides are the same for all DNAi, the only differences being in the central 50 nt-long sequence. It is known that a low complexity library might encounter problems during sequencing due to the design of Illumina instruments: nucleotide diversity, especially in the first sequencing cycles, is critical for cluster filtering, optimal run performance and high-quality data generation. To overcome this limitation, the obtained libraries were run together with more complex and diverse library preparations: the ADSE sequences were about 1-2% of the total reads per run, corresponding to only a few million reads.

      This discussion is now in Appendix 1, Intrinsic limitations of the molecular approach.

      1.4) Some DNA sequences would bind to the beads instead of the resource sequence coated on them. Should the author run the experiment using bead alone as a control?<br /> : We performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

      After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

      This indicates that the drift due to non-specific binding (+ PCR amplification) is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.<br /> This part is now discussed in Appendix 1, Experimental controls.

      2) It would be interesting to study the impact of environmental factors, for example, changing pH, salt concentration, and detergent. Would these factors accelerate/decelerate the evolution?

      We agree that the approach we propose and the set of results we obtained call for further investigations. However, performing these additional experiments, which would require a minimum of 6 generations each, is a long and expensive job, which we have started and will not be completed in the near future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population in the selected conditions and leave the exploration of new conditions, potentially opening new evolutionary scenarios, to a future contribution. In fact, our aim was to show that through our platform we can indeed observe fundamental elements of evolution in a non-biological system, which, in the set of chosen parameters, we do.

      3) The concentration of individual oligo is apparently one of the most important factors in determining the interactions. In later cycles, some oligos become dominant, namely with extremely higher concentrations compared to their concentration in earlier cycles. This would definitely affect its interaction with resources, or self-interaction, or interaction with other oligos in the pool. However, the authors failed to discuss this factor, which may explain the exponential enrichment in later cycles.

      We agree with the reviewer that this is an important point, but we disagree that we have not discussed it. We introduce the topic at the end of the “Null Model and Eco-evolutionary Algorithm”, where we comment on the change of the gamma parameter by saying that there must be a shift in the evolution process, first dominated by the interactions with the resources, and in later stages by some other factors (lines 227230) that we then discuss in “Self and mutual DNAi interactions are evolutionary drivers”. In this latter chapter and in the following, we indeed discussed the effects of mutual and self interactions between DNAi.

      Indeed, a key point in our paper is the change in the gamma parameter necessary to match the IBEE model to experiments, as it is now more openly stated (p5 lines 217218 where we also mention figure 2-supplement 8 which clearly shows the necessity of a variable gamma). The two regimes enlightened by the gamma value must reflect a change in the competition for the resources and interactions among species. In the first generations, where the diversity of species is large (there are few strings for each species) and binding to the resources generally very week (small <omega>), the affinity with the resource is the main driving force (fast growth of <omega>), while mutual interactions remain too random to favor any species in particular. In the later cycles instead, when <omega> becomes large enough to provide a significant stability to the resource-binding of the majority of species, the dominating species compete more intensively on the basis of their structure and capacity of self-defense, parasitism and mutualism, a condition in which evolution affects more modifications in sequences than in <omega>.

      Certainly, our understanding of this shift is based on statistical behavior and it is inferential, based on the study of specific DNAi described in the last part of the manuscript. For a better molecular model, more experiments with selected DNAi competing, cooperating or being parasitic would be necessary, with the final aim of defining a predictive fitness function. Alas, this requires months of further investigation. :

      4) The author observed the different behaviors of medium 𝜔 in early and late cycles, referring to Fig 2h. Using the IBEE model, they found out it is the change of gamma. However, the authors did not further discuss the molecular mechanism. It could be very interesting to understand the evolutionary change of these individuals.

      This comment might be related to the previous one. It is true that our discussion and understanding of the whole process is statistical, and misses a molecular model to predict the value of gamma.

      However, the specific behavior that the reviewer asks about (those in Fig. 2h) is not related to the change in gamma. Even if gamma remains as in the first part of the evolution (gamma = 3), the species with overlap between 6 and 10 would first grow in number and later decrease. Indeed, during the first cycles they have an advantage with respect to the majority of species with lower maximum overlap, a condition that favors their amplification. However, in the second stage of the evolution dominant species with a larger affinity emerge and outcompete the individuals of this class. We added a sentence in the text to clarify this point (p7 lines 227-229).

      5) In Figure 2f, some high w become quite missing. Should the authors give some interpretation? It is not observed in cycle 12 though (panel e).

      Such an effect is just due to under-sampling. In a pool of 10^n oligomers, any sequence with a given 𝜔 with P(omega) < 10E-n will have a vanishing probability to appear in that sample.<br /> At cycle 12 the overall number of sequenced strands is larger than at cycle 24, due to the growing presence of PCR by-products. Thus, the right tail of the cyan distribution at the last cycle is sampled with less accuracy than at cycle 12. We have added a sentence in the revised manuscript (p5 lines 177-178) to clarify this point.

      6) It would be interesting to further explore if another type of selection resource is used, for example protein that binds to particular sequences, i.e. transcription factors. Previous studies have used a large amount of sequence-specific transcription factors to run SELELX. Since the data have existed there, why not explore?

      This is an interesting suggestion: can we use data from “ordinary” SELEX favoring specific sequences to explore sequence evolution? Two limitations make us a bit skeptical on this path: first, the consensus sequences of DNA-binding proteins are rather short and typically target dsDNA rather than ssDNA; second, the free energy of interaction is known only for the consensus sequence but not for sequences with all possible mutations with respect to the consensus sequence, making very hard to develop any molecular understanding of the process.

      Minor:

      1) There is no figure legend or in-text citation of Figure 2b.

      2) Please correct "⁃C" with "{degree sign}C" in lines 470, 471, 472, 477 et al.

      3) Typos and grammar issues should be corrected. Examples are shown below (but not limited to these only):

      • mixed use of past and present tense.

      • Line 152, "basis" should be "bases".

      • Line 277, "a impediment" should be "an impediment"

      • Line 278, "a major deadly threats" should be "major deadly threats"<br /> :<br /> We are sorry for the mistakes, and we have corrected them. Many thanks to the reviewer!

    1. Author Response

      Reviewer #1 (Public Review):

      The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain. Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies. The authors could have also better integrated the RNAseq findings into mechanistic experiments, including testing whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain.

      While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain.

      We agree with the reviewer regarding the importance of examining sex differences on stimulation-evoked BBB changes. To address this issue we have: (1) clarified in the methods section that the human study involved both males and females; (2) added a section to the discussion highlighting the male bias as a key limitation of our animal experiments; and (3) stated that future work should examine whether stimulation-evoked BBB changes differ between makes and females.

      Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies.

      We are grateful for this comment and agree with the reviewer that the potential effects of anesthesia should be discussed. We have added the following discussion paragraph:

      “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018).”

      Reviewer #2 (Public Review):

      Summary:

      This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the bloodbrain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium, and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood-brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity, and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a breakdown in the blood-brain barrier following activation of brain areas through a motor task.

      Strengths:

      This study is novel and the results are potentially important as they demonstrate an unexpected breakdown of the blood-brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity.

      The strengths of the study are:

      1) The use of an in vivo model with multiple methods to investigate the blood-brain barrier response to a forelimb stimulation.

      2) The determination of a potential functional role for the observed leakage of the blood-brain barrier from both a genetic and electrophysiological viewpoint.

      3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.

      4) Preliminary experiments demonstrating a similar observation of activity-dependent breakdown of the blood-brain barrier in humans.

      Weaknesses:

      There are both conceptual and experimental weaknesses.

      1) The stimulation is in an animal anesthetized with ketamine, which can affect critical receptors (ie NMDA receptors) in synaptic plasticity.

      We agree that the potential effects of anesthesia should be considered. The Discussion was revised to address this point: “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018)”

      2) The stimulation protocol is prolonged and it would be helpful to know if briefer stimulations have the same effect or if longer stimulations have a greater effect ie does the leakage give a "readout" of the stimulation intensity/length.

      Thank you for this important comment. We are also very curious about the potential relationship between stimulation magnitude/duration and subsequent leakage and have added the following statement to the discussion:

      “Future studies should also explore the effects of stimulation magnitude/duration on BBB modulation, as well as the stimulation threshold between physiological and pathological increase in BBB permeability.”

      Our current findings indicate that a one-minute stimulation does not affect vascular permeability or SEP and we aim to test additional stimulation paradigms in future studies.

      3) For some of the experiments (see below), the numbers of animals are low and the statistical tests used may not be the most appropriate, making the results less clear cut.

      We appreciate this comment and have revised the statistical analysis of Figure 1J,K. We now use a nested t-test to test for differences between rats (as opposed to sections). The differences remain significant (EB, p=0.0296; Alexa, p=0.0229). The text was modified accordingly.

      4) The experimental paradigms are not entirely clear, especially the length of time of drug application and the authors seem to try to detect enhancement of a blocked SEP.

      Thank you for pointing this out. Figures 2&3 were revised for clarification and a ‘Drug Application’ subsection was added to the methods section.

      5) It is not clear how long the enhancement lasts. There is a remark that it lasts longer than 5 hours but there is no presentation of data to support this.

      Thank you for this comment. As the length of experiments differed between animals, the exact length could not be specifically stated. To clarify this point, we revised the text to indicate that LTP was recorded until the end of each experiment (between 1.5-5 hours, depending on the condition the animal was in). We also added a panel to figure 2 (Figure 2d) with exemplary data showing potentiation 60, 90, and 120 min post stimulation.

      6) The spatial and temporal specificity of this effect is unclear (other than hemispheric in rats) and even less clear in humans.

      Our animal experiments (using both in vivo imaging and histological analysis) showed no evidence of BBB modulation outside the cortical somatosensory area corresponding to the limbs. We looked at the entirety of the coronal section of the brain and found enhancement solely in the somatosensory area corresponding to limb. The right side of panels h and i in Figure 1 show an x20 magnification of the section, focusing on the enhanced area. The whole section was not shown, as no fluorescence was found outside the magnified area. Moreover, our quantification showed that the enhancement was specific to the contralateral and not ipsilateral somatosensory cortex (Figure 1 j-k).

      We agree that temporal specificity needs to be further explored, and we have now stated that in the discussion: “Future studies are needed to explore the BBB modulating effects of additional stimulation protocols – with varying durations, frequencies, and magnitudes. Such studies may also elucidate the temporal and ultrastructural characteristics that may differentiate between physiological and pathological BBB modulation.”

      We also agree that larger studies are needed to better understand the specificity of the observed effect in humans, and to account for potential inter-human variability in vascular integrity and brain function due to different schedules, diets, exercise habits, etc.

      8) The experimenters rightly use separate controls for most of the experiments but this is not always the case, also raising the possibility that the application of drugs was not done randomly or interleaved, but possibly performed in blocks of animals, which can also affect results.

      Thank you for pointing out this lack of clarity. We have now highlighted that drug application was done randomly.

      9) Methyl-beta-cyclodextrin clears cholesterol so the effect on albumin transport is not specific, it could be mediating its effect through some other pathway.

      We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10uM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is observed at 5mM mβCD and not at 2.5mM/5mM (Koudinov & Koudinova, 2001). This point was added to the discussion.

      10) Since the breakdown of the blood-brain barrier can be inhibited by a TGF-beta inhibitor, then this implies that TGFbeta is necessary for the breakdown of the blood-brain barrier. This does not sit well with the hypothesis that TGF-beta activation depends upon blood-brain barrier leakage.

      Thank you for pointing out this lack of clarity. We have added a discussion paragraph that clarifies our hypothesis: “As mentioned above, albumin is a known activator of TGF-β signaling, and TGF-β has a well-established role in neuroplasticity. Interestingly, emerging evidence suggests that TGF-β also increases cross-BBB transcytosis (Betterton et al., 2022; Kaplan et al., 2020; McMillin et al., 2015; Schumacher et al., 2023). Hence, we propose the following two-part hypothesis for the TGF-β/BBB-mediated synaptic potentiation observed in our experiments: (1) prolonged stimulation triggers TGF-β signaling and increased caveolae-mediated transcytosis of albumin; and (2) extravasated albumin induces further TGF-β signaling, leading to synaptogenesis and additional cross-BBB transport – in a self-reinforcing positive feedback loop. Future research is needed to examine the validity of this hypothesis.

      Reviewer #3 (Public Review):

      Summary:

      This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood-brain barrier (BBB) was opened by prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity-related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggested that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB.

      Strengths:

      The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves the opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data.

      Weaknesses:

      The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

      The conclusions are not compelling however because of a lack of explanation of methods and quantification.

      Thank you for this comment. In the revised paper, we expanded the Methods section to better describe the procedures and approaches we used for data analysis.

      It also is not clear whether the prolonged stimulation in the rat was normal conditions.

      We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

      1) In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

      2) Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

      3) The levels of SEP potentiation we observed are similar to those reported in:

      a) Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

      b) Humans following a 15 min task (McGregor et al., 2016).

      This important point is now presented in the discussion.

      Reviewer #1 (Recommendations For The Authors):

      The discussion would benefit from additional discussion of the potential impacts of sex and anesthesia in their findings.

      We agree with the reviewer and have added the following paragraph to the discussion:

      “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can potentially alter neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketaminexylazine anesthesia, which unlike other anesthetics, was shown to maintain robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018). Another limitation of our animal study is the potentially non-specific effect of mβCD – an agent that disrupts caveola transport but may also lead to cholesterol depletion (Keller & Simons, 1998). To mitigate this issue, we used a very low mβCD concentration (10uM), orders of magnitude below the concentration reported to deplete cholesterol (Koudinov et al). Lastly, our animal study is limited by the inclusion of solely male rats. While our findings in humans did not point to sex-related differences in stimulation-evoked BBB modulation, larger animals and human studies are needed to examine this question.”

      The figure text is quite small.

      Thank you for pointing this out, we revised all figures and increased font size for clarity.

      Including pharmacological concentrations within the figure legends would improve the readability of the manuscript.

      Thank you for this suggestion, the figure legends were modified accordingly.

      In methods for immunoassays the 5 groups could be more clear by stating that there are 3 timepoints for stimulation experiments. There is a typo in this section where the 24-hour post is stated twice in the same sentence.

      Thank you for pointing this out, the text was modified accordingly.

      Reviewer #2 (Recommendations For The Authors):

      1) In Figure 1, J and K seem to indicate that in these experiments the statisitics were done per slice and not per animal. This is not a reasonable approach, a repeat measure ANOVA or averaging for each animal are more appropriate statistical approaches.

      We thank the reviewer for pointing this out. The statistical analysis for Figure 1j,k was modified. We now use a nested ttest to test for differences between rats and not sections. The differences are still significant (EB, p=0.0296; Alexa, p=0.0229). The manuscript was modified accordingly.

      2) In Figure 2, the protocol does not seem to give much idea about time course. There was a stimulation test for 1 minute before and then 1 minute after the 30-minute stimulation train. How was potentiation assessed for the next 5 hours and where are the data?

      Potentiation was assessed by repeating 1min test stim every 30 min for the duration of the experiment, we added a panel to show late potentiation, see response above.

      3) In Figure 2, there is a notable lack of controls eg the effect of sham stimulation and application of saline. These are important as the drift of response magnitude can be a problem in long experiments.

      We did test for the potential presence of response drift, by examining whether SEPs of non-stimulated animals change over time (at baseline, 30 or 60 minutes of recording; n=6). No statistical differences were found. Our analysis focused on using each animal as its own control (i.e., comparing baseline SEP to SEP post albumin perfusion), because SEP studies highlight the importance of comparing each animal to its own baseline, due to the large inter-animal variability (All et al., 2010; Mégevand et al., 2009; Zandieh et al., 2003).

      4) Figure 3 a is not clear – were the drugs applied throughout?

      Thank you for pointing this out. We have revised Figure 3 a to show that the drugs were applied for 50 min before the stimulation.

      5) In Figure 3 panel d is repeated in panel j. This needs correcting

      Thank you. This mistake was fixed.

      6) In LTP-type experiments usually the antagonist is applied during the stimulation and then washed out. This avoids the problem in this figure in which CNQX effectively blocks transmission and so it is not possible to detect any enhancement if it were there. Eg in panel e, CNQX block transmission, and then the assessment is performed when the AMPA receptors are blocked after 30 minutes of stimulation. If receptors are blocked no enhancement will be detectable. Moreover, surely the question is the ratio of the effect of 30-minute stimulation on the SEP in the presence of CNQX and so the statistics should be done on the fold change in the SEP following 30-minute stimulation in the presence of CNQX.

      Thank you. The protocol might have been misrepresented in the original figure. We modified Fig 3a to clarify that the antagonists were indeed washed out upon stimulation start to make sure the receptors are not blocked during the test stimulation following the 30 min stimulation. In addition, we tested for the difference in fold change between 30 min stim, and 30 min stimulation following antagonists wash-in (Fig 3f and Fig S2a).

      7) Interesting in Figure f, stimulation, albumin, and AP5 all seem to have the same enhancement of the SEP. Is the lack of effect of 30-minute stimulation in the presence of AP5, a ceiling effect ie AP5 has enhanced the SEP, and no further enhancement from stimulation is possible.

      This is a very interesting point that will require further research.

      8) SJN seems to block neurotransmission. What is the mechanism? The same analysis as for CNQX should be performed ie what is the fold change not compared to baseline but in the presence of SJN.

      Our quantification showed that SJN did not significantly reduce the SEP max amplitude, and we therefore did not include this graph in the figure.

      9) Please acknowledge that the effect of mbetaCD is non-specific. There is a large literature on the effects of cholesterol depletion on LTP.

      We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10µM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is only observed at a concentration of 5mM (Koudinov & Koudinova, 2001). This point is now discussed under the discussion paragraph describing the study’s limitations.

      10) k&l seem to have used the same control in which case they should not be analysed separately (they are all part of the same experiment).

      We agree with the reviewer and have revised the figure accordingly.

      11) The difference in gene expression in Figure 4 would be more convincing if it could be prevented by for example a TGFbeta inhibitor.

      We agree and acknowledge the impact such experiments could provide. We plan to incorporate these experiments into our future studies.

      12) Figure 5 seems to indicate bilateral and widespread BBB modulation arguing that this may be a non-specific effect. Panel g should look at other neocortical regions eg occipital cortex.

      We agree and thank the reviewer for this comment. We revised the figure to include other cortical areas, such as the frontal and occipital cortices (Figure 5g)

      Minor comments

      1) Paired data eg in Fig 2D are better represented by pairing the dots usually with a line.

      2) Please correct the %fold baseline in axes in graphs which show % change for baseline.

      3) Figure 4 is not correctly referred to in the text.

      We agree with all the points raised by the reviewer and revised the figures and text accordingly.

      Reviewer #3 (Recommendations For The Authors):

      The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

      Major concerns:

      Methods need more explanation. Rationales need more justification. Examples are provided below.

      Throughout many sections of the paper, sample sizes and stats are often missing. For stats, please provide p-values and other information (tcrit, U statistic, F, etc.)

      Thank you, we added the relevant information where it was missing throughout the manuscript.

      For transcriptomics, they might have found changes in BBB-related genes if they assayed vessels but they assayed the cortex.

      We agree with the reviewer that this would be a very interesting future direction. The present study could not include this kind of analysis due to lack of access to vasculature isolation methods or single-cell RNA seq.

      What were the inclusion/exclusion criteria for the subjects?

      Thank you for pointing out this lack of clarity. The methods section (under ‘Magnetic Resonance Imaging’ – ‘Participants’) was expanded to include the following:

      “Male and female healthy individuals, aged 18-35, with no known neurological or psychiatric disorders were recruited to undergo MRI scanning while performing a motor task (n=6; 3 males and 3 females). MRI scans of 10 sex- and age- matched individuals (with no known neurological or psychiatric disorders) who did not perform the task were used as control data (n=10; 5 males and 5 females.

      Were they age and sex-matched?

      They were, indeed, age and sex-matched. This was now clarified in the relevant Methods section.

      Were there other factors that could have influenced the results?

      Certainly. Human subjects are difficult to control for due to different schedules, diets, exercise habits, and other factors that may impact vascular integrity and brain function. Larger multimodal studies are needed to better understand the observed phenomenon.

      Fig. 1. Images are very dim. Text here and in other figures is often too small to see. Some parts of the figures are not explained.

      Our apologies. Figures and legends were revised accordingly.

      Fig 2a, f. I don't see much difference here- do the authors think there was?

      We agree that the difference may not be visually obvious. The quantification of trace parameters (amplitude and area under curve) does, however, reveal a significant SEP difference in response to both stimulation (panels X and y) and albumin (panels z and q).

      Fig 3 d and j seem the same.

      We thank the reviewer for noticing. This was a copy mistake that was now rectified.

      Lesser concerns and examples of text that need explana9on:

      Introduction

      Insulin-like growth factor is transported. From where to where?

      The text was edited to clarify that this was cross-BBB influx of insulin-like growth factor-I.

      RMT that underlies the transport of plasma proteins was induced by physiological or non-physiological stimulation.

      This was shown without stimulation, in normal physiology of young and aged healthy mice. The text was edited to clarify this point.

      What was the circadian modulation that was shown to implicate BBB in brain function?

      The text was edited for clarity.

      Results

      When the word stimulation is used please be specific if whiskers are moved by an experimenter, an electrode is used to apply current, etc.

      We have now moved the ‘Stimulation protocol’ section closer to beginning of the Methods and emphasized that we administered electrical stimulation to the forepaw or hindlimb using subdermal needle electrodes.

      Please explain how the authors are convinced they localized the vascular response.

      The vascular response was localized via: (1) visual detection of arterioles that dilated in response to stimulation (due to functional hyperemia / neurovascular coupling) [figure 1 d]; and (2) quantitative mapping of increased hemoglobin concentration (Bouchard et al., 2009) [Figure 1 b]. This is now mentioned in the methods (under ‘In vivo imaging’) and results (under the ‘Stimulation increases BBB permeability’).

      "30 min of limb stimulation" means what exactly? 6 Hz 2mA for 30 min?

      Thank you. The text was revised for clarity (Methods under ‘Stimulation protocol’):

      “The left forelimb or hind limb of the rat was stimulated using Isolated Scmulator device (AD Instruments) attached with two subdermal needle electrodes (0.1 ms square pulses, 2-3 mA) at 6 Hz frequency. Test stimulation consisted of 360 pulses (60 s) and delivered before (as baseline) and after long-duration stimulation (30 min, referred throughout the text as ‘stimulation’). In control and albumin rats, only short-duration stimulations were performed. Under sham stimulation, electrodes were placed without delivering current.”

      Histology that was performed to confirm extravasation needs clarification because if tissue was removed from the brain, and fixed in order to do histology, what is outside the vessels would seem likely to wash away.

      Thank you for pointing out the need to clarify this point. The Histology description in the Methods section was revised in the following manner:

      “Albumin extravasacon was confirmed histologically in separate cohorts of rats that were anesthetized and stimulated without craniotomy surgery. Assessment of albumin extravasacon was performed using a well-established approach that involves peripheral injection of either labeled-albumin (bovine serum albumin conjugated to Alexa Flour 488, Alexa488-Alb) or albumin-labeling dye (Evans blue, EB – a dye that binds to endogenous albumin and forms a fluorescent complex), followed by histological analysis of brain tissue (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Obermeier et al., 2013; Veksler et al., 2020). Since extravasated albumin is taken up by astrocytes (Ivens et al., 2007; Obermeier et al., 2013), it can be visualized in the brain neuropil after brain removal and fixation (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Veksler et al., 2020). Five rats were injected with Alexa488-Alb (1.7 mg/ml) and five with EB (2%, 20 mg/ml, n=5). The injections were administered via the tail vein. Following injection, rats were transcardially perfused with…”

      It is not clear why there was extravasacon contralateral but not ipsilateral if there are cortical-cortical connections.

      Interpersonally, we also did not observe ipsilateral SEP in response to limb stimulation, with evidence of SEP and BBB permeability only in the contralateral sensorimotor region. This finding is consistent with electrophysiological and fMRI studies showing that peripheral stimulation results in predominantly contralateral potentials (Allison et al., 2000; Goff et al., 1962).

      After injection of Evans blue or Alexa-Alb, how was it shown that there was extravasacon?

      Extravasalon in cortical sections was visualized using a fluorescent microscope (Figure 1 h-i). Since extravasated albumin is taken up by astrocytes, fluorescent imaging can be used for visualizing and quantifying labeled albumin (Ahishali & Kaya, 2020; Ivens et al., 2007; Knowland et al., 2014). Here is the relevant methods excerpt:

      “Coronal sections (40-μm thick) were obtained using a freezing microtome (Leica Biosystems) and imaged for dye extravasacon using a fluorescence microscope (Axioskop 2; Zeiss) equipped with a CCD digital camera (AxioCam MRc 5; Zeiss).”

      How is a sham control not stimulated - what is the sham procedure?

      In the sham stimulation protocol electrodes were placed, but current was not delivered. A section titled ‘Stimulation protocol’ was added to the methods to clarify this point.

      What was the method for photothrombosis-induced ischemia?

      The procedure for photothrombosis-induced ischemia is described under the Methods section ‘Immunoassays’ – ‘Enzyme-linked immunosorbent assay (ELISA) for albumin extravasalon’:

      “Rats were anesthetilzed and underwent … photothrombosis stroke (PT) as previously described (Lippmann et al., 2017; Schoknecht et al., 2014). Briefly, Rose Bengal was administered intravenously (20 mg/kg) and a halogen light beam was directed for 15 min onto the intact exposed skull over the right somatosensory cortex.”

      Fig 1d. All parts of d are not explained.

      Thank you for pointing this out. In the revised manuscript, the panels of this figure were slightly reordered, and we made sure all panels are explained in the legend.

      e. Is the LFP a seizure? How physiological is this- it does not seem very physiological.

      Thank you for your comment. We believe that this activity is not a seizure because it lacks the typical slow activity that corresponds to the “depolarizalon shir” observed during seizures (Ivens et al., 2007; Milikovsky et al., 2019; Zelig et al., 2022).

      f. Permeability index needs explanation. How was the area chosen for each rat? Randomly? Was it the same across rats?

      We have now revised the Methods section to provide a clearer description of the permeability index calculation and the choice of the imaging area:

      “Across all experiments, acquired images were the same size (512 × 512 pixel, ~1x1 mm), centered above the responding arteriole. Images were analyzed offline using MATLAB as described (Vazana et al., 2016). Briefly, image registration and segmentation were performed to produce a binary image, separating blood vessels from extravascular regions. For each extravascular pixel, a time curve of signal intensity over time was constructed. To determine whether an extravascular pixel had tracer accumulation over time (due to BBB permeability), the pixel’s intensity curve was divided by that of the responding artery (i.e., the arterial input function, AIF, representing tracer input). This ratio was termed the BBB permeability index (PI), and extravascular pixels with PI > 1 were identified as pixels with tracer accumulation due to BBB permeability.”

      g. For Evans blue and Alexa-Alb was the sample size rats or sections?

      Thank you for this question. We revised the statistical analysis for Figure 1j,k to appropriately asses the differences between rats. We used a nested t-test to test for differences between rats (and not sections). The differences remained significant (EB, p=0.0296; Alexa, p=0.0229) and the text was modified accordingly.

      h, i, j need more contrast and/or brightness to appreciate the images. Arrows would help. The text is too small to read.

      Thank you. This issue was addressed in the revised paper.

      To induce potentiation, 6 Hz 2 mA stimuli were used for 30 min. Please justify this as physiological.

      Thank you for the comment. We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

      1. In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

      2. Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

      3. The levels of SEP potentiation we observed are similar to those reported in:

      a. Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

      b. Humans following a 15 min task (McGregor et al., 2016).

      We have revised the Discussion of the paper to clarify this important point.

      The test stimulus to evoke somatosensory evoked potentials was 1 min. Was this 6 Hz 2 mA for 1 min? Please justify.

      Yes. We chose these parameters as these ranges were shown to induce the largest changes in blood flow (with laserdoppler flowmetry) and summated SEP (Ngai et al., 1999), corresponding with our findings. We also show that these stimulation parameters do not induce changes in BBB permeability nor synaptic potentiation, therefore served as test control.

      How long after the 30 min was the test stimulus triggered- immediately? 30 sec afterwards?

      The test stimulus was applied 5 min afterwards to allow for BBB imaging protocol (now explained in the Methods section).

      How were amplitude and AUC measured? Baseline to peak? For AUC is it the sum of the upward and downward deflections comprising the LFP?

      Yes, and yes. This is now clarified in the ‘Analysis of electrophysiological recordings’ section in the Methods.

      How was the same site in the somatosensory cortex recorded for each animal?<br /> Potentiation was said to last >5 hrs. How often was it measured? Was potentiation the same for the amplitude and the AUC?

      The location of the cranial window over the somatosensory cortex was the same in all rats. The location of the specific responding arteriole may change between animals, but the recording electrode was places around the responding arteriole in the same approaching angle and depth for all animals.

      As the length of experiments differed between animals, the exact length could not be specifically stated. We therefore revised the text to clarify that LTP was recorded until the end of each experiment (depending on the animal condition, between 1.5-5 hours) and added a panel to figure 2 (Figure 2f) with exemplary data showing potentiation 120 min (2hr) post stimulation.

      Why was 25% of the serum level of albumin selected- does the brain ever get exposed to that much? Was albumin dissolved in aCSF or was aCSF chosen as a control for another reason?

      Yes, albumin was dissolved in aCSF and the solution was allowed to diffuse through the brain. The relatively high concentration of albumin was chosen to account for factors that lower its effective tissue concentration:

      1. The low diffusion rate of albumin (Tao & Nicholson, 1996).

      2. The likelihood of albumin to encounter a degradation site or a cross-BBB efflux transporter (Tao & Nicholson, 1996; Zhang & Pardridge, 2001).

      Figure 2.

      a. Please show baseline, the stimulus, and aftier the stimulus.

      Please point out when there was stimulacon.

      What is the inset at the top?

      The inset on top is the example trace of the stimulus waveform, the legend of the figure was modified for clarity.

      b. Please show when the stimulus artifact occurred. The end of the 1-minute test stimulus period is fine. Why are the SEPs different morphologies? It suggests the different locations in the cortex were recorded.

      What is shown is the averaged SEP response over 1min test stimulus, each SEP is time locked to each stimulus. Regarding SEP waveform, it does indeed show different morphology between animals, as sometimes different arterioles respond to the stimulation, and we localize the recording to the responding vessel in each rat. However, in each rat the recording is only from one location. Once the electrode was positioned near the responding arteriole it was not moved.

      d, e. What are the stats?

      h, i. Add stats. Are all comparisons Wilcoxon? Please provide p values.

      The comparisons were performed with the Wilcoxon test. We now state that and provide the exact p values.

      j. What was selected from the baseline and what was selected during Albumin and how long of a record was selected?

      What program was used to create the spectrogram?

      What is meant by changes at frequencies above 200 Hz, the frequencies of HFOs?

      The Method section (under ‘Electrophysiology – Data acquisition and analyses’) has been revised for clarification. Spectrogram was created with MATLAB and graphed with Prism. For analysis, we selected a 10 min recorded segment before starting albumin perfusion, and 10 min after terminating albumin perfusion.

      When the cortial window was exposed to drugs, what were concentrations used that were selective for their receptor? How long was the exposure?

      Was the vehicle tested?

      We have revised the Methods section (under ‘Animal preparation and surgical procedures - Drug application’) to clarify the duration and concentration used and justification. All blockers were exposed for 50 min. The vehicle was an artificial cerebrospinal fluid solution (aCSF).

      For PSD-95, what was the area of the cortex that was tested?

      Were animals acutely euthanized and the brain dissected, frozen, etc?

      We have revised the Methods section (under ‘Immunoassays’) for clarity.

      What is mbetaCD?

      The full term was added to the results section. It is also mentioned in the Methods.

      Is SJN specific at the concentration that was chosen? Did it inhibit the SEP?

      In the concentration used in our experiments, SJN is a selective TGF-β type I receptor ALK5 inhibitor (see (Gellibert et al., 2004)).

      Fig. 3b. It looks like CNQX increased the width of the vessels quite a bit. Please explain.

      For AP5, very large vessels were imaged, making it hard to compare to the other data.

      The vascular dilation in response to the stimulation under CNQX was similar to that seen under “normal” conditions (i.e. aCSF). As for AP5, in some experiments the responding arteriole was in close proximity to a large venule that cannot be avoidable while imaging. For quantification we always measured arterioles within the same diameter range.

      e. Sometimes CNQX did not block the response after 30 min stimulation. Why?

      CNQX is washed out before the 30 min stimulation starts, so it is not expected to block the response to stimulation. However, in some cases the response to stimulation was lower in amplitude, likely due to residual CNQX that did not wash out completely.

      Regarding DEGs, on the top of p 10 what are the percentages of?

      In this analysis we tested in each hemisphere how many genes expressed differentially between 1 and 24 hours post stimulation (either up- or down- regulated). The results were presented as the percentages of differentially expressed genes in each hemisphere (13.2% contralateral, and 7.3% ipsilateral). The text was rephrased for clarity.

      Please add a ref for the use of the JSD metric methods and support for its use as the appropriate method. Other methods need explanation/references.

      References were added to the text to clarify. The Jensen-Shannon Divergence metric is commonly used to calculate the statistical pairwise distance among two distributions (Sudmant et al., 2015). From comparing a few different distance metric calculations including JSD, our results were similar irrespective of the distance metric applied. Therefore, we demonstrate the variability between paired samples of stimulated and non-stimulated cortex of each animal at two time points following stimulation (24 h vs. 1 h) using JSD.

      What synaptic plasticity genes were selected for assay and what were not?

      What does "largely unaffected" mean? Some of the genes may change a small amount but have big functional effects.

      The selected genes of interest were taken from a large list compiled from previous publications (see (Cacheaux et al., 2009; Kim et al., 2017)) and are well documented in gene ontology databases and tools (e.g., Metascape, (Zhou et al., 2019)).

      We agree that the term ‘largely unaffected’ is suboptimal, and we rephrased this section of the results to indicate that “No significant differences were found in BBB or inflammation related genes between the hemispheres”. We also agree that a small number of genes can have big functional effects. Future studies are needed to better understand the genes underlying the observed BBB modulation.

      Please note that Slc and ABCs are not only involved in the BBB.

      Thank you. We modified the text to no longer specify that these are BBB-specific transporters.

      Please explain the choice of the stress ball squeeze task, and DCE.

      DCE is a well-established method for BBB imaging in living humans, and it is cited throughout the manuscript. The ball squeeze task was chosen as it is presumed to involve primarily sensory motor areas, without high-level processing (Halder et al., 2005). This is now stated in the discussion.

      What is Gd-DOTA?

      Gd-DOTA is a gadolinium-based contrast agent (gadoterate meglumine, AKA Dotarem). Text was revised for clarity. Please see the Methods section under ‘Magnetic Resonance Imaging’ - ‘Data Acquisition’.

      What does a higher percentage of activated regions mean- how was activacon defined and how were regions counted?

      Higher percentage of activated regions refers to regions in which voxels showed significant BOLD changes due to the motor task preformed. The statistical approaches and analyses are detailed in the Methods section under ‘Magnetic Resonance Imaging - Preprocessing of functional data, and fMRI Localizer Motor Task’.

      Figure. 4

      Was stimulation 1 min or 30 min.?

      30 min, Text has been revised for clarity.

      What is the Wald test and how were p values adjusted-please add to the Stats section.

      The Methods section under ‘Statistical analysis’ was revised to clarify this point.

      Is there a reason why p values are sometimes circles and otherwise triangles?

      The legend was revised to explain that ”Circles represent genes with no significant differences between 1 and 24 h poststimulation. Upward and downward triangles indicate significantly up- and down- regulated genes, respectively.”

      How can a p-value be zero? Please explain abbreviations.

      The p-value is very low (~10-10) and therefore appears to be zero due to the scale of the y-axis.

      Fig. 5b.

      There are unexplained abbreviations.

      The x on the ball and hand is not clear relative to the black ball and hand.

      Thank you for noticing. We revised the figure for clarity.

      c. What was the method used to make an activator map and what is meant by localizer task?

      The explanation of the “fMRI Localizer Motor Task” section in the methods was revised for added clarity.

      f. What is the measurement "% area" that indicates " BBB modulation"?

      Is it in f, the BBB permeable vessels (%)? f. Please explain: "Heatmap of BBB modulated voxels percentage in motor/sensory-related areas of task vs. controls."

      The %area measurement indicates the percentage of voxels within a specific brain region that have a leaky BBB. See Methods.

      Is Task - the control?

      Yes.

      Supplemental Fig. 2.

      Why is AUC measured, not amplitude?

      The amplitude, and now also the AUC are shown in Figure 3.

      b. There is no comparison to baseline. The arrowhead points to the start of stimulation but there is no arrowhead marking the end.

      In the revised paper we added a grey shade over the stimulation period to better visualize the difference to baseline. In this panel we wanted to show that NMDA receptor antagonist did not block the SEP, while AMPA receptor antagonist did.

      c. In the blot there are two bands for PSD95- which is the one that is PSD95? There is no increase in PSD95 uncl 24 hrs but in the graph in d there is. In the blot, there is a strong expression of PSD95 ipsilateral compared to contralateral in the sham-why?

      What is the percent change fold?

      The PSD-95 is the top and larger band. The lower band was disregarded in the analysis. The example we show may not fully reflect the group statistics presented in panel d. Upon quantification of 8 animals, PSD-95 is significantly higher 30 min and 24 hours post stimulation in the contralateral hemisphere. No significant changes were found in sham animals. The % change fold refers to the AUC change compared to baseline. This panel was now incorporated in Figure 3 (panel h), and the title was corrected to “|AUC|, % change from baseline”.

      Supplemental Fig. 4.

      a. If ipsilateral and contralateral showed many changes why do the authors think the effects were only contralateral?

      Our gene analysis was designed to complement our in vivo and histological findings, by assessing the magnitude of change in differentially expressed genes (DEGs). This analysis showed that: (1) the hemisphere contralateral to the stimulus has significantly more DEGs than the ipsilateral hemisphere; and (2) the DEGs were related to synaptic plasticity and TGF-b signaling. These findings strengthen the hypothesis raised by our in vivo and histological experiments.

      Supplemental Fig. 5 includes many processes not in the results. Examples include dorsal cuneate and VPL, dynamin, Kir, mGluR, etc. The top right has numbers that are not mentioned. If the drawings are from other papers they should be cited.

      The drawings of Figure 5 are original and were not published before. This hypothesis figure points to mechanisms that may drive the phenomena described in the paper. The legend of the figure was revised to include references to mechanisms that were not tested in this study.

      Papers referenced in this letter:

      Ahishali, B., & Kaya, M. (2020). Evaluation of Blood-Brain Barrier Integrity Using Vascular Permeability Markers: Evans Blue, Sodium Fluorescein, Albumin-Alexa Fluor Conjugates, and Horseradish Peroxidase. Methods in Molecular Biology, 2367, 87–103. https://doi.org/10.1007/7651_2020_316

      Aksenov, D. P., Li, L., Miller, M. J., Iordanescu, G., & Wyrwicz, A. M. (2015). Effects of anesthesia on BOLD signal and neuronal activity in the somatosensory cortex. Journal of Cerebral Blood Flow and Metabolism, 35(11), 1819–1826. https://doi.org/10.1038/jcbfm.2015.130

      All, A. H., Agrawal, G., Walczak, P., Maybhate, A., Bulte, J. W. M., & Kerr, D. A. (2010). Evoked potential and behavioral outcomes for experimental autoimmune encephalomyelitis in Lewis rats. Neurological Sciences, 31(5), 595–601. https://doi.org/10.1007/s10072-010-0329-y

      Allison, J. D., Meador, K. J., Loring, D. W., Figueroa, R. E., & Wright, J. C. (2000). Functional MRI cerebral activation and deactivation during finger movement. Neurology, 54(1), 135–142. https://doi.org/10.1212/wnl.54.1.135

      Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & Ullén, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8(9), 1148–1150. https://doi.org/10.1038/nn1516

      Betterton, R. D., Abdullahi, W., Williams, E. I., Lochhead, J. J., Brzica, H., Stanton, J., Reddell, E., Ogbonnaya, C., Davis, T. P., & Ronaldson, P. T. (2022). Regula/on of Blood-Brain Barrier Transporters by Transforming Growth Factor-β/Activin Receptor-Like Kinase 1 Signaling: Relevance to the Brain Disposition of 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase Inhibitors (i.e., Sta/ns). Drug Metabolism and Disposition, 50(7), 942–956. https://doi.org/10.1124/dmd.121.000781

      Bouchard, M. B., Chen, B. R., Burgess, S. A., & Hillman, E. M. C. (2009). Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics. Optics Express, 17(18), 15670. https://doi.org/10.1364/oe.17.015670

      Cacheaux, L. P., Ivens, S., David, Y., Lakhter, A. J., Bar-Klein, G., Shapira, M., Heinemann, U., Friedman, A., & Kaufer, D. (2009). Transcriptome profiling reveals TGF-β signaling involvement in epileptogenesis. Journal of Neuroscience, 29(28), 8927–8935. https://doi.org/10.1523/JNEUROSCI.0430-09.2009

      Classen, J., Liepert, J., Wise, S. P., Hallett, M., & Cohen, L. G. (1998). Rapid plasticity of human cortical movement representation induced by practice. Journal of Neurophysiology, 79(2), 1117–1123. https://doi.org/10.1152/JN.1998.79.2.1117/ASSET/IMAGES/LARGE/JNP.JA47F4.JPEG

      Franceschini, M. A., Radhakrishnan, H., Thakur, K., Wu, W., Ruvinskaya, S., Carp, S., & Boas, D. A. (2010). The effect of different anesthetics on neurovascular coupling. NeuroImage, 51(4), 1367–1377. https://doi.org/10.1016/j.neuroimage.2010.03.060

      Gellibert, F., Woolven, J., Fouchet, M. H., Mathews, N., Goodland, H., Lovegrove, V., Laroze, A., Nguyen, V. L., Sautet, S., Wang, R., Janson, C., Smith, W., Krysa, G., Boullay, V., De Gouville, A. C., Huet, S., & Hartley, D. (2004). Identification of 1,5-naphthyridine derivatives as a novel series of potent and selective TGF-β type I receptor inhibitors. Journal of Medicinal Chemistry, 47(18), 4494–4506. https://doi.org/10.1021/jm0400247

      Goff, W. R., Rosner, B. S., & Allison, T. (1962). Distribution of cerebral somatosensory evoked responses in normal man. Electroencephalography and Clinical Neurophysiology, 14(5), 697–713. https://doi.org/10.1016/0013-4694(62)90084-6

      Halder, P., Sterr, A., Brem, S., Bucher, K., Kollias, S., & Brandeis, D. (2005). Electrophysiological evidence for cortical plasticity with movement repetition. European Journal of Neuroscience, 21(8), 2271–2277. https://doi.org/10.1111/J.1460-9568.2005.04045.X

      Ivens, S., Kaufer, D., Flores, L. P., Bechmann, I., Zumsteg, D., Tomkins, O., Seiffert, E., Heinemann, U., & Friedman, A. (2007). TGF-β receptor-mediated albumin uptake into astrocytes is involved in neocortical epileptogenesis. Brain, 130(2), 535–547. https://doi.org/10.1093/brain/awl317

      Kaplan, L., Chow, B. W., & Gu, C. (2020). Neuronal regulation of the blood–brain barrier and neurovascular coupling. In Nature Reviews Neuroscience (Vol. 21, Issue 8, pp. 416–432). Nature Research. https://doi.org/10.1038/s41583-020-0322-2

      Keller, P., & Simons, K. (1998). Cholesterol is required for surface transport of influenza virus hemagglutinin. Journal of Cell Biology, 140(6), 1357–1367. https://doi.org/10.1083/jcb.140.6.1357

      Kim, S. Y., Senatorov, V. V., Morrissey, C. S., Lippmann, K., Vazquez, O., Milikovsky, D. Z., Gu, F., Parada, I., Prince, D. A., Becker, A. J., Heinemann, U., Friedman, A., & Kaufer, D. (2017). TGFβ signaling is associated with changes in inflammatory gene expression and perineuronal net degradation around inhibitory neurons following various neurological insults. Scientific Reports, 7(1), 7711. https://doi.org/10.1038/s41598-017-07394-3

      Knowland, D., Arac, A., Sekiguchi, K. J., Hsu, M., Lutz, S. E., Perrino, J., Steinberg, G. K., Barres, B. A., Nimmerjahn, A., & Agalliu, D. (2014). Stepwise Recruitment of Transcellular and Paracellular Pathways Underlies Blood-Brain Barrier Breakdown in Stroke. Neuron, 82(3), 603–617. https://doi.org/10.1016/j.neuron.2014.03.003

      Koudinov, A. R., & Koudinova, N. V. (2001). Essen/al role for cholesterol in synaptic plasticity and neuronal degeneration. The FASEB Journal, 15(10), 1858–1860. https://doi.org/10.1096/r.00-0815re

      Lapilover, E. G., Lippmann, K., Salar, S., Maslarova, A., Dreier, J. P., Heinemann, U., & Friedman, A. (2012). Periinfarct blood-brain barrier dysfunction facilitates induction of spreading depolarization associated with epileptiform discharges. Neurobiology of Disease, 48(3), 495–506. htttts://doi.org/10.1016/j.nbd.2012.06.024

      Lindauer, U., Villringer, A., & Dirnagl, U. (1993). Characterization of CBF response to somatosensory stimulation: Model and influence of anesthetics. American Journal of Physiology - Heart and Circulatory Physiology, 264(4 33-4), 223–1228. https://doi.org/10.1152/ajpheart.1993.264.4.h1223

      Lippmann, K., Kamintsky, L., Kim, S. Y., Lublinsky, S., Prager, O., Nichtweiss, J. F., Salar, S., Kaufer, D., Heinemann, U., & Friedman, A. (2017). Epileptiform activity and spreading depolarization in the bloodbrain barrier-disrupted peri-infarct hippocampus are associated with impaired GABAergic inhibition and synaptic plasticity. Journal of Cerebral Blood Flow and Metabolism, 37(5), 1803–1819. https://doi.org/10.1177/0271678X16652631

      Masamoto, K., & Kanno, I. (2012). Anesthesia and the quantitative evaluation of neurovascular coupling. In Journal of Cerebral Blood Flow and Metabolism (Vol. 32, Issue 7, pp. 1233–1247). SAGE PublicationsSage UK: London, England. https://doi.org/10.1038/jcbfm.2012.50

      McGregor, H. R., Cashaback, J. G. A., & Gribble, P. L. (2016). Functional Plasticity in Somatosensory Cortex Supports Motor Learning by Observing. Current Biology, 26(7), 921–927. https://doi.org/10.1016/j.cub.2016.01.064

      McMillin, M. A., Frampton, G. A., Seiwell, A. P., Patel, N. S., Jacobs, A. N., & DeMorrow, S. (2015). TGFβ1 exacerbates blood-brain barrier permeability in a mouse model of hepatic encephalopathy via upregulation of MMP9 and downregulation of claudin-5. Laboratory Investigation, 95(8), 903–913. https://doi.org/10.1038/labinvest.2015.70

      Mégevand, P., Troncoso, E., Quairiaux, C., Muller, D., Michel, C. M., & Kiss, J. Z. (2009). Long-term plasticity in mouse sensorimotor circuits after rhythmic whisker stimulation. Journal of Neuroscience, 29(16), 5326– 5335. https://doi.org/10.1523/JNEUROSCI.5965-08.2009

      Milikovsky, D. Z., Ofer, J., Senatorov, V. V., Friedman, A. R., Prager, O., Sheintuch, L., Elazari, N., Veksler, R., Zelig, D., Weissberg, I., Bar-Klein, G., Swissa, E., Hanael, E., Ben-Arie, G., Schefenbauer, O., Kamintsky, L., Saar-Ashkenazy, R., Shelef, I., Shamir, M. H., … Friedman, A. (2019). Paroxysmal slow cortical activity in Alzheimer’s disease and epilepsy is associated with blood-brain barrier dysfunction. Science Translational Medicine, 11(521), eaaw8954–eaaw8954. https://doi.org/10.1126/scitranslmed.aaw8954

      Ngai, A. C., Jolley, M. A., D’Ambrosio, R., Meno, J. R., & Winn, H. R. (1999). Frequency-dependent changes in cerebral blood flow and evoked potentials during somatosensory stimulation in the rat. Brain Research, 837(1–2), 221–228. https://doi.org/10.1016/S0006-8993(99)01649-2

      Obermeier, B., Daneman, R., & Ransohoff, R. M. (2013). Development, maintenance and disruption of the blood-brain barrier. In Nature Medicine (Vol. 19, Issue 12, pp. 1584–1596). Nature Publishing Group. https://doi.org/10.1038/nm.3407

      Schoknecht, K., Prager, O., Vazana, U., Kamintsky, L., Harhausen, D., Zille, M., Figge, L., Chassidim, Y., Schellenberger, E., Kovács, R., Heinemann, U., & Friedman, A. (2014). Monitoring stroke progression: In vivo imaging of cortical perfusion, blood-brain barrier permeability and cellular damage in the rat photothrombosis model. Journal of Cerebral Blood Flow and Metabolism, 34(11), 1791–1801. https://doi.org/10.1038/jcbfm.2014.147

      Schumacher, L., Slimani, R., Zizmare, L., Ehlers, J., Kleine Borgmann, F., Fitzgerald, J. C., Fallier-Becker, P., Beckmann, A., Grißmer, A., Meier, C., El-Ayoubi, A., Devraj, K., Mittelbronn, M., Trautwein, C., & Naumann, U. (2023). TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes. Biomedicines, 11(1), 1–19. https://doi.org/10.3390/biomedicines11010214

      Shim, H. J., Jung, W. B., Schlegel, F., Lee, J., Kim, S., Lee, J., & Kim, S. G. (2018). Mouse fMRI under ketamine and xylazine anesthesia: Robust contralateral somatosensory cortex ac/va/on in response to forepaw stimulation. NeuroImage, 177, 30–44. https://doi.org/10.1016/J.NEUROIMAGE.2018.04.062

      Sudmant, P. H., Alexis, M. S., & Burge, C. B. (2015). Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome Biology, 16(1), 287. https://doi.org/10.1186/s13059-015-0853-4

      Tao, L., & Nicholson, C. (1996). Diffusion of albumins in rat cortical slices and relevance to volume transmission. Neuroscience, 75(3), 839–847. https://doi.org/10.1016/0306-4522(96)00303-X

      Vazana, U., Veksler, R., Pell, G. S., Prager, O., Fassler, M., Chassidim, Y., Roth, Y., Shahar, H., Zangen, A., Raccah, R., Onesti, E., Ceccanti, M., Colonnese, C., Santoro, A., Salvati, M., D’Elia, A., Nucciarelli, V., Inghilleri, M., & Friedman, A. (2016). Glutamate-mediated blood–brain barrier opening: Implications for neuroprotection and drug delivery. Journal of Neuroscience, 36(29), 7727–7739. https://doi.org/10.1523/JNEUROSCI.0587-16.2016

      Veksler, R., Vazana, U., Serlin, Y., Prager, O., Ofer, J., Shemen, N., Fisher, A. M., Minaeva, O., Hua, N., SaarAshkenazy, R., Benou, I., Riklin-Raviv, T., Parker, E., Mumby, G., Kamintsky, L., Beyea, S., Bowen, C. V., Shelef, I., O’Keeffe, E., … Friedman, A. (2020). Slow blood-to-brain transport underlies enduring barrier dysfunction in American football players. Brain, 143(6), 1826–1842. https://doi.org/10.1093/brain/awaa140

      Zandieh, S., Hopf, R., Redl, H., & Schlag, M. G. (2003). The effect of ketamine/xylazine anesthesia on sensory and motor evoked potentials in the rat. Spinal Cord, 41(1), 16–22. https://doi.org/10.1038/sj.sc.3101400

      Zelig, D., Goldberg, I., Shor, O., Ben Dor, S., Yaniv-Rosenfeld, A., Milikovsky, D. Z., Ofer, J., Imtiaz, H., Friedman, A., & Benninger, F. (2022). Paroxysmal slow wave events predict epilepsy following a first seizure. Epilepsia, 63(1), 190–198. https://doi.org/10.1111/epi.17110

      Zhang, Y., & Pardridge, W. M. (2001). Mediated efflux of IgG molecules from brain to blood across the blood– brain barrier. Journal of Neuroimmunology, 114(1–2), 168–172. https://doi.org/10.1016/S01655728(01)00242-9

      Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-09234-6

    1. Author Response

      Reviewer #1 (Public Review)

      Midbrain dopamine neurons have attracted attention as a part of the brain's reward system. A different line of research, on the other hand, has shown that these neurons are also involved in higher cognitive functions such as short-term memory. However, these neurons are thought not to encode short-term memory itself because they just exhibit a phasic response in short-term memory tasks, which cannot seem to maintain information during the memory period. To understand the role of dopamine neurons in short-term memory, the present study investigated the electrophysiological property of these neurons in rodents performing a T-maze version of a short-term memory task, in which a visual cue indicated which arm (left or right) of the T-maze was associated with a reward. The animal needed to maintain this information while they were located between the cue presentation position and the selection position of the T-maze. The authors found that the activity of some dopamine neurons changed depending on the information while the animals were located in the memory position. This dopamine neuron modulation was unable to explain the motivation or motor component of the task. The authors concluded that this modulation reflected the information stored as short-term memory.

      I was simply surprised by their finding because these dopamine neurons are similar to neurons in the prefrontal cortex that store memory information with sustained activity. Dopamine neurons are an evolutionally conserved structure, which is seen even in insects, whereas the prefrontal cortex is developed mainly in the primate. I feel that their findings are novel and would attract much attention from readers in the field. But the authors need to conduct additional analyses to consolidate their conclusion.

      We thank reviewer #1 for the positive assessment and for the valuable and constructive comments on our manuscript.

      Reviewer #1 (Recommendations to The Authors)

      (1) The authors found the dopamine neuron modulation that reflected the memory information during the delay period. Here the dopamine neuron activity was aligned by the position, not by time, in which the animals needed to maintain the information. Usually, the activity was aligned by time, and many studies found that dopamine neurons exhibited a short duration burst in response to rewards and behaviorally relevant stimuli including visual cues presented in short-term memory tasks. For comparison, I (and probably other readers) want to see the time-aligned dopamine neuron modulation that reflected the memory information. Did the modulation still exist? Did it have a long duration? The authors just showed the time-aligned "population" activity that exhibited no memory-dependent modulation.

      We agree that the point raised by the reviewer is important. To address this question, we added a new paragraph to the Methods section titled “Methodological considerations” (in line 793 of the revised manuscript), where we explain the caveats of using time alignment in the T-maze task study. We also created a new sup figure 5 to clarify our argument. As the figure shows, we did not observe major differences in the firing rates when they were arranged by position or time. More importantly, we did not detect brief bursts of activity in response to the visual cue which could reflect an RPE signaling scheme. Our interpretation is that in the T-maze task, DA neurons encode “miniature” RPE signals between successive states in the T-maze, which are hard to detect, especially when neurons receive a continuous sensory input during trials.

      (2) Several studies have reported that dopamine neurons at different locations encode distinct signals even within the VTA or SNr. Were the locations of dopamine neurons maintaining the memory information different from those of other dopamine neurons?

      We thank the reviewer’s comment. Indeed, there is evidence from recent studies demonstrating that DA neurons form functional and anatomical clusters in the VTA and SN. Following the reviewer’s advice, we report the anatomical structure of memory and non-memory-specific neurons in the revised manuscript. You can read these results in the paragraph “Anatomical organization of trajectory-specific neurons.” in the “Results” section (in line 383 of the revised manuscript) and in the new sup figure 11. We only observed a clear functional-anatomical segregation in GABA neurons, but not in DA neurons. But we should note that the absence of segregation in the DA neurons could be accounted for by the fact that we recorded mostly from the lateral VTA, therefore we do not have any numbers from the medial VTA.

      (3a) Did the dopamine neurons maintaining the memory information respond to reward?

      We believe that we have already provided the data that can partially answer this question by correlating the firing rate difference between the reward and memory delay sections. This result was described in the “Neuronal activities in delay and reward are unrelated.” paragraph and in Figure 6. Moreover, motivated by the reviewer’s question, we also performed additional analysis, which is included in the revised manuscript. Briefly, we clustered significant responses between the memory delay and reward sections (Category 1: Left-signif, R-signif or No-signif / Category 2: Memory delay or Reward). We discovered that only a very small number of neurons showed the same significant trajectory preference in the memory delay and reward sections (i.e., significant preference for left trials in the memory delay and significant preference for the left reward). In fact, more significant neurons showed a preference for opposite trajectories (i.e. significant preference for left trials in memory delay and a significant preference for right rewards). A description of the new results is included in the “Neuronal activities in delay and reward are unrelated.” paragraph (in line 349 of the revised manuscript) and in the new supplementary Figure 11.

      (3b) Did they encode reward prediction error? The relationship between the present data and the conventional theory may be valuable.

      We understand that the readers of this study will come up with the question of how memory-specific activities are related to RPE signaling. However, the T-maze task we used in this research was designed for studying working memory and was not adequate to extract information about the RPE signaling of DA neurons.

      RPE signaling is mainly studied in Pavlovian conditioning. These are low-dimensional tasks with usually four (4) states (state1: ITI, state2: trial start, state3: stimulus presentation, state4: reward delivery). Evidence of RPE signaling is extracted from the firing activity of states 3 and 4 (which is theorized to be related to the difference in the values for states 3 and 4).

      However, in the T-maze task, the number of states is hard to define and practically countless. In these conditions, it has been suggested that numerous small RPEs are signaled while the mice navigate the maze; Thus, they are very difficult to detect. To our knowledge, only Kim et al 2020, Cell, vol183, pg1600, managed to detect the RPE signaling activity of DA neurons while mice were teleported in a virtual corridor.

      Another confounding factor in extracting RPE signals in the T-maze task is that the environment is high-dimensional and DA neurons are multitasking. Therefore, it is likely that RPE signaling could be masked by other parallel encoding schemes.

      We have added these descriptions in the “Methodological considerations” (in line 793 of the revised manuscript).

      (4) Did the dopamine neurons maintaining the memory information (left or right) prefer a contralateral direction like neurons in the motor cortex?

      We thank the reviewer for this comment. Indeed, the majority of the memory-specific DA neurons showed a preference for the contralateral direction. We report this result in the legend of the new sup fig 10 (in line 1668 of the revised manuscript).

      (5) As shown in Table S2, the proportion of GABA neurons maintaining the memory information (left or right during delay) was much larger than that of dopamine neurons. It seems to be strange because the main output neurons in the VTA are dopaminergic. What is the role of these GABA neurons?

      We thank the reviewer for pointing this out. The present study shows that in both populations a sizeable portion of neurons show memory-specific encoding activities. However, the percentage of memory-encoding GABA neurons is more than twice as large as in the DA neurons. Moreover, we show that GABA neurons are functionally and anatomically segregated.

      From this evidence, one could raise the hypothesis that the GABA neurons have a primary role and that the activity of DA neurons is a collateral phenomenon, triggered in a sequence of events within the VTA network. To characterize the (1) role and (2) importance of GABA neurons in memory-guided behavior, one should first identify the afferent and efferent projections of these cells in great detail. Unfortunately, we do not provide anatomical evidence.

      So far, with the electrophysiological data we have collected (unit and field recordings), we can address an alternative hypothesis. It has been reported earlier (but we have also observed) that the VTA circuit engages in behaviorally related network oscillations which range from 0.4Hz up to 100Hz. Converging evidence from different brain regions, in vitro preparations but also in vivo recordings agree that local networks of inhibitory neurons are crucial for the generation, maintenance, and spectral control of network oscillations. Ongoing analysis, which we hope will lead to a publication, is looking for the behavioral correlates of network oscillations on the T-maze task, as well as the correlation of single-unit firing activity to the field oscillations. We expect to detect a higher field-unit coherence in GABA neurons, which could explain their stronger engagement in memory-specific encoding activity.

      The potential role of GABA neurons in network oscillations is discussed in the revised manuscript in a newly added paragraph in line 564.

      Reviewer #2 (Public Review)

      The authors phototag DA and GABA neurons in the VTA in mice performing a t-maze task, and report choice-specific responses in the delay period of a memory-guided task, more so than in a variant task w/o a memory component. Overall, I found the results convincing. While showing responses that are choice selective in DA neurons is not entirely novel (e.g. Morris et al NN 2006, Parker et al NN 2016), the fact that this feature is stronger when there is a memory requirement is an interesting and novel observation.

      I found the plots in 3B misleading because it looks like the main result is the sequential firing of DA neurons during the Tmaze. However, many of the neurons aren't significant by their permutation test. Often people either only plot the neurons that are significant, or plot with cross-validation (ie sort by half of the trials, and plot the other half).

      Relatedly, the cross-task comparisons of sequences (Fig, 4,5) are hampered by the fact that they sort in one task, then plot in the other, which will make the sequences look less robust even if they were equally strong. What happens if they swap which task's sequences they use to order the neurons? I do realize they also show statistical comparisons of modulated units across tasks, which is helpful.

      We thank reviewer #2 for the valuable and constructive comments on our manuscript. If, as the reviewer commented, the rate differences between left and right trajectories were only the result we want to claim, there may be a way to show only those whose left and right are significant. However, the sequential activity is also one of the points we wanted to display. We did not emphasize this result because it has already been shown by Engelhard et al. 2019. However, after reading the reviewer's comments, we decided to add a few lines in the "Results" (in lines 205 - 215 of the revised manuscript) and "Discussion" (in line 453 of the revised manuscript) describing the sequential activity of the VTA circuit. In those lines, we explained that DA activity is position-specific (resulting in sequential activity) and that a fraction of them also have left-right specificity.

      Overall, the introduction was scholarly and did a good job covering a vast literature. But the explanation of t-maze data towards the end of the introduction was confusing. In Line 87, I would not say "in the same task" but "in a similar task" because there are many differences between the tasks in question.

      We thank the reviewer for pointing out this mistake. In the revised manuscript, we replaced “in the same task” with “in a similar task” (in line 85 of the revised manuscript).

      And not clear what is meant by "by averaging neuronal population activities, none of these computational schemes would have been revealed. " There was trial averaging, at least in Harvey et al. I thought the main result of that paper related to coding schemes was that neural activity was sequential, not persistent. I think it would help the paper to say that clearly.

      We admit that this sentence leaves room for misunderstanding. We were mainly referring to DA studies using microdialysis or fiber photometry techniques. We decided to delete this sentence in the revised manuscript.

      Also, I'm not aware it was shown that choice selectivity diminishes when the memory demand of the task is removed - please clarify if that is true in both referenced papers.

      The reviewer’s remark is correct. None of these reports show explicitly that memory-specific activities are diminished without the memory component. Therefore, we deleted this sentence in the revised manuscript.

      If so, an interpretation of this present data could be found in Lee et al biorxiv 2022, which presents a computational model that implies that the heterogeneity in the VTA DA system is a reflection of the heterogeneity found in upstream regions (the state representation), based on the idea that different subsets of DA neurons calculate prediction errors with respect to different subsets of the state representation.

      We thank the reviewer for sharing this interpretation. We agree that this theory would support our results. In the revised manuscript we briefly discuss the Lee et al. report (in line 460 of the revised manuscript).

      I am surprised only 28% of DA neurons responded to the reward - the reward is not completely certain in this task. This seems lower than other papers in mice (even Pavlovian conditioning, when the reward is entirely certain). It would be helpful if the authors comment on how this number compares to other papers.

      In Pavlovian conditioning, neuronal responses to rewards are compared to a relatively quiet period of firing activity (usually the inter-trial interval epoch). As the reviewer pointed out, in the present study, the number of DA neurons responding to reward is smaller compared to the earlier studies. We hypothesize that this is due to our comparison method. We compared the post-reward response to an epoch when the animal was running along the side arms and the majority of neurons were highly active, instead of comparing it to a quiescent baseline epoch.

      Reviewer #2 (Recommendations to The Authors)

      Can you clarify what disparity you are referring to here? "Disparities between this 438 and our study in the proportions of modulated neurons could be attributed to the 439 different recording techniques applied as well as the maze regions of interest; for 440 example, Engelhard et al. analyzed neuronal firing activities in the visual-cue period 441 (Engelhard et al., 2019), whereas we focused on memory delay.". Is it the fact that Engelhard et al did not report choice-selective activity? They did report cue-side-selective activity, with some neurons responsive to cues on one side, and other neurons responsive to cues on the other side. Because there are more cues on the left when the mouse turns left, these neurons do indeed have choice-selective responses.

      We thank the reviewer for this comment. We agree that we need to clarify further our argument. As the reviewer pointed out, Engelhard et al identified choice-specific DA neurons. However, they reported the encoding properties of DA neurons only in the visual-cue period and the reward period. Remarkably, although the task has a memory delay, they did not report the neuronal firing activities for this delay period. Instead, in the present study we dedicated most of our analysis to characterizing the firing properties of VTA neurons in the delay period.

      Also, in response to your comment, we edited the paragraph where we describe the disparities between our study and Engelhard et al (in line 466 in the revised manuscript).

      I don't think this sentence of intro is needed since it doesn't really contain new info: "Therefore, we looked for hints 116 of memory-related encoding activities in single DA and GABA neurons by 117 characterizing their firing preference for opposite behavioral choices.".

      We agree with the reviewer. Therefore, we deleted this sentence in the revised manuscript.

      I didn't understand this line of discussion: "Our evidence does not question the validity of this computational model, since we do not provide evidence of how the selective preference for one response over the other translates into the release site.".

      The gating theory is based on experimental evidence of neuronal firing activities of DA neurons but also takes into consideration (to a lesser degree) the pre- and post-synaptic processes at the DA release sites (inverted U-shape of D1R activity). We thought that the reader may come to the conclusion that we question the validity of the gating theory. But this is not our intention, especially when we do not provide important evidence such as (1) the projection sites of DA and GABA neurons and (2) the sequence of events that take place at the synaptic triads following the DA and GABA release.

      After reading your comment we came to the conclusion that this sentence should be omitted because it is not within the scope of this study to question the validity of the gating theory. Instead, we dedicated a few lines of text to explaining which components of the gating theory (“update”, “maintenance & manipulation” and “motor preparation”) could be attributed to the trajectory-specific activities in the memory delay of the T-maze task. (section “Activities of midbrain DA neurons in short-term memory” in line 417 of the revised manuscript).

      In 1B, please illustrate when the light pulses are on & off?

      Following the reviewer’s instruction, we added colored bars on top of the raster plots in Figure 1B, indicating the light induction conditions.

      In legend for 6C, please clarify it's a correlation between the difference in R and L choice activity across the epochs (if my understanding is correct).

      The reviewer’s understanding is correct. We took this advice into consideration to further clarify the methods of analysis that led to the plot in Figure 6C (in line 1246 in the revised manuscript).

    1. Reviewer #2 (Public Review):

      The authors demonstrate convincingly the potential of single mesodermal cells, removed from zebrafish embryos, to show cell-autonomous oscillatory signaling dynamics and differentiation. Their main conclusion is that a cell-autonomous timer operates in these cells and that additional external signals are integrated to tune cellular dynamics. Combined, this is underlying the precision required for proper embryonic segmentation, in vivo. I think this work stands out for its very thorough, quantitative, single-cell real-time imaging approach, both in vitro and also in vivo. A very significant progress and investment in method development, at the level of the imaging setup and also image analysis, was required to achieve this highly demanding task. This work provides new insight into the biology underlying embryo axis segmentation.<br /> The work is very well presented and accessible. I think most of the conclusions are well supported. Here a my comments and suggestions:

      1) The authors state that "We compare their cell-autonomous oscillatory and arrest dynamics to those we observe in the embryo at cellular resolution, finding remarkable agreement."

      I think this statement needs to be better placed in context. In absolute terms, the period of oscillations and the timing of differentiation are actually very different in vitro, compared to in vitro. While oscillations have a period of ~30 minutes in vivo, oscillations take twice as long in vitro. Likewise, while the last oscillation is seen after 143 minutes in vivo, the timing of differentiation is very significantly prolonged, i.e.more than doubled, to 373min in vitro (Supplementary Figure 1-9). I understand what the authors mean with 'remarkable agreement', but this statement is at the risk of being misleading. I think the in vitro to in vivo differences (in absolute time scales) needs to be stated more explicitly. In fact, the drastic change in absolute timescales, while preserving the relative ones,i.e. the number of oscillations a cell is showing before onset of differentiation remains relatively invariant, is a remarkable finding that I think merits more consideration (see below).

      2) One timer vs. many timers<br /> The authors show that the oscillation clock slowing down and the timing of differentiation, i.e. the time it takes to activate the gene mesp, are in principle dissociable processes. In physiological conditions, these are however linked. We are hence dealing with several processes, each controlled in time (and hereby space). Rather than suggesting the presence of 'a timer', I think the presence of multiple timing mechanisms would reflect the phenomenology better. I would hence suggest separating the questions more consistently, for instance into the following three:<br /> a. what underlies the slowing down of oscillations?<br /> b. what controls the timing of onset of differentiation?<br /> c. and finally, how are these processes linked?

      Currently, these are discussed somewhat interchangeably, for instance here: "Other models posit that the slowing of Her oscillations arise due to an increase of time-delays in the negative feedback loop of the core clock circuit (Yabe, Uriu, and Takada 2023; Ay et al. 2014), suggesting that factors influencing the duration of pre-mRNA splicing, translation, or nuclear transport may be relevant. Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock."(page 14). In the first part, the slowing down of oscillations is discussed and then the authors conclude on 'the timer', which however is the one timing differentiation, not the slowing down. I think this could be somewhat misleading.

      3) From this and previous studies, we learn/know that without clock oscillations, the onset of differentiation still occurs. For instance in clock mutant embryos (mouse, zebrafish), mesp onset is still occurring, albeit slightly delayed and not in a periodic but smooth progression. This timing of differentiation can occur without a clock and it is this timer the authors refer to "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14). This 'timer' is related to what has been previously termed 'the wavefront' in the classic Clock and Wavefront model from 1976, i.e. a "timing gradient' and smooth progression of cellular change. The experimental evidence showing it is cell-autonomous by the time it has been laid down,, using single cell measurements, is an important finding, and I would suggest to connect it more clearly to the concept of a wavefront, as per model from 1976.

      4) Regarding question a., clearly, the timer for the slowing down of oscillations is operating in single cells, an important finding of this study. It is remarkable to note in this context that while the overall, absolute timescale of slowing down is entirely changed by going from in vivo to in vitro, the relative slowing down of oscillations, per cycle, is very much comparable, both in vivo and in vivo. To me, while this study does not address the nature of this timer directly, the findings imply that the cell-autonomous timer that controls slowing down is, in fact, linked to the oscillations themselves. We have previously discussed such a timer, i.e. a 'self-referential oscillator' mechanism (in mouse embryos, see Lauschke et al., 2013) and it seems the new exciting findings shown here in zebrafish provide important additional evidence in this direction. I would suggest commenting on this potential conceptual link, especially for those readers interested to see general patterns.

      5) Regarding question c., i.e. how the two timing mechanisms are functionally linked, I think concluding that "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14), might be a bit of an oversimplification. It is correct that the timer of differentiation is operating without a clock, however, physiologically, the link to the clock (and hence the dependence of the timescale of clock slowing down), is also evident. As the author states, without clock input, the precision of when and where differentiation occurs is impacted. I would hence emphasize the need to answer question c., more clearly, not to give the impression that the timing of differentiation does not integrate the clock, which above statement could be interpreted to say.

      6) A very interesting finding presented here is that in some rare examples, the arrest of oscillations and onset of differentiation (i.e. mesp) can become dissociated. Again, this shows we deal here with interacting, but independent modules. Just as a comment, there is an interesting medaka mutant, called doppelkorn (Elmasri et al. 2004), which shows a reminiscent phenotype "the Medaka dpk mutant shows an expansion of the her7 expression domain, with apparently normal mesp expression levels in the anterior PSM.". The authors might want to refer to this potential in vivo analogue to their single cell phenotype.

      7) One strength of the presented in vitro system is that it enables precise control and experimental perturbations. A very informative set of experiments would be to test the dependence of the cell-autonomous timing mechanisms (plural) seen in isolated cells on ongoing signalling cues, for instance via Fgf and Wnt signaling. The inhibition of these pathways with well-characterised inhibitors, in single cells, would provide important additional insight into the nature of the timing mechanisms, their dependence on signaling and potentially even into how these timers are functionally interdependent.

    1. Reviewer #1 (Public Review):

      Summary:<br /> This study examines a hypothesized link between autism symptomatology and efference copy mechanisms. This is an important question for several reasons. Efference copy is both a critical brain mechanism that is key to rapid sensorimotor behaviors, and one that has important implications for autism given recent empirical and theoretical work implicating atypical prediction mechanisms and atypical reliance on priors in ASD.

      The authors test this relationship in two different experiments, both of which show larger errors/biases in spatial updating for those with heightened autistic traits (as measured by AQ in neurotypical (NT) individuals).

      Strengths:<br /> The empirical results are convincing - effects are strong, sample sizes are sufficient, and the authors also rule out alternative explanations (ruling out differences in motor behavior or perceptual processing per se).

      Weaknesses:<br /> My main concern is that the paper should be more transparent about both (1) that this study does not include individuals with autism, and (2) acknowledging the limitations of the AQ.

      On the first point, and I don't think this is intentional, there are several instances where the line between heightened autistic traits in the NT population and ASD is blurred or absent. For example, in the second sentence of the abstract, the authors state "Here, we examine the idea that sensory overload in ASD may be linked to issues with efference copy mechanisms". I would say this is not correct because the authors did not test individuals with ASD. I don't see a problem with using ASD to motivate and discuss this work, but it should be clear in key places that this was done using AQ in NT individuals.

      For the second issue, the AQ measure itself has some problems. For example, reference 38 in the paper (a key paper on AQ) also shows that those with high AQ skew more male than modern estimates of ASD, suggesting that the AQ may not fully capture the full spectrum of ASD symptomatology. Of course, this does not mean that the AQ is not a useful measure (the present data clearly show that it captures something important about spatial updating during eye movements), but it should not be confused with ASD, and its limitations need to be acknowledged. My recommendation would be to do this in the title as well - e.g. note impaired visuomotor updating in individuals with "heightened autistic traits".

      Suggestions for improvement:<br /> - Figure 5 is really interesting. I think it should be highlighted a bit more, perhaps even with a model that uses the results of both tasks to predict AQ scores.<br /> - Some discussion of the memory demands of the tasks will be helpful. The authors argue that memory is not a factor, but some support for this is needed.<br /> - With 3 sessions for each experiment, the authors also have data to look at learning. Did people with high AQ get better over time, or did the observed errors/biases persist throughout the experiment?

    1. Reviewer #2 (Public Review):

      Summary:

      We often have prior expectations about how the sensory world will change, but it remains an open question as to how these expectations are integrated into perceptual decisions. In particular, scientists have debated whether prior knowledge principally changes the decisions we make about the perceptual world, or directly alters our perceptual encoding of incoming sensory evidence.

      The authors aimed to shed light on this conundrum by using a novel psychophysical task while measuring EEG signals that have previously been linked to either the sensory encoding or response selection phase of perceptual choice. The results convincingly demonstrate that both features of perceptual decision making are modulated by prior expectations - but that these biases in neural process emerge over different time courses (i.e., decisional signals are shaped early in learning, but biases in sensory processing are slower to emerge).

      Another interesting observation unearthed in the study - though not strictly linked to this perceptual/decisional puzzle - is that neural signatures of focused attention are exaggerated on trials where participants are given neutral (i.e. uninformative) cues. This is consistent with the idea that observers are more attentive to incoming sensory evidence when they cannot rely on their expectations.

      In general, I think the study makes a strong contribution to the literature, and does an excellent job of separating 'perceiving' from 'responding'. More perhaps could have been done though to separate 'perceiving' and 'responding' from 'deciding' (see below).

      Strengths:

      The work is executed expertly and focuses cleverly on two features of the EEG signals that can be closely connected to specific loci of the perceptual decision making process - the SSVEP which connects closely to sensory (visual) encoding, and Mu-Beta lateralisation which connects closely to movement preparation. This is a very appropriate design choice given the authors' research question.

      Another advantage of the design is the use of an unusually long training regime (i.e., for humans) - which makes it possible to probe the emergence of different expectation biases in the brain over different timecourses, and in a way that may be more comparable to work with nonhuman animals (who are routinely trained for much longer than humans).

      Weaknesses:

      In my view, the principal shortcoming of this study is that the experimental task confounds expectations about stimulus identity with expectations about to-be-performed responses. That is, cues in the task don't just tell participants what they will (probably) see, but what they (probably) should do.

      In many respects, this feature of the paradigm might seem inevitable, as if specific stimuli are not connected to specific responses, it is not possible to observe motor preparation of this kind (e.g., de Lange, Rahnev, Donner & Lau, 2013 - JoN).

      However, the theoretical models that the authors focus on (e.g., drift diffusion models) are models of decision (i.e., commitment to a proposition about the world) as much as they are models of choice (i.e., commitment to action). Expectation researchers interested in these models are often interested in asking whether predictions influence perceptual processing, perceptual decision and/or response selection stages (e.g., Feuerriegel, Blom & Hoogendorn, 2021 - Cortex), and other researchers have shown that parameters like drift bias and start point bias can be shifted in paradigms where observers cannot possibly prepare a response (e.g., Thomas, Yon, de Lange & Press, 2020 - Psych Sci).

      The present paradigm used by Walsh et al makes it possible to disentangle sensory processing from later decisional processes, but it blurs together the processes of deciding about the stimulus and choosing/initiating the response. This ultimately limits the insights we can draw from this study - as it remains unclear whether rapid changes in motor preparation we see reflect rapid acquisition of new decision criterion or simple cue-action learning. I think this would be important for comprehensively testing the models the authors target - and a good avenue for future work.

      In revising the manuscript after an initial round of revisions, the authors have done a good job of acknowledging these complexities - and I don't think that any of these outstanding scientific puzzles detract from the value of the paper as a whole.

    1. No man would keep his hands off what was not his own when he could safely take what he liked out of the market, or go into houses and lie with any one at his pleasure, or kill or release from prison whom he would, and in all respects be like a God among men.

      This is all a hypothetical situation that cannot be proven. However, I do believe the same could be said regarding the statement that every person who has previously been just will act with the same integrity when placed in a similar situation. It IS all hypothetical. But really thinking about it, I think we all have the capacity to act unjust in situations and justify ourselves. Just think if your loved one's life was at risk and you HAD to be unjust to save that person, would you do it? Many people may say no, or it depends on what I would have to do. But I think in reality, we do not want to admit that we would indeed do anything even if it means being unjust. When we are put under pressure, we show our true colors. We just fear what others may think of us. Or we fear what we may think of ourselves. Either way, we tend to justify ourselves and not view things as they are.

      We have a tendency to want to be above others in society and make our own rules and excuses.

    2. And this we may truly affirm to be a great proof that a man is just, not willingly or because he thinks that justice is any good to him individually, but of necessity, for wherever any one thinks that he can safely be unjust, there he is unjust.

      Earlier I mentioned conscience as something that can drive a person to justice. So, I'm also curious what Plato would think about altruism and how natural it is to humans?

    1. Background Applying good data management and FAIR data principles (Findable, Accessible, Interoperable, and Reusable) in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.Findings We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.Conclusions Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

      Reviewer 3 Megan Hagenauer - Original Submission

      Review of "A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object" by Niehues et al. for GigaScience08-31-2023I want to begin by apologizing for the tardiness of this review - my whole family caught Covid during the review period, and it has taken several weeks for us to be functional again.OverviewAs a genomics data analyst, I found this manuscript to be a fascinating, inspiring, and, quite honestly, intimidating, view into the process of making analysis code and workflow truly meet FAIR standards. I have added recommendations below for elements to add to the manuscript that would help myself and other analysts use your case study to plan out our own workflows and code release. These recommendations fall quite solidly into the "Minor Revision" category and may require some editorial oversight as this article type is new to me. Please note that I only had access to the main text of the manuscript while writing this review.Specific Comments1) As a case study, it would be useful to have more explicit discussion of the expertise and effort involved in the FAIR code release and the anticipated cost/benefit ratio:As a data analyst, I have a deep, vested interest in reproducible science and improved workflow/code reusability, but also a limited bandwidth. For me, your overview of the process of producing a FAIR code release was both inspiring and daunting, and left me with many questions about the feasibility of following in your footsteps. The value of your case study would be greatly enhanced by discussing cost/benefit in more detail:a. What sort of expertise or training was required to complete each step in the FAIR release? E.g.,i. Was your use of tools like Github, Jupyter notebook, WorkflowHub, and DockerHub something that could be completed by a scientist with introductory training in these tools, or did it require higher level use?ii. Was there any particular training required for the production of high quality user documentation or metadata? (e.g., navigating ontologies?)b. With this expertise/training in place, how much time and effort do you estimate that it took to complete each step of adapting your analysis workflow and code release to meet FAIR standards?i. Do you think this time and effort would differ if an analyst planned to meet FAIR standards for analysis code prior to initiating the analysis versus deciding post-hoc to make the release of previously created code fit FAIR standards?c. The introduction provides an excellent overview of the potential benefits of releasing FAIR analysis code/workflows. How did these benefits end up playing out within your specific case study?i. e.g., I thought this sentence in your discussion was a particularly important note about the benefits of FAIR analysis code in your study: "Developing workflows with partners across multiple institutions can pose a challenge and we experienced that a secure shared computing environment was key to the success of this project."ii. Has the FAIR analysis workflow also been useful for collaboration or training in your lab?iii. How many of the analysis modules (or other aspects of the pipeline) do you plan on reusing? In general, what do you think is the size for the audience for reuse of the FAIR code? (e.g., how many people do you think will have been saved significant amounts of work by you putting in this effort?)iv. … Or is the primary benefit mostly just improving the transparency/reproducibility of your science?d. If there is any way to easily overview these aspects of your case study (effort/time, expertise, immediate benefits) in a table or figure, that would be ideal. This is definitely the content that I would be skimming your paper to find.2) As a reusable code workflow, it would be useful to provide additional information about the data input and experimental design, so that readers can determine how easily the workflow could be adapted to their own datasets. This information could be added to the text or to Fig 1. E.g.,i. The dimensionality of the input (sample size, number of independent variables & potential co-variates, number of dependent variables in each dataset, etc)ii. Data types for the independent variables, co-variates, and dependent variables (e.g., categorical, numeric, etc)iii. Any collinearity between independent variables (e.g., nesting, confounding).3) As documentation of the analysis, it would be useful to provide additional information about how the analysis workflow may influence the interpretation of the results.a. It would be especially useful to know which aspects of the analysis were preplanned or following a standard procedure/protocol, and which aspects of the analysis were customized after reviewing the data or results. This information can help the reader assess the risk of overfitting or HARKing.b. It would also be useful to call out explicitly how certain analysis decisions change the interpretation of the results. In particular, the decision to use dimension reduction techniques within the analysis of both the independent and dependent variables, and then focus only on the top dimensions explaining the largest sources of variation within the datasets, is especially important to justify and describe its impact on the interpretation of the results. Is there reason to believe that externalizing behavior should be related to the largest sources of variation within buccal DNA methylation or urinary metabolites? Within genetic analyses, the assumption tends to be the opposite - that genetic variation related to behavior (such as externalizing) is likely to be present in a small percent of the genome, and that the top sources of variation within the genetics dataset are uninteresting (related to population) and therefore traditionally filtered out of the data prior to analysis. Within transcriptomics, if a tissue is involved in generating the behavior, some of the top dimensions explaining the largest sources of variation in the dataset may be related to that behavior, but the absolute largest sources of variation are almost always technical artifacts (e.g., processing batches, dissection batches) or impactful sources of biological noise (e.g., age, sex, cell type heterogeneity in the tissue). Is there reason to believe that cheek cells would have their main sources of epigenetic variation strongly related to externalizing behavior? (maybe as a canary in a coal mine for other whole organism events like developmental stress exposure?). Is there reason to believe that the primary variation in urinary metabolites would be related to externalizing behavior? (perhaps as a stand-in for other largescale organismal states that might be related to the behavior - hormonal states? metabolic states? inflammation?). Since the goal of this paper is to provide a case study for creating a FAIR data analysis workflow, it is less important that you have strong answers for these questions, and more important that you are transparent about how the answers to these questions change the interpretation of your results. Adding a few sentences to the discussion is probably sufficient to serve this purpose. Thank you for your hard work helping advance our field towards greater transparency and reproducibility. I look forward to seeing your paper published so that I can share it with the other analysts in our lab.

    1. And, as to the faculties of the mind, setting aside the arts grounded uponwords and especially that skill of proceeding upon general and infallible rulescalled science, which very few have and but in few things, as being not a nativefaculty born with us, nor attained, as prudence, while we look after somewhatelse, I find yet a greater equality amongst men than that of strength. Forprudence is but experience, which equal time equally bestows on all men inthose things they equally apply themselves unto. That which may perhaps makesuch equality incredible is but a vain conceit of one’s own wisdom, which almostall men think they have in a greater degree than the vulgar, that is, than all menbut themselves, and a few others whom by fame or for concurring withthemselves they approve. For such is the nature of men that, howsoever theymay acknowledge many others to be more witty or more eloquent or morelearned, yet they will hardly believe there be many so wise as themselves, forthey see their own wit at hand and other men’s at a distance. But this provethrather that men are in that point equal than unequal. For there is not ordinarily agreater sign of the equal distribution of anything than that every man iscontented with his share.2

      To me, Hobbes is saying that In simple terms, people are generally equally smart. The idea that some are wiser might be because individuals often think they are smarter than others. Hobbes argues that, in reality, people are more equal in their mental abilities than they realize.

    2. For such is the nature of men that, howsoever theymay acknowledge many others to be more witty or more eloquent or morelearned, yet they will hardly believe there be many so wise as themselves, forthey see their own wit at hand and other men’s at a distance. But this provethrather that men are in that point equal than unequal.

      This is such an interesting point. We like to assume that we better than, in any aspect, our peers, but when it comes down to it, we are all equal. We all carry that belief that we are better then someone else, believing we are unequal to said person. They may carry that same idea about you, they think they outrank you in some aspect, making them believe they are better then you. Therefore, we are all the same. The same arrogant people.

    1. Content (posts, photos, articles, etc.)# Content recommendations can go well when users find content they are interested in. Sometimes algorithms do a good job of it and users are appreciative. TikTok has been mentioned in particular as providing surprisingly accurate recommendations, though Professor Arvind Narayanan argues that TikTok’s success with its recommendations relies less on advanced recommendation algorithms, and more on the design of the site making it very easy to skip the bad recommendations and get to the good ones. Content recommendations can go poorly when it sends people down problematic chains of content, like by grouping videos of children in a convenient way for pedophiles, or Amazon recommending groups of materials for suicide.

      I think we need to understand the nuances of recommendation algorithms, which is critical in addressing their influence on individual experiences. As these systems are designed to enhance user interaction, they can inadvertently perpetuate biases and present content that may not always align with the best interests or intentions of the users.

    1. To whom our general Ancestor repli'd. Daughter of God and Man, accomplisht Eve, [ 660 ] Those have thir course to finish, round the Earth, By morrow Eevning, and from Land to Land In order, though to Nations yet unborn, Ministring light prepar'd, they set and rise; Least total darkness should by Night regaine [ 665 ] Her old possession, and extinguish life In Nature and all things, which these soft fires Not only enlighten, but with kindly heate Of various influence foment and warme, Temper or nourish, or in part shed down [ 670 ] Thir stellar vertue on all kinds that grow On Earth, made hereby apter to receive Perfection from the Suns more potent Ray. These then, though unbeheld in deep of night, Shine not in vain, nor think, though men were none, [ 675 ] That heav'n would want spectators, God want praise; Millions of spiritual Creatures walk the Earth Unseen, both when we wake, and when we sleep: All these with ceasless praise his works behold Both day and night: how often from the steep [ 680 ] Of echoing Hill or Thicket have we heard Celestial voices to the midnight air, Sole, or responsive each to others note Singing thir great Creator: oft in bands While they keep watch, or nightly rounding walk, [ 685 ] With Heav'nly touch of instrumental sounds In full harmonic number joind, thir songs Divide the night, and lift our thoughts to Heaven.

      In this section, Adam responds to Eve as to why the stars and heavens shine. He explains to her that the sun must shine over all the earth, for those who will inhabit it in the future. They sleep at night, so that they may work harder in the day. He also talks about various "celestial voices"(4. 682) that he has heard at night, praising the glory of God. This heavenly chorus will protect them, as they “divide the night”(4. 688) to keep watch over Adam and Eve, while continuing to exalt their Creator. While reading this section, it seemed to me that the difference between night and day was emphasised heavily. This is an important distinction to make, as God is attributed as the giver of light, and Satan as a bringer of darkness.

    1. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      1. General Statements

      We thank the reviewers for their excellent work that greatly improved our work. We are very content that reviewer #1 considered our work to be “novel, interesting and important for understanding the mitochondrial biology of PD”. This reviewer also valued our work as “a significant advancement” and suggested further study of the relationship of CISD1 (dimerization) to general mitophagy/autophagy. We addressed this in the already transferred revision (version 1, v1).

      Also reviewer #2 considered our work to be “an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate”. This reviewer had a very specific suggestion on how to improve our manuscript which makes a lot of sense and is feasible. As the suggested experiments include fly breeding and behavioral analysis, these experiments will be included in the second revision to be uploaded as soon as possible (version 2, v2).

      Finally, reviewer #3 gathered that parts of our results “are confirmatory to recently published work” but also appreciated that our results established that iron-depleted apo-Cisd is an important determinant of toxicity which has not been shown before. I would like to comment here, that in contrast to the paper mentioned by this reviewer, our contribution includes data from dopaminergic neurons obtained from human patients suffering from familial Parkinson’s disease that demonstrate the same increase in apo-Cisd levels as the flies. This reviewer mainly suggested that the manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data which we did in the revised version of our manuscript. We also added data on the expression levels of Cisd and apo-Cisd in transgenic flies as also suggested.

      2. Description of the planned revisions

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary: The manuscript focuses on mitochondrial CISD1 and its relationship to two Parkinson's disease (PD) proteins PINK1 and Parkin. Interestingly, CISD1 is a mitochondrial iron sulfur binding protein and an target of Parkin-mediated ubiquitinylation. Disruption of iron metabolism and accumulation of iron in the brain has long since been reported in PD but the involvement of iron sulfur binding is little studied both in vivo and in human stem cell models of PD. This work addresses the relationship between CISD1 and two mitochondrial models of PD (PINK1 and Parkin) making use of in vivo models (Drosophila), PINK1 patient models (iPSC derived neurons) and Mouse fibroblasts. The authors report a complex relationship between CISD1, PINK1 and Parkin, where iron-depleted CISD1 may illicit a toxic gain of function downstream of PINK1 and Parkin.

      Major comments:

      The conclusions are overall modest and supported by the data. One question remains unaddressed. Is mitochondrial CISD1 a downstream target that specifically mediates PINK1 and Parkin loss of function phenotypes or are the phenotypes being mediated because CISD1 is downstream of mitophagy in general?

      It would be interesting to know what happens to CISD1 (dimerization?) upon initiation of mitophagy in wild type cells? Would dissipation of mitochondrial membrane potential be sufficient to induce changes to CISD1 in wild type cells or PINK1 deficient cells? Since iron chelation is a potent inducer of mitophagy (Loss of iron triggers PINK1/Parkin-independent mitophagy. George F G Allen, Rachel Toth, John James, Ian G Ganley. EMBO Reports (2013)14:1127-1135) it would be useful to show one experiment addressing the role of CISD1 dimerization under mitochondrial depolarizing and non-depolarizing conditions in cells.

      Based on the overall assumption of the reviewer that our work is “novel, interesting and important for understanding the mitochondrial biology of PD” and “a significant advancement” we understand the word “modest” here as meaning “not exaggerated”. To address this question, we studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

      Alternatively, the authors should discuss the topic of mitophagy (including PINK1-parkin independent mitophagy), the limitation of the present study not being able to rule out a general mitophagy effect and previous work on the role of iron depletion on mitophagy induction in the manuscript.

      The data and the methods are presented in such a way that they can be reproduced.

      The experiments are adequately replicated and statistical analysis is adequate.

      Minor comments:

      Show p values even when not significant (ns) since even some of the significant findings are borderline < p0.05.

      Here, I decided to leave it as it is, because the figures became very cluttered and less easy to understand. Borderline findings are however indicated and mentioned in the text.

      Because the situation for CISD1 is complicated (overexpression, different models etc.) it would be helpful if in the abstract the authors could summarize the role. E.g. as in the discussion that iron-depleted CISD1 could represent a toxic function.

      The abstract has been completely rewritten and now mentions the potential toxic function of iron-depleted CISD1.

      If there is sufficient iron (accumulation in PD) why would CISD1 be deactivated? Perhaps that could be postulated or discussed in a simplified way?

      We actually think that apo-CISD1 without its iron/sulfur cluster is incapable of transferring its Fe/S cluster to IRP1 and IRP2. This then results in increased levels of apo-IRP1/2 and subsequent changes that lead to iron overload. Such a sequence of events would place CISD1 upstream of the changes in iron homeostasis observed in PD and models of PD. This is now discussed in more detail.

      In the methods section both reducing and non-reducing gel/Western blotting is mentioned but the manuscript only describes data from blots under reducing conditions. Are there blots under non-reducing conditions that could be shown to see how CISD1 and dimerized CISD1 resolve?

      We now show these blots as supplemental data in new supplemental Figure 2.

      In the results section, PINK1 mutant flies, it is said that the alterations to CISD1 (dimerization) are analogous to the PINK1 mutation patient neurons. The effect is seen in old but not young flies. Since iPSC-derived neurons are relatively young in the dish, would one not expect that young flies and iPSC-derived neurons have similar CISD1 phenotypes? Could the authors modify the text to reflect that? or discuss the finding in further context.

      We only studied one time point in PINK1 mutation patient neurons and controls. It would indeed be interesting whether neuronal aging (as far as this can be studied in the dish) would result in increased CISD1 dimerization. This is now discussed.

      Reviewer #1 (Significance):

      The strengths of this work are in the novelty of the topic and the use of several well established in vivo and cell models including patient-derived neurons. The findings discussed in the text are honest and avoid over-interpretation. The findings are novel, interesting and important for understanding the mitochondrial biology of PD.

      We thank the reviewer for their kind words.

      Limitations include the lack of strong phenotypes in the CISD1 models and the lack of robust, sustained and consistent increase in CISD1 dimers in the patient and fly models (just significant because of variability). The relationship of CISD1 (dimerization) to general mitophagy/autophagy is not shown here.

      We do not completely agree with the assumption that all CISD1 models lack a strong phenotype. At least the CISD1-deficient fibroblasts exhibit a strong phenotype consisting of fragmented mitochondria and increased oxidative stress. The lack of a strong phenotype in Cisd-deficient flies could actually hint to a potential compensatory mechanism that could also protect the Pink1 mutant x Cisd-deficient double-knockout flies. It is correct that the increase in CISD1/Cisd dimers in the PD models are not overwhelming but – as also mentioned by the reviewer – this could be increased in “older” cultures. This is now discussed in more detail. As suggested by the reviewer, we have now added experiments that study the relationship between CISD1 dimerization and conventional mitophagy as described above.

      There is a significant advancement. So far researchers were able to describe the importance of iron metabolism in PD (For example refer to work from the group of Georg Auburger such as PMID 33023155 and discussion of therapeutic intervention such as reviewed by Ma et al. PMID: 33799121) but few papers describe involvement of iron sulfur cluster proteins specifically (such as Aconitase) in relation to PINK1 and parkin (these are cited). The fact that CISD1 is a protein of the mitochondrial outer membrane makes it particularly interesting and further studies looking more closely at the interaction of CISD1 with mitochondrial proteins associated with PD will be of interest.

      We thank the reviewer for pointing out these excellent publications. Key et al present an enormous wealth of data on protein dysregulation of wildtype and Pink1-/- fibroblast cell lines upon perturbation of the iron homeostasis (Key et al, 2020). Both cell lines exhibit a downregulation of CISD1 levels upon iron deprivation with the agent 2,2′ -Bipyridine possibly as a compensatory mechanism to limit the toxic gain of function of iron-depleted CISD1. The other paper, Ma et al. is a recent review on changes in iron homeostasis in PD and PD models (Ma et al, 2021). Both papers are now cited in the manuscript.

      This paper describes CISD1 as a new and relevant player in PINK1 and Parkin biology. Further work could lead to exploration of whether CISD1 could be a therapeutic target, considering its role in maintaining mitochondrial redox and mitochondrial health. This is of particular interest to mitochondrial biologists and pre-clinical research in PD.

      This preprint was reviewed by three scientists whose research focus in the mitochondrial biology underlying Parkinson's disease. The group has a special interest in the functions of the mitochondrial outer membrane. We work with several cell models of Parkinson's disease and work with patient donated samples. We do not have expertise in Drosophila models of PD nor the quantification of iron described in the manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary: In the paper entitled 'Mitochondrial CISD1 is a downstream target that mediates PINK1 and Parkin loss-of-function phenotypes', Bitar and co-workers investigate the interaction between CISD1 and the PINK1/Parkin pathway. Mutations in PINK1 and PARKIN cause early onset Parkinson's disease and CISD1 is a homodimeric mitochondrial iron-sulphur binding protein. They observed an increase in CISD1 dimer formation in dopaminergic neurons derived from Parkinson's disease patients carrying a PINK1 mutation. Immuno-blots of cells expressing CISD1 mutants that affects the iron sulphur cluster binding and as well as cells treated with iron chelators, showed that the tendency of CISD1 to form dimers is dependent on its binding to iron-sulphur clusters. Moreover, the Iron-depleted apo-CISD1 does not rescue mitochondrial phenotypes observed in CISD1 KO mouse cells. Finally, In vivo studies showed that overexpression of Cisd and mutant apo-Cisd in Drosophila shortened fly life span and, using a different overexpression model, apo-Cisd caused a delay in eclosion. Similar as patient derived neurons, they observed an increase in Cisd dimer levels in Pink1 mutant flies. Additionally, the authors showed that double mutants of Cisd and Pink1 alleviated all Pink1 mutant phenotypes, while double mutants of Prkn and Cisd rescued most Prkn mutant phenotypes.

      Major comments:

      1) The authors observed an increase in the levels of Cisd dimers in Pink1 mutant flies and removing Cisd in Pink1 mutant background rescues all the mutant phenotypes observed in Pink1 mutant flies, suggesting that the Cisd dimers are part and partial of the Pink1 mutant phenotype. The authors also generated a UAS_C111S_Cisd fly which can overexpress apo-Cisd. Overexpression of the C111S_Cisd construct with Tub-Gal4 showed a developmental delay. Since apo-Cisd forms more dimeric Cisd, my question is: does the strong overexpression (e.g. with Tub-Gal4) of the C111S_Cisd in wild type flies shows any of the Pink1 mutant phenotypes? If not, the authors should mention this and elaborate on it.

      We thank the reviewer for their comments. In fact, we only observed very few flies ecclosing after overexpression of wildtype Cisd or C111S Cisd using the strong tubP-Gal4 driver during development. We considered these very few flies to be escapees (also indicated by the rather low induction of Cisd mRNA suggesting compensatory downregulation) and only used them to conduct the analysis shown in Figure 4c-e. This is now mentioned in more detail in the manuscript.

      2) Figure 6g: Shows the TEM pictures of the indirect flight muscles of Pink1 mutant flies and Pink1, Cisd double mutants. To me, the Picture of Pink1 mutant mitochondria is not very convincing. We expect swollen (enlarged) mitochondria with disrupted mitochondrial matrix. However, this is not clear in the picture. Moreover, in my opinion, Figure6 g, is missing an EM Picture of the Cisd mutant indirect flight muscles.

      We now show exemplary pictures from Pink1 mutant and DKO in a higher magnification which better demonstrate the rounded Pink1 mutant mitochondria and the disrupted cristae structure. EM pictures of all four genotypes in different magnifications are now shown in new supplemental Figure 6.

      3) OPTIONAL: The authors suggest that most probably apo-Cisd, assumes a toxic function in Pink1 mutant flies and serves as a critical mediator of Pink1-linked phenotypes. If this statement is correct, we can hypothesize that increasing apo-Cisd in Pink1 mutant background should worsen the pink1 mutant defects.

      Therefore, I suggest overexpressing Cisd1 wild type (and/or C111S Cisd) in pink1 mutant flies, as pink1 is on the X chromosome, and mild overexpression of Cisd1 with da is not lethal, these experiments could be done in 3-4 fly crosses and hence within 1.5 - 2 months.

      We have set up this experiment and will report in the second revision (v2) of our manuscript.

      Since Pink1 mutant flies contain higher levels of endogenous Cisd dimers, we can expect that overexpression of wild type Cisd will result in an even stronger increase of dimers. If these dimers indeed contribute to Pink1 mutant phenotypes we can expect that overexpression of Cisd will result in a worsening of the Pink1 mutant phenotypes.

      We have set up this experiment and will report in the second revision of our manuscript.

      Minor Comments:

      -) In the Introduction (Background) there are some parts without references:

      E.g., there is not a single reference in the following part between

      'However, in unfit mitochondria with a reduced mitochondrial membrane potential ...&... compromised mitochondria safeguards overall mitochondrial health and function.'

      We thank the reviewer for pointing out this flaw. We have now added a suitable reference to the introduction.

      -) In the introduction there is some confusion about the nomenclature used in the article: e.g. following comments are made in the text: Cisd2 (in this publication referred to as Dosmit) or fly Cisd2 (in this publication named MitoNEET).

      However, the names Dosmit and MitoNEET do not appear in the manuscript (except in references)

      The literature and nomenclature for CISD1 are indeed confusing. We have now revised the introduction.

      -) Figure 1: I am not sure why some gels are shown in this figure. The two last lanes of figure 1c are redundant and Figure 1c' which is also not mentioned in the text, is also a repetition of figure 1c.

      The blots in 1c and 1c’ represent all data points (different patients and different individual differentiations) shown in the quantification in 1d. This is now explained better in the revised manuscript.

      -) The authors mention in material and methods that T2A sites are used at the C-terminus of CISD1 to avoid tagging of CISD1. However, this is not entirely true as T2A will leave some amino acids (around 20) after the self-cleaving and therefore CISD1 will be tagged.

      This is indeed true and we have now changed the wording in the revised manuscript.

      -) In figure 5 P1 is used to abbreviate Pink1 mutants, however P1, to me, refers to pink1 wild type. It would be clearer to abbreviate Pink1 mutants as P1B9 in the graphs as B9 is the name of the mutant pink1 allele.

      We thank the reviewer for pointing out this flaw. We have now altered Fig. 5 to be clearer.

      -) In figure 7: Parkin is abbreviated both as Prkn and as Park

      We thank the reviewer for pointing out this flaw, we indeed mixed up both names because it is complicated. The gene symbol is Prkn, the fly line is called Park25. We have now clarified this in the text and Fig. 7.

      -) I suggest changing the title. Recently an article (Ham et al, 2023 PMID: 37626046) was published showing similar genetic interactions between Pink1/Prkn and Cisd. However, the article of Ham et al, 2023 was focused on Pink1/Prkn regulation of ER calcium release, while this article is more related to iron homeostasis. I suggest that the title shows this distinction.

      This is indeed a very good suggestion. We have now altered the title to “Iron/sulfur cluster loss of mitochondrial CISD1 mediates PINK1 loss-of-function phenotypes”.

      Reviewer #2 (Significance):

      In general, this is an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate. Very recently, a similar genetic interaction between Cisd and Pink1/Prkn in flies was published (Ham et al, 2023 PMID: 37626046) however, from a different angle. While, Ham et al focused on the role of Pink1/Prkn and Cisd in IP3R related ER calcium release, this manuscript approaches the Pink1/Prkn - Cisd interaction from an iron homeostasis point of view. Since, iron dysregulation contributes to the pathogenesis of Parkinson's disease, the observations in this manuscript are relevant for the disease. Hence, the work is sufficiently novel and deserves publication. However, additional experiments are suggested to strengthen the authors' conclusions.

      We thank the reviewer for their kind words. As mentioned above, these additional experiments are on their way and will be included in version 2 of our revised manuscript (v2).

      I work on Drosophila models of Parkinson's disease

      Referees cross-commenting

      I agree with the reviewer number 1 that it would be interesting to investigate CISD1 dimerisation status during mitophagy.

      As mentioned above, we now studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Here the authors provide evidence that Cisd is downstream of Parkin/Pink1 and suggest that the levels of apo-Cisd correlate with neurotoxicity. The data presented generally supports the conclusions of the authors and will be useful to those in the field. The manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data.

      We thank the reviewer for their comments aimed to improve our manuscript. We have now discussed the strengths and weaknesses of our study in more detail.

      Introduction. While iron has been implicated in Parkinson's disease, it is an overstatement to say that disruption in iron metabolism contributes significantly to the pathogenesis of the disease.

      There is certainly a plethora of data implicating perturbed iron homeostasis in PD as also pointed out by reviewer #1. We have tried to tone down our wording in the text and added a recent review on the topic (Ma et al, 2021) as also suggested by reviewer #1.

      Introduction. The discussion of the various names for Cisd2 is important, but confusing as written. Specifically, the use of "this" makes the wording unclear.

      We thank the reviewer for pointing out this flaw. We have altered the wording in the introduction.

      Methods. It would be preferable to use heterozygous driver lines or a more similar genetic control rather than w-1118.

      The exact controls were indeed not well explained in the Methods section, this has been corrected in the revised version. In brief, homozygous driver and UAS lines were indeed used in Fig. 4, this will be addressed in the second revision of our manuscript together with the experiments reviewer #2 suggested. The data shown in Fig. 5, 6, and 7 all used w1118 as control because all other fly strains are on the same genetic background.

      Page 10. It appears that the PINK1 lines have been described previously. The authors should clarify this point and ensure that the new data presented in the current manuscript (presumably the mRNA levels, Fig. 1a) is indicated, as well as data that is confirmatory of prior findings (Fig. 1b).

      Yes, these PINK1 lines have been described previously as pointed out in the manuscript. The original paper did not quantify the PINK1 mRNA levels shown in Fig. 1a. The blots shown in Fig. 1b are from new differentiations and have also not been shown before but confirm findings published in Jarazo et al. (Jarazo et al, 2022). This has been clarified in the revised version of our manuscript.

      Fig. 3 legend. There is a typographical error, "ne-way ANOVA."

      We thank the reviewer for pointing out this flaw. This has been corrected in the revised version.

      Page 15. The nature of the Pink1-B9 mutant should be specified.

      We now added a supplemental Figure 1 that depicts the specific mutation in these flies.

      Fig. 4. Levels of mutant and wild type Cisd should be compared in transgenic flies.

      We now added a quantification of mutant and wildtype Cisd levels to the new Figure 4d.

      Fig. 5b,d. The striking change seems to be the decrease in dimers in young Pink1 mutant animals, not the small increase in dimers in the older Pink1 mutants.

      It is always difficult to find a “typical” picture that reflects all changes observed in quantitative data. This Figure actually shows a decrease of total Cisd levels in young flies in Fig. 5c but no difference of the dimer/monomer ratio in Fig. 5d.

      Fig. 5f. Caution should be used in interpreting the results. Deferiprone has toxicity to wildtype flies (trend) and may simply be making sick Pink1 mutants sicker.

      There is certainly a tendency for wildtype flies to thrive less in food containing deferiprone. To make this more obvious, we have now added the exact p value (0.0764, which we don’t consider borderline but a tendency) to this figure and mention this fact in the text.

      Fig. 5e. The data are hard to interpret. The number of animals is very small for a viability study and the strains are apparently in different genetic backgrounds, though this is not clearly specified. The experiment in Supplementary Fig. 1 appears better controlled and supports the Pink1 data; however, a similar concern pertains to Fig. 7. The authors may thus wish to be more circumspect in their interpretation, especially of the Parkin data.

      In Fig 5e we quantified total iron levels and the Fe3+/Fe2+ ratio using capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICP-MS). Although indeed not so many flies were used in this quantification, the results are highly significant. If the reviewer was referring to Fig. 5f, we agree that this experiment was not well (to be honest, even wrongly explained) which we corrected in the revised version of this manuscript. We thank the reviewer for pointing out this flaw.

      Reviewer #3 (Significance):

      The major significance of the study is in putting downstream of Parkin/Pink1 (largely confirmatory to recently published work) and suggesting that the levels of apo-Cisd are an important determinant of toxicity. The work will be of interest to those in the field.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      The changes already carried out and included in the transferred manuscript (v1) are indicated above in bold orange. All changes pending on ongoing experiments to be included in the second revision of the manuscript are indicated above in bold magenta.

      4. Description of analyses that authors prefer not to carry out

      All changes suggested by the reviewers were addressed (v1) or will be addressed (v2).

      References

      Jarazo J, Barmpa K, Modamio J, Saraiva C, Sabaté-Soler S, Rosety I, Griesbeck A, Skwirblies F, Zaffaroni G, Smits LM, et al (2022) Parkinson’s Disease Phenotypes in Patient Neuronal Cultures and Brain Organoids Improved by 2-Hydroxypropyl-β-Cyclodextrin Treatment. Mov Disord 37: 80–94

      Key J, Sen NE, Arsović A, Krämer S, Hülse R, Khan NN, Meierhofer D, Gispert S, Koepf G & Auburger G (2020) Systematic Surveys of Iron Homeostasis Mechanisms Reveal Ferritin Superfamily and Nucleotide Surveillance Regulation to be Modified by PINK1 Absence. Cells 9

      Ma L, Gholam Azad M, Dharmasivam M, Richardson V, Quinn RJ, Feng Y, Pountney DL, Tonissen KF, Mellick GD, Yanatori I, et al (2021) Parkinson’s disease: Alterations in iron and redox biology as a key to unlock therapeutic strategies. Redox Biol 41: 101896

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      1. General Statements [optional]

      __We thank all the reviewers for their time and their constructive criticism, based on which we will revise our manuscript. All our responses are indicated in red. __

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

      The manuscript by Nguyen and Cheng is investigating the timing and mechanism of cessation of neuroblasts in the pupal optic lobe. Previous studies by several groups have determined the spatial and temporal factors required for the neuroepithelial to neuroblast transition and neuroblast to neural/glycogenesis in third instar larvae such that neuroblasts are eliminated. The mechanism of elimination of neuroblasts in the VNC or mushroom bodies have been investigated, but the mechanism(s) and the timing of elimination of medulla neuroblasts has not been investigated. The authors suggest that medulla neuroblasts are eliminated via a combination of mechanisms including apoptosis, prospero induced size symmetric terminal differentiation and a switch to gliogenesis by gcm expression. Expression of Tailless also was found to affect the timing of medulla neuroblast termination. They also ruled out several mechanisms such as ecdysone pulses.

      Major comments

      Clearly written and logical flow to experiments and results not over interpreted.

      Clearly show that the neuroblast number and size decrease (12 to 18 hrs) and are eliminated by 30 hours

      Figure 2a Marking of the Neuroepithelium. Would be more convincing if shown by PatJ expression and is clonal analysis. While the following panels use PatJ in clones suggesting are NE and NBs present it is more difficult to put into the context in the higher magnification images (Figure 2 D- M) and the Miranda expression in F' seems to be the entire lobe and it is not clear if would be any NE which does not agree with what is shown in panel A.

      We will perform clonal analysis using MARCM to show that the elimination of medulla NBs (marked by Dpn) is accompanied by the depletion of NE (marked by PatJ). For Figure 2 D, E, I, L, we will change the images to the whole lobes to clearly show the shift in the NE-NB transition upon Notch OE/KD.

      Is difficult to see the neuroblasts in Figure 2 D D" and E. The figure does not match what is stated in the results in that the neuroblasts are difficult to observe. If the point is that there is fewer NE cells and more neuroblasts then this is hard to see. It has been previously shown that with Notch RNAi clones prematurely extrude form the NE (Egger 20210; Keegan 2023) and could be expressing more Neuroblast markers but this is not visible in the panels as shown. Are the images single focal plane or maximum projections? Imaging more deeply in the brain or viewing in cross section would account for these possibilities. The possibility that are more neuroblasts but not all at the surface of the OL should be addressed as this could also alter the overall results.

      Figure 2 is key to first point of the paper so needs to be addressed.

      The images are single focal plane of the superficial layer of the medulla. We will specify this information in the figure legends. We will include cross-section of the notch RNAi clones to show the delamination of precocious NBs.

      Minor comments

      Why express volume of DPN in clone volume. Would make the point more clear and more strong be to express as number of NB in the 3-D volume of the clone. This measurement occurs in several figures.

      We will redo the quantification as suggested.

      Use of Miranda to mark NBs is unclear in Figure 2. Perhaps more clear in B&W.

      We will redo the staining with Dpn instead of Mira to mark medulla NBs. Figures will be presented in B&W as suggested.

      Make clear in figures (or figure legend) if single focal plane or projections.

      We will do so.

      It is unclear what percentage of NB the Gal4 line eyR16F10 are expressed in. Veen 2023 state that the GAL4 is also expressed in neurons and at different levels whether deeper within the brain or superficially on the surface of the brain. At 16 APF it is expressed but it is not clear whether it is in all cells at a low level or only within a few cells

      We will further characterize the expression of eyR16F10-GAL4 in the pupal medulla as suggested.

      Some RNAi lines referenced as previously validated and other are not. For example: EcR, Oxphos, Med27, Notch need references or confirmation of specificity to the intended target (qRT)

      We will perform RT-qPCR to validate the use of UAS-med27 RNAi. For RNAi stocks such as UAS-EcR RNAi, UAS-Atg1 RNAi, UAS-notch RNAi that have been previously used in other publications, we will provide appropriate references.

      At least 2 animals per genotype were used. While I appreciate the technical difficulty of working in pupae this seems a bit low in terms of number of samples and data would be more robust with more numbers.

      Any experiments in which less than 3 animals were used, we will redo the experiments.

      Reviewer #1 (Significance (Required)):

      This provides mechanism and timing for the elimination of neuroblasts (NE to NB) that arise from the medulla. As these are most similar to mammalian brain development (Radial glial to NSC) this information provides more context to interpret the formation of glial and neurons in the adult optic lobe given the effect on timing and mechanisms of elimination.

      This paper would be of interest to developmental biologist who work with Drosophila or mice who are looking at neural development. An understanding of how neural diversity is achieved and the mechanisms behind this that can be dysfunctional in terms of etiology of neural diseases. Is a well done study for the most part that would be improved by clarifying some data and provided more replicates for robustness of the data.

      I am a developmental biologist working with Drosophila in larval and adult neural development.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

      Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.

      In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla.

      The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?

      The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.

      There are no major issues affecting the conclusions

      • The paper shows that the end of production of neural stem cells occurs the neuroepithelium is completely transformed. The experiments performed by the authors are fine and show that, if the transition is delayed, neural stem cells terminate their life later, and vice versa. However, the lifespan of the neural stem cells is not affected by the timing of the transition. Therefore, these experiments do not tell us how neural stem cells terminate their life, which is the central question of the study. The discussion should be written accordingly and the title and the model in Fig 6 modified to reflect the importance of the end of life of the stem cells, the main theme of the paper.

      We agree that our said experiments did not elucidate how NBs terminate at the end of neurogenesis. Nevertheless, our aim is to show that the timing of NB termination in the medulla is dependent on the timing of the NE-NB transition.

      In Supplementary Figure 1, we showed that factors previously shown to be involved in NB termination in other lineages did not play similar roles in the medulla NBs. Thus, we think that NB termination in the medulla is likely regulated at the levels of the NE, but not the NBs themselves. Although we have briefly mentioned this in our manuscript, we hope by conducting the experiments suggested by the reviewer (see below), we can subsequently modify our model in Figure 6 and our discussion.

      • The authors talk about Pros-dependent symmetric division and gliogenic switch as two separate processes, but these may be two sides of the same phenomenon. Tll+ gcm+ neural stem cells undergo Pros-dependent cell cycle exit, generating glial progeny. If the authors agree with this, could they update their model (and discussion) to reflect the fact that gliogenic switch occurs via a Pros-dependent symmetric division, and these are not two separate processes independently contributing to the depletion of the neural stem cell pool? Ideally, a triple staining between Dpn, Pros, and gcm would show that the symmetrically dividing cells seen by the authors are committed to the glial fate.

      We will further test how gliogenesis is affected in pros RNAi clones. The results may shed light on whether Pros-mediated symmetric division is required for Gcm-mediated gliogenesis in the medulla. Regarding the model, we have summarized our findings and suggestions in Figure 5K, however, we will integrate this information into our final model.

      In Figure 5C, we showed that at 12h APF, there are Dpn+ NBs in the medulla that expressed both Pros and Gcm, suggesting that it is very likely that Pros is upstream of Gcm to induce the glial cell fate switch of the medulla NBs.

      • Why were Notch RNAi experiments assessed for the presence of neural stem cells at P12 and gcm RNAi experiments at P24? Given that most optic lobe neural stem cells disappear between P12-18, a subtle effect of gcm RNAi may have been missed. Do the authors have data for gcm RNAi at P12?

      We hypothesized that the timing of NE-NB transition affects the timing of NB termination in the medulla. Because Notch KD was previously shown to induce precocious NE-NB transition in the OL, meaning that medulla NBs are born prematurely, we expected that this manipulation will lead to a corresponding premature elimination of the NBs. In contrast, gcm RNAi which inhibits the switch into the glial cell fate of the NBs, is expected to prolong the neurogenic phase of the NBs, and thereby, their persistence by 24h APF when WT NBs are eliminated.

      • The authors should acknowledge that the inhibition of either apoptosis or autophagy alone may not be fully sufficient to prevent the death of NBs. In mushroom body neural stem cells, both processes must be inhibited simultaneously to produce a strong effect on their survival (Pahl et al. 2019, PMID 30773368).

      We will add this information in our discussions.

      • There is an important missing point that should be addressed: is there a specific point in time when all neural stem cells must stop their lineage wherever they are in the temporal series and either die or divide symmetrically? One possibility that is not discussed is that most neural stem cells end their life through a gliogenic symmetric division while those that were generated late must stop en route and die by apoptosis and/or autophagy. This would solve the strange diversity of end-of-life, which could be easily addressed by identifying the temporal stage of the neural stem cells that undergo apoptosis

      We agree that it would be of interest to understand how there are diverse mechanisms by which medulla NBs terminate during pupal development. To address if temporal progression is involved in apoptosis of the medulla NBs, we will first characterize the expression of some temporal TFs (e.g., Ey, Slp, Tll) at 12h APF when we found a subset of medulla NBs undergo apoptosis in the wildtype animals.

      Minor suggestions:

      We agree with these minor modifications.

      • Line 46: Specify that there are 8 type II neural stem cells in each hemisphere*.

      • The statement in lines 181-182 that "cell death, and not autophagy, makes a minor contribution to..." should be replaced with "apoptosis, and not autophagy," as autophagy is also a type of cell death.

      • The authors should adjust the logic of the section "Medulla neuroblasts terminate during early pupal development": Describe the wild-type pattern first (the decrease in the number of neural stem cells and their size with age) and then describe the perturbations aimed at disrupting the number and the size of neural stem cells

      • Line 151 should refer to Fig. 2I-K, not Fig. 2J-K.

      **Referees cross-commenting**

      How can NBs die by different mechanisms?? This might only happen is they are in a different states, an issue that is not addressed.

      it has been shown that optic lobe NBs end their life by a symmetric, gliogenic last division at the end of the last temporal window, and not by PCD.

      It is likely, and the authors do hint at it, that NBs only die by PCD when they prematurely interrupt the temporal series in early pupation when neurons synchronously start undergoing maturation.

      I believe that the authors should explain this, if this is indeed their model, and show that NBs die while still in early temporal windows.

      Reviewer #2 (Significance (Required)):

      Lineages of neural stem cells are of great interest to understand how many neural types are generated. They produce very diverse neurons, often in a highly stereotyped series. However, they must terminate their life when the animal becomes functional or if neurons need time to become mature before birth.

      In the Drosophila optic lobes, neural stem cells are produced over a period of several days by a wave of neurogenesis that transforms a neuroepithelium into neural stem cells that undergo a series of temporal patterning steps. It has been reported that they finish their life when a symmetric division generates glial cells. The authors however analyze the end of a particular lineage, that of the latest born neural stem cells of the medulla.

      The paper shows that neural stem cells stop being produced when the neuroepithelium is consumed. But how do the latest born neural stem cells stop their lineage?

      The results show that they do so by several means, which is quite unexpected: they may die from apoptosis, or autophagy, by becoming glioblasts or by a terminal symmetric division.

      There are no major issues affecting the conclusions

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary

      In this manuscript, the authors address the timing and mechanisms responsible for the termination of medulla neuroblasts in Drosophila visual processing centres, also known as optic lobes. Through time course experiments the authors demonstrate the medulla NBs are completely eliminated by 30h APF during early pupal development. By manipulating the Notch signalling pathway as well as proneural genes such as lethal of scute, the authors show that altering the NE-NB transition is sufficient to change the timing of NB termination. In contrast, ecdysone signalling and components of the mediator complex, known to terminate proliferation of central brain NBs, are not required for the termination of medulla NBs. Medulla NBs sequentially express a variety of temporal transcription factors to promote cellular diversity, however, the authors demonstrate that altering temporal factors such as Ey, Sco or Hth, does not affect the timing of the medulla NBs termination. Interestingly however overexpression of the transcription factor tailless can cease medulla NB termination via the conversion of type I to type II NB fate. They further go on to show the importance of the differentiation factor, Prospero, in promoting the differentiation of medulla NBs as well as terminating medulla neurogenesis during pupal development. Finally, in addition to differentiation, the authors show another mechanism responsible for the cessation of neurogenesis which is the commencement of gliogenesis. Through manipulation of the neurogenic to gliogenic switch by knockdown or overexpressing the glial regulatory gene, gcm, the authors show that even though the downregulation of gcm is is not sufficient to induce NB persistence, gcm overexpression can cause premature termination of NBs.

      Major comments:

      • Are the key conclusions convincing?

      Yes, the key conclusions are convincing with proper controls, quantifications and statistical analyses.

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      The conclusion that temporal transcription factors (TTF) do not affect the timing of medulla NB termination is somewhat preliminary. The authors investigated a simplified temporal series including Homothorax, Eyeless, Sloppy-paired, Dichaete and Tailless. However, there are additional temporal factors that have not been examined for their potential involvement in medullar NB termination. Previous reports have identified several other temporal factors that play a role in medulla TTF cascade, such as, SoxNeuro (SoxN) and doublesex-Mab related 99B (Dmrt99B) that start their expression in the NE similar to Hth, however, Dmrt99B is likely to be repressed much later than Hth (Li, Erclik et al. 2013, Zhu, Zhao et al. 2022). At this point, it remains challenging to completely rule out the possibility that other temporal factors play a role in medullar NB termination or have redundant functions in regulating the timing of medulla NB cessation. It is suggested to tone down this claim and provide a brief discussion on alternative possibilities, citing relevant papers on the functions of other temporal factors in medullar NBs.

      We agree.

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Loss of pros by RNAi caused the formation of ectopic NBs and the NBs persist even at 24h APF. Do these NBs persist at 30h or 48h APF? Does overexpression of Pros result in early termination of medulla NBs?

      We will do these experiments in clones as suggested.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      Yes, I believe the suggested experiments are realistic in terms of time and resources, with an estimation of 3 months to complete the experiments.

      • Are the data and the methods presented in such a way that they can be reproduced?

      Yes.

      • Are the experiments adequately replicated and statistical analysis adequate?

      The experiments are straight forward and were performed with proper controls, supported by quantifications and proper statistical analyses. However, there is no mention about how many replicates were used.

      We will add this information in our Material and Methods section.

      Minor comments:

      1. The authors use the eyR6F10-Gal4 driver in certain experiments. The eyR6F10-Gal4 driver is however expressed only in a subset of medulla NBs. Can the authors comment on what percentage of medulla NBs is the driver expressed in? We will characterize this.

      Does the EGFR signalling pathway or JAK/STAT pathway affect the timing of termination of medulla NBs? Experiments are not necessary. The author can speculate on their roles.

      We will modify our discussion accordingly.

      Figure 1C has a p value of only 0.03 (*) but shows a strong reduction in the number of Dpn+ cells from 12h to 18h, etc. Is this correct? Also, is the p value the same for the comparison between 12h and 24h as well as 12h and 30h APF?

      Yes. P-values showed no significant differences between 28-24h and 24-30h APF.

      The controls in figure 2B and to some extent figure 2H show one major outlier (much higher than the other brain lobes in the control). Will the removal of this outlier affect the significance/ p-value of the experiment?

      No, removing the outliers do not change the statical results.

      In figure 2B what is the p-value between 12h and 18h APF? Is it *** as well?

      No, it’s not significant.

      Line 84 of the introduction introduces Tll, Gcm and Pros for the first time in the manuscript and should be written out in full.

      We will change this.

      • Are prior studies referenced appropriately?

      Yes.

      • Are the text and figures clear and accurate?

      Yes.

      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Quite a few of data mentioned in the manuscript have been described as data not shown. I think it would be nice to show quantifications or representative images in the supplementary figures.

      We will add the data which was previously not shown.

      Reviewer #3 (Significance (Required)):

      Since the mechanisms by which medulla NBs are terminated are currently unknow, this is an important and interesting study to understand how medulla neuroblasts in the optic lobe are terminated. The balance between stem cell maintenance and differentiation is critical for proper brain development and the results presented in this paper are impactful. Furthermore, Drosophila melanogaster is an excellent model to study stem cell niches and neuroblast temporal patterning. The authors provide key mechanisms namely cell death, Pros-mediated differentiation and the gliogenic switch that contribute to a better understanding of how the NB progenitor pool can be terminated in the Drosophila OL, which is largely supported by the data.

      • Place the work in the context of the existing literature (provide references, where appropriate).

      So far, most work in this field has focused on the regulation of the temporal factors to promote the progression of the TTF transcriptional cascade and thereby diversity of the neural progenitors (Li, Erclik et al. 2013, Naidu, Zhang et al. 2020, Ray and Li 2022, Zhu, Zhao et al. 2022). Furthermore, work on pathways such as EGFR and Notch signalling that allows the proneural wave to progress and subsequently induce neuroblast formation in a precise and orderly manner have also been studied (Yasugi, Umetsu et al. 2008, Yasugi, Sugie et al. 2010). Here, considering previous literature, the authors move one step forward to determine how and when these neuroblast progenitors cease proliferation during development thus providing mechanisms for the regulation of the neuroepithelial stem cell pool, its timely conversion into NSCs and the switch from neurogenesis to gliogenesis thus providing important implications for brain size determination and function.

      • State what audience might be interested in and influenced by the reported findings.

      Stem cell research, neurobiologists and developmental biologists.

      • Define your field of expertise

      Stem cells, developmental biology

    1. One big category of website that produces writing are the professional media outlets that employ journalists, editors, researchers, and writers to produce daily or weekly content. On these sites, the writing itself is the product.

      Although thee are so many different styles of writing, especially on the internet, I think we can all agree in one way or another that knowing how to write professionally, whether it may be for a professional media outlet or even for a company website, is very important. Consider journalism. If a journalist working for the New York Times were to publish a story that was written in the style of something that came from Reddit, they would be fired immediately. Therefore, knowing how to differentiate when to use different writing styles is extremely important as a writer.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment:

      This valuable study, of interest for students of the biology of genomes, uses simulations in combination with published data to examine how many TADs remain after cohesin depletion. The authors suggest that a significant subset of chromosome conformations do not require cohesin, and that knowledge of specific epigenetic states can be used to identify regions of the genome that still interact in the absence of cohesin. The theoretical approaches and quantitative analysis are state-of-the-art, and the data quality and strength of the conclusions are solid. However, because "physical boundaries (of domains?)" in the model appear to be a consequence of preserved TADs, rather than the other way around, the functional insights are limited.

      Summary of the reviewer discussion for the authors:

      While the simulations are state of the art and the reviewers appreciated that the approaches used here might help to resolve apparent discrepancies between conclusions from single-cell and bulk/ensemble techniques to study chromosome conformation, the work would benefit from clarification of what precisely is meant with "physical boundaries" and from a comparison of CCM and HIPPS models to understand commonalities and differences between them. In addition, more discussion of the relation of the current work to previous studies, such as Schwarzer et al., 2017, and Nuebler et al., 2018, would elevate the work and make the key claims more compelling. Please see also the detailed comments from the expert reviewers.

      We thank the editor for the assessment and the reviewers for the incisive comments. We will address these comments one by one. In particular, we attempt to clarify the concept of “physical boundaries” and its relevance in our study. We hope our responses are satisfactory. We believe that our manuscript has benefitted substantially by revising the manuscript following the comments by the reviewers.

      Below is our point-by-point response to the comments:

      Reviewer #1 (Public Review):

      Summary:

      In this paper, Jeong et al. investigate the prevalence and cause of TADs that are preserved in eukaryotic cells after cohesin depletion. The authors perform an extensive analysis of previously published Hi-C data, and find that roughly 15% of TADs are preserved in both mouse liver cells and in HCT-116 cells. They confirm previous findings that epigenetic mismatches across the boundaries of TADs can cause TAD preservation. However, the authors also find that not all preserved TADs can be explained this way. Jeong et al. provide an argument based on polymer simulations that "physical boundaries" in 3D structures provide an additional mechanism that can lead to TAD preservation. However, in its current form, we do not find the argumentation and evidence that leads to this claim to be fully compelling.

      Strengths:

      We appreciate the extensive statistical analysis performed by the authors on the extent to which TAD's are preserved; this does seem like a novel and valuable contribution to the field.

      We thank the reviewer for a succinct and comprehensive summary of our work and for appreciating value of our work.

      Weaknesses:

      1) As the authors briefly note, the fact that compartmentalization due to epigenetic mismatches can cause TAD-like structures upon cohesin depletion has already been discussed in the literature; see for example Extended Data Figure 8 in (Schwarzer et al., 2017) or the simulation study (Nuebler et al., 2018). We are hence left with the impression that the novelty of this finding is somewhat overstated in this manuscript.

      It is unclear to us by studying the results in the Extended Data Figure 8 that the authors have shown that epigenetic mismatches cause TAD-like structures. As far as we can discern, the data, without a quantitative analysis, shows that may be new TAD-like structures that are not in the wild type appear when cohesin is deleted.

      The studies by Schwarzer et al 2017 and Nuebler et al 2018 are relevant to our own investigation, which we undertook after scrutinizing the experiments in Schwarzer et al 2017 and the related work by Rao et. al in 2017 on a different cell line. In the summary of the Reviewer discussion, it is suggested we discuss the relation to the experimental study by Schwarzer et al 2017 and the computational work by Nuebler et al 2018.

      (1) The results and the corresponding discussion in these two studies are different (may be complimentary) from our results. When referring to the Extended Data Figure 8 Schwarzer and co-authors state in the main text, “The finer compartmentalization explains most of the remaining or new domains and boundaries seen in Nipbl Hi-C maps”. We are not 100% sure what “remaining” means in this context. The Extended Data Fig. 8(a) shows the “common boundaries” is correlated with the eigenvectors of compartmentalization. If this indeed is what the reviewer is referring to, we believe that our study differs from theirs in two important ways: First, Extended Data Fig.8 (a) is a statistical analysis at the “ensemble” level. In our study, we examined the preservation of TADs at both individual and ensemble level with more detailed analysis. Second, in Extended Data Fig. 8(a), the “common boundaries” (incidentally we are uncertain how that was calculated) are compared to the eigenvectors of PCA analysis of the compartments (larger length scales). In contrast, in our study, we examined the correlation between TAD boundaries and the epigenetic profiles. We believe that this is an important distinction. The PCA analysis of compartments and “common boundaries” defined using (presumably) the insulation score are both derived from the Hi-C contact map. Epigenetic profile, on the other hand, is independent of Hi-C data. We believe our contribution, is to build the connection between epigenetic profiles with the preservation of TADs, and link it to 3D structures. For these reasons, we assert that our results are novel, and are not contained (or even implied) in the Schwarzer et al 2017 study.

      The simulations in Neubler et al 2018, which were undertaken to rationalize the experimenrs, revealed that compartmentalization of small segments is enhanced after cohesin depletion, while TADs disappear, which support the broad claims that are made in the experiments. They assert that the structures generated are non-equilibrium. They do not address the emergence of preserved nor the observation of TAD-like structures at the single cell level. However, our goal was to elucidate the reasons for of preservation of TADs. By that we mean, the reasons why certain TADs are present in both the wild and cohesin depleted cells? Through a detailed analyses of two cells, polymer simulations, we have provided a structural basis to answer the question. Finally, we have provided a plausible between TAD preservation and maintenance of enhancer-promoter interactions by analyzing the Micro-C data. For all these reasons, we believe that our study is different from the results in the Extended Figure 8 or the simulations described by Neubler.

      Let us summarize the new results in our study that are not contained in the studies referred to by this Reviewer. (1) We showed by analyzing the Hi-C data for mouse liver and HCT-16 that a non-negligible fraction of TAPs is preserved, which set in motion our detailed investigation. (2) Then, using polymer simulations on a different cell type, we generated quantitative insights (epigenetic mismatches as well as structural basis) for the preservation of TADs. Although not emphasized, we showed that deletion of cohesin in the GM12878 cells also give rise to P-TADs a prediction that suggests that the observations might be “universal”. (3) Rather than perform, time consuming polymer simulations, we calculated 3D structures directly from Hi-C data for the mouse liver and HCT-16 cells, which provided a structural basis for TAP preservation. (4) The 3D structures also showed how TAD-like features appear at the single cell level, which is in accord with imaging experiments. (5) Finally, we suggest that P-TADs may be linked to the maintenance of enhancer-promoter and promoter-promoter interactions by calculating the 3D structures using the recent Micro-C data.

      For the reasons given above, we assert that our results are novel, and bring new perspectives that are not in the aforementioned insightful studies cited by the Reviewer.

      2) It is not quite clear what the authors conceptually mean by "physical boundaries" and how this could offer additional insight into preserved TADs. First, the authors use the CCM model to show that TAD boundaries correlate with peaks in the single cell boundary probability distribution of the model. This finding is consistent with previous reports that TAD-like structures are present in single cells, and that specific TAD boundaries only arise as a population average.

      The finding based on the CCM simulations hence seems to be that preserved TADs also arise as a population average in cohesin-depleted cells, but we do not follow what the term "physical boundaries" refers to in this context. The authors then use the Hi-C data to infer a maximumentropy-based HIPPS model. They find that preserved TADs often have boundaries that correspond to peaks in the single cell boundary probabilities of the inferred model. The authors seem to imply that these peaks in the boundary probability correspond to "physical boundaries" that cause the preservation of TADs. This argument seems circular; the model is based on inferring interaction strengths between monomers, such that the model recreates the input Hi-C map. This means that the ensemble average of the model should have a TAD boundary where one is present in the input Hi-C data. A TAD boundary in the Hi-C data would then seem to imply a peak in the model's single-cell boundary probability. (The authors do display two examples where this is not the case in Fig.3h, but looking at these cases by eye, they do not seem to correspond to strong TAD boundaries.) "Physical boundaries" in the model are hence a consequence of the preserved TADs, rather than the other way around, as the authors seem to suggest. At the very least the boundary probability in the HIPPS model is not an independent statistic from the Hi-C map (on which their model is constrained), so we have concerns about using the physical boundaries idea to understand where some of the preserved TADs come from.

      There are many statements in this long comment that require us to unpack separately. First, using both the CCM simulations, and even more importantly using data-driven approach, we established that TAD-like structures are present in single cells with and without cohesin. The latter finding is fully consistent with imaging experiments. We are unaware of other computational efforts, before our work, demonstrating that this is the case. Perhaps, the Reviewer can point to the papers in the literature.

      Regarding the statement that our arguments are circular, and lack of clarity of the meaning of physical boundary, please allow us to explain. First, we apologize for the confusion. Let us clarify our approach. We first used CCM to understand the potential origin of substantial fraction of P-TADs in the GM. The simulations, allowed us to generate the plausible mechanisms, for the origin of P-TADs. Because the CCM does reproduce the Hi-C data, we surmised that the general mechanisms inferred from these simulations could be profitably used to analyze the experiments. The simulations also showed that knowledge of 3D structures produces a muchneeded structural basis of P-TADs , and potentially for emergence of new TADs upon cohesin depletion.

      Because 3D coordinates are needed to obtain structural insights into the role of cohesin, we use a novel method to obtain them without the need for simulations. In particular, we used the HIPPS method to obtain 3D coordinates with the Hi-C data as the sole input, which allowed us to calculate directly the boundary probabilities. The excellent agreement between the predicted 3D structures and imaging experiments suggests that meaningful information, not available in Hi-C, may be gleaned from the ensemble of calculated 3D structures.

      Although “physical boundary”, a notion introduced by Zhuang, is defined in in the method section, it is apparently unclear for which we apologize. Because this is an important technical tool, we have added a summary in the main text in the revision. We did not mean to imply that the physical boundaries cause the preservation of TADs, although we found that maintenance of the enhancer-promoter contacts (see Fig. 8 in the revision) could be one of the potential reasons for the emergence of physical boundaries. We agree with the reviewer that physical boundaries are structural evidence of preserved TADs (not the cause), that is when a TAD is preserved, we can detect it by prominent physical boundary. The purpose and benefit of physical boundary analysis and using HIPPS in general is to obtain three-dimensional structures of chromosomes. Although both CCM simulations and HIPPS use Hi-C contact maps, three-dimensional structures provide additional information that is not present in the Hi-C data.

      The arguments that the authors use to justify their claims could be clarified and strengthened. Here are some suggestions: -Explain the concept of "physical boundaries" more clearly in the main text.

      As explained above, we have revised the text to clarify the concept and purpose of physical boundaries analysis. See Page 7.

      • Justify why the boundary probabilities and the physical boundaries concept can be used to offer novel insight into where preserved TADs may come from.

      Boundary probabilities and physical boundaries provide previously unavailable 3D structural information on the TADs structures both at the single-cell and population level. This provides a direct structural basis for determining which TADs are preserved. But in order to understand where P-TADs may come from, physical boundaries analysis alone is not sufficient. As we have shown in the analysis of enhancer-promoter contact, using physical boundary analysis from 3D structures, we can conclude that conservation of enhancer-promoter contact could be one of the reasons for the P-TAD.

      • Explain more clearly what the additional value of using the HIPPS model to study TAD preservation is.

      Our goal, as announced in the title is to elucidate the structural basis for the emergence of PTADs. The HIPPS method, which avoids doing simulations (like CCM and other polymer models used in the literature) provides an ensemble of 3D conformations using averaged contact map generated in Hi-C experiments. Even more importantly, HIPPS produce an ensemble of structures, which can be the basis for predicting the outcomes at the single-cell level. The accuracy of the generated structures has been shown in our previous work (Shi and ThirumalaiPRX 2021). In ensemble-averaged Hi-C experiments, TADs appear to be relatively stable. However, imaging experiments (Bintu et. al, 2018) have revealed that TADs are not fixed structures present in every single cell, but instead exhibit variability at the single-cell level. TADlike structures with distinct boundaries are observed in individual cells, and the location of these boundaries varies from cell to cell. However, these TAD-like structures still show a preferential positioning in 3D structures. Interestingly, the preferential positioning often corresponds to TAD boundaries observed in population-averaged Hi-C data. This suggests that while cohesin is involved in establishing the overall organization of TADs, other factors and mechanisms could also contribute to TAD formation at the individual cell level. In this study, we showed some boundaries of P-TADs upon cohesin loss in the Hi-C maps, align with preferential boundaries in individual 3D structures of chromosomes. The makes the finding that a subset of TADs is preserved upon cohesin is robust.

      From a technical perspective, the use of HIPPS avoids time-consuming polymer simulations. The HIPPS is rapid and can be used to generate arbitrarily large ensemble of structures, allowing us calculate properties both at the single cell and ensemble level.

      In addition, we'd like to offer the following feedback to the authors.

      3) The discussion of enhancer-promoter loops as a cause of TAD preservation is interesting, but it would be interesting to know fraction of preserved TADs enhancer-promoter loops might explain.

      We thank the reviewer for the excellent suggestion. We have done the suggested calculation. The results are shown in a new Figure.8 in the main text. We also moved the results on enhancer-promoter to the main results section from the Discussion section.

      4) The last paragraph of the introduction seems to state that only the HIPPS model was used to find single-cell 3D structures and boundary probabilities. However, the main text suggests that the CCM model was also used for these purposes.

      We have revised the text to clarify this point on pages 3-4. Also please see the discussion on the utility of HIPPS above.

      5) When referring to the boundary probability, it would be useful if the authors always specified whether they refer to the boundary probability before or after cohesin depletion (or loop depletion in the CCM model). Statements such as "This implies that peaks in the boundary probabilities should correspond to P-TADs" are ambiguous; it is unclear if the authors mean that boundary probabilities before cohesin depletion predict that the boundary will be preserved, rather than that preserved TAD boundaries correlate with peaks in the boundary probability after cohesin depletion.

      We thank the reviewer for the suggestion. Indeed, it may be confusing. Hence, we have revised the text in numerous places to clarify this point.

      6) It would be interesting to analyze all TAD boundaries that are present after cohesin depletion, rather than just those that overlap with TAD boundaries in WT cells. This would give better statistics for answering the question what causes TAD-like structures in cells without cohesin.

      We thank the reviewer for this excellent suggestion. First, this would we believe this deviate from the primary goal of this study: what leads to TAD preservation after cohesin deletion? Second, this has to be done very systematically, as we did here for P-TADs, in order draw meaningful conclusions. This is a very useful study for another occasion.

      7) The use of a plethora of acronyms (P-TAD, CM, DM, CCM, HLM...) makes the paper difficult to read.

      We have revised the text to change CM to “contact map” and “DM” to “distance map”. For PTADs, CCM, and WLM, we would argue that P-TAD is rather a clear and intuitive abbreviation and CCM/WLM refers to specific methods/models and replacing them with full names would make text more difficult to read. We hope the reviewer is okay with us keeping these acronyms.

      Reviewer #2 (Public Review):

      Summary:

      Here Jeong et al., use a combination of theoretical and experimental approaches to define molecular contexts that support specific chromatin conformations. They seek to define features that are associated with TADs that are retained after cohesin depletion (the authors refer to these TADs as P-TADs). They were motivated by differences between single cell data, which suggest that some TADs can be maintained in the absence of cohesin, whereas ensemble HiC data suggest complete loss of TADs. By reananalyzing a number of HiC datasets from different cell types, the authors observe that in ensemble methods, a significant subset of TADs are retained. They observe that P-TADs are associated with mismatches in epigenetic state across TAD boundaries. They further observe that "physical boundaries" are associated with P-TAD maintenance. Their structure/simulation based approach appears to be a powerful means to generate 3D structures from ensemble HiC data, and provide chromosome conformations that mimic the data from single-cell based experiments. Their results also challenge current dogma in the field about epigenetic state being more related to compartment formation rather than TAD boundaries. Their analysis is particularly important because limited amounts of imaging data are presently available for defining chromosome structure at the single-molecule level, however, vast amounts of HiC and ChIP-seq data are available. By using HiC data to generate high quality simulated structural data, they overcome this limitation. Overall, this manuscript is important for understanding chromosome organization, particularly for contacts that do not require cohesin for their maintenance, and for understanding how different levels of chromosome organization may be interconnected. I cannot comment on the validity of the provided simulation methods and hope that another reviewer is qualified to do this.

      We appreciate the reviewer for a comprehensive summary of our work, and we are happy that the reviewer finds our work important, which provides valuable insights to the field.

      Specific comments

      • It is unclear what defines a physical barrier. From reading the text and the methods, it is not entirely clear to me how the authors have designated sites of physical barriers. It may help to define this on pg 7, second to last paragraph, when the authors first describe instances of PTAD maintenance in the absence of epigenetic mismatch.

      We thank the reviewer for the suggestions. The details of physical boundary designation are provided in the appendix data analysis. To make the concept and idea of physical boundary easy to understand, we have revised the text on page 7 in the revised main text.

      • Figure 7 adds an interesting take to their approach. Here the authors use microC data to analyze promoter-enhancer/promoter-promoter contacts. These data are included as part of the discussion. I think this data could be incorporated into the main text, particularly because it provides a biological context where P-TADs would have a rather critical role.

      We thank the reviewers for the suggestion. We also agree that results in Figure 7 provide novel insights on TAD formation and its possible preservation upon perturbation. We have followed the reviewer’s suggestion to move it to an independent section in the main results section as the last subsection.

      • Figure 3a- the numbers here do not match the text (page 6, second to last paragraph). The numbers have been flipped for either chromosome 10 or chromosome 13 in the text or the figures.

      We thank the reviewer for pointing out this error. In the revised main text, it has been corrected.

      Reviewer #3 (Public Review):

      This manuscript presents a comprehensive investigation into the mechanisms that explain the presence of TADs (P-TADs) in cells where cohesin has been removed. In particular, to study TADs in wildtype and cohesin depleted cells, the authors use a combination of polymer simulations to predict whole chromosome structures de novo and from Hi-C data. Interestingly, they find that those TADs that survive cohesin removal contain a switch in epigenetic marks (from compartment A to B or B to A) at the boundary. Additionally, they find that the P-TADs are needed to retain enhancer-promoter and promoter-promoter interactions.

      Overall, the study is well-executed, and the evidence found provides interesting insights into genome folding and interpretations of conflicting results on the role of cohesin on TAD formation.

      We are pleased with the reviewer’s positive assessment of our work.

      To strengthen their claims, the authors should compare their de-novo prediction approach to their data-driven predictions at the single cell level.

      We thank the reviewer for the very good suggestion. We are assuming that the Reviewer is asking us to compare the CCM simulations with HIPPS generated structures at the single cell level. We have shown, using the GM12878 cell data, that the polymer simulations reproduce the Hi-C contact maps (an average quantity) well (see Appendix Fig. 2 and Fig. 3). In addition, we show in Appendix Fig. 8 the comparison with ensemble averaged distance maps as well as at the single cell level for Chr 13 from the GM12878 cell. There are TAD-like structures at the single cell level just as we find for HCT-116 cell (Fig. 5 in the main text). Thus, the conclusions from de-novo prediction and data-driven predictions are consistent. In addition, in our previous publication introducing HIPPS in Phys Rev X 11: 011051 (2021), we showed that the method is quantitatively accurate in reproducing experimental data for all the interphase chromosomes.

      Having demonstrated this consistency, we used computationally simple data-driven predictions to analyze HCT-116 and mouse liver cell lines for which Hi-C data with and without cohesin rather than perform multiple laborious polymer simulations.

      Please see below for our response to specific comments.

      1) It is confusing that the authors change continuously their label for describing B-A and A-B switches. They should choose one expression. I think that the label "switch" between A and B is more precise than "mismatch".

      We have revised the text to make it consistent. Now it all reads “A-B”. Yes, the suggestion that we use switch is good but we think that mismatch is more concise. We trust that this Reviewer will indulge us on this point.

      2) In the Abstract, the authors mention HCT-116 cells but do not specify which cells are these.

      We have changed “HCT-116” in the abstract to “human colorectal carcinoma cell line”.

      3) In the Abstract, it is unclear what the authors mean by "without any parameters"

      In the theoretically based HIPPS method, there is no “free” parameter. In other words, the only parameter is uniquely determined. To avoid confusion, we have removed “without any parameters” from abstract.

      4) In Results, what do the authors mean by 16% (26%)?

      This refers the percentage of how many TADs are preserved after Nipbl and RAD21 removal in mouse and HCT-116 cells, respectively. Using TopDom method, we identified TAD boundaries in Wild and cohesin-depleted cells. There are 16% (959 out of 4176 – Fig. 1a) and 26% (1266 out of 4733 – Fig. 1b) of TADs are preserved after Nipbl and RAD21 removal in mouse and HCT-116 cells, respectively. We removed the percentages in the revised version.

      5) In Results, the authors mention "more importantly, we did tune the value of any parameter to fit the experimental CMs". Did they mean that instead they didn't tune any parameter?

      We apologize for the confusion. In the CCM, there is a single controlled parameter. We have changed the sentence to reflect this correctly.

      6) In Results, section "CCM simulations reproduce wild-type Hi-C maps", Kullback-Leibler (KL) divergence is used to assess the correlation between two loci, but it is unclear what the value 0.04 stands for; is it a good or a bad correlation?

      The value for Kullback-Leibler divergence can vary from 0 to infinity with 0 give the perfect correlation. Thus, 0.04 means that the correlation is excellent.

      7) The authors use two techniques to obtain 3D structures, one is CCM, which takes the cohesin as constraints, and another is HIPPS, which reconstructs from Hi-C maps. Both seem to have good agreement with the Hi-C contact maps. However, did the authors compare the CCM with the HIPPS 3D structures?

      This is detailed in response at the start of the reply to this Reviewer. As detailed in this response as well in the main text we used the CCM to generate hypotheses for the origin of P-TADs. In the process, we established the accuracy of CCM, which gives us confidence about the hypotheses. As explained above and emphasized in the revised version, CCM simulations are time consuming whereas generating 3D structures using HIPPS is computationally simple. Because HIPPS is also accurate, we used it to analyze the Hi-C data on mouse liver, HCT-116 as well as Micro-Data on mESC.

      In our paper in Phys Rev X 11: 011051 (2021) we showed that HIPPS reproduces Hi-C data. In the current manuscript, we showed in Appendix Fig. 2 and Fig. 3 as well as in a study in 2018 (Shi and Thirumalai, Nat Comm.) that CCM is accurate as well. Thus, there is little doubt about the accuracies of the methods that we have developed.

      8) In Results, section "P-TADs have prominent spatial domain boundaries", the authors constructed individual spatial distance matrices (DMs) using 10,000 simulated 3D structures. What are the differences among these 10,000 simulations? Do they start them with different initial structures?

      The structures are generated using HIPPS which is data-driven method that uses Hi-C contact map as constraints. The method, which uses the maximum entropy theory, samples from a distribution that describe the structural ensemble of chromosome. The 10,000 structures are randomly sampled and are independent from each other. The HIPPS method is not a simulation, and hence the issue of initial structures does not arise.

      9) In Methods, when the authors mention the "unknown parameter", do they use one parameter for all simulations (+/- cohesin) or is this parameter different for each system? Would this change the results?

      We apologize for the confusion. The “unknown parameter” is the energy scale 𝜖 that describes the interaction strength between chromosome loci. We have revised the text in the method (page 27) to clarify it. The same value of 𝜖 is used for all CCM simulation with or without cohesin.

      10) In Methods, when the authors perform DBSCAN clustering, they mention that they optimize the clustering parameters for each system. However, if they want to compare between different systems, the clustering parameters should be the same.

      The purpose of DBSCAN is to capture the spatial clustering topology of chromosome loci. However, different cell types and chromosomes may have different overall density, which will impact the average distance between loci. If using the same parameters, such global changes will impact the result of clustering most and the intended spatial clustering topology can be distorted. Hence, we tune the clustering parameter for each system in order to ignore the global effect but only capture the local and topology of clustering of chromosome loci.

      Grammar comments:

      1) "structures, with sharp boundaries are present, at.."

      We thank the reviewer for pointing out the error. We have fixed it.

      2) "Three headlines emerge from these studies are:"

      We have fixed it.

      3) "both the cell lines"

      We have fixed it.

    1. Author Response

      Reviewer #1 (Public Review):

      This manuscript presents the first evidence for a plastic enhancement in the response of pial cortical arterioles to external stimulation. Specifically, they show (p8; Figure 3A-C) that repeated application of a visual stimulus at 0.25 Hz, at the upper edge of the vasomotor response, leads to a greater change in the diameter of pial arterioles at that frequency. This adds to the earlier, referenced work of Mateo et al (2017) that showed locking - or entrainment of pial arteriole vasomotion - by stimuli at different (0.0 to 0.3 Hz) frequencies.

      We thank the reviewer for positively identifying the value of our manuscript.

      The manuscript has a major flaw. Much as there is plasticity that leads to an increase in the amplitude of vasomotion at the drive frequency, the authors need to show reversibility. This could possibly be accomplished by driving the visual system at a different frequency, say 0.15 Hz, and observing if the 0.25 Hz response is then diminished. The authors could then test if their observation is repeatable by again driving at 0.25 Hz. Unless I missed the presentation on this point, there is no evidence for reversibility.

      The reviewer has raised a very important point of view. In our experiments, the visually induced vasomotion (or visual stimulus-triggered vasomotion) was always entrained by repeated trials of the 0.25 Hz temporal frequency stimuli. When the visual stimulation stops, the vasomotion frequency lock to 0.25 Hz quickly dissipates. After saturated training with this stimulus, the parameters of the visual stimulus were switched, for example to 0.15 Hz. The animal quickly adapted to this new stimulus paradigm and the vasomotion was frequency-locked to 0.15 Hz. The adaptation to this new paradigm occurred well within 5 minutes. In Fig. 5, various paradigms were randomly tested. In some of the trials, 0.25 Hz stimulus was tested after 0.15 Hz. The vasomotion also quickly adapted back to the 0.25 Hz. We agree with the reviewer that this reversibility could have been explicitly documented in the manuscript.

      Drew, P. J., A. Y. Shih, J. D. Driscoll, P. M. Knutsen, D. Davalos, P. Blinder, K. Akassoglou, P. S. Tsai, and D. Kleinfeld. 2010. 'Chronic optical access through a polished and reinforced thinned skull', Nature Methods, 7: 981-84.

      Morii, S., A. C. Ngai, and H. R. Winn. 1986. 'Reactivity of rat pial arterioles and venules to adenosine and carbon dioxide: With detailed description of the closed cranial window technique in rats', Journal of Cerebral Blood Flow & Metabolism, 6: 34-41.

      Reviewer #2 (Public Review):

      Sasaki et al. investigated methods to entrain vasomotion in awake wild-type mice across multiple regions of the brain using a horizontally oscillating visual pattern which induces an optokinetic response (HOKR) eye movement. They found that spontaneous vasomotion could be detected in individual vessels of their wild-type mice through either a thinned cranial window or intact skull preparation using a widefield macro-zoom microscope. They showed that low-resolution autofluorescence signals coming from the brain parenchyma could be used to capture vasomotion activity using a macro-zoom microscope or optical fibre, as this signal correlates well with the intensity profile of fluorescently-labelled single vessels. They show that vasomotion can also be entrained across the cortical surface using an oscillating visual stimulus with a range of parameters (with varying temporal frequencies, amplitudes, or spatial cycles), and that the amplitude spectrum of the detected vasomotion frequency increases with repeated training sessions. The authors include some control experiments to rule out fluorescence fluctuations being due to artifacts of eye movement or screen luminance and attempt to demonstrate some functional benefit of vasomotion entraining as HOKR performance improves after repeat training. These data add in an interesting way to the current knowledge base on vasomotion, as the authors demonstrate the ability to entrain vasomotion across multiple brain areas and show some functional significance to vasomotion with regards to information processing as HOKR task performance correlates well with vascular oscillation amplitudes.

      We thank the reviewer for summarizing the value of our study and recognizing its significance.

      The aims of the paper are mostly well supported by the data, but some streamlining of the data presentation would improve overall clarity. The third aim to establish the functional significance of vasomotion in relation to plasticity in information processing could be better supported by the inclusion of some additional control experiments.

      We thank the reviewer for recognizing our vast amount of data supporting our findings. We agree that better data presentation could have improved the clarity of the manuscript.

      Specifically:

      1) The clarity and comprehensibility of the paper could be significantly enhanced by incorporating additional details in both the introduction and discussion sections. In the introduction, a succinct definition of the frequency range of vasomotion should be provided, as well as a better description of the horizontal optokinetic response (i.e. as they have in the results section in the first paragraph below the 'Entrainment of vasomotion with visual stimuli presentation' sub-heading). The discussion would benefit from the inclusion of a clear summary of the results presented at the start, and the inclusion of stronger justification (i.e. more citations) with regards to the speculation about vasomotion and neuronal plasticity (e.g. paragraph 5 includes no citations).

      We agree that a better description of vasomotion and horizontal optokinetic response could have been provided in the introduction. As the reviewer suggests, the discussion could also have started with the following summary of the results.

      “We show that visually induced vasomotion can be frequency-locked to the visual stimulus and can be entrained with repeated trials. The initial drive for the vasomotion, or the sensory-evoked hyperemia, must be coming from the neuronal activity in the visual system. The vasomotion is likely triggered by activation of the neurovascular interaction (Kayser, 2004; van Veluw et al., 2020). Surprisingly, the entrained vasomotion was observed not only in the visual cortex but also widely throughout the surface of the brain and deep in the cerebellar flocculus. The global entrainment could be realized through separate mechanisms from the local neurovascular coupling. What is also unknown is where the plasticity occurs. The neuronal visual response in the primary visual cortex could potentially decrease with repeated visual stimulation presentation as the adaptive movement of the eye should decrease the retinal slip. With repeated training sessions, a more static projection of the presented image will likely be shown to the retina. The neurovascular coupling could be enhanced with increased responsiveness of the vascules and vascular-to-vascular coupling could also be potentiated.”

      2) The novel methods for detecting vasomotion using low-resolution imaging techniques are discussed across the first four figures, but this gets a little bit confusing to follow as the authors jump back and forth between the different imaging and analysis techniques they have employed to capture vasomotion. The data presentation could be better streamlined - for instance by presenting only the methods most relevant for the functional dataset (in Figures 5-7), with the additional information regarding the various controls to establish the use of autofluorescence intensity imaging as a valid method for capturing vasomotion reduced to fewer figure panels, or moved to supplementary figures so as to not detract from the main novel findings contributed in this study.

      We apologize for the confusing presentation of the data. Many of the initial figures were technical; however, we feel that following these steps was necessary to logically conclude that shadow imaging of the autofluorescence could be used as an indicator of vasomotion. We do agree with the reviewer that going back and forth between different techniques can be confusing. We could have added separate supplementary figures to introduce the various methods used upfront before going into the findings.

      3) The authors heavily rely on representative traces from individual vessels to illustrate their findings, particularly evident in Figures 1-4. While these traces offer a valuable visualization, augmenting their approach by presenting individual data points across the entire dataset, encompassing all animals and vessels, would significantly enhance the robustness of their claims. For instance, in Figures 1 and 2, where average basal and dilated traces are depicted for a representative vessel, supplementing these with graphs showcasing peak values across all measured vessels would enable the authors to convey a more holistic representation of their data. Or in Figure 3, where the amplitude spectrum is presented for individual Texas red fluorescence intensity changes in V1 across novice, trained, and expert mice, incorporating a summary graph featuring the amplitude spectrum value at 0.25Hz for each individual trace (across animals/imaging sessions), followed by statistical analysis, would fortify the strength of their assertions. Moreover, providing explicit details on sample sizes for each individual figure panel (where not a representative trace), including the number of animals or vessels/imaging sessions, would contribute to transparency and aid readers in assessing the generalisability of the findings.

      We agree with the reviewer that summarization of the data across a number of vessels/imaging sessions would lead to more generalization of the findings. However, contrary to what the reviewer described, we did summarize the vessel diameter expansion events across multiple vessel observations in Fig. 1F, G. The vasomotion parameters were not summarized for observation in intact skull shown in Fig. 2. However, this figure was intended just to show that vessel boundary cannot be well defined in intact skull imaging and Texas Red intensity or autofluorescence intensity fluctuation would give a better indication of vessel diameter fluctuation. In Fig. 3G, the peak ratio of 0.25 Hz was calculated for individual animals at Novice, Trained, and Expert levels and summarized for n = 5 animals. Statistical analysis was also done. The variability between imaging sessions within individual animals was not analyzed; thus, this could have been indicated.

      4) In the experiments where mice are classed as "novice", "trained" or "expert", the inclusion of the specific range of the number of training sessions for each category would improve replicability.

      We agree with the reviewer that classification on the level of training should have been explicitly indicated. Mice experiencing the first visual training session were defined as “Novice”. The mice that have experienced 3 training sessions are the “Trained” mice and the performance of the “Trained” mice during the 4th training session was evaluated. Mice that experienced 8 to 11 rounds of visual training sessions are the “Expert” mice.

      5) The authors don't state whether mice were habituated to the imaging set-up prior to the first data collection, as head-fixation and restraint can be stress-inducing for animals, especially upon first exposure, which could impact their neurovascular coupling responses differentially in "novice" versus "trained" imaging sessions (e.g. see Han et al., 2020, DOI: https://doi.org/10.1523/JNEUROSCI.1553-20.2020). The stress associated with a tail vein injection prior to imaging could also partially explain why mice didn't learn very well if Texas Red was injected before the training session. If no habituation was conducted in these experiments, the study would benefit from the inclusion of some control experiments where "novice" responses were compared between habituated and non-habituated animals.

      We agree with the reviewer that stress could well affect spontaneous vasomotion as well as visually induced vasomotion (or visual stimulus-triggered vasomotion). As the reviewer suggested, we could have compared the habituated and non-habituated mice to the initial visually induced vasomotion response. In addition, whether the experimentally induced increase in stress would interfere with the vasomotion or not could also be studied. With the Texas Red experiments, we observed that tail-vein injection stress appeared to interfere with the HOKR learning process. In the experiments presented in Fig. 3, Texas Red was injected before session 1. Vasomotion entrainment likely progressed with sessions 2 and 3 training. Before session 4, Texas Red was injected again to visualize the vasomotion. The vasomotion was clearly observed in session 4, indicating that the stress induced by tail-vein injection could not interfere with the generation of visually induced vasomotion.

      6) The experiments regarding the brain-wide vasomotion entrainment across the cortical surface would benefit from some additional information about how brain regions were identified (e.g. particularly how V1 and V2 were distinguished given how close together they are).

      The brain regions were identified by referring to the Mouse Brain Atlas. As the skull was intact, the location of bregma, lambda, and midline was clearly visible. We agree with the reviewer that strict separation of V1 and V2 could be difficult if we rely on the brain atlas alone. However, what we wanted to emphasize was that there was no specific localization of the vasomotion entrainment effect.

      7) Whilst the authors show that HOKR task performance and vasomotion amplitude are increased with repeated training to provide some support to their aim of investigating the functional significance of vasomotion with regards to information processing plasticity, the inclusion of some additional control experiments would provide stronger evidence to address this aim. For instance, if vasomotion signalling is blocked or reduced (e.g. using optogenetics or in an AD mouse model where arteriole amyloid load restricts vasomotion capacity), does flocculus-dependent task performance (e.g. HOKR eye movements) still improve with repeated exposure to the external stimulus.

      We agree that experimental intervention to vasomotion is ideal to test the functional significance of vasomotion. As pharmacological intervention lacks specificity, we are currently exploring the optogenetic approach. We have never thought of using the AD mouse as a model of restricted vasomotion by amyloid, and we agree this would be an interesting model to study. However, the AD mouse model would also have deficits other than the restricted vasomotion. On the other hand, we could test whether the repeated presentation of slowly oscillating visual stimuli can have beneficial effects in improving the cognitive abilities of AD model mice.

      Reviewer #3 (Public Review):

      Summary:

      Here the authors show global synchronization of cerebral blood flow (CBF) induced by oscillating visual stimuli in the mouse brain. The study validates the use of endogenous autofluorescence to quantify the vessel "shadow" to assess the magnitude of frequency-locked cerebral blood flow changes. This approach enables straightforward estimation of artery diameter fluctuations in wild-type mice, employing either low magnification wide-field microscopy or deep-brain fibre photometry. For the visual stimuli, awake mice were exposed to vertically oscillating stripes at a low temporal frequency (0.25 Hz), resulting in oscillatory changes in artery diameter synchronized to the visual stimulation frequency. This phenomenon occurred not only in the primary visual cortex but also across a broad cortical and cerebellar surface. The induced CBF changes adapted to various stimulation parameters, and interestingly, repeated trials led to plastic entrainment. The authors control for different artefacts that may have confounded the measurements such as light contamination and eye movements but found no influence of these variables. The study also tested horizontally oscillating visual stimuli, which induce the horizontal optokinetic response (HOKR). The amplitude of eye movement, known to increase with repeated training sessions, showed a strong correlation with CBF entrainment magnitude in the cerebellar flocculus. The authors suggest that parallel plasticity in CBF and neuronal circuits is occurring. Overall, the study proposes that entrained "vasomotion" contributes to meeting the increased energy demand associated with coordinated neuronal activity and subsequent neuronal circuit reorganization.

      We thank the reviewer for providing a thorough summarization of our manuscript.

      Strengths:

      • The paper describes a simple and useful method for tracking vasomotion in awake mice through an intact skull.

      • The work controls for artefacts in their primary measurements.

      • There are some interesting observations, including the nearly brain-wide synchronization of cerebral blood flow oscillations to visual stimuli and that this process only occurs after mice are trained in a visual task.

      • This topic is interesting to many in the CBF, functional imaging, and dementia fields.

      We thank the reviewer for positively recognizing the strength of the paper.

      Weaknesses:

      • I have concerns with the main concepts put forward, regarding whether the authors are actually studying vasomotion as they state, as opposed to functional hyperemia which is sensory-induced changes in blood flow, which is what they are actually doing. I recommend several additional experiments/analyses for them to explore. This is mostly further characterizing their effect which will benefit the interpretations.

      We recognized that the terminology used in our paper was not explicitly explained. Traditionally, “vasomotion” is defined as the dilation and constriction of the blood vessels that occurs spontaneously at low frequencies in the 0.1 Hz range without any apparent external stimuli. Sensory-induced changes in the blood flow are usually called “hyperemia”. However, in our paper, we used the term, vasomotion, literally, to indicate both forms of “vascular” “motion”. Therefore, the traditional vasomotion was called “spontaneous vasomotion” and the hyperemia induced with slow oscillating visual stimuli was called “visually induced vasomotion”.

      Using our newly devised methods, we show the presence of “spontaneous vasomotion”. However, this spontaneous vasomotion was often fragmented and did not last long at a specific frequency. With visual stimuli that slowly oscillated at temporal frequencies close to the frequency of spontaneous vasomotion, oscillating hyperemia, or “visually induced vasomotion” was observed.

      • Neuronal calcium imaging would also benefit the study and improve the interpretations.

      In our paper, we mainly studied the visually induced vasomotion (or visual stimulus-triggered vasomotion). Therefore, visual stimulation must first activate the neurons and, through neurovascular coupling, the initial drive for vasomotion is likely triggered. However, visually induced vasomotion is not observed in novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to neuronal activity in the primary visual cortex. We also do not know how the synchronized vasomotion can spread throughout the whole brain. Where the plasticity for vasomotion entrainment occurs is also unknown. To identify the extent of the neuronal contribution to the vasomotion triggering, whole brain synchronization, and vasomotion entrainment, simultaneous neuronal calcium imaging would be ideal. However, due to the fact that fluorescent Ca2+ indicators expressed in neurons would also be distorted by the “shadow” effect from the vasomotion, exquisite imaging techniques would be required.

      • The plastic effects in vasomotion synchronization that occur with training are interesting but they could use an additional control for stress. Is this really a plastic effect, or is it caused by progressively decreasing stress as trials and progress? I recommend a habituation control experiment.

      As also pointed out by reviewer #2, we agree that, whether stress would affect visually induced vasomotion or not could be studied. Studying the visually induced vasomotion in mice well-habituated to the experimental apparatus would give an idea of whether stress could truly be a profounding factor affecting vasomotion. On the other hand, whether acutely induced stress can interfere with the already entrained vasomotion could also be studied. In the experiments presented in Fig. 3, Texas Red was injected via the tail vein, which would be quite stressful for the mouse. However, in the trained mouse, visually induced vasomotion could be observed regardless of the stress. It is likely that stress can interfere with the acquisition of vasomotion entrainment, but the already acquired entrainment will not be canceled with acute stress induced by tail-vein injection. We agree that further relationship between stress and vasomotion and plasticity related to vasomotion entrainment could be investigated.

      Appraisal

      I think the authors have an interesting effect that requires further characterization and controls. Their interpretations are likely sound and additional experiments will continue to support the main hypothesis. If brain-wide synchrony of blood flow can be trained and entrained by external stimuli, this may have interesting therapeutic potential to help clear out toxic proteins from the brain as seen in several neurodegenerative diseases.

      We thank the reviewer for the positive evaluation of our manuscript. Strong entrainment of visually induced vasomotion was observed with a simple presentation of slowly oscillating visual stimuli for several days. This is a totally non-invasive method to train the vasomotion capacity. As the reviewer recognizes, potential benefits for the treatment of dementia and neurodegenerative diseases could be evaluated with further studies.

    1. A disability is an ability that a person doesn’t have, but that their society expects them to have.1 For example: If a building only has staircases to get up to the second floor (it was built assuming everyone could walk up stairs), then someone who cannot get up stairs has a disability in that situation. If a physical picture book was made with the assumption that people would be able to see the pictures, then someone who cannot see has a disability in that situation. If tall grocery store shelves were made with the assumption that people would be able to reach them, then people who are short, or who can’t lift their arms up, or who can’t stand up, all would have a disability in that situation. If an airplane seat was designed with little leg room, assuming people’s legs wouldn’t be too long, then someone who is very tall, or who has difficulty bending their legs would have a disability in that situation. Which abilities are expected of people, and therefore what things are considered disabilities, are socially defined. Different societies and groups of people make different assumptions about what people can do, and so what is considered a disability in one group, might just be “normal” in another. There are many things we might not be able to do that won’t be considered disabilities because our social groups don’t expect us to be able to do them. For example, none of us have wings that we can fly with, but that is not considered a disability, because our social groups didn’t assume we would be able to. Or, for a more practical example, let’s look at color vision: Most humans are trichromats, meaning they can see three base colors (red, green, and blue), along with all combinations of those three colors. Human societies often assume that people will be trichromats. So people who can’t see as many colors are considered to be color blind, a disability. But there are also a small number of people who are tetrachromats and can see four base colors2 and all combinations of those four colors. In comparison to tetrachromats, trichromats (the majority of people), lack the ability to see some colors. But our society doesn’t build things for tetrachromats, so their extra ability to see color doesn’t help them much. And trichromats’ relative reduction in seeing color doesn’t cause them difficulty, so being a trichromat isn’t considered to be a disability. Some disabilities are visible disabilities that other people can notice by observing the disabled person (e.g., wearing glasses is an indication of a visual disability, or a missing limb might be noticeable). Other disabilities are invisible disabilities that other people cannot notice by observing the disabled person (e.g., chronic fatigue syndrome, contact lenses for a visual disability, or a prosthetic for a missing limb covered by clothing). Sometimes people with invisible disabilities get unfairly accused of “faking” or “making up” their disability (e.g., someone who can walk short distances but needs to use a wheelchair when going long distances). Disabilities can be accepted as socially normal, like is sometimes the case for wearing glasses or contacts, or it can be stigmatized as socially unacceptable, inconvenient, or blamed on the disabled person. Some people (like many with chronic pain) would welcome a cure that got rid of their disability. Others (like many autistic people), are insulted by the suggestion that there is something wrong with them that needs to be “cured,” and think the only reason autism is considered a “disability” at all is because society doesn’t make reasonable accommodations for them the way it does for neurotypical people. Many of the disabilities we mentioned above were permanent disabilities, that is, disabilities that won’t go away. But disabilities can also be temporary disabilities, like a broken leg in a cast, which may eventually get better. Disabilities can also vary over time (e.g., “Today is a bad day for my back pain”). Disabilities can even be situational disabilities, like the loss of fine motor skills when wearing thick gloves in the cold, or trying to watch a video on your phone in class with the sound off, or trying to type on a computer while holding a baby. As you look through all these types of disabilities, you might discover ways you have experienced disability in your life. Though please keep in mind that different disabilities can be very different, and everyone’s experience with their own disability can vary. So having some experience with disability does not make someone an expert in any other experience of disability.

      The differentiation between visible and invisible disabilities in this text serves as a crucial reminder of the broad spectrum of disabilities and the varied experiences of those living with them. It underscores the need for greater awareness and sensitivity towards individuals whose disabilities may not be immediately apparent. The mention of the unjust stigma faced by individuals with invisible disabilities raises important questions about societal attitudes and the need for a shift towards more compassionate and informed perspectives. This section also implicitly advocates for a more inclusive definition of disability, one that acknowledges the complexity and diversity of individual experiences, thereby fostering a more accommodating and supportive community.

    1. There are several ways of managing disabilities. All of these ways of managing disabilities might be appropriate at different times for different situations. 10.2.1. Coping Strategies# Those with disabilities often find ways to cope with their disability, that is, find ways to work around difficulties they encounter and seek out places and strategies that work for them (whether realizing they have a disability or not). Additionally, people with disabilities might change their behavior (whether intentionally or not) to hide the fact that they have a disability, which is called masking and may take a mental or physical toll on the person masking, which others around them won’t realize. For example, kids who are nearsighted and don’t realize their ability to see is different from other kids will often seek out seats at the front of classrooms where they can see better. As for us two authors, we both have ADHD and were drawn to PhD programs where our tendency to hyperfocus on following our curiosity was rewarded (though executive dysfunction with finishing projects created challenges)1. This way of managing disabilities puts the burden fully on disabled people to manage their disability in a world that was not designed for them, trying to fit in with “normal” people. 10.2.2. Modifying the Person# Another way of managing disabilities is assistive technology, which is something that helps a disabled person act as though they were not disabled. In other words, it is something that helps a disabled person become more “normal” (according to whatever a society’s assumptions are). For example: Glasses help people with near-sightedness see in the same way that people with “normal” vision do Walkers and wheelchairs can help some disabled people move around closer to the way “normal” people can (though stairs can still be a problem) A spoon might automatically balance itself when held by someone whose hands shake Stimulants (e.g., caffeine, Adderall) can increase executive function in people with ADHD, so they can plan and complete tasks more like how neurotypical people do. Assistive technologies give tools to disabled people to help them become more “normal.” So the disabled person becomes able to move through a world that was not designed for them. But there is still an expectation that disabled people must become more “normal,” and often these assistive technologies are very expensive. Additionally, attempts to make disabled people (or people with other differences) act “normal” can be abusive, such as Applied Behavior Analysis (ABA) therapy for autistic people, or “Gay Conversion Therapy.” 10.2.3. Making an environment work for all# Another strategy for managing disability is to use Universal Design, which originated in architecture. In universal design, the goal is to make environments and buildings have options so that there is a way for everyone to use it2. For example, a building with stairs might also have ramps and elevators, so people with different mobility needs (e.g., people with wheelchairs, baby strollers, or luggage) can access each area. In the elevators the buttons might be at a height that both short and tall people can reach. The elevator buttons might have labels both drawn (for people who can see them) and in braille (for people who cannot), and the ground floor button may be marked with a star, so that even those who cannot read can at least choose the ground floor. In this way of managing disabilities, the burden is put on the designers to make sure the environment works for everyone, though disabled people might need to go out of their way to access features of the environment. 10.2.4. Making a tool adapt to users# When creating computer programs, programmers can do things that aren’t possible with architecture (where Universal Design came out of), that is: programs can change how they work for each individual user. All people (including disabled people) have different abilities, and making a system that can modify how it runs to match the abilities a user has is called Ability based design. For example, a phone might detect that the user has gone from a dark to a light environment, and might automatically change the phone brightness or color scheme to be easier to read. Or a computer program might detect that a user’s hands tremble when they are trying to select something on the screen, and the computer might change the text size, or try to guess the intended selection. In this way of managing disabilities, the burden is put on the computer programmers and designers to detect and adapt to the disabled person. 10.2.5. Are things getting better?# We could look at inventions of new accessible technologies and think the world is getting better for disabled people. But in reality, it is much more complicated. Some new technologies make improvements for some people with some disabilities, but other new technologies are continually being made in ways that are not accessible. And, in general, cultures shift in many ways all the time, making things better or worse for different disabled people. 1 We’ve also noticed many youtube video essayists have mentioned having ADHD. This is perhaps another job that attracts those who tend to hyperfocus on whatever topic grabbed their attention, and then after releasing their video, move on to something completely different. 2 Universal Design has taken some criticism. Some have updated it, such as in acknowledging that different people’s needs may be contradictory, and others have replaced it with frameworks like Inclusive Design..

      This explains different ways to help people with disabilities, like using special tools or making places easier for everyone to use. An example could be the tiktok update allowing for autogenerated subtitles so people didn't have to consciously write out every line. It shows that helping disabled people often means changing our surroundings or technology to fit their needs better.

    1. kinds of facts and events which are facts for ushave already been shaped up and given theircharacter and substance as facts, as relations, etc.,by the methods and practice of governing. Men-tal illness, crimes, riots, violence, work satisfac-tion, neighbors and neighborhoods, motiva-tion, etc., these are the constructs of the practiceof government. In many instances such as mentalillness, crimes, neighborhoods, etc., they are con-stituted as discrete phenomena primarily byadministrative procedures and others arise asproblems in relation to the actual practice ofgovernment, as for example concepts of motiva-tion, work satisfaction, etc.The governing processes of our society areorganized as social entities constituted externallyto those persons who participate in and performthem. The managers, the bureaucrats, the admin-istrators, are employees, are people who are used.They do not own the enterprises or otherwise ap-propriate them. Sociologists study these entitiesunder the heading of formal organization. Theyare put together as objective structures with goals,activities, obligations, etc., other than those whichits employees can have as individuals. The acad-emic professions are also set up in a mode whichexternalizes them as entities vis-8-vis their practi-tioners. The body of knowledge which its mem-bers accumulate is appropriated by the disciplineas its body. The work of members aims at con-tributing to that body of knowledge.As graduate students learning to become sociol-ogists, we learn to think sociology as it is thoughtand to practice it as it is practiced. We learnthat some topics are relevant and some are not.We learn to discard our experienced world as asource of reliable information or suggestionsabout the character of the world; to confine andfocus our insights within the conceptual frame-works and relevances which are given in thediscipline. Should we think other kinds ofthoughts or experience the world in a differentway or with edges and horizons that pass beyondthe conceptual we must practice a discipline whichdiscards them or find some procedure whichmakes it possible to sneak them in. We learn away of thinking about the world which is recog-nizable to its practitioners as the sociologicalway of thinking.We learn to practice the sociological subsump-tion of the actualities of ourselves and of otherpeople. We find out how to treat the world asinstances of a sociological body of knowledge.The procedure operates as a sort of conceptualimperialism. When we write a thesis or a paper,we learn that the first thing to do is to latch it onto the discipline at some point. This may be byThe profession of sociology is predicated on auniverse which is occupied by men and it is itselfstill largely appropriated by men as their “ter-ritory.”

      Цікаво, наскільки змінилася ситуація з часу написання цієї статті. Звичайно, "базові" соціологічні твори були написані переважно білими чоловіками (переважно, бо були Рут Бенедикт та Маргарет Мід, наприклад), проте з огляду на авторство джерел, що вивчаються принаймні в Могилянці на соціології, кількість впливових соціологинь значно зросла

    1. Ethics provides a foundation for what teachers should do in their roles and responsibilities as an educator. It is a framework that a teacher can use to help make decisions about what is right or wrong in a given situation.

      I think it is important to have a foundation for us teachers to refer to in order to help lead us in the right direction to make the "right" solution. This is important because sometimes teachers make connections with their students or have certain feelings towards an individual due to there behavior or whatever it may be which can hinder there decisions making. So it is important that we do not let our feelings get in the way of our decision making but rather rely on the ethics of the situation.

    1. Author Response

      We thank the editors and the reviewers for their assessment of our revised manuscript. Please see bellow, our answers to the recommendations by reviewer #2.

      Figure S2F - Seems like a very narrow range of parameters. Is there some fine tuning here?

      The range of values of tau_P that yields previous-trial biases is bounded by below and above for the following reasons: above a certain value of tau_P (therefore large integration time), the bump that had formed in the previous trial is not strong enough to remain stable for a long time, and therefore dissipates by the time the current trial starts (especially when adaptation is fast, towards the left of the third panel). Below a certain value, instead, this integration timescale is small enough to quickly form a representation of the current trial, hence the bump from the previous trial quickly dissipates (due to mutual inhibition). This interplay between the integration and the adaptation timescale as well as considering a phenomenon which is bounded in time (how close the activity bump is to the second stimulus of the previous trial which is presented between -22.4 and -5.6 seconds from the moment we are considering) yields a region for tau_P which is bounded. This region, however, appears narrow due to the limited number of points we have considered for the simulation grid.

      Regarding my comment on lapse at the boundaries (old line 221). Lapse parameters in psychometric curves correspond to errors on the "easy" trials. But the mechanistic explanation for lapse trials is that there is a non-zero probability for the subject to respond in a manner that is random and independent of the stimulus. In the case of extreme stimuli, this is the only reason for errors, and thus looking at the edges of the psychometric curves allows to calculate lapse rate. But - the usual assumption for underlying mechanism is that the subject lapses in all trials, regardless of stimulus. If I understand correctly, this is different than the mechanistic reason for lapses in the network model, which was described as something that happens more in the edges than in the center. Or more generally, to be a stimulus-dependent effect.

      We thank the reviewer for this clarification. The reviewer is right that in our mechanistic model, lapses (as defined by errors on easy trials) are more likely to occur for extreme stimuli, due to the vicinity to the boundary of the attractor. Such errors also occur for non-extreme stimuli, when delay intervals are long enough for the bump in PPC to drift to the boundaries. In experiments, lapse trials as described by the reviewer occur due to multiple different reasons; for lapse that is independent of the stimuli, mechanisms such as attention have been thought to play a role, this however is not included in our model.

      What are the parameters for the distributions (skewed, bimodal, ...)?

      These parameters are reported in the legend of Fig.6, where the distributions appear.

      Bump with adaptation. Sorry for the draft-like comment. I don't think the existing studies are in the form you describe. I do think it might be useful to point readers to these studies. If an interested reader wishes to understand network dynamics in this and similar scenarios, it might be useful to have the pointers. The reference I had in mind was Romani, S., & Tsodyks, M. (2015). Short‐term plasticity based network model of place cells dynamics. Hippocampus, 25(1), 94-105.

      We thank the reviewer for the clarification, and we will include this reference in the Version of Record.


      The following is the authors’ response to the original reviews.

      eLife assessment

      This is an important study about the mechanisms underlying our capacity to represent and hold recent events in our memory and how they are influenced by past experiences. A key aspect of the model put forward here is the presence of discrete jumps in neural activity with the posterior parietal region of the cortex. The strength of evidence is largely solid, with some weaknesses noted in the methodology. Both reviewers suggested ways in which this aspect of the model can to be tested further and resolve conflicts with previously published experimental results, in particular the study by Papadimitriou et al 2014 in Journal of Neurophysiology.

      We thank the editors for their assessment. As mentioned in the cover letter, we have addressed all the reviewers’ concerns and would like to request and update of the assessment to reflect the revisions we have made.

      Public Reviews:

      We thank both reviewers for their careful reading and feedback that helped clarify many aspects of the model. Below, we address their comments.

      Reviewer #1 (Public Review):

      This paper aims to explain recent experimental results that showed deactivating the PPC in rats reduced both the contraction bias and the recent history bias during working memory tasks. The authors propose a twocomponent attractor model, with a slow PPC area and a faster WM area (perhaps mPFC, but unspecified). Crucially, the PPC memory has slow adaptation that causes it to eventually decay and then suddenly jump to the value of the last stimulus. These discrete jumps lead to an effective sampling of the distribution of stimuli, as opposed to a gradual drift towards the mean that was proposed by other models. Because these jumps are single-trial events, and behavior on single events is binary, various statistical measures are proposed to support this model. To facilitate this comparison, the authors derive a simple probabilistic model that is consistent with both the mechanistic model and behavioral data from humans and rats. The authors show data consistent with model predictions: longer interstimulus intervals (ISIs) increase biases due to a longer effect over the WM, while longer intertrial intervals (ITIs) reduce biases. Finally, they perform new experiments using skewed or bimodal stimulus distributions, in which the new model better fits the data compared to Bayesian models.

      The mechanistic proposed model is simple and elegant, and it captures both biases that were previously observed in behavior, and how these are affected by the ISI and ITI (as explained above). Their findings help rethink whether our understanding of contraction bias is correct.

      On the other hand, the main proposal - discrete jumps in PPC - is only indirectly verified.

      We agree with the reviewer that the evidence for discrete jumps in PPC has been provided in behavioural results (short-term, n-back trial biases), and not from neural data. However, we believe electrophysiological investigations are out of the scope of the current manuscript and future works are needed to further verify the results.

      The model predicts a systematic change in bias with inter-trial-interval. Unless I missed it, this is not shown in the experimental data. Perhaps the self-paced nature of the experiments allows to test this?

      We thank the reviewer for this great suggestion.

      We had not previously looked at this in the data for the reason that in the simulations, the ITI is set to either 2.2, 6 or 11 seconds, whereas the experiment is self-paced. Therefore, any comparison with the simulation should be made carefully.

      However, after the reviewer’s suggestion, we did look at the change in the bias with the inter-trial interval, by dividing trials according to ITIs lower than 3 seconds (“short” ITI), and higher than 3 seconds (“long” ITI). This choice was motivated by the shape of the distribution of ITIs, which is bimodal, with a peak around 1 second, and another after 3 seconds (new Fig 8F). Hence, we chose 3 seconds as it seemed a natural division. However, 3 seconds also happens to be approximately the 75th percentile of the distribution, and this means that there is much more data in the “short” ITI than the “long” ITI set. In order to have sufficient data in the “long” ITI for clearer effects we used all of our dataset – the negatively skewed, and also two bimodal distributions (of which only one was shown in the manuscript, for succinctness). This larger dataset allows us to clearly see not only a decreasing contraction bias with increasing ITI (Fig 8G), but also a decreasing onetrial-back attractive bias with increasing ITI (Fig 8H). We have uploaded all the datasets as well as scripts used to analyze them to this repository: https://github.com/vboboeva/ParametricWorkingMemory_Data.

      The data in some of the figures in the paper are hard to read. For instance, Figure 3B might be easier to understand if only the first 20 trials or so are shown with larger spacing. Likewise, Figure 5C contains many overlapping curves that are hard to make out.

      We have limited the dynamics in Fig 3B to the first 50 trials for better visibility. Likewise, as suggested, we report the standard error of the mean instead of the standard deviation in old Fig 5C (new Fig 6C) – this allows for the different curves to be better discernible.

      There is a gap between the values of tau_PPC and tau_WM. First - is this consistent with reports of slower timescales in PFC compared to other areas?

      Recent studies by Xiao-Jing Wang and colleagues (Refs. 1-3 below) suggest that may be the case. In Wang et al 2023, Ref 1 below), the authors use a generative model to study the concept of bifurcation in space in working memory, that is accompanied by an inverted-V shape of the time constants as a function of cortical hierarchy.

      Briefly, they propose a generative model of the cortex with modularity, incorporating repeats of a canonical local circuit connected via long-range connections. In particular, the authors define a hierarchy for each local circuit. At a critical point in this hierarchy axis, there is a phase transition from monostability to bistability in the firing rate. This means that a local circuit situated below the critical point will only display a low activity steady state, while those above the critical point additionally display a persistent activity steady state.

      The model predicts a critical slowing down of the neural fluctuations at the critical point, resulting in an inverted-V shape of the time constants as a function of the hierarchy. They test the predictions of their model – the bifurcation in space and that inverted-V-shaped time constants as a function of the hierarchy - on connectome-based models of the macaque and mouse cortex. Interestingly both datasets show similar behavior. In particular, during working memory, frontal areas (higher in the hierarchy, e.g. area 24c in macaques) has a smaller time constant relative to posterior parietal areas (lower in the hierarchy, like LIP or f7). We have now cited this new work.

      [1] https://www.biorxiv.org/content/10.1101/2023.06.04.543639v1

      [2] https://elifesciences.org/articles/72136

      [3] https://www.biorxiv.org/content/10.1101/2022.12.05.519094v3.abstract

      Second - is it important for the model, or is it mostly the adaptation timescale in PPC that matters?

      We have run simulations producing a phase diagram with tau_theta^P on the x-axis, tau^P on the y-axis, and in color, the fraction of trials in which the bump is in the vicinity of a target (Fig S2 F), before the network is presented with the second stimulus. This target can be the first stimulus s_1 (left), mean over stimuli (middle) and previous trial’s stimulus (right)). White point corresponds to parameters of the default network.

      In this phase diagram, the lowest value that tau_P takes is tau_WM=0.01. When tau_P=tau_WM, the bump is rarely in the vicinity of 1-trial-back stimulus, and we can see that tau_PPC should be greater than tau_WM in order for the model to yield 1-trial back effects. We conclude that it is indeed important for tau_PPC > tau_WM.

      We have included this in Fig S2 F of the manuscript.

      Regarding the relation to other models, the model by Hachen et al (Ref 45) also has two interacting memory systems. It could be useful to better state the connection, if it exists.

      The model proposed by Hachen et al is conceptually different in that one module stores the mean of the sensory stimulus; it could be related to a variant of our model where adaptation is turned off in the PPC network (Fig S2 A). However, the task they model is also different: subjects have to learn the location of a boundary according to which the stimulus is classified as ‘weak’ or ‘strong’, set by the experimenter. Hence, it is a task where learning is needed - this contrasts with the task we are modelling, where only working memory is required. How task demands reconfigure existing circuits via dynamics and/or learning to perform different computations is a fascinating area of research that is outside the scope of this work.

      Reviewer #2 (Public Review):

      Working memory is not error free. Behavioral reports of items held in working memory display several types of bias, including contraction bias and serial dependence. Recent work from Akrami and colleagues demonstrates that inactivating rodent PPC reduces both forms of bias, raising the possibility of a common cause.

      In the present study, Boboeva, Pezzotta, Clopath, and Akrami introduce circuit and descriptive variants of a model in which the contents of working memory can be replaced by previously remembered items. This volatility manifests as contraction bias and serial dependence in simulated behavior, parsimoniously explaining both sources of bias. The authors validate their model by showing that it can recapitulate previously published and novel behavioral results in rodents and neurotypical and atypical humans.

      Both the modeling and the experimental work is rigorous, providing compelling evidence that a model of working memory in which reports sometimes sample past experience can produce both contraction bias and serial dependence, and that this model is consistent with behavioral observations across rodents and humans in the parametric working memory (PWM) task.

      Evidence for the model advanced by the authors, however, remains incomplete. The model makes several bold predictions about behavior and neural activity, untested here, that either conflict with previous findings or have yet to be reported but are necessary to appropriately constrain the model.

      First, in the most general (descriptive) formulation of the Boboeva et al. model, on a fraction of trials items in working memory are replaced by items observed on previous trials. In delayed estimation paradigms, which allow a more direct behavioral readout of memory items on a trial-by-trial basis than the PWM task considered here, reports should therefore be locked to previous items on a fraction of trials rather than display a small but consistent bias towards previous items. However, the latter has been reported (e.g., in primate spatial working memory, Papadimitriou et al., J Neurophysiol 2014). The ready availability of delayed estimation datasets online (e.g., from Rademaker and colleagues, https://osf.io/jmkc9/) will facilitate in-depth investigation and reconciliation of this issue.

      As pointed out by the reviewer, in the PWM task that we are modelling here, the activity in the network is used to make a binary decision. However, it is possible to directly analyse the network activity before the onset of the second stimulus.

      In their manuscript, Papadimitriou et al. study a memory-guided saccade task in nonhuman primates and argue that the animals display a small but consistent bias towards previous items (Fig 2). In that figure, the authors compute the error as the difference between the saccade direction and target direction in each trial. They compute this error for all trials in which the preceding trial’s target direction is between 35° and 85° relative to the current trial (counterclockwise with respect to the current trial’s target). They discover that the residual error distribution is unimodal with a mode at 1.29° and a mean at 2.21° (positive, so towards the preceding target’s direction), from which they deduce a small but systematic bias towards previous trial targets.

      We have computed a similar measure for our network with default parameters (Table 1), by subtracting the location of the bump at the end of the delay interval (s_hat(t), ‘saccade’) from the initial location of the first stimulus in the current trial (s1(t) or the ‘target’). We have done this for all trials where s1(t)=0.2, and where s2(t-1) takes specific values. These distributions are characterized by two modes. The first corresponds to those trials where the bump is not displaced in WM (i.e. mean of zero). We can also see the appearance of a second mode at the location of s1(t) - s2(t-1), corresponding to the displacements towards the preceding trial’s stimulus described in the main text. If, instead, we limit the analysis to a small range of previous trials close to s1(t) (similar to Papadimitriou et al) then the distribution of residual errors will appear unimodal, as the two modes merge. Importantly, note that there is a large variability around the second mode, expressing a more complex dynamics in the network. As can be seen in Fig 3B, the location of the bump is not always slaved to the one in the PPC in a straightforward way -- due to the adaptation in the PPC, the global inhibition in the connectivity kernel, as well as interleaved design for various delay intervals, the WM bump can be displaced in nontrivial ways (see also Recommendation no 4), yielding the dispersion around the second peak. It remains to be seen whether such patterns can be observed in the data from previous works on continuous working memory recall (including Papadimitriou et al). However, to our knowledge, such detailed and full analysis of errors at the level of individual trials has not been done.

      In summary, this analysis shows that the type of dynamics in our network is not one of the two cases: 1) small and systematic bias in each and every trial or 2) large error that occurs only rarely; rather, the dispersion around both modes suggests that the dynamics in our model are a mixture of these two limit cases.

      We have also performed another typical analysis, reported in several continuous recall tasks (e.g. Jazayeri and Shadlen 2010) where contraction bias has been reported. We plot WM bump locations after the delay period for every trial (s_hat(t)), and their averages, against the nominal value of s1(t). We see that the mean WM location deviates from the identity line toward the mean values of s1(t), again showing contraction bias as an average effect, while individual trials follow the dynamics explained above.

      We have now included a new section on continuous recall (Sect. 1.5 and a new figure (Fig 5)), which details the two above-mentioned analyses. The analysis of freely available datasets of delayed estimation tasks, unfortunately, is out of the scope of this work, and we leave such analyses to future studies.

      Second, the bulk of the modeling efforts presented here are devoted to a circuit-level description of how putative posterior parietal cortex (PPC) and working-memory (WM) related networks may interact to produce such volatility and biases in memory. This effort is extremely useful because it allows the model to be constrained by neural observations and manipulations in addition to behavior, and the authors begin this line of inquiry here (by showing that the circuit model can account for effects of optogenetic inactivation of rodent PPC).

      Further experiments, particularly electrophysiology in PPC and WM-related areas, will allow further validation of the circuit model. For example, the model makes the strong prediction that WM-related activity should display 'jumps' to states reflecting previously presented items on some trials. This hypothesis is readily testable using modern high-density recording techniques and single-trial analyses.

      As mentioned in response to the previous comment, we note again that in the WM network, the bump ‘displacement’ has a complex dynamics -- the examples we have provided in Fig 1A and 2B mainly show the cases in which jumps occur in the WM network, but this is not the only type of dynamics we observe in the model. We do have instances in which the continuity of the model causes drift across values, and we have now replaced the right panel in Fig 2B with one such instance, in order to emphasize that this displacement towards the previous trial’s stimulus (s2(t-1)) can occur in various ways. For a more thorough analysis, we have analyzed the distance between s1(t) and the position of the bump in the WM network at the end of the delay period s_hat(t), conditioned on specific values of s1(t) and s2(t-1) (Fig 5C). In this figure, we can see the appearance of two modes: one centered around 0, corresponding to the correct trials where the stimulus is kept in WM (s1(t) = s_hat(t)), and another mode centered around s2(t-1), the location of the second stimulus of the previous trial, where the bump is displaced. Note, as we explain in Sect. 1.5, the large dispersion around this second mode, which suggests that the bump is not always displaced to that specific location and may undergo drift.

      We agree with the reviewer that future electrophysiological experiments (or analysis of existing datasets) are necessary for validation of these results.

      Finally, while there has been a refreshing movement away from an overreliance on p-values in recent years (e.g., Amrhein et al., PeerJ 2017), hypothesis testing, when used appropriately, provides the reader with useful information about the amount of variability in experimental datasets. While the excellent visualizations and apparently strong effect sizes in the paper mitigate the need for p-values to an extent, the paucity of statistical analysis does impede interpretation of a number of panels in the paper (e.g., the results for the negatively skewed distribution in 5D, the reliability of the attractive effects in 6a/b for 2- and 3- trials back).

      We share the reviewer’s criticism towards the misuse of p-values – in order for a clearer interpretation of old Fig 5D (new Fig 7E), we have looked at the 2 and 3 trials-back biases by using all of our dataset – the negatively skewed, and also two bimodal distributions (of which only one was shown in the manuscript). This larger dataset of 43 subjects (approximately 17,200 trials) allows us to clearly see the 2 and 3 trial back attractive biases, and the effect that the delay interval exerts on them.

      Reviewer #1 (Recommendations For The Authors):

      Fig 5 A&C - It might be beneficial to separate the distribution of stimuli from the performance. It is hard to read the details of the performance, especially with error bars.

      Following the next recommendation, we have exchanged the standard deviation to standard errors of the mean, hopefully this allows to better read the performance.

      Fig 5C. The number of participants should be written. Perhaps standard errors instead of standard deviation?

      We have now changed the standard deviation to standard errors of the mean and included the number of participants in the figure.

      Fig 2B - hard to understand, because there is no marking of where "perfect" memory of s1 would be.

      The perfect memory of s1 is shown in the upper panel as black bars.

      Fig 3B. dot number 9 (blue, around 0.7) - why is WM higher than stimulus?

      This trial has a long ISI (blue means 10s). During this delay, the bump in the PPC, under the influence of adaptation, drifts far below the first stimulus (note that the previous trial also had its first stimulus in the same location, as a result of which the adaptative thresholds have built up significantly, causing the bump to move away from that location). During this delay period, neurons in the WM network receive inputs from the PPC network: if this input is strong enough, it can disrupt an existing bump; if not, this input still exerts inhibiting influence on the existing bump via the global inhibition in the connectivity. This can cause an existing bump to slowly drift in a random direction, and finally dissipate. Note that the lines in Fig 2B represent the neuron with the maximal activity, this activity may be a stable bump, or an unstable bump that may soon dissipate.

      Other examples with similar dynamics include trials 43 and 54.

      L167 fewer -> smaller

      We have now corrected this.

      Fig 3C - bump can also be in between. Is this binned?

      We have not binned the length of the attractor; to produce that figure, we check whether the position of the neuron with the maximal firing rate is within a distance of ±5% of the length of the whole line attractor from the target location.

      L221 Lapse at the boundary of attractor. This seems very different from behavior. Specifically, if it is in the boundaries, it should be stimulus dependent.

      Very sorry, we did not manage to understand the reviewer’s comment.

      L236 are -> is

      We have now corrected this.

      Fig S4 - should be mostly in main text.

      Part of this figure is in Fig 6A, but given the amount of detail, we think Supplementary Material is better suited.

      L253-254. Differences across all distributions - very minor except the bimodal case.

      That is correct, this is why we conducted the experiment with the bimodal distribution, to better differentiate the predictions of the two models.

      L273 extra comma after "This probability"

      We have now corrected this.

      ITI was only introduced in section 1.5.2. Perhaps worth mentioning the default 5s value earlier in the paper.

      We have now mentioned this in line 97-98.

      Fig S6B title: perhaps "previous stimuli"?

      We have now corrected this.

      L364 i"n A given trial"

      Equation 2 - no decay term?

      Thank you for pointing out this error, we have now corrected this.

      Equation 5,6 are j^W and j^P indices of neurons in those populations?

      Yes, j^W indexes neurons in the WM network, and j^P those in the PPC. We have now added this in the text for clarity.

      Bump with adaptation - other REFs? Sandro?

      We are aware of continuous bump attractors implementing short-term synaptic plasticity in various studies (including by Sandro Romani), but not in the form we have described. May the reviewer kindly point us towards the relevant literature.

      Free boundary - what is the connectivity for neurons 1 and N? Is it weaker than others? Is the integral still 1? Does this induce some bias on the extreme values?

      The connectivity of the network is all-to-all. However, as expressed by Eq. (3), the distance-dependent contribution to the weights, K, decreases exponentially as we move from neuron 1 onwards, and from neuron N down. The sum (or integral, in the large-N limit) of the K_ij for j on either side of neuron i is unity only when i is sufficiently far from 1 or N. We have rephrased the paragraph starting in line 516 to make this clearer.

      The presence of a boundary could introduce a bias in theory, but in practice, it affects the dynamics only when the bump drifts sufficiently close to it. The smallest stimulus in the simulated task has amplitude 0.2, with width 0.05, which implies the activation of 50 neurons on either side of neuron 400. If one compares this with the width of the kernel K in stimulus space (d_0 = 0.02), which spans ~10 neurons, we can see that the bump of activity stays mostly far from the boundary. It is possible, though it is observed rarely, when several consecutive long delay intervals happen to occur, that the bump in PPC drifts beyond the location corresponding to either the minimum or maximum stimulus.

      Code availability?

      Code simulating the dynamics of the network as well as analysing the resulting data can be found in the following repository: https://github.com/vboboeva/ParametricWorkingMemory Code used to analyse human behavioural data and fit them with our statistical model can be found in this repository: https://github.com/vboboeva/ParametricWorkingMemory_Data Code used to run the auditory PWM experiments with human subjects (adapted from Akrami et al 2018) can be found here: https://github.com/vboboeva/Auditory_PWM_human

      L547 stimuli

      We have now corrected this.

      Equation 14 uses both stimuli. Was this the same for the rest of analysis in the paper (first figures for instance)?

      This equation was used for all GLM analyses (Figs 9 and S6).

      D0 is very small (0.02). Does this mean that activity is essentially discrete in the model? Fig 1A & 2B - the two examples of model activity suggest this is the case. In other words - are there cases where the continuity of the model causes drift across values? Can you show an example (similar to Fig 1A)?

      Since this point has been raised beforehand, we refer to the first comment, Fig 2B and Sect. 1.5 for the response to this question.

      Table 1 - inter trial interval 6. Text says 5

      We have now corrected this in the text.

      Reviewer #2 (Recommendations For The Authors):

      In addition to my review above, I just have a few minor comments:

      • If I understood correctly, the squares inside the purple rectangle in Figure 1B are meant to show a gradation from red to blue, but this was hard to make out in the pdf.

      Actually the squares are all on one side or the other of the diagonal, therefore they do not have any gradation.

      • line 164: "The resulting dynamics... [are]?"

      We have corrected this in the text.

      • Fig 7B legend: "The network performance is on average worse for longer ITIs" – correct?

      This was a mistake, we have replaced worse with better.

      Other comments

      We realized that the colorbar reported the incorrect fraction classified in Figs 1B, 2C, 7B (new 8B), S2C, S3A, S5B. We have corrected this in the new version of the manuscript.

      We also found a minor mistake in one of our analysis codes that computed the n-trial back biases for different delay intervals. This did not change our results, actually made the effects clearer. The figures concerned are Fig 3F and new Fig 7E.

    1. Author Response

      eLife assessment

      This study presents important findings for understanding cortical processing of color, binocular disparity, and naturalistic textures in the human visual cortex at the spatial scale of cortical layers and columns using state-of-the-art high-resolution fMRI methods at ultra-high magnetic field strength (7 T). Solid evidence supports an interesting layer-specific informational connectivity analysis to infer information flow across early visual areas for processing disparity and color signals. While the question of how the modularity of representation relates to cortical hierarchical processing is interesting and fundamental, the findings that texture does not map onto previously established columnar architecture in V2 is suggestive but would benefit from further controls. The successful application of high-resolution fMRI methods to study the functional organization along cortical columns and layers is relevant to a broad readership interested in general neuroscience.

      Thank you for your assessment of our manuscript "Mesoscale functional organization and connectivity of color, disparity, and naturalistic texture in human second visual area ". We have carefully considered the public reviews and have outlined our plans of revision by providing point-by-point responses to the reviewers’ comments.

      Reviewer #1 (Public Review):

      To support the finding that texture is not represented in a modular fashion, additional possibilities must be considered. These include the effectiveness and specificity of the texture stimulus and control stimuli, (b) further analysis of possible structure in images that may have been missed, and (c) limitations of imaging resolution.

      Thank you for your suggestions. We will provide evidence and additional analyses to show that there was indeed a large difference in high-order statistical information between the texture and control stimuli in our study, and thus the contrast between the two stimuli should be effective in localizing the processing of high-order texture information. Compared to the previous studies, another reason for the weaker texture selectivity in the current study could be the smaller number of images used and the slower rate of image presentation. Although our fMRI result at 1-mm isotropic resolution did not show a modular processing of naturalistic texture in CO-stripe columns, this does not exclude the possibility that smaller modules exist beyond the current fMRI resolution. We will discuss these limitations in the revised manuscript.

      More in-depth analysis of subject data is needed. The apparent structure in the texture images in peripheral fields of some subjects calls for more detailed analysis. e.g. Relationship to eccentricity and the need for a 'modularity index' to quantify the degree of modularity. A possible relationship to eccentricity should also be considered.

      We will perform further analysis based on your suggestion, especially regarding the relationship between eccentricity and modulation index. We will discuss this possibility in the revised manuscript.

      Given what is known as a modular organization in V4 and V3 (e.g. for color, orientation, curvature), did images reveal these organizations? If so, connectivity analysis would be improved based on such ROIs. This would further strengthen the hierarchical scheme.

      Thank you for your suggestion. The informational connectivity analyses used highly informative voxels by feature selection, which may already represent information from the modular organizations in these higher visual areas. We will examine the functional maps for possible modular organizations.

      Reviewer #2 (Public Review):

      In lines 162-163, it is stated that no clear columnar organization exists for naturalistic texture processing in V2. In my opinion, this should be rephrased. As far as I understand, Figure 2B refers to the analysis used to support the conclusion. The left and middle bar plots only show a circular analysis since ROIs were based on the color and disparity contrast used to define thin and thick stripes. The interesting graph is the right plot, which shows no statistically significant overlap of texture processing with thin, thick, and pale stripe ROIs. It should be pointed out that this analysis does not dismiss a columnar organization per se but instead only supports the conclusion of no coincidence with the CO-stripe architecture.

      Reviewer #1 also raised a similar concern. We agree that there may be a smaller functional module of textures in area V2 at a finer spatial scale than our fMRI resolution. We will rephrase our conclusions to be more precise.

      In Figure 3, cortical depth-dependent analyses are presented for color, disparity, and texture processing. I acknowledge that the authors took care of venous effects by excluding outlier voxels. However, the GE-BOLD signal at high magnetic fields is still biased to extravascular contributions from around larger veins. Therefore, the highest color selectivity in superficial layers might also result from the bias to draining veins and might not be of neuronal origin. Furthermore, it is interesting that cortical profiles with the highest selectivity in superficial layers show overall higher selectivity across cortical depth. Could the missing increase toward the pial surface in other profiles result from the ROI definition or overall smaller signal changes (effect size) of selected voxels? At least, a more careful interpretation and discussion would be helpful for the reader.

      We will discuss the limitations of cortical depth-dependent analysis using GE-BOLD fMRI. All our stimuli produced robust activations in these visual areas, thus the flat laminar profiles of modulatory indices are unlikely to be caused by smaller signal changes. We will show the original BOLD responses in addition to the modulation index.

      I was slightly surprised that no retinotopy data was acquired. The ROI definition in the manuscript was based on a retinotopy atlas plus manual stripe segmentation of single columns. Both steps have disadvantages because they neglect individual differences and are based on subjective assessment. A few points might be worth discussing: (1) In lines 467-468, the authors state that V2 was defined based on the extent of stripes. This classical definition of area V2 was questioned by a recent publication (Nasr et al., 2016, J Neurosci, 36, 1841-1857), which showed that stripes might extend into V3. Could this have been a problem in the present analysis, e.g., in the connectivity analysis? (2) The manual segmentation depends on the chosen threshold value, which is inevitably arbitrary. Which value was used?

      The retinotopic atlas on the standard surface is usually quite accurate in defining the boundaries of early visual areas. Although some stripes may extend into V3, these patterns should be more robust in V2. In our analysis, we selected only those with clear organizations within the retinotopic atlas. Thus, the signal contribution from V3 is likely to be small and would not affect the pattern of results. In addition, the results between V3 and V2 could be very different, we will compare the pattern of results from these areas in additional analyses. The threshold for segmentation is abs(T)>2, we will clarify this in the method.

      The use of 1-mm isotropic voxels is relatively coarse for cortical depth-dependent analyses, especially in the early visual cortex, which is highly convoluted and has a small cortical thickness. For example, most layer-fMRI studies use a voxel size of around isotropic 0.8 mm, which has half the voxel volume of 1 mm isotropic voxels. With increasing voxel volume, partial volume effects become more pronounced. For example, partial volume with CSF might confound the analysis by introducing pulsatility effects.

      We agree that the 1-mm isotropic voxel is much smaller in volume than the 0.8-mm isotropic voxel, but the resolution along the cortical depth is not a large difference. In addition to our study, there are also other studies showing that fMRI at 1-mm isotropic resolution is capable of resolving cortical depth-dependent signals. Also, our fMRI slices were oriented perpendicular to the calcarine sulcus, the higher in-plane resolution will also benefit in resolving depth-dependent signals. We will discuss these issues about fMRI resolution in the revised manuscript.

      The SVM analysis included a feature selection step stated in lines 531-533. Although this step is reasonable for the training of a machine learning classifier, it would be interesting to know if the authors think this step could have reintroduced some bias to remaining draining vein contributions.

      Several precautions have been taken in the ROI definition to reduce the influence of large draining veins. The same number of voxels were selected from each cortical depth for the SVM analysis, thus there was no bias from the superficial layers susceptible to draining veins. Also, since both feedforward and feedback connections involved the superficial voxels, the remaining influence of large draining veins should be comparable between the two connections.

      Reviewer #3 (Public Review):

      The authors tend to overclaim their results.

      Thank you for your comments. We will add more control analyses to strengthen our findings, and have appropriate discussion of results.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary: The authors study the appearance of oscillations in motifs of linear threshold systems, coupled in specific topologies. They derive analytical conditions for the appearance of oscillations, in the context of excitatory and inhibitory links. They also emphasize the higher importance of the topology, compared to the strength of the links. Finally, the results are confirmed with WC oscillators, which are also linear. The findings are to some extent confirmed with spiking neurons, though here results are less clear, and they are not even mentioned in the Discussion.

      Overall, the results are sound from a theoretical perspective, but I still find it hard to believe that they are of significant relevance for biological networks, or in particular for the oscillations of BG-thalamus-cortex loop in PD. I find motifs in general to be too simplistic for multiscale and generally large networks as is the case in the brain. Moreover, the division of regions is more or less arbitrary by definition, and having such a strong dependence on an odd/even number of inhibitory links is far from reality. Another limitation is the fact that the cortex is considered a single node. Similarly, decomposing even such a coarse network in all possible (238 in this case) motifs doesn't seem of much relevance, when I assume that the emergence of pathological rhythms is more of an emergent phenomenon.

      Strengths:

      From the point of view of nonlinear dynamics, the results are solid, and the intuition behind the proofs of the theorems is well explained.

      Weaknesses:

      As stated in the summary, I find the work to be too theoretical without a real application in biological systems or the brain, where the networks are generally very large.

      We respectfully disagree with the reviewer here. The second half of the paper is all about explaining a biological problem. We have shown the validity of our theoretical results (which indeed were obtained in idealized settings) to explain emergence of oscillations in the basal ganglia. We clearly show that our theoretical results hold both in a rate-based model and in a network model with spiking neurons. The model with spiking neurons is one of the most complete network models of the basal ganglia available in the literature. So we emphasize that we have provided a clear application of our results for the brain networks.

      It is not the problem in the simplicity of the model or of the topology, it is often the case that the phenomena are explained by very reduced systems, but the problem is that the applicability of the finding cannot be extended. E.g. the Kuramoto model uses all-to-all coupling, or similar with QIF neurons which also need to follow a Lorentzian distribution in order to derive a mean field.

      We do not understand this comment. There is no need to extend these results to a network of Kuramoto models because in that setting we already assume that individual nodes/populations are oscillating – there is no problem of emergence of oscillations. Here, we are specifically considering a setting in which nodes themselves are not oscillators. We agree that we, at this point, have no insight into how to extend our analytical proof to a situation where individual nodes are spiking.

      But in those cases, relaxing the strict conditions that were necessary for the derivations, still conserves the main findings of the analysis, which I don't see being the case here. The odd/even number rule is too strict, and talking about a fixed and definite number of cycles in the actual brain seems too simplistic.

      We have clearly relaxed most of our assumptions when we considered a network model of basal ganglia in which each subpopulation is a collection of spiking neurons. And as we have shown our results still hold (see Figure 5). Again our model is about oscillations in a network of networks i.e. network of brain regions.

      At meso-scale it is not unreasonable to find such cycles and even-odd number rules. We have shown this for the case of a cortico-basal ganglia model. We can also extend this to cortico-thalamic networks and so on. We have already emphasized this point in the introduction to avoid any confusion: see lines 62-66 – “We prove this conjecture for the threshold-linear network (TLN) model without delays which can closely capture the dynamics of neural populations. Therefore, it is implicit that our results do not hold at the neuronal level but rather at the level of neuron populations/brain regions e.g. the basal ganglia (BG) network which can be described a network of different nuclei.” and lines 69-70 – ’Within the framework of the odd-cycle theory, distinct nuclei are associated with either excitatory or inhibitory nodes.’

      Being linear is another strong assumption, and it is not clear how much of the results are preserved for spiking neurons, even though there is such an analysis, or maybe for other nonlinear types of neuronal masses.

      Clearly our results hold in a network of spiking neurons (see Figure 5). It is of course interesting to ask whether our results hold in a network where individual spiking neurons have more complex spiking behavior like AdEx or Quadratic IF. But that kind of analysis deserves a full manuscript on its own.

      Delays are also mentioned, and their impact on the oscillatory networks is as expected: it reduces the amplitude, but there is no link to the literature, where this is an established phenomenon during synchronization. Finally, the authors should also discuss the time-delays as a known phenomenon to cause or amplify oscillations at different frequencies in a network of coupled oscillators, e.g Petkoski & Jirsa Network Neuroscience 2022, Tewarie et al. NeuroImage 2019, Davis et al. Nat Commun 2021.

      This is indeed a weakness of our model. But as the reviewer already knows, dynamical systems with delays are very difficult to analyze analytically. We have mentioned this in the limitations of the model and the analysis. In our simulations we have considered delays and when the delays are within reasonable limits our results hold.

      Reviewer #2 (Public Review):

      Summary:

      The authors present here a mathematical and computational study of the topological/graph theory requirements to obtain sustained oscillations in neural network models. A first approach mathematically demonstrates that a given network of interconnected neural populations (understood in the sense of dynamical systems) requires an odd number of inhibitory populations to sustain oscillations. The authors extend this result via numerical simulations of (i) a simplified set of Wilson-Cowan networks, (ii) a simplified circuit of the cortico-basal ganglia network, and (iii) a more complex, spike-based neural network of basal ganglia network, which provides insight on experimental findings regarding abnormal synchrony levels in Parkinson's Disease (PD).

      Strengths:

      The work elegantly and effectively combines solid mathematical proof with careful numerical simulations at different levels of description, which is uncommon and provides additional layers of confidence to the study. Furthermore, the authors included detailed sections to provide intuition about the mathematical proof, which will be helpful for readers less inclined to the perusal of mathematical derivations. Its insightful and well-informed connection with a practical neuroscience problem, the presence of strong beta rhythms in PD, elevates the potential influence of the study and provides testable predictions.

      Weaknesses:

      In its current form, the study lacks a more careful consideration of the role of delays in the emergence of oscillations. Although they are addressed at certain points during the second part of the study, there are sections in which this could have been done more carefully, perhaps with additional simulations to solidify the authors' claims. Furthermore, there are several results reported in the main figures which are not explained in the main text. From what I can infer, these are interesting and relevant results and should be covered. Finally, the text would significantly benefit from a revision of the grammar, to improve the general readability at certain sections. I consider that all these issues are solvable and this would make the study more complete.

      This point has been made by the first reviewer as well. So we repeat our answer:

      This is indeed a weakness of our model. But as the reviewer already knows, dynamical systems with delays are very difficult to analyze analytically. We have mentioned this in the limitations of the model and the analysis. In our simulations we have considered delays and when the delays are within reasonable limits our results hold.

      Reviewer #2 (Recommendations For The Authors):

      As mentioned in my comments above, I think that the work is already quite solid and relevant but would significantly improve if some issues were addressed:

      We would like to thank the reviewer for valuable comments and constructive feedback which has helped us greatly improve the manuscript.

      1) While the authors acknowledge early on the limitations of this study in terms of not considering plasticity or neuron biophysics (line 72), I think that the absence of propagation delays should be explicitly included here. This absence leads to inaccuracies --for example, the sentence "Consider a small network of two nodes. If we connect them mutually with excitatory synapses, intuitively we can say that the two-population network will not oscillate" (line 74) is only correct if the delays (or signal latencies) are zero. With a proper delay, two excitatory neurons can engage in oscillations with a period given by two times the value of the delay.

      A similar situation happens for inhibitory neurons, where the winner-take-all dynamics described in line 77 is only valid for zero delay. It is known that a homogeneous population of inhibitory spiking neurons with delayed synapses can lead to fast oscillations (Brunel and Hakim 1999), something which is also valid for the equivalent inhibitory single node with delayed self-inhibition. Indeed, a circuit of two inhibitory populations with delayed self- and cross-inhibition can generate oscillations, contradicting the main conclusion of the odd number of inhibitory nodes needed for oscillations.

      Because of these considerations, I think the authors should be more careful when explaining the effects of delays, and state that their main results on the link between oscillations and having an odd number of inhibitory nodes are not valid when delays are considered. They could modify the sentences in lines 72-77 above and include a supplementary figure right after their simulation study for the Wilson-Cowan (to explain the examples above, and also the one in the next point).

      The reviewer has brought up a critical point regarding the impact of propagation delays, and we completely concur with your assessment. In our study, we indeed did not comprehensively consider the effects of propagation delays in cycles with even inhibition, which may introduce inaccuracies in our conclusions.

      We note that in the Wilson-Cowan model with delays, certain cycles with even number of inhibitory links can also generate oscillations with a period equal to twice the delay value. However, in our hand such oscillations were transient and dissipated quickly.

      To better reflect the limitations of our research, we have made significant modifications to the relevant sections in our manuscript.

      In line 100, we've added text to explicitly state that we considered delays in our simulations and acknowledged their potential to generate oscillations ("Given the importance of delays in biological network such as BG, we will consider them in the simulations.").

      In line 102, we've clarified that our conclusions are based on a scenario without delays ("In this following, we give simple examples of the possibility of oscillation (or not) based on the connectivity characteristics of small networks without delays. Let us start with a network of two nodes.").

      Additionally, in line 230, we've included a reference figure supplement 3-2 to highlight the outcomes in terms of oscillations ("EII network only resulted in transient oscillations (Fig. 3, figure supplement 3-1, figure supplement 3-2)").

      In lines 234-237, we've added a sentence discussing the role of synaptic delays in generating transient oscillations in cycles with an even number of inhibitory components, referring to figure supplement 3-2 ("In networks with even number of inhibitory connections (e.g. EII, EEE, II), synaptic delays are the sole mechanism for initiating oscillations, however, unless delays are precisely tuned such oscillations will remain transient (see Supplementary figure supplement 3-2)").

      Moreover, in response to the reviewer’s suggestion, we have included an additional figure supplement 3-2 to illustrate how cycles with even inhibitory components generate transient oscillations when propagation delays are taken into account. This figure provides a visual representation of the phenomenon and enhances the clarity of our findings.

      2) In Figure 3, two motifs (III and EII) are explored to demonstrate the validity of the results across different parameters. Delays don't seem to play a disruptive role in these two cases, but the results seem to be different for other motifs not considered here. Aside from the examples mentioned above, I can imagine how a motif of EEE (i.e. a circle of three excitatory Wilson-Cowan neurons) would display oscillations when delays are included, as the activation would 'circulate' along the ring. However, this EEE motif has an even number of inhibitory units (or perhaps zero is considered an exception, but if so it's not mentioned in the text).

      We thank the reviewer for this observation regarding Figure 3. Indeed, the impact of delays may differ for other motifs not considered in our study. For example, as the reviewer has correctly anticipated, a motif of EEE (a circular network of three excitatory Wilson-Cowan neurons) would exhibit oscillations when delays are included, as activation could 'circulate' along the ring.

      To address this concern,we have performed new simulations (added as a new supplementary figure supplement 3-2). As illustrated in figure supplement 3-2, oscillations may indeed arise in the EEE motif when delays are introduced. However, these oscillations will eventually dissipate – at least with our settings.

      3) Figures 1b, 1c, and 4e display interesting results, but these are absent from the main text. Please include the description of those results. Particularly the case of Figs 1b and 1c seems very relevant to understanding the main results in the context of more complex networks, in which multiple loops with odd and even numbers of inhibitory units would coexist in the network. Does the number of odd-inhibitory loops in a given network affect somehow the power or frequency of the resulting network oscillations? It would be interesting to show this.

      Indeed, we did not explain Figs 1b,c and 4e properly. Now we have revised the manuscript in the following way to incorporate these results:

      In lines 124-128, we added the following text to introduce the concept: "We can generalize these results to cycles of any size, categorizing them into two types based on the count of their inhibitory connections in one direction (referred to as the odd cycle rule, as illustrated in Fig. 1b). More complex networks can also be decomposed into cycles of size 2…N (where N is number of nodes), and predict the ability of the network to oscillate (as shown in Fig. 1c)" In line 298, we included the following text to highlight the relevant result: "Next, we removed the STN output (equivalent to inhibition of STN), the Proto-D2-Arky subnetwork generated oscillations for weak positive inputs to the D2-SPNs (Fig.4e, bottom)."

      How the number of odd/even loops affect the frequency is an interesting question. Intuitively there should be a relation between the two. However, a complete treatment of this question is beyond the scope of the manuscript but we think that in a network with identical node properties, more odd cycles should imply higher oscillation power.

      4) The cortico-BG model is focused on how inactivating STN could suppress (or not) beta oscillations, following experimental observations. However, besides mechanisms for extinguishing oscillations, it would be interesting to see if the progressive emergence of pathological beta oscillations could be explained by the modification of some of the nodes in the model (for example, explicitly mimicking the loss of dopaminergic neurons in the substantia nigra). This could be a very interesting additional figure in the main text.

      This is an interesting suggestion. Something similar has been already done – e.g. Kumar et al. (2010) showed that progressive increase of inhibition of GPe can lead to oscillations. Similarly Holgado et al. (2008) showed how progressive change in the mutual connectivity between STN and GPe can cause oscillations. More recently, Ortone et al. (PloS Comp. Biol 2023) and Azizpour et al. (2023 Bioarxiv) have also shown the effect of progressive change in individual node properties on oscillations in basal ganglia using numerical simulations. Our work in a way provides the theoretical backing to their work. Therefore, we think it is not necessary to again show these results in our model. Instead we have cited these papers. Lines 392-396

      5) I observed some grammatical inconsistencies in the text, some of them are indicated below. I would suggest carefully going through the text to correct those issues or seeking help with editing.

      -line 32 "...which can closely capture the neural population dynamics". Which population dynamics? Do the authors refer to general neural dynamics?

      -line 33 "long term behavior" -> long-term behavior

      -line 68 "given the ionic channel composition" -> "given its ionic channel composition"

      We apologize for the grammatical inconsistencies in our manuscript. We have made the necessary corrections to improve the clarity and accuracy of our text.

      Reviewer #3 (Recommendations For The Authors):

      This manuscript is useful for analytically showing that a cyclic network of threshold-linear neural populations can only oscillate if it has an odd number of inhibitory nodes with strong enough connections. Establishing this result, which holds under rather narrow assumptions, relies on standard tools from dynamical system theory. I find the strength of support for this result to be incomplete for the reasons detailed below:

      Although the mathematical arguments used appear to be correct, the manuscript lacks in rigor and clarity. For instance, the main result presented in theorem 2 is stated in a very unclear fashion: aside from the oddity of the number of inhibitory nodes, there are two conditions to check, which determines four cases. This can be explained in a much more straightforward way without introducing four relations in equations 4-7.

      We acknowledge the reviewer’s concern regarding the presentation of the main result in Theorem 2.

      We would like to emphasize that the introduction of four relations in equations 4-7 was intended to provide a detailed and transparent exposition of the conditions for the main result. While we understand that this approach may appear less straightforward, it allows for a more comprehensive understanding of the underlying logic and the multiple factors influencing the outcomes.

      However, we are open to suggestions for more concise and clear ways to express these conditions if the reviewer has specific recommendations or if there are alternative approaches that the reviewer believes would be more effective in conveying the information.

      Moreover, equation 3 in that same theorem is clearly wrong.

      We sincerely apologize for the typographical error in equation 3 within the same theorem. We thank the reviewer for noticing this. We have revised the text to rectify this mistake. The equation has now been corrected to ensure its accuracy.

      The proof of theorem 2 relies on standard linear algebra and can be improved as well: there are typos, approximations, and missing words (see line 664). The rigor of the exposition is also unsatisfactory. For instance, the proof of Lemma 1 ends with the sentence: "Similarly as before, the convergence of the dynamics driven by the left and right terms ends the proof". I don't know what this means.

      We thank the reviewer for the comments and suggestions. We have made the necessary adjustments to enhance the rigor and clarity of our mathematical reasoning in the revised manuscript.

      In line 644, we have provided clarification for the sentence you found unclear. The revised version now offers a more precise explanation that should help in understanding the proof.

      At the same time, the intuitive arguments presented in the main text are vague at best and do not really help grasping the possible generalizability of the results. For instance, I do not understand the message of panel B in Figure 2 and there seems to be no explanation about it in the main text.

      The main purpose of Figure 2B is to offer a visual representation of the concept and to serve as an aid for readers who may prefer a graphical illustration over extensive equations. While we understand that the figure may not provide a complete explanation on its own, it is intended to complement the text and mathematical content presented in the main text. In the revised version we have added the explanation of Figure 2B.

      Aside from the analytical result, most of the paper consists in simulating networks with distinct inhibitory cyclic structure to validate the theoretical argument. I do not find this approach particularly convincing due to the qualitative nature of the numerical results presented. There is little quantitative analysis of the network structure in relation to the emergence of oscillations. It is also hard to judge whether the examples discussed are cherry picked or truly representative of a large class of dynamics.

      The reviewer has a valid concern about numerical simulations and qualitative nature of the results. We would like to provide some perspective on our approach.

      In our paper, the primary focus is on the mathematical proof, which rigorously establishes the existence of our results. However, we understand that numerical simulations are valuable for illustrating the applicability of the theoretical framework and providing insights into the practical implications.

      If we get into the quantitative description of all the results, the manuscript will become prohibitively long. We acknowledge that there is a balance to be struck between theory and numerical examples in a research paper. We believe that, in conjunction with the mathematical proof, the numerical simulations serve the purpose of illustrating the existence of our results in specific examples. While we cannot provide an exhaustive exploration of all possible network structures, we have chosen representative cases to demonstrate the applicability of our findings. Some of these are already provided in figure supplements S3-1 and S3-3. In the absence of specific suggestions from the reviewer we would like to leave it as is.

      Moreover, the authors apply their cycle analysis to real-world networks by considering cycles of inhibitory nodes independently, whereas the same nodes can belong to several cycles. I find it hard to believe that considering these cycles independently should be enough to make predictions about the emergence of oscillations, as these cycles must interact with one another via shared nodes. I do not understand the color coding used to mark distinct cycles in supplementary figures. There is also not enough information to understand figures in the main text. For instance, I do not understand what the grids are representing in panel B, Figure 4.

      We have clarified the color coding and added more information to understand the figures. We appreciate the reviewer’s concern about our application of cycle analysis to real-world networks and the clarity of our figures. It is not a matter of belief – we have provided a mathematical proof and complemented that with illustrative examples from real-world networks i.e. cortico-basal ganglia network with both rate-based and spiking neurons. Clearly our results hold.

      Regarding the color coding in supplementary figures, we have revised the color scheme to make it more intuitive and informative in caption of figure 4: we use different colors to mark potential oscillators in each motif in BG, and each color means an oscillator from panel a. For more details, see figure supplements 4-1–4-6. The colors now represent distinct cycles more clearly, helping readers better interpret the figures.

    1. Reviewer #1 (Public Review):

      Summary & Assessment:

      The catalytic core of the eukaryotic decapping complex consists of the decapping enzyme DCP2 and its key activator DCP1. In humans, there are two paralogs of DCP1, DCP1a, and DCP1b, that are known to interact with DCP2 and recruit additional cofactors or coactivators to the decapping complex; however, the mechanisms by which DCP1 activates decapping and the specific roles of DCP1a versus DCP1b, remain poorly defined. In this manuscript, the authors used CRISPR/Cas9-generated DCP1a/b knockout cells to begin to unravel some of the differential roles of human DCP1a and DCP1b in mRNA decapping, gene regulation, and cellular metabolism. While this manuscript presents some new and interesting observations on human DCP1 (e.g. human DCP1a/b KO cells are viable and can be used to investigate DCP1 function; only the EVH1 domain, and not its disordered C-terminal region which recruits many decapping cofactors, is apparently required for efficient decapping in cells; DCP1a and b target different subsets of mRNAs for decay and may regulate different aspects of metabolism), there are several major issues that undercut some of the main conclusions of the paper, and some key claims that are incompletely or inconsistently supported by the presented data.

      Strengths & well-supported claims:

      • Through in vivo tethering assays in CRISPR/Cas9-generated DCP1a/b knockout cells, the authors show that DCP1 depletion leads to significant defects in decapping and the accumulation of capped, deadenylated mRNA decay intermediates.

      • DCP1 truncation experiments reveal that only the EVH1 domain of DCP1 is necessary to rescue decapping defects in DCP1a/b KO cells.

      • RNA and protein immunoprecipitation experiments suggest that DCP1 acts as a scaffold to help recruit multiple decapping cofactors to the decapping complex (e.g. EDC3, DDX6, PATL1 PNRC1, and PNRC2), but that none of these cofactors are essential for DCP2-mediated decapping in cells.

      • The authors investigated the differential roles of DCP1a and DCP1b in gene regulation through transcriptomic and metabolomic analysis and found that these DCP1 paralogs target different mRNA transcripts for decapping and have different roles in cellular metabolism and their apparent links to human cancers. (Although I will note that I can't comment on the experimental details and/or rigor of the transcriptomic and metabolomic analyses, as these are outside my expertise.)

      Weaknesses & incompletely supported claims:

      1) A central mechanistic claim of the paper is that "DCP1a can regulate DCP2's cellular decapping activity by enhancing DCP2's affinity to RNA, in addition to bridging the interactions of DCP2 with other decapping factors. This represents a pivotal molecular mechanism by which DCP1a exerts its regulatory control over the mRNA decapping process." Similar versions of this claim are repeated in the abstract and discussion sections. However, this appears to be entirely at odds with the observation from in vitro decapping assays with immunoprecipitated DCP2 that showed DCP1 knockout does not significantly affect the enzymatic activity of DCP2 (Figures 2B-D; I note that there may be a very small change in DCP2 activity shown in panel C, but this may be due to slightly different amounts of immunoprecipitated DCP2 used in the assay, as suggested by panel D). If DCP1 pivotally regulates decapping activity by enhancing RNA binding to DCP2, why is no difference in decapping activity observed in the absence of DCP1? Furthermore, the authors show only weak changes in relative RNA levels immunoprecipitated by DCP2 with versus without DCP1 (~2-3 fold change; consistent with the Valkov 2016 NSMB paper, which shows what looks like only modest changes in RNA binding affinity for yeast Dcp2 +/- Dcp1). Is the argument that only a 2-3 fold change in RNA binding affinity is responsible for the sizable decapping defects and significant accumulation of deadenylated intermediates observed in cells upon Dcp1 depletion? (and if so, why is this the case for in-cell data, but not the immunoprecipitated in vitro data?)

      The authors acknowledge this apparent discrepancy between the in vitro DCP2 decapping assays and in-cell decapping data, writing: "this observation could be attributed to the inherent constraints of in vitro assays, which often fall short of faithfully replicating the complexity of the cellular environment where multiple factors and cofactors are at play. To determine the underlying cause, we postulated that the observed cellular decapping defect in DCP1a/b knockout cells might be attributed to DCP1 functioning as a scaffold." This is fair. They next show that DCP1 acts as a scaffold to recruit multiple factors to DCP2 in cells (EDC3, DDX6, PatL1, and PNRC1 and 2). However, while DCP1 is shown to recruit multiple cofactors to DCP2 (consistent with other studies in the decapping field, and primarily through motifs in the Dcp1 C-terminal tail), the authors ultimately show that *none* of these cofactors are actually essential for DCP2-mediated decapping in cells (Figures 3A-F). More specifically, the authors showed that the EVH1 domain was sufficient to rescue decapping defects in DCP1a/b knockout cells, that PNRC1 and PNRC2 were the only cofactors that interact with the EVH1 domain, and finally that shRNA-mediated PNRC1 or PNCR2 knockdown has no effect on in-cell decapping (Figures 3E and F). Therefore, based on the presented data, while DCP1 certainly does act as a scaffold, it doesn't seem to be the case that the major cellular decapping defect observed in DCP1a/b knockout is due to DCP1's ability to recruit specific cofactors to DCP2.

      So as far as I can tell, the discrepancy between the in vitro (DCP1 not required) and in-cell (DCP1 required) decapping data, remains entirely unresolved. Therefore, I don't think that the conclusions that DCP1 regulates decapping by (a) changing RNA binding affinity (authors show this doesn't matter in vitro, and that the change in RNA binding affinity is very small) or (b) by bridging interactions of cofactors with DCP2 (authors show all tested cofactors are dispensable for robust in-cell decapping activity), are supported by the evidence presented in the paper (or convincingly supported by previous structural and functional studies of the decapping complex).

      2) Related to the RNA binding claims mentioned above, are the differences shown in Figure 3H statistically significant? Why are there no error bars shown for the MBP control? (I understand this was normalized to 1, but presumably, there were 3 biological replicates here that have some spread of values?). The individual data points for each replicate should be displayed for each bar so that readers can better assess the spread of data and the significance of the observed differences. I've listed these points as major because of the key mechanistic claim that DCP1 enhances RNA binding to DCP2 hinges in large part on this data.

      3) Also related to point (1) above, the kinetic analysis presented in Figure 2C shows that the large majority of transcript is mostly decapped at the first 5-minute timepoint; it may be that DCP2-mediated decapping activity is actually different in vitro with or without DCP1, but that this is being missed because the reaction is basically done in less than 5 minutes under the conditions being assayed (i.e. these are basically endpoint assays under these conditions). It may be that if kinetics were done under conditions to slow down the reaction somewhat (e.g. lower Dcp2 concentration, lower temperatures), so that more of the kinetic behavior is captured, the apparent discrepancy between in vitro and in-cell data would be much less. Indeed, previous studies have shown that in yeast, Dcp1 strongly activates the catalytic step (kcat) of decapping by ~10-fold, and reduces the KM by only ~2 fold (Floor et al, NSMB 2010). It might be beneficial to use purified proteins here (only a Western blot is used in Figure 2D to show the presence of DCP2 and/or DCP1, but do these complexes have other, and different, components immunoprecipitated along with them?), if possible, to better control reaction conditions.

      This contradiction between the in vitro and in-cell decapping data undercuts one of the main mechanistic takeaways from the first half of the paper. This needs to be addressed/resolved with further experiments to better define the role of DCP1-mediated activation, or the mechanistic conclusions significantly changed or removed.

      4) The second half of the paper compares the transcriptomic and metabolic profiles of DCP1a versus DCP1b knockouts to reveal that these target a different subset of mRNAs for degradation and have different levels of cellular metabolites. This is a great application of the DCP1a/b KO cells developed in this paper and provides new information about DCP1a vs b function in metazoans, which to my knowledge has not really been explored at all. However, the analysis of DCP1 function/expression levels in human cancer seems superficial and inconclusive: for example, the authors conclude that "...these findings indicate that DCP1a and DCP1b likely have distinct and non-redundant roles in the development and progression of cancer", but what is the evidence for this? I see that DCP1a and b levels vary in different cancer cell types, but is there any evidence that these changes are actually linked to cancer development, progression, or tumorigenesis? If not, these broader conclusions should be removed.

      5) The authors used CRISPR-Cas9 to introduce frameshift mutations that result in premature termination codons in DCP1a/b knockout cells (verified by Sanger sequencing). They then use Western blotting with DCP1a or DCP1b antibodies to confirm the absence of DCP1 in the knockout cell lines. However, the DCP1a antibody used in this study (Sigma D5444) is targeted to the C-terminal end of DCP1a. Can the authors conclusively rule out that the CRISPR/Cas-generated mutations do not result in the production of truncated DCP1a that is just unable to be detected by the C-terminally targeted antibody? While it is likely the introduced premature termination codon in the DCP1a gene results in nonsense-mediated decay of the resulting transcript, this outcome is indeed supported by the knockout results showing large defects in cellular decapping which can be rescued by the addition of the EVH1 domain, it would be better to carefully validate the success of the DCP1a knockout and conclusively show no truncated DCP1a is produced by using N-terminally targeted DCP1a antibodies (as was the case for DCP1b).

      Some additional minor comments:

      • More information would be helpful on the choice of DCP1 truncation boundaries; why was 1-254 chosen as one of the truncations?<br /> • Figure S2D is a pretty important experiment because it suggests that the observed deadenylated intermediates are in fact still capped; can a positive control be added to these experiments to show that removal of cap results in rapid terminator-mediated degradation?

    1. We have, as a bedrock value in our society, long agreed on thevalue of open access to information, and recognize the problems thatarise with attempts to restrict access to and development of knowledge.

      Many academics and modern people may think this way, but it is far from a "bedrock value".

      In many indigenous cultures knowledge was carefully sectioned and cordoned off.

      And as we know that knowledge itself is power (ipsa scientia potestas est - Francis Bacon) many people have frequently cordoned off access to information.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This valuable study advances our understanding of the forces that shape the genomic landscape of transposable elements. By exploiting both long-read sequencing of mutation accumulation lines and in vivo transposition assays, the authors offer compelling evidence that structural variation rather than transposition largely shapes transposable element copy number evolution in budding yeast. The work will be of interest to the transposable element and genome evolution communities.

      Public Reviews:

      Reviewer #1 (Public Review):

      Henault et al build on their own previous work investigating the longstanding hypothesis that hybridization between divergent populations can activate transposable element mobilization (transposition). Previously they created crosses of increasing sequence divergence, using both intra- and inter-species hybrids, and passaged them neutrally for hundreds of generations. Their previous work showed that neither hybrids isolated from natural environments nor hybrids from their mutation accumulation lines showed consistent evidence of increased transposable element content. Here, they sequence and assemble long-read genomes of 127 of their mutation-accumulation lines and annotate all existing and de novo transposable elements. They find only a handful of de novo transposition events, and instead demonstrate that structural variation (ploidy, aneuploidy, loss of heterozygosity) plays a much larger role in the transposable element load in a given strain. They then created transposable element reporter constructs using two different Ty1 elements from S. paradoxus lineages and measured the transposition rate in a number of intraspecific crosses. They demonstrate that the transposition rate is dependent on both the Ty1 sequence and the copy number of genomic transposable elements, the latter of which is consistent with what has been observed in the literature on transposable element copy number control in Saccharomyces. To my knowledge, others have not directly tested the effect of Ty1 sequence itself (have not created diverse Ty1 reporter constructs), and so this is an interesting advance. Finally, the authors show that mitotype has a moderate effect on transposition rate, which is an intriguing finding that will be interesting to explore in future work.

      This study represents a large effort to investigate how genetic background can influence transposable element load and transposition rate. The long read sequencing, assembly, and annotation, and the creation of these reporter constructs are non-trivial. Their results are straightforward, well supported, and a nice addition to the literature.

      The authors state that the results from their current work support results taken from their previous study using short-read sequencing data of the same lines. The argument that follows is whether the authors gained anything novel from long-read sequencing. I would like to see the authors make a stronger argument for why this new work was necessary, and a more detailed view of similarities or differences from their previous study (when should others choose to do long read vs. short read of evolved lines?).

      We thank the reviewer for the suggestion. While we initially aimed to justify the relevance and novelty of the current in relation to our previous study, we understand that this justification may not have been strong enough.

      In the second paragraph of the introduction, we explain how the multidimensional nature of TE load makes it more complex to characterize that simply reporting the abundance of a given TE family in a given genome. We added the following concluding sentence to further emphasize the importance of long reads in TE-focused genome inference:

      “As such, ongoing technological and computational advances in genome inference, including long-read sequencing, will certainly be key to getting a detailed understanding of the dynamics of TEs and the underpinning evolutionary forces.”

      In the penultimate introductory paragraph, we summarize our previous work from 2020 and highlight that the evolution of Ty contents in MA lines was inferred from aggregate measures of genomic abundance of TE families using short reads. We then make the point that combinations of multiple SVs could affect the landscape of TEs in ways that are not reflected by crude short-read measures. We added the following sentence to further emphasize this point and contrast it with the necessity of using more powerful methodologies for genome resolution:

      “Under this scenario, measuring Ty family abundance would yield no significant net change, and the dissection of the underlying SVs using short reads could often be challenging.”

      Relatedly, the authors should report the rates of structural variants that they observe. How are these results similar/different from other mutation-accumulation work in S. cerevisiae?

      Since this work does not attempt to provide an exhaustive report of all the SVs in the MA lines, but rather focus on attributing an SV type to individual loci occupied by TEs, we cannot include these estimates, excepted for de novo transposition itself (see below). We added the following sentence to the Results section on the classification of Ty loci by SV types:

      “We note that the current methodology does not aim at providing an exhaustive quantification of all SVs in the MA lines, as previously done for some SV types (Marsit et al., 2021), but focuses solely on loci containing Ty elements.”

      We added estimates of the average retrotransposition rate in the MA experiment based on the number of de novo insertions detected in the MA lines genomes.

      Figure 4:

      “The average retrotransposition rates estimated from the counts of de novo insertions (per line per generation per element) are the following: CC1, 1.0✕10-5; CC2, 4.9✕10-6; CC3, 7.6✕10-6; BB1, 1.5✕10-5; BC2, 1.7✕10-5; BA1, 6.5✕10-6; BA2, 2.2✕10-5; BSc1, 3.6✕10-5.”

      We added the following paragraph in the Discussion section to specifically discuss these estimates in relation to the in vivo measurements.

      “We note that while the CC crosses tend to have the lowest retrotransposition rates as estimated from the de novo insertions (~1✕10-5 per line per generation per element; Figure 4), these values are several orders of magnitude higher than the in vivo measures in SpC backgrounds. The discrepancy between these estimates could be due to uncharacterized biases inherent to each method. They could also be linked to differences between the parental genotypes used to generate the MA crosses and the fluctuation assays. One major difference is the use of ade2 genotypes in the MA parents, a strategy that was initially adopted to provide a marker for the loss of mitochondrial respiration (Joseph and Hall, 2004; Lynch et al., 2008). It has been shown that the induction of adenine starvation through minimal adenine concentration in the medium and deletion of ADE2, which inactivates the adenine de novo biosynthesis pathway, increases Ty1 transcript levels (Todeschini et al., 2005), resulting in higher transposition rates. Rich complex medium like the one that was used for the MA experiment (YPD) can exhibit substantial variation in adenine concentration (VanDusen et al., 1997), and adenine can quickly become the limiting nutrient for ade2 strains (Kokina et al., 2014). Thus, we cannot exclude that the choice of initial ade2 genotypes could have inflated the transposition rates in the MA experiment.”

      Since the authors show a small, but consistent influence of mitotype on transposition rates, adding further evidence for the role of mtDNA in regulating transposition, I'm curious what the transposition rate of a p0 strain is. I think including these results could make this observation more compelling.

      We agree that measuring in vivo transposition rates in ρ0 backgrounds would be an interesting avenue. However, there is a large distinction between having non-functional mitochondrial respiration in ρ0 strains and inheriting diverse functional mtDNA haplotypes. The effects we show are all linked to the reciprocal inheritance of intact mtDNAs, producing ρ+ strains that are all respiration-competent, as shown by our growth confirmations on non-fermentable carbon sources for all the diploid backgrounds generated. While potentially interesting, adding transposition rates measures for the ρ0 backgrounds seems hard to justify in the context of our results.

      Reviewer #2 (Public Review):

      This is an interesting follow-up study that uses long-read sequencing to examine previously constructed mutation accumulation lines between wild populations of S. cerevisiae and S. paradoxus. They also complement this work with reporter assays in hybrid backgrounds. The authors are attempting to test the hypothesis that hybridization leads to genome shock and unrestrained transposition. The paper largely confirms previous results (suggesting hybridization does not increase transposition) that are well cited and discussed in the paper, both from this group and from the Smukowski Heil/Dunham group but extends them to a new set of species/hybrids and with some additional resolution via the long read sequencing. The paper is well written and clear and I have no serious complaints.

      In the abstract, the authors make three primary claims:

      Structural variation plays a strong role in TE load.

      Transposition plays only a minor role in shaping the TE landscape in MA lines.

      Transposition rates are not increased by hybridization but are affected by genotype-specific factors.

      I found all three claims supported, albeit with some minor questions below:

      Structural variation plays a strong role in TE load.

      Convinced of this result. However:

      Line 185-187/Figure 3C: I'm curious given that the changes in Ty count are so often linked to changes in gross DNA sequence whether the count per total DNA sequence is actually changing on average in these genomes. Ie., does hybridization tend to increase TE count via CNV or does hybridization tend to increase DNA content in the MA lines and TEs come along for the ride?

      The Ty content definitely “rides along” with the rest of the genome that is affected by retrotransposition-unrelated SVs. To further highlight this point, we added a panel (E) to Figure 3 in which we correlate the net Ty copy number change (same as panel D, formerly C) to the corresponding genome size, which reflects the amount of DNA lost/gained by all SV types. We added the following to the results section:

      “The distributions of net Ty CN change per MA line showed that most crosses had significant gains (Figure 3D), suggesting that Ty load can often increase as a result of random genetic drift. Some (but not all) of these crosses also exhibited significant increases in genome size after evolution (Supplemental Figure S7A). The net Ty CN changes per MA line subgenome were globally correlated to the corresponding changes in subgenome size (Figure 3E). Even after excluding polyploid lines (which have the largest changes in both Ty CN and genome size), we found a significant relationship between the two variables (mixed linear model with random intercepts and slopes for MA crosses, P-value=3.71✕10-9; Supplemental Figure S7B), indicating that SVs affecting large portions of the genome have a substantial impact on the Ty landscape.”

      One question about ploidy (lines 175-177):

      Both aneuploidy and triploidy seem easy to call from this data. A 3:1 tetraploidy as well. However, in Figure 2B there are tetraploids that are around the 1:1 line. How are the authors calling ploidy for these strains? This was not clear to me from the text.

      This detail was indeed missing from the manuscript. The ploidy level of all MA lines was previously measured by DNA staining and flow cytometry, and the ploidy level of the subgenomes of each polyploid MA line was previously inferred from short-read sequencing. We modified the figure captions and the main text to include this along with the corresponding references:

      Figure 2:

      “The ploidy level of each line was previously determined by DNA staining and flow cytometry (Charron et al., 2019; Marsit et al., 2021).”

      Main text:

      “The ratio of classified bases per subgenome was consistent with the corresponding ploidy levels: triploid BC lines had two copies of the SpC subgenome, while tetraploid lines had both SpC subgenomes duplicated (Charron et al., 2019; Marsit et al., 2021) (Figure 2B).”

      “Finally, we used the ploidy level of each MA line subgenome as previously measured by flow cytometry and short-read sequencing (Charron et al., 2019; Marsit et al., 2021).”

      Reviewer #3 (Public Review):

      Henault et al. address the important open question of whether hybridization could trigger TE mobilization. To do this they analysed MA lines derived from crosses of Saccharomyces paradoxus and Saccharomyces cerevisiae using long-read sequencing. These MA lines were already analysed in a previous publication using Illumina short-read data but the novelty of this work is the long-read sequencing data, which may reveal previously missed information. It is an interesting message of this study that hybridization between the two species did not lead to much TE activity. Due to this low activity, the authors performed an additional TE activity assay in vivo to measure transposition rates in hybrid backgrounds. The study is well written and I cannot spot any major problems. The study provides some important messages (like the influence of the genotype and mitochondrial DNA on transposition rates).

      Major comments

      • What I miss the most in this work is the perspective of the host defence against TEs in Saccharmoces. Based on such a mechanistic perspective, why do the authors think that hybridization could lead to a TE reactivation? For example, in Drosophila small RNAs important for the defence against a TE, are solely maternally transmitted. Hybrid offspring will thus solely have small-RNAs complementary to the TEs of the mother but not to the TEs of the father, therefore a reactivation of the paternal TEs may be expected. I was thus wondering, what is the situation in yeast. Why would we expect an upregulation of TEs? Without such a mechanistic explanation the hypothesis that TEs should be upregulated in hybrids is a bit vague, based on a hunch.

      We agree with the reviewer that in the first version of the manuscript, the justification for the investigation of the reactivation hypothesis in the first place was not self-sufficient and relied too much on our previous work, upon which this article builds. We extensively remodeled the introduction to better justify the investigation of this hypothesis in the context of the current knowledge on the regulation of Ty elements in Saccharomyces.  

      Reviewer #1 (Recommendations For The Authors):

      It's interesting that the net change in transposable element copy number in mutation accumulation lines is either insignificant or gain, and never a significant loss. I think this could make a nice discussion point regarding the roles of drift and selection on TE load.

      We thank the reviewer for the suggestion and agree that this is an interesting perspective that we did not explore in the first version of the manuscript. We thus included a short discussion point in the Results:

      “The distributions of net Ty CN change per MA line showed that most crosses had significant gains (Figure 3D), suggesting that Ty load can often increase as a result of random genetic drift.”

      We also added the following paragraph to the discussion section:

      “Our experiments illustrate how under weakened natural selection efficiency, TE load can increase in hybrid genomes by the action of transposition-unrelated SVs. This offers a nuanced perspective on the classical interpretation of the transposition-selection balance model (Charlesworth et al., 1994; Charlesworth and Langley, 1989), in which increased TE load would be predominantly driven by the relaxation of purifying selection against TE insertions generated by de novo transposition. Our results suggest that SVs arising in the context of hybridization can act as a significant source of TE insertion polymorphisms which natural selection can purge more or less efficiently, depending on the population genetic context. This is closely related to the idea that sexual reproduction could favor the spread of TE families, contributing to their evolutionary success (Hickey, 1982; Zeyl et al., 1996). Since the insertion polymorphisms that contribute to increase TE load mostly originate from standing genetic variation, they could be less deleterious and thus harder for natural selection to purge efficiently.”

      The point about the role of LOH in TE load is cool!

      We thank the reviewer for their enthusiasm, it is one of our favorite results as well.

      Figure 1: Add a figure component of the green box and label it Ty1 or TE.

      We modified Figure 1 accordingly.

      Figure 2C: what is the assembly size ratio?

      We added the following sentence to the figure caption to clarify what we define as assembly size ratio:

      “Assembly size ratio refers to the ratio of subgenome assembly size to the corresponding parental assembly size.”

      Something cut off in the N50 plot axis

      Unfortunately, we can’t seem to understand what the reviewer meant with this comment, nothing seems cut out of the figure panel 2C in any of our versions of the manuscript.

      Reviewer #2 (Recommendations For The Authors):

      These are all minor comments/suggestions that the authors can take or leave.

      Line 42: "fuels" should be "fuel".

      Since the verb refers to “source” and not “variants”, we believe it should be at the third person singular.

      Line 43: unclear what the authors mean by "regroup".

      We understand how this phrasing may sound strange. We modified the sentence accordingly:

      “Structural variation is a term that encompasses a broad variety of large-scale sequence alterations”

      Line 51-52: There are a couple of really nice papers that could be cited here from Anna Selmecki's group (Todd et al. 2020, Todd and Selmecki 2019, both in eLife).

      We thank the reviewer for the suggestions, we included some of these references in the manuscript.

      Figure 1: This is a nice cartoon! I'd suggest spelling out LOH here for a truly naive reader.

      We modified the Figure 1 accordingly.

      Figure 3A: One thing that is slightly lost here in the presentation is the relative frequency of the different events because of the changing scales across 3A. I can see why you want to do it this way, but would consider whether there may be a way to present this that makes it more obvious how much more frequent polyploidy is than excision for example.

      We agree with the reviewer that the focus of this visualization is to compare crosses and individual MA lines within SV types, and fails to display the relative importance of each SV type. We solved this by including an additional panel (new 3A) that shows how the number of Ty loci affected by each SV type scales in comparison to others.

      Figure 5: I'm not a fan of the gray bars highlighting the individual strains. This made the graph less intuitively readable for me.

      We tend to agree with the reviewer and rolled back to a previous version of Figure 5 that was lighter on annotations.

      One thing I would like to see in the future from this data (definitely not in this paper) is genome rearrangements within these hybrid MA lines. How often are there structural changes and how often are those changes mediated by repeats including TEs?

      We completely agree with the reviewer that this would be a very interesting avenue, with a distinct (and likely higher) set of challenges at the analysis level compared to simply focusing on TE sequences like we did here. We hope to be able to tackle this goal in the future of this project.

      Reviewer #3 (Recommendations For The Authors):

      • I'm not from the yeast field. But why this focus on the Ty-load? Are Ty's the only active TEs in yeast? Provide some background on the TE landscape in yeast and a justification for focusing on Ty's.

      We agree with the reviewer that this point was only implicit in the introduction. We modified the introductory segment on Saccharomyces yeasts to mention that Ty retrotransposons are the only TEs found in these genomes, thus explaining the exclusive focus on them. It now reads as follows:

      “In the case of Saccharomyces cerevisiae, the only TEs found are five families of long terminal repeat (LTR) retrotransposons families named Ty1-Ty5 (Kim et al., 1998).”

      • 56 I would argue that Petrov et al 2003 is not the best citation for arguing that TEs can lead to genomic rearrangement through ectopic recombination. Petrov solely showed that some long TE families are at lower population frequency than short TE families ones. This could be due to many reasons (e.g. recent activity of long TEs - mostly LTRs) but Petrov interpreted the data as being due to ectopic recombination. Petrov, therefore, did not demonstrate any direct evidence for the involvement of ectopic recombination.

      We agree with the reviewer that this reference is not the best choice to simply support the role of TEs in generating ectopic recombination events and modified the references accordingly.

      • For the assembly the authors used two steps 1) separate the reads based on similarity to a subgenome 2) and assembly the reads from the resulting two sets separately. This is probably the only viable approach, but I'm wondering if this step can lead to some biases (many reads may not be assigned to one sub-genome or assigned to the wrong sub-genome). An alternative, possibly less biased approach, would be to use one of the emerging assemblers that promise to assemble sub-genomes. Maybe discuss why this approach was not pursued.

      We completely agree that our method has some level of bias. We adopted it because it seemed the most appropriate to answer our question, which required to resolve individual TE insertions at the level of single haplotype sequences. One specific challenge of this dataset is that we have a relatively wide range of nucleotide divergence between parental subgenomes in the different MA crosses, from <1% to ~15%. The efficiency of haplotype separation from tools that are not necessarily designed to be tunable with respect to the level of nucleotide divergence seemed uncertain, which is why we opted for a custom methodology. Although read non-classification remains a problem that is hard to solve (and would remain so using orthogonal strategies), we believe that read misclassification is minimized by our stringent criteria for read classification. The goal of this study was not to develop a tool nor to benchmark our approach against existing diploid assembly tools. It yielded phased genome representations that were of sufficient completeness and contiguity to confidently answer our questions, and we believe that pushing the discussion towards technical considerations would fall outside of our main objective.

      • The authors used a decision tree to classify Ty loci. What were the training data? How were the trees validated? Decision tree is a technical term for a classifier in machine learning. I do not think the authors used machine learning in this work, but rather an "an ad-hoc set of rules". The term decision tree in this study is misleading.

      We believe that the term “decision tree” can simply refer to a hierarchy of conditional rules implemented as a classification algorithm. As the reviewer pointed, it is clear from the manuscript that none of the analyses performed include any form of training or fitting of a machine learning classifier. However, we agree that its specific reference to the machine learning classifier can create unnecessary confusion. We thus agree to remove this term from the manuscript and replaced all its instances by “a hierarchy of binary rules”.

      • 272: as it is the CNC explanation does not make a lot of sense to me; some information is missing, is p22 expression increasing with copy numbers?

      Yes, p22 expression correlates positively with the CN of p22-expressing Ty1 elements.

      Why are the two alternative downstream codons important?

      We thought it would be useful to mention the two start codons at this point because later in the discussion, we bring the conservation of the first start codon as an observation consistent with the putative expression of p22 in S. paradoxus. We also thought that it helped clarify the mechanism by which the N-truncated version of the protein is expressed.

      p22 interferes with assembly viral particles when in high copy numbers, but what happens when at low copy numbers, is it essential for retroviral activity? Is it even necessary for the virus or just some garbage product (they mention N-truncated).

      To our knowledge, these questions regarding the potential molecular functions of p22 outside of a retrotransposition restriction factor are still open. We added details to the background on CNC in the Introduction and Results section to help clarify some the points raised:

      Introduction:

      “The best known regulation mechanism in yeast is termed copy number control (CNC) and was characterized in the Ty1 family of S. cerevisiae. This mechanism is a potent copy-number dependent negative feedback loop by which increasing the CN of Ty1 elements strengthens their repression (Czaja et al., 2020; Garfinkel et al., 2003; Saha et al., 2015).”

      Results:

      “The mechanism of negative copy-number dependent self-regulation of retrotransposition (CNC) was characterized in the Ty1 family of S. cerevisiae (Garfinkel et al., 2016). This mechanism relies on the expression of an N-truncated variant of the Ty1 capsid/nucleocapsid Gag protein (p22) from two downstream alternative start codons (Nishida et al., 2015; Saha et al., 2015). p22 expression scales up with the CN of Ty1 elements that encode it (Tucker et al., 2015), which gradually interferes with the assembly of the viral-like particles essential for Ty1 replication (Cottee et al., 2021; Saha et al., 2015). Thus, CNC yields a steep negative relationship between the retrotransposition rate measured with a tester element and the number of Ty1 copies in the genome (Garfinkel et al., 2003; Tucker et al., 2015).”

      • mtDNA influences transposition, is anything known about the mechanism?

      When presenting this result, we make it clear that this finding is not new and was previously observed in S. cerevisiae x S. uvarum hybrids by Smukowski-Heil et al. (2021). In this reference, the authors discuss multiple mechanisms by which mitochondrial biology and mito-nuclear interplay may affect transposition rate, although their data cannot support one specific hypothesis. Our data does not to allow to further dissect the mechanistic basis of the mtDNA effect, not more than the effect of distinct Ty1 natural variants. Since we simply provide new independent evidence for the mtDNA effect, it seems to us that repeating the discussion on putative mechanisms while bringing no support to any given hypothesis would be of limited relevance.

      • During the first reading, I got quite confused about what CN means (copy number as it turned out). I suggest using abbreviations only if absolutely necessary, and I'm not entirely convinced it is necessary here. But I leave this to the discretion of the authors.

      We agree that the excessive use of abbreviations in manuscripts is annoying. However, in this case, “copy number” is used so extensively that its abbreviation seemed to improve the reading experience. Thus, we would prefer to keep it unchanged.

      • Fig 3D: Wilcoxon Rank sum test. It is not clear to me what was tested here? Which data were used?

      We confirm that the statistical test employed is the Wilcoxon signed-rank test, and not the Wilcoxon rank-sum test (also known as Mann-Whitney U-test). The Wilcoxon signed-rank test is used here as a non-parametric one-sample test against the null hypothesis that the distribution is centered around zero.

      • de novo -> italics

      We choose to follow the recommendation of the general style conventions of the ACS guide for scholarly communications not to italicize common Latin terms like “de novo”, “e.g.” and “i.e.”.

    1. Instead of seeking discrete answers to complex problems, experts un-derstand that a given issue may be characterized by several compet-ing perspectives as part of an ongoing conversation in which infor-mation users and creators come together and negotiate meaning.

      This part of the assignment confused me, but now I think I understand better. I realized that sometimes we don't have all the answers, and sometimes the "answers" are just a collaboration of opinions and it is up to you to decide.All the sources I am finding are not direct answers to my question, but they do relate and spark my thoughts and more questions.

    1. Author Response

      The following is the authors’ response to the original reviews.

      The reviewers make some suggestions aimed towards increasing the clarity of the manuscript, and I suggest that the authors examine those carefully. In particular, the figure is difficult to read and could contain additional information to help the reader's interpretation. For example, Reviewer 1 suggests including sample age estimates alongside depth, while Reviewer 3 also notes that there is missing information in the figure. Apart from the figure, Reviewer 1 suggests two additional analysis to help explain the amount of mammoth DNA recovered, which they observe is much higher than previous similar investigations. This would seem to be an important issue to address, given the surprising nature of the findings. In addition to this larger issue, the Reviewer makes a few important suggestions for supplementary material that may be needed to support the authors' statements.

      Some additional recommended edits -- in particular to the text and included references to related studies -- are suggested by Reviewers 2 and 3, and both commented on the lack of a publicly-available data repository. The authors may also wish to comment on or revisit their differential treatment of wooly mammoth vs. wooly rhinoceros samples, though I suspect this has more to do with low read numbers for the rhinos.

      Thank you very much for the positive assessment of our manuscript and clear suggestions for revision. We address these points below.

      Reviewer #1 (Recommendations For The Authors):

      I have a few suggestions that might further improve the manuscript:

      It is difficult for the reader to follow which core slices exactly have been sampled and sequenced. The authors mention 23 samples were taken from core LK-001 and 16 samples from core LK-007. From the text it remains unclear to me what the exact age of each of these samples is. Figure 1 shows the depth at which the LK-001 core was sampled, maybe sample age estimates could be included here.

      Thanks for pointing this out. We have added approximate ages to Figure 1, added the depth range to the text (“from 1.5 to 80 cm”; l. 73-74, caption Figure 1), and reworked the table of the sampling depths in the supplement.

      Line 84-87. The authors mention the retrieval of DNA from several expected Arctic taxa, however no further data regarding these findings is given in the manuscript. It would be useful to report the same numbers for these species as the ones given for the Mammuthus and woolly rhinoceros, which would allow for a comparison of the relative abundance of the DNA between these species. Are the expected Arctic species for instance at much higher (DNA) abundance in the samples? It would also be interesting to know if the authors discovered DNA from extant species that are unlikely to have occurred in the geographic region. A (supplementary)table listing the number of mapped reads to each of the respective mitogenomes for each sequence library would be useful for the reader.

      We added a supplementary table (S8) indicating the numbers of reads assigned to mammals.

      Line 90: I am somewhat amazed by the amount of mammoth DNA the authors recovered from these cores. A total depth of over 400X of the mitogenome is quite extraordinary and I am not aware of any ancient sediment study to date that has retrieved a similar amount of data. For instance, the Wang et al. 2021 paper, which the authors cite, sequenced over 400 samples and did not find any mammoth DNA in 70% of those. For the 30% of samples showing signs of mammoth DNA they retrieved on average 530 sequence reads. In this study the authors find on average ~20.000 reads, in 22 out of the 23 sequence libraries. This makes me wonder if the way the mapping was performed has been too lenient, resulting in possible spurious mappings? To really confirm the authenticity of the mammoth (and woolly rhino data) I would suggest two additional analysis:

      1) Mapping all the sequence libraries to a reference consisting of the complete Asian-elephant genome (for instance https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_024166365.1/), the complete human genome (+mitogenome) and the Asian elephant mitogenome. This could possibly reduce spurious mappings as conserved regions between the genomes are filtered out and could also reduce the possible mapping of NUMTS. If the authors could show that after such a mapping approach a significant number of reads are still assigned to the Asian elephant part (including the mitogenome) of the reference, the reported findings would be strengthened.

      2) I also suggest to construct a mitochondrial haplotype network from the obtained DNA, while also including previously published Asian and African elephants as well as previously published mammoth mitogenomes. If the obtained haplotypes indeed show that they cluster within the known haplotype diversity of mammoth, that would be strong support for the authenticity of the data

      The same analysis could be considered for the woolly rhino data, although the lower read numbers might make this analysis challenging.

      We agree that the amount of mammoth DNA is surprising, which is why we opted for further laboratory experiments for confirmation of the hybridization capture results of the first core, i.e., 1) DNA extraction from a second core of a different lake, 2) a quantitative PCR approach (ddPCR), and 3) metabarcoding. Our results of the highly specific ddPCR and metabarcoding assays confirmed considerable amounts of mammoth DNA in two sediment cores of different lakes, thus we have no doubts regarding the authenticity of the data. Considering the large amount of mammoth DNA, the high number of reads, and particularly the high mitogenome coverage, we argue that the effect of some spurious mapping is negligible and does not affect the main outcome and conclusions of our study. Although we agree that a haplotype network would be interesting, such analyses would stretch beyond the focus of this publication.

      Line 91: The authors mention negative controls (extraction and library blanks) did not produce any reads assigned to mammals. This is quite remarkable, as in my experience low levels of (human)contamination are almost always present in the blanks. Could the authors comment on why they think the blanks did not show any signal of mammalian DNA?

      The hybridization capture enrichment and the filtration and mapping procedures likely eliminated human contamination. Also, the data were mapped against Arctic mammal mitogenomes, which did not include human reference sequences. However, six of the sediment samples contained human sequences (now shown in supplementary table S8), albeit at low read counts (mean = 65)

      Line 97: "mapping suggested that the sequences throughout the core originated from multiple individuals" The authors do not provide any supporting data showing this. I think that an analysis (for instance based on allele frequencies) has to be included in manuscript to support this claim.

      We agree that his claim was not sufficiently supported. We performed further analyses including genomic data of previously retrieved mammoth remains and assigned our data to these haplogroups; the results were added to the main text and are shown as a figure (Fig. 2).

      Line 98: "Signatures of post-mortem DNA decay were comparably minor."

      Do the authors know if the used hybridisation enrichment method can distort the measurement of post-mortem damage? Are for instance reads with C-T substitutions less likely to be captured by the baits?

      To our knowledge, there is no study suggesting that damaged sites are less likely to be captured. In general, the hybridization capture procedure is not overly specific, and studies report that DNA is readily and preferentially captured as long as the difference between baits and DNA is not above 10%.

      Line 100: "The proportions of bases did not suggest a substantial deviation from those in the reference genomes or in the closest extant relative of Mammuthus, the Asian elephant (Elephas maximus)."

      It is not clear to me what the authors mean by this. Could the authors explain how this was measured and what their interpretation of this result is?

      We realize that the sentence was unclear. We meant that the nucleotide composition was similar to that of the reference genomes or the closest extant relative. However, as we do not consider this important for the argument, we have removed this sentence from the manuscript.

      Given the high number of recovered mammoth reads in the samples, it would be interesting to know how much mammoth reads are present in the sample before enrichment capture with the baits. Shotgun sequencing the raw extract of one of the samples with the highest number of mammoth reads might allow for a rough estimate of mammoth DNA abundance compared to the other extant species (e.g. reindeer, Arctic lemming and hare) found in the sample(s). This could give further clarification about the extent of stratigraphy disturbance and its overall effect on the DNA based community reconstruction. However, this is just a suggested additional analysis and not something I believe crucial for supporting the overall findings in this manuscript.

      We fully agree that this would be a highly interesting and informative additional analysis to perform. It was, however, not possible to perform this additional analyses in the course of the current experiments.

      Finally, I could not find a public link to the (sequence)data produced in this study. I strongly encourage the authors to make their data publicly available.

      Thank you for pointing this out. We have added a Data Availability paragraph, including the respective reference.

      Reviewer #2 (Recommendations For The Authors):

      In the Discussion it is mentioned that the reasons for Mammoth extinction are not entirely clear but are largely attributed to sudden climate warming (and add some relevant citations). However, there is also abundant literature that suggest humans also played a role in their extinction (for instance, a recent one, Damien et al. (2022) at Ecology Letters 25: 127-137).

      We agree with the reviewer and have added some the recent citation highlighting the possible influence of humans.

      One possibility to add further interest to this paper would be to conduct a phylogenetic tree with the Mammoth mitogenome(s) retrieved and a reference dataset; it could be interesting to know where do they fall in the phylogeny -already abundant with tens of individuals- and maybe it could be even possible to roughly estimate their date. There are some papers that report many Mammoth mitogenomes, including of course some from Siberia; for instance Chang et al. (2017) at Sci Reports and also Fellow Yates et al. (2017) also at Sci Reports (the latter mainly from Central Europe).

      We are well aware of the amount of mt genomes available for mammoth, and such an analyses would be an interesting addition, potentially also offering the possibility to date the DNA. However, the analyses was hampered and would be less secure for this dataset, as our sequences display quite some variation among each other, suggesting that we have a mix of multiple mt genomes, which we cannot readily distinguish. We thus refrain from this, also because we instead provide multiple lines of evidence for the existence of the mammoth DNA in the surface sediment core (metabarcoding, ddPCR).

      Minor points:

      -Correct wooly to woolly

      Revised.

      -In the sampling description it is not totally clear if the samples were taken at 1 cm each (it is mentioned that core LK-001 is sliced in the field at 1-cm steps for radiometric dating and later it is explained that 23 samples were analyzed from this core, but it is unclear if they represent 23 cm of core)

      -Maybe the authors could briefly define some terms such as "talik"

      Revised.

      Reviewer #3 (Recommendations For The Authors):

      Maybe I missed this but I could not find a data availability statement or the location of the repository

      We have added a Data Availability paragraph, including the respective reference.

      It would be good to see some additional analysis on the distribution of the woolly rhinoceros DNA through the sediment core - like the figure for the mammoth i.e read numbers vs depth.

      We have added to the supplements a table showing the numbers of assigned mammal reads over the core depths (Table S8). However, as rhinoceros reads are considerable rarer in our results, we did not produce a figure.

      Would it be possible to be more explicit about the multiple mammoth individuals, could you calculate a minimum number or haplotypes for example.

      We agree that his claim was not sufficiently supported and added results from additional analyses (incl. Fig. 2). Please see our response above.

      Based on the aim stated in the introduction, the analysis of the Arctic biodiversity of this area is missing, it would be nice to see these result added or maybe the focus needs to be changed for clarity.

      We now explicitly state that this objective pertains to a different study, which is currently still in preparation for publication.

      The single main figure needs a bit more consideration. For example in panel A - there was no information on the transformation performed or what the general trend line refers to. Do the results in panel B refer to all 22 libraries? What is the x-axis in Panel C and what do the coloured lines refer to? Additionally, I think the figure needs to be in higher resolution with increased text size on all axes.

      We revised the figure and the caption for clarity and readability.

      Finally this might be an accidental typo - but when referring to the sample aged at around 8,677 years in text it states this the 36.5 cm sample (line 130 and 192), but the supplementary says this is the 51cm sample (Table S6). This would maybe impact potential conclusions. Would you be able to clarify this.

      Thank you for noting this error, we revised it.

    1. Author Response

      Reviewer #1 (Public Review):

      Summary:

      The manuscript by Dubicka and co-workers on calcification in miliolid foraminifera presents an interesting piece of work. The study uses confocal and electron microscopy to show that the traditional picture of calcification in porcelaneous foraminifera is incorrect.

      Strengths:

      The authors present high-quality images and an original approach to a relatively solid (so I thought) model of calcification.

      Weaknesses:

      There are several major shortcomings. Despite the interesting subject and the wonderful images, the conclusions of this manuscript are simply not supported at all by the results. The fluorescent images may not have any relation to the process of calcification and should therefore not be part of this manuscript. The SEM images, however, do point to an outdated idea of miliolid calcification. I think the manuscript would be much stronger with the focus on the SEM images and with the speculation of the physiological processes greatly reduced.

      Reply: We would like to give thanks for all of the highly valuable comments. Prior to our study, we were also convinced that the calcification model of Miliolid (porcelaneous) foraminifera was relatively solid. Nevertheless, our SEM imaging results surprisingly contradicted the old model. The main difference is the in situ biomineralization of calcitic needles that precipitate within the chamber wall after deposition of ACC-bearing vesicles. We agree that our fluorescence studies presented in the paper are not conclusive evidence for the calcification model used by the studied Miliolid species. However, our fluorescent results show that “the old model” (sensu Hemleben et al., 1986) is not completely outdated. Most of the fluorescent imaging data show a vesicular transport of substrates necessary for calcification. This transport is presented by Calcein labelling experiments (Movie 1 that show a high number of dynamic endocytic vesicles of sea water circulation within the cytoplasm. These very fine Calcein-labelled vesicles are most likely responsible for transport and deposition of Ca2+ ions. This is partly consistent with the model presented by Hemleben et al. (1986). We may speculate that calcite nucleation is already occurring within the transported vesicles, but at this stage of research we have no evidence for this phenomenon.

      Further live imaging fluorescence data show autofluorescence of vesicles upon excitation at 405 nm (emission 420–480 nm) associated with acidic vesicles marked by pH-sensitive LysoGlow84, may be a hint indicating association of ACC-bearing vesicles with acidic vesicles. Such spatial association of these vesicles may indicate a mechanism of pH elevation in the vesicles transporting Ca2+-rich gel to the calcifying wall of the new chamber.

      We will do our best to limit the physiological interpretation presented based on fluorescence studies in the revised version of the manuscript. We are convinced that our fluorescent live imaging experiments provide important observations in biomineralizing Miliolid foraminifera, which are still missing in the existing literature. It should be stressed that all the fluorescent experiments and SEM observations were based on specimens constructing and biomineralizing new chambers. All of them belong to the same species and come from the same culture. Due to the aforementioned reasons, it is worthwhile presenting these complimentary results of our study. In the future they may be helpful in further exploration and understanding of all aspects of calcification in foraminifera.

      Reviewer #2 (Public Review):

      Summary:

      Dubicka et al. in their paper entitled " Biocalcification in porcelaneous foraminifera" suggest that in contrast to the traditionally claimed two different modes of test calcification by rotallid and porcelaneous miliolid formaminifera, both groups produce calcareous tests via the intravesicular mineral precursors (Mg-rich amorphous calcium carbonate). These precursors are proposed to be supplied by endocytosed seawater and deposited in situ as mesocrystals formed at the site of new wall formation within the organic matrix. The authors did not observe the calcification of the needles within the transported vesicles, which challenges the previous model of miliolid mineralization. Although the authors argue that these two groups of foraminifera utilize the same calcification mechanism, they also suggest that these calcification pathways evolved independently in the Paleozoic.

      Reply: We would like to acknowledge the review and all valuable comments. We do not argue that Miliolida and Rotallida utilise an identical calcification mechanism, but both groups utilize less divergent crystallization pathways, where mesocrystalline chamber walls are created by accumulating and assembling particles of pre-formed liquid amorphous mineral phase.

      Strengths:

      The authors document various unknown aspects of calcification of Pseudolachlanella eburnea and elucidate some poorly explained phenomena (e.g., translucent properties of the freshly formed test) however there are several problematic observations/interpretations which in my opinion should be carefully addressed.

      Weaknesses:

      1) The authors (line 122) suggest that "characteristic autofluorescence indicates the carbonate content of the vesicles (Fig. S2), which are considered to be Mg-ACCs (amorphous MgCaCO3) (Fig. 2, Movies S4 and S5)". Figure S2 which the authors refer to shows only broken sections of organic sheath at different stages of mineralization. Movie S4 shows that only in a few regions some vesicles exhibit red autofluorescence interpreted as Mg-ACC (S5 is missing but probably the authors were referring to S3). In their previous paper (Dubicka et al 2023: Heliyon), the authors used exactly the same methodology to suggest that these are intracellularly formed Mg-rich amorphous calcium carbonate particles that transform into a stable mineral phase in rotaliid Aphistegina lessonii. However, in Figure 1D (Dubicka et al 2023) the apparently carbonate-loaded vesicles show the same red autofluorescence as the test, whereas in their current paper, no evidence of autofluorescence of Mg-ACC grains accumulated within the "gel-like" organic matrix is given. The S3 and S4 movies show circulation of various fluorescing components, but no initial phase of test formation is observable (numerous mineral grains embedded within the organic matrix - Figures 3A and B - should be clearly observed also as autofluorescence of the whole layer). Thus the crucial argument supporting the calcification model (Figure 5) is missing. There is no support for the following interpretation (lines 199-203) "The existence of intracellular, vesicular intermediate amorphous phase (Mg-ACC pools), which supply successive doses of carbonate material to shell production, was supported by autofluorescence (excitation at 405 nm; Fig. 2; Movies S3 and S4; see Dubicka et al., 2023) and a high content of Ca and Mg quantified from the area of cytoplasm by SEM-EDS analysis (Fig. S6)."

      Reply: We used laser line 405nm and multiphoton excitation to detect ACCs. These wavelengths (partly) permeate the shell to excite ACCs autofluorescence. The autofluorescence of the shells is present as well, but it is not clearly visible in movieS4 as the fluorescence of ACCs is stronger. This may be related to the plane/section of the cell which is shown. The laser permeates the shell above the ACCs (short distance), but to excite the shell CaCO3 around foraminifera in the same three-dimensional section where ACCs are shown, the light must pass a thick CaCO3 area due to the three-dimensional structure of the foraminifera shell. Therefore, the laser light intensity is reduced. In a revised version a movie/image with reduced threshold will be shown.

      2) The authors suggest that "no organic matter was detected between the needles of the porcelain structures (Figures 3E; 3E; S4C, and S5A)". Such a suggestion, which is highly unusual considering that biogenic minerals almost by definition contain various organic components, was made based only on FE-SEM observation. The authors should either provide clearcut evidence of the lack of organic matter (unlikely) or may suggest that intense calcium carbonate precipitation within organic matrix gel ultimately results in a decrease of the amount of the organic phase (but not its complete elimination), alike the pure calcium carbonate crystals are separated from the remaining liquid with impurities ("mother liquor"). On the other hand, if (249-250) "organic matrix involved in the biomineralization of foraminiferal shells may contain collagen-like networks", such "laminar" organization of the organic matrix may partly explain the arrangement of carbonate fibers parallel to the surface as observed in Fig. 3E1.

      Reply: We agree with the reviewer that biogenic minerals should, by definition, contain some organic components. We wrote that "no organic matter was detected between the needles of the porcelain structures” as we did not detect any organic structures based only on our FE-SEM observations. We are convinced that the shell incorporates a limited amount of organic matrix. We will rephrase this part of the text to avoid further confusion.

      3) The author's observations indeed do not show the formation of individual skeletal crystallites within intracellular vesicles, however, do not explain either what is the structure of individual skeletal crystallites and how they are formed. Especially, what are the structures observed in polarized light (and interpreted as calcite crystallites) by De Nooijer et al. 2009? The author's explanation of the process (lines 213-216) is not particularly convincing "we suspect that the OM was removed from the test wall and recycled by the cell itself".

      Reply: Thank you for this comment. We will do our best to supplement our explanations. We are aware of the structures observed in polarized light by De Nooijer et al. (2009). However, Goleń et al. (2022, Protist, https://doi.org/10.1016/j.protis.2022.125886) showed that organic polymers may also exhibit light polarization. Additional experimental studies are needed to distinguish these types of polarization. We will aim to investigate this issue in our future research.

      4) The following passage (lines 296-304) which deals with the concept of mesocrystals is not supported by the authors' methodology or observations. The authors state that miliolid needles "assembled with calcite nanoparticles, are unique examples of biogenic mesocrystals (see Cölfen and Antonietti, 2005), forming distinct geometric shapes limited by planar crystalline faces" (later in the same passage the authors say that "mesocrystals are common biogenic components in the skeletons of marine organisms" (are they thus unique or are they common)? It is my suggestion to completely eliminate this concept here until various crystallographic details of the miliolid test formation are well documented.

      Reply: Our intention was to express that mesocrystals are common biogenic components in the skeletons of marine organisms, however Miliolid needles that form distinct geometric shapes limited by planar crystalline faces are unique type of mesocrystals.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Summary:

      In this interesting work, the authors investigated an important topical question: when we see travelling waves in cortical activity, is this due to true wave-like spread, or due to sequentially activated sources? In simulations, it is shown that sequential brain module activation can show up as a travelling wave - even in improved methods such as phase delay maps - and a variety of parameters is investigated. Then, in ex-vivo turtle eye-brain preparations, the authors show that visual cortex waves observable in local field potentials are in fact often better explained as areas D1 and D2 being sequentially activated. This has implications for how we think about travelling wave methodology and relevant analytical tools.

      Strengths:

      I enjoyed reading the discussion. The authors are careful in their claims, and point out that some phenomena may still indeed be genuine travelling waves, but we should have a higher evidence bar to claim this for a particular process in light of this paper and Zhigalov & Jensen (2023) (ref 44). Given this careful discussion, the claims made are well-supported by the experimental results. The discussion also gives a nice overview of potential options in light of this and future directions.

      The illustration of different gaussian covariances leading to very different latency maps was interesting to see.

      Furthermore, the methods are detailed and clearly structured and the Supplementary Figures, particularly single trial results, are useful and convincing.

      We are glad the reviewer found our manuscript “interesting”, the questions we raise “important”, our claims “well-supported by the experimental results”, and our methods “detailed and clearly structured”.

      The details of the sequentially activated Gaussian simulations give some useful results, but the fundamental idea still appears to be "sequential activation is often indistinguishable from a travelling wave", an idea advanced e.g. by Zhigalov & Jensen (2023). It takes a while until the (in my opinion) more intriguing experimental results.

      To emphasize the experimental results, we switched between the analytical results and the experimental results. Correspondingly, figure 2 now illustrates the more intriguing experimental results and figure 3 the analytical results. In addition, we added subtitles to the different sections of the results to ease the navigation through the paper and to enable the readers to access the different sections more easily.

      One of the key claims is that the spikes are more consistent with two sequentially activated modules rather than a continuous wave (with Fig 3k and 3l key to support this). Whilst this is more consistent, it is worth mentioning that there seems to be stochasticity to this and between-trial variability, especially for spikes.

      In the revised manuscript we added the reviewer’s comment about stochasticity, and we discuss its possible origins:

      "The transition was also not clear when examining spiking responses in some of the trials (as indicated by high DIP scores, Figure 2K). However, the observation that temporal grouping became more pronounced when using ALSA (a more robust estimate of local excitability) (Figure 2L,N), suggests that high DIP values may result from variability in the spike times of single neurons, and not necessarily from the lack of modular activation. Such issues can be resolved by denser sampling of spiking activity in the tissue."

      Recommendations For The Authors:

      The eye-cortex turtle preparation is not the most common. I would add more context about how specific the results are to this preparation vs how comparable it is to human data.

      We added a sentence explaining the relevance of our preparation: “Finally, while the layered organization of turtle cortex is different than that of mammalian cortex, the basic excitability features of both tissues are similar (Connors and Kriegstein, 1986; Hemberger et al., 2019; Kriegstein and Connors, 1986; Larkum et al., 2008; Shein-Idelson et al., 2017b), and substantial differences in the manner by which field potentials and spikes spread through the tissue are not to be expected.”

      Philosophical question: when does a 'module' become small enough for it to count as a travelling wave? More on this could be added to the discussion. I think we are in the very early days for a true understanding of travelling waves, and I wonder if these sequentially activated modules will functionally correspond to the known cortical segregation, or if it varies by area/task.

      We agree with the reviewer that macroscopic waves could be composed of smaller modules (or single neurons at the smallest scale). Our results suggest that modular patterns can be classified as wave patterns both at large scales (of brain areas) and smaller scales of local neural circuits. Therefore, we believe it is necessary to make this distinction across different scales. We sharpened this point in the first paragraph of the discussion:

      "…We showed that LFP measurements indicative of waves propagating across turtle cortex are underlined by discrete and consecutively activated neuronal populations, and not by a continuously propagating wavefront of spikes (Figure 2). Similarly, activation profiles that resemble continuous travelling waves in EEG simulations can be underlined by consecutive activation of two discrete cortical regions (Figure 1). We replicated these results using an analytical model and demonstrated that a simple scenario of sequentially activated Gaussians can exhibit WLPs with a rich diversity of spatiotemporal profiles (Figure 3). Our results offer insight into the scenarios and conditions for WLP detection by identifying failure points that should be considered when identifying travelling waves and therefore suggest caution when interpreting continuous phase latency maps as microscopically propagating wave patterns. Such failure points may exist both when examining activity at the scale of brain regions (Figure 1) and smaller neural circuits (Figure 2). Therefore, our results suggest that the discrepancy between modular and wave activation should be examined across spatial scales. Specifically, it is not necessarily the case that at the fine grained (single neuron) scale activation patterns are modular, but, following coarse graining, smooth wave patterns emerge. Rather, modular activation may hierarchically exist across scales (Kaiser and Hilgetag, 2010; Meunier et al., 2010) and may be masked by smeared spatial supra-threshold excitability boundaries. Below we discuss these limitations across techniques and their implications.”

      I would advise the authors to focus on the experimental data, perhaps by putting the simulations second, and by putting some of the equation details that are in Methods into the Supplementary Information. Whilst the simulation parameter space is well-explored, the fundamental idea of spreading Gaussians is relatively simple, and the current manuscript organization detracted from the main message for me a little bit.”

      Following the referee’s suggestion, we switched between the section with experimental data and the one with the analytic model (see response to comment 1). In addition, to ease the reading of the methods, we moved the mathematical derivation and related equations to appendix 1.

      Things I thought about that you may also enjoy thinking about: Could we tell something about sequential sources vs travelling waves by the nature of the wave - e.g. shape or dispersion? If some wave properties are conserved whilst travelling, this could be evidence for travelling vs two sources.

      This is a wonderful suggestion. We are currently working on a follow up publication with a new approach to do exactly that! We think that this new body of work is outside the scope of this paper.

      Could synaptic potentials spread like waves, but spikes more in modular bursts? This would also explain the LFP vs spikes difference - maybe travelling waves of EPSPs are there priming the network, 'looking' for suitable modules to activate, which then activate sequentially. The current discussion is quite spike-focused - could some information be in synaptic potentials after all?

      This is an interesting idea with intriguing functional implications. We added this idea to our discussion (see paragraph below). In addition, to emphasize our discussion on synaptic potentials, we reorganized the paragraphs in the discussion to separate between our discussion on sub-threshold excitability (which is mostly synaptic) and supra-threshold excitability which is the focus of the second part of the discussion.

      “Variability in responses may also be explained by differences in propagation mechanisms (Ermentrout and Kleinfeld, 2001; Muller et al., 2018; Wu et al., 2008). Several reports suggest that waves are underlined by propagation along axonal collaterals (Muller et al., 2018, 2014). Both the transmembrane voltage-gated currents excited during action potentials as well as the post-synaptic currents along axonal boutons can potentially contribute to measured signals. However, such waves travel at high propagation speeds and are not compatible with the wide diversity of wave velocities and mechanisms of local neuronal interactions (Ermentrout and Kleinfeld, 2001; Feller et al., 1996). An intriguing possibility is that such axonal waves prime neuronal excitability by sub-threshold inputs that later result in modular supra-threshold activation. The ability to experimentally discriminate between axonal inputs and local spiking excitability (e.g. by reporters with different wavelengths) can potentially resolve such discrepancies.

      Our turtle cortex results (Figure 2) exemplify how contrasting sub-threshold LFP measurements with supra-threshold spiking measurements can yield different conclusions about the nature of activity spread….”

    2. Joint Public Review:

      Summary:

      In this interesting work, the authors investigated an important topical question: when we see travelling waves in cortical activity, is this due to true wave-like spread, or due to sequentially activated sources? In simulations, it is shown that sequential brain module activation can show up as a travelling wave - even in improved methods such as phase delay maps - and a variety of parameters is investigated. Then, in ex-vivo turtle eye-brain preparations, the authors show that visual cortex waves observable in local field potentials are in fact often better explained as areas D1 and D2 being sequentially activated. This has implications for how we think about travelling wave methodology and relevant analytical tools.

      Strengths:

      I enjoyed reading the discussion. The authors are careful in their claims, and point out that some phenomena may still indeed be genuine travelling waves, but we should have a higher evidence bar to claim this for a particular process in light of this paper and Zhigalov & Jensen (2023) (ref 44). Given this careful discussion, the claims made are well-supported by the experimental results. The discussion also gives a nice overview of potential options in light of this and future directions.

      The illustration of different gaussian covariances leading to very different latency maps was interesting to see.

      Furthermore, the methods are detailed and clearly structured and the Supplementary Figures, particularly single trial results, are useful and convincing.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Joint Public Review:

      […] While this does not rule out criticality in the brain, it decidedly weakens the evidence for it, which was based on the following logic: critical systems give rise to power law behavior; power law behavior is observed in cortical networks; therefore, cortical networks operate near a critical point. Given, as shown in this paper, that power laws can arise from noncritical processes, the logic breaks. Moreover, the authors show that criticality does not imply optimal information transmission (one of its proposed functions). This highlights the necessity for more rigorous analyses to affirm criticality in the brain. In particular, it suggests that attention should be focused on the question "does the brain implement a dynamical latent variable model?".

      These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in the critical state.

      We thank the reviewers for their thoughtful assessment of our paper.

      We would push back on the assessment that our model ‘has nothing to do with criticality,’ and that we observed ‘signatures of criticality [that] emerge through fundamentally non-critical mechanisms.’ This assessment partially stems from the definition of criticality provided in the Public Comment, that ‘criticality is a very specific set of phenomena in physics in which fundamentally local interactions produce unexpected long-range behavior.’

      Our disagreement is largely focused on this definition, which we do not think is a standard definition. Taking the favorite textbook example, the Ising model, criticality is characterized by a set of power-law divergences in thermodynamic quantities (e.g., susceptibility, specific heat, magnetization) at the critical temperature, with exponents of these power laws governed by scaling laws. It is not defined by local interactions. All-to-all Ising model is generally viewed as showing a critical behavior at a certain temperature, even though interactions there are manifestly non-local. It is possible that, by “local” in the definition, the Public Comment meant that interactions are “collective” and among microscopic degrees of freedom. However, that same all-to-all Ising model is mathematically equivalent to the mean-field model, where criticality is achieved through large fluctuations of the mean field, but not through microscopic interactions.

      More commonly, criticality is defined by power laws and scaling relationships that emerge at a critical value of a parameter(s) of the system. That is, criticality is defined by its signatures. What is crucial in all such definitions is that this atypical, critical state requires fine tuning. For example, in the textbook example of the Ising model, a parameter (the temperature) must be tuned to a critical value for critical behavior to appear. In the branching process model that generates avalanche criticality, criticality requires tuning m=1. The key result of our paper is that all signatures expected for avalanche criticality (power laws, crackling, and, as shown below, estimates of the branching rate m), and hence the criticality itself, appear without fine-tuning.

      As we discussed in our introduction, there are a few other instances of signatures of criticality (and hence of criticality itself) emerging without fine-tuning. The first we are aware of was the demonstration of Zipf’s Law (by Schwab, et al. 2014, and Aitchison et al. 2016), a power-law relationship between rank and frequency of states, which was shown to emerge generically in systems driven by a broadly distributed latent variable. A second example, arising from applications of coarse-graining analysis to neural data (cf., Meshulam et al. 2019; also, Morales et al., 2023), was demonstrated in our earlier paper (Morrell et al. 2021). Thus, here we have a third example: the model in this paper generates signatures of criticality in the statistics of avalanches of activity, and it does so without fine-tuning (cf., Fig. 2-3).

      The rate at which these ‘criticality without fine-tuning' examples are piling up may inspire revisiting the requirement of fine-tuning in the definition of criticality, and our ongoing work (Ngampruetikorn et al. 2023) suggests that criticality may be more accurately defined through large fluctuations (variance > 1/N) rather than through fine-tuning or scaling relations.

      References:

      • Schwab DJ, Nemenman I, Mehta P. “Zipf’s Law and Criticality in Multivariate Data without FineTuning.” Phys Rev Lett. 2014 Aug; doi::101103/PhysRevLett.113.068102,

      • Aitchison L, Corradi N, Latham PE. “Zipf’s Law Arising Naturally When There Are Underlying, Unobserved Variables.” PLOS Computational biology. 2016 12; 12(12):1-32. doi:10.1371/journal.pcbi.1005110

      • Meshulam L, Gauthier JL, Brody CD, Tank DW, Bialek W. “Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons.” Phys Rev Lett. 2019 Oct; doi: 10.1103/PhysRevLett.123.178103.

      • Morales GB, di Santo S, Muñoz MA. “Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics.” Proceedings of the National Academy of Sciences. 2023; 120(9):e2208998120.

      • Morrell MC, Sederberg AJ, Nemenman I. “Latent Dynamical Variables Produce Signatures of Spatiotemporal Criticality in Large Biological Systems.” Phys Rev Lett. 2021 Mar; doi: 10.1103/PhysRevLett.126.118302.

      • Ngampruetikorn, V., Nemenman, I., Schwab, D., “Extrinsic vs Intrinsic Criticality in Systems with Many Components.” arXiv: arXiv:2309.13898 [physics.bio-ph]

      Major comments:

      1) For many readers, the essential messages of the paper may not be immediately clear. For example, is the paper criticizing the criticality hypothesis of cortical networks, or does the criticism extend deeper, to the theoretical predictions of "crackling" relationships in physical systems as they can emerge without criticality? Statements like "We show that a system coupled to one or many dynamical latent variables can generate avalanche criticality ..." could be misinterpreted as affirming criticality. A more accurate language is needed; for instance, the paper could state that the model generates relationships observed in critical systems. The paper should provide a clearer conclusion and interpretation of the findings in the context of the criticality hypothesis of cortical dynamics.

      Please see the response to the Public Review, above. To clarify the essential message that the dynamical latent variable model produces avalanche criticality without fine-tuning, we have made revisions to the abstract and introduction. This point was already made in the discussion (first sentence).

      Key sentences changed in the abstract:

      "… We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases, avalanche criticality is observed without fine-tuning of model parameters. … Our results suggest that avalanche criticality arises in neural systems in which activity is effectively modeled as a population driven by a few dynamical variables and these variables can be inferred from the population activity."

      In the introduction, we changed the final sentence to read:

      "These results demonstrate how criticality in neural recordings can arise from latent dynamics in neural activity, without need for fine-tuning of network parameters."

      2) On lines 97-99, the authors state that "We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from recurrent dynamics locally". This idea is also repeated at the beginning of the Summary section. Perhaps being agnostic isn't such a good idea: it's possible that the recurrent dynamics is in a critical regime, which would just push the problem upstream. Presumably you're thinking of recurrent dynamics with slow timescales that's not critical? Or are you happy if it's in the critical regime? This should be clarified.

      We have amended this sentence to clarify that any latent dynamics with large fluctuations would suffice:

      ”We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from large fluctuations in local recurrent dynamics.”

      3) Even though the model in Equation 2 has been described in a previous publication and the Methods section, more details regarding the origin and justification of this model in the context of cortical networks would be helpful in the Results section. Was it chosen just for simplicity, or was there a deeper reason?

      This model was chosen for its simplicity: there are no direct interactions between neurons, coupling between neurons and latent variables is random, and simulation is straightforward. More complex latent dynamics or non-random structure in the coupling matrices could have been used, but our aim was to explore this model in the simplest setting possible.

      We have revised the Results (“Avalanche scaling in a dynamical latent variable model,” first paragraph) to justify the choice of the model:

      "We study a model of a population of neurons that are not coupled to each other directly but are driven by a small number of dynamical latent variables -- that is, slowly changing inputs that are not themselves measured (Fig.~\ref{fig:fig1}A). We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from large fluctuations in local recurrent dynamics. The model was chosen for its simplicity, and because we have previously shown that this model with at least about five latent variables can produce power laws under the coarse-graining analysis \citep{Morrell2021}."

      We have added the following to the beginning of the Methods section expanding on the reasons for this choice:

      "We study a model from Morrell 2021, originally constructed as a model of large populations of neurons in mouse hippocampus. Neurons are non-interacting, receiving inputs reflective of place-field selectivity as well as input current arising from a random projection from a small number of dynamical latent variables, representing inputs shared across the population of neurons that are not directly measured or controlled. In the current paper, we incorporate only the latent variables (no place variables), and we assume that every cell is coupled to every latent variable with some randomly drawn coupling strength."

      4) The Methods section (paragraph starting on line 340) connects the time scale to actual time scales in neuronal systems, stating that "The timescales of latent variables examined range from about 3 seconds to 3000 seconds, assuming 3-ms bins". While bins of 3 ms are relevant for electrophysiological data from LFPs or high-density EEG/MEG, time scales above 10 seconds are difficult to generate through biophysically clear processes like ionic channels and synaptic transmission. The paper suggests that slow time scales of the latent variables are crucial for obtaining power law behavior resembling criticality. Yet, one way to generate such slow time scales is via critical slowing down, implying that some brain areas providing input to the network under study may operate near criticality. This pushes the problem toward explaining the criticality of those external networks. Hence, discussing potential sources for slow time scales in latent variables is crucial. One possibility you might want to consider is sources external to the organism, which could easily have time scales in the 1-24 hour range.

      As the reviewers note, it is a possibility that slow timescales arise from some other brain area in which dynamics are slow due to critical dynamics, but many other plausible sources exist. These include slowly varying sensory stimuli or external sources, as suggested by the reviewers. It is also possible to generate “effective” slow dynamics from non-critical internal sources. One example, from recordings in awake mice, is the slow change in the level of arousal that occurs on the scale of many seconds to minutes. These changes arise from release of neuromodulators that have broad effects on neural populations and correlations in activity (for a focused review, see Poulet and Crochet, 2019).

      We have added the following sentence to the Methods section where timescales of latent variables was discussed:

      "The timescales of latent variables examined range from about $3$ seconds to $3000$ seconds, assuming $3$-ms bins. Inputs with such timescales may arise from external sources, such as sensory stimuli, or from internal sources, such as changes in physiological state."

      5) It is common in neuronal avalanche analysis to calculate the branching parameter using the ratio of events in consecutive bins. Near-critical systems should display values close to 1, especially in simulations without subsampling. Including the estimated values of the branching parameter for the different cases investigated in this study could provide more comprehensive data. While the paper acknowledges that the obtained exponents in the model differ from those in a critical branching process, it would still be beneficial to offer the branching parameter of the observed avalanches for comparison.

      The reviewers requested that the branching parameter be computed in our model. We point out that, for the quasi-stationary latent variables (as in Fig. 3), a branching parameter of 1 is expected because the summed activity at time t+k is, on average, equal to the summed activity at time t, regardless of k. Numerics are consistent with this expectation. Following the methodology for an unbiased estimate of the branching parameter from Wilting and Priesemann (2018), we checked an example set of parameters (epsilon = 8, eta = 3) for quasi-stationary latent fields. We found that the naïve (biased) estimate of the branching parameter was 0.94, and that the unbiased estimator was exp(−1.4⋅10−8) ≈ 0.999999986.

      For faster time scales, it is no longer true that summed activity is constant over time, as the temporal correlations in activity decay exponentially. Using the five-field simulation from Figure 2, we calculated the branching parameter for several values of tau. The biased estimates of m are 0.76 (𝜏=50), 0.79 (𝜏=500), and 0.79 (𝜏=5000). The corrected estimates are 0.98 (𝜏=50), 0.998 (𝜏=500), and 0.9998 (𝜏=5000).

      6) In the Discussion (l 269), the paper suggests potential differences between networks cultured in vitro and in vivo. While significant differences indeed exist, it's worth noting that exponents consistent with a critical branching process have also been observed in vivo (Petermann et al 2009; Hahn et al. 2010), as well as in large-scale human data.

      We thank the reviewers for pointing out these studies, and we have added the missing one (Hahn et al. 2010) to our reference list. The following was added to the discussion, in the section “Explaining Experimental Exponents:”

      "A subset of the in vivo recordings analyzed from anesthetized cat (Hahn et al. 2010) and macaque monkeys (Petermann et al. 2009) exhibited a size distribution exponent close to 1.5."

      Along these lines, we noted two additional studies of high relevance that have been published since our initial submission (Capek et al. 2023, Lombardi et al. 2023), and we have added these references to the discussion of experimental exponents.

      Minor comments:

      1) The term 'latent variable' should be rigorously explained, as it is likely to be unfamiliar to some readers.

      Sentences and clauses have been added to the Introduction, Results and the Methods to clarify the term:

      Intro: “Numerous studies have reported relatively low-dimensional structure in the activity of large populations of neurons [refs], which can be modeled by a population of neurons that are broadly and heterogeneously coupled to multiple dynamical latent (i.e., unobserved) variables.”

      Results: “We studied a population of neurons that are not coupled to each other directly but are driven by a small number of dynamical latent variables -- that is, slowly changing inputs that are not themselves measured.”

      Methods: “Neurons are non-interacting, receiving inputs reflective of place-field selectivity as well as input current reflecting a random projection from a small number of dynamical latent variables, representing inputs shared across the population of neurons that are not directly measured.”

      2) There's a relatively important typo in the equations: Eq. 2 and Eq. 6 differ by a minus sign in the exponent. Eqs. 3 and 4 use the plus sign, but epsilon_0 on line 198 uses the minus sign. All very confusing until we figured out what was going on. But easy to fix.

      Thank you for catching this. We have made the following corrections:

      1) Figures adopted the sign convention that epsilon > 0, with larger values of epsilon decreasing the activity level. Signs in Eqs. 3 and 4 have been corrected to match.

      2) Equation 5 was missing a minus sign in front of the Hamiltonian. Restoring this minus sign fixed the discrepancy between 2 and 6.

      3) In Eq. 7, the left hand side is zeta'/zeta', which is equal to 1. Maybe it should be zeta'/zeta? Fixed, thank you.

      Additional comments:

      The authors are free to ignore these; they are meant to improve the paper.

      We are extremely grateful for the close reading of our paper and note the actions taken below.

      1) We personally would not use the abbreviation DLV; we find abbreviations extremely hard to remember. And DLV is not used that often.

      Done, thank you for the suggestion.

      2) l 198: epsilon_0 = -log(2^{1/N}-1) was kind of hard to picture -- we had to do a little algebra to make sense of it. Why not write e^{-epsilon_0} = 2^{1/N}-1 \approx log(2)/N, which in turn implies that epsilon_0 ~ log(N)?

      Thank you, good point. We have added a sentence now to better explain:

      "...which is maximized at $\epsilon_0 = - \log (2^{1/N} - 1)$, independent of $J_i$ and $\eta$. After some algebra, we find that $\epsilon_0 \sim \log N$ for large $N$."

      3) Typo on l 202: "We plot P_ava as a function of epsilon in Fig. 4B". 4B --> 4D.

      Done

      4) It would be easier on the reader if the tables were all in one place. It would be even nicer to put the parameters in the figure captions. Or at least N; that one is kind of important.

      Table placement was a Latex issue, which we have now fixed. We also have included links between tables and relevant figures and indicated network size.

      5) What's x_i in Eqs. 7 and 8?

      We added a sentence of explanation. These are the individual observations of avalanche sizes or durations, depending on what is being fit.

      6) The latent variables evolve according to an Ornstein-Uhlenbeck process. But we might equally expect oscillations or non-normal behavior coupling dynamical modes, and these are likely to give different behavior with respect to avalanches. It might be worth commenting on this.

      7) The model assumes a normal distribution of the coupling strengths between the latent variables and the binary units. Discussing the potential effects of different types of random coupling could provide interesting insights.

      Both 6 and 7 are interesting questions. At this point, we could speculate that the main results would be qualitatively unchanged, provided dynamics are sufficiently slow and that the distribution of coupling strengths is sufficiently broad (that is, there is variance in the coupling matrix across individual neurons). Further studies would be needed to make these statements more precise.

      8) In Fig 1, tau_f = 1E4 whereas in Fig 2 tau_f = 5E3. Why the difference?

      For Figure 1, we chose a set of parameters that gave clear scaling. In Figure 2, we saw some value in showing more than one example of scaling, hence different parameters for the examples in Fig 2 than Fig 1. Note that the Fig 1 simulations are represented in Fig. 2 G-J, as the 5-field simulation with tau_F = 1e4.

    1. Reviewer #2 (Public Review):

      Summary:<br /> The dominant paradigm in the past decade for modeling the ventral visual stream's response to images has been to train deep neural networks on object classification tasks and regress neural responses from units of these networks. While object classification performance is correlated to the variance explained in the neural data, this approach has recently hit a plateau of variance explained, beyond which increases in classification performance do not yield improvements in neural predictivity. This suggests that classification performance may not be a sufficient objective for building better models of the ventral stream. Lindsey & Issa study the role of factorization in predicting neural responses to images, where factorization is the degree to which variables such as object pose and lighting are represented independently in orthogonal subspaces. They propose factorization as a candidate objective for breaking through the plateau suffered by models trained only on object classification. They claim that (i) maintaining these non-class variables in a factorized manner yields better neural predictivity than ignoring non-class information entirely, and (ii) factorization may be a representational strategy used by the brain.

      The first of these claims is supported by their data. The second claim does not seem well-supported, and the usefulness of their observations is not entirely clear.

      Strengths:<br /> This paper challenges the dominant approach to modeling neural responses in the ventral stream, which itself is valuable for diversifying the space of ideas.

      This paper uses a wide variety of datasets, spanning multiple brain areas and species. The results are consistent across the datasets, which is a great sign of robustness.

      The paper uses a large set of models from many prior works. This is impressively thorough and rigorous.

      The authors are very transparent, particularly in the supplementary material, showing results on all datasets. This is excellent practice.

      Weaknesses:<br /> 1. The primary weakness of this paper is a lack of clarity about what exactly is the contribution. I see two main interpretations: (1-A) As introducing a heuristic for predicting neural responses that improve over-classification accuracy, and (1-B) as a model of the brain's representational strategy. These two interpretations are distinct goals, each of which is valuable. However, I don't think the paper in its current form supports either of them very well:

      (1-A) Heuristic for neural predictivity. The claim here is that by optimizing for factorization, we could improve models' neural predictivity to break through the current predictivity plateau. To frame the paper in this way, the key contribution should be a new heuristic that correlates with neural predictivity better than classification accuracy. The paper currently does not do this. The main piece of evidence that factorization may yield a more useful heuristic than classification accuracy alone comes from Figure 5. However, in Figure 5 it seems that factorization along some factors is more useful than others, and different linear combinations of factorization and classification may be best for different data. There is no single heuristic presented and defended. If the authors want to frame this paper as a new heuristic for neural predictivity, I recommend the authors present and defend a specific heuristic that others can use, e.g. [K * factorization_of_pose + classification] for some constant K, and show that (i) this correlates with neural predictivity better than classification alone, and (ii) this can be used to build models with higher neural predictivity. For (ii), they could fine-tune a state-of-the-art model to improve this heuristic and show that doing so achieves a new state-of-the-art neural predictivity. That would be convincing evidence that their contribution is useful.

      (1-B) Model of representation in the brain. The claim here is that factorization is a general principle of representation in the brain. However, neural predictivity is not a suitable metric for this, because (i) neural predictivity allows arbitrary linear decoders, hence is invariant to the orthogonality requirement of factorization, and (ii) neural predictivity does not match the network representation to the brain representation. A better metric is representational dissimilarity matrices. However, the RDM results in Figure S4 actually seem to show that factorization does not do a very good job of predicting neural similarity (though the comparison to classification accuracy is not shown), which suggests that factorization may not be a general principle of the brain. If the authors want to frame the paper in terms of discovering a general principle of the brain, I suggest they use a metric (or suite of metrics) of brain similarity that is sensitive to the desiderata of factorization, e.g. doesn't apply arbitrary linear transformations, and compare to classification accuracy in addition to invariance.

      Overall, I suggest the authors clarify exactly what their claim is, then focus on that claim and present results to justify it. If neither of the claims above can be supported by evidence, then this paper still has value as an idea that they spent effort trying to test, but they should not suggest these claims in the paper. In that case, it may also be possible to increase the value of the contribution by characterizing how the structure of class-free variable representations impacts correlation with neural fit, instead of just comparing existence vs absence (invariance) of this information. For example, evaluate the degree to which local or global orthogonality matters, or the degree to which curvature of the embedding matters.

      2. I think the comparison to invariance, which is pervasive throughout the paper, is not very informative. First, it is not surprising that invariance is more weakly correlated with neural predictivity than factorization, because invariant representations lose information compared to factorized representations. Second, there has long been extensive evidence that responses throughout the ventral stream are not invariant to the factors the authors consider, so we already knew that invariance is not a good characterization of ventral stream data.

      3. The formalization of the factorization metric is not particularly elegant, because it relies on computing top K principal components for the other-parameter space, where K is arbitrarily chosen as 10. While the authors do show that in their datasets the results are not very sensitive to K (Figure S5), that is not guaranteed to be the case in general. I suggest the authors try to come up with a formalization that doesn't have arbitrary constants. For example, one possibility that comes to mind is E[delta_a x delta_b], where 'x' is the normalized cross product, delta_a, and delta_b are deltas in representation space induced by perturbations of factors a and b, and the expectation is taken over all base points and deltas. This is just the first thing that comes to mind, and I'm sure the authors can come up with something better. The literature on disentangling metrics in machine learning may be useful for ideas on measuring factorization.

      4. The authors defined the term "factorization" according to their metric. I think introducing this new term is not necessary and can be confusing because the term "factorization" is vague and used by different researchers in different ways. Perhaps a better term is "orthogonality", because that is clear and seems to be what the authors' metric is measuring.

      5. One general weakness of the factorization paradigm is the reliance on a choice of factors. This is a subjective choice and becomes an issue as you scale to more complex images where the choice of factors is not obvious. While this choice of factors cannot be avoided, I suggest the authors add two things: First, an analysis of how sensitive the results are to the choice of factors (e.g. transform the basis set of factors and re-run the metric); second, include some discussion about how factors may be chosen in general (e.g. based on temporal statistics of the world, independent components analysis, or something else).

  5. Jan 2024
    1. everything in my own immediate experience supports my deep belief that I am the absolute centre of the universe; the realest, most vivid and important person in existence. We rarely think about this sort of natural, basic self-centredness because it’s so socially repulsive.

      We did an exercise in my "Models of Effective Helping" class yesterday where we were given a list of about 12 people and the list had their ages and a brief description of who they are. We had to choose as a group eight people from the list to go on a life raft and the rest of them would die. You could also choose to save yourself as one of the eight. There was a heated debate over whether its ethical or proper to choose to save yourself over somebody else. I couldn't fathom the thought of me choosing for somebody to die over myself, but the majority of the class agreed to save themselves and kill somebody else. What I thought was even more shocking was that a few of the people on the list were teenagers and people were trying to justify killing them over themselves. What I learned is that there are many people who truly believe that they are the center of the universe, and it may just be human nature to think so.

    1. These functions include the following: (1) poor people do the work that other people do not want to do; (2) the programs that help poor people provide a lot of jobs for the people employed by the programs; (3) the poor purchase goods, such as day-old bread and used clothing, that other people do not wish to purchase, and thus extend the economic value of these goods; and (4) the poor provide jobs for doctors, lawyers, teachers, and other professionals who may not be competent enough to be employed in positions catering to wealthier patients, clients, students, and so forth (Gans, 1972)

      This seems like the thinking of someone who does not believe in equality. I don't think that we need such severe levels of poverty to achieve a healthy social order. It should not be impossible to get out of poverty. Poverty should not be a life sentence.

    1. A disability is an ability that a person doesn’t have, but that their society expects them to have.1 For example: If a building only has staircases to get up to the second floor (it was built assuming everyone could walk up stairs), then someone who cannot get up stairs has a disability in that situation. If a physical picture book was made with the assumption that people would be able to see the pictures, then someone who cannot see has a disability in that situation. If tall grocery store shelves were made with the assumption that people would be able to reach them, then people who are short, or who can’t lift their arms up, or who can’t stand up, all would have a disability in that situation. If an airplane seat was designed with little leg room, assuming people’s legs wouldn’t be too long, then someone who is very tall, or who has difficulty bending their legs would have a disability in that situation. Which abilities are expected of people, and therefore what things are considered disabilities, are socially defined. Different societies and groups of people make different assumptions about what people can do, and so what is considered a disability in one group, might just be “normal” in another. There are many things we might not be able to do that won’t be considered disabilities because our social groups don’t expect us to be able to do them. For example, none of us have wings that we can fly with, but that is not considered a disability, because our social groups didn’t assume we would be able to. Or, for a more practical example, let’s look at color vision: Most humans are trichromats, meaning they can see three base colors (red, green, and blue), along with all combinations of those three colors. Human societies often assume that people will be trichromats. So people who can’t see as many colors are considered to be color blind, a disability. But there are also a small number of people who are tetrachromats and can see four base colors2 and all combinations of those four colors. In comparison to tetrachromats, trichromats (the majority of people), lack the ability to see some colors. But our society doesn’t build things for tetrachromats, so their extra ability to see color doesn’t help them much. And trichromats’ relative reduction in seeing color doesn’t cause them difficulty, so being a trichromat isn’t considered to be a disability. Some disabilities are visible disabilities that other people can notice by observing the disabled person (e.g., wearing glasses is an indication of a visual disability, or a missing limb might be noticeable). Other disabilities are invisible disabilities that other people cannot notice by observing the disabled person (e.g., chronic fatigue syndrome, contact lenses for a visual disability, or a prosthetic for a missing limb covered by clothing). Sometimes people with invisible disabilities get unfairly accused of “faking” or “making up” their disability (e.g., someone who can walk short distances but needs to use a wheelchair when going long distances). Disabilities can be accepted as socially normal, like is sometimes the case for wearing glasses or contacts, or it can be stigmatized as socially unacceptable, inconvenient, or blamed on the disabled person. Some people (like many with chronic pain) would welcome a cure that got rid of their disability. Others (like many autistic people), are insulted by the suggestion that there is something wrong with them that needs to be “cured,” and think the only reason autism is considered a “disability” at all is because society doesn’t make reasonable accommodations for them the way it does for neurotypical people. Many of the disabilities we mentioned above were permanent disabilities, that is, disabilities that won’t go away. But disabilities can also be temporary disabilities, like a broken leg in a cast, which may eventually get better. Disabilities can also vary over time (e.g., “Today is a bad day for my back pain”). Disabilities can even be situational disabilities, like the loss of fine motor skills when wearing thick gloves in the cold, or trying to watch a video on your phone in class with the sound off, or trying to type on a computer while holding a baby. As you look through all these types of disabilities, you might discover ways you have experienced disability in your life. Though please keep in mind that different disabilities can be very different, and everyone’s experience with their own disability can vary. So having some experience with disability does not make someone an expert in any other experience of disability. As for our experience with disability, Kyle has been diagnosed with generalized anxiety disorder and Susan has been diagnosed with depression. Kyle and Susan also both have: near sightedness: our eyes cannot focus on things far away (unless we use corrective lenses, like glasses or contacts) ADHD: we have difficulty controlling our focus, sometimes being hyperfocused and sometimes being highly distracted and also have difficulties with executive dysfunction. 1 There are many ways to think about disability, such as legal (what legally counts as a disability?), medical (what is a problem to be cured?), identity (who views themselves as “disabled”), etc. We are focused here more on disability as it relates to design and who things in our world are designed for. 2 Trying to name the four base colors seen by tetrachromats is not straightforward since our color names are based on trichromat vision. It seems that for tetrachromats blue would be the same, but they would see three different base colors in the red/green range instead of two.

      This paragraph of the article tells how technological developments have the potential to redefine disability. For example, the development of cochlear implants and hearing aids has changed the way society views hearing loss. Similarly, Braille displays and screen reading technology have revolutionized the way blind people access information. I think these are all very significant things, and increasingly people are seeing disability as a spectrum rather than a binary state (disabled vs. non-disabled). Individuals may have different levels of skills in different areas. For example, a person may have a mild visual impairment that has no effect on most activities, but can be a major handicap in specific situations, such as dimly lit areas. I've known people with this problem so I think the point is important.

    2. There are many ways to think about disability, such as legal (what legally counts as a disability?), medical (what is a problem to be cured?), identity (who views themselves as “disabled”), etc. We are focused here more on disability as it relates to design and who things in our world are designed for.

      Certainly, considering disability in the context of design involves creating products, environments, and systems that are inclusive and accessible to individuals with a diverse range of abilities. Engaging individuals with disabilities in the design process helps to identify specific needs and challenges they may face. This approach ensures that the final design reflects the diverse perspectives and requirements of the user community.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Nitrogen metabolism is of fundamental importance to biology. However, the metabolism and biochemistry of guanidine and guanidine containing compounds, including arginine and homoarginine, have been understudied over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new type of guanidine forming enzyme. It was previously known that 2-oxogluturate oxygenase catalysis in bacteria can produce guanidine via oxidation of arginine. Interestingly, the same enzyme that produces guanidine from arginine also oxidises 2-oxogluturate to give the plant signalling molecule ethylene. Funck et al show that a mechanistically related oxygenase enzyme from plants can also produce guanidine, but instead of using arginine as a substrate, it uses homoarginine. The work will stimulate interest in the cellular roles of homoarginine, a metabolite present in plants and other organisms including humans and, more generally, in the biochemistry and metabolism of guanidines.

      1) Significance

      Studies on the metabolism and biochemistry of the small nitrogen rich molecule guanidine and related compounds including arginine have been largely ignored over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new guanidine forming enzyme that works by oxidation of homoarginine, a metabolite present in organisms ranging from plants to humans. The new enzyme requires oxygen and 2oxogluturate as cosubstrates and is related, but distinct from a known enzyme that oxidises arginine to produce guanidine, but which can also oxidise 2-oxogluturate to produce the plant signalling molecule ethylene.

      Overall, I thought this was an exceptionally well written and interesting manuscript. Although a 2-oxogluturate dependent guanidine forming enzyme is known (EFE), the discovery that a related enzyme oxidises homoarginine is really interesting, especially given the presence of homoarginine in plant seeds. There is more work to be done in terms of functional assignment, but this can be the subject of future studies. I also fully endorse the authors' view that guanidine and related compounds have been massively understudied in recent times. I would like to see the possibility that the new enzyme makes ethylene explored. Congratulations to the authors on a very nice study.

      Response: We thank the reviewer for the positive evaluation of our manuscript. In the revised version, we have emphasized more clearly that we found no evidence for ethylene production by the recombinant enzymes. The other suggestions of the reviewer are also considered in the revised version as detailed below.

      Reviewer #2 (Public Review):

      In this study, Dietmar Funck and colleagues have made a significant breakthrough by identifying three isoforms of plant 2-oxoglutarate-dependent dioxygenases (2-ODD-C23) as homo/arginine-6-hydroxylases, catalyzing the degradation of 6-hydroxyhomoarginine into 2aminoadipate-6-semialdehyde (AASA) and guanidine. This discovery marks the very first confirmation of plant or eukaryotic enzymes capable of guanidine production.

      The authors selected three plant 2-ODD-C23 enzymes with the highest sequence similarity to bacterial guanidine-producing (EFE) enzymes. They proceeded to clone and express the recombinant enzymes in E coli, demonstrating capacity of all three Arabidopsis isoforms to produce guanidine. Additionally, by precise biochemical experiments, the authors established these three 2-ODD-C23 enzymes as homoarginine-6-hydroxylases (and arginine-hydroxylase for one of them). Furthermore, the authors utilized transgenic plants expressing GFP fusion proteins to show the cytoplasmic localization of all three 2-ODD-C23 enzymes. Most notably, using T-DNA mutant lines and CRISPR/Cas9-generated lines, along with combinations of them, they demonstrate the guanidine-producing capacity of each enzyme isoform in planta. These results provide robust evidence that these three 2-ODD-C23 Arabidopsis isoforms are indeed homoarginine-6-hydroxylases responsible for guanidine generation.

      The findings presented in this manuscript are a significant contribution for our understanding of plant biology, particularly given that this work is the first demonstration of enzymatic guanidine production in eukaryotic cells. However, there are a couple of concerns and potential ways for further investigation that the authors should (consider) incorporate.

      Firstly, the observation of cytoplasmic and nuclear GFP signals in the transgenic plants may also indicate cleaved GFP from the fusion proteins. Thus, the authors should perform Western blot analysis to confirm the correct size of the 2-ODD-C23 fusion proteins in the transgenic protoplasts.

      Secondly, it may be worth measuring pipecolate (and proline?) levels under biotic stress conditions (particularly those that induce transcript changes of these enzymes, Fig S8). Given the results suggesting a potential regulation of the pathway by biotic stress conditions (eg. meJA), these experiments could provide valuable insights into the physiological role of guanidine-producing enzymes in plants. This additional analysis may give a significance of these enzymes in plant defense mechanisms.

      Response: We thank also reviewer 2 for the positive evaluation and useful suggestions. We performed the proposed GFP Western blot, which indeed indicated the presences of both, fulllength fusion proteins and free GFP, which can explain the partial nuclear localization. We fully agree that further experiments with biotic and abiotic stress will be required to determine the physiological function of the 2-ODD-C23 enzymes. However, the list of potential experiments is long and they are beyond the scope of the present manuscript.

      Reviewer #1 (Recommendations For The Authors):

      Specific points

      Overall, I thought this was a very interesting study, comprising biochemical, cellular, and in vivo studies. Of course more could be done on each of these, and likely will be, but I think the assignment of biochemical function is very strong, across all three approaches. The one new experiment I would like to see is a clear demonstration of whether ethylene is produced - unlikely but should be tested.

      We had mentioned our failure to detect ethylene production by the plant enzymes in the previous version and have made it more prominent and reliable by including ethylene production as positive control in the new supplementary figure S5.

      Abstract

      Delete 'hitherto overlooked' - this is implicit 'but is more likely' to 'is likely'?

      Agreed and modified

      Introduction

      Second sentence - what about relevant small molecule primary metabolites including precursors of proteins/nucleic acids.

      We modified the sentence accordingly.

      Paragraph 2 - maybe also note EFE produces glutamate semi aldehyde, via arginine C-5 oxidation.

      Paragraph 2 has been re-phrased according to your suggestion.

      Overall, I thought the introduction was exceptionally well written.

      Perhaps either in the introduction, or later, note there are other 2OG oxygenases that oxidise arginine/arginine derivatives in various ways, e.g. clavaminate synthase/arginine hydroxylases/desaturases.

      We added a sentence mentioning the arginine hydroxylases VioC and OrfP to the introduction and included VioC into the sequence comparison in supplementary figure 2 to show that these enzymes, as well as NapI, are very different from EFE and the plant hydroxylases.

      Results

      Paragraph 1 - qualify similarity and refer to/give a structurally informed sequence alignment, including EFE

      A new supplemental figure S2 was added with sequence identity values and a structurally informed alignment. The text has been modified accordingly.

      Paragraph 2 - briefly state method of guanidine analysis

      We included a reference to the M&M section and mentioned LC-MS in paragraph 2.

      Figure 1 - trivial point - proteins are not expressed/genes are

      We have modified the legend to figure 1. However, we would like to point out that terms like “recombinant protein expression” are widely used in the field. A quick search with google Ngram viewer shows that “protein expression” started to appear in the mid-80ies and its use stayed constantly at 1/8th of “gene expression”.

      Define errors clearly in all figure legends, clearly defining biological/technical repeats<br /> Page 6 - was the His-tag cleared to ensure no issues with Ni contamination?

      We treat individual plants or independent bacterial cultures as biological replicates. Only in the case of enzyme activity assays with NAD(P)H, technical replicates were used and this has been indicated in the legend of figure 6.

      Lower case 'p' in pentafluorobenzyl corrected

      In Figure 2 make clear the hydroxylated intermediates are not observed

      We now use grey color for the intermediates and have put them in brackets. Additionally we state in the figure legend that these intermediates were not detected.

      Pages 6-7 - I may have missed this but it's important to investigate what happens to the 2OG. Is succinate the only product or is ethylene also produced? This possibility should also be considered in the plant studies, i.e. is there any evidence for responses related to perturbed ethylene metabolism. The authors consider a signalling role relating to AASA/P6C, but seem to ignore a potential ethylene connection.

      As stated above, we checked for ethylene production with negative result. EFE produced 6 times more guanidine than the plant enzymes under the same condition, but even 100-fold lower ethylene production would have been clearly detected.

      Page 12 - 'plants have been shown to....' Perhaps note how hydroxy guanidine is made?

      We now mention the canavanine-γ-lyase that cleaves canavanine into hydroxyguanidine and homoserine.

      Overall, I thought the discussion was good, but perhaps a bit long/too speculative on pages 12/13 and this detracted from the biochemical assignment of the enzyme. I'd suggest shortening the discussion somewhat - the precise roles of the enzyme can be the subject of future work. As indicated above, some discussion on potential links to ethylene would be appreciated.

      Since reviewer 2 wanted more (speculative) discussion on the role of the 2-ODD-C23 enzymes and there was no detectable ethylene production, we took the liberty to leave the discussion largely unaltered.

      I'd also like to see some more consideration/metabolic analyses of guanidine related metabolism in the genetically modified plants.

      Such analyses will certainly be included in future experiments once we get an idea about the physiological role of the 2-ODD-C23 enzymes.

      Page 16 - mass spectrometry

      Corrected.

      Please add a structurally informed sequence alignment with EFE and other 2OG oxygenases acting on arginine/derivatives.

      An excerpt of the alignment is now presented in supplementary figure S2.

      Reviewer #2 (Recommendations For The Authors):

      I would like to see more discussion in the manuscript about the possible interconnection/roles between 2-ODD-C23 guanidine-producing, lysine- ALD1-Pipecolate producing, and proline metabolism pathways during both biotic and abiotic stresses.

      Since we were unable to detect pipecolate in any of our plant samples and also our preliminary results with biotic stress did not produce any evidence for a function of the 2ODD-C23 enzymes in the tested defense responses, we would like to postpone such extended discussion until we find a condition where the physiological function of these enzymes is evident.

      Fig. 4: Authors should change colors for Col-0, 0.2 HoArg and ctrl? They look too similar in my pdf file.

      We changed the colors in figure 4 and hope that the enhanced contrast is maintained during the production of the final version of our article.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Sender et al describe a model to estimate what fraction of DNA becomes cell-free DNA in plasma. This is of great interest to the community, as the amount of DNA from a certain tissue (for example, a tumor) that becomes available for detection in the blood has important implications for disease detection.

      However, the authors' methods do not consider important variables related to cell-free DNA shedding and storage, and their results may thus be inaccurate. At this stage of the paper, the methods section lacks important detail. Thus, it is difficult to fully assess the manuscript and its results.

      Strengths:

      The question asked by the authors has potentially important implications for disease diagnosis. Understanding how genomic DNA degrades in the human circulation can guide towards ways to enrich for DNA of interest or may lead to unexpected methods of conserving cell-free DNA. Thus, the question "how much genomic DNA becomes cfDNA" is of great interest to the scientific and medical community. Once the weaknesses of the manuscript are addressed, I believe this manuscript has the potential to be a widely used resource.

      Weaknesses:

      There are two major weaknesses in how the analysis is presented. First, the methods lack detail. Second, the analysis does not consider key variables in their model.

      Issues pertaining to the methods section.

      The current manuscript builds a flux model, mostly taking values and results from three previous studies: 1) The amount of cellular turnover by cell type, taken from Sender & Milo, 2021

      2) The fractions of various tissues that contribute DNA to the plasma, taken from Moss et al, 2018 and Loyfer et al, 2023

      My expertise lies in cell-free DNA, and so I will limit my comments to the manuscripts in (2). Paper by Loyfer et al (additional context):

      Loyfer et al is a recent landmark paper that presents a computational method for deconvoluting tissues of origin based on methylation profiles of flow-sorted cell types. Thus, the manuscript provides a well-curated methylation dataset of sorted cell-types. The majority of this manuscript describes the methylation patterns and features of the reference methylomes (bulk, sorted cell types), with a smaller portion devoted to cell-free DNA tissue of origin deconvolution.

      I believe the data the authors are retrieving from the Loyfer study are from the 23 healthy plasma cfDNA methylomes analyzed in the study, and not the re-analysis of the 52 COVID-19 samples from Cheng et al (MED 2021).

      Paper by Moss et al (additional context):

      Moss et al is another landmark paper that predates the Loyfer et al manuscript. The technology used in this study (methylation arrays) is outdated but is an incredible resource for the community. This paper evaluates cfDNA tissues of origin in health and different disease scenarios. Again, I assume the current manuscript only pulled data from healthy patients, although I cannot be sure as it is not described in the methods section.

      This manuscript:

      The current manuscript takes (I think) the total cfDNA concentration from males and females from the Moss et al manuscript (pooled cfDNA; 2 young male groups, 2 old male groups, 2 young female groups, 2 old female groups, Supplementary Dataset; "total_cfDNA_conc" tab). I believe this is the data used as total cfDNA concentration. It would be beneficial for all readers if the authors clarified this point.

      The tissues of origin, in the supplemental dataset ("fraction" tab), presents the data from 8 cell types (erythrocytes, monocytes/macrophages, megakaryocytes, granulocytes, hepatocytes, endothelial cells, lymphocytes, other). The fractions in the spreadsheet do not match the Loyfer or Moss manuscripts for healthy individuals. Thus, I do not know what values the supplementary dataset represents. I also don't know what the deconvolution values are used for the flux model.

      The integration of these two methods lack detail. Are the authors here using yields (ie, cfDNA concentrations) from Moss et al, and tissue fractions from Loyfer et al? If so, why? There are more samples in the Loyfer manuscript, so why are the samples from Moss et al. being used? The authors are also selectively ignoring cell-types that are present in healthy individuals (Neurons from Moss et al, 2018). Why?

      Appraisal:

      At this stage of the manuscript, I think additional evidence and analysis is required to confirm the results in the manuscript.

      Impact:

      Once the authors present additional analysis to substantiate their results, this manuscript will be highly impactful on the community. The field of liquid biopsies (non-invasive diagnostics) has the potential to revolutionize the medical field (and has already in certain areas, such as prenatal diagnostics). Yet, there is a lack of basic science questions in the field. This manuscript is an important step forward in asking more "basic science" questions that seek to answer a fundamental biological question.

      We thank the reviewer for the valuable comments on our analysis. In response to the feedback, we have updated the analysis to address all critical points as described below and revised the text to enhance the clarity of our methodology. One notable improvement to our analysis involved ensuring better alignment between the cohort data for cfDNA plasma concentration and cell turnover estimates. To achieve this, we utilized the total plasma concentration of cfDNA from a study conducted by Meddeb et al. 2019, taking into account the influence of age and sex on these concentrations and specifically focusing on a cohort of relatively young and healthy individuals. Additionally, we considered expected variations related to sex, age, and other pertinent factors, as outlined in the studies by Meddeb et al. 2019 and Madsen et al. 2019.

      In addition, we have addressed concerns regarding the technical aspects of cfDNA analysis, providing detailed explanations of their limited impact on our analysis and the resulting conclusions.

      Reviewer #2 (Public Review):

      Summary:

      Cell-free DNA (cfDNA) are short DNA fragments released into the circulation when cells die. Plasma cfDNA level is thought to reflect the degree of cell-death or tissue injury. Indeed, plasma cfDNA is a reliable diagnostic biomarker for multiple diseases, providing insights into disease severity and outcomes. In this manuscript, Dr. Sender and colleagues address a fundamental question: What fraction of DNA released from cell death is detectable as plasma cfDNA? The authors use public data to estimate the amount of DNA produced from dying cells. They also utilize public data to estimate plasma cfDNA levels. Their calculations showed that <10% of DNA released is detectable as plasma cfDNA, the fraction of detectable cfDNA varying by tissue sources. The study demonstrates new and fundamental principles that could improve disease diagnosis and treatment via cfDNA.

      Strengths:

      1) The experimental approach is resource-mindful taking advantage of publicly available data to estimate the fraction of detectable cfDNA in physiological states. The authors did not assess if the fraction of detectable cfDNA changes in disease conditions. Nonetheless, their pioneering study lays the foundation and provides the methods needed for a similar assessment in disease states.

      2) The findings of this study potentially explain discrepancies in measured versus expected tissue-specific cfDNA from some tissues. For example, the gastrointestinal tract is subject to high cell turnover and release of DNA. Yet, only a small fraction of that DNA ends up in plasma as gastrointestinal cfDNA.

      3) The study proposes potential mechanisms that could account for the low fraction of detectable cfDNA in plasma relative to DNA released. This includes intracellular or tissue machinery that could "chew up" DNA released from dying cells, allowing only a small fraction to escape into plasma as cfDNA. Could this explain why the gastrointestinal track with an elaborate phagosome machinery contributes a small fraction of plasma cfDNA? Given the role of cfDNA as damage-associated molecular pattern in some diseases, targeting such a machinery may provide novel therapeutic opportunities.

      Weaknesses:

      In vitro and in vivo studies are needed to validate these findings and define tissue machinery that contribute to cfDNA production. The validation studies should address the following limitations of the study design: -

      1) Align the cohorts to estimate DNA production and plasma cfDNA levels. Cellular turnover rate and plasma cfDNA levels vary with age, sex, circadian clock, and other factors (Madsen AT et al, EBioMedicine, 2019). This study estimated DNA production using data abstracted from a homogenous group of healthy control males (Sender & Milo, Nat Med 2021). On the other hand, plasma cfDNA levels were obtained from datasets of more diverse cohort of healthy males and females with a wide range of ages (Loyfer et al. Nature, 2023 and Moss et al., Nat Commun, 2018).

      2) "cfDNA fragments are not created equal". Recent studies demonstrate that cfDNA composition vary with disease state. For example, cfDNA GC content, fraction of short fragments, and composition of some genomic elements increase in heart transplant rejection compared to no-rejection state (Agbor-Enoh, Circulation, 2021). The genomic location and disease state may therefore be important factors to consider in these analyses.

      3) Alternative sources of DNA production should be considered. Aside from cell death, DNA can be released from cells via active secretion. This and other additional sources of DNA should be considered in future studies. The distinct characteristics of mitochondrial DNA to genomic DNA should also be considered.

      We appreciate the reviewer's comments on our analysis. In response to the feedback, we have updated to address key points and revised the text accordingly.

      1) We have incorporated several enhancements to improve the coherence of our analysis. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

      There was no specific estimate for a cohort of young males in both Meddeb et al. and Loyfer et al.; however, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in literature (Meddeb et al. 2019; Madsen et al. 2019). Thus, we demonstrate that sex and age have a small effect on the cfDNA concentrations and thus are unlikely to alter our conclusions substantially when considering a healthy population. We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion.

      2) In this study, we addressed the total amount of cfDNA in healthy individuals without regard to GC content, representation of different genomic regions, or fragment length, as the goal was to understand if cell death rates are fully accounted for by cfDNA concentration. We agree that it will be interesting to study the relative representation of the genome in cfDNA and the processes that determine cfDNA concentration in pathologies beyond the rate of cell death. These topics for future research fall beyond this study's scope.

      3) We know only a few specific cases whereby DNA is released from cells that are not dying. These include the release of DNA from erythroblasts and megakaryocytes to generate anucleated erythrocytes and platelets (Moss et al. 2022, cited in our paper) and the release of NETs from neutrophils.

      The presence of cfDNA fragments originating from megakaryocytes and erythroblasts indicates the elimination of megakaryocytes and erythroblasts and the birth of erythrocytes and platelets. However, the considerations in the rest of the paper still apply: the concentration of cfDNA from these sources is far lower than expected from the cell turnover rate.

      Concerning NETosis: the presence of cfDNA originating in neutrophils that have not died would reduce the concentration of cfDNA from dying neutrophils and thus further increase the discrepancy, which is the topic of our study (under-representation of DNA from dying cells in plasma).

      We neglected mitochondrial DNA, as it is not measured in methylation cell-of-origin analysis. Similarly to the argument above, if some of the total DNA measured in plasma is in fact, mitochondrial, this would mean that genomic cfDNA concentration is actually lower than the estimates, meaning that an even smaller fraction of DNA from dying cells is measured in plasma.

      Recommendations For The Authors

      Reviewer #1 (Recommendations For The Authors):

      I think readers would appreciate the authors commenting or addressing the following points, in addition to addressing the concerns I raised about the methods section in the public review:

      What variables and considerations did the authors omit in this study?

      1) Cell-free DNA is found in virtually every biofluid.

      Thus, the fact that cell-free DNA is not present in the plasma does not mean it cannot be detected elsewhere. This also implies that phagocytosis may not be the only factor related to cfDNA not being present in the blood. One example (of many, many others) is neutrophil-derived cell-free DNA, which is present in the urine.

      Indeed, dying cells and their DNA can be consumed locally, released into the blood, or shed outside the body. The latter is a function of tissue topology. For example, intestinal epithelial cell turnover releases material to the lumen of the gut (i.e., stool); kidney and bladder cell turnover releases material to urine; and lung epithelium releases material to the air spaces. In these cases, the absence of cfDNA in plasma is expected. However, in cases where tissue topology dictates release to blood, low representation in cfDNA indicates local consumption or a related mechanism. In Figure 1 of the manuscript, we distinguish between tissues according to their topology, labeling organs that shed material to the outside denoted by open circles.

      Neutrophil-derived DNA in urine likely represents a local process in the kidney (neutrophils that penetrate the epithelium and fall into the urine). Neutrophils that die elsewhere in the body must release cfDNA to the blood before it can reach the urine. Hence, quantifying plasma cfDNA is a legitimate approach for assessing the relationship between cell death and cfDNA. The revised text clarifies this point. We made revisions to the initial paragraph in the results section and a paragraph within the discussion to provide clarity on this topic:

      “Based on atlases of human cell type-specific methylation signatures, Moss et al. and Loyfer et al. analyzed the main cell types contributing to plasma cfDNA. They found the primary sources of plasma cfDNA to be blood cells: granulocytes, megakaryocytes, macrophages, and/or monocytes (the signature could not differentiate between the last two), lymphocytes, and erythrocyte progenitors. Other cells that had detectable contributions are endothelial cells and hepatocytes. Qualitatively, these cells represent most of the leading cell types in cellular turnover, as shown in Sender & Milo 2021 (Sender and Milo 2021). Epithelial cells of the gastrointestinal tract, lung, kidney, bladder, and skin are other cell types that significantly contribute to cellular turnover. Dying cells in these tissues are shed into the gut lumen, the air spaces, the urine, or out of the skin (note that while DNA from gut, lung, and kidney epithelial cells can be found in stool, bronchoalveolar lavage, and urine, the fate of DNA from skin cells is not known). This arrangement may explain why DNA from these cell types is not represented in plasma cfDNA in healthy conditions. Therefore, it appears that cells with high cfDNA plasma levels are those with relatively high turnover that are not being shed out of the body.”

      “A comparison between the different types of cells shows a trend in which less DNA flux from cells with higher turnover gets to the bloodstream. In particular, a tiny fraction (1 in 3x104) of DNA from erythroid progenitors arrives at the plasma, indicating an extreme efficiency of the DNA recovery mechanism. Erythroid progenitors are arranged in erythroblastic islands. Up to a few tens of erythroid progenitors surround a single macrophage that collects the nuclei extruded during the erythrocyte maturation process (pyrenocytes) (Chasis and Mohandas 2008). The amount of DNA discarded through the maturation of over 200 billion erythrocytes per day (Sender and Milo 2021) exceeds all other sources of homeostatic discarded DNA. Our findings indicate that the organization of dedicated erythroblastic islands functions highly efficiently regarding DNA utilization. Neutrophils are another high-turnover cell type with a low level of cfDNA. When contemplating the process of NETosis (Vorobjeva and Chernyak 2020), the existence of cfDNA originating from live neutrophils would potentially diminish the concentration of cfDNA released by dying neutrophils, thereby amplifying the observed ratio for this particular cell type. The overall trend of higher turnover resulting in a lower cfDNA to DNA flux ratio may indicate similar design principles, in which the utilization of DNA is better in tissues with higher turnover. However, our analysis is limited to only several cell types (due to cfDNA test and deconvolution sensitivities), and extrapolation to cells with lower cell turnover is problematic.”

      2) Effect of biofluid storage.

      Cell-free DNA continues to degrade after it is extracted via blood draw. This is not expected to change tissue of origin predictions (although that remains to be shown in the literature), but definitely affects extraction yield. This is not accounted for (or even discussed) in the manuscript. It would be important to understand how this was done for the data presented here.

      The paper integrates data from multiple recent studies that adhered to state-of-the-art procedures requiring rapid processing of blood samples. In fact, earlier studies that were not careful to isolate plasma quickly typically reported very high concentrations due to the lysis of leukocytes and artifactual release of genomic DNA. Rapid plasma isolation and DNA extraction typically yield 5ng/ml in healthy donors, as stated in the paper (last paragraph of Results).

      3) Batch effects

      Batch effects are not discussed here and can affect cfDNA yields.

      Our analysis relies on data reported by multiple studies from different groups, which independently results in similar key findings (total concentration of cfDNA and the relative contribution of different tissues). Thus, batch effects are unlikely to affect the calculations markedly.

      4) Cell-free DNA extraction kits

      Different kits and methods extract cell-free DNA at different quantities. Importantly, much research has been done recently that most kits are not sensitive for ultrashort cell-free DNA (of lengths ~50bp). This may represent most of the DNA present in plasma. This raises an important question: are the yields that are being used in Moss et al (where I presume the total concentration is taken from) accurate? Is there more cell-free DNA that was missed? While the importance of this ultrashort cfDNA has yet to be shown, it is in the blood. Thus, the authors' model may underestimate ratios by not accounting for this. This is mentioned in the discussion, but it is not evident why it was not added into the model.

      The Qiagen cfDNA extraction kit can detect 50bp fragments. As shown in the specification sheets of the kit (https://www.qiagen.com/us/products/diagnostics-and-clinical-research/solutions-for -laboratory-developed-tests/qiasymphony-dsp-circulating-dna-kit), urine DNA contains abundant DNA fragments that peak at 50bp. In contrast, plasma cfDNA does not contain such fragments at appreciable concentrations. This suggests that small fragments, 50-150bp long, are not a major component of cfDNA, and thus, our measurements of the total concentration of cfDNA are not dramatically underestimated.

      The convention regarding the size distribution of cfDNA fragments is based on extensive evidence using multiple approaches. For example, a study that profiled the DNA released by multiple cell lines in vitro (Aucamp et al. 2017) used another kit for DNA isolation – the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel, Düren, Germany). This kit does extract fragments that are 50bp long (nucleospin-gel-and-pcr-clean-up-mini). Indeed, the DNA released from cultured cells did contain a peak at 50bp, but it was minor compared with the nucleosome-size peak.

      More recently, several studies did suggest the presence of ultra-short cfDNA fragments, 50 bp long on average, and concluded that such fragments might be present at a molar concentration that is comparable to that of nucleosome-protected DNA (for example, (Hisano et al. 2021)).

      Thus, our model estimates can be off by up to 2-fold (that is, actual cfDNA concentration measured in most studies overlooks the small fragments and thus underestimates the actual concentration of cfDNA by 2-fold). This is incorporated into the revised manuscript.

      We note that we cannot exclude the presence of abundant ultra-short DNA fragments (e.g., 10bp long). However, such fragments are not measurable in cfDNA analysis. Thus, we can refine our conclusion and state that only a small fraction of DNA of dying cells appears as measured cfDNA. We included a section in the methods detailing the integration of a potential factor for the short fragments and revised the discussion:

      “The overall plasma cfDNA concentration was multiplied by a factor of 1.5 to accommodate for the presence of small fragments of approximately 50 base pairs of cfDNA in the plasma. These fragments are suggested to contribute comparable molar concentrations (Hisano, Ito, and Miura 2021). Despite having approximately one-third of the mass, it is reasonable to presume that these fragments represent a similar number of genomes. This assumption is based on the idea that their source is a broken nucleosome unit, and the fragments represent the portion that was not degraded. Given the restricted data and its interpretation, we consider factors spanning the range of 1 (negligible effect) and 2 (doubling of the amount). The chosen factor, 1.5, is selected as the midpoint within this range of uncertainty.”

      “In this study, we report a surprising, dramatic discrepancy between the measured levels of cfDNA in the plasma and the potential DNA flux from dying cells. One hypothetical explanation for that discrepancy is the limited sensitivity of typical cfDNA assays to short DNA fragments, which may contribute a significant fraction of the overall cfDNA mass. Regular cfDNA analysis shows a size distribution concentrated around a length of 165 base pairs (bp). The sizes in ctDNA vary more, but most are longer than 100 bp (Alcaide et al. 2020; Udomruk et al. 2021). Recent studies suggested a significant fraction of single-strand ultrashort fragments (length of 25-60 bp) (Cheng et al. 2022; Hisano, Ito, and Miura 2021). However, the total amount of DNA contained in these fragments is less than or comparable to that of the longer “regular” nucleosome-protected cfDNA fragments (Cheng et al. 2022; Hisano, Ito, and Miura 2021), arguing against ultrashort fragments as a dominant explanation for the “missing” cfDNA material. We integrated the estimate provided by Hisano et al. into our analysis as a modifying factor for both the total concentration and uncertainty of plasma cfDNA. Importantly, this incorporation did not alter the overall conclusions, as the discrepancy between the cfDNA plasma concentration and potential DNA flux remains on the same order of magnitude. We note that we cannot exclude the presence of abundant DNA fragments that are even shorter (e.g., 10bp long) and are not measurable in cfDNA analysis. Thus, our formal conclusion is that only a small fraction of the DNA of dying cells appears as measurable cfDNA.”

      5) Health status of samples analyzed.

      Health, sex and physical activity affects cfDNA yields. This is not accounted for or discussed in the manuscript.

      We incorporated several enhancements to improve our analysis in response to the provided feedback. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

      Furthermore, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in the works of (Meddeb et al. 2019; Madsen et al. 2019). Our intent in doing so was to demonstrate that these factors are unlikely to alter our conclusions substantially when considering a healthy population. We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion:

      “Our estimates for total plasma cfDNA concentration were derived from the median concentration observed in individuals below 47 years of age (n=52), as reported by (Meddeb et al. 2019). To complement this, we integrated our total concentration estimates with data on the proportion of cfDNA originating from specific cell types, leveraging a plasma methylome deconvolution method described by (Loyfer et al. 2023), which did not provide absolute quantities of cfDNA). To quantify the uncertainty associated with our cfDNA concentration estimates, we employed a methodology that considered several sources of variation. First, we incorporated the confidence interval of the median concentration reported by Meddeb et al. as a measure of uncertainty. Additionally, we accounted for individual-specific and analytic variations based on the study by (Madsen et al. 2019), encompassing factors such as the precise timing of measurements and assay precision. These sources of uncertainty were combined using the approach outlined below.”

      “Our current analysis focused on estimating plasma cfDNA concentration and cellular turnover in a cohort of healthy, relatively young individuals. The total plasma cfDNA concentrations were sourced from healthy individuals below 47 years, as reported by (Meddeb et al. 2019). We use data analyzed based on plasma samples from healthy individuals to estimate the proportion of cfDNA originating from specific cell types (Loyfer et al. 2023). These values were then compared to the potential DNA flux resulting from homeostatic cellular turnover, estimated for reference healthy males aged between 20 and 30 (Sender and Milo 2021). In our analysis, we considered various sources of uncertainty, including inter-individual variation, variability in the timing of sample collection, and analytical precision (Madsen et al. 2019; Meddeb et al. 2019). These factors collectively contributed to an uncertainty factor of less than 3. Importantly, this level of uncertainty does not alter our conclusion regarding the relatively small fraction of DNA present in plasma as cfDNA. Furthermore, we acknowledge that age and sex can impact total cfDNA concentration, as demonstrated by (Meddeb et al. 2019), with potential variations of up to 30%. However, as the results of our analysis present a much larger difference, these effects do not change the conclusions drawn from our analysis. Nevertheless, age and health status may influence the proportion of cfDNA originating from specific cell types and their corresponding cellular turnover rates. Consequently, the ratios themselves may vary in the elderly population or individuals with underlying health conditions.”

      Reviewer #2 (Recommendations For The Authors):

      1) Align the cohorts to estimate DNA production and plasma cfDNA levels. Cellular turnover rate and plasma cfDNA levels vary with age, sex, circadian clock, and other factors (Madsen AT et al, EBioMedicine, 2019). This study estimated DNA production using data abstracted from a homogenous group of healthy control males (Sender & Milo, Nat Med 2021). On the other hand, plasma cfDNA levels were obtained from datasets of more diverse cohort of healthy males and females with a wide range of ages (Loyfer et al. Nature, 2023 and Moss et al., Nat Commun, 2018).

      We have incorporated several enhancements to improve the coherence of our analysis. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

      There was no specific estimate for a cohort of young males in both Meddeb et al. and Loyfer et al.; however, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in literature (Meddeb et al. 2019; Madsen et al. 2019). Thus, we demonstrate that sex and age have a small effect on the cfDNA concentrations and thus are unlikely to alter our conclusions substantially when considering a healthy population.

      We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion.

      “Our estimates for total plasma cfDNA concentration were derived from the median concentration observed in individuals below 47 years of age (n=52), as reported by (Meddeb et al. 2019). To complement this, we integrated our total concentration estimates with data on the proportion of cfDNA originating from specific cell types, leveraging a plasma methylome deconvolution method described by (Loyfer et al. 2023), which did not provide absolute quantities of cfDNA). To quantify the uncertainty associated with our cfDNA concentration estimates, we employed a methodology that considered several sources of variation. First, we incorporated the confidence interval of the median concentration reported by Meddeb et al. as a measure of uncertainty. Additionally, we accounted for individual-specific and analytic variations based on the study by (Madsen et al. 2019), encompassing factors such as the precise timing of measurements and assay precision. These sources of uncertainty were combined using the approach outlined below.”

      “Our current analysis focused on estimating plasma cfDNA concentration and cellular turnover in a cohort of healthy, relatively young individuals. The total plasma cfDNA concentrations were sourced from healthy individuals below 47 years, as reported by (Meddeb et al. 2019). We use data analyzed based on plasma samples from healthy individuals to estimate the proportion of cfDNA originating from specific cell types (Loyfer et al. 2023). These values were then compared to the potential DNA flux resulting from homeostatic cellular turnover, estimated for reference healthy males aged between 20 and 30 (Sender and Milo 2021). In our analysis, we considered various sources of uncertainty, including inter-individual variation, variability in the timing of sample collection, and analytical precision (Madsen et al. 2019; Meddeb et al. 2019). These factors collectively contributed to an uncertainty factor of less than 3. Importantly, this level of uncertainty does not alter our conclusion regarding the relatively small fraction of DNA present in plasma as cfDNA. Furthermore, we acknowledge that age and sex can impact total cfDNA concentration, as demonstrated by (Meddeb et al. 2019), with potential variations of up to 30%. However, as the results of our analysis present a much larger difference, these effects do not change the conclusions drawn from our analysis. Nevertheless, age and health status may influence the proportion of cfDNA originating from specific cell types and their corresponding cellular turnover rates. Consequently, the ratios themselves may vary in the elderly population or individuals with underlying health conditions.”

      2) "cfDNA fragments are not created equal". Recent studies demonstrate that cfDNA composition vary with disease state. For example, cfDNA GC content, fraction of short fragments, and composition of some genomic elements increase in heart transplant rejection compared to no-rejection state (Agbor-Enoh, Circulation, 2021). The genomic location and disease state may therefore be important factors to consider in these analyses.

      In this study, we addressed the total amount of cfDNA in healthy individuals without regard to GC content, representation of different genomic regions, or fragment length, as the goal was to understand if cell death rates are fully accounted for by cfDNA concentration. We agree that it will be interesting to study the relative representation of the genome in cfDNA and the processes that determine cfDNA concentration in pathologies beyond the rate of cell death. These topics for future research fall beyond this study's scope.

      3) Alternative sources of DNA production should be considered. Aside from cell death, DNA can be released from cells via active secretion. This and other additional sources of DNA should be considered in future studies. The distinct characteristics of mitochondrial DNA to genomic DNA should also be considered.

      We know only a few specific cases whereby DNA is released from cells that are not dying. These include the release of DNA from erythroblasts and megakaryocytes to generate anucleated erythrocytes and platelets (Moss et al. 2022, cited in our paper) and the release of NETs from neutrophils.

      The presence of cfDNA fragments originating from megakaryocytes and erythroblasts indicates the elimination of megakaryocytes and erythroblasts and the birth of erythrocytes and platelets. However, the considerations in the rest of the paper still apply: the concentration of cfDNA from these sources is far lower than expected from the cell turnover rate.

      Concerning NETosis: the presence of cfDNA originating in neutrophils that have not died would reduce the concentration of cfDNA from dying neutrophils and thus further increase the discrepancy, which is the topic of our study (under-representation of DNA from dying cells in plasma).

      We updated a paragraph in the discussion regarding this issue:

      “A comparison between the different types of cells shows a trend in which less DNA flux from cells with higher turnover gets to the bloodstream. In particular, a tiny fraction (1 in 3x104) of DNA from erythroid progenitors arrives at the plasma, indicating an extreme efficiency of the DNA recovery mechanism. Erythroid progenitors are arranged in erythroblastic islands. Up to a few tens of erythroid progenitors surround a single macrophage that collects the nuclei extruded during the erythrocyte maturation process (pyrenocytes) (Chasis and Mohandas 2008). The amount of DNA discarded through the maturation of over 200 billion erythrocytes per day (Sender and Milo 2021) exceeds all other sources of homeostatic discarded DNA. Our findings indicate that the organization of dedicated erythroblastic islands functions highly efficiently regarding DNA utilization. Neutrophils are another high-turnover cell type with a low level of cfDNA. When contemplating the process of NETosis (Vorobjeva and Chernyak 2020), the existence of cfDNA originating from live neutrophils would potentially diminish the concentration of cfDNA released by dying neutrophils, thereby amplifying the observed ratio for this particular cell type. The overall trend of higher turnover resulting in a lower cfDNA to DNA flux ratio may indicate similar design principles, in which the utilization of DNA is better in tissues with higher turnover. However, our analysis is limited to only several cell types (due to cfDNA test and deconvolution sensitivities), and extrapolation to cells with lower cell turnover is problematic.”

      We neglected mitochondrial DNA, as it is not measured in methylation cell-of-origin analysis. Similarly to the argument above, if some of the total DNA measured in plasma is in fact mitochondrial, this would mean that genomic cfDNA concentration is actually lower than the estimates, meaning that an even smaller fraction of DNA from dying cells is measured in plasma.

    1. Late infection

      Given that we assume the effect of revision is conditional on the nature of that revision, it's not clear to me what "DAIR vs. Revision" means for late infection silo,

      Just thinking through it for myself...

      The options are:

      • A = DAIR -> B = 12w
      • A = Revision(one) -> B in {12w, 6w}
      • A = Revision(two) -> B in {12w, 7d}
      • C in (no rifampicin, rifampicin)

      Currently, (just focusing on surgery/duration, and making 12w the reference for all groups rather than 7w/6d) cell parameters as specified in the model are:

      $$ \begin{matrix} \text{DAIR} \ \text{Revision(one), 12w} \ \text{Revision(two), 12w} \ \text{Revision(one), 6w} \ \text{Revision(two), 7d} \end{matrix} \begin{pmatrix} \alpha \ \alpha + \beta_A \ \alpha + \beta_A \ \alpha + \beta_A + \beta_{B1} \ \alpha + \beta_A + \beta_{B2} \end{pmatrix} $$

      So, effect of one-stage + 12 w assumed equal to the effect of two-stage + 12w, then revision type specific deviations from those. And "DAIR vs Revision" (beta_A) is really DAIR vs. weighted average of one-stage 12w and two-stage 12w, i.e. ignores the duration options.

      I'm guessing this is the only randomised comparison we can make: a weighted average of one/two + 12w is the "default" revision.

      As an alternative, I assume it's plausible that one-stage 12w and two-stage 12w differ due to clinician selection of one/two stage. So there may be preference (or maybe it just makes things messy) to have something like

      $$ \begin{matrix} \text{DAIR} \ \text{Revision(one), 12w} \ \text{Revision(two), 12w} \ \text{Revision(one), 6w} \ \text{Revision(two), 7d} \end{matrix} \begin{pmatrix} \alpha \ \alpha + \beta_{A1} \ \alpha + \beta_{A2} \ \alpha + \beta_{A1} + \beta_{B1} \ \alpha + \beta_{A2} + \beta_{B2} \end{pmatrix} $$

      Noting that \beta_{A1} and \beta_{A2} aren't "causal" in the sense that any differences could just be due to selection bias rather than differences in effectiveness of one/two stage.

      The effect of revision versus DAIR will depend on what "revision" means. We can't just compare one-stage 12 weeks to DAIR vs 12 weeks because surgeon's choose who gets one-stage. Only comparison that seems to make sense is weighted combination of one/two stage, with weight as observed in the trial. I think that comparison makes sense, but maybe not.

      Assume that $$p_{A1}$$ is the proportion randomised to revision who are selected to receive one-stage and $$1 - p_{A1}$$ the proportion selected to receive two-stage. Then the comparison for any revision versus DAIR might be taken to mean

      $$ p_{A1}(0.5\beta_{A1} + 0.5(\beta_{A1} + \beta_{B1}))\ + (1 - p_{A1})(0.5\beta_{A2} + 0.5(\beta_{A2} + \beta_{B2})) $$

      which just explicitly allows for the differences. Or some other combination of groups, where we assume that selection of one/two stage in the trial is the same in the population. Presumably though there are issues in interpreting such a comparison as "causal", unless also adjust for factors determining one/two stage selection.

      The \beta_{A1} and \beta_{A2} are necessary for estimation of the duration effects, unless willing to assume no differences between one/two stage 12w.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their reading of our manuscript, which we believe has led to substantial improvements.

      To aid clarity, we have split Fig. 1 into three separate figures.

      For convenience, we have put all major changes in the text in blue.

      Reviewer #1

      Evidence, reproducibility and clarity

      Summary: Hui et al. tackle a crucial question in biology: what factors influence the preference for carbon sources in yeasts?

      They reveal that the growth rate on palatinose exceeds that on glucose,

      The above statement is incorrect --- we think the reviewer may have confused sugars.

      despite palatinose utilization being repressed in the presence of glucose. Consequently, the favored carbon source does not necessarily align with the one supporting the fastest growth rate. The study also delves into potential regulatory mechanisms governing carbon source preference and dismisses certain existing theories, such as the general carbon flux sensing mechanism proposed by Okano et al. [25].

      Major comments: None

      Minor comments:

      The authors suggest that a higher growth rate implies a higher glycolytic flux (l63), a crucial assumption underpinning their interpretation of the absence of a ``general carbon flux sensing mechanism' (l65). To substantiate this significant conclusion, they could calculate the extracellular uptake fluxes (based on the time-course concentrations of biomass and substrates).

      This suggestion is a good one, but unfortunately the number of data points in the new Fig. 3 are insufficient to estimate the uptake flux reliably.

      To address whether glycolytic flux increases, we have added a new paragraph to the introduction explaining how all the sugars we consider feed upper glycolysis, providing either its first or second metabolite. We therefore think it highly likely that any differences in growth rate are generated by differences in glycolytic flux. Indeed, Hackett et al., 2016, showed that the glycolytic flux increases with growth rate when they changed extracellular glucose concentrations. We now include this reference in the Discussion.

      The accumulation of certain by-products is known to be toxic, reducing cellular growth rate (e.g., acetate DOI: 10.1038/srep42135, ethanol DOI: 10.1016/B978-0-12-040308-0.50006-9, etc.), while they can also enhance growth under specific conditions (e.g., acetate DOI: 10.15252/embj.2022113079). Considering this is crucial to rule out certain hypotheses, such as the possibility that a by-product produced during growth on the first carbon source would not modulate growth on the second carbon source, potentially influencing the growth rate differentially in each phase. Although the authors use mutant strains to eliminate the role of some C2 compounds (acetate and ethanol), alternative pathways could be implicated in the (co-)utilization of these by-products. This aspect should be discussed, and ideally, the authors could quantify the time-course concentrations of by-products to assess their potential role.

      We agree with the reviewer that extracellular acetate and ethanol may inhibit growth, although budding yeast might be less sensitive than E. coli, the subject of most of the studies provided.

      Nevertheless, we think it unlikely that these chemicals modify the decision-making we see. First, the icl1Δ mutant we tested is unable to consume ethanol (Fernandez et al., 1992) or acetate (Lee et al., 2011) --- we now include these references in the SI --- and yet has wild-type behaviour (Fig. S2D). Second, we observe that isomaltase expression strongly decreases in the presence of galactose when we grow cells in a microfluidic device (Fig. S4), just like it does in batch culture (Fig. 3A), even though the constant flow of medium through the device removes any chemicals the cells excrete.

      The general flux-sensing regulatory mechanism proposed by Okano et al. [25], which has been dismissed by this study, has recently been questioned, as discussed in DOI: 10.15252/embj.2022113079. This aspect should be included in the discussion.

      Okano et al. studied E. coli while we study budding yeast. We therefore have shown that the understanding for that organism does not transfer to our eukaryotic example. We suspect that control in budding yeast combines both flux-sensing and specific regulation, as we say in the discussion, and so we consider our results to build on those of Okano et al.

      Significance

      Strengths & limitations: The work is robust, and the experiments in the study have been appropriately designed and conducted. The primary question of this study has been tackled using a combination of experimental and computational methods to thoroughly assess various regulatory and functional aspects. However, there are gaps in the data that could enhance key conclusions, notably the absence of glycolytic flux measurements. Moreover, further evidence is needed to substantiate the assertion that by-products do not play a role in carbon source preference.

      Advance: This study represents a significant step forward in comprehending the nutritional strategy of microbes. The authors demonstrate that the preferred carbon source may not necessarily be the one supporting the fastest growth rate. Furthermore, they dismiss certain theories that have been proposed to explain the growth strategy of microbes on mixed carbon sources.

      Audience: By addressing a fundamental question in life science, this work is important in the field of biology in general and of particular interest in systems biology, biotechnology, synthetic biology, and health. Consequently, it will be of interest to a broad audience.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary: The authors have used microtiter plates to produce growth profiles on combinations of different sugars. From this data they have evaluated whether the sugars are co-consumed or if there is a preference for either sugar, seen as a diauxic shift. They found diauxie between galactose and the disaccharide palatinose, but co-consumption between palatinose and fructose. They further used strains with perturbations in their GAL regulon to attempt to explain this discrepency.

      Major comments:

      I unfortunately found a large portion of the present manuscript unintelligable.

      Firstly, figures were incorrect to the point I could not dechiffre them: Figure 2A-C have black solid and dashed lines in the legend that are not found in the graph, instead there are orange and blue dashed lines in the graph with no legends. Figure 4C has no description of the y-axis. The growth rates in Figure 1C are very hard to follow, and there are definitely local maxima in both the blue and green profiles that are not being discussed (at 15-20 h). I cannot evaluate the conclusions drawn from the data until these issues have been resolved.

      We apologise for the difficulties experienced by this reviewer.

      The black lines in the old Fig. 2's legend, now Fig. 4, explain the different styles used: dashed lines are for single sugars regardless of their concentration and full lines are for mixtures regardless of their concentration. We now explicitly say this in the caption.

      We have fixed the missing label in what is now Fig. 6C and have moved the statement that we are showing two biological replicates for each set of concentrations earlier in Fig. 2's caption.

      We now explore the meaning of the shoulder for the fructose-palatinose mixture in Fig. 2B in the Discussion. This point is not a local maximum, unlike the case for diauxie, because the growth rate always decreases. The shoulder for the glucose-palatinose mixture was likely an artefact generated by measurement noise at low ODs because it was not present when we repeated the experiment. We now use that data for Fig. 2A & B. We also include a new Fig. S5 showing that there are sucrose-palatinose concentrations too that have a similar shoulder.

      Secondly, the language in the Results and Discussion sections is confusing. Alternating between present and imperfect tense as well as active and passive form makes it hard to distinguish the authors own results from literature findings (Results are usually written in passive, imperfect tense). Examples are found on lines 24, 29, 37-38, 59, 84, 131, and 165.

      We have made both sections flow more smoothly with substantial re-writing. As before, we cite all results that are not our own.

      The authors also do not consider the differences and similarities in catabolic pathways for assimilation of galactose, fructose and palatinose. Even if they do not see a reason to continue that as a possible explanation for the co-consumption between fructose and palatinose a discussion of why it is disregarded would not be out of place here.

      A good point, and we now state in the Introduction that all the sugars we study feed upper glycolysis.

      Significance

      There is some novelty to the authors findings, but I would argue it is being overstated in the present manuscript. Some examples of studies looking at catabolite repression, the main cause of diauxie, of sugars other than glucose can be found in: Simpson-Lavy and Kupiec (2019), Gancedo (1998), Prasad and Venkatesh (2008) and Borgstrom et al (2022).

      We strongly disagree with this statement. The papers cited do not address, as we do, the co-consumption between two sugars neither of which is glucose. Where they study two sugars, they always study glucose.

      Simpson-Lavy and Kupiec, 2019, investigate the interaction between acetate and ethanol, neither of which are sugars. Further, they are not independent carbon sources because cells convert ethanol into acetate when catabolising ethanol.

      Gancedo, 1998, is a review of glucose repression and describes how glucose represses the expression of genes for other sugars. Although Gancedo mentions ``galactose repression', this repression is of genes encoding enzymes for gluconeogenesis and the TCA and glyoxylate cycles, not of other sugar regulons, our subject.

      Prasad and Venkatesh, 2008, also focus on glucose and the well studied diauxie between glucose and galactose.

      Borgstrom et al., 2022, focus too on glucose and growth on glucose and xylose in recombinant strains. The standard laboratory strains we study have not be artificially engineered to consume xylose. They do mention that galactose causes repression of TPS1, which encodes an enzyme that synthesises the storage carbohydrate trehalose. This repression is again not of a sugar catabolic regulon, our subject.

      I would not say that the field would be significantly advanced by the publication of this manuscript, and the authors have themselves not explained the application of futhering the understanding palatinose metabolism in yeast. As mentioned above, the catabolite repression potential of galactose is already known, it just hasn't been shown for palatinose specifically before.

      We again strongly disagree. Our findings are novel. The reviewer did not provide any evidence for galactose repression of other sugar regulons, which is not widely recognised as we emphasised in the Discussion. We believe that the reviewer has confused the known "galactose repression' of gluconeogenic or TCA-cycle genes with our new report of repression of other sugar regulons in the presence of the sugar catabolised by the regulon.

      I would recommend a complete rewrite of the manuscript as presented, with a lower stated novelty, clearer language and comprehensible figures.

      Reviewer #3

      Evidence, reproducibility and clarity

      Summary: Microbes grow at different growth rates in different carbon sources. When more than one carbon sources are present in the media microbes often show a preference over certain carbon sources, and 'non-preferred' carbons sources are used only when the preferred carbon source is exhausted in the media, this process called diauxic shift.

      Why microbes exhibit such utilization preference over certain carbon sources, is an interesting question in microbiology and evolutionary biology, and the molecular mechanisms that enable microbes to preferentially use one carbon over another is worth investigating. It is intuitive to think that microbes will prefer to use a carbon source that confers maximum growth rate, but when tested experimentally it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used.

      Although the reviewer states that "it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used“, we are unaware of this work and would appreciate references, particularly for budding yeast. The most systematic study we know, in E. coli by Aidelberg et al., 2014 --- reference 13, concludes that "the faster the growth rate, the higher the sugar on the hierarchy“, the opposite behaviour.

      In this study authors demonstrate that budding yeast prefer to use galactose over palatinose, but not over sucrose or fructose where all three sugars can support faster growth rate compared to palatinose. Authors presented data where preferential galactose use and diauxic shift is observed in the growth curve when galactose and palatinose or glucose and palatinose combinations were used.

      No diauxic shift was observed in the growth curve when fructose-palatinose, or sucrose-palatinose combination were used. In fructose-palatinose and sucrose-palatinose combinations growth curves agree more with co-utilization strategies. Authors used transcriptomics and genetic perturbations to decipher the molecular mechanism of such preferential carbon use, and reports preference of galactose over palatinose is achieved by preventing positive feedback of MAL regulon, which encodes the genes for palatinose catabolism. We found this observation is interesting and the molecular mechanism of such preferential carbon use is nicely described in this paper. We also find claims authors made are well supported by experiments. Although catabolite repression and diauxic transitions are known in yeast, and authors also pointed out such previous references, but preferential use of a slower carbon source, i.e. galactose over at least one other fast-growing carbon is interesting enough for publication. We would like to support the publication of this article, but we have major concerns about the data analysis and data presentation. Authors must address our concerns which are mentioned below.

      Major comments:

      1. This study mainly hinges on growth rate measurements, but we found growth rates are not properly represented in the figures. Growth curves are always shown in linear scale, which makes it almost impossible to compare fast and slow growth when presented in same plot. All growth curves must be shown on log scale.

      We have changed all growth curves to log2 scale, following New et al., 2014, rather than Monod's choice of linear scale that we had originally.

      Our conclusions are unaffected.

      1. Growth rates of the Yeast strain growing individual single carbon sources (galactose, palatinose, sucrose and fructose) should be shown as a figure panel and t-test should be performed to conclude if the individual growth rates are significantly different or not.

      We already showed these growth rates in their own panel in Fig. 1B. Following the reviewer's suggestion, we have now added their statistical significance to the caption.

      1. Growth phase, lag phase, diauxic shift and post shift growth should be clearly shown in figure 2 and 4, each phase should be clearly marked, carbons used in each phase should be mentioned on the plot. Also, the growth curve must be plotted using log scale.

      Although we have changed all growth curves to log scale, we decided against include this additional labelling for two reasons. First, we are presenting evidence that some of the growth we observe is diauxic and labelling the curves as diauxic before we discuss this evidence undermines that discussion. Second, any further labels would clutter the figures, and we believe would hinder rather than help the reader.

      Instead we changed the colour scheme and the boldness of the diauxic growth curves in Fig. 2, which we hope the reviewer agrees adds the clarity they felt was missing.

      1. Authors has taken in account that MAL12 gene overexpression causes long lag when cells need to switch to maltose from glucose, and shown deletion of IMA1 decreases the lag with subsequent 2% growth rate increase in palatinose. How significant is this increase?

      We have confirmed the statistical significance through a t-test and added the results to the caption of Fig. 6C.

      1. Authors have an interesting observation that in sucrose-palatinose and fructose palatinose combinations, most probably co utilization of the carbons is taking place. Authors should discuss this in more details. In galactose-palatinose scenario intracellular galactose-based repression of gal80 and subsequent lack of feed forward of the Mal regulon is expected to stop co-utilization of palatinose. As authors have RNA seq data, can they make predictions for other carbon pairs, where sequential utilization can occur based on their model?

      We agree and have added more discussion of the fructose- and sucrose-palatinose mixtures to the Discussion and a new figure, Fig. S5.

      Our RNAseq data reveals the difference in gene expression caused by an active versus an inactive GAL regulon. In Fig. S11, we show that the hexose transporters HXT2 and HXT7 are down regulated in 0.1% fructose when the GAL regulon is active, perhaps implying that cells are able to prioritise galactose over other hexoses. Nevertheless, to predict if particular carbon sources are therefore favoured, we would need to know whether cells use specific hexose transporters to drive growth on different carbon sources, which has been little investigated.

      Minor comments:

      1. In figure 5, authors attempted to summarize the model, which is informative, but it will be more useful for non-specific reader if a cell-based cartoon, with transports on surface and catabolic enzymes inside is also added.

      We have re-designed Fig. 5, now Fig. 7, following this suggestion and agree it improves clarity.

      In this schematic diagram, switch from galactose (blue line) to red line (palatinose) shows a mixed color zone, it's a bit confusing, as this represents a bi-stable state. Authors should clearly comment on possibility of biostability while discussing their proposed mechanism.

      In the new figure, this part has been removed.

      1. The author may want to put their work in the context of other recent observations that bacteria do not try to maximize their growth rates in many conditions. Fast growth is often associated with expansive tradeoffs, and a carbon source which confers fast growth rate may confer selective disadvantage. Thus, there are evolutionary benefits of sub-optimal growth, which could be discussed in the manuscript. In this regard a recent study (bioRxiv (2023) doi:10.1101/2023.08.22.554312.) has established the link between resource allocation strategies, growth rates and tradeoffs, which may be taken in account while discussing. Are there any known tradeoffs, when galactose is used over palatinose and which is not the case sucrose or fructose?

      This is an interesting reference looking at growth on a single carbon source. We are unaware of similar tradeoffs relevant to our study. For example, we see little evidence for a constraint on the proteome because in a strain with a constitutively active GAL regulon there is no change in phenotype if we delete the genes for the three highly expressed GAL enzymes (Fig. S6B). Nevertheless and as we state in the penultimate paragraph of the Discussion, we agree that such a constraint must exist, although perhaps this constraint is ecological.

      Referees cross-commenting

      As other reviewers pointed out, this study has merit and addressed interesting questions, but needed to be written well in a more understandable form, we agree with this assessment. Also figures must be made much clearer, as all of the reviewers pointed out. In summary, this is an interesting study, but needs some work before publication.

      Significance

      General assessment: Strength and limitations:

      This study addressed an interesting question regarding resource preference and growth rate optimization in microbes. This is an important question in the field. Study is well designed and claims are backed up with experimental results. One of the limitations of the study is lack of predictability. Authors explained the mechanism for one pair of carbon sources, but how applicable that will be in general is not clear.

      We would argue that one of our important findings is to demonstrate that the scientific community is missing the information needed to make such predictions. We provide a counter example to the generally accepted belief that accurate predictions can be made using growth rates. Our work poses the question: what then are the physiological variables required to predict how a cell will consume a pair of carbon sources?

      Advance: This study helps to advance our knowledge. Their observation regarding preferential utilization of a carbon source which supports slower growth over a carbon source which can support faster growth, and the molecular mechanism provided will help researchers to understand resource allocation strategies better.

      Audience: Microbiology, systems biology, evolutionary biology, fermentation and bio process engineering research.

      Reviewer expertise: Biochemistry, systems biology, metabolic strategies and tradeoffs in microbes, microbial ecology.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Review of the paper by Yu Huo et al.

      Summary:

      Microbes grow at different growth rates in different carbon sources. When more than one carbon sources are present in the media microbes often show a preference over certain carbon sources, and 'non-preferred' carbons sources are used only when the preferred carbon source is exhausted in the media, this process called diauxic shift. Why microbes exhibit such utilization preference over certain carbon sources, is an interesting question in microbiology and evolutionary biology, and the molecular mechanisms that enable microbes to preferentially use one carbon over another is worth investigating. It is intuitive to think that microbes will prefer to use a carbon source that confers maximum growth rate, but when tested experimentally it has been often observed that a carbon source in which microbes grow at sub optimal growth rate is actually preferentially used. In this study authors demonstrate that budding yeast prefer to use galactose over palatinose, but not over sucrose or fructose where all three sugars can support faster growth rate compared to palatinose. Authors presented data where preferential galactose use and diauxic shift is observed in the growth curve when galactose and palatinose or glucose and palatinose combinations were used.

      No diauxic shift was observed in the growth curve when fructose-palatinose, or sucrose-palatinose combination were used. In fructose-palatinose and sucrose-palatinose combinations growth curves agree more with co-utilization strategies. Authors used transcriptomics and genetic perturbations to decipher the molecular mechanism of such preferential carbon use, and reports preference of galactose over palatinose is achieved by preventing positive feedback of MAL regulon, which encodes the genes for palatinose catabolism. We found this observation is interesting and the molecular mechanism of such preferential carbon use is nicely described in this paper. We also find claims authors made are well supported by experiments. Although catabolite repression and diauxic transitions are known in yeast, and authors also pointed out such previous references, but preferential use of a slower carbon source, i.e. galactose over at least one other fast-growing carbon is interesting enough for publication. We would like to support the publication of this article, but we have major concerns about the data analysis and data presentation. Authors must address our concerns which are mentioned below.

      Major comments:

      1. This study mainly hinges on growth rate measurements, but we found growth rates are not properly represented in the figures. Growth curves are always shown in linear scale, which makes it almost impossible to compare fast and slow growth when presented in same plot. All growth curves must be shown on log scale.
      2. Growth rates of the Yeast strain growing individual single carbon sources (galactose, palatinose, sucrose and fructose) should be shown as a figure panel and t-test should be performed to conclude if the individual growth rates are significantly different or not.
      3. Growth phase, lag phase, diauxic shift and post shift growth should be clearly shown in figure 2 and 4, each phase should be clearly marked, carbons used in each phase should be mentioned on the plot. Also, the growth curve must be plotted using log scale.
      4. Authors has taken in account that MAL12 gene overexpression causes long lag when cells need to switch to maltose from glucose, and shown deletion of IMA1 decreases the lag with subsequent 2% growth rate increase in palatinose. How significant is this increase?
      5. Authors have an interesting observation that in sucrose-palatinose and fructose palatinose combinations, most probably co utilization of the carbons is taking place. Authors should discuss this in more details. In galactose-palatinose scenario intracellular galactose-based repression of gal80 and subsequent lack of feed forward of the Mal regulon is expected to stop co-utilization of palatinose. As authors have RNA seq data, can they make predictions for other carbon pairs, where sequential utilization can occur based on their model?

      Minor comments

      1. In figure 5, authors attempted to summarize the model, which is informative, but it will be more useful for non-specific reader if a cell-based cartoon, with transports on surface and catabolic enzymes inside is also added.

      In this schematic diagram, switch from galactose (blue line) to red line (palatinose) shows a mixed color zone, it's a bit confusing, as this represents a bi-stable state. Authors should clearly comment on possibility of biostability while discussing their proposed mechanism. 2. The author may want to put their work in the context of other recent observations that bacteria do not try to maximize their growth rates in many conditions. Fast growth is often associated with expansive tradeoffs, and a carbon source which confers fast growth rate may confer selective disadvantage. Thus, there are evolutionary benefits of sub-optimal growth, which could be discussed in the manuscript. In this regard a recent study (bioRxiv (2023) doi:10.1101/2023.08.22.554312.) has established the link between resource allocation strategies, growth rates and tradeoffs, which may be taken in account while discussing. Are there any known tradeoffs, when galactose is used over palatinose and which is not the case sucrose or fructose?

      Referees cross-commenting

      As other reviewers pointed out, this study has merit and addressed interesting questions, but needed to be written well in a more understandable form, we agree with this assessment. Also figures must be made much clearer, as all of the reviewers pointed out. In summary, this is an interesting study, but needs some work before publication.

      Significance

      General assessment: Strength and limitations: This study addressed an interesting question regarding resource preference and growth rate optimization in microbes. This is an important question in the field. Study is well designed and claims are backed up with experimental results. One of the limitations of the study is lack of predictability. Authors explained the mechanism for one pair of carbon sources, but how applicable that will be in general is not clear.

      Advance: This study helps to advance our knowledge. Their observation regarding preferential utilization of a carbon source which supports slower growth over a carbon source which can support faster growth, and the molecular mechanism provided will help researchers to understand resource allocation strategies better.

      Audience: Microbiology, systems biology, evolutionary biology, fermentation and bio process engineering research.

      Reviewer expertise: Biochemistry, systems biology, metabolic strategies and tradeoffs in microbes, microbial ecology.

    1. Author Response

      The following is the authors’ response to the current reviews.

      We would firstly like to thank all reviewers for their comments and support of this manuscript.

      Reviewer #1 (Recommendations For The Authors):

      No further recommendations.

      Reviewer #2 (Recommendations For The Authors):

      All of my comments have been sufficiently addressed.

      Reviewer #3 (Recommendations For The Authors):

      Thanks for responding to my former recommendations constructively. I believe these points have been fully addressed in this new version.

      However, I have not seen any comments on the points I raised in my former public review concerning the I-2 dependence of the FonSIX4 cell death. Do you know whether FonSIX4 would trigger cell death in tissues not expressing any I-2?

      We are a little confused concerning this comment. I-2 is a different class of resistance protein (NLR) that recognises Avr2 and this is likely to be intracellular. From the previous public review, we believe reviewer 3 may have been asking us to clarify the dependence of I (MM or M82) on FonSIX4 cell death. We have performed these controls by expressing FonSIX4 and associated FonSIX4/Avr1 chimeras in N. benthamiana (with the PR-1 signal peptide for efficient secretion of effectors) and it does not cause cell death in the absence of the I receptor – see S11F Fig. This was not explicitly conveyed in text so we have included the following in text: “Using the N. benthamiana assay we show FonSIX4 is recognised by I receptors from both cultivars (IM82 and iMoneymaker) and cell death is dependent on the presence of IM82 or iMoneymaker (Fig 5B, S11 Fig).”

      I still recommend discussing whether the Avr1 residues crucial for Avr activity are in the same structural regions of the C-terminal domain where previous work has identified residues under diversifying selection in symbiotic fungal FOLD proteins.

      The region important for recognition does encompass some residues within the structural region identified to be under diversifying selection in FOLD effectors from Rhizophagus irregularis previously reported (two residues within one beta-strand). However, we also see residues that don’t overlap to this area. We also note that the mycFOLD proteins analysed in symbiotic fungi are heavily skewed towards strong structurally similarity with FolSIX6 (similar cysteine spacing within both N and C-domains and structural orientation of the N and C-domains) rather than Avr1. We are under the impression that Avr1 was not included in the analysis of diversifying selection in symbiotic fungal FOLD proteins, it also is unclear to us if close Avr1 homologues are present. With this in mind, and considering our already lengthy discussion (as previously highlighted during reviewer), we have decided not to include further discussion concerning this point.


      The following is the authors’ response to the original reviews.

      We would like to thank the editor(s) and reviewers for their work concerning our manuscript. Most of the suggested changes were related to text changes which we have incorporated into the revised version. Please find our response to reviewers below.

      Reviewer #1 (Recommendations For The Authors):

      I only have very minor suggestions for the authors. The first one comes from reading the manuscript and finding it very dense with so many acronyms. This will limit the audience that will read the study and appreciate its impact. This is more noticeable in the Results, with many passages that I would suggest moving to Methodology.

      We thank reviewer 1 for their very positive review. We understand that due to the nature of this study, which includes many protein alleles/mutations that were expressed with different boundaries etc., it is difficult to achieve this. Reviewer 2 asked for more details to be provided. We hope we have achieved a nice balance in the revised manuscript.

      Something else that would facilitate the reading of the manuscript is the effectors name. The authors use the SIX name or the Avr name for some effectors and it makes it difficult to follow up.

      We have tried to make this consistent for Avr1 (SIX4), Avr2 (SIX3) and Avr3 (SIX1). Other SIX effectors are not known Avrs so the SIX names were used.

      Reading the manuscript and seeing how in most of the sections the authors used a computational approach followed by an experimental approach, I wonder why Alphafold2-multimer was not used to investigate the interaction between the effector and the receptor?

      This is a great suggestion, we have certainly investigated this, however to date there is no experimental evidence to directly support the direct interaction between I and Avr1. Post review, we spent some time trying to capture an interaction using a co-immunoprecipitation approach however to date we have not been able to obtain robust data that support this. We are currently looking to study this utilising protein biophysics/biochemistry but this work will take some time.

      Reviewer #2 (Recommendations For The Authors):

      We thank reviewer 2 for the very thorough editing and recommendations. We have incorporated all minor text edits below into the manuscript.

      Line 43: perhaps "Effector recognition" instead of "Effector detection", to be consistent with line 51?

      Line 60: Change to "leads".

      Line 79: Italicise Avr2.

      Line 94: Add the acronym ETI in parentheses after "effector-triggered immunity".

      Line 106: "(Leptosphaeria Avirulence-Supressing)" should be "(Leptosphaeria Avirulence and Supressing)".

      Line 112: Change "defined" to "define".

      Line 119: Spell out the species name on first use.

      Line 205: Glomeromycota is a division rather than a genus. Consistent with Fig 2, it also does not need to italicized.

      Line 207: Change "basidiomycete" to "Division Basidiomycota", consistent with Fig 2.

      Line 214: Change "alignment of Avr1, Avr3, SIX6 and SIX13" to "alignment of the mature Avr1, Avr3, SIX6 and SIX13 sequences".

      Line 324: Change "solved structures" to "solved protein structures".

      Line 335: Spell out acronyms like "MS" on first use in figure legends. Also dpi in other figure legends.

      Line 341: replace "effector-triggered immunity (ETI)" with "(ETI)" - see comment on Line 94.

      Line 370: Change "domains" to "domain".

      Line 374: In the title, change "C-terminus" to C-domain", consistent with the rest of the figure legend.

      Line 404: Change "(basidiomycetes and ascomycetes)" to "(Basidiomycota and Ascomycota fungi)", consistent with Fig 2C.

      Line 416: Change "in" to "by".

      Line 427: un-italicize the parentheses.

      Line 519: First mention of NLR. Spell out the acronym on first use in main text. S5 and S11 figure titles should be bolded.

      Line 852: Replace "@" with "at".

      S4 Table: Gene names should be italicised.

      S5 Table: Needs to be indicated that the primer sequences are in the 5´-3´ orientation.

      With regards to the Agrobacterium tumefaciens-mediated transient expression assays involving co-expression of the Avr1 effector and I immune receptor, the authors need to make clear how many biological replicates were performed as this information is only provided for the ion leakage assay.

      We have added these data to the figure legend

      Line 57: For me, the text "Fol secretes a limited number of structurally related effectors" reads as Fol secretes structurally related effectors, but very few of them are structurally related. Perhaps it would be better to say that the effector repertoire of Fol is made up of proteins that adopt a limited number of structural folds, or that the effector repertoire can be classified into a reduced set of structural families?

      This edit has been incorporated.

      Lines 66-67: Subtle re-wording required for "The best-characterized pathosystem is F. oxysporum f. sp. lycopersici (Fol)", as a pathosystem is made up of a pathogen and its host. Perhaps "The best-characterized pathosystem involves F. oxysporum f. sp. lycopersici (Fol) and tomato".

      Sentence has been reworded.

      Line 113 and throughout: Stick with one of "resistance protein", "receptor", "immune receptor" and "immunity receptor" throughout the manuscript.

      We have decided to use both receptor and immunity receptor as not all receptors investigated in the manuscript provide immunity.

      Lines 149-150: The title does not fully represent what is shown in the figure. The text "that is unique among fungal effectors" can be deleted as there is nothing in Fig 1 that shows that the fold is unique to fungal effectors.

      Figure title has been changed.

      Line 173: The RMSD of Avr3 is stated as being 3.7 Å, but in S3 Fig it is stated as being 3.6 Å.

      This was a mistake in the main text and has been corrected.

      Lines 202-204: This sentence needs to be reworded, as the way that it is written implies that the Diversispora and Rhizophagus genera are in the Ascomycota division. Also, "Ascomycetes" should be changed to "Ascomycota fungi", consistent with Fig 2.

      Sentence has been reworded.

      Line 233: "Scores above 8". What type of scores? Z-scores?

      These are Z-scores. This has been added in text.

      Lines 242-246: It is stated that SIX9 and SIX11 share structural similarity to various RNA-binding proteins, but no scores used to make these assessments is given. The scores should be provided in the text.

      Z-scores have been added.

      Fig 4A: SIX3 should be Avr2, consistent with line 292. The gene names should be italicised in Fig 4A.

      SIX3 was changed to Avr2. Gene names have been italicised.

      Line 356: Subtle rewording required, as "co-infiltrated with both IM82 and iMoneymaker" implies that you infiltrated with protein rather than Agrobacterium strains.

      Sentence has been reworded.

      Fig 5A, Fig 5C and Line 380: Light blue is used, but this looks grey. Perhaps change colour, as grey is already used to show the pro-domain in Fig 5A (or simply change the colour used to highlight the pro-domain)?

      Colour depicting the C-domain was changed.

      Lines 530-531: This text is no longer correct. Rlm4 and Rlm3 are now known to be alleles of Rlm9. See: Haddadi, P., Larkan, N. J., Van deWouw, A., Zhang, Y., Neik, T. X., Beynon, E., ... & Borhan, M. H. (2022). Brassica napus genes Rlm4 and Rlm7, conferring resistance to Leptosphaeria maculans, are alleles of the Rlm9 wall‐associated kinase‐like resistance locus. Plant Biotechnology Journal, 20(7), 1229.

      We thank the reviewer for picking this up. This text has been updated.

      Line 553: Provide more information on what the PR1 signal peptide is.

      More information about the PR1 signal peptide has been added.

      Lines 767-781: Descriptions and naming conventions of proteins throughout the figure legend need to be consistent and better reflect their makeup. For example, I think it would be best to put the sequence range after each protein mentioned - e.g. Avr118-242 or Avr159-242 instead of Avr1, PSL1_C37S18-111 instead of PSL1_C37S, etc. Furthermore, it is often stated that a protein is full-length when it lacks a signal peptide - my thought is that if a proteins lack its signal peptide, it is not full-length. The acronym "PD" also needs to be spelled out as "pro-domain (PD)" in the figure legend.

      We have incorporated sequence range for proteins that were produced upon first use. Sequence ranges that were modelled in AlphaFold2 were not added in text because they can be found in Supplementary Table 3.

      Lines 853-845: It is stated the sizes of proteins are indicated above the chromatogram in S10 Fig, but this is not the case. It is also not clear from S10B Fig that the faint peaks correspond to the peaks in the Fig 4B chromatogram. In S10D Fig, the stick of C58S is difficult to see. Perhaps change the colour or use an arrow/asterisk?

      Protein size estimates have been added above the chromatogram. Added text to indicate that the faint peaks correspond to peaks in Fig 4B. Added an asterisk in S10D Fig to identify the location of C58.

      S14 Fig is not mentioned/referenced in the main text of the manuscript.

      This was a mistake and has been added.

      The reference list needs to be updated to accommodate those referenced bioRxiv preprints that have now been published in peer-reviewed journals.

      The reference list has been updated.

      Reviewer #3 (Recommendations For The Authors):

      It would be good to discuss whether the pro-domains affecting virulence or avirulence activity.

      Kex2, the protease that cleaves the pro-domain functions in the golgi. We therefore suspect that the pro-domain is removed prior to secretion. For recombinant protein production in E. coli we find that these pro-domains are necessary to obtain soluble protein (doi: 10.1111/nph.17516). As we require the pro-domain for protein production and can not completely removing them from our preps, we cannot perform experiments to test this and subsequently comment further. In a paper that identified SIX effectors in tomato utilising proteomics approach (https://bsppjournals.onlinelibrary.wiley.com/doi/10.1111/j.1364-3703.2007.00384.x), it appears that the pro-domains were not captured in this analysis. This supports the conclusion that they are not associated with the mature/secreted protein.

      The authors stated that the C-terminal domain of SIX6 has a single disulfide bond unique to SIX6. Please clarify in which context is it unique: in Fusarium or across all FOLD proteins?

      This is in direct comparison to Avr1 and Avr3. The disulfide in the C-domain of SIX6 is unique compared to Avr1 and Avr3. This has been made clear in text.

      The structural similarity of FOLD proteins to other known structures have been discussed (lines 460ff), but it is not clear whether all structures and models identified in this work would yield cysteine inhibitor and tumor necrosis factors as best structural matches in the database or whether this is specific to a single FOLD protein. Please consider discussing recently published findings by others (Teulet et al. 2023, New Phytologist) on this aspect.

      This analysis was performed for Avr1, we obtained relatively low similarity hits for Avr3/Six6. We have updated this text accordingly… “Unfortunately, the FOLD effectors share little overall structural similarity with known structures in the PDB outside of the similarity with each other. At a domain level, the N-domain of the FOLD effector Avr1 has some structural similarities with cystatin cysteine protease inhibitors (PDB code: 4N6V, PDB code: 5ZC1) [60, 61], and the C-domain with tumour necrosis factors (PDB code: 6X83) [62] and carbohydrate-binding lectins (PDB code: 2WQ4) [63]. Relatively weak hits were observed for Avr3/Six6.”

      It might be useful to clearly point out that the ToxA fold and the C-terminus of the FOLD fold are different.

      We have secondary structural topology maps of the FOLD and ToxA-like families in S8 Fig which highlight the differences in topology between these two families.

      Please add information to Fig.S8 listing the approach to generate the secondary structure topology maps.

      We have added this information in the figure caption.

    1. Author Response

      Reviewer #1 (Public Review):

      “A sample size of 3 idiopathic seems underpowered relative to the many types of genetic changes that can occur in ASD. Since the authors carried out WGS, it would be useful to know what potential causative variants were found in these 3 individuals and even if not overlapping if they might expect to be in a similar biological pathway.

      If the authors randomly selected 3 more idiopathic cell lines from individuals with autism, would these cell lines also have altered mTOR signaling? And could a line have the same cell biology defects without a change in mTOR signaling? The authors argue that the sample size could be the reason for lack of overlap of the proteomic changes (unlike the phosphor-proteomic overlaps), which makes the overlapping cell biology findings even more remarkable. Or is the phenotyping simply too crude to know if the phenotypes truly are the same?”

      We appreciate these thoughtful comments and also agree that of several models, our studies indicate the possibility of mTOR alteration in multiple forms of ASD. As above, we are currently pursuing this hypothesis with newly acquired DOD support. With regard to the I-ASD population, we agree that there are a large variety of genetic changes that can occur in genetically undefined ASDs. Indeed, this is precisely why we expected to see “personalized” phenotypes in each I-ASD individual when we embarked on this study. At that time, several years ago, we had planned to expand the analyses to more I-ASD individuals to assess for additional personalized phenotypes. However, as our studies progressed, we were surprised to find convergence in our I-ASD population in terms of neurite outgrowth and migration and later proteomic results showing convergence in mTOR. We found it particularly remarkable that despite a sample size of 3 that this convergence was noted. When we had the opportunity to extend our studies to the 16p11.2 deletion population, we were thrilled to conduct the first comparison between I-ASD and a genetically defined ASD and, as such, the scope of the paper turned towards this comparison. We do agree that analyses of the other I-ASD individuals would be a beneficial endeavor, both to understand how pervasive NPC migration and neurite deficits are in autism and to assess the presence of mTOR dysregulation. Furthermore, it would be important to see whether alterations in other pathways could also lead to similar cell biological deficits, though we know that other studies of neurodevelopmental disorders have found such cellular dysregulations without reporting concurrent mTOR dysregulation. Given our current grant funding to extend these analyses, such experiments within this manuscript would not be feasible.

      Regarding the phenotyping methods used, we decided to assess neurite outgrowth and migration as they are both cytoskeleton dependent processes that are critical for neurodevelopment and are often regulated by the same genes. Furthermore, similar analyses have been applied to Fragile-X Syndrome, 22q11.2 deletion syndrome, and schizophrenia NPCs (Shcheglovitov A. et al., 2013; Mor-Shaked H. et al., 2016; Urbach A. et al., 2010; Kelley D. J. et al., 2008; Doers M. E. et al., 2014; Brennand K. et al., 2015; Lee I. S. et al., 2015; Marchetto M. C. et al., 2011). As such, it seems that multiple underlying etiologies can lead to similar dysregulated cellular phenotypes that can contribute to a variety of neurodevelopmental disorders. On a more global level, there are only a few different cellular functions a developing neuron can undergo, and these include processes such as proliferation, survival, migration, and differentiation. Thus, to understand neurodevelopmental disorders, it is important to study the more “crude” or “global” cellular functions occurring during neurodevelopment to determine whether they are disrupted in disorders such as ASD. In our studies we find that there are indeed dysregulations in many of these basic developmental processes, indicating that the typical steps that occur for normal brain cytoarchitecture may be disrupted in ASD. To understand why, we then further utilized molecular studies to “zoom” in on potential mechanisms which implicated common dysregulation in mTOR signaling as one driver for these common cellular phenotypes. As suggested, we did complete WGS on all the I-ASD individuals and did not see any overlapping genetic variants between the three I-ASD individuals as mentioned in our manuscript. The genetic data was published in a larger manuscript incorporating the data (Zhou A. et al., 2023). However, there were variants that were unique to each I-ASD individual which were not seen in their unaffected family members, and it is possible these variants could be contributing to the I-ASD phenotypes. We also utilized IPA to conduct pathway analysis on the WGS data utilizing the same approach we did in analysis of p- proteome and proteome data. From WGS data, we selected high read-quality variants that were found only in I-ASD individuals and had a functional impact on protein (ie excluding synonymous variants). The enriched pathways obtained from this data were strikingly different from the pathways we found in the p-proteome analysis and are now included in supplemental Figure 6 in the manuscript. Briefly, the top 5 enriched pathways were: O-linked glycosylation, MHC class 1 signaling, Interleukin signaling, Antigen presentation, and regulation of transcription.

      Reviewer #2 (Public Review):

      1) I found that interpreting how differential EF sensitivity is connected to the rest of the story difficult at times. First, it is unclear why these extracellular factors were picked. These are seemingly different in nature (a neuropeptide, a growth factor and a neuromodulator) targeting largely different pathways. This limits the interpretation of the ASD subtype-specific rescue results. One way of reframing that could help is that these are pro-migratory factors instead of EFs broadly defined that fail to promote migration in I-ASD lines due to a shared malfunctioning of the intracellular migration machinery or cell-cell interactions (possibly through tight junction signaling, Fig S2A). Yet, this doesn't explain the migration/neurite phenotypes in 16p11 lines where EF sensitivity is not altered, overall implying that divergent EF sensitivity independent of underlying mTOR state. What is the proposed model that connects all three findings (divergent EF sensitivity based on ASD subtypes, 2 mTOR classes, convergent cellular phenotypes)?

      We thank you for the kind assessment of our manuscript and for the thought-provoking questions posed. In terms of extracellular factors, for our study, we defined extracellular factor as any growth factor, amino acid, neurotransmitter, or neuropeptide found in the extracellular environment of the developing cells. The EFs utilized were selected due to their well-established role in regulation of early neurodevelopmental phenotypes, their expression during the “critical window” of mid-fetal development (as determined by Allan Brain Atlas), and in the case of 5-HT, its association with ASD (Abdulamir H. A. et al., 2018; Adamsen D. et al., 2014; Bonnin A. et al., 2011; Bonnin A. et al., 2007; Chen X. et al., 2015; El Marroun H. et al., 2014; Hammock E. et al., 2012; Yang C. J. et al., 2014; Dicicco-Bloom E. et al., 1998; Lu N. et al., 1998; Suh J. et al., 2001; Watanabe J. et al., 2016; Gilmore J. H. et al., 2003; Maisonpierre P. C. et al., 1990; Dincel N. et al., 2013; Levi- Montalcini R., 1987). Lastly, prior experiments in our lab with a mouse model of neurodevelopmental disorders, had shown atypical responses to EFs (IGF-1, FGF, PACAP). As such, when we first chose to use EFs in human NPCs we wanted to know 1) whether human NPCs even responded to these EFs, 2) whether EFs regulated neurite outgrowth and migration and 3) would there be a differential response in NPCs derived from those with ASD. Our studies were initiated on the I-ASD cohort and given the heterogeneity of ASD we had hypothesized we would get “personalized” neurite and migration phenotypes. Due to this reason, we also wanted to select multiple types of EFs that worked on different signaling pathways. Ultimately, instead of personalized phenotypes we found that all the I-ASD NPCs did not respond to any of the EFs tested whereas the 16p11.2 deletion NPCS did – this was therefore the only difference we found between these two “forms” of ASD. As noted, in I-ASD the lack of response to EFs can be ameliorated by modulating mTOR. However, in the 16p11.2 deletion, despite similar mTOR dysregulation as seen in I-ASD, there is no EF impairment. We do not have a cohesive model to explain why the 16pDel individuals differ from the I-ASD model other than to point to the p- proteomes which do show that the 16pDel NPCs are distinct from the I-ASD NPCs. It seems that mTOR alteration can contribute to impaired EF responsiveness in some NPCs but perhaps there is an additional defect that needs to be present in order for this defect to manifest, or that 16p11.2 deletion NPCs have specific compensatory features. For example, as noted in the thoughtful comment, the p-proteome canonical pathway analysis shows tight junction malfunction in I-ASD which is not present in the 16pDel NPCs and it could be the combination of mTOR dysregulation + dysregulated tight junction signaling that has led to lack of response to EFs in I-ASD. Regardless, we do not think the differences between two genetically distinct ASDs diminish the convergent mTOR results we have uncovered. That is, regardless of whatever defects are present in the ASD NPCs, we are able to rescue it with mTOR modulation which has fascinating implications for treatment and conceptualization for ASD. Lastly, we see our EF studies as an important inclusion as it shows that in some subtypes of ASD, lack of response to appropriate EFs could be contributing to neurodevelopmental abnormalities. Moreover, lack of response to these EFs could have implications for treatment of individuals with ASD (for example, SSRI are commonly used to treat co-morbid conditions in ASD but if an individual is unresponsive to 5- HT, perhaps this treatment is less effective). We have edited the manuscript to include an additional discussion section to address the EFs more thoroughly and have included a few extra sentences in the introduction as well!

      2) A similar bidirectional migration phenotype has been described in hiSPC-derived human cortical interneurons generated from individuals with Timothy Syndrome (Birey et al 2022, Cell Stem Cell). Here, authors show that the intracellular calcium influx that is excessive in Timothy Syndrome or pharmacologically dampened in controls results in similar migration phenotypes. Authors can consider referring to this report in support of the idea that bimodal perturbations of cardinal signaling pathways can converge upon common cellular migration deficits.

      We thank you for pointing out the similar migration phenotype in the Timothy Syndrome paper and have now cited it in our manuscript. We have also expanded on the concept of “too much or too little” of a particular signaling mechanism leading to common outcomes.

      3) Given that authors have access to 8 I-ASD hiPSC lines, it'd very informative to assay the mTOR state (e.g. pS6 westerns) in NPCs derived from all 8 lines instead of the 3 presented, even without assessing any additional cellular phenotypes, which authors have shown to be robust and consistent. This can help the readers better get a sense of the proportion of high mTOR vs low- mTOR classes in a larger cohort.

      We have already addressed this in response to reviewer 1 and the essential revisions section, providing our reasoning for not expanding the study to all 8 I-ASD individuals.

      4) Does the mTOR modulation rescue EF-specific responses to migration as well (Figure 7)

      We did not conduct sufficient replicates of the rescue EF specific responses to migration due to the time consuming and resource intensive nature of the neurosphere experiments. Unlike the neurite experiments, the neurosphere experiments require significantly more cells, more time, selection of neurospheres based on a size criterion, and then manual trace measurements. We did one experiment in Family-1 where we utilized MK-2206 to abolish the response of Sib NPCs to PACAP. Likewise, adding SC-79 to I-ASD-1 neurospheres allowed for response to PACAP.

      Author response image 1.

      Author response image 2.

      Reviewer #3: Public Review

      We appreciate the kind, detailed and very thorough review you provided for us!

      The results on the mTOR signaling pathway as a point of convergence in these particular ASD subtypes is interesting, but the discussion should address that this has been demonstrated for other autism syndromes, and in the present manuscript, there should be some recognition that other signaling pathways are also implicated as common factors between the ASD subtypes.

      With regards to the mTOR pathway, we had included the other ASD syndromes in which mTOR dysregulation has been seen including tuberous sclerosis, Cowden Syndrome, NF-1, as well as Fragile-X, Angelman, Rett and Phelan McDermid in the final paragraph of the discussion section “mTOR Signaling as a Point of Convergence in ASD”. We have now expanded our discussion to include that other signaling pathways such as MAPK, cyclins, WNT, and reelin which have also been implicated as common factors between the ASD subtypes.

      The conclusions of this paper are mostly well supported by data, but for the cell migration assay, it is not clear if the authors control for initial differences in the inner cell mass area of the neurospheres in control vs ASD samples, which would affect the measurement of migration.

      Thank you for this thoughtful comment! When we first started our migration data, inner cell mass size was indeed a major concern for which we controlled in our methods. First, when plating the neurospheres, we would only collect spheres when a majority of spheres were approximately a diameter of 100 um. Very large spheres often could not be imaged due to being out of focus and very small spheres would often disperse when plated. Thus, there were some constraints to the variability of inner cell mass size.

      Furthermore, when we initially collected data, we conducted a proof of principal test to see if initial inner cell mass area (henceforth referred to as initial sphere size or ISS) influenced migration data. To do so, we obtained migration and ISS data from each diagnosis (Sib, NIH, I-ASD, 16pASD). Then we utilized R studio to see if there is a relationship between Migration and ISS in each diagnosis category using the equation (lm(Migration~ISS, data=bydiagnosis). In this equation, lm indicates linear modeling and (~) is a term used to ascertain the relationship between Migration and ISS and the term data=bydiagnosis allows the data to be organized by diagnosis

      The results were expressed as R-squared values indicating the correlation between ISS and Migration for each diagnosis and the p-value showing statistical significance for each comparison. As shown in Author response table 1, for each data set, there is minimal correlation between Migration and ISS in each data set. Moreover, there are no statistically significant relationships between Migration and ISS indicating that initial sphere size DOES NOT influence migration data in any of our data-sets.

      Author response table 1.

      Lastly, utilizing R, we modeled what predicted migration would be like for Sib, NIH, I-ASD, and 16pASD if we accounted for ISS in each group. Raw migration data was then plotted against the predicted data as in Author response image 3.

      Author response image 3.

      As shown in the graph, there are no statistical differences between the raw migration data (the data that we actually measured in the dish) and the modeled data in which ISS is accounted for as a variable. As such, we chose not to normalize to or account for ISS in our other experiments. We have now included the above R studio analyses in our supplemental figures (Figure S1) as well.

      Also, in Fig 5 and 6, panels I and J omit the effects of drug on mTOR phosphorylation as shown for other conditions.

      Both SC-79 and MK2206 were selected in our experiments after thorough analysis of their effects on human epithelial cells and other cultured cells (citations in manuscript). However, initially, we did not know whether either of these drugs would modulate the mTOR pathway in human NPCs, thus, in Figures 5A,5D, 6A and 6D we chose to focus on two of our data-sets to establish the effect of these drugs in human NPCs. Our experiments in Family-1 and Family-2 showed us that SC-79 increases PS6 in human NPCs while MK-2206 downregulates it. Once this was established, we knew the drugs would have similar effects in the NPCs from the other families. Thus, we only conducted a proof of principle test to confirm the drug does indeed have the intended effect in I-ASD-3 and 16pDel. We have included these proof of principle westerns in Figure 5I, 5K, 6I and 6K to show that the effects of these drugs are reproducible across all our NPC lines. We did not include quantification since the data is only from our single proof of principle western.

    1. Author response

      eLife assessment

      Using a genetically controlled experimental setting, the authors find that the lack of Polycomb-dependent epigenetic programming in the oocyte and early embryo influences the developmental trajectory through gestation in the mouse. By showing a two-phase outcome of early growth restriction followed by enhancement, the authors address previous inconsistencies in the field. However, the link with placenta function and gene misregulation is not yet fully supported.

      We thank the Reviewers for their constructive comments. In response we have added significantly more data to the study and substantially rewritten the manuscript. New data include analyses of glucose, amino acid and metabolite levels in fetal and maternal blood samples, more highly resolved fetal growth analyses, a more detailed study of the hyperplastic placenta including IF analyses of labyrinth area, labyrinth to placenta and capillary to labyrinth ratios. We have also added analyses of placental DNA methylation state in offspring from oocytes lacking EED, which reveals a range of DNA methylation changes at imprinted and non-imprinted genes in HET-hom offspring compared to HET-het or WT-wt controls.

      Reviewer #1 (Public Review):

      Oberin, Petautschnig et. al investigated the developmental phenotypes that resulted from oocyte-specific loss of the EED (Embryonic Ectoderm Development) gene - a core component of the Polycomb repressive complex 2 (PRC2), which possess histone methyltransferase activity and catalyses trimethylation of histone H3 at lysine 27 (H3K27). The PRC2 complex plays essential roles in regulating chromatin structure, being an important regulator of cellular differentiation and development during embryogenesis. As novel findings, the authors find that PRC2-dependent programming in the oocyte, via loss of the core component EE2, causes placental hyperplasia and propose that the increase of placental transplacental flux of nutrients leads to fetal and postnatal overgrowth. At the mechanistic level, they show altered expression of genes previously implicated in placental hyperplasia phenotypes. They also establish interesting parallelism with the placental hyperplasia phenotype that is frequently observed in cloned mice.

      Strengths:

      The mouse breeding experiments are very well designed and are powerful to exclude potential confounding genetic effects on the developmental phenotypes that resulted from the loss of EED in oocytes. Another major strength is the developmental profiling across gestation, from pre-implantation to late gestation.

      Weaknesses:

      The evidence for 'oocyte' programming is restricted to phenotypic and gene expression analysis, without measurements of epigenetic dysregulation. It would be an added value if the authors could show evidence for altered H3K27me3 or DNA methylation in the placenta, for example.

      In an earlier previous study we identified a large number of developmentally important genes that accumulated H3K27me3 in primary-secondary stage growing oocytes and were repressed by EED (Jarred et al., 2022 Clinical Epigenetics). However, H3K27me3 was removed from all from these genes during preimplantation development, indicating that maternal inheritance of H3K27me3 at a wide range of genes is unlikely (Jarred et al., 2022 Clinical Epigenetics). Consistent with this only a small number of genes, including Slc38a4 and C2MC, have been shown to be functionally important in H3K27me3-dependent imprinting (Matoba et al., 2022 Genes and Development). Moreover, a related study showed that deletion of Setd2 and consequent loss of H3K36me3 in oocytes led to spreading of H3K27me3 into regions that were otherwise marked by H3K36me3 and DNA methylation (Xu et al. 2019 Nature Genetics 51:844–56). Based on these studies, we proposed that loss of EED and H3K27me3 may result in the ectopic spreading of H3K36me3 and DNA methylation in oocytes and that altered DNA methylation may then be transmitted to offspring and affect developmental outcomes (Jarred et al., 2022 Clinical Epigenetics)

      Given this hypothesis we analysed DNA methylation rather than H3K27me3 in the placenta of WT-wt, HET- het and HET-hom offspring. This revealed differentially methylated regions (DMRs) in HET-hom placentas at two H3K27me3 imprinted genes Sfmbt2 (C2MC) and Mbnl2, five classically imprinted genes and at 74 DMRs not associated with imprinted loci. Together, our data supports the hypothesis from Jarred et al., 2022 Clinical Epigenetics that loss of EED in oocytes results in altered DNA methylation patterning at both imprinted and non-imprinted genes in offspring and that this is likely to affect offspring growth and development. However, whether these changes result from direct alteration of DNA methylation in oocytes remains unclear.

      These new data are now included in results (Lines 387-409), Figure 6I, Supplementary File H-J and Discussion Lines 569-581.

      Reviewer Comment 1. The claim that placental hyperplasia drives offspring catch-up growth is not supported by current experimental data. The authors do not address if transplacental flux is increased in the hyperplastic placentae, measure amino acids and glucose in fetal/maternal plasma, or perform tetraploid rescue experiments to ascertain the contribution of the placenta to growth phenotypes. Furthermore, it is unclear, from the current data, if the surface area for nutrient transport is actually increased in the hyperplastic placenta and the extent to which other cell populations (i.e. spongiotrophoblasts) are affected in addition to glycogen cells. In addition, one of the supporting conclusions that the placenta is a key contributor to fetal overgrowth is based on a very crude measurement - placenta efficiency - which the authors claim is increased in the homozygous mutants compared to controls. After analysing the data carefully, I find evidence for decreased placental efficiency instead. I believe that the authors mistakenly present the data as placenta to fetal weight ratios, which led to the misinterpretation of the 'efficiency' concept.

      We thank the reviewer for pointing out our error in the placental efficiency data and we have now corrected the placental efficiency graphs (fetal/placental weight ratios) and updated the text throughout the manuscript as required (Figure 3I-K). As requested and described below, we have also added significantly more data, which support the conclusion that placental function is not enhanced in HET-hom mice and is unlikely to support fetal growth recovery.

      The new data and analyses we have added include:

      1. Further analyses of glycogen-enriched and non-glycogen-enriched cell counts in the decidua and junctional zones (Figure 4F-J)

      2. Total glycogen cell counts for male and female placentas (Figure 4 – figure supplement 1F)

      3. New analyses of fetal blood glucose levels at E17.5 and E18.5 and matching data from the mothers of each litter (Figure 4M)

      4. New analyses of the circulating amino acid levels and metabolites in fetal blood of E17.5 offspring and matching data from the mothers of each litter (Figure 8)

      5. New IF analyses of CD31 (PECAM-1) and combined this with machine learning assisted quantitative analyses of labyrinth and capillary areas using HALO (Figure 5)

      6. Separated male and female offspring and placental weights at E14.5 and E17.5 and total areas of the placenta, decidua, junctional zone and labyrinth (Figure 3 – figure supplement 1) which provide more insight into potential sex-specific differences in HET-hom offspring and placenta

      We have significantly re-written the results and discussion to reflect our new data and interpretation.

      While we did not assess transplacental flux, our new data revealed: 1. HET-hom fetuses had lower blood glucose levels at E18.5; 2. Circulating levels of amino acids and a wide range of metabolites did not differ between HET-hom and control offspring, or between the mothers of these offspring; 3. HET-hom placentas had lower total labyrinth area, labyrinth/placenta and capillary/labyrinth ratios based on analysis of total capillary and labyrinth areas, indicating that the surface area for nutrient transfer is not increased

      Together these data strongly indicate that hyperplastic HET-hom placentas do not provide greater support to HET-hom fetuses than controls, and that increased placental function in HET-hom offspring is unlikely to explain the late gestation fetal growth recovery we observed in HET-hom offspring or how HET-hom offspring were able to attain normal weights by birth.

      While we have not directly counted the spongiotrophoblast populations, we have now included analyses of both the glycogen-enriched and non-glycogen cell populations in the junctional zone and the decidua (Figure 4H-K). This revealed an increased area of both glycogen-enriched and non-glycogen cells in the junctional zone and in the decidua of HET-hom placentas, consistent with the greater junctional zone/placenta ratio observed in HET-hom placentas (Figure 4D). Together with data in Figure 4C-F and Supp. Fig. 3, our observations demonstrate that the overall decidua and junctional zone areas were increased in HET-hom offspring, but there was a disproportionate expansion of the junctional zone that was caused by increased areas of both glycogen and non-glycogen-enriched cells.

      Tetraploid rescue experiments would require a very significant amount of time and investment and are technically very demanding. While creation of complementary tetraploid offspring would be informative, unfortunately these experiments are beyond the scope of this current study.

      Reviewer Comment 1 cont. The authors do not mention alternative explanations for the observed fetal catch-up and postnatal overgrowth. Why would oocyte epigenetic programming effects be restricted to the placenta, and not include fetal organs?

      Our intention was certainly not to convey a message that effects may be placenta specific. Indeed, our ongoing work beyond the scope of this study provides evidence for effects in other tissues (brain and bones) that will be published elsewhere. Our new data clearly show low placental efficiency, fetal blood glucose, low capillary/labyrinth ratio and no impact on circulating fetal amino acid or metabolite levels in HET-hom offspring. In light of these new data, we have reinterpreted the findings of this study and substantially updated the discussion.

      Given our observations that fetal growth rate markedly increased during late gestation, but placental efficiency was reduced, our data strongly indicate that the effects of altered epigenetic oocyte programming due to loss of Eed affect both the placenta and the fetus. While our findings are significant, the precise mechanism underlying this growth response in HET-hom fetuses remains unknown. Understanding this mechanism will require substantially more work that will be the subject of future studies.

      Reviewer #2 (Public Review):

      Consistent fetal growth trajectories are vital for survival and later life health. The authors utilise an elegant and novel animal model to tease apart the role of Eed protein in the female germline from the role of somatic Eed. The authors were able to experimentally attribute placental overgrowth - particularly of the endocrine region of the placenta - to the function of Eed protein in the oocyte. Loss of Eed protein in the oocyte was also associated with dynamic changes in fetal growth and prolonged gestation. It was not determined whether the reported catch-up growth apparent on the day of birth was due to enhanced fetal growth very late in gestation, a longer gestational time ie the P0 pups are effectively one day "older" compared to the controls, or the pups catching up after birth when consuming maternal milk.

      To understand if increased growth occurred in HET-hom fetuses prior to birth, we have now included analyses of offspring weight at E18.5 (Figure 2F), all pups collected with a verified E19.5 birth date (Figure 2J) and for pups from similar litter sizes (5-7 pups) at E19.5 (Figure 2K). Together with our existing data, these additional analyses provide average weights for fetuses at E14.5, E17.5, E18.5 and pups born on E19.5. This confirmed that HET-hom offspring undergo enhanced growth in the last few days of pregnancy, resulting in the progression of substantially growth and developmentally restricted HET-hom fetuses at E14.5, to pups with normal weight at birth within the 40% of pregnancies that were born on E19.5 in a normal gestational time.

      However, in addition, gestational length was increased by one to two days in 60% of pregnancies from hom oocytes, but not in control pregnancies from het or wt oocytes. As average weights were significantly greater in all surviving HET-hom offspring at P0 (i.e. surviving pups born on E19.5-E21.5; Figure 2G), it appears that this additional gestational time contributed to the offspring overgrowth. This is logical, however it does not explain how growth and developmentally delayed fetuses at E14.5 attained normal weight and developmental stage by E19.5 (Figure 2J-K).

      Together our data clearly show that HET-hom offspring undergo enhanced growth during the late stages of pregnancy, allowing them to resolve the developmental delay and growth insufficiency observed at E14.5 so that they were born at normal weight and stage at E19.5. In addition, increased gestational time contributes to weight of pups delivered on E20.5 or 21.5, partly explaining the overgrowth phenotype observed in this model.

      The idea that increased milk consumption may explain the overgrowth of HET-hom offspring is interesting. It is possible that the increased growth rate of HET-hom offspring continues after birth and contributes to overgrowth. However, examining this outcome in a tightly controlled manner is complicated given that we cannot predict the day of birth of HET-hom litters, and that these litters are generally small and would need to be fostered on the day of birth alongside control litters. Given these challenges and that our primary observation is that HET-hom offspring underwent fetal growth recovery during pregnancies of normal length and via extension of gestational length, we have not examined the possibility of increased milk consumption after birth.

      We have updated the results to reflect the new analyses and have provided relevant discussion to address these data. Our description of these data can be found in Results (lines 165-197) and in Figure 2.

      Reviewer #3 (Public Review):

      My understanding of the main claims of the paper, and how they are justified by the data are discussed below:

      Overall, loss of PRC2 function in the developing oocyte and early embryo causes:

      1) Growth restriction from at least the blastocyst stage with low cell counts and midgestational developmental delay.

      Strengths:

      • Live embryo imaging added an important dimension to this study. The authors were able to confirm an unquantified finding from a previous lab (reduced time to 2-cell stage in oocyte-deletion Eed offspring, Inoue 2018, PMID: 30463900) as well as identify developmental delay and mortality at the blastocyst- hatching transition.

      • For the weight and morphological analysis the authors are careful to provide isogenic controls for most of the experiments presented. This means that any phenotypes can be attributed to the oocyte genotype rather than any confounding effects of maternal or paternal genotype.

      • Overall, there is good evidence that oocyte deletion of Eed results in early embryonic growth restriction, consistent with previous observations (Inoue 2018, PMID: 30463900).

      Reviewer 3, Comment 1: Weaknesses: Gaps in the reporting of specific features of the methodology make it difficult to interpret/understand some of the results.

      While we are unsure exactly which methods Reviewer 3 would like expanded, we have updated parts that we thought required further detail and allow more informed interpretation of the results. These include methods for placental histology (Lines 650-669) and immuno- histochemistry (Lines 671-690), and new methods for CD31 immunofluorescence (Lines 692-714), glucose and metabolomics (Lines 752-769) and DNA methylation (RRBS; Lines 734-750) analyses.

      To clarify the approach taken for histology, immunohistochemical and immunofluorescent staining, sections were cut in compound series from the centre of each placenta, ensuring that we collected representative data for each sample. QuPath was used to quantify the decidual and junctional zone areas in one complete, fully intact midline section for each placenta as close to the midline as possible. This provided data from 10 placentas for each genotype. In addition, glycogen-enriched and non-glycogen-enriched cells were identified and quantified using machine learning assisted QuPath analyses of the whole placenta, decidua and junctional zone regions. We have also added quantitative analyses of the labyrinth and labyrinth capillary network using immunofluorescent CD31 staining and machine learning assisted HALO software. This new analysis of placental morphology is included in the methods section.

      Moreover, as there were no sex-specific differences in placental morphology or weight, we combined the samples from both sexes to provide greater numbers for analysis in each genotype. For example, as described for the analyses of labyrinth and capillaries using CD31 IF, 4 placentas of each sex were used for data collection. This provided data from a total of 8 placentas (4 male and 4 female) for each genotype from a total of 17 WT-wt (9 male and 8 female), 21 HET-het (9 male and 12 female) and 24 HET-hom (16 male and 8 female) sections (2-3 sections/placenta).

      Reviewer 3, Comment 2: Placental hyperplasia with disproportionate overgrowth of the junctional trophoblast especially the glycogen trophoblast (GlyT) cells.

      Strengths: • The authors provide a comprehensive description of how placental and embryo weight is affected by the oocyte-Eed deletion through mid-to-late gestation development. The case for placentomegaly is clear.

      Weaknesses:

      • The placental efficiency data presented in Figure 3G-I is incorrect. Placental efficiency is calculated as embryo mass/placental mass, and it increases over the late gestation period. For e14.5 for example (Fig3G), WT-wt embryo mass = ~0.3g, placenta mass = 0.11g (from Fig 3D) = placental efficiency 2.7; HET-hom = 0.25/0.12 = 2.1. The paper gives values: WT-wt 0.5, HET-hom 0.7. Have the authors perhaps divided placenta weight by embryo mass? This would explain why the E17.5 efficiencies are so low (WT-wt 0.11 rather than a more usual figure of 8.88. If this is the case then the authors' conclusion that placental efficiency is improved by oocyte deletion of Eed is wrong - in fact, placental efficiency is severely compromised.

      The authors have performed cell type counting on histological sections obtained from placentas to discover which cells are contributing to the placentomegaly. This data is presented as %cell type area in the main figure, though the untransformed cross-sectional area for each cell type is shown in the supplementary data. This presentation of the data, as well as the description of it, is misleading because, while it emphasises the proportional increase in the endocrine compartment of the placenta it downplays the fact that the exchange area of the mutant placentas is vastly expanded. This is important for two reasons.

      Firstly, the whole placenta is increased in size suggesting that the mechanism is not placental lineage- specific and instead acting on the whole organ. Secondly in relation to embryonic growth, generally speaking, genetic manipulations that modify labyrinthine volume tend to have a positive correlation with fetal mass whereas the relationship between junctional zone volume and embryonic mass is more complex (discussed in Watson PMID: 15888575, for example). The authors should reconsider how they present this data in light of the previous point.

      We thank the reviewer for pointing out our error in the placental efficiency analysis and apologise for this error. We have corrected the presentation and interpretation of these data and have described this in detail in our response to Reviewer 1, Comment 1.

      As discussed in our response to Reviewer 1, Comment 1, we have added a range of analyses to determine whether placental efficiency was enhanced in HET-hom offspring. These include measuring fetal and maternal circulating glucose levels (Figure 4K), individual amino acids and an extensive range of metabolites (Figure 8) and providing CD31 immunofluorescent analyses of labyrinth area, labyrinth/placental ratio and capillary/labyrinth ratio in HET-hom and control placentas (Figure 5).

      We also added analyses of glycogen enriched and non-glycogen-enriched cell counts in the decidua and junctional zones. As suggested by Reviewer 3, both glycogen-enriched and non-enriched cell populations are significantly increased in HET-hom placentas.

      Combined, these new analyses significantly expand the study and support the conclusion that placental efficiency in HET-hom offspring was either compromised or not different from controls, depending on the analysis. We find no evidence that placental efficiency was increased in HET-hom offspring and have reworked our results and discussion sections to reflect these new data and interpretation.

      Reviewer 3, Comment 2 cont: Again, some of the methods are not clearly reported making interpretation difficult - especially how they have estimated their GlyT number.

      As outlined in our response to Reviewer 3 Comment 1, in the methods section we have added further detail of how we counted glycogen-enriched and non-enriched cells in the decidua and junctional zone regions of sections for the middle of WT-wt, WT-het, HET-het and HET-hom placentas (Lines 650-669).

      Reviewer 3, Comment 3: Perinatal embryonic/pup overgrowth.

      Strengths:

      • The overgrowth exhibited by the oocyte-Eed-deleted pups is striking and confirms the previous work by this group (Prokopuk, 2018). This is an important finding, especially in the context of understanding how PRC2-group gene mutations in humans cause overgrowth syndromes. It is also intriguing because it indicates that genetic/environmental insults in the mother that affect her gamete development can have long-term consequences on offspring physiology.

      Weaknesses:

      • Is the overgrowth intrauterine or is it caused by the increase in gestation length? The way the data is reported makes it impossible to work this out. The authors show that gestation time is consistently lengthened for mothers incubating oocyte-Eed-deleted pups by 1-2 days. In the supplementary material, the mutant embryos are not larger than WT at e19.5, the usual day of birth. Postnatal data is presented as day post-parturition. It would probably be clearer to present the embryonic and postnatal data as days post coitum. In this way, it will be obvious in which period the growth enhancement is taking place. This is information really important to determine whether the increased growth of the mutants is due to a direct effect of the intrauterine environment, or perhaps a more persistent hormonal change in the mother that can continue to promote growth beyond the gestation period.

      We have used embryonic day (E) to denote embryo and fetal age throughout the study – this is the same as using DPC (i.e. E19.5 is equivalent to 19.5 DPC). As described in the Methods “Collection of post-implantation embryos, placenta and postnatal offspring”, mice were time mated for two-four nights, with females plug checked daily. Positive plugs were noted as day E0.5.

      To make the data presentation clearer, we have shown the data for surviving HET-hom pups born on E19.5 (Figure 2J) separately from all HET-hom surviving pups born on E19.5-E21.5. (Figure 2G). As discussed in our response to Reviewer 2, we have also included growth data for pregnancies at E14.5, E17.5, E18.5 (Fig. 2C-F) and E19.5 (Figure 2J,K), as well as P0 (combined data for surviving pups born E19.5-E21.5), and P3 (combined data for surviving pups born E19.5-E21.5, Figure 2G,H).

      These data clearly show that HET-hom fetuses are substantially growth and developmentally delayed at E14.5 (Figure 2D), but HET-hom pups born on E19.5 are the same weight as WT-wt, WT-het and HET-het control pups (Figure 2J). This demonstrates that weight of HET-hom fetuses is normalised in utero between E14.5 and day of birth on E19.5.

      Importantly, as requested by Reviewer 3, we have separated average weight for all surviving pups with a day of birth of E19.5-21.5 (Figure 2G) from average weight of pups born on E19.5 only (Figure 2J). These analyses revealed that the average weight of surviving pups born between E19.5-21.5 was significantly higher than for controls (Figure 2G), but the average weight of pups born on E19.5 only was not. It is therefore clear that extended gestation also contributed to increased HET-hom pup birth weight. We have updated these additional analyses in Results (Lines 165-197) and Figure 2

      As revealed in Figure 2H, it is also possible/likely that growth of HET-hom pups during the three days post- partum may have contributed to the offspring overgrowth we observed in this and our previous study (Prokopuk et al., 2018 Clinical Epigenetics). However, we cannot determine whether there is a contribution from a persistent maternal hormonal change that promotes post-natal offspring growth or whether there is an innate growth benefit in HET-hom pups. As this is very difficult to dissect, separating these possibilities is beyond the scope of our study.

      Reviewer 3, Comment 4: "fetal growth restriction followed by placental hyperplasia, .. drives catch-up growth that ultimately results in perinatal offspring overgrowth".

      Here the authors try to link their observations, suggesting that i) the increased perinatal growth rate is a consequence of placentomegaly, and ii) the placentomegaly/increased fetal growth is an adaptive consequence of the early growth restriction. This is an interesting idea and suggests that there is a degree of developmental plasticity that is operating to repair the early consequences of transient loss of Eed function.

      Strengths:

      • Discrepancies between earlier studies are reconciled. Here the authors show that in oocyte-Eed-deleted embryos growth is initially restricted and then the growth rate increases in late gestation with increased perinatal mass.

      Weaknesses:

      • Regarding the dependence of fetal growth increase on placental size increase, this link is far from clear since placental efficiency is in fact decreased in the mutants (see above).

      • "Catch-up growth" suggests that a higher growth rate is driven by an earlier growth restriction in order to restore homeostasis. There is no direct evidence for such a mechanism here. The loss of Eed expression in the oocyte and early embryo could have an independent impact on more than one phase of development.

      Firstly, there is growth restriction in the early phase of cell divisions. Potentially this could be due to depression of genes that restrain cell division on autosomes, or suppression of X-linked gene expression (as has been previously reported, Inoue, 2018 PMID: 30463900). The placentomegaly is explained by the misregulation of non-canonically imprinted genes, as the authors report (and in agreement with other studies, e.g. Inoue, 2020. PMID: 32358519).

      • Explaining the perinatal phase of growth enhancement is more difficult. I think it is unlikely to be due to placentomegaly. Multiple studies have shown that placentomegaly following somatic cell nuclear transfer (SCNT) is caused by non-canonically imprinted genes, and can be rescued by reducing their expression dosage. However, SCNT causes placentomegaly with normal or reduced embryonic mass (for example -Xie 2022, PMID: 35196486), not growth enhancement. Moreover, since (to my knowledge) single loss of imprinting models of non-canonically imprinted genes do not exist, it is not possible to understand if their increased expression dosage can drive perinatal overgrowth, and if this is preceded by growth restriction and thus constitutes 'catch up growth'.

      Reviewer 3 is correct in their assessment that placental efficiency was decreased in HET- hom offspring and we have corrected the placental efficiency analysis based on fetal/placental weight ratios (discussed in detail in our response to Reviewer 1 Comment 1). We have added substantially more data (glucose, amino acids, metabolites, labyrinth capillary area and density). These data support the conclusion that a placentally driven advantage for HET-hom fetal growth is unlikely, despite our observation that HET- hom fetuses are developmental delayed and underweight at E14.5, but are born at normal weight after a normal gestational length (19.5 days) (discussed in our responses to Reviewer 3, Comment 3 and Reviewer 2).

      This demonstrates that HET-hom fetuses are able to attain normal birth weight despite being initially growth restricted state at E14.5, and that this occurs despite low placental function. Moreover, as we compared isogenic offspring with heterozygous loss of Eed (Het-het compared to HET-hom offspring) the outcomes we observed in HET-hom offspring originate from loss of EED in the growing oocyte or loss of maternal EED in the zygote strongly suggesting that a non-genetic mechanism is involved.

      As pointed out by Reviewer 3, the initial developmental delay in HET-hom offspring may be due to increased expression of genes that regulate cell proliferation – this could clearly explain the lower number of cells we observed in the ICM and the growth delay at later stages of embryonic and fetal development. Another possibility is that maternal PRC2 provided by the oocyte promotes cell divisions in preimplantation embryos We have discussed these possibilities on Lines 467-476.

      In addition, Matoba et al 2022 demonstrated that deletion of maternal Xist together with Eed was able to rescue male-biased lethality in offspring from oocytes lacking Eed, revealing a clear role for X-linked genes in this phenotype (Matoba et al 2022, Genes and Development). However, deletion of maternal Xist did not properly normalise survival offspring from Eed null oocytes (i.e. Eed/Xist double maternal null litters were smaller than litters derived from wild type oocytes) strongly suggesting other mechanisms provide the capacity for HET-hom offspring to attain normal weight at birth. We have added further discussion of the Matoba study in the context of our study on of the Discussion (Lines 544-555)

      Finally, with respect to the outcomes for SCNT derived offspring, we extracted SCNT fetal growth and placental weight data from the supplementary data included in Matoba et al., 2018 Cell Stem Cell. 2018;23(3):343-54.e5 and compared it with data collected in our study (Figure 7). This analysis revealed that the weights of placentas and fetuses of offspring derived via SCNT were very similar to the HET-hom offpsring in our study and we have discussed the similarities and potential differences between HET-hom and SCNT offspring in the Discussion (Lines 478-500).

      As pointed out by Reviewer 3, deletion of maternal non-canonically imprinted genes partially or fully rescued the placental hyperplasia phenotype in both SCNT derived and offspring from oocyte lacking EED. However, as we have discussed, the mechanisms underlying other aspects of the offspring phenotype, such as fetal growth recovery of HET-hom offspring observed in our study, remain unknown. Moreover, the comparison we provide in Figure 7 strongly indicates that HET-hom and SCNT fetuses are similarly delayed at E14.5 and undergo similar fetal growth recovery before birth, but the mechanism also remains unknown. Together, it appears that offspring derived from either Eed-null oocytes or by SCNT have an innate ability to remediate fetal growth restriction during the late stages of pregnancy without a requirement to correct maternally inherited impacts mediated by Xist or H3K27me3-dependent imprinting.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This well done and interesting paper examining the connection between TXNIP and GDF15. The main thrust is that TXNIP upregulation chemotherapies, such as Oxa, results in an a down regulation of GDF15 early in tumorigenesis. Later in tumorigenesis, TXNIP upregulation is less pronounced, elevating GFP15 resulting in a blockage of tumor suppressive immune responses. Generally the work is convincing. For example, it's clear that TXNIP is up regulated by Oxa in an ROS and MondoA-dependent manner. Likewise its quite clear TXNIP loss reads to an upregulation of GDF15. However, it's also quite clear that Oxa suppresses GDF15 in a manner that appears to be completely independent of TXNIP. The writing in the paper implies strongly that there is a mechanistic connection between TXNIP and GDF15, but no experiments investigate this possibility.

      We feel this is very fair and is reflective of a) perhaps an overemphasis of the TXNIP knockout observation and supportive tissue data, which suggests a relationship but not a mechanistic understanding b) an underemphasis of the data in Figure 3 that shows a decrease in GDF15 after oxaliplatin treatment in TXNIP knockout lines.

      We have addressed these concerns in several ways:

      1. We have carried out knockdown experiments using siRNA for ARRDC4, which we felt, given its regulation by MondoA and ROS, and homology to TXNIP, may also regulate GDF15. This was found to be the case and may explain the data in Figure 3. At the very least it shows that other factors involved in oxidative stress management may have similar impacts – a form of functional redundancy. Lines 553-559 “Finally, given our previous data (Figure S4) we looked to assess the role of ARRDC4 on GDF15 expression. In the absence of oxaliplatin, knocking down ARRDC4 in DLD1 and HCT15 cells drove an increase in GDF15. When challenged with oxaliplatin, both ARRDC4 and TXNIP expression increased and GDF15 decreased. When the ARRDC4 knockdown was challenged TXNIP increased further and GDF15 decreased further (Figure S6G-J). Given the common regulatory pathways and homology between TXNIP and ARRDC4, and their similar functional roles, we suggest these data are evidence of redundancy within this system. “

      We have included some context in the discussion:

      Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572

      We have tempered the text:

      Lines 522-524 “It is important to note however that here we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment, with decreased levels seen in knockout lines (Figure 3C-G, S5E).”

      Lines 926-929 “It must be stressed that these data do not place TXNIP as the sole regulator of GDF15, for example ARRDC4 can also be seen to regulate GDF15. We envisage TXNIP as one of a number of ROS-dependent GDF15 regulators, with this redundancy potential evidence of the importance of this regulatory framework.”

      We have carried out additional analysis detailed in the discussion and in Figure S12 which suggests TXNIP impacts MYC function, as reported elsewhere (detailed below). For ease, the key paper can be accessed through this link https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001778

      Lines 934-956: “The main shortcoming of this paper is the lack of mechanistic understanding linking TXNIP to GDF15. There are 650 transcription factors that have been shown, or are predicted, to bind to GDF15 promoter and/or enhancer regions. By assessing our list of differentially expressed genes (Suppl. Table 1-2) for the presence of these factors we identified 6 GDF15 binding TFs that show significantly decreased expression after oxaliplatin treatment in both cell lines (ATF4, MYC, SREBF1, PHB2, HBP1, KLF9). There was only one, MYC, that was downregulated by oxaliplatin treatment (validated; Figure S12A), and with this downregulation partially being rescued in a matched TXNIP knockout line (Figure S12B). We then observed that c-myc has been shown or is predicted to bind to promoter/enhancer regions of the top five transcriptomic and proteomic differentials in TXNIP knockout lines, including TXNIP itself (apart from C16orf90). Even with c-myc’s promiscuity (binds to 10-20% of all promoters/enhancers) this may be suggestive of a specific relationship. Finally, when looking at the correlations between these 6 TFs and TXNIP and GDF15 in the TCGA COAD dataset, MYC has the greatest and most significant negative correlation to TXNIP (r=-0.4631 p=1.42e-28) and the greatest and most significant positive correlation to GDF15 (r=0.4653 p=7.32e-29). ATF4 and PHB2 are the other TFs in the list, that show the same significant trends (Figure S12C), and therefore may play a role in the TXNIP-independent oxaliplatin-dependent regulation of GDF15. Further exploration of these additional TFs is outside the scope of the current manuscript.

      MYC’s role in bridging from TXNIP to GDF15 is further supported by a recent paper which shows that TXNIP is “a broad repressor of MYC genomic binding” and that “TXNIP loss mimics MYC overexpression”73. Furthermore, the inter-dependent regulatory relationship between MondoA, TXNIP, and MYC has been seen in a variety of models74, whilst the impact of NAC on MYC-dependent pathways has been seen in lymphoma75. These studies lend credence to the idea that MYC is the most likely TXNIP-regulated TF that regulates GDF15 in our systems.”

      It seems equally likely that TXNIP and GDF15 represent independent parallel pathways. Even if TXNIP is a direct regulator of GDF15, it's also clear that other "factors" up or down-regulated by Oxa also contribute to the regulation of GDF15. These are not explored and even though TXNIP is highly regulated genes shown Figure 2 that are not identified or discussed that may also be contributing to GDF15 regulation.

      As mentioned above, the new data suggests that at least one other factor, ARRDC4, can regulate GDF15 (changes upon oxaliplatin treatment) and that MYC is a potential mechanistic bridge between TXNIP and GDF15. Whilst assessing for the transcription factor that may link TXNIP and GDF15 we found an additional 5 TXNIP-independent factors (ATF4, PHB2, SREBF1, HBP1, KLF9) that bind to GDF15 promoter/enhancer regions and are downregulated post-oxaliplatin treatment. When looking at correlations between these factors and GDF15 in the TCGA COAD dataset, ATF4 and PHB2 correlate most closely with GDF15 (when removing MYC) and so we would cautiously suggest that these may be the most pertinent. This data is now included.

      Further, the experiments treating PBMCs with conditioned media contain other cytokines/factors, in addition to GDF15, that likely also contribute the observed effects on the different immune cells understudy. The conditioned media from GDF15 knock out cells are a good experiment, but the media is not rigorously tested to see what other cytokines/factors might have also been depleted.

      The TXNIP knockout media is the same as that analysed by mass spec and the protein array, however as the reviewer states there is no analysis (excluding assessing for the presence or absence of GDF15) on the double knockout supernatant or over-expression supernatant. The text has been corrected as follows:

      Lines 675-679. “In light of other secreted factors being seen to be regulated by TXNIP (Figure 3A-B), we included double knockouts (TXNIP and GDF15 knockout; GTKO) as well as an overexpression system (GDF15a) to test for GDF15 specific effects. However, we do not know the impact of knocking out or overexpressing GDF15 on the broader secretome.”

      Perhaps a GDF15 complementation experiment would help here.

      We felt that the association between GDF15 and Treg induction is reasonably well established in the literature, and so once we saw that the supernatant from our GDF15 overexpression system (+/- CD48 blockade) complemented what has already been demonstrated, we were encouraged. However we needed more – hence the TCGA data and IHC staining.

      Finally, even if completely independent, a TXNIP/GDF15 ratio does seem to have utility in determining chemo-therapeutic response.

      We agree – we feel that conceptually this may be the most interesting part of the project and is an example of what can be done with these tools.

      Other major points: 1. Please label the other highly regulated genes shown in Fig 2A and B. Might they also explain some of the underlying biology. This could be on the current figures or in a supplement, though the former is preferred.

      Many thanks – we have done this.

      Please address why the TXNIP induction is so much less in patient-derived organoids vs. cell line spheroids (Fig S2). By the western blots, TXNIP inductions in the organoids looks quite modest. Further, the text is quite cryptic and implies that the "upregulation" is similar in both organoids and spheroids.

      You are absolutely correct. Many apologies, the wording has changed:

      Lines 320-323 “In both models we observed the upregulation of TXNIP mRNA (Figure S2E-H) and TXNIP protein (Figure S2I-L) after oxaliplatin treatment, with spheroids showing greater responsiveness. This difference is most likely due to culturing conditions or differences in the number and location of cycling cells.”

      We have two possible explanations. Firstly the media in which the organoids are cultured contains a lower glucose concentration than that used for the spheroids. As per some of our new data (Figure S3 – later in the rebuttal), the upregulation of TXNIP after oxaliplatin is glucose dependant, with lower concentrations resulting in less of a differential. Secondly, while restricted to the periphery, the Ki67 signal in DLD1 spheroids is quite pronounced indicating that, within the outer zone, many cells (probably the majority) are in the S/G1/G2 phase of the cell cycle at any given point in time (figure below this text).

      This is not the case for the organoids, where the Ki67 (and pCDK1) signal is quite weak, and only sporadic in the outer layer. So we believe that there are many more rapidly cycling cells in the most drug-exposed layer of spheroids when compared to the comparable region in organoids. As the spheroid cells are likely cycling more rapidly, they would also be expected to be more adversely affected by the drug within the finite drug treatment window. Indeed, these spheroids grow large, and quite quickly. If the organoid cells are cycling more slowly and if, within the cell layer most exposed to drug, these cycling cells are less abundant, then the TXNIP response may well be subdued in organoids when compared with spheroids.

      We have decided to not include the above (full) explanation and figure within the new draft, as we feel it may distract from the central message. However do let ourselves and the editor know if you disagree.

      What was the rationale of performing the MS experiment on control and TXNIP KO DLD1 cells in the absence of oxaliplatin? The other experiments in Fig 3 clearly show that Oxa can repress GDF15 even in the absence of TXNIP, which implicates other pathways. ARRDC4? Or something else? This needs to be addressed.

      We adopted this approach because of the order in which the assays occurred and technical issues surrounding the use of post-oxaliplatin treated supernatant. By the time we moved to the proteomics we had already identified, and validated, GDF15 as our number one candidate (initially from the protein array), in terms of response to oxaliplatin and dependence on TXNIP. This led us to the next stage of the project – to assess the environmental impacts of this factor in vitro before validation in situ. To do this, aware of the issue of contaminated recombinant GDF15, we decided early on to use cell line supernatant. We carried out some pilot studies on immune cells using supernatant from oxaliplatin treated cell lines and we had several technical issues (difficulty in determining the correct controls, immune cell death…). This changed the emphasis to using supernatant from knockout models rather than knockout and treated models. Before we began these assays in earnest we wanted to assess exactly what was enriched in TXNIP knockout supernatant and so we turned to proteomics. When this further validated GDF15, we then generated GDF15 and TXNIP/GDF15 knockouts to further elucidate GDF15’s role specifically.

      With regards the other pathways, as you correctly predicted, ARRDC4 also appears to regulate GDF15 – many thanks for helping with this line of enquiry. Please see earlier in the rebuttal for more details and the data.

      The data in 3J with the MondoA knockdown is not convincing. The knockdown is weak and TXNIP goes down a smidge. Agree that GDF15 goes up

      We agree. We have re-run this and pooled the densitometry data – see new figure below (Panel 3J).

      Minor points 1. Line 79. The "loss" of TXNIP/GDF15 axis is confusing. It's really loss of TXNIP and upregulation of GDF15, right?

      Absolutely - corrected to responsiveness.

      Lines 144-147: “Intriguingly, multiple models including patient-derived tumor organoids demonstrate that the loss of TXNIP and GDF15 responsiveness to oxaliplatin is associated with advanced disease or chemotherapeutic resistance, with transcriptomic or proteomic GDF15/TXNIP ratios showing potential as a prognostic biomarker.”

      Please provide an explanation for the different stages in tables 1 and 2. This will likely not be clear to non-clinicians.

      Many thanks. The following has been added at the bottom of the second table.

      Lines 304-309: “The TNM staging system stands for Tumor, Node, Metastasis. T describes the size of the primary tumor (T1-2; 5cm). N describes the presence of tumor cells in the lymph nodes (N0; no lymph nodes. N1-3 >0). M describes whether there are any observable metastases (M0; no metastases. M1; metastases). The clinical stage system is as follows: I/II; the tumor has remained stable or grown, but hasn’t spread. III/IV; the tumor has spread, either locally (III) or systemically (IV).”

      Line 231 should probably read ...cysteine (NAC), a reactive oxygen species inhibitor,

      Many thanks - corrected

      Line 247, should be RT-qPCR I think.

      Many thanks - corrected

      Lines 343-345. I don't quite understand the wording. Does this mean to say that 675 soluble proteins were not changed between the condition media from both cell populations?

      Yes, exactly this. We have removed as this is superfluous and confusing.

      The data in FigS1 B and C don't seem to reach the standard p value of > 0.05

      Very true – we have rewritten the text to make sure the reader knows there is no significance.

      Lines 269-271. “High levels of both the protein (significantly) and the transcript (not significantly) were seen to be associated with favourable prognosis (Figure 1G,H and S1B,C).”

      **Referee Cross-Commenting**

      cross comment regarding referees 2 and 3 above. I'm am convinced that TXNIP is at least contemporaneously upregulated with GDF15 downregulation. However, the strong implication from the writing is that TXNIP regulates GDF15 directly. I agree with the comment above that exploring mechanisms may be open-ended especially as TXNIP has been implicated in gene regulation by several different mechanism. I'd be satisfied with a more open-minded discussion of potential mechanisms by which TXNIP may repress GDF15 and the possibility of other parallel pathways that likely contribute to GDF15 repression.

      Many thanks, this is a generous and understanding approach. As described above we have carried out extra analysis and have found 6 differentially regulated transcription factors which have been shown to bind GDF15 promoter or enhancer regions with 1 of these, MYC, being significantly affected in the TXNIP knockout cell lines, which in combination with supportive literature suggests a degree of TXNIP dependence. We have also identified ARRDC4 as an additional regulator of GDF15 – again please see above.

      Reviewer #1 (Significance (Required)):

      This is an interesting contribution but the mechanistic connection between GDF15 and TXNIP is relatively weak. That said, even as independent variables they do seem to have utility in predicting therapeutic response.

      Many thanks for the comment – we concur. We have reanalysed our data looking for relevant transcription factors (those that bind GDF15 promoter / enhancer regions) finding MYC as the most likely bridge. Please see above.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Deng et al. investigates a mechanistic link between TXNIP and GDF15 expression and oxaliplatin treatment and acquired resistance. They observe an upregulation in TXNIP expression in the tumors of patients who have previously received chemotherapy. They demonstrate oxaliplatin-driven MondoA transcriptional activity is what underlies the induction of TXNIP. They further demonstrate that TXNIP is a negative regulator of GDF15 expression. Together, oxaliplatin induces MondoA activity and TXNIP expression, resulting in a downregulation of GDF15 expression and consequently decreased Treg differentiation.

      Major Comments

      1. The authors suggest that TXNIP induction and GDF15 downregulation are a common effect of chemotherapies; however, the mechanistic studies were limited to oxaliplatin. The authors should clarify this point through further investigation using other commonly used CRC chemotherapies (5-FU, irinotecan, etc.),or through textual changes. To be clear, I think that the oxaliplatin results could potentially stand on their own but would require additional clarification. For example, regarding the patient samples analyzed in 1D and 4F, which patients received oxaliplatin? Could the analysis of publicly available molecular data be drilled down to just the patients who received oxaliplatin?

      Many thanks – this is an excellent point. Firstly, all the patients in 1D and 4F received oxaliplatin. Secondly, we have included new data looking at the impact of other chemotherapies (FOLRIRI, FU-5 and SN-38) on aspects of the study, ultimately finding that these processes (especially an anti-correlation between GDF15 and TXNIP changes upon chemo treatment) appear to be specific to oxaliplatin. These data have been added (Figure S11) and throughout the emphasis has been switched from chemotherapeutic treatment to oxaliplatin treatment.

      Lines 796-799: “To check if the pre-treatment GDF15/TXNIP ratio could be used for patients treated with FOLFIRI we performed the same analyses finding no significance (S11A-D). This oxaliplatin specificity was then confirmed by western blot analysis in DLD1 and HCT15 cells treated with 5-FU or SN38 (Figure S11E-F).

      Example of change of emphasis from ‘chemotherapy’ to ‘oxaliplatin’ – lines 139-142: “Here, in colorectal adenocarcinoma (CRC) we identify oxaliplatin-induced Thioredoxin Interacting Protein (TXNIP), a MondoA-dependent tumor suppressor gene, as a negative regulator of Growth/Differentiation Factor 15 (GDF15).”

      The data demonstrating the induction of MondoA transcriptional activity and TXNIP expression in response to oxaliplatin treatment is quite convincing. The data regarding ROS induction of TXNIP is interesting, especially in light of other studies arguing that ROS limits MondoA activity (PMID: 25332233). Given this apparent disparity, I think that this study could really be strengthened by also investigating other potential mechanisms of oxaliplatin induction of MondoA. In particular, given many studies arguing for direct nutrient-regulation of MondoA, the authors should address the potential for oxaliplatin regulation of glucose availability and a potential glucose dependence of oxaliplatin-induced TXNIP. 2

      In line with the previous point, since MondoA activity and TXNIP expression are sensitive to glucose levels, the authors should investigate oxaliplatin-regulation of TXNIP under physiological glucose levels. No need to replicate everything, just key experiments.

      We feel these are excellent point and really help the piece – many thanks. We have carried out assays around these points suggested and have included the findings in the new draft – see below.

      Lines 332-339: “As such, we went back to first principles and assessed the impact of different concentrations of glucose on TXNIP induction +/- oxaliplatin treatment, finding a concentration dependent effect (Figure S3A). Intriguingly, high glucose alone was able to induce increased TXNIP expression. We then assessed if oxaliplatin treatment drove an increase in glucose uptake, with this seen at concentrations >10mM (Figure S3B). Next, to investigate the impact of glucose metabolism, and consequent ROS generation, on TXNIP induction we treated cells with Antimycin A, an inhibitor of oxidative phosphorylation, finding a complete block in oxaliplatin-induced TXNIP (Figure S3C).”

      The authors did a good job of linking TXNIP and GDF15 in untreated conditions; however, the data arguing for oxaliplatin regulation of GDF15 through TXNIP is less clear. For example, in 3B-H, oxaliplatin treatment reduces GDF15 approximately to the same extent in the NTC and TKO cells, potentially in line with a mechanism of downregulation that doesn't involve TXNIP.

      A very salient point and completely in line with the other reviewers. We have carried out a few additional analyses mentioned previously in this letter. The most pertinent for this specific point are the experiments around ARRDC4, where we found evidence to suggest that, like TXNIP, it regulates GDF15.

      Minor Comments

      1. The presentation of data in Figure 5 is confusing. A-B include raw cell numbers, whereas C-F show "normalized proliferation." What does this mean? And how was the normalization done?

      Apologies for this. Legend test has been corrected to “Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 727-729).

      **Referee Cross-Commenting**

      cross-comment regarding reviewer #1

      I agree with the referee that the link between TXNIP and GDF15 is weak, though as I mentioned before, this is particularly true in the context of oxaliplatin-regulation of TXNIP. I agree that given all the presented data, it is likely that oxaliplatin-regulation of TXNIP and GDF15 are independent. In my opinion, the referee brought up all valid concerns, but this is by far the biggest concern that I share.

      We agree that this is the weakest aspect of the paper, however our new analyses plus supportive literature, suggests that the relationship between TXNIP and GDF15 may be mediated by MYC (please see above)

      cross-comment regarding reviewer #3

      The major concern that this referee addresses is whether another transcription factor supersedes the proposed MondoA/TXNIP induction in regulating GDF15 expression in later stage CRC. In my opinion, this another other concerns of the referee are all valid, but still I remain unconvinced that TXNIP induction underlies the oxaliplatin-regulation of GDF15. I think fleshing out that aspect of the study would potentially help the authors tease apart how this potential MondoA-TXNIP-GDF15 axis is dysregulated later in CRC progression.

      This is a great discussion. Interestingly enough, c-myc is seen at higher levels in late stage CRC (Hu X, Fatima S, Chen M, Huang T, Chen YW, Gong R, Wong HLX, Yu R, Song L, Kwan HY, Bian Z. Dihydroartemisinin is potential therapeutics for treating late-stage CRC by targeting the elevated c-Myc level. Cell Death Dis. 2021 Nov 5;12(11):1053. Doi: 10.1038/s41419-021-04247-w. PMID: 34741022; PMCID: PMC8571272.), is seen as an important factor in resistance, and as this review argues, is driven by stress (Saeed H, Leibowitz BJ, Zhang L, Yu J. Targeting Myc-driven stress addiction in colorectal cancer. Drug Resist Updat. 2023 Jul;69:100963. Doi: 10.1016/j.drup.2023.100963. Epub 2023 Apr 20. PMID: 37119690; PMCID: PMC10330748.). So it is very plausible that the partial TXNIP-mediated regulation of myc in early / sensitive CRCs that we may be observing, and has been reported recently (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778) is lost in late stage / resistant CRCs. If this is the case, in effect what we would have observed is the loss of a stress-associated method (TXNIP) of controlling c-myc activity. What makes our collective lives difficult is that, as reported “this expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Myc’s intrinsic capacity to activate transcription and without increasing Myc levels.” (TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding Lim TY, Wilde BR, Thomas ML, Murphy KE, Vahrenkamp JM, et al. (2023) TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding. PLOS Biology 21(3): e3001778. https://doi.org/10.1371/journal.pbio.3001778)

      Reviewer #2 (Significance (Required)):

      Generally speaking the experiments are well controlled and the findings are significant and novel. Though the link between MondoA activity and ROS could be strengthened, and the data could be validated under more physiological settings. Further, the authors should clarify their interpretations so as to not overstate the findings.

      Many thanks for the comments. We have taken onboard the need for more physiological settings and have included varying levels of glucose to reflect concentrations in different environments. We have repeated the siMondoA work in 3J strengthening the conclusions wrt its impact on TXNIP and GDF15 expression (see above).

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this well-written manuscript, the authors show that chemotherapy increases a MondoA-dependent oxidative stress-associated protein, TXNIP, in chemotherapy-responsive colorectal cancer cells. They show that TXNIP negatively regulates GDF-15 expression. GDF-15, in turn, correlates with the presence of T cells (Treg), and inhibits CD4 and CD8 T cell stimulation. In advanced disease and chemo-resistant cancers, upregulation of TXNIP and downregulation of GDF-15 appear to get lost. Based on a somewhat smallish data set, the authors suggest that the pre-treatment GDF-15/TXNIP ratio can predict responses to oxaliplatin treatment. This is a very interesting, novel finding. In general, the quality of the experiments and the data are high and the conclusions appear sound. Still, there are a number of aspects that should still be improved:

      The observed loss of the ROS - MondoA - TXNIP - GDF15 axis in chemoresistant and/or metastatic tumors implies that another transcription factor or pathway becomes dominant upon tumor progression. As this switch would be key to better understanding the mechanism underlying the prognostic role of the TXNIP/GDF15 ratio, the authors should at least do data mining followed by ChEA or Encode (or other) analysis to identify transcription factors or pathways that become activated in late-stage/metastatic CRC cells. There is a high likelihood that a transcription factor or pathway involved in GDF-15 upregulation in cancer (e.g. p53, HIF1alpha, Nrf2, NF-kB, MITF, C/EBPß, BRAF, PI3K/AKT, MAPK p38, EGR1) supersedes the inhibitory effect of the MondoA-TXNIP axis. As it stands, the proposed loss of function of the ROS - MondoA - TXNIP - GDF-15 axis is far less convincing than almost all other aspects of the study.

      An extremely fair point. We adopted a similar approach to that suggested – as mentioned above, we looked at TFs that bind to GDF15 promoter/enhancer regions and then looked at the presence of these in our transcriptomic data – specifically any evidence of change post oxaliplatin treatment. We found 6 such TFs that were decreased post-oxaliplatin treatment. We then looked for any evidence of TXNIP dependence in these TFs by comparing post-oxaliplatin treatment across NTC and TXNIP knockout lines, when we did this we found only one GDF15 promoter/enhancer binding TF was significantly changed: MYC. We then looked at the relationship between MYC,TXNIP, and GDF15 against the other 5 ‘control’ TFs in the TCGA COAD dataset, we found that MYC showed the strongest correlations, in the ‘correct’ directions. This finding was further backed up in the literature where a TXNIP knockout in a breast cancer model drove c-myc-dependent transcription, whilst c-myc has been observed to increase in later stage CRC patients, is associated with cellular stress and resistance. The collective evidence therefore suggests that MYC is the factor that is initially at least partially regulated by TXNIP, before this regulation is lost in advanced / resistant disease. Continuing on this line, it is likely that the predictive GDF15/TXNIP ratio is at least in part, a measure of c-myc responsiveness to oxaliplatin. All the while we must bear in mind TXNIP-independent oxaliplatin-dependent regulation of GDF15, most likely ARRDC4, as described earlier in this document.

      Using pathway analysis software to compare our transcriptomic data from cell lines treated with/without oxaliplatin, the most likely pathways upstream of MYC/c-myc that are negatively affected by chemotherapy are BAG2, Endothelin-1, telomerase, ErbB2-ErbB3 and Wnt/B-catenin. When looking at the comparison of UTC and resistant lines’ transcripts there is only one key component of these pathways which is upregulated in both lines - ERBB3 – which has already been shown to be important in CRC metastasis and resistance (Desai O, Wang R. HER3- A key survival pathway and an emerging therapeutic target in metastatic colorectal cancer and pancreatic ductal adenocarcinoma. Oncotarget. 2023 May 10;14:439-443. doi: 10.18632/oncotarget.28421. PMID: 37163206; PMCID: PMC10171365.). It is highly speculative, but our data suggests the most likely pathway to supersede TXNIP in its (partial) regulation of MYC is the ErbB2-ErbB3 pathway.

      My further criticisms are mostly more technical:

      Figure 2 I-L: What was the extent of MondoA downregulation achieved by siRNA treatment? Could the effects also be seen with the small molecule mondoA inhibitor SBI-477 (or a related substance)?

      This experiment has been repeated. The pooled densiometric data is also now given (please see above).

      How do you explain the different GDF-15 levels between untreated non-target control cells (NTC) and TXNIP knock-down cells (TKO) in Figures 3C-F?

      The only way to interpret this is that there is a TXNIP-independent pathway regulating GDF15 expression after oxaliplatin treatment, as described this is most likely to be ARRDC4 - the text has been updated to:

      Lines 522-524: “It is important to note, however, that we saw clear evidence that TXNIP was not solely responsible for the downregulation of GDF15 post oxaliplatin treatment (Figure 3C-G, S6E).”

      In figures 3 E-G the dots for the individual measurements should be indicated. This would be more informative than just the bar graphs.

      Completed.

      Figure 4C,D and Table 3: Data on the role of GDF-15 in CRC are largely valedictory of previous work (e.g. Brown et al. Clin Cancer Res 2003, 9(7):2642-2650, Wallin et al., Br J Cancer. 2011 May, 10;104(10):1619-27). Therefore, the previous studies should be cited.

      Apologies for the oversight and many thanks – this is an excellent addition.

      Figure 5C-F: Please indicate in the figure legend how proliferation was assessed.

      Many thanks. This was noticed by another reviewer also. We have changed the text to include how the data was normalised: “(C-F) Labelled PBMCs were stimulated with anti-CD3 and anti-CD28 for 4 days in the presence of fresh supernatant from indicated cell lines, before being stained with anti-CD3 and anti-CD8 (C-D) or anti-CD4 (E-F) antibodies and measured by flow cytometry. Normalised proliferation (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) on gated CD3+CD8+ or CD3+CD4+ cells is shown. n=6. (G-H) Normalised IFNg concentrations (normalised to MFI from control: i.e. cells treated with supernatant from NTC cells) in the supernatant of cells from C-F.” (lines 724-730)

      Figure S8E-G: Please indicate the analysed parameters in the graphs. In Figure S8G, the legend just indicates that "aggression of tumour" is dichotomized and plotted. This clearly requires a better definition.

      Many thanks, this has been changed as per the below.

      Lines 862-868: “(E-G) Receiver operating characteristic (ROC) curves showing area under the curve and p values for the use of GDF15/TXNIP ratio in predicting origin of cell line (E; primary; DLD1, HCT15, HT29, SW48 [n=4] or secondary; DiFi, LIM1215 [n=2]), sensitivity to oxaliplatin (F; parental DLD1 (plus biological repeat), HCT15 [n=3] or resistant DLD1 (plus biological repeat), HCT15 [n=3]), aggression of tumor (G; non-aggressive; The authors propose a novel ROS - MondoA - TXNIP - GDF15 - Treg axis, where MondoA activation, TXNIP up- and GDF-15 downregulation enhance tumor immunogenicity. While this axis has been analyzed in some detail, GDF-15 is not only linked to induction of regulatory T cells. There has been a report showing that GDF-15/MIC-1 expression in colorectal cancer correlates with the absence of immune cell infiltration (Brown et al. Clin Cancer Res 2003, 9(7):2642-2650). The link between GDF-15 and immune cell exclusion has also been confirmed in other conditions, including different cancers (Kempf et al. Nat Med 2011, 17(5):581-588, Roth P et al. Clin Cancer Res 2010, 16(15):3851-3859, Haake et al. Nat Commun 2023, 14(1):4253). A key mechanism is the GDF-15 mediated inhibition of LFA-1 activation on immune cells. As the authors argue that the described pathways turns cold tumors hot in response to oxaliplatin-based chemotherapy, this GDF-15 dependent immune cell exclusion mechanism might be at least as relevant than induction of Treg. Likewise, inhibition of dendritic cell maturation by GDF-15 (Zhou et al. PLoS One 2013, 8(11):e78618) could explain why GDF-15high tumors are immunologically cold. Reviewed in 3

      The authors propose that the pathways discovered by them contributed to the "heating up" of the tumor microenvironment after oxaliplatin-based chemotherapy. The authors should thus look in their data sets for the presence of cytotoxic T cells and their possible correlation with TXNIP and GDF-15 levels.

      This is a wonderful explanation – many thanks. We have taken the opportunity to assess the impact of GDF15 expression on a variety of T cell markers (Figure S9). In this data a negative association between GDF15 and CD8 CTLs can clearly be seen, as predicted by the reviewer.

      Lines 712-717: “To assess if the GDF15-dependent presence of Tregs may be associated with a decrease in activated cytotoxic CD8 T cells, we interrogated the TCGA COAD dataset. We found that low GDF15 tumors carried significantly higher levels of CD8, CD69, IL2RA, CD28, PRF1, GZMA, GZMK, TBX21, EOMES and IRF4 (Figure S9); transcripts indicative of activated cytotoxic CD8 T cells. High GDF15 tumors were enrichment for FOXP3 and, interestingly, RORC (Figure S9). These data support the hypothesis that GDF15 induces Foxp3+ve Tregs which inhibit CD8 T cell proliferation and activation in the TME.”

      The paragraph on GDF-15 receptors needs to be corrected: The purported role of a type 2 transforming growth factor (TGF)-beta receptor in GDF-15 signalling had been due to a frequent contamination of recombinant GDF-15 with TGF-beta (Olsen et al. PLoS One 2017, 12(11):e0187349). There have been a number of screenings for GDF-15 receptors that have all failed to show an interaction between GDF-15 and TGF-beta receptors. Instead, only GFRAL was found in these large-scale screenings (Emmerson et al. Nat Med 2017, 23(10):1215-1219, Hsu et al. Nature 2017, 550(7675):255-259, Mullican et al. Nat Med 2017, 23(10):1150-1157, Yang et al. Nat Med 2017, 23(10):1158-1166). The one subsequent report that shows a link between GDF-15, engagement of CD48 on T cells and induction of a regulatory phenotype (Wang et al. J Immunother Cancer 2021, 9(9)) still awaits independent validation. Considering that CD48 lacks an intracellular signaling domain that would be critical for a classical receptor function, I recommend to be more cautious regarding the role of CD48 as GDF-15 receptor. Given the mechanism outlined by Wang et al. the word interaction partner might be more apt. Moreover, an anti-GDF-15 antibody would be a good control for the experiments involving an anti-CD48 antibody in Figure 5.

      Thank you so much for this concise and highly informative paragraph. We have changed the text to read:

      202-204: “As a soluble protein, GDF15 exerts its effects by binding to its cognate receptor, GDNF-family receptor a-like (GFRAL)44,45,46,47 or interaction partner, CD48 receptor (SLAMF2)43, with the latter still requiring additional verification.”

      We would have ideally included an anti-GDF15 antibody in the CD48 assay at the time but didn’t have the foresight. We have included the additional text to temper any conclusions.

      Lines 701-711: “Furthermore, when stimulating naïve CD4 T cells in the presence of GDF15 enriched supernatant we were able to both differentiate these cells into functional Tregs and also block the generation of this functionality using an anti-CD48 antibody (Figure 5M-N). However, it must be stressed that the binding and functional impacts of GDF15’s interaction with CD48 still require further verification.”

      Cell surface externalization of annexin A1 has been described as a failsafe mechanism to prevent inflammatory responses during secondary necrosis (PMID: 20007579). Thus, I am surprised that the authors list annexin A1 among the immune-stimulatory molecules exposed or released in response to chemotherapy-induced cell death (line 103). Please clarify!

      We agree – it shouldn’t be there!! Removed. Many thanks.

      **Referee Cross-Commenting**

      Regarding the cross-comment by referee 2: In my opinion, the data shown in Figure 3C-H clearly demonstrates that TXNIP can repress GDF-15 expression. I agree that there will likely be further regulators. The GDF-15 promoter is constantly regulated by a multitude of factors (which mostly induce transcription). As downregulation of GDF-15 in response to oxaliplatin is the opposite of the frequently described induction of GDF-15 upon chemotherapy, net effects may always be "smudged" by contributions from different pathways (e.g. by cell stress due to siRNA transfection). Therefore, I believe that the data are as good as it will get. Accordingly, I would not force the authors to further amplify the observed effect.

      Many thanks for your understanding – yes, GDF15 has >650 TFs that bind its promoter/enhancer regions – a number we found rather daunting. Happily your comments and those of the other reviewers inspired us to dig and we now have data that is supportive of MYC’s and ARRDC4’s involvement – detailed throughout this reply.

      cross comment regarding referee #1: I share the general assessment of the referee and recognize the very detailed mechanistic analysis. To further support the moderate effects of the MondoA knockdown, a small molecule inhibitor like SBI-477 might be useful. (I had already suggested using this inhibitor to support these data.)

      Many thanks for the suggestion. We opted to increase the number of siRNA repeats instead – with the data included in Figure 3J (above).

      Still, my view on the potential relevance of oxaliplatin-induced, TXNIP-independent downregulation of GDF-15 differs from that of referee 1. In the clinics, platinum-based chemotherapy is one of the strongest inducers of GDF-15 (compare Breen et al. GDF-15 Neutralization Alleviates Platinum-Based Chemotherapy-Induced Emesis, Anorexia, and Weight Loss in Mice and Nonhuman Primates. Cell Metabolism 32(6), P938-950, 2020.DOI:https://doi.org/10.1016/j.cmet.2020.10.023). I was thus surprised that the authors found a pathway, which leads to an outcome that an exactly opposite effect.

      This is fascinating that oxaliplatin drives this increase in GDF15 – we were unaware of this paper. Looking at figure 2(H-K), GDF15 is being produced from multiple non-diseased tissues after systemic chemotherapy – even at day 19 post-treatment – this suggests that wrt this study, systemic GDF15 could not be used as a readout of success or otherwise – which is extremely helpful! Thank you.

      Thus far, the only obvious reason for reduced GDF-15 secretion upon treatment with cytotoxic drugs was a reduction in tumor cell number due to cytotoxicity.

      Please do not discount this. This study was focused on the cells which survived oxaliplatin treatment – the cells which did not were discarded. Our view, given your input, would be a complex picture where in early stages systemic GDF15 goes up, due to off-target effects, but locally levels drop owing to cell death and this, and other, stress-related pathways in the remaining tumor cells.

      Still, the authors managed to convince me that the described pathway (ROS - MondoA - TXNIP - GDF-15) exists. (Here, I still largely concur with referee 1.) Moreover, as we have identified some factors required for GDF-15 biosynthesis that could easily interact with TXNIP, I find the proposed mechanism plausible.

      Extremely encouraging for us to hear!

      Nevertheless, as a downregulation of GDF-15 in response to chemotherapy is hardly ever observed in late-stage cancers, I believe that the observed switch in pathway activation between early- and late-stage cancers might be highly relevant - in particular, as there is so much evidence for platinum-based induction of GDF-15 in late-stage cancer patients. Emphasizing the divergent clinical observations (e.g. by Breen et al.) could thus help to put the finding into perspective.

      Very much agree. We did see this phenomenon in LIM1215 cells (Figure 6B) and the resistant lines we generated continually produced higher levels.

      Analysing TXNIP-independent mechanisms involved in the oxaliplatin-dependent repression of GDF-15, as suggested by referee #1, will require enormous efforts and resources, and may still turn out to be fruitless. Personally, I would thus be content if the authors just mentioned possible contributions from other pathways upon cancer progression. To me, the described pathway seems to be limited to early-stage cancers, and the actual finding that GDF-15 is downregulated is an interesting observation, irrespective of further involved pathways.

      Many thanks – this is extremely fair. Happily we have managed to make some tentative steps forward in highlighting the potential role of MYC, and the suggestion of redundancy wrt ARRDC4, but as you say, much more work needs to be done to fully understand these processes.

      cross comment regarding referee #2: I fully agree with the referee that activation of the pathway by further chemotherapeutic drugs could be a valuable addition. As Guido Kroemer´s lab has described oxaliplatin to induce a more immunogenic cell death compared to other platinum-based chemotherapies, even a rather limited comparison between oxaliplatin and cisplatin could be very interesting.

      Absolutely agree – extra data on this has been included in Figure S11, which is included earlier in this letter. We also uncovered a meta-analysis using metformin, which has been seen to inhibit ROS, where TXNIP and ARRDC4 are the top two downregulated transcripts whilst GDF15 appears in the top four upregulated. This may suggest that chemotherapeutic immunogenicity, at least through the presence or absence of GDF15, may in part be driven by ROS.

      Lines 930-933: “Further support for both TXNIP and ARRDC4’s role in regulating GDF15 after the induction of ROS comes from a pan cancer meta-analysis assessing the impact of metformin (which has been reported to inhibit ROS) on gene expression. Here the top two downregulated genes were TXNIP and ARRDC4 and the top four upregulated genes were DDIT4, CHD2, ERN1 and GDF1572 “

      Reviewer #3 (Significance (Required)):

      In general, this is a very interesting manuscript describing a cascade of events that may contribute to successful chemotherapy (which likely requires induction of an immune response against dying tumor cells.) The observation that this pathway is only active in early/non-metastatic cancer cells is striking. Unfortunately, the authors cannot explain inactivation of this pathway in later stage/ metastatic/ highly aggressive cancers. Understanding this switch could easily be the most important finding triggered by this report. Therefore, I highly recommend to make some effort in this direction. Strikingly, the authors find that disruption of TXNIP-mediated GDF-15 downregulation is strongly associated with worse prognosis. They also suggest that this ratio could indicate whether a patient will respond to oxaliplatin-based chemotherapy.

      This is again very fair – we have posited a potential mechanism for the loss of this switch elsewhere in this reply– one which involves a change in TXNIP-mediated MYC regulation and/or increased HER2-HER3 signalling – but although reasonable for a rebuttal (and publication in that context) we do not feel we have the evidence to include this within the full manuscript.

      Altogether, the findings described in manuscript are very novel and may have prognostic (or, in case of the presumed loss of the MondoA - TXNIP - GDF-15 pathway) therapeutic implications. Thus, the manuscript certainly fills various gaps and should be of major interest for cell biologists working on immunogenic cell death, or colorectal cancer, or MondoA, TXNIP or GDF-15. Still, due to its translational implications, it would also be worthwhile reading for a large number of researchers in the oncology field.

      We are very grateful for your kind comments.

      1 Sinclair, L. V., Barthelemy, C. & Cantrell, D. A. Single Cell Glucose Uptake Assays: A Cautionary Tale. Immunometabolism 2, e200029, doi:10.20900/immunometab20200029 (2020).

      2 Yu, F. X., Chai, T. F., He, H., Hagen, T. & Luo, Y. Thioredoxin-interacting protein (Txnip) gene expression: sensing oxidative phosphorylation status and glycolytic rate. J Biol Chem 285, 25822-25830, doi:10.1074/jbc.M110.108290 (2010).

      3 Wischhusen, J., Melero, I. & Fridman, W. H. Growth/Differentiation Factor-15 (GDF-15): From Biomarker to Novel Targetable Immune Checkpoint. Front Immunol 11, 951, doi:10.3389/fimmu.2020.00951 (2020).

    1. trauma reenactment narrative is by getting the child manipulating the child convincing the child to adopt the victimized child role within that trauma reenactment there and so all we have to do is get the child to believe that the

      This ominous realization did not occur and come together for me until just now:

      Kate's influence did not start with Kate directly. It would have started with her son Liam. I've not recognized until now the likely significant role he plays in this. He is her son. He would have already been fully traumatized by Kate or by the situation with his dad, depending on if it existed, but if it did or didn't, the fear/abandonment/insecure attachment disorder would be entrenched in both Kate and Liam and they would be reinforcing it in each other. Rhyanna working with Liam at Subway would have been the first contact in which casual conversation would begin the subtle campaign by Liam via trauma reenactment (and also fueled by being a teenage boy meets girl savior/peacocking mentality) that at first innocuously and then overtly was showing (manipulating into false belief) that she is victimized. Liam then notifies Mom of "the recruit", probably a genuine felt statement like "Mom, there's this girl at work and it sounds like she's going through what we went through and we could help her". Then Mom [Kate], which we know this happened, took the initiative to contact her (or told Liam to bring her over to the house to hangout so she could then introduce herself and have 'a talk' with her). Phone numbers were shared, instructions to not let Dad know where they lived were given, taking out to dinners were done, sharing of "stories about my husband we don't tell other people so please don't share this" were given about "my dangerous psychotic husband that Liam and I had to flee from and go on the run because the system couldn't save us so we had to act outside it". This matches the dynamic and origination story of every cult/radical "church"/scientology/NXIVM story I know and it is the same dynamic whether it's the pathogenic parent or pathogenic adult influence which in this way has an extra component of evolution. Ie, the pathogenic adult has created/obtained a pathogenic "victimized" subordinate follower. The follower then acts as a relatable/ice-breaking recruiter that has the effect on the target of " they're my peer, they're like me, I can therefore trust the accuracy of what they're saying more and am more willing to listen". Then when the follower eventually introduces the pathogenic adult, the critical judgement defence of the target is suppressed/ignored because the target has made the naive judgment error that since I believe and feel trusting in this peer, I can put that trust into someone he is introducing me too. And because that person is "the adult in the room" this person instantly gets, erroneously, the elevated security clearance in the target's mind that this person is a "trusted"+"adult"+"who understands me"+"has my best interest"+"and knows what I need". Additionally, when speaking with this adult, should the target's defense mechanisms of critical judgement start turning on, the target then looks to a reference point to "reality test", and the follower, Liam, is immediately on hand and present almost daily to act as that reference point nodding reassuringly when the target glances over [literally or metaphorically]. ..... Combine this with a parent who is getting sicker and sicker, who's observably by the child who knows her father well can tell his fear, anxiety (particularly regarding his ability to provide for them both), and sadness because of his non-improving sickness from a mysterious unknown deadly pandemic disease, a parent who is the SOLE parent and there is no second parent to reality test against and get reassuring grounded perspective (ie you are not victimized, dad isn't going to kill himself, yes this is a tough situation but we and you are not a victim and this is not a Hallmark/teen drama, and tough situations like this have long been and are a prolific part of human life and we can more than handle this situation and frankly will serve to accelerate your empowered growth and deeper understanding, meaning, passion, joy of life and further shedding of vulnerability to irrational and mismanagement of uncontrollable fear as a general skill set in your personal quiver. This all is the loss of the second, of which there may only be 2, fundamental defense mechanisms to safeguard a child's sound critical analytical/judgement skills. It is easy to empathize with a child's daily living experience, especially an adolescent, how these are the 2 mechanisms which are functioning by which they are consuming and assembling all new knowledge and understanding. #1 They first use their incumbent developed analytical/judgement skills to self analyze a concept or problem or question. #2 They verify that determination with their trusted source of truth and protection, ie their parents (a reality test). Perhaps this at the root of the common report "teenagers think they know everything". It's probably the first time the first mechanism is developed strongly enough to feel like it can safely be used in its own. And in being the first time, many errors will be made and in many of those errors the use of verification of mechanism 2 will not be used. An ill unimproving parent will exacerbate the error to not use mechanism 2. Fear and anxiety will exacerbate errors in mechanism number 1. Severity of those insults would proportionally affect the rate of error. Malfunctions in both mechanisms would have a multiplicative effect on damaging erroneous conclusions the child arrives at and the damage further choices on those erroneous conclusions causes. Then when the "virus" of the narcissistic/BLP cross generational shared persecutory delusion boundary violation gains entry into this now much increased "analytically vulnerable" child, it has the critically added effect of disabling mechanism 2 since the patent now becomes "all bad [splitting]". ..... Then ..... add to this child a history that she is a survivor, albeit exceptionally so, of incurring the pain and largely successful battle for separation from a very narcissistic mother and the family that produced that narcissism in her mother. The entire repercussions of that I am not sure, but relevant here is I think that means my child's developmental reality has a biased understanding and emotional sensitivity to the fear that a parent "I thought was normal, changed into a monster" and second "I fully believed a truth about the 1 of 2 people I trusted and depend on the most, and I was wrong. How can I trust my own conclusions now if I can't trust my own analytical and emotional judgement abilities?". No doubt also a fear and anxiety upregulating mechanism in and if itself, as well as providing a data point which can add confusion to a child frantically looking for understanding and/or can be leveraged to falsely rationalize the false narrative is correct especially when the pain of the truth is building and she is looking for any tool to suppress confronting that pain.

      Then, as Rhyanna further looks for, or rather it is imposed onto her, the naive drama thirsty peer group, whom many know Liam and Kate, and whom with very good intention but naivety of teenagers who in Boulder Colorado are conditioned to both be very helpful and that money and wealth (like them) combined with middle aged Caucasian combined with a "Boulderite" personality with an air of non-confrontational superiority and cancel-culture tendencies is the equivalent of "insightful, wise, holder of truth, and generally the definition of what is good, righteous, and hold the authority to declare whom is bad and further that it is expected that they will declare whom is good and bad and that action further validates that they are and have such authorities" in these teenagers minds reenforces this false truth as accurate.. Then the school, then CIRT team "mental health professionals", then the mental health hospital centennial peaks, then Boulder county child welfare via multiple staff, then the court and the judge personally all buy in and propagate this false truth and reinforce it overtly or indirectly overtly, and some propagate it by simply ignoring and not speaking out against or in questioning validity, all reinforcing this false truth. ..... And given all this, given all these goddamn ignorant spineless children of men in their lack of knowledge or past traumas, and under the weight of their ignorance and cowardice and laziness, and then under the unreal weight and fear and confusion of her and her dad, her one parent who's been her warrior defender of knowledge, self discovery, safety, character, food, and shelter, and whom no other family support exists is now very possibly dying and cannot speak for himself or to her (because her confusion and outside influence is not allowing it) to tell her the truth and reassurance of the situation ....... her heart and mind refuse to yield. The pain from her heart refusing to give way to the lie, they are trying to make her believe had caused her to want to kill herself. My daughter s unyielding heart and character brought tears to a police officer who'd not had the fortune of experiencing someone like my daughter. And still, after a year and a half, my daughter, MY daughter, still holds fast and is unwilling to tell the COURT that her resistance is because of me and is instead because of her. Yeah, that's who my daughter is. That is the caliber here. She is her father's daughter.

      I see you kid. You hold fast. I'm comin' for you.

      PS - Attention needs to be given to Liam. With consideration towards his possible and to what degree of trauma, and the validity of the story regarding his father.. It is now a real question, is his father above and well, normative, searching for his son and or fallen into decline, suicidality, doom? Is Liam about to lose a father and be irreversibly severely damaged because of the complete irreversible devastation, which will also include the self blame he incurs and will not be able to reconcile.

      PSS - likely it is both important and the is the time to revisit with focus Rhyannas feelings and understanding of her mom. She possibly stands to gain 1, a self confidence and esteem and complete obliteration of any feeling/false rationalization that she is somehow "less", that she is at fault, or that she is somehow "less capable" of a person now and going forward, 2) stamp out reactions of hate, tolerance, splitting, and walls she might form that would prevent problem solving, truth finding, and understanding so crucial to both abilities and finding of joy, particularly in relationships of love and family, 3) she stands to gain a mother and an entire side of a family and which is attained by a fulfilling relationship of her own architecture and which she is fully empowered to control and manage and nurture at her pleasure.

    1. Empiricism involves acquiring knowledge through observation and experience. Once again many of you may have believed that all swans are white because you have only ever seen white swans.

      I think this the reason why Karl Popper proposed an alternative approach based on falsification rather than confirmation. because when we saying that all swans are white, confirming this statement would involve finding as many white swans as possible However, with Popper's approach it shows that the statement is scientific only if there is a way to show it is false. In this case, finding a single black swan would falsify the statement). Therefore, we can also say that confirmation alone cannot provide certainty or proof of a theory's validity.

    1. ZK II note 9/8b 9/8b On the general structure of memories, see Ashby 1967, p. 103 . It is then important that you do not have to rely on a huge number of point-by-point accesses , but rather that you can rely on relationships between notes, i.e. references , that make more available at once than you would with a search impulse or with one thought - has fixation in mind.

      This underlies the ideas of songlines and oral mnemonic practices and is related to Vannevar Bush's "associative trails" in As We May Think.

      Luhmann, Niklas. “ZK II Zettel 9/8b.” Niklas Luhmann-Archiv, undated. https://niklas-luhmann-archiv.de/bestand/zettelkasten/zettel/ZK_2_NB_9-8b_V.

    1. us of that. As regards the third source, the social source of suffering, our attitude is adifferent one. We do not admit it at all; we cannot see why the regulations made by ourselves shouldnot, on the contrary, be a protection and a benefit for every one of us. And yet, when we consider howunsuccessful we have been in precisely this field of prevention of suffering, a suspicion dawns on us thathere, too, a piece of unconquerable nature may lie behind -this time a piece of our own psychicalconstitution.When we start considering this possibility, we come upon a contention which is so astonishing that wemust dwell upon it. This contention holds that what we call our civilization is largely responsible for ourmisery, and that we should be much happier if we gave it up and returned to primitive conditions. I callthis contention astonishing because, in whatever way we may define the concept of civilization, it is acertain fact that all the things with which we seek to protect ourselves against the threats that emanatefrom the sources of suffering are part of that very civilization.How has it happened that so many people have come to take up this strange altitude of hostility tocivilization? I believe that the basis of it was a deep and long-standing dissatisfaction with the thenexisting state of civilization and that on that basis a condemnation of it was built up, occasioned bycertain specific historical events. I think I know what the last and the last but one of those occasionswere. I am not learned enough to trace the chain of them far back enough in the history of the humanspecies; but a factor of this land hostile to civilization must already have been at work in the victory ofChristendom over the heathen religions, for it was very closely related to the low estimation put uponearthly life by the Christian doctrine. The last but one of these occasions was when the progress of

      I believe the focus of the reading is happiness can't be fully observed from the outside due to the lack self understanding we may have pertaining to happiness. It's hard for a man to be happy when they're so many different outlooks on what happiness should look like.

    2. If there had been no railway to conquer distances, my child wouldnever have left his native town and I should need no telephone to hear has voice; if travelling across theocean by ship had not been introduced, my friend would not have embarked on his sea-voyage and Ishould not need a cable to relieve my anxiety about him. What is the use of reducing infantile mortalitywhen it is precisely that reduction which imposes the greatest restraint on us in the begetting ofchildren, so that, taken all round, we nevertheless rear no more children than in the days before thereign of hygiene, while at the same time we have created difficult conditions for our sexual life inmarriage, and have probably worked against the beneficial effects of natural selection? And, finally,what good to us is a long life if it is difficult and barren of joys, and if it is so full of misery that we canonly welcome death as a deliverer?

      It seems lie Freud is trying to say that thirst for technological advancements have birthed new problems related to human existence. When you look at it from that perspective it does paint a rather bleak picture. However I do not think it's quite that simple. A dilemma such this may never have a clear cut answer.

    3. And yet, when we consider howunsuccessful we have been in precisely this field of prevention of suffering, a suspicion dawns on us thathere, too, a piece of unconquerable nature may lie behind -this time a piece of our own psychicalconstitution

      I don't know why but this made me think about fate vs free as we may make all the right choices but somethings may not fall in our favor.

    1. We also engage in social comparison based on similarity and difference. Since self-concept is context specific, similarity may be desirable in some situations and difference more desirable in others. Factors like age and personality may influence whether or not we want to fit in or stand out. Although we compare ourselves to others throughout our lives, adolescent and teen years usually bring new pressure to be similar to or different from particular reference groups.

      People put so much focus on social comparison. I think people get tunnel vision on trying to find a group to fit into, rather than find a group that fits them. Both are important in the right context. Just as it's important to step out of your comfort zone for new people, it's just as important to seek out people with shared interests and hobbies.

    1. history can seem more difficult to deny than those of engineering or medicine.

      Though history may seem trivial at times, I think that the real purpose of learning history is how we learn what works, as well as what we should never do again. Our goal should be to learn from our mistakes as a society. It also tells us so much about culture and in that we can also learn about our future.

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study advances our understanding of the ways in which different types of communication signals differentially affect mouse behaviors and amygdala cholinergic/dopaminergic neuromodulation. Researchers interested in the complex interaction between prior experience, sex, behavior, hormonal status, and neuromodulation should benefit from this study. Nevertheless, the data analysis is incomplete at this stage, requiring additional analysis and description, justification, and - potentially - power to support the conclusions fully. With the analytical part strengthened, this paper will be of interest to neuroscientists and ethologists.

      GENERAL COMMENTS ON REVIEWS AND REVISIONS

      Experimental design

      Here we address questions from several reviewers regarding our periods of neuromodulator and behavioral analysis. First, we recognize that the text would benefit from an overview of the experimental structure different from the narrative we provide in the first paragraphs of the Results. We now include this near the beginning for the Materials and Methods (page 17). We further articulate that the 10-minute time periods were dictated by the sampling duration required to perform accurate neurochemical analyses (and to reserve half of the sample in the event of a catastrophic failure of batch-processing samples). Since neurochemical release may display multiple temporal components (e.g., ACh: Aitta-aho et al., 2018) during playback stimulation, and since these could differ across neurochemicals of interest, we decided to collect, analyze, and report in two stimulus periods as well as one Pre-Stim control. We now clarify this in additional text in the Material and Methods (p. 24, lines 20-22; p. 26, lines 17-19). We decided not to include analyses of the post-stimulus period because this is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback.

      We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13) to clarify these periods.

      For behavioral analyses, observation periods were much shorter than 10 mins, but the main purpose of behavioral analyses in this report is to relate to the neurochemical data. As a result, we matched the temporal features of the behavioral and neurochemical analyses (p. 22, lines 17-22). We plan a separate report, focused exclusively on a broader set of behavioral responses to playback, that may examine behaviors at a more granular level.

      Data and statistical analyses

      Reviewers 1 and 3 expressed concerns about our normalization of neurochemical data, suggesting that it diminishes statistical power or is not transparent. We note that normalization is a very common form of data transformation that does not diminish statistical power. It is particularly useful for data forms in which the absolute value of the measurement across experiments may be uninformative. Normalization is routine in microdialysis studies, because data can be affected by probe placement and factors affecting neurochemical recovery and processing. Recent examples include:

      Li, Chaoqun, Tianping Sun, Yimu Zhang, Yan Gao, Zhou Sun, Wei Li, Heping Cheng, Yu Gu, and Nashat Abumaria. "A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice." Neuron (2023).

      Gálvez-Márquez, Donovan K., Mildred Salgado-Ménez, Perla Moreno-Castilla, Luis Rodríguez-Durán, Martha L. Escobar, Fatuel Tecuapetla, and Federico Bermudez-Rattoni. "Spatial contextual recognition memory updating is modulated by dopamine release in the dorsal hippocampus from the locus coeruleus." Proceedings of the National Academy of Sciences 119, no. 49 (2022): e2208254119.

      Holly, Elizabeth N., Christopher O. Boyson, Sandra Montagud-Romero, Dirson J. Stein, Kyle L. Gobrogge, Joseph F. DeBold, and Klaus A. Miczek. "Episodic social stress-escalated cocaine self-administration: role of phasic and tonic corticotropin releasing factor in the anterior and posterior ventral tegmental area." Journal of Neuroscience 36, no. 14 (2016): 4093-4105.

      Bagley, Elena E., Jennifer Hacker, Vladimir I. Chefer, Christophe Mallet, Gavan P. McNally, Billy CH Chieng, Julie Perroud, Toni S. Shippenberg, and MacDonald J. Christie. "Drug-induced GABA transporter currents enhance GABA release to induce opioid withdrawal behaviors." Nature neuroscience 14, no. 12 (2011): 1548-1554.

      However, since all reviewers requested raw values of neurochemicals, we provide these in supplementary tables 1-3. The manuscript references these table early in the Results (p. 6, lines 18-19) and in the Material and Methods (p. 27, lines 3-4)

      All reviewers commented on correlation analyses that we presented, with different perspectives. Reviewer 2 questioned the validity of such analyses, performed across experimental groups, while Reviewer 1 pointed out that the analyses were redundant with the GLM. We agree with these criticisms, and note the challenges associated with correlations involving behaviors for which there is a “floor” in the number of observations. As a result, we have removed most correlation analyses from the manuscript. The text and figures have been modified accordingly. Due these changes, we have to decline requests of Reviewer 3 to include many more such analyses. While correlation analyses could still be performed between neurochemicals and behaviors for each group, the relatively small size of each experimental group, the large number of groups, and the even larger numbers of pairings between neurochemicals and behavior, the statistical power is very low. The only correlations we utilize in the manuscript concern the interpretation of our increased acetylcholine levels.

      As part of this revision, we re-ran our statistical analyses on neuromodulators because of a calculation error in 3 animals (regarding baseline values). In a few instances, a significance level changed, but none of these changed a conclusion regarding neuromodulator changes under our experimental conditions.

      Other revisions

      INTRODUCTION: We modified the Introduction to provide both a more general framework and specific gaps in our understanding relating neuromodulators with vocal communication.

      DISCUSSION: We have added material in the first two pages of the Discussion to provide more framework to our conclusions, to address the issues of the temporal aspects of neurochemical release and behavioral observations, and to identify limitations that should be addressed in future studies.

      FIGURES: All figures are now in the main part of the manuscript. We modified most figures in response to reviewer comments. We removed neuromodulator – behavior correlations from several figures. We modified all box plots to ensure that all data points are visible. The visible data points match the numbers reported in figure captions. We brought 5-HIAA data into the main figures reporting on neuromodulator results.

      Public Reviews:

      Reviewer #1 (Public Review):

      The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain states and neurochemistry. In addition, the manuscript highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized. The authors are thoughtful about their quantitative approaches and interpretations of the data.

      That being said, the authors need to work on justifying some of their analytical approaches (e.g., normalization of neurochemical data, dividing the experimental period into two periods (as opposed to just analyzing the entire experimental period as a whole)) and should provide a greater discussion of how their data also demonstrate dissociations between neurochemical release in the basolateral amygdala and behavior (e.g., neurochemical differences during both of the experimental periods but behavioral differences only during the first half of the experimental period). The normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and could be problematic; by normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power.

      Please see our general responses to structure of observation periods and normalization of neuromodulator data. Normalization is a common and appropriate procedure in microdialysis studies that does not alter statistical power.

      We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback (p. 12, lines 3-17). We note where the linkage is particularly strong (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

      The Introduction could benefit from a priori predictions about the differential release of specific neuromodulators based on previous literature.

      We added some material to the Introduction to provide additional rationale for the study. However, we did not attempt to develop predictions for the range of neuromodulators that we sought to test. The literature can lead to opposite predictions for a given neuromodulator. For example, acetylcholine could be associated with both positive and negative valence. Instead, we note in the Introduction the association of both DA and ACh with vocalizations.

      The manuscript would also benefit from a description of space use and locomotion in response to different valence vocalizations.

      We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report a few correlations based on these data in the Results to demonstrate that increased ACh in Restraint males and Mating estrus females was not related to the amount of locomotion (p. 9, lines 8-14).

      Nevertheless, the current manuscript seems to provide some compelling support for how positive and negative valence vocalizations differentially affect behavior and the release of acetylcholine and dopamine in the basolateral amygdala. The research is relevant to broad fields of neuroscience and has implications for the neural circuits underlying social behavior.

      Reviewer #2 (Public Review):

      Ghasemahmad et al. report findings on the influence of salient vocalization playback, sex, and previous experience, on mice behaviors, and on cholinergic and dopaminergic neuromodulation within the basolateral amygdala (BLA). Specifically, the authors played back mice vocalizations recorded during two behaviors of opposite valence (mating and restraint) and measured the behaviors and release of acetylcholine (ACh), dopamine (DA), and serotonin in the BLA triggered in response to those sounds.

      Strength: The authors identified that mating and restraint sounds have a differential impact on cholinergic and dopaminergic release. In male mice, these two distinct vocalizations exert an opposite effect on the release of ACh and DA. Mating sounds elicited a decrease of Ach release and an increase of DA release. Conversely, restraint sounds induced an increase in ACh release and a trend to decrease in DA. These neurotransmission changes were different in estrus females for whom the mating vocalization resulted in an increase of both DA and ACh release.

      Weaknesses: The behavioral analysis and results remain elusive, and although addressing interesting questions, the study contains major flaws, and the interpretations are overstating the findings.

      Although Reviewer 2 raises several valid issues that we have addressed in our response and revision, we believe that none represent “major flaws” in the study that challenge the validity of our central conclusions. In brief, we will:

      --provide enhanced description of behaviors (pp. 22-23 and Table 1)

      --clarify / modify box-plot representations of data (p 28. Lines 3-9)

      --point to our methods that describe corrections for multiple comparisons (p. 27; lines 15-16)

      --revise figures to clarify sample size (Figs. 3-6)

      Reviewer #3 (Public Review):

      Ghasemahmad et al. examined behavioral and neurochemical responses of male and female mice to vocalizations associated with mating and restraint. The authors made two significant and exciting discoveries. They revealed that the affective content of vocalizations modulated both behavioral responses and the release of acetylcholine (ACh) and dopamine (DA) but not serotonin (5-HIAA) in the basolateral amygdala (BLA) of male and female mice. Moreover, the results show sex-based differences in behavioral responses to vocalizations associated with mating. The authors conclude that behavior and neurochemical responses in male and female mice are experience-dependent and are altered by vocalizations associated with restraint and mating. The findings suggest that ACh and DA release may shape behavioral responses to context-dependent vocalizations. The study has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the BLA while an animal listens to social vocalizations; however, multiple concerns must be addressed to substantiate their conclusions.

      Major concerns:

      1) The authors normalized all neurochemical data to the background level obtained from a single pre-stimulus sample immediately preceding playback. The percentage change from the background level was calculated based on a formula, and the underlying concentrations were not reported. The authors should report the sample and background concentrations to make the results and analyses more transparent. The authors stated that NE and 5-HT had low recovery from the mouse brain and hence could not be tracked in the experiment. The authors could be more specific here by relating the concentrations to ACh, DA, and 5-HIAA included in the analyses.

      Please see our general statement regarding normalization of neurochemical data. We have added supplemental tables that shows concentrations of dopamine, acetylcholine, 5-HIAA. We do not report serotonin or noradrenalin since these were below the detection threshold.

      2) For the EXP group, the authors stated that each animal underwent 90-min sessions on two consecutive days that provided mating and restraint experiences. Did the authors record mating or copulation during these experiments? If yes, what was the frequency of copulation? What other behaviors were recorded during these experiences? Did the experiment encompass other courtship behaviors along with mating experiences? Was the female mouse in estrus during the experience sessions?

      In the mating experience, mounting or attempted mounting was required for the animal to be included in subsequent testing. Since the session lasted 90 minutes, more general courtship behavior was likely. However, we did not record detailed behaviors or track estrous stage for the mating experience. See p. 21, line 20-22.

      3) For the mating playback, the authors stated that the mating stimulus blocks contained five exemplars of vocal sequences emitted during mating interactions. The authors should clarify whether the vocal sequences were emitted while animals were mating/copulating or when the male and female mice were inside the test box. If the latter was the case, it might be better to call the playback "courtship playback" instead of "mating playback".

      We have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

      4) Since most differences that the authors reported in Figure 3 were observed in Stim 1 and not in Stim 2, it might be better to perform a temporal analysis - looking at behaviors and neurochemicals over time instead of dividing them into two 10-minute bins. The temporal analysis will provide a more accurate representation of changes in behavior and neurochemicals over time.

      Please see our general response to the structuring of experimental periods. The 10-min periods are the minimum for the neurochemical analyses, and we adopted the same periods for behavioral analyses to match the two types of observations. Our repeated measures analysis is a form of temporal analysis, since it compares values in three observation periods.

      5) In Figures 2 and 3, the authors show the correlation between Flinching behavior and ACh concentration. The authors should report correlations between concentrations of all neurochemicals (not just ACh) and all behaviors recorded (not just Flinching), even if they are insignificant. The analyses performed for the stim 1 data should also be performed on the stim 2 data. Reporting these findings would benefit the field.

      Please see general comments regarding correlation analyses. We removed almost all such analyses and references to them from the manuscript based on concerns of the other reviewers.

      6) The mice used in the study were between p90 - p180. The mice were old, and the range of ages was considerable. Are the findings correlated with age? The authors should also discuss how age might affect the experiment's results.

      Our p90-p180 mice are not “old”. CBA/CaJ mice display normal hearing for at least 1 year (Ohlemiller, Dahl, and Gagnon, JARO 11: 605-623, 2010) and adult sexual and social behavior throughout our observation period. They are sexually mature adults, appropriate for this study. We decline to perform correlation analyses with age, both because this was not a question for this study and because the very large number of correlations, for each experimental group (as requested by reviewer #2), render this approach statistically problematic.

      7) The authors reported neurochemical levels estimated as the animals listened to the sounds played back. What about the sustained effects of changes in neurochemicals? Are there any potential long-term effects of social vocalizations on behavior and neurochemical levels? The authors might consider discussing long-term effects.

      We have not included discussion of long term effects of neuromodulatory release, both because our data analysis doesn’t address it (see response to Comment #10) and because we desired to keep the Discussion focused on topics more closely related to the results.

      8) Histology from a single recording was shown in supplementary figure 1. It would benefit the readers if additional histology was shown for all the animals, not just the colored schematics summarizing the recording probe locations. Further explanation of the track location is also needed to help the readers. Make it clear for the readers which dextran-fluorescein labeling image is associated with which track in the schematic.

      Based on the recent publications cited in our overall response to reviewer comments about statistical methods, our reporting of histological location of microdialysis exceeds the standard. We believe that the inclusion of all histology is unnecessary and not particularly helpful. Raw photomicrographs do not always illustrate boundaries, so interpretation is required. However, we added a second photomicrograph example and we identified which tracks correspond to these photomicrographs (see Figure 2; now in main body of manuscript).

      9) The authors did not control for the sounds being played back with a speaker. This control may be necessary since the effects are more pronounced in Stim 1 than in Stim 2. Playing white noise rather than restraint or courtship vocalizations would be an excellent control. However, the authors could perform a permutation analysis and computationally break the relationship between what sound is playing and the neurochemical data. This control would allow the authors to show that the actual neurochemical levels are above or below chance.

      We considered a potential “control” stimulus in our experimental design. We concluded, based on our previous work (e.g., Grimsley et al., 2013; Gadziola et al., 2016), that white noise is not or not necessarily a neutral stimulus and therefore the results would not clarify the responses to the two vocal stimuli. Instead, we opted to use experience as a type of control. This control shows very clearly that temporal patterns and across-group differences in neurochemical response to playback disappear in the absence of experience with the associated behavior.

      10) The authors indicated that each animal's post-vocalization session was also recorded. No data in the manuscript related to the post-vocalization playback period was included. This omission was a missed opportunity to show that the neurochemical levels returned to baseline, and the results were not dependent on the normalization process described in major concern #1. The data should be included in the manuscript and analyzed. It would add further support for the model described in Figure 6.

      We decided not to include analyses of the post-stimulus period because this period is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback. We agree that the general question is of interest to the field, but we don’t think our study is best designed to answer that question.

      11) The authors could use a predictive model, such as a binary classifier trained on the CSF sampling data, to predict the type of vocalizations played back. The predictive model could support the conclusions and provide additional support for the model in Figure 6.

      We recognize that a binary classifier could provide an interesting approach to support conclusions. However, we do not believe that the sample size per group is sufficient to both create and test the classifier.

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      • Introduction: It would be useful to set up an experimental framework before delving into the results. What are the predictions about specific neuromodulators based on previous literature?

      Because this narrative is laid out in the first two paragraphs of the Results, which immediately follow the Introduction, we believe that additional text in the Introduction on the experimental framework is redundant. As stated above, detailing predictions for a range of neuromodulators would make for a long and not particularly illuminating Introduction. We instead have related our findings to more general understanding of DA and ACh in the Discussion.

      • There really isn't a major difference in stimuli during the "Stim 1" and "Stim 2" phases, and it's not clear why the authors divided the experimental period into two phases. Therefore, the authors need to justify their experimental approach. For example, the authors could first anecdotally mention that behavioral responses to playbacks seem to be larger in the first half of the playbacks than during the second half, therefore they individually analyzed each half of the experimental period. Or adopt a different approach to justify their design. Overall, the analytical approach is reasonable but it is currently not justified.

      See general comment for analysis periods. As noted, we clarified these issues in several locations with Materials and Methods (pp. 24, lines 20-22; p. 26, lines 17-19). We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13).

      • The normalization of neurochemical data seems problematic and unnecessary. By normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) and this has implications for statistical power. Because the analysis is a within-subjects analysis, this normalization is not necessary for the analysis itself. It can be useful to normalize data for visualization purposes, but raw data should be analyzed. Indeed, behavioral data are qualitatively similar to the neurochemical data, and those data are not normalized to baseline values.

      Please see our general comment on this issue. We believe normalization does not affect statistical power and is both the standard way and an appropriate way to analyze microdialysis results. We include concentrations of ACh, DA, and 5-HIAA in supplementary tables?

      • The authors should include a discussion (in the Discussion section) of how behavior and neurochemical release are associated during the first half of the experimental session but not in the second half (e.g., differences in Ach and DA release between mating and restraint groups during stim 1 and 2, but behavioral differences only during stim 1).

      We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback. We note that the linkage is particularly strong in some cases (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

      Minor comments:

      • Keywords: add "serotonin" (even though there are no significant differences on 5-HIAA, people interested in serotonin would find this interesting).

      Added to keywords list.

      • Do the authors collect data on the vocalizations of mice in response to these playbacks?

      We monitored vocalizations during playback, noting that vocalizations–especially “Noisy” vocalization–were common. However, we did not record vocalizations and are therefore unable quantify our observations.

      • First line of page 7: readers do not know about "stim 1" and "stim 2". Therefore, the authors need to describe their approach to analyzing behavior and neurochemical release.

      We first introduce these terms earlier, citing Figure 1D,E. We have added some additional wording for further clarification. page 7, lines 4-5.

      • Make sure citations are uniformly formatted (e.g., Inconsistencies in: "As male and female mice emit different vocalizations during mating (Finton et al., 2017; J. M. S. Grimsley et al., 2013; Neunuebel et al., 2015; Sales (née Sewell), 1972)").

      We have reviewed and corrected citations throughout the manuscript.

      • Last paragraph of page 7: "attending behavior" has not been defined yet.

      Table 1 contains our description of the behaviors analyzed in this study. We have now inserted a reference to Table 1 earlier in the Results (p. 6, line 12).

      • Figure 2E and 3G: I find these correlations to be redundant with the GLMs. This is because the significant relationship is likely to be driven by group differences in behavior and in neurochemical release.

      Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

      • Page 2, 2nd paragraph, 2nd sentence: this paragraph seems to be rooted in comparing and contrasting experienced and inexperienced mice, so there should be explicit comparisons in each sentence. For example, the 2nd sentence should read: "Whereas EXP estrus females demonstrated increased flinching behaviors in response to mating vocalizations, INEXP ....". This paragraph overall could use some refining.

      We believe this refers to page 9. We have revised the paragraph to clarify our findings (Beginning p. 9, line 23).

      • Page 9: "Further, there were no significant differences across groups during Stim 1 or Stim 2 periods. These results contrast sharply with those from all EXP groups, in which both ACh and DA release changed significantly during playback (Figs. 2C, 2D, 3E, 3F)." While I understand their perspective, this is misleading because changes were only observed during the Stim 1 period.

      We have slightly revised the wording in this paragraph, because the restraint males did not show significant ACh decreases. However, we do not believe our statements mislead readers just because some changes are observed in only one of the stimulation periods (p 10, lines 13-16).

      • Last paragraph of page 14: it would be useful to mention the increase in flinching in experienced females in response to mating vocalizations.

      We have added a sentence in this paragraph relating flinching in estrus females to increased ACh (p. 15, lines 18-20).

      • Was there a full analysis of locomotion in response to playbacks? I see that locomotion was correlated with neurochemical release but was it different in response to different stimuli? Were there changes to the part of the arena that mice occupied in response to restraint vs. mating vocalizations? Given their methods section, it would be useful for the authors to mention the results of the analyses of these aspects of movement.

      We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report additional results associated with these analyses (p. 8, lines 13-15; p. 9, lines 8-14).

      • I believe that each experimental mouse only heard one of the stimuli (given the analytical approach). Because it is plausible to measure neurochemical release in response to both types of stimuli, I encourage the authors to be more explicit about this aspect of the experimental design (e.g., mention in Results section).

      Sentence modified to read: “Each mouse received playback of either the mating or restraint stimuli, but not both: same-day presentation of both stimuli would require excessively long playback sessions, the condition of the same probe would likely change on subsequent days, and quality of a second implanted probe on a subsequent day was uncertain.” (p. 7, lines 5-9).

      • Figure 1A and 1B: add labels to the panels so readers don't have to read the legend to know what spectrogram is associated with what context.

      We added these labels to Figure 1.

      • Table 1: in the definition of "still and alert", should this mention "abrupt attending" instead of "abrupt freezing"? The latter isn't described.

      Yes, we intended “abrupt attending”, and now indicated that in Table 1

      Reviewer #2 (Recommendations For The Authors):

      Major comments:

      • The authors report they performed manual behavioral analysis, and provide a table defining the different behaviors. However, it remains unclear how some of these behaviors were detected (such as still-and-alert events). A thorough description of the criteria used to define these events needs to be provided.

      We have modified some descriptions of manually analyzed behaviors in Table 1, and have added additional description of how we developed this set of behaviors for analysis in the study (pp. 22-23).

      • The box plots do not appear to represent the "minimum, first quartile, median, third quartile, and maximum values." as specified on page 24 (Methods). Indeed, the individual data points sometimes do not reach the max or min of the bar plot, and sometimes are way beyond them.

      We used the “inclusive median” function in Excel to generate final boxplots. These boxplots will sometimes result in a data point being placed outside of the whiskers. SPSS considers these to be “outliers”, but our GLM analysis includes these values. We describe this in Data Analysis section of Materials and Methods (p. 28, lines 3-9)

      • Some of the data are replicated in different Figures: Figure 2A and Figure 3C. While this is acceptable, the authors did not correct for multiple comparisons (dividing the p value by the number of comparisons).

      Our analysis included corrections for multiple comparisons, as we have indicated on p. 27, lines 15-16.

      • Overall, the sample sizes are too small (for example in Figure 3, non-estrus females are at n=3), and are different in experiments where they should be equal (Figure 2B: mating stim 1 is at n=5 and mating stim 2 is at n=3).

      We apologize that sample sizes were not properly displayed in figures. Please note that sample sizes are identified in the figure captions. For neuromodulator data, all sample sizes are at least 7. For behavioral data, the minimum sample size is 5. We have revised Figures 3-6 to ensure that all data points are visible.

      • It remains unclear why the impact of mating vocalizations has been tested only in males.

      We assume the reviewer meant that only males were tested in restraint. We now indicate that our preliminary evidence indicated no difference in behavioral responses to restraint vocalization between males and females, so we opted to perform the neurochemical analysis for restraint only in males (page 22 lines 4-5). If there were no limitations to time and cost, we would have preferred to test responses to restraint in females as well. We note that such inclusion would have added up to 4 experimental groups (estrus and non-estrus groups in both EXP and INEXP groups).

      • The correlation between the number of flinching and ACh release changes (Figure 2E) visually appears to be opposite between mating and restraint playbacks. The authors should perform independent correlations for these 2 playbacks.

      Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

      • The authors state that their findings "indicate that behavioral responses to salient vocalizations result from interactions between sex of the listener or context of vocal stimuli with the previous behavioral experience associated with these vocalizations.". However, in male mice, they do not report any difference in previous experience on flinching for both restraint and mating sounds, as well as no difference in rearing for the restrain sounds (Figure 4A-B). Thus, the discussion of these results should be completely revisited.

      We revised the paragraph in question (p. 9, line 22 through p. 10, line 9). For instance, we note that significant differences between EXP male-mating and male-restraint flinching do not exist between the INEXP groups. We believe that the last sentence correctly summarizes findings described in this paragraph.

      • For serotonin experiments in Figure S2 there are strong outliers (150% increase in 5HIAA release). Did the authors correlate these levels with the behavior of the animals?

      Outliers are identified by the Excel function that generated the boxplots, but we have no reason to consider these as outliers and exclude them. As noted above, we have clarified that these “outliers” are the result of the Excel function in the Materials and Methods (p. 28, lines 3-9) and we have revised the plotting of data points

      Minor comments:

      • Mating vocalization playback is mainly emitted by males, thus, instead of a positive valence signal, this could also be interpreted as a competitive signal to other males.

      There is support in the literature for viewing our mating stimulus as having positive valence. Gaub et al., 2016 describe the emission of stepped calls, lower frequency harmonics, and increased sound level as indicators of “positive emotion”. We have shown (Grimsley et al, 2013) that the female LFH vocalization can be highly attractive to male mice, under the right conditions, indicating something like “sex is happening”. The inclusion of both the male and female vocalizations in our stimuli was a key piece of our experimental design, based on our understanding of the contributions of both vocalizations to the meaning of the overall acoustic experience.

      • Figure 1 should include panel titles.

      No change. This information is available in the Figure caption.

      • n=31 should be indicated in the EXP group.

      We’re not sure where the reviewer is referring to this value.

      • The color legend of Figure 1E is absent, making the Figure not understandable.

      We added text in the Figure 1 caption to indicate that each color represents a different exemplar. We don’t think a legend provides additional useful information.

      • The point of making two blocks (stim 1 and stim2) should be stated more clearly.

      Please see general statement regarding experimental blocks. We have modified our description of these in an Experimental overview section in the Material and Methods.

      • Including raw data of micro-dialysis in the supplementary figures would allow assessment of the variability and quality of the measurements.

      We have added concentrations of neurochemicals in supplemental tables 1-3.

      • Baseline (prestimulus) number of flinch and rearing should systematically be indicated (missing in Figure 4).

      The focus in this figure is on the differences that occur in Stim 1 values. There are no differences between EXP and INEXP animals of any group during the Pre-Stim period. We now state that in the Figure 4 caption.

      • Discussion: "increase in AMPA/NMDA currents". We believe the authors are referring to the ratio of AMPA to NMDA currents. This sentence should be reformulated.

      These are modified to refer to “… the AMPA/NMDA current ratio…” in two locations in the Discussion (p. 14, lines 8-9; p. 15, line 4)

      • Overall the discussion is very speculative and should rely more on the data.

      We believe that the Discussion provides appropriate speculation that is based on our experimental data and previous literature. We have added a paragraph to identify limitations of our findings and recommendations of future experiments to resolve some issues (p. 12, lines 3-17)

      Reviewer #3 (Recommendations For The Authors):

      Minor concerns:

      1) The authors stated that USVs are most likely to be emitted by males, and LFH are likely to be emitted by females. However, Oliveira-Stahl et al. 2023, Matsumoto et al. 2022, Warren et al. 2018, Heckman et al. 2017, Neunuebel et al., 2015 showed that females also emit USVs. The authors should mention that USVs are emitted by both males and females and discuss how the sex of the vocalizing animal (both males and females) can influence neuromodulator release.

      The reviewer slightly mis-stated the wording of our text, changing the meaning significantly. Our wording is “These sequences included ultrasonic vocalizations (USVs) with harmonics, steps, and complex structure, mostly emitted by males, and low frequency harmonic calls (LFHs) emitted by females (Fig. 1A,C)…” This phrasing is correct and carefully chosen. The Discussion in Oliveira-Stahl et al 2023 (p. 10-11) supports our statement: “The exact fraction of USVs emitted by females as concluded in all previous studies on dyadic courtship has varied, ranging from 18%, 17.5%, and 16% to 10.5% in the present study…”.

      2) The authors should explain why ECF from BLA was collected unilaterally from the left hemisphere.

      p. 23, lines 9-11: We inserted a sentence to explain why we targeted the BLA unilaterally. “Since both left and right amygdala are responsive to vocal stimuli in human and experimental animal studies (Wenstrup et al., 2020), we implanted microdialysis probes into the left amygdala to maintain consistency with other studies in our laboratory..” Beyond that, the choice was arbitrary.

      3) The authors said each animal recovered in its home cage for four days before the playback experiment. A 4-day period may not be sufficient for every animal to recover from surgery, so the authors should describe how a mouse's recovery was assessed.

      p. 23, lines 20-23: We provide more description about the recovery and how it was assessed. Except for a few animals that were not included in the experiments, all animals recovered within 4 days.

      4) The authors stated that each animal was exposed to 90-min sessions with mating and restraint behaviors in a counterbalanced design. This description for Figure 1D should also include the duration of the mating and restraint experience.

      The Results that immediately precede citation to this figure include this information.

      5) The authors stated, "Data are reported only from mice with more than 75% of the microdialysis probe implanted within the BLA". What are the implications of having 25% of the probe outside the BLA? The authors should shed more light on this by discussing this issue as it relates to the findings and commenting on where the other 25% of the probe was located.

      We inserted a sentence to explain the rationale for this inclusion criterion. “We verified placement of microdialysis probes to minimize variability that could arise because regions surrounding BLA receive neurochemical inputs from different sources (e.g., cholinergic inputs to putamen and central amygdala).” (p. 25, lines 21-23).

      All brain regions that surround BLA, dorsal, medial, ventral, or lateral, could have been sampled by the “other” 25%. Some of these, e.g., the central amygdala or caudate-putamen, have different sources of cholinergic input that may not have the same release pattern. We do not think it is worthy of further speculation in the Discussion. Due to the high cost of the neurochemical analysis, we often did not process the neurochemistry data if histology indicated that a probe missed the BLA target.

      6) The authors confirmed that the estrus stage did not change during the experiment day by evaluating and comparing estrus prior to and after data collection. This strategy was a fantastic experimental approach, but the authors should have discussed the results. How did the results the authors included change when the females were in estrus before but not after data collection? What percentage of females started in estrus but ended in metestrus? Assuming that some females changed estrus state, were these animals excluded from the analyses?

      All animals were in the same estrus state at the beginning and end of the playback session.

      7). Authors cite Neunuebel et al., 2015 for the sentence "As male and female mice emit different vocalizations during mating". However, Neunuebel et al., 2015 showed vocalizations emitted during chasing--not mating. If mating is a general term for courtship, then this reference is appropriate, but see major concern #3.

      In the Results (p. 8, line 5), we changed the phrasing to “courtship and mating” to include the Neunubel et al study.

      As we indicate in our response to Public Comment #3, we have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

      8) Authors interpret Figure 3F as DA release showed a "consistent" increase during mating playback across all three experimental groups. However, the increase in the estrus female group is inconsistent, as seen in the graph. This verbiage should be reworded to describe the data more accurately.

      p. 8, line 23 “consistent” was deleted.

      9) In all the box plots, multiple data points overlay each other. A more transparent way of showing the data would be adding some jitter to the x value to make each data point visible. The mean (X's) in Figure 3D (pre-stim mating and mating estrus) are difficult to see, as are all the data points in mating non-estrus. Adding all the symbols to the figure legend or a key in the figure instead of the method section would aid the reader and make the plots easier to interpret

      We have revised the boxplots to ensure that all data points are visible.

      10) Some verbiage used in the discussion should be toned down. For example, "intense" experiences and "emotionally charged" vocalizations should be removed.

      We have not changed these terms, which we believe are appropriate to describe these experiences and vocalizations.

      11) The authors include "Emotional Vocalizations" in the title. It would be beneficial if the authors included more detail and references in the introduction to help set up the emotional content of vocalizations. It may benefit a broader readership as typically targeted by eLife.

      We now cite Darwin and some more recent publications that articulate the general understanding that social vocalizations carry emotional content.

    1. 7.6. Ethics and Trolling# 7.6.1. Background: Forming Groups# Every “we” implies a not-“we”. A group is constituted in part by who it excludes. Think back to the origin of humans caring about authenticity: if being able to trust each other is so important, then we need to know WHICH people are supposed to be entangled in those bonds of mutual trust with us, and which are not from our own crew. As we have developed larger and larger societies, states, and worldwide communities, the task of knowing whom to trust has become increasingly large. All groups have variations within them, and some variations are seen as normal. But the bigger groups get, the more variety shows up, and starts to feel palpable. In a nation or community where you don’t know every single person, how do you decide who’s in your squad? One answer to this challenge is that we use various heuristics (that is, shortcuts for thinking) like stereotypes and signaling to quickly guess where a person stands in relation to us. Sometimes wearing items of a certain brand signals to people with similar commitments that you might be on the same page. Sometimes features that are strongly associated with certain social groups—stereotypes—are assumed to tell us whether or not we can trust someone. Have you ever tried to change or mask your accent, to avoid being marked as from a certain region? Have you ever felt the need to conceal something about yourself that is often stereotyped, or to use an ingroup signal to deflect people’s attention from a stereotyped feature? There is a reason why stereotypes are so tenacious: they work… sort of. Humans are brilliant at finding patterns, and we use pattern recognition to increase the efficiency of our cognitive processing. We also respond to patterns and absorb patterns of speech production and style of dress from the people around us. We do have a tendency to display elements of our history and identity, even if we have never thought about it before. This creates an issue, however, when the stereotype is not apt in some way. This might be because we diverge in some way from the categories that mark us, so the stereotype is inaccurate. Or this might be because the stereotype also encodes value judgments that are unwarranted, and which lead to problems with implicit bias. Some people do not need to think loads about how they present in order to come across to people in ways that are accurate and supportive of who they really are. Some people think very carefully about how they curate a set of signals that enable them to accurately let people know who they are or to conceal who they are from people outside their squad. Because patterns are so central to how our brains process information, patterns become extremely important to how societies change or stay the same. TV tropes is a website that tracks patterns in media, such as the jump scare The Seven Basic Plots Patterns build habits. Habits build norms. Norms build our reality. To create a social group and have it be sustainable, we depend on stable patterns, habits, and norms to create the reality of the grouping. In a diverse community, there are many subsets of patterns, habits, and norms which go into creating the overall social reality. Part of how people manage their social reality is by enforcing the patterns, habits, and norms which identify us; another way we do this is by enforcing, or policing, which subsets of patterns, habits, and norms get to be recognized as valid parts of the broader social reality. Both of these tactics can be done in appropriate, just, and responsible ways, or in highly unjust ways. 7.6.2. Ethics of Disruption (Trolling)# Trolling is a method of disrupting the way things are, including group structure and practices. Like these group-forming practices, disruptive trolling can be deployed in just or unjust ways. (We will come back to that.) These disruptive tactics can also be engaged with different moods, ranging from playful (like some flashmobs), to demonstrative (like activism and protests), to hostile, to warring, to genocidal. You may have heard people say that the difference between a coup and a revolution is whether it succeeds and gets to later tell the story, or gets quashed. You may have also heard that the difference between a traitor and a hero depends on who is telling the story. As this class discusses trolling, as well as many of the other topics of social media behavior coming up in the weeks ahead, you are encouraged to bear this duality of value in mind. Trolling is a term given to describe behavior that aims to disrupt (among other things). To make value judgments or ethical judgments about instances of disruptive behavior, we will need to be thoughtful and nuanced about how we decide to pass judgments. One way to begin examining any instance of disruptive behavior is to ask what is being disrupted: a pattern, a habit, a norm, a whole community? And how do we judge the value of the thing being disrupted? Returning to the difference between a coup and a revolution, we might say that a national-level disruption is a coup if it fails, and a revolution if it succeeds. Or we might say that such a disruption is a coup if it intends to disrupt a legitimate instance of political domination/statehood, but a revolution if the instance of political domination is illegitimate. If you take a close look at English-language headlines in the news about uprisings occurring near to or far from here, it should become quickly apparent that both of these reasons can drive an author’s choice to style an event as a coup. To understand what the author is trying to say, we need to look inside the situation and see what assumptions are driving their choice to characterize the disruption in the way that they do. Trolling is disruptive behavior, and whether we class it as problematic or okay depends in part on how we judge the legitimacy of the social reality which is being disrupted. Trolling can be used, in principle, for good or bad ends. 7.6.3. Trolling and Nihilism# While trolling can be done for many reasons, some trolling communities take on a sort of nihilistic philosophy: it doesn’t matter if something is true or not, it doesn’t matter if people get hurt, the only thing that might matter is if you can provoke a reaction. We can see this nihilism show up in one of the versions of the self-contradictory “Rules of the Internet:” 8. There are no real rules about posting … 20. Nothing is to be taken seriously … 42. Nothing is Sacred Youtuber Innuendo Studios talks about the way arguments are made in a community like 4chan: You can’t know whether they mean what they say, or are only arguing as though they mean what they say. And entire debates may just be a single person stirring the pot [e.g., sockpuppets]. Such a community will naturally attract people who enjoy argument for its own sake, and will naturally trend oward the most extremte version of any opinion. In short, this is the free marketplace of ideas. No code of ethics, no social mores, no accountability. … It’s not that they’re lying, it’s that they just don’t care. […] When they make these kinds of arguments they legitimately do not care whether the words coming out of their mouths are true. If they cared, before they said something is true, they would look it up. The Alt-Right Playbook: The Card Says Moops by Innuendo Studios While there is a nihilistic worldview where nothing matters, we can see how this plays out practically, which is that they tend to protect their group (normally white and male), and tend to be extremely hostile to any other group. They will express extreme misogyny (like we saw in the Rules of the Internet: “Rule 30. There are no girls on the internet. Rule 31. TITS or GTFO - the choice is yours”), and extreme racism (like an invented Nazi My Little Pony character). Is this just hypocritical, or is it ethically wrong? It depends, of course, on what tools we use to evaluate this kind of trolling. If the trolls claim to be nihilists about ethics, or indeed if they are egoists, then they would argue that this doesn’t matter and that there’s no normative basis for objecting to the disruption and harm caused by their trolling. But on just about any other ethical approach, there are one or more reasons available for objecting to the disruptions and harm caused by these trolls! If the only way to get a moral pass on this type of trolling is to choose an ethical framework that tells you harming others doesn’t matter, then it looks like this nihilist viewpoint isn’t deployed in good faith1. Rather, with any serious (i.e., non-avoidant) moral framework, this type of trolling is ethically wrong for one or more reasons (though how we explain it is wrong depends on the specific framework). 7.6.4. Reflection Exercise# Revisit the K-Pop protest trolling example in section 7.3. Take your list of ethical frameworks from Chapter 2 and work through them one by one, applying each tool to the K-Pop trolling. For each theory, think of how many different ways the theory could hook up with the example. For example, when using a virtue ethics type of tool, consider how many different people’s character and flourishing could be developed through this? When using a tool based on outcomes, like consequentialism, how many different elements of the outcome can you think of? The goal here is to come up with as many variations as you can, to see how the tools of ethical analysis can help us see into different aspects of the situation. Once you have made your big list of considerations, choose 2-3 items that, in your view, feel most important. Based on those 2-3 items, do you evaluate this trolling event as having been morally good? Why? What changes to this example would change your overall decision on whether the action is ethical?

      The section provides a profound exploration of the complexities involved in understanding and evaluating disruptive behaviors in social media contexts. It compellingly illustrates how the formation of groups, the use of stereotypes, and the enforcement of norms are all deeply intertwined with our cognitive processes and societal structures. The examination of trolling as a form of disruption that can be deployed for both just and unjust ends invites readers to reflect on the multifaceted nature of these actions and their ethical implications.

    1. Reviewer #1 (Public Review):

      This valuable study demonstrates a novel mechanism by which implicit motor adaptation saturates for large visual errors in a principled normative Bayesian manner. Additionally, the study revealed two notable empirical findings: visual uncertainty increases for larger visual errors in the periphery, and proprioceptive shifts/implicit motor adaptation are non-monotonic, rather than ramp-like. This study is highly relevant for researchers in sensory cue integration and motor learning. However, I find some areas where statistical quantification is incomplete, and the contextualization of previous studies to be puzzling.

      Issue #1: Contextualization of past studies.

      While I agree that previous studies have focused on how sensory errors drive motor adaptation (e.g., Burge et al., 2008; Wei and Kording, 2009), I don't think the PReMo model was contextualized properly. Indeed, while PReMo should have adopted clearer language - given that proprioception (sensory) and kinaesthesia (perception) have been used interchangeably, something we now make clear in our new study (Tsay, Chandy, et al. 2023) - PReMo's central contribution is that a perceptual error drives implicit adaptation (see Abstract): the mismatch between the felt (perceived) and desired hand position. The current paper overlooks this contribution. I encourage the authors to contextualize PReMo's contribution more clearly throughout. Not mentioned in the current study, for example, PReMo accounts for the continuous changes in perceived hand position in Figure 4 (Figure 7 in the PReMo study).

      There is no doubt that the current study provides important additional constraints on what determines perceived hand position: Firstly, it offers a normative Bayesian perspective in determining perceived hand position. PReMo suggests that perceived hand position is determined by integrating motor predictions with proprioception, then adding a proprioceptive shift; PEA formulates this as the optimal integration of these three inputs. Secondly, PReMo assumed visual uncertainty to remain constant for different visual errors; PEA suggests that visual uncertainty ought to increase (but see Issue #2).

      Issue #2: Failed replication of previous results on the effect of visual uncertainty.

      2a. A key finding of this paper is that visual uncertainty linearly increases in the periphery; a constraint crucial for explaining the non-monotonicity in implicit adaptation. One notable methodological deviation from previous studies is the requirement to fixate on the target: Notably, in the current experiments, participants were asked to fixate on the target, a constraint not imposed in previous studies. In a free-viewing environment, visual uncertainty may not attenuate as fast, and hence, implicit adaptation does not attenuate as quickly as that revealed in the current design with larger visual errors. Seems like this current fixation design, while important, needs to be properly contextualized considering how it may not represent most implicit adaptation experiments.

      2b. Moreover, the current results - visual uncertainty attenuates implicit adaptation in response to large, but not small, visual errors - deviates from several past studies that have shown that visual uncertainty attenuates implicit adaptation to small, but not large, visual errors (Tsay, Avraham, et al. 2021; Makino, Hayashi, and Nozaki, n.d.; Shyr and Joshi 2023). What do the authors attribute this empirical difference to? Would this free-viewing environment also result in the opposite pattern in the effect of visual uncertainty on implicit adaptation for small and large visual errors?

      2c. In the current study, the measure of visual uncertainty might be inflated by brief presentation times of comparison and referent visual stimuli (only 150 ms; our previous study allowed for a 500 ms viewing time to make sure participants see the comparison stimuli). Relatedly, there are some individuals whose visual uncertainty is greater than 20 degrees standard deviation. This seems very large, and less likely in a free-viewing environment.

      2d. One important confound between clear and uncertain (blurred) visual conditions is the number of cursors on the screen. The number of cursors may have an attenuating effect on implicit adaptation simply due to task-irrelevant attentional demands (Parvin et al. 2022), rather than that of visual uncertainty. Could the authors provide a figure showing these blurred stimuli (gaussian clouds) in the context of the experimental paradigm? Note that we addressed this confound in the past by comparing participants with and without low vision, where only one visual cursor is provided for both groups (Tsay, Tan, et al. 2023).

      Issue #3: More methodological details are needed.

      3a. It's unclear why, in Figure 4, PEA predicts an overshoot in terms of perceived hand position from the target. In PReMo, we specified a visual shift in the perceived target position, shifted towards the adapted hand position, which may result in overshooting of the perceived hand position with this target position. This visual shift phenomenon has been discovered in previous studies (e.g., (Simani, McGuire, and Sabes 2007)).

      3b. The extent of implicit adaptation in Experiment 2, especially with smaller errors, is unclear. The implicit adaptation function seems to be still increasing, at least by visual inspection. Can the authors comment on this trend, and relatedly, show individual data points that help the reader appreciate the variability inherent to these data?

      3c. The same participants were asked to return for multiple days/experiments. Given that the authors acknowledge potential session effects, with attenuation upon re-exposure to the same rotation (Avraham et al. 2021), how does re-exposure affect the current results? Could the authors provide clarity, perhaps a table, to show shared participants between experiments and provide evidence showing how session order may not be impacting results?

      3d. The number of trials per experiment should be detailed more clearly in the Methods section (e.g., Exp 4). Moreover, could the authors please provide relevant code on how they implemented their computational models? This would aid in future implementation of these models in future work. I, for one, am enthusiastic to build on PEA.

      3f. In addition to predicting a correlation between proprioceptive shift and implicit adaptation on a group level, both PReMo and PEA (but not causal inference) predict a correlation between individual differences in proprioceptive shift and proprioceptive uncertainty with the extent of implicit adaptation (Tsay, Kim, et al. 2021). Interestingly, shift and uncertainty are independent (see Figures 4F and 6C in Tsay et al, 2021). Does PEA also predict independence between shift and uncertainty? It seems like PEA does predict a correlation.

      References:

      Avraham, Guy, Ryan Morehead, Hyosub E. Kim, and Richard B. Ivry. 2021. "Reexposure to a Sensorimotor Perturbation Produces Opposite Effects on Explicit and Implicit Learning Processes." PLoS Biology 19 (3): e3001147.<br /> Makino, Yuto, Takuji Hayashi, and Daichi Nozaki. n.d. "Divisively Normalized Neuronal Processing of Uncertain Visual Feedback for Visuomotor Learning."<br /> Parvin, Darius E., Kristy V. Dang, Alissa R. Stover, Richard B. Ivry, and J. Ryan Morehead. 2022. "Implicit Adaptation Is Modulated by the Relevance of Feedback." BioRxiv. https://doi.org/10.1101/2022.01.19.476924.<br /> Shyr, Megan C., and Sanjay S. Joshi. 2023. "A Case Study of the Validity of Web-Based Visuomotor Rotation Experiments." Journal of Cognitive Neuroscience, October, 1-24.<br /> Simani, M. C., L. M. M. McGuire, and P. N. Sabes. 2007. "Visual-Shift Adaptation Is Composed of Separable Sensory and Task-Dependent Effects." Journal of Neurophysiology 98 (5): 2827-41.<br /> Tsay, Jonathan S., Guy Avraham, Hyosub E. Kim, Darius E. Parvin, Zixuan Wang, and Richard B. Ivry. 2021. "The Effect of Visual Uncertainty on Implicit Motor Adaptation." Journal of Neurophysiology 125 (1): 12-22.<br /> Tsay, Jonathan S., Anisha M. Chandy, Romeo Chua, R. Chris Miall, Jonathan Cole, Alessandro Farnè, Richard B. Ivry, and Fabrice R. Sarlegna. 2023. "Implicit Motor Adaptation and Perceived Hand Position without Proprioception: A Kinesthetic Error May Be Derived from Efferent Signals." BioRxiv. https://doi.org/10.1101/2023.01.19.524726.<br /> Tsay, Jonathan S., Hyosub E. Kim, Darius E. Parvin, Alissa R. Stover, and Richard B. Ivry. 2021. "Individual Differences in Proprioception Predict the Extent of Implicit Sensorimotor Adaptation." Journal of Neurophysiology, March. https://doi.org/10.1152/jn.00585.2020.<br /> Tsay, Jonathan S., Steven Tan, Marlena Chu, Richard B. Ivry, and Emily A. Cooper. 2023. "Low Vision Impairs Implicit Sensorimotor Adaptation in Response to Small Errors, but Not Large Errors." Journal of Cognitive Neuroscience, January, 1-13.

    2. Reviewer #3 (Public Review):

      Summary<br /> In this paper, the authors model motor adaptation as a Bayesian process that combines visual uncertainty about the error feedback, uncertainty about proprioceptive sense of hand position, and uncertainty of predicted (=planned) hand movement with a learning and retention rate as used in state space models. The model is built with results from several experiments presented in the paper and is compared with the PReMo model (Tsay, Kim, et al., 2022) as well as a cue combination model (Wei & Körding, 2009). The model and experiments demonstrate the role of visual uncertainty about error feedback in implicit adaptation.

      In the introduction, the authors notice that implicit adaptation (as measured in error-clamp-based paradigms) does not saturate at larger perturbations, but decreases again (e.g. Moorehead et al., 2017 shows no adaptation at 135{degree sign} and 175{degree sign} perturbations). They hypothesized that visual uncertainty about cursor position increases with larger perturbations since the cursor is further from the fixated target. This could decrease the importance assigned to visual feedback which could explain lower asymptotes.

      The authors characterize visual uncertainty for 3 rotation sizes in the first experiment, and while this experiment could be improved, it is probably sufficient for the current purposes. Then the authors present a second experiment where adaptation to 7 clamped errors is tested in different groups of participants. The models' visual uncertainty is set using a linear fit to the results from experiment 1, and the remaining 4 parameters are then fit to this second data set. The 4 parameters are 1) proprioceptive uncertainty, 2) uncertainty about the predicted hand position, 3) a learning rate, and 4) a retention rate. The authors' Perceptual Error Adaptation model ("PEA") predicts asymptotic levels of implicit adaptation much better than both the PReMo model (Tsay, Kim et al., 2022), which predicts saturated asymptotes, or a causal inference model (Wei & Körding, 2007) which predicts no adaptation for larger rotations. In a third experiment, the authors test their model's predictions about proprioceptive recalibration, but unfortunately, compare their data with an unsuitable other data set. Finally, the authors conduct a fourth experiment where they put their model to the test. They measure implicit adaptation with increased visual uncertainty, by adding blur to the cursor, and the results are again better in line with their model (predicting overall lower adaptation) than with the PReMo model (predicting equal saturation but at larger perturbations) or a causal inference model (predicting equal peak adaptation, but shifted to larger rotations). In particular, the model fits experiment 2 and the results from experiment 4 show that the core idea of the model has merit: increased visual uncertainty about errors dampens implicit adaptation.

      Strengths<br /> In this study, the authors propose a Perceptual Error Adaptation model ("PEA") and the work combines various ideas from the field of cue combination, Bayesian methods, and new data sets, collected in four experiments using various techniques that test very different components of the model. The central component of visual uncertainty is assessed in the first experiment. The model uses 4 other parameters to explain implicit adaptation. These parameters are 1) learning and 2) retention rate, as used in popular state space models, and the uncertainty (variance) of 3) predicted and 4) proprioceptive hand position. In particular, the authors observe that asymptotes for implicit learning do not saturate, as claimed before, but decrease again when rotations are very large and that this may have to do with visual uncertainty (e.g. Tsay et al., 2021, J Neurophysiol 125, 12-22). The final experiment confirms predictions of the fitted model about what happens when visual uncertainty is increased (overall decrease of adaptation). By incorporating visual uncertainty depending on retinal eccentricity, the predictions of the PEA model for very large perturbations are notably different from and better than, the predictions of the two other models it is compared to. That is, the paper provides strong support for the idea that visual uncertainty of errors matters for implicit adaptation.

      Weaknesses<br /> Although the authors don't say this, the "concave" function that shows that adaptation does not saturate for larger rotations has been shown before, including in papers cited in this manuscript.

      The first experiment, measuring visual uncertainty for several rotation sizes in error-clamped paradigms has several shortcomings, but these might not be so large as to invalidate the model or the findings in the rest of the manuscript. There are two main issues we highlight here. First, the data is not presented in units that allow comparison with vision science literature. Second, the 1 second delay between the movement endpoint and the disappearance of the cursor, and the presentation of the reference marker, may have led to substantial degradation of the visual memory of the cursor endpoint. That is, the experiment could be overestimating the visual uncertainty during implicit adaptation.

      The paper's third experiment relies to a large degree on reproducing patterns found in one particular paper, where the reported hand positions - as a measure of proprioceptive sense of hand position - are given and plotted relative to an ever-present visual target, rather than relative to the actual hand position. That is, 1) since participants actively move to a visual target, the reported hand positions do not reflect proprioception, but mostly the remembered position of the target participants were trying to move to, and 2) if the reports are converted to a difference between the real and reported hand position (rather than the difference between the target and the report), those would be on the order of ~20{degree sign} which is roughly two times larger than any previously reported proprioceptive recalibration, and an order of magnitude larger than what the authors themselves find (1-2{degree sign}) and what their model predicts. Experiment 3 is perhaps not crucial to the paper, but it nicely provides support for the idea that proprioceptive recalibration can occur with error-clamped feedback.

      Perhaps the largest caveat to the study is that it assumes that people do not look at the only error feedback available to them (and can explicitly suppress learning from it). This was probably true in the experiments used in the manuscript, but unlikely to be the case in most of the cited literature. Ignoring errors and suppressing adaptation would also be a disastrous strategy to use in the real world, such that our brains may not be very good at this. So the question remains to what degree - if any - the ideas behind the model generalize to experiments without fixation control, and more importantly, to real-life situations.

      Specific comments:<br /> A small part of the manuscript relies on replicating or modeling the proprioceptive recalibration in a study we think does NOT measure proprioceptive recalibration (Tsay, Parvin & Ivry, JNP, 2020). In this study, participants reached for a visual target with a clamped cursor, and at the end of the reach were asked to indicate where they thought their hand was. The responses fell very close to the visual target both before and after the perturbation was introduced. This means that the difference between the actual hand position, and the reported/felt hand position gets very large as soon as the perturbation is introduced. That is, proprioceptive recalibration would necessarily have roughly the same magnitude as the adaptation displayed by participants. That would be several times larger than those found in studies where proprioceptive recalibration is measured without a visual anchor. The data is plotted in a way that makes it seem like the proprioceptive recalibration is very small, as they plot the responses relative to the visual target, and not the discrepancy between the actual and reported hand position. It seems to us that this study mostly measures short-term visual memory (of the target location). What is astounding about this study is that the responses change over time to begin with, even if only by a tiny amount. Perhaps this indicates some malleability of the visual system, but it is hard to say for sure.

      Regardless, the results of that study do not form a solid basis for the current work and they should be removed. We would recommend making use of the dataset from the same authors, who improved their methods for measuring proprioception shifts just a year later (Tsay, Kim, Parvin, Stover, and Ivry, JNP, 2021). Although here the proprioceptive shifts during error-clamp adaptation (Exp 2) were tiny, and not quite significant (p<0.08), the reports are relative to the actual location of the passively placed unseen hand, measured in trials separate from those with reach adaptation and therefore there is no visual target to anchor their estimates to.

      Experiment 1 measures visual uncertainty with increased rotation size. The authors cite relevant work on this topic (Levi & Klein etc) which has found a linear increase in uncertainty of the position of more and more eccentrically displayed stimuli.

      First, this is a question where the reported stimuli and effects could greatly benefit from comparisons with the literature in vision science, and the results might even inform it. In order for that to happen, the units for the reported stimuli and effects should (also) be degrees of visual angle (dva).

      As far as we know, all previous work has investigated static stimuli, where with moving stimuli, position information from several parts of the visual field are likely integrated over time in a final estimate of position at the end of the trajectory (a Kalman filter type process perhaps). As far as we know, there are no studies in vision science on the uncertainty of the endpoint of moving stimuli. So we think that the experiment is necessary for this study, but there are some areas where it could be improved.

      Then, the linear fit is done in the space of the rotation size, but not in the space of eccentricity relative to fixation, and these do not necessarily map onto each other linearly. If we assume that the eye-tracker and the screen were at the closest distance the manufacturer reports it to work accurately at (45 cm), we would get the largest distances the endpoints are away from fixation in dva. Based on that assumed distance between the participant and monitor, we converted the rotation angles to distances between fixation and the cursor endpoint in degrees visual angle: 0.88, 3.5, and 13.25 dva (ignoring screen curvature, or the absence of it). The ratio between the perturbation angle and retinal distance to the endpoint is roughly 0.221, 0.221, and 0.207 if the minimum distance is indeed used - which is probably fine in this case. But still, it would be better to do fit in the relevant perceptual coordinate system.

      The first distance (4 deg rotation; 0.88 dva offset between fixation and stimulus) is so close to fixation (even at the assumed shortest distance between eye and screen) that it can be considered foveal and falls within the range of noise of eye-trackers + that of the eye for fixating. There should be no uncertainty on or that close to the fovea. The variability in the data is likely just measurement noise. This also means that a linear fit will almost always go through this point, somewhat skewing the results toward linearity. The advantage is that the estimate of the intercept (measurement noise) is going to be very good. Unfortunately, there are only 2 other points measured, which (if used without the closest point) will always support a linear fit. Therefore, the experiment does not seem suitable to test linearity, only to characterize it, which might be sufficient for the current purposes. We'd understand if the effort to do a test of linearity using many more rotations requires too much effort. But then it should be made much clearer that the experiment assumes linearity and only serves to characterize the assumed linearity.

      Final comment after the consultation session:<br /> There were a lot of discussions about the actual interpretation of the behavioral data from this paper with regards to past papers (Tsay et al. 2020 or 2021), and how it matches the different variables of the model. The data from Tsay 2020 combined both proprioceptive information (Xp) and prediction about hand position (Xu) because it involves active movements. On the other hand, Tsay et al. 2021 is based on passive movements and could provide a better measure of Xp alone. We would encourage you to clarify how each of the variables used in the model is mapped onto the outcomes of the cited behavioral experiments.

      The reviewers discussed this point extensively during the consultation process. The results reported in the Tsay 2020 study reflect both proprioception and prediction. However, having a visual target contributes more than just prediction, it is likely an anchor in the workspace that draws the response to it. Such that the report is dominated by short-term visual memory of the target (which is not part of the model). However, in the current Exp 3, as in most other work investigating proprioception, this is calculated relative to the actual direction.

      The solution is fairly simple. In Experiment 3 in the current study, Xp is measured relative to the hand without any visual anchors drawing responses, and this is also consistent with the reference used in the Tsay et al 2021 study and from many studies in the lab of D. Henriques (none of which also have any visual reach target when measuring proprioceptive estimates). So we suggest using a different data set that also measures Xp without any other influences, such as the data from Tsay et al 2021 instead.

      These issues with the data are not superficial and can not be solved within the model. Data with correctly measured biases (relative to the hand) that are not dominated by irrelevant visual attractors would actually be informative about the validity of the PEA model. Dr. Tsay has so much other that we recommend using a more to-the-point data set that could actually validate the PEA model.

    1. 4.4. How Data Informs Ethics# Think for a minute about consequentialism. On this view, we should do whatever results in the best outcomes for the most people. One of the classic forms of this approach is utilitarianism, which says we should do whatever maximizes ‘utility’ for most people. Confusingly, ‘utility’ in this case does not refer to usefulness, but to a sort of combo of happiness and wellbeing. When a utilitarian tries to decide how to act, they take stock of all the probable outcomes, and what sort of ‘utility’ or happiness will be brought about for all parties involved. This process is sometimes referred to by philosophers as ‘utility calculus’. When I am trying to calculate the expected net utility gain from a projected set of actions, I am engaging in ‘utility calculus’ (or, in normal words, utility calculations). Now, there are many reasons one might be suspicious about utilitarianism as a cheat code for acting morally, but let’s assume for a moment that utilitarianism is the best way to go. When you undertake your utility calculus, you are, in essence, gathering and responding to data about the projected outcomes of a situation. This means that how you gather your data will affect what data you come up with. If you have really comprehensive data about potential outcomes, then your utility calculus will be more complicated, but will also be more realistic. On the other hand, if you have only partial data, the results of your utility calculus may become skewed. If you think about the potential impact of a set of actions on all the people you know and like, but fail to consider the impact on people you do not happen to know, then you might think those actions would lead to a huge gain in utility, or happiness. When we think about how data is used online, the idea of a utility calculus can help remind us to check whether we’ve really got enough data about how all parties might be impacted by some actions. Even if you are not a utilitarian, it is good to remind ourselves to check that we’ve got all the data before doing our calculus. This can be especially important when there is a strong social trend to overlook certain data. Such trends, which philosophers call ‘pernicious ignorance’, enable us to overlook inconvenient bits of data to make our utility calculus easier or more likely to turn out in favor of a preferred course of action. Can you think of an example of pernicious ignorance in social media interaction? What’s something that we might often prefer to overlook when deciding what is important? One classic example is the tendency to overlook the interests of children and/or people abroad when we post about travels, especially when fundraising for ‘charity tourism’. One could go abroad, and take a picture of a cute kid running through a field, or a selfie with kids one had traveled to help out. It was easy, in such situations, to decide the likely utility of posting the photo on social media based on the interest it would generate for us, without thinking about the ethics of using photos of minors without their consent. This was called out by The Onion in a parody article, titled “6-Day Visit To Rural African Village Completely Changes Woman’s Facebook Profile Picture”. The reckoning about how pernicious ignorance had allowed many to feel comfortable leaving the interests of many out of the utility calculus for use of images online continued. You can read an article about it here, or see a similar reckoning discussed by National Geographic: “For Decades, Our Coverage Was Racist. To Rise Above Our Past, We Must Acknowledge It”.

      This section particularly the exploration of utilitarianism in the context of social media, provides a thought-provoking perspective on ethical decision-making. The concept of the utility calculus as a method of predicting the outcomes and moral implications of our actions highlights the importance of comprehensive data collection and the potential pitfalls of biased or incomplete data. The discussion cleverly highlighted the challenges of navigating social media in an ethical manner, which must consider

    1. Author Response

      The following is the authors’ response to the original reviews.

      eLife assessment

      This important study reports jAspSnFR3, a biosensor that enables high spatiotemporal resolution of aspartate levels in living cells. To develop this sensor, the authors used a structurally guided amino acid substitution in a glutamate/aspartate periplasmic binding protein to switch its specificity towards aspartate. The in vitro and in cellulo functional characterization of the biosensor is convincing, but evidence of the sensor's effectiveness in detecting small perturbations of aspartate levels and information on its behavior in response to acute aspartate elevations in the cytosol are still lacking.

      We thank the reviewers and editors for the detailed assessment of our work and for their constructive feedback. Most comments have now been experimentally addressed in the revised manuscript, which we feel is substantially improved from the initial draft.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this manuscript, Davidsen and coworkers describe the development of a novel aspartate biosensor jAspSNFR3. This collaborative work supports and complements what was reported in a recent preprint by Hellweg et al., (bioRxiv; doi: 10.1101/2023.05.04.537313). In both studies, the newly engineered aspartate sensor was developed from the same glutamate biosensor previously developed by the authors of this manuscript. This coincidence is not casual but is the result of the need to find tools capable of measuring aspartate levels in vivo. Therefore, it is undoubtedly a relevant and timely work carried out by groups experienced in aspartate metabolism and in the generation of metabolite biosensors.

      Reviewer #2 (Public Review):

      In this work the IGluSnFR3 sensor, recently developed by Marvin et al (2023) is mutated position S72, which was previously reported to switch the specificity from Glu to Asp. They made 3 mutations at this position, selected a S72P mutant, then made a second mutation at S27 to generate an Asp-specific version of the sensor. This was then characterized thoroughly and used on some test experiments, where it was shown to detect and allow visualization of aspartate concentration changes over time. It is an incremental advance on the iGluSnFR3 study, where 2 predictable mutations are used to generate a sensor that works on a close analog of Glu, Asp. It is shown to have utility and will be useful in the field of Asp-mediated biological effects.

      Reviewer #3 (Public Review):

      In this manuscript, Davidsen and collaborators introduce jAspSnFR3, a new version of aspartate biosensor derived from iGluSnFR3, that allows monitoring in real-time aspartate levels in cultured cells. A selective amino acids substitution was applied in a key region of the template to switch its specificity from glutamate to aspartate. The jAspSnFR3 does not respond to other tested metabolites and performs well, is not toxic for cultured cells, and is not affected by temperature ensuring the possibility of using this tool in tissues physiologically more relevant. The high affinity for aspartate (KD=50 uM) allowed the authors to measure fluctuations of this amino acid in the physiological range. Different strategies were used to bring aspartate to the minimal level. Finally, the authors used jAspSnFR3 to estimate the intracellular aspartate concentration. One of the highlights of the manuscript was a treatment with asparagine during glutamine starvation. Although didn't corroborate the essentiality of asparagine in glutamine depletion, the measurement of aspartate during this supplementation is a glimpse of how useful this sensor can be.

      Reviewer #1 (Recommendations For The Authors):

      The authors should evaluate the effectiveness of the sensor in detecting small perturbations of aspartate levels and its behavior in response to acute aspartate elevations in the cytosol. In vivo aspartate determinations were performed exclusively in conditions that cause aspartate depletion. By means the use of mitochondrial respiratory inhibitors or aspartate withdrawal, it was determined the reliability of the sensor performing readings during relatively long periods, until reaching a steady-state of aspartate-depletion 12-60 hours later. Although in Hellweg and coworkers, it has been demonstrated that a related aspartate sensor could detect increases in aspartate in cell overexpressing the aspartate-glutamate GLAST transporter, the differences reported here between both sensors advise testing whether this aspect is also improved, or not, using jAspSNFR3.

      Similarly, Davidsen et al. did not test if the sensor can be able to detect transient variations in cytosolic aspartate levels. In proliferative cells aspartate synthesis is linked to NAD+ regeneration by ETC (Sullivan et al., 2015, Cell), indeed the authors deplete aspartate using CI or CIII inhibitors but do not analyze if those are recovered, and increased, after its removal. Furthermore, the sequential addition of oligomycin and uncouplers could generate measurable fluctuations of aspartate in the cytosol.

      We agree with the reviewer that only including situations of aspartate depletion in our cell culture experiments provided an incomplete evaluation of the utility of this biosensor. In the revised manuscript we provide three additional experiments using secondary treatments that restore aspartate synthesis to conditions that initially caused aspartate depletion. First, we conducted experiments where cells expressing jAspSnFR3/NucRFP were changed into media without glutamine, inducing aspartate depletion, with glutamine being replenished at various time points to observe if GFP/RFP measurements recover. As expected, glutamine withdrawal caused a decay in the GFP/RFP signal and we found that restoring glutamine caused a subsequent restoration of the GFP/RFP signal at all time points, with each fully recovering the GFP/RFP signal over time (Revised Manuscript Figure 2E). Next, we conducted the experiment suggested by the reviewer, testing whether the published finding, that oligomycin induced aspartate limitation can be remedied by co-treatment with electron transport chain uncouplers, could be visualized using jAspSnFR3 measurements of GFP/RFP. Indeed, after 24 hours of oligomycin induced aspartate depletion, treatment with the ETC uncoupler BAM15 dose dependently restored GFP/RFP signal (Revised Manuscript Figure 2G). Finally, we also measured whether the ability of pyruvate to mitigate the decrease in aspartate upon co-treated with rotenone (Figure 2B) could also be detected in a sequential treatment protocol after aspartate depletion. Indeed, after 24 hours of aspartate depletion by rotenone treatment, the GFP/RFP signal was rapidly restored by additional treatment with pyruvate (Revised Manuscript Figure 2, figure supplement 1C). Collectively, these results provide support for the utility of jAspSnFR3 to measure transient changes in aspartate levels in diverse metabolic situations, including conditions that restore aspartate to cells that had been experiencing aspartate depletion.

      Reviewer #2 (Recommendations For The Authors):

      Weaknesses: Sensor basically identical to iGluSnFR3, but nevertheless useful and specific. The results support the conclusions, and the paper is very straightforward. I think the work will be useful to people working on the effects of free aspartate in biology and given it is basically iGluSnFR3, which is widely used, should be very reproducible and reliable.

      We appreciate the reviewer’s comment that sensor is useful for specific detection of aspartate. We agree that the advance of the paper is primarily in demonstrating its utility to measure aspartate, rather than any fundamental innovation on the biosensor approach. We hope the fact that jAspSnFR3 derives from a well validated biosensor (iGluSnFR3) will support its adoption.

      Reviewer #3 (Recommendations For The Authors):

      Although this is a well-performed study, I have some comments for the authors to address:

      1) A red tag version of the sensor (jAspSnFR3-mRuby3) was generated for normalization purposes, with this the authors plan to correct GFP signal from expression and movement artifacts. I naturally interpret "movement artifacts" as those generated by variations in cell volume and focal plane during time-lapse experiments. However, it was mentioned that jAspSnFR3-mRuby3 included a histidine tag that may induce a non-specific effect (responses to the treatment with some amino acids). This suggests that a version without the tag needs to be generated and that an alternative design needs to be set for normalization purposes. A nuclear-localized RFP was expressed in a second attempt to incorporate RFP as a normalization signal. Here the cell lines that express both signals (sensor and RFP) were generated by independent lentiviral transductions (insertions). Unless the number of insertions for each construct is known, this approach will not ensure an equimolar expression of both proteins (sensor and RFP). In this scenario is not clear how the nuclear expression of RFP will help the correction by expression or monitor changes in cell volume. The authors may be interested in attempting a bicistronic system to express both the sensor and RFP.

      The reviewer noted several potential issues concerning the use of RFP for normalization, which will be separated into sections below:

      Movement artifacts:

      We are glad the reviewer raised this issue since we see how it was confusingly worded. We have deleted the text “and movement artefacts” from the sentence.

      His-tag and non-specific responses to some amino acids:

      We also found it concerning that non-specific responses to amino acids could potentially contribute to our RFP normalization signal, and so we conducted additional experiments to address whether this was likely to be an issue in intracellular measurements. We first tested whether the non-specific signal was related to the histidine tag, or was intrinsic to the mRuby3 protein itself, by comparing the fluorescence response to a titration of histidine (which showed the largest effect of red fluorescence), aspartate, and GABA (structurally related to glutamate and aspartate, but lacking a carboxylate group) across a group of mRuby containing variants, with or without histidine tags. We replicated the non-specific signal originally observed in jAspSnFR3-mRuby3-His and found that another biosensor with a histidine tagged on the C terminus of mRuby3 had a similar response (iGlucoSnFR2.mRuby3-His), as did mRuby3-His alone, indicating that the aspect of being fused with jAspSnFR3 or another binding protein was not required for this effect. Additionally, we also compared the fluorescence response of lysates expressing mRuby2 and mRuby3 without histidine tags and found that the non-specific signal was essentially absent (Revised Manuscript Figure 1, figure supplement 4B-D). Collectively. These data support our original hypothesis that the histidine tag was responsible for the non-specific signal, alleviating concerns about more substantial protein design issues or with using nuc-RFP for normalization. Since we also found that measuring aspartate signal using GFP/RFP ratios from cells with linked the jAspSnFR3-Ruby3-His agreed with measurements from cells separately expressing jAspSnFR3 and nucRFP (without a His tag), and the amino acid concentrations needed to significantly alter His tagged Ruby3 signal are above those typically found in cells, we conclude that this is unlikely to be a significant factor in cells. Nonetheless, we have added all the relevant data to the manuscript to allow readers to make their own decision about which construct would be best for their purposes.

      Original text:

      "Surprisingly, the mRuby3 component responds to some amino acids at high millimolar concentrations, indicating a non-specific effect, potentially interactions with the C-terminal histidine tag (Figure 1—figure Supplement 2, panel B). Notably, this increase in fluorescence is still an order of magnitude lower than the green fluorescence response and it occurs at amino acid concentrations that are unlikely to be achieved in most cell types."

      Revised text:

      "Surprisingly, the mRuby3 fluorescence of affinity-purified jAspSnFR3.mRuby3 responds to some amino acids at high millimolar concentrations, indicating a non-specific effect (Figure 1—figure Supplement 4, panel A). This was determined to be due to an unexpected interaction with the C-terminal histidine tag and could be reproduced with other proteins containing mRuby3 and purified via the same C-terminal histidine tag (Figure 1—figure Supplement 4, panel B and C). Interestingly, a structurally related, non-amino acid compound, GABA, does not elicit a change in red fluorescence; indicating, that only amino acids are interacting with the histidine tag (Figure 1—figure Supplement 4, panel D). Nevertheless, most of our cell culture experiments were performed with nuclear localized mRuby2, which lacks a C-terminal histidine tag, and these measurements correlated with those using the histidine tagged jAspSnFR3-mRuby3 construct (Figure 1—figure Supplement 1 panel D)."

      Lentiviral transductions

      We agree that splitting the two fluorescent proteins across two expression constructs and infections effectively guarantees that there will not be equimolar expression of jAspSnFR3 and RFP, however we do not think equimolar expression is necessary in this context. The primary goal of RFP measurements in these experiments (and in experiments using the jAspSnFR3-mRuby3 fused construct) is to control for global alterations in protein expression that might confound the interpretation that a change in GFP fluorescence corresponds to a change in aspartate levels. While a bicistronic system is arguably a better approach to improve the similarity of expression of jAspSnFR3 and nuc-RFP in a cell, we only require that the cells have consistent expression of both proteins across all cells in the population, not that the expression of one necessarily be a similar molarity to the other. We accomplish consistent expression of proteins by single cell cloning after expression of jAspSnFR3 and nucRFP (or jAspSnFR3-mRuby3), and screening for clones that have high enough expression of both proteins such that they are well detected by standard Incucyte conditions. Given that our data do not identify an obvious downside to separate expression of jASPSnFR3 and nuc-RFP compared to the fused jAspSnFR3-mRuby3 construct (where the fluorescent proteins are truly equimolar) (Figure 2, Figure Supplement 1C), we elected to prioritize the separate jAspSnFR3 and nuc-RFP combination, which provides additional opportunities to measure cell number in the same experiment (see below).

      2) The authors were interested in establishing the temporal dynamics of aspartate depletion by genetics and pharmaceutical means. For the inhibition of mitochondrial complex I rotenone and metformin were used. Although the assays are clearly showing aspartate depletion the report of cell viability is missing. Considering that glutamine deprivation induces arrest in cell proliferation, I think will be important to know the conditions of the cell cultures after 60 hours of treatment with such inhibitors.

      We agree that ensuring that cells are still viable in conditions where aspartate is depleted, as determined by GFP/RFP in jAspSnFR3 expressing cells, is an important goal. To this end, we added a new experiment investigating the restoration of glutamine on the GFP/RFP signal at different time points after glutamine depletion (Revised Manuscript Figure 2E, see response to reviewer 1). One advantage of using the nuclear RFP as a normalization marker is that it also enables measurements of nuclei counts, a surrogate measurement for cell number. In the same glutamine depletion experiment we therefore measured cell counts using nuclear RFP incidences and confluency as measurements of cell proliferation/growth. In both cases, the arrest in cell proliferation upon glutamine withdrawal was obvious, as was the restoration of cell proliferation following glutamine replenishment, with the amount of growth delay corresponding to the length of glutamine withdrawal (Revised Manuscript Figure 2, Figure Supplement 2A-B). Nonetheless, there was no obvious lasting defects in restarting cell proliferation even after 12 hours of glutamine withdrawal, indicating that cell viability is preserved. In the case of mitochondrial inhibitors, we also observe even that after 24 hours of treatment with oligomycin or rotenone, restoration of aspartate synthesis from BAM15 or pyruvate, respectively, can also restore GFP/RFP signal, supporting the conclusion that cellular metabolism is still active in these conditions (Revised Manuscript Figure 2G; Revised Manuscript Figure 2, figure supplement 1C).

      3) The pH sensitivity was checked in vitro with jAspSnFR3-mRuby3 and the sensor reported suitable for measurements at physiological pH. It would be an opportunity to revisit the analysis for pH sensitivity in cultured cells using an untagged version of jAspSnFR3 coupled, for example, to a sensor for pH.

      We thank the reviewer for the suggestion and agree that pH effects on sensor signal could be a confounding factor in some conditions. Unfortunately, measuring intracellular pH is not trivial and using multiple fluorescent sensors that change simultaneously would be complex to interpret, particularly in the absence of controls to unambiguously control intracellular pH and aspartate concentrations. Thus, we believe that proper investigation of the variable of pH is beyond the scope of this study. Nonetheless, we agree that measuring the contribution of pH to sensor signal is an important goal for future work, particularly if deploying it in conditions likely to cause substantial pH differences, such as comparing compartmentalized signal of jAspSnFR3 in the cytosol and mitochondria. We have added the following italicized text to the conclusions section to underscore this point:

      “Another potential use for this sensor would be to dissect compartmentalized metabolism, with mitochondria being a critical target, although incorporating the influence of pH on sensor fluorescence will be an important consideration in this context.”

      4) While the authors take an interesting approach to measuring intracellular aspartate concentration, it will be highly desirable if a calibration protocol can be designed for this sensor. Clearly, glutamine depletion grants a minimal ("zero") aspartate concentration. However, having a more dynamic way for calibration will facilitate the introduction of this tool for metabolism studies. This may be achieved by incorporating a cultured cell that already expresses the transporter or by ectopic expression in the cells that have already been used.

      We appreciate the suggestion and would similarly desire a calibration protocol to serve as a quantitative readout of aspartate levels from fluorescence signal, if possible. While we do calibrate jAspSnFR3 fluorescence in purified settings, conducting an analogous experiment intracellularly is currently difficult, if not impossible. While we have several methods to constrain the production rate of aspartate (glutamine withdrawal, mitochondrial inhibitors, and genetic knockouts of GOT1 and GOT2), we cannot prevent cells from decreasing aspartate consumption and so cannot get a true intracellular zero to aid in calibration. Additionally, the impermeability of aspartate to cell membranes makes it challenging to specifically control intracellular concentrations using environmental aspartate, and the best-known aspartate transporter (SLC1A3) is concentrative and so has the reciprocal problem. Considering these issues, we are wary of implying to readers that any specific fluorescence measurement can be used to directly interpret aspartate concentration given the many variables that can impact its signal, both related to the biosensor system itself (expression of jAspSnFR3, expression of Nuc-RFP, sensitivity and settings of the fluorescence detector) and based on cell intrinsic variability (differences in basal ASP levels, different sensitivity to treatments, influence of pH, etc.). We maintain that jAspSnFR3 has utility to measure relative changes in aspartate within a cell line across treatment conditions and over time, but absolute quantitation of aspartate still will require complementary approaches, like mass spectrometry, enzymatic assays, or NMR.

      5) jAspSnFR3 seems to have the potential to be incorporated easily for several research groups as a main tool. In general, a minor correction to replace F/F with ΔF/F in the text.

      Thank you for catching this error, the text has been edited accordingly.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this work, the authors provide evidence to show that an increase in Kv7 channels in hilar mossy cells of Fmr1 knock out mice results in a marked decrease in their excitability. The reduction in excitatory drive onto local hilar interneurons produces an increased excitation/inhibition ratio in granule cells. Inhibiting Kv7 channels can help normalize the excitatory drive in this circuit, suggesting that they may represent a viable target for targeted therapeutics for fragile-x syndrome.

      Strengths:

      The work is supported by a compelling and thorough set of electrophysiological studies. The authors do an excellent job of analysing their data and present a very complete data set.

      We thank the Reviewer for the positive comments.

      Weaknesses:

      There are no significant weaknesses in the experimental work, however the complexity of the data presentation and the lack of a schematic showing the organizational framework of this circuit make the data less accessible to non-experts in the field. I highly encourage a graphical abstract and network diagram to help individuals understand the implications of this work.

      We thank the Reviewer for the suggestion, and added a schematic of the dentate network organization (Figure 1A).

      The work is important as it identifies a unique regional and cell-specific abnormality in Fmr1 KO mice, showing how the loss of one gene can result in region-specific changes in brain circuits.

      Reviewer #2 (Public Review):

      Summary:

      Deng et al. investigate, for the first time to my knowledge, the role that hippocampal dentate gyrus mossy cells play in Fragile X Syndrome. They provide strong evidence that, in slice preparations from Fmr1 knockout mice, mossy cells are hypoactive due to increased Kv7 function whereas granule cells are hyperactive compared to slices from wild-type mice. They provide indirect evidence that the weakness of mossy cell-interneuron connections contributes to granule cell hyperexcitability, despite converse adaptations to mossy cell inputs. The authors show that application of the Kv7 inhibitor XE991 is able to rescue granule cell hyperexcitability back to wild-type baseline, supporting the overall conclusion that inhibition of Kv7 in the dentate may be a potential therapeutic approach for Fragile X Syndrome. However, any claims regarding specific circuit-based intervention or analysis are limited by the exclusively pharmacological approach of the manipulations.

      Strengths:

      Thorough electrophysiological characterization of mossy cells in Fmr1 knockout mice, a novel finding.

      Their electrophysiological approach is quite rigorous: patched different neuron types (GC, MC, INs) one at a time within the dentate gyrus in FMR1 KO and WT, with and without 'circuit blockade' by pharmacologically inhibiting neurotransmission. This allows the most detailed characterization possible of passive membrane/intrinsic cell differences in the dentate gyrus of Fmr1 knockout mice.

      Provide several examples showing the use of Kv7 inhibitor XE991 is able to rescue excitability of granule cell circuit in Fmr1 knockout mice (AP firing in the intact circuit, postsynaptic current recordings, theta-gamma coupling stimulation).

      We thank the Reviewer for the positive comments.

      Weaknesses:

      The implications for these findings and the applicability of the potential treatment for the disorder in a whole animal are limited due to the fact that all experiments were done in slices.

      We appreciate the Reviewer’s point and agree. To address this concern, we have revised the Discussion to state that “the applicability of a circuit-wide approach as a potential treatment in vivo will require extensive future behavioral analyses, which are beyond the scope of the current study”. We also now emphasize in Discussion that “these findings provide a proof-of-principle demonstration that a circuit-based intervention can normalize dynamic E/I balance and restore dentate circuit output in vitro”.

      The authors' interpretation of the word 'circuit-based' is problematic - there are no truly circuit-specific manipulations in this study due to the reliance on pharmacology for their manipulations. While the application of the Kv7 inhibitor may have a predominant effect on the circuit through changes to mossy cell excitability, this manipulation would affect many other cells within the dentate and adjacent brain regions that connect to the dentate that express Kv7 as well.

      We appreciate the reviewer’s point but would like to clarify that by using a term “circuit-based” we did not intend to imply that it is a “’circuit-specific” intervention. Our intended interpretation of the term ‘circuit-based’ stems from the following reasoning: the dentate circuit has two types of excitatory neurons which show opposite excitability defects in FXS mice, thus presenting an irreconcilable conflict to correct pharmacologically for each cell type individually. Instead, we sought an approach to correct the overall dentate circuit output, rather than to restore excitability defects of individual cell types. Notably, when we pharmacologically isolated granule cells from the circuit, inhibition of Kv7 failed to restore their excitability, suggesting that normalization of the dentate output depends on the circuit activity. Since we focused on correcting dentate output using such a circuit-dependent approach, we used the term ‘circuit-based intervention’ to emphasize this notion.

      Reviewer #3 (Public Review):

      The paper by Deng, Kumar, Cavalli, Klyachko describes that, unlike in other cell types, loss of Fmr1 decreases the excitability of hippocampal mossy cells due to up-regulation of Kv7 currents. They also show evidence that while muting mossy cells appears to be a compensatory mechanism, it contributes to the higher activity of the dentate gyrus, because the removal of mossy cell output alleviates the inhibition of dentate principal cells. This may be important for the patho-mechanism in Fragile X syndrome caused by the loss of Fmr1.

      These experiments were carefully designed, and the results are presented ‎in a very logical, insightful, and self-explanatory way. Therefore, this paper represents strong evidence for the claims of the authors. In the current state of the manuscript, there are only a few points that need additional explanation.

      We thank the Reviewer for the positive comments.

      One of the results, which is shown in the supplementary dataset, does not fit the main conclusions. Changes in the mEPSC frequency suggest that in addition to the proposed network effects, there are additional changes in the synaptic machinery or synapse number that are independent of the actual activity of the neurons. Since the differences of the mEPSC and sEPSC frequencies are similar and because only the latter can signal network effects, while the former is typically interpreted as a presynaptic change, it cannot be claimed that sEPSC frequency changes are due to the hypo-excitability of mossy cells.

      We thank the Reviewer for this important point and agree. To address this concern, we now state in Results that “We note that changes in the excitatory drive onto interneurons include both mEPSC and sEPSC frequencies, which reflect not only potential deficits in excitability of their input cells, such as MCs, but also changes in synaptic connectivity/function, that may arise from homeostatic circuit reorganization/compensation (see Discussion)”.

      We also now emphasize this point in Discussion by stating that “alterations in excitatory drives, including both mEPSC and sEPSC frequencies onto interneurons, suggest changes in the excitatory synapse number and/or function. Together with alterations in inhibitory drives these changes may reflect compensatory circuit reorganization of both excitatory and inhibitory connections, including mossy cell synapses”.

      We also note in Discussion that “Such circuit reorganization can explain the balanced E/I drive onto granule cells in Fmr1 KO mice we observed in the basal state, which can result from reorganization of excitatory and inhibitory axonal terminals”.

      Notably, our findings that Kv7 blocker acting by increasing MC excitability is sufficient to correct dentate output, supports the notion that hypo-excitability of mossy cells is a major factor contributing to dentate circuit E/I imbalance. This does not exclude the presence of additional mechanisms contributing to E/I imbalance, such as changes of synaptic connectivity or release machinery. To reflect this point, we revised the Results to temper the initial claim that “this analysis supports the notion that the hypo-excitability of MCs in Fmr1 KO mice caused (now replaced with “is a major factor contributing to”) the reduction of excitatory drive onto hilar interneurons, which ultimately results in reduced local inhibition”.

      An apparent technical issue may imply a second weak point in the interpretation of the results. Because the IPSCs in the PP stimulation experiments (Fig 8) start within a few milliseconds, it is unlikely that its first ‎components originate from the PP-GC-MC-IN feedforward inhibitory circuit. The involvement of this circuit and MCs in the Kv7-dependent excitability changes is the main implication of the results of this paper. But this feedforward inhibition requires three consecutive synaptic steps and EPSP-AP couplings, each of them lasting for at least 1ms + 2-5ms. Therefore, the inhibition via the PP-GC-MC-IN circuit can be only seen from 10-20ms after PP stimulation. The earlier components of the cPSCs should originate from other circuit elements that are not related to the rest of the paper. Therefore, more isolated measurements on the cPSC recordings are needed ‎which consider only the later phase of the IPSCs. This can be either a measurement of the decay phase or a pharmacological manipulation that selectively enhances/inhibits a specific component of the proposed circuit.

      We appreciate the Reviewer’s point. As we mentioned in Results: “The EPSP measured in granule cells in response to the PP stimulation integrates both excitatory and inhibitory synaptic inputs onto granule cells, including the direct synaptic input from the PP and all the PP stimulation-associated feedforward and feedback synaptic inputs. In other words, the EPSP in granule cells integrates all dentate circuit ‘operations’.” As the Reviewer pointed out, this is also the case in the measurements of cPSCs, which comprise all of PP stimulation-associated feedforward and feedback inhibition. We thank the Reviewer for the suggestion to isolate specific components of IPSC. However, we did not attempt to do it in this study for three reasons. First, activity of all of these circuit components likely overlaps extensively in time and it is difficult to identify the specific time point that can separate contributions from earlier canonical feed-forward and feed-back components from the contribution of the later MC-dependent PP-GC-MC-IN feed-forward component. Notably the tri-synapse PP-GC-MC-IN component differs temporarily from the canonical di-synaptic (PP-GC-IN) feed-back inhibition only by a single synaptic activation step, resulting in only a few milliseconds difference. Moreover, the temporal differences in the contributions of these components vary widely among different recordings making a uniform analysis very difficult. Second, we used three different metrics to assess E/I changes in cPSC measurements, which capture a wide range of temporal processes and their integration, including peak-to-peak measurements, the charge transfer, and the excitation window metrics. Third, the principal readout in our study was the overall dentate output (i.e., granule cell firing), which reflects the integration of all dentate circuit ‘operations’ thus making the overall cPSC measurements appropriate, in our view, for this readout.

      I suggest refraining from the conclusions saying "‎MCs provide at least ~51% of the excitatory drive onto interneurons in WT and ~41% in KO mice", because too many factors (eg. IN cell types, slice condition, synaptic reliability) are not accounted for in these actual numbers, and these values are not necessary for the general observation of the paper.

      We thank the reviewer for this suggestion, and have revised the manuscript accordingly.

      There are additional minor issues about the presentation of the results.

      We have carefully checked and corrected the minor errors that reviewer pointed out.

      Recommendations for the authors:

      Revisions that are considered essential for improved assessment regarding the strengths of support of the claims:

      • Temper claims regarding circuit-based effects

      • Temper claims regarding very specific quantitative assessments of synaptic drives

      • Differentiate between monosynaptic inputs and inputs arriving through multiple synaptic contacts with proper analytical techniques.

      We appreciate these suggestions and have revised the manuscript to address the concerns raised by the reviewers.

      Reviewer #1 (Recommendations For The Authors):

      The authors do an outstanding job of reviewing and presenting all of their data. This is a paper I will recommend all of my trainees read, as it is an excellent example of a complete research project. While I am impressed with the effort involved, I also wondered if the complexity and thoroughness of their presentations could make the story less accessible to non-expert readers. My comments are simply intended to help them present a more coherent and succinct story to a wider audience, though I am not sure I really provide any meaningful changes. This is simply a very thorough and complete body of work that the authors should be commended for. After reading it I felt they had gone above and beyond what most authors would provide in terms of data to support their story, and thus I had no doubt that a change in Kv7 plays a role in changing the excitability of the network.

      We thank the Reviewer for the positive comments and great suggestions. We have made numerous changes to present our work in a more coherent and succinct way, in part by re-plotting some of the figures, as well as by adding a schematic of the dentate circuit in Figure 1.

      Figure 1. A visual of mossy cells and the local circuit they are studying would be a useful addition to Figure. 1. I also feel this is important for conveying the story of how hypo-excitability can impact the E/I of the network. I think it has to be more of a cell structure/circuit-based figure than is presented in Supplementary Figure 8.

      We thank the reviewer for this suggestion. We have added a schematic of the dentate circuit with all major cell types involved in Figure 1A.

      Figure 1. A, B, and C tell a coherent story and are easy to understand. The interpretation of the phase plot in D is harder to access. Perhaps having this as a separate figure and providing a clearer presentation of the way the phaseplot was created (see Figure 3 Bove et al., 2019, Neuroscience 418; DOI: 10.1016/j.neuroscience.2019.08.048)

      We appreciate the Reviewer’s point and agree. In order to keep Figure 1 more concise and readable, we removed the phase plot in the revised version. This change did not negatively impact the result presentation because the primary aim of this plot was to visualize changes in voltage threshold in an alternative way, but it was already clearly shown by the ramp-evoked AP traces (revised Figure 1D, insert), and thus was not essential to show.

      Figure 1 E-N might be better situated in a supplementary graph as the characteristics of the AP aren't changing.

      We understand the Reviewer’s point, but we feel it would be better to keep all action potential metrics together in one figure, to show that only a specific subset of parameters was affected in Fmr1 KO mice.

      Figure 2: (A-D) I am not sure having so many figures is required given the focus is on having a small change in Ir at one membrane potential. I do worry that the significance appears to be due to 2 cells with an IR of over 100 in the WT group and 2 with an IR of around 62 in the KO group. All other cells are between 75-100 in both groups. I also worry a bit bc in the literature IRs between 55 and 125 seem to be commonly reported by groups that do this work normally (Buzsacki, Westbrook, etc.). I would be cautious about making too much out of this result.

      We thank the Reviewer for these comments. We have performed additional analyses of these data, as also suggested by Reviewer 3 (Point #1), and improved presentation of the data in Figure 2D-F by showing the effect of XE991 on increasing input resistance in WT vs KO. We also plotted other panels in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991, which makes the results easy to follow. For more details, please also see the response to Reviewer 3, Point 1.

      Figure 2D-E: As in the text, this result is really pointing towards there being a Kv7 issue. Worries about the data in D aside, I think these two figures alone tell a clearer story. Figure 3 on the other hand tells a story of the effects of blocking Kv7 on membrane potential. Is this central to the story the others are trying to tell?

      We thank the reviewer for this point. We believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

      Figure 3. This is an interesting finding that shows how detailed their analysis was. Showing that the change in holding current in KO animals is greater than in WT is the first solid piece of evidence that there is a change in Kv7 in these cells that affects their excitability.

      We appreciate the reviewer’s comment. As mentioned above, we believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

      Figures 4 and 5 provide additional detail to support the idea that Kv& changes by showing how the E/I ratio and spontaneous minis are shifted in KO animals.

      We thank the Reviewer for the comments.

      Figures 6-8 build a compelling story for the reduction in excitatory drive in mossy cells affecting the network dynamics in excitatory/inhibitory interactions in DG cells.

      We appreciate the Reviewer’s comment.

      Reviewer #2 (Recommendations For The Authors):

      1) Other than location and characteristic morphology, the other parameters that were used to identify mossy cells and granule cells were also parameters used to find differences in cellular properties between wild-type and Fmr1 KO mice (RMP, sEPSC frequency, etc.), which would confound the results shown. The use of available transgenic mouse lines would provide for a more unbiased screen of these cells. Afterhyperpolarization was also used as a parameter while screening cells, yet none of the data on this measurement is shown.

      We thank the reviewer for this point and agree that transgenic mouse lines provide a more unbiased way to identify various types of neurons. However, since the present study involves analyses of at least three different types of neurons, establishing multiple transgenic lines labeling different types of dentate neurons in the Fmr1 KO mouse model would be very time consuming and beyond the current resources of the lab. We would also like to clarify that the three types of dentate neurons are easily distinguished according to the large differences in location, morphology and basal electrophysiological properties, none of which were essential in defining differences between genotypes. Specifically, granule cells are located in the granule cell layer, have a small cell body (<10 m), RMP around -80mV, capacitance ~20 pF, and infrequent sEPSCs (<20 events/min); mossy cells are located in the hilus, have a large cell body (>15 m), RMP around -65 mV, capacitance >100 pF, and fast afterhyperpolarization less than -10 mV (WT –5.1 ± 0.7 mV, KO -5.8 ± 0.5 mV); interneurons are located in the hilus or border of granule cell layer, have a relative smaller cell body (10-15 m), RMP around -55 mV, capacitance <60 pF, and afterhyperpolarization larger than -15 mV (WT -20.4 ± 1.3 mV, KO -19.8 ±1.4 mV). We note that the cells that could not be definitively classified into the three categories were not included in analyses, and we have now clarified this further in the Methods. To address the reviewer’s second concern regarding AHP, we now provided the corresponding values in the Methods.

      2) A definitive way to test the cell-autonomous nature of the Kv7 changes would be to use female mice, who will have a mosaic of cells affected by the fragile X chromosome, and the Fmr1 KO cells could be engineered to express GFP to help identify them from wild-type cells.

      We agree and appreciate this suggestion. This could be an interesting follow up study to further verify the cell-autonomous nature of Kv7 changes.

      3) The authors heavily rely on XE991 as a selective Kv7 blocker. Is it blocking all Kv7 channels at the concentration used? If so, given the significant expression of Kv7 in the dentate as shown by Western blot, is it surprising that there is no effect of this inhibitor on wild-type slices in most cases?

      We thank the reviewer for this important point. We used 10x of IC50 concentration in the present study, suggesting that more than 80% of Kv7 should be blocked. Notably, we observed several effects of XE991 in WT mice: it significantly increased input resistance (new Figure 2D-F), and strongly enhanced AP firing evoked by step depolarization (Figure 7E-H), although we did not observe effect of XE991 in WT in the analyses of spiking evoked by theta-gamma stimulation in Figure 8. However, this is not surprising. If a parameter we measured is predominately cell-autonomous (for example, input resistance), the effects of XE991 are easy to observe. However, if a parameter reflects integration of all dentate circuit operations (for example, AP probability in response to theta-gamma stimulation), it is difficult to detect the effect of XE991 in WT mice because the dentate circuit of WT mice has larger capability to maintain E/I balance in response to XE991.

      4) E/I ratio is a helpful concept, and it is heavily relied upon in the results text, but statistically shaky, especially for sEPSC:sIPSCs since you are combining uncertainty in the sEPSC and sIPSC to make one very uncertain ratio that doesn't undergo any subsequent statistical confirmation (such as in Fig 4I).

      We appreciate the reviewer’s point and apologize for the confusion in presentation of Fig 4I (and 5I), due to lack of detailed explanation. The E/I ratio shown in Figs. 4I (and 5I) is a single data-point estimate calculated from the mean values of independent sEPSC and sIPSC measurements (Figs. 4G-H and 5G-H, respectively). This ratio was used only as an estimate/illustration of the changes, rather than a precise determination of the shift in E/I balance. Because there is only one data-point for this ratio, statistical analysis is not possible. For this reason we performed extensive additional analyses in Figures 7 and 8, in which the EPSC and IPSC were measured from the same cells and at the same time to define the actual E/I ratio with the corresponding statistical analyses (i.e., a real matched and dynamic E/I ratio).

      5) Is this mGlur2/CB1 specificity to PP/granule and MC axons, respectively, true in the Fmr1 KO mice? It is possible that mGluR2 and CB1 expression patterns are altered in FMR1 KO, thus the assumption used to isolate these distinct inputs may not hold true.

      This is a very good point. We do assume that the specificity of Group II mGluR and CB1 is similar between Fmr1 KO and WT mice, but this is an assumption that we have not directly verified. However, our results in Figures 7 and 8 strongly support this assumption, because if it were not true, then our intervention would be unlikely to correct the excessive dentate output.

      6) XE991 only normalized GC firing when other cells were not pharmacologically blocked. The authors suggest this means blockage of MC Kv7 reduces GC excitability back to normal...presumably by increasing MC --> IN --> GC firing. This is a conclusion from many indirect comparisons (comparing XE991 effect on GC with/without GABA and glutamate blockers; comparing MC firing rates with/without XE991, and using CB1 agonist versus mGluR2 agonist to say it is mossy cells that are mostly controlling INs) - a clincher experiment would be to acutely knockdown Kv7 in mossy cells specifically and measure GC and IN firing.

      Thank you, this is a great suggestion. Indeed, as an expansion of this project, in the future studies we are planning to manipulate excitability of mossy cells through manipulating Kv7, or using chemogenetic or optogenetic approaches.

      7) The reasoning behind the FMRP-Kv7 connection is quite weak, citing the paper Darnell 2011 as "translational target", but FMRP has myriad translational targets.

      We agree, and attempted to define the mechanism of increased Kv7 function using co-immunoprecipitation approach, as well as immunostaining to look at cell-type specific expression changes. However, both of these approaches were difficult to interpret due to technical limitations of the available antibodies. We also note that “We did not further investigate the precise mechanisms underlying enhancement of Kv7 function in the absence of FMRP, since the present study primarily focuses on the functional consequences of abnormal cellular and circuit excitability”. To address this concern, we extensively discussed the potential mechanisms of FMRP-Kv7 connection, acknowledged in Discussion that “further studies will be needed to elucidate the precise mechanism responsible for the increased Kv7 function in Fmr1 KO mice”, and will continue to investigate it in the future studies.

      8) The authors attempt to look for changes in Kv7 expression with Western blot, but since they hypothesize that Kv7 changes are mainly in the mossy cells, it is perhaps not surprising that they would not be able to see any changes when they look at dentate as a whole. Staining for Kv7 subunits to look at expression on a cellular level would be beneficial.

      We appreciate the reviewer’s suggestion. We attempted to perform the suggested experiments using immunostaining for KCNQ2, KCNQ3 and KCNQ5 in different subtypes of dentate neurons. However, these experiments failed to produce interpretable results due to technical limitations of the available antibodies.

      9) Is Kv7 localization or splice/composition different in FMR1 KO mice?

      This is a very good point. As we mentioned in Point 8 above, we were not able to perform these experiments and do not have the answer at this point.

      10) Regarding the 3 subtypes of interneurons in the dentate, the authors are pooling data based on similar intrinsic properties, but this conclusion may be affected by the low number of recorded neurons for the regular-spiking type. In addition, it is unclear whether these different interneuron types have differential circuit connectivity (most likely) which would make it imperative to keep circuit analysis for interneurons segregated into these cell types.

      We appreciate the reviewer’s point. Indeed, these different interneuron types may have distinct circuit connectivity and contributions to circuit activity. However, identification of these 3 types of interneurons and determination of their respective functions is in itself a very extensive set of experiments which is beyond the scope of the current manuscript. We also note that the functional readout of circuit activity in our measurements was the AP firing and EPSPs evoked in granule cells by PP stimulation, which integrate all dentate circuit operations, including all of the feedforward and feedback loops which are mediated by all of these different types of interneurons. For simplicity, we thus pooled all interneuron data for the purposes of this study. But we fully agree that extensive future work is required to elucidate interneuron-type specific changes in Fmr1 KO mice and their contributions to the dentate circuit dysfunction.

      11) To do statistics treating each cell individually, and therefore assuming each cell is independent of one another, is not correct. Two cells from the same mouse will be more similar than two cells from different mice, therefore they are not independent data points. Nested statistical methods (n cells from o slices from p mice) will be important in future work, as discussed by (Aarts et al., Nat. Neurosci. 2014).

      We agree with the Reviewer’s point and appreciate this suggestion. In the present study, the cells tested in electrophysiological experiments were from at least 3 different mice for each condition, which help minimize this kind of errors.

      Reviewer #3 (Recommendations For The Authors):

      Is there a difference in the Rin at -45mV of the control cell after the application of XE991? This is important to appreciate whether the XE991-sensitive conductances contribute to the basal excitability of MCs. Furthermore, the statistical comparison of the Rin at -45mV of the FXS animals in the control solution and in the presence of XE991 would be also important‎. Actually, the most accurate measurement would be to show a difference in the acute Kv7-blockade between control and FXS animals, if that is possible with this blocker. Additionally, it would be also informative if the bar graphs in Fig.2 D & E were merged for this purpose, similarly as in the later figures.

      We thank the Reviewer for this suggestion and agree. Following this suggestion, we have re-plotted the data in Figure 2 accordingly. Specifically, we now show that XE991 significantly increased input resistance in both WT and KO mossy cells, and the effect of XE991 on increasing input resistance was markedly larger in KO than WT mossy cells. For other figures, we have plotted data in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991.

      Because of the cell-to-cell variability of the voltage responses, it would be more informative and representative if the average of traces from all cells were shown in Fig.2 D & E.

      We agree with the Reviewer’s point. For clarity of presentation, we presented the cell-to-cell variability of the data as scatter points of input resistance values in the bar graph (Figure 2E), together with the representative traces (Figure 2D). Plotting the average traces from all cells would result in a total of 30 traces for all the WT and KO mice, which is difficult to visually assess clearly.

      On page 7, please clarify the recorded cell type in this sentence: "In ‎contrast, WIN markedly reduced the number of sEPSCs in both WT and KO mice...".

      We thank the Reviewer for pointing out this omission and have clarified it in the revised version.

      In Figures 6 C, F, and I, the title of the Y-axis should be normalized frequency. Please also correct the figure legend accordingly because the current sentence can be also interpreted as the absolute or total number of events that were compared, irrespective of the duration of the recordings.

      We thank the Reviewer for this point and have corrected the revised version accordingly.

    1. Interpretation is the third part of the perception process, in which we assign meaning to our experiences using mental structures known as schemata.

      I feel like when people think about perception, this is the aspect that usually comes to mind. So much so that other aspects are often disregarded. I can attest to this myself. When I think about how I perceive things, I don't usually think about selecting information, or even organizing it. This gives some interesting perspective going forward in life. I'm realizing I may have more things to consider as I take in the world.

    1. Hope is a disposition of the soul to be convinced that what it desires willcome about. It is caused by a particular movement of the spirits,consisting of the movement of joy mixed with that of desire. And anxietyis another disposition of the soul, which convinces it that its desires willnot be fulfilled. It should be noted that these two passions, althoughopposed, may nevertheless occur together, namely when we think ofreasons for regarding the fulfilment of the desire as easy, and at the sametime we think of other reasons which make it seem difficult

      lol me af

    1. Author Response

      The following is the authors’ response to the original reviews.

      We thank the reviewers and the editors for their constructive and critical comments/ suggestions regarding our paper. We have since extensively revised the manuscript accordingly, including the addition of new experimental data. Hope the readers, reviewers, and editors are now satisfied with the quality and significance of the revised paper.

      Our responses to the eLife assessment and the reviewers’ comment as well as the details of the revisions are described below.

      Wang et al present a useful manuscript that builds modestly on the group's previous publication on KLF1 (EKLF) K47R mice focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo (Shyu et al., Adv Sci (Weinh). 2022). The data demonstrates that Eklf (K74R) imparts these advantages in a background, age, and gender independent manner, not the consequence of the specific amino acid substitution, and transferable by BMT. However, the authors overstate the meaning of these results and the strength of evidence is incomplete, since only a melanoma model of cancer is used, it is unclear why only homozygous mutation is needed when only a small fraction of cells during BMT confer benefit, they do not show EKLF expression in any cells analyzed, and the PD-1 and PDL-1 experiments are not conclusive. The definitive mechanism relative to the prior publication from this group on this topic remains unclear.

      The issues in the assessment by the editor on our paper were also brought up by the reviewers. We have taken care of them by carrying out new experiments as well as rewriting of the paper to highlight the rationales and novel aspects of the current study, as described below in our responses to the three reviewers.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors Wang et al. present a study of a mouse model K74R that they claim can extend the life span of mice, and also has some anti-cancer properties. Importantly, this mechanism seems to be mediated by the hematopoietic system, and protective effects can be transferred with bone marrow transplantation.

      The authors need to be more specific in the title and abstract as to what is actually novel in this manuscript (a single tumor model), and what relies on previously published data (lifespan). Because many of these claims derive from previously published data, and the current manuscript is an extension of previously published work. The authors need to be more specific as to the actual data they present (they only use the B16 melanoma model) and the actual novelty of this manuscript.

      Especially experiments on life span are published and not sufficiently addressed in this actual paper, as the title would suggest.

      Indeed important to point out the novelty of this paper in comparison to the previous paper. First, we have modified the title, the abstract, and the text so to emphasize that the extended lifespan as well as tumor resistance could be transferred by from Eklf(K74R) mice to WT mice by a single transplantation of the Eklf(K74R) bone marrow mononuclear cells (BMT) to the WT mice at their young age (2 months).

      We now also provide several new experimental data including the one demonstrating that Eklf(K74R) mice are resistant to tumorigenesis of hepatocellular carcinoma as well (new Fig. 1E). These points are elaborated in more details below in my responses to the reviewers’ comments/ suggestions.

      Reviewer #2 (Public Review):

      The manuscript by Wang et al. follows up on the group's previous publication on KLF1 (EKLF) K47R mice and reduced susceptibility to tumorigenesis and increased life span (Shyu et al., Adv Sci (Weinh). Sep 2022;9(25):e2201409. doi:10.1002/ advs.202201409). In the current manuscript, the authors have described the dependence of these phenotypes on age, gender, genetic background, and hematopoietic translation of bone marrow mononuclear cells. Considering the current study is centered on the phenotypes described in the previous study, the novelty is diminished. Further, there are significant conceptual concerns in the study that make the inferences in the manuscript far less convincing. Major concerns are listed below:

      1) The authors mention more than once in the manuscript that KLF1 is expressed in range of blood cells including hematopoietic stem cells, megakaryocytes, T cells and NK cells. In the case of megakaryocytes, studies from multiple labs have shown that while EKLF is expressed megakaryocyte-erythroid progenitors, EKLF is important for the bipotential lineage decision of these progenitors, and its high expression promotes erythropoiesis, while its expression is antagonized during megakaryopoiesis. In the case of HSCs, the authors reference to their previous publication for KLF1's expression in these cells- however, in this study nor in the current study, there is no western blot documented to convincingly show that KLF1 protein is expressed at detectable levels in these cells. For T cells, the authors have referenced a study which is based on ectopic expression of KLF1. For NK cells, the authors reference bioGPS: however, upon inspection, this is also questionable.

      2) The current study rests on the premise that KLF1 is expressed in HSCs, NK cells and leukocytes, and the references cited are not sufficient to make this assumption, for the reasons mentioned in the first point. Therefore, the authors will have to show both KLF1 mRNA and protein levels in these cells, and also compare them to the expression levels seen in KLF1 wild type erythroid cells along with knockout erythroid cells as controls, for context and specificity.

      Regarding the novelties of the current story. Besides demonstration of the independence of the healthy longevity characteristics on age, gender, and genetic background, as exemplified by the tumor resistance, another novelty of the current study is that the healthy longevity characteristics, in particular the tumor resistance and extended lifespan, could be transferred by one-time long-term transplantation of the Eklf(K74R) bone marrow mononuclear cells from young Eklf(K74R) mice to young WT mice. Also, since submission of the last version of the paper, we have carried out new experiments, including the characterization of the anti-cancer capability of NK cells (new Fig. 6) as well as assay of the tumor-resistance of Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E), etc.

      We have also modified the title, Abstract, and different parts of the text to highlight the novelties of the current study.

      As to the expression of EKLF in different hematopoietic blood cell types, we have now added a paragraph in Result (p.6 and p.7) describing what have been known in literature in relation to our data presented in the paper. Importantly, following the reviewer’s comments, we have since carried out Western blot analysis of EKLF expression in NK, T, and B cells (p. 6, p.7 and new Fig. S4B). Also noted is that the level of EKLF in B cells is very low and only could be detected by RT-qPCR (Fig. S4C) and RNA-Seq (Bio-GPS database)

      3) To get to the mechanism driving the reduced susceptibility to tumorigenesis and increased life span phenotypes in EKLF K74R mice, the authors report some observations- However, how these observations are connected to the phenotypes is unclear.

      a. For example, in Figure S3, they report that the frequency of NK1.1+ cells is higher in the mutant mice. The significance of this in relation to EKLF expression in these cells and the tumorigenesis and life span related phenotypes are not described. Again, as mentioned in the second point, KLF1 protein levels are not shown in these cells.

      b. In Figure 4, the authors show mRNA levels of immune check point genes, PD-1 and PD-l1 are lower in EKLF K74R mice in PB, CD3+ T cells and B220+ B cells. Again, the questions remain on how these genes are regulated by EKLF, and whether and at what levels EKLF protein is expressed in T cells and B cells relative to erythroid cells. Further, while the study they reference for EKLF's role in T cells is based on ectopic expression of EKLF in CD4+ T cells, in the current study, CD3+ T cells are used. Also, there are no references for the status of EKLF in B cells. These details are not discussed in the manuscript.

      Regarding this part of the questions and comments by the reviewer.

      First, we have since assayed the effect of the K74R substitution of EKLF on the in vitro cancer cell-killing ability of NK cells (termed NK1.1 cells in the previous version). The data showed that NK(K74R) cells have higher ability than the WT NK cells (new Fig. 6). This property together with the higher expression level of NK(K74R) cells in 24 month-old Eklf (K74R) mice than NK cells in 24 month-old WT mice would contribute to the higher tumor-resistance of the Eklf (K74R) mice. This point is also addressed on p. 8 andp.9.

      Second, as stated in previous sections, we have since carried out comparative Western blot analysis of the expression of EKLF protein in NK, CD3 T, and B cells of the WT and Eklf(K74R) mice, respectively (please see the new Fig. S4B). Also, description regarding what are known in literature in relation to our data on the expression of EKLF protein/ Eklf mRNA in different types of hematopoietic blood cells is now included in the Result (please see p.6 and p.7). Notably though, the level of EKLF protein in B cells was too low to be detected by WB (Fig. S4B).

      4) The authors perform comparative proteomics in the leukocytes of EKLF K74R and WT mice as shown in Figure S5. What is the status of EKLF levels in the mutant lysate vs wild type lysates based on this analysis? More clarity needs to be provided on what cells were used for this analysis and how they were isolated since leukocytes is a very broad term.

      The leukocytes used by us were isolated from the peripheral blood after removal of red blood cells, as described in the Materials and Methods.

      Also, the Western blot analysis of EKLF expression in the lysates of leukocytes/ white blood cells (WBC) has been shown previously, now presented in the new Figure S4A.

      5) In the discussion the authors make broad inferences that go beyond the data shown in the manuscript. They mention that the tumorigenesis resistance and long lifespan is most likely due to changes in transcription regulatory properties and changes in global gene expression profile of the mutant protein relative to WT leukocytes. And based on reduced mRNA levels of Pd-1 Pd-l1 genes in the CD3+ T cells and B220+ B cells from mutant mice, they "assert" that EKLF is an upstream regulator of these genes and regulates the transcriptomes of a diverse range of hematopoietic cells. The lack of a ChIP assay to show binding of WT EKLF on genes in these cells and whether this binding is reduced or abolished in the mutant cells, make the above statements unsubstantiated.

      We have since carried out ChIP-PCR analysis of EKLF-binding in the Pd-1 promoter (new Fig. S5). The data showed that EKLF was bound on the CACCC box at -103 of the promoter in WT CD3+T as well as in CD3+T(K74R) cells. This result is discussed on p.7.

      6) Where westerns are shown, the authors need to show the molecular weight ladder, and where qPCR data are shown for EKLF, it will be helpful to show the absolute levels and compare these levels to those in erythroid cells, along the corresponding EKLF knock out cells as controls.

      We have since included the molecular weight markers by the side of Western blots in Fig. S4. Also, we have added a new figure (Fig.S4C) showing the comparison of the expression levels of Eklf mRNA in B cells and CD3+ T cells to the mouse erythroleukemia (MEL) cells, as analyzed by RT-qPCR.

      Also, as indicated now in the Material and Methods section, the specificity of the primers used for RT-qPCR quantitation of mouse Eklf mRNA has been validated before by comparative analysis of wild type and EKLF-knockout mouse erythroid cells (Hung et al., IJMS, 2020).

      7) Figure S1D does not have a figure legend. Therefore, it is unclear what the blot in this figure is showing. In the text of the manuscript where they reference this figure, they mention that the levels of the mutant EKLF vs WT EKLF does not change in peripheral blood, while in the figure they have labeled WBCs for the blot, and the mRNA levels shown do seem to decrease in the mutant compared to WT peripheral blood.

      We apologize for this ignorance on our side. The data shown in the original Fig. SID (new Fig. S4A) are from Western blot analysis of EKLF protein and RT-qPCR analysis of Eklf mRNA in leukocytes/ white blood cells (WBC) isolated from the peripheral blood samples. We have now added back the figure legend and also rewritten the corresponding description in the text on p.6.

      Reviewer #3 (Public Review):

      Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated. The major strengths of the manuscript include the clarity of the effect and the appropriate controls. For instance, the authors query whether Eklf (K74R) imparts these advantages in a background, age, and gender dependent manner, demonstrating that the findings are independent. In addition, the authors demonstrate that the effect is not the consequence of the specific amino acid substitution, with a similar effect on anticancer activity. Furthermore, the authors provide some evidence that PD-1 and PDL-1 are altered in Eklf (K74R) mice.

      Here we thank the encouraging comments by this reviewer.

      Finally, they demonstrate that the effects are transferrable with BMT. Several weaknesses are also evidence. For instance, only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach.

      We have now included new data showing that the Eklf(K74R) mice also carry a higher anti-cancer ability against hepatocellular carcinoma than the WT mice (new Fig. 1E).

      It is also unclear why a homozygous mutation is needed when only a small fraction of cells during BMT can confer benefit. It is also difficult to explain how transplanted donor Eklf (K74R) HSCs confer anti-melanoma effect 7 and 14 days after BMT.

      First, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). These points are now clarified in Discussion (p.9 and p.10).

      Second, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT. This point is now touched upon on p.8 and p.9.

      Furthermore, it would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

      As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

      Finally, the data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation without changing EKLF expression impacts immune cells. The work is impactful as it provides evidence that healthspan and lifespan may be modulated by specific hematological mutation but the mechanism by which this occurs is not completely elucidated by this work.

      As described in a previous section, we have since also carried out ChIP-qPCR analysis of the binding of WT EKLF and EKLF (K74R) on the Pd-1 promoter (new Fig. S5).

      Reviewer #1 (Recommendations For The Authors):

      The authors present interesting melanoma model data but need to tone down their claim of multiple effects of their model system. It needs to be clear what is new and what is previously known.

      As respond in the Public Reviews, we have since added new data on the tumor resistance of the Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E). We have also modified the title as well as highlighted the novel points in the Abstract and text of the revised draft.

      Reviewer #2 (Recommendations For The Authors):

      In addition to the major concerns listed in the public review, the minor concerns that the authors could address are listed below:

      1) Will be helpful to describe why was the pulmonary melanoma focus assay chosen for metastasis assay?

      We now describe on p. 4 the rationale behind the initial choice of this assay for analysis of the anti-cancer capability of the Eklf(K74R) mice. Also, we have since included data from experiment using the subcutaneous cancer cell inoculation assay for comparative analysis of the anti-hepatocellular carcinoma capability of Eklf(K74R) and WT mice (Fig. 1E and p.5).

      2) Reference #61 for B16-F10-luc cells cited in the methods does not have details on the generation of these cells. What these cells are and why this model was chosen needs to be described.

      Sorry about not providing this information before. We now describe the generation of B16F10-luc cells in the Material and Methods section (p.13). The rationale of choosing the B16-F10 cells for the pulmonary lung foci assay is also added on p.4.

      3) The DNA binding consensus site for EKLF needs to be expanded in the introduction.

      This part has been taken care of now on p.13.

      Reviewer #3 (Recommendations For The Authors):

      Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated.

      1) Only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach. The authors, therefore, need to provide evidence of a second type of malignancy to which Eklf mutation confers anticancer and longevity advantages or temper the claims in the discussion that the effect still needs to be tested in non-melanoma cancer models to determine the broad anti-cancer effect.

      As responded in the Public Reviews, we have since shown that Eklf(K74R) mice also exhibited a higher resistance to the carcinogenesis of hepatocellular carcinoma (new Fig. 1E).

      2) Why is a homozygous mutation needed when only a small fraction of cells during BMT can confer benefit of Eklf mutation? Is there evidence that the cellular effect is binary but only a few such cells are needed? This is confusing and requires further clarification.

      As responded in the Public Reviews, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). This point is now clarified in Discussion (p.9).

      3) BMT typically requires at least 3-4 weeks to reconstitute the marrow compartment but the authors are able to see effects of Eklf mutation as early as 7 days following BMT. This is surprising and brings into question the mechanism of effect.

      As responded in the Public Reviews, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT (please see discussion of this point on p. 9).

      4) It would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

      As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

      5) The data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation WITHOUT changing EKLF expression impacts immune cells.

      Indeed, the RNAi knockdown experiment only demonstrated a positive regulatory role of EKLF in Pd1/Pd-l1 gene expression. We have followed the reviewer’s suggestion and carried out ChIP-qPCR analysis and shown that the factor is bound on the Pd-1 promoter in both WT CD3+T cells and CD3+T(K74R) cells (new Fig. S5). We briefly discuss these data on p.7 in relation to the possible effect of K74R substitution of EKLF on Pd-1 expression.

      We have now further clarified this point on p. 7.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      Congratulations on the very nice structure! In my opinion, which you can feel free to take or leave, this would work better as a short report focused on the improvement of the structure relative to the current published model. To my mind, while the functional and dimerization studies are supportive of the cryo-EM studies (specifically, the purified protein is functional, and does tend to dimerize in various membrane mimetics), these experiments don't provide a lot of new mechanistic insight on their own. The dimerization, in particular, could be developed further.

      Response: Thank you for the comments. We have chosen to stick with the current article format. That the protein is dimeric is exciting in our view and we are working to further define the functional significance of this formation.

      Reviewer #2 (Recommendations For The Authors):

      Ln 48. Abstract. "highlighting feature of the complex interface" sounds a bit vague. I was wondering if the authors considered including more specific findings here.

      Response: This sentence has been removed.

      Ln 149 and elsewhere. The authors refer to the previously published structure of HiSiaQM as "low resolution". It may just be me and likely not the intention of the authors, but this comes across as an attempt to diminish the validity of this previous work from another group, which is not necessary. I would recommend rewording these parts slightly, even if it is just to say "lower resolution" instead of "low resolution".

      Response: It was not our intention to diminish the excellent work published by another group, we have changed “low resolution” to “lower resolution” throughout.

      Ln 160. The authors state that the inward-open conformation is likely "the resting state of the transporter". I think this statement should be modified slightly to acknowledge that this is only true under these conditions, i.e. in the absence of the bilayer, membrane potential and chemical gradients.

      Response: We have edited this as follows “That we observe the inward-open conformation without either a bound P-subunit or fiducial marker, suggests that this is the resting state of the transporter under experimental conditions (in the absence of a membrane bilayer, membrane potential and chemical gradients).”

      Ln 202. I'm not convinced that the use of the word "probable" is appropriate here; "possible" would likely fit better in the absence of compelling evidence that this dimer forms in a bacterial cell membrane with physiological levels of HiSiaQM expression.

      Response: We have changed “probable” to “possible”.

      The authors show an SEC trace for DDM solubilised protein, which is a single peak, whereas the LMNG extracted protein has 2 distinctly different elution profiles depending on the LMNG concentration. Was the same phenomenon observed when varying the DDM concentration?

      Response: We observed significantly more aggregation with DDM than L-MNG, so it was infrequently used and considerably less well characterised. In one purification, moderately higher DDM shifted the elution peak to be slightly later but retained a similar profile. Overall, we did not observe the same phenomenon of distinctly different elution profiles with DDM, but we have limited data.

      Ln 245. The two positions cited as important for the elevator-type mechanism are the fusion helix and the dimer interface. However, there is no evidence that the dimer interface observed in this work has any relevance to the transport mechanism. To make this statement, the interface would need to be disrupted and the effects on transport evaluated.

      Response: This has been edited as follows. “Evident in our cryo-EM maps are well-defined phospholipid densities associated with areas of HiSiaQM that may be important for the function of an elevator-type mechanism (Figure 4), but require further testing.”

      Ln 257. The authors state that the lipids form "specific and strong interactions" with the protein, but without knowing the identity of the lipids present, it is difficult to say anything about the specificity of this interaction. I think the authors could consider rewording this. Response: We have edited this by removing the term “specific” and describing the lipid interactions only as strong interactions.

      Ln 270. The authors identify a lipid-binding site and residues that likely interact with the headgroup. It would be interesting if the authors could speculate on the purpose of this lipid binding site and how it could affect transport. The residues are not conserved, which the authors suggest reflects the variety of lipid compositions in different bacteria. Are the authors suggesting that this lipid binding site is a general feature for all fused TRAP transporters and that the identity of the lipid changes depending on the species?

      Response: Yes, we speculate that the lipid binding site may be a general feature for fused TRAP transporters. We have added speculation about this binding site, specifically that “the fusion helix and concomitant lipid molecule may provide a more structurally rigid scaffold than a Q-M heterodimer, i.e., PpSiaQM, although how this impacts the elevator transition requires further testing” at Line 283.

      Though we believe that a binding pocket is likely found in a number of fused TRAPs (based on sequence and Alphafold predictions, e.g., FnSiaQM and AaSiaQM), we have now acknowledged that some fusions may not necessarily bind a lipid molecule here, by stating “While this binding pocket is likely found in a number of fused TRAPs (based on sequence predictions, e.g., FnSiaQM and AaSiaQM in Supplementary Figure 8), it is not clear whether they also bind lipids here without experimental data” at Line 290.

      Ln 306. The authors state that the HiSiaPQM has a 10-fold higher transport activity than PpSiaPQM. Unless the transport assays were performed in parallel (to mitigate small changes in experimental set-up) and the reconstitution efficiency for each proteoliposome preparation was carefully analysed, it is very difficult for this to be a meaningful comparison. Even if the amount of protein incorporated into the proteoliposomes is quantified (e.g. by evaluating protein band intensity when the proteoliposomes are analysed using SDS-PAGE), this does not account for an inactive protein that was incorporated, nor the proportion of the protein that was incorporated in the inside-out orientation, which would be functionally silent in these assays. I'm not suggesting these assays actually need to be performed, but I think the text should be modified to reflect what can actually be compared.

      Response: We agree with the reviewer that a meaningful comparison is difficult to make without a careful analysis of the reconstitution efficiency and have modified the text to reflect this. We have altered the paragraph beginning at Line 319 to the following: “The fused HiSiaPQM system appears to have a higher transport activity than the non-fused PpSiaPQM system. With the same experimental setup used for PpSiaPQM (5 M Neu5Ac, 50 M SiaP) (33), the accumulation of [3H]-Neu5Ac by the fused HiSiaPQM is ~10-fold greater. Although this difference may reflect the reconstitution efficiency of each proteoliposome preparation, it is possible that it has evolved as a result of the origins of each transporter system—P. profundum is a deep-sea bacterium and as such the transporter is required to be functional at low temperatures and high pressures… ”

      Ln 335. "S298A did not show an effect on growth when mutated to alanine previously." Suggest changing "S298A" here to "S298".

      Response: This has been changed.

      Ln 340. In addition to PpSiaQM, the large cavity was also presumably observed in the lower resolution structure of HiSiaQM?

      Response: The cavity is detectable in the lower resolution structure (7qe5), though very poorly defined by the density. Furthermore, the AlphaFold model fitted to this density has positioned sidechains inside the cavity, which we consider very likely to be an error (in comparison to our structures, VcINDY and our estimates of the volume required to house sialic acid). The cavity is generally much better defined by the structures we have referenced.

      Ln 345. Reference missing after "previously reported"? Response: This has been added. Measuring the affinity for the P-to-QM interaction is very useful, but it would have enhanced the study if some of the residues identified as important for this interaction (detailed on p.13) had been tested for their contributions to binding using this approach.

      Response: We do aim to perform this assay with these mutants in the future, but are also developing parallel assays to further test this interaction in different membrane mimetics.

      Ln 436. As stated previously, it is more accurate to say that "this is the most stable conformation" under these conditions.

      Response: We have edited this to say “The ‘elevator down’ (inward-facing) conformation is preferred in experimental conditions”. We have also changed the last sentence of this paragraph to say “However, the dimeric structures we have presented have no other proteins bound, yet exist stably in the elevator down state, suggesting this is the most stable conformation in experimental conditions, where there is no membrane bilayer, membrane potential, or chemical gradient present.”

      Ln 438. "Lipids associated with HiSiaQM are structurally and mechanistically important." This conclusion is not supported by the data presented; there is no evidence that the bound lipids influence the mechanism at all. The lipids observed are certainly interestingly placed and one could speculate about their relevance, but this statement of fact is not supported. Therefore, their importance to the mechanism needs to be tested or this conclusion needs to be substantially softened.

      Response: We have softened this statement by changing it to “Lipids have strong interactions with HiSiaQM and are likely to be important for the transport mechanism.”

      Reviewer #3 (Recommendations For The Authors):

      The fact that HiSiaQM samples consist of a mixture of compact monomer and dimer is clear, from Fig. S5 and S6. However, the analysis displayed in Fig 3 and Fig S4 would require more explanation. To my understanding, it requires the values of the sedimentation and diffusion coefficients. It could be good to provide the experimental values of D, and explain a little more about the method in the material and method section.

      Response: Yes, the analysis requires the experimental diffusion coefficients. These have been added to the Figure 3 and S4 legends and more detail has been added to the method section.

      In addition, I am puzzled when reading, in the legend of Fig 3, considerations that peak 2 could not correspond to a monomer or trimer: do these sentences correspond to other mathematical solutions, or is a given frictional ratio considered, or do they refer to Fig. S5 analysis?

      We can see where this confusion could arise from. These sentences do not correspond to a given frictional ratio or the Fig. S5 analysis (this is a separate, complementary analysis). For peak 2 not existing as a monomer is strictly a physical justification – with pure protein and an observed peak smaller than peak 2, a monomer is not possible for peak 2. For peak 2 not existing as a trimer is a mathematical solution using the s and D coefficients. The solutions identify that an unreasonably low amount of detergent would be bound to a trimer (32 molecules for L-MNG or 0 for DDM) to exist at those s and D values so we have ruled the trimer out. Reassuringly, the complementary analysis in Fig. S5/S6 agrees with the monomer-dimer outputs from the s and D analysis. We have adjusted the text in the legends of Fig. 3 and S4 to better convey these points.

    1. Author Response

      The following is the authors’ response to the original reviews.

      First of all, we'd like to thank the three reviewers for their meticulous work that enable us to present now an improved manuscript and substantial changes were made to the article following reviewers' and editors' recommendations. We read all their comments and suggestions very carefully. Apart from a few misunderstandings, all comments were very pertinent. We responded positively to almost all the comments and suggestions, and as a result, we have made extensive changes to the document and the figures. This manuscript now contains 16 principal figures and 15 figure supplements.

      The number of principal figures is now 16 (1 new figure), and additional panels have been added to certain figures. On the other hand, we have added 7 additional figures (supplement figures) to answer the reviewers' questions and/or comments.

      Main figures

      ▪ Figures 1, 4, 5, 10, 11, 12, 13, 14: unchanged ▪ Figure 7 and 8 were switched.

      ▪ Figure 2: we added panel F in response to reviewer 3's and request for sperm defect statistics

      ▪ Figure 3: the contrast in panel B has been taken over to homogenize colors

      ▪ Figure 6: This figure was recomposed. The WB on testicular extract was suppressed and we present a new WB allowing to compare the presence of CCDC146 in the flagella fraction. Using an anti-HA Ab, we demonstrate that the protein is localized in the flagella in epididymal sperm. Request of the 3 reviewers.

      ▪ Figure 7 (old 8): to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. Moreover, the WB was removed and is now presented in figure 6 (improved as requested).

      ▪ Figure 8. Was old figure 7

      ▪ Figure 9: figure 9 was recomposed and improved for increased clarity as suggested by reviewer 2 and 3.

      ▪ Figure 16 was before appendix 11

      Figure supplements and supplementary files

      ▪ Figure 1-Figure supplement 1 New. Sperm parameters of the 2 patients. requested by editor (remark #1) by the reviewer 1 (Note #3)

      ▪ Figure 2-Figure supplement 1 new. Sperm parameters of the line 2 (KO animals) requested by the reviewer 1 (Note #5)

      ▪ Figure 4-Figure supplement 1 New. Experiment to evaluate the specificity of the human CCDC146 antibody. Minimal revision request and reviewer 1 note #8

      ▪ Figure 6-Figure supplement 1 New. Figure recomposed; Asked by reviewer 2 note #4 and reviewer 3

      ▪ Figure 8-Figure supplement 1 New. We now provide new images to show the non-specific staining of the midpiece of human sperm by secondary Abs in ExM experiments; Asked by reviewer 2

      ▪ Figure 10-Figure supplement 1 New. We added new images to show the non-specific staining of the midpiece of mouse sperm by secondary Abs in IF (panel B). Rewiever 1 note #9 and reviewer 2 note #5

      ▪ Figure 12-Figure supplement 1 New. Control requested by reviewer 3 Note #23

      ▪ Figure 13-Figure supplement 1 New. We provide a graph and a statistical analysis demonstrating the increase of the length of the manchette in the Ccdc146 KO. Requested by editor and reviewer 3 Note 24

      ▪ Figure 15-Figure supplement 1 New. Control requested by reviewer 2. Minor comments

      ▪ Figure supplementary 1 New. Answer to question requested by reviewer 2 note #1

      All the reviewers' and editors’ comments have been answered (see our point to point response) and we resubmit what we believe to be a significantly improved manuscript. We strongly hope that we meet all your expectations and that our manuscript will be suitable for publication in "eLife". We look forward to your feedback,

      Point by point answer

      Please note that there has been active discussion of the manuscript and the summarize points below is the minimal revision request that the reviewers think the authors should address even under this new review model system. It was the reviewers' consensus that the manuscript is prepared with a lot of oversights - please see all the minor points to improve your manuscript.

      All minimal revision requests have been addressed

      Minimal revision request

      1) Clinical report/evaluation of the two patients should be given as it was not described even in their previous study as well as full description of CCDC146.

      We provide now a new Figure 1-figure supplement 1 describing the patients sperm parameters

      2) Antibody specificity should be provided, especially given two of the reviewers were not convinced that the mid piece signal is non-specific as the authors claim. As both KO and KI model in their hands, this should be straightforward.

      To validate the specificity of the Antibody, we transfected HEK cells with a human DDK-tagged CCDC146 plasmid and performed a double immunostaining with a DDK antibody and the CCDC146 antibody. We show that both staining are superimposable, strongly suggesting that the CCDC146 Ab specifically target CCDC146. This experiment is now presented in Figure 4-Figure supplement 1. Next, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

      3) The authors should improve statistical analysis to support their experimental results for the reader can make fair assessment. Combined with clear demonstration of ab specificity, this lack of statistical analysis with very few sample number is a major driver of dampening enthusiasm towards the current study.

      Several statistical analyses were carried out and are now included:

      1) distribution of the HA signal in mouse sperm cells (see point 2 Figure 7 panel B)

      2) quantification and statistical analyses of the defect observed in Ccdc146 KO sperm (figure 2 panel E)

      3) Quantification and statistical analyses of the length of the manchette in spermatids 13-15 steps (Figure 13-Figure supplement 1 new)

      4) The authors need to clarify (peri-centriolar vs. centriole)

      In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein in somatic cells. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein, and this is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described, and the best example is maybe gamma-tubulin (PMID: 8749391).

      or tone down (CCDC146 to be a MIP) of their claim/description.

      Concerning its localization in sperm, we agree with the reviewer that our demonstration that CCDC146 is MIP would deserve more results. Because of that, we have toned down the MIP hypothesis throughout the manuscript. See lines 491495

      Testis-specific expression of CCDC146 as it is not consistent with their data.

      We have also modified our claim concerning the testis-expression of CCDC146. Line 176

      Reviewer #1 (Recommendations For The Authors):

      Major comments

      1) As described in general comments, this study limits how the CCDC146 deficiency impairs abnormal centriole and manchette formation. The authors should explain their relationship in developing germ cells.

      In fact, there are limited information about the relationship between the manchette and the centriole. However, few articles have highlighted that both organelles share molecular components. For instance, WDR62 is required for centriole duplication in spermatogenesis and manchette removal in spermiogenesis (Commun Biol. 2021; 4: 645. doi: 10.1038/s42003-021-02171-5). Another study demonstrates that CCDC42 localizes to the manchette, the connecting piece and the tail (Front. Cell Dev. Biol. 2019 https://doi.org/10.3389/fcell.2019.00151). These articles underline that centrosomal proteins are involved in manchette formation and removal during spermiogenesis and support our results showing the impact of CCDC146 lack on centriole and manchette biogenesis. This information is now discussed. See lines 596-603

      2) The authors generated knock-in mouse model. If then, are the transgene can rescue the MMAF phenotype in CCDC146-null mice? This reviewer strongly suggest to test this part to clearly support the pathogenicity by CCDC146.

      We indeed wrote that we created a “transgenic mice”, which was misleading. We actually created a CCDC16 knock-in expressing a tagged-protein. The strain was actually made by CRISPR-Cas9 and a sequence coding for the HA-tag was inserted just before the first amino acid in exon 2, leading to the translation of an endogenous HA-tagged CCDC146 protein. We have removed the word transgenic from the text and made changes accordingly (see lines 250-253). We can therefore not use this strain to rescue the MMAF phenotype as suggested by the reviewer.

      3) Although the authors cite the previous study (Coutton et al., 2019), the study does not describe any information for CCDC146 and clinical information for the patients. The authors must show the results for clinical analysis to clarify the attended patients are MMAF patients without other phenotypic defects.

      We have now inserted a table, indicating all sperm parameters for the patients harboring a mutation in the CCDC146 gene (Figure 1-Figure supplement 1) and is now indicated lines 159-160

      4) The authors describe CCDC146 expression is dominant in testes, However, the level in testis is only moderate in human (Supp Figure 1). Thus, this description is not suitable.

      In Figure 1-figure supplement 2 (old FigS1), the median of expression in testis is around 12 in human, a value considered as high expression by the analysis software from Genevestigator. However, for mouse, it is true that the level of expression is medium. We assumed that reviewer’s comment concerned testis expression in mouse. To take into account this remark, we changed the text accordingly. See line 176.

      5) Although the authors mentioned that two mice lines are generated, only one line information is provided. Authors must include information for another line and provide basic characterization results to support the shared phenotype within the lines.

      We now provide a revised Figure 2-figure supplement 1CD, presenting the second line and the corresponding text in the main text is found lines 178-183.

      6) In somatic cells, the CCDC146 localizes at both peri-centriole and microtubule but its intracellular localization in sperm is distinguished. The authors should explain this discrepancy.

      The multi-localization of a centriolar protein is already discussed in detail in discussion lines 520-526. We have written:

      “Despite its broad cellular distribution, the association of CCDC146 with tubulin-dependent structures is remarkable. However, centrosomal and axonemal localizations in somatic and germ cells, respectively, have also been reported for CFAP58 [37, 55], thus the re-use of centrosomal proteins in the sperm flagellar axoneme is not unheard of. In addition, 80% of all proteins identified as centrosomal are found in multiple localizations (https://www.proteinatlas.org/humanproteome/subcellular/centrosome). The ability of a protein to home to several locations depending on its cellular environment has been widely described, in particular for MAP. The different localizations are linked to the presence of distinct binding sites on the protein…. “

      7) Authors mention CCDC146 is a centriolar protein in the title and results subtitle. However, the description in results part depicts CCDC146 is a peri-centriolar protein, which makes confusion. Do the authors claim CCDC146 is centrosomal protein?

      In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein in somatic cells, and is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described and the best example is maybe gamma-tubulin (PMID: 8749391).

      8) Verification of the antibody against CCDC146 must be performed and shown to support the observed signal are correct. 2nd antibody only signal is not proper negative control.

      It is a very important remark. The commercial antibody raised against human CCDC146 was validated in HEK293-cells expressing a DDK-tagged CCDC146 protein. Cells were co-marked with anti-DDK and anti-CCDC146 antibodies. We have a perfect colocalization of the staining. This experiment is now presented in Figure 4-figure supplement 1 and presented in the text (lines 206-208).

      9) In human sperm, conventional immunostaining reveals CCDC146 is detected from acrosome head and midpiece. However, in ExM, the signal at acrosome is not detected. How is this discrepancy explained? The major concern for the ExM could be physical (dimension) and biochemical (properties) distortion of the sample. Without clear positive and negative control, current conclusion is not clearly understood. Furthermore, it is unclear why the authors conclude the midpiece signal is non-specific. The authors must provide experimental evidence.

      Staining on acrosome should always be taken with caution in sperm. Indeed, numerous glycosylated proteins are present at the surface of the plasma membrane regarding the outer acrosomal membrane for sperm attachment and are responsible for numerous nonspecific staining. Moreover, this acrosomal staining was not observed in mouse sperm, strongly suggesting that it is not specific.

      Concerning the staining in the midpiece observed in both conventional and Expansion microscopy, it also seems to be nonspecific and associated with secondary Abs.

      For IF, we now provide new images showing clearly the nonspecific staining of the midpiece when secondary Ab were used alone (see Figure 10-figure supplement 1B).

      For ExM, we provide new images in Figure 8-figure supplement 1B (POC5 staining) showing a staining of the midpiece (likely mitochondria), although POC5 was never described to be present in the midpiece. Both experiments (CCDC146 and POC5 staining by ExM) shared the same secondary Ab and the midpiece signal was likely due to it.

      Moreover, we now provide new images (figure 7C) in ExM on mouse sperm showing no staining in the midpiece and demonstrating that the punctuated signal is present all along the flagellum. Finally, we would like to underline that we now provide new IF results, using an anti-HA conjugated with alexafluor 488 and confirming the ExM results.

      These points are now discussed lines 498-502 for acrosome and lines 503-511 for midpiece staining.

      10) For intracellular localization of the CCDC146 in mouse sperm, the authors should provide clear negative control using WT sperm which do not carry the transgene.

      This experiment was performed.

      To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

      11) Current imaging data do not clearly support the intracellular localization of the CCDC146. Although western blot imaging reveal that CCDC146 is detected from sperm flagella, this is crude approach. Thus, this reviewer highly recommends the authors provide more clear experimental evidence, such as immuno EM.

      We provide now a WB comparing the presence of the protein in the flagellum and in the head fractions; see new figure 6. We show that CCDC146 is only present in the flagellum fraction; The detection of the band appeared very quickly at visualization and became very strong after few minutes, demonstrating that the protein is abundant in the flagella. It is important to note that epididymal sperm do not have centrioles and therefore this signal is not a centriolar signal. We also now provide new statistical analyses showing that the immuno-staining observed in the principal piece is very specific (Figure 7B). Altogether, these results demonstrate unequivocally the intracellular localization of CCDC146 in the flagellum. This point is now discussed lines 480-489

      12) Although sarkosyl is known to dissociate tubulin, it is not well understood and accepted that the enhanced detection of CCDC146 by the detergent indicates its microtubule inner space. Sperm axoneme to carry microtubule is also wrapped peri-axonemal components with structural proteins, which are even not well solubilized by high concentration of the ionic detergent like SDS.

      We agree with the reviewer that the solubilization of the protein by sarkozyl is not a proof of the presence of the protein inside microtubule. Taking into account this point, the MIP hypothesis was toned down and we now discuss alternative hypothesis concerning these results; See discussion lines 490-497

      13) SEM image is not suitable to explain internal structure (line 317-323).

      We agree with the reviewers and changes were made accordingly. See lines 354-357

      Minor comments

      1) In main text, supplementary figures are cited "Supp Figure". And the corresponding legends are written in "Appendix - Figure". Please unify them.

      Done Labelled now “Figure X-figure supplement Y”

      2) Line 159, "exon 9/19" is not clear.

      We have written now exons 9 and indicated earlier that the gene contains 19 exons

      3) Line 188, "positive cells" are vague.

      Positive was changed by “fluorescent”

      4) Representative TUNEL assay image for knockout testes were not shown in Supp Figure 3B.

      It was a mistake now Figure 2-figure supplement 2C

      5) Please provide full description for "IF" and "AB" when described first.

      Done

      6) Line 262, It is unclear what is "main piece".

      Changed to principal piece

      7) Line 340, Although the "stage" information might be applicable, this is information for "seminiferous tubule" rather than "spermatid". This reviewer suggests to provide step information rather than stage information.

      We agree with the reviewer that there was a confusion between “stage” and “step”. We change to step spermatids

      8) Line 342, Step 1 is not correct in here.

      OK corrected. now steps 13-15 spermatids

      9) Line 803, "C." is duplicated.

      Removed

      10) Figure 3A, it will be good to mark the defective nuclei which are described in figure legends.

      These cells are now indicated by white arrow heads

      11) Figure 5, Please provide what MT stands for.

      Now explained in the legend of figure 5

      12) Figure 6. Author requires clear blot images for C. In addition, Panel B information is not correct. If the blot was performed using HA antibody, then how "WT" lane shows bands rather than "HA" bands?

      The reviewer is correct. It was a mistake; The figure was recomposed and improved.

      Reviewer #2 (Recommendations For The Authors):

      Overall, editing oversights are present throughout the manuscript, which has made the review process quite difficult. Some repetitive figures can be removed to streamline to grasp the overall story easier. Some claims are not fully supported by evidence that need to tone down. Some figures not referenced in the main text need to be mentioned at least once.

      All figures are now referenced in the text

      Major comments:

      1) 163-164 - Please clarify the claim that there is going to be an absence of the protein or nonfunctional protein, especially for the patient with a deletion that could generate a truncated protein at two third size of the full-length protein. Similarly, 35% of the protein level is present for the patient with a nonsense mutation. Some in silico structural analysis or analysis of conserved domains would be beneficial to support these claims.

      Both mutations are predicted to produce a premature stop codons: p.Arg362Ter and p.Arg704serfsTer7, leading either to the complete absence of the protein in case of non-sense mediated mRNA decay or to the production of a truncated protein missing almost two third or one fourth of the protein respectively. CCDC146 is very well conserved throughout evolution (Figure supplementary 1), including the 3’ end of the protein which contains a large coil-coil domain (Figure 1B). In view of the very high degree of conservation, it is most likely that the 3’ end of the protein, absent in both subjects, is critical for the CCDC146 function and hence that both mutations are deleterious. This explanation is now added to the discussion. see lines 439-448

      2) 173, 423 - Please clearly state a rationale of your mouse model design (i.e., why a mouse model that recapitulate human mutation is not generated) as the truncations identified in human patients are located further towards the C-terminus, and it is not clear whether truncated proteins are present, and if so, they could still be functional. Basically, the current mouse model supports the causality of the human mutations.

      This is an important question, which goes beyond the scope of this article, and raises the question of how to confirm the pathogenicity of mutations identified by high-throughput sequencing. The production of KO or KI animals is an important tool to help confirm one’ suspicions but the first element to take into consideration is the nature of the genetic data.

      Here we had two patients with homozygous truncating variants. In human, it is well established that the presence of premature stop codons usually induces non-sense mediated mRNA decay (NMD), inducing the complete absence of the protein or a strong reduction in protein production. In the unlikely absence of NMD in our two patients, the identified variants would induce the production of proteins missing 60% and 30% of their C terminal part. Often (and it is particularly true for structural proteins) the production of abnormal proteins is more deleterious than the complete absence of the protein (and it is most likely the purpose of NMD, to limit the production of abnormal “toxic” proteins). For these reasons, to try to recapitulate the most likely consequences of the human variants, without risking obtaining an even more severe effect, we decided to introduce a stop codon in the first exon in order to remove the totality of the protein in the KO mice.

      The second element is to interpret the phenotype of the KO animals. Here, the human sperm phenotype is perfectly recapitulated in the KO mice.

      Overall, we have strong genetic arguments in human and the reproduction of the phenotype in KO mice confirming the pathogenicity of the variants identified in men.

      This point is now discussed see lines 433-438

      3) Figure 6A - the labelling is misleading as it seems to suggest that the specific cells were isolated from the testes for RT-PCR.

      We have modified the labelling to avoid any confusion.

      Figure 6B -Signal of HA-tag is shown in WT, not in transgenic. Please check the order of the labels. Figure 6C - This blot is NOT a publication-quality figure. The bands are very difficult to observe, especially in lane D18. Because it is one of the important data of this study, replacing this figure is a must.

      The figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed.

      4) Supplementary fig 6 is also not a publication-level figure, and the top part seems largely unnecessary (already in the figure legend).

      The figure has been completely remade as well (now Figure 6-Figure Supplement 1).

      5) 261/267- The conclusion that mitochondrial staining in the flagellum (in both mice and humans) is non-specific is not convincing. Supplementary fig 8 shows that the signal from secondary only IF possibly extends beyond the midpiece - but it is hard to determine as no mitochondrial-specific staining is present. Either need to tone down the conclusion or provide supporting experimental evidence.

      First, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. These experiments are now described lines 271-279

      Second, we provide new images of the signal obtained with secondary Abs only that shows more clearly that the secondary Ab gave a non-specific staining (Figure 10-Figure supplement 1B). This point is discussed lines 503-511

      6) Figure 9 A - Please relate the white line to Fig. 9B label in X-axis. The information from Fig 9A+D and 9E+F are redundant. The main text nor the figure legends indicate why these specific two sperm were chosen for quantification and demonstrating the outcomes. One of them could be moved to supplementary information or removed, or the two could be combined.

      As suggested by the reviewer, we have combined the two sperm to demonstrate that CCDC146 staining is mostly located on microtubule doublets. Moreover, the figure was recomposed to make it clearer.

      Minor comments:

      All of the supplementary figures are referred to as Supp Fig X in the text, however, they are actually titled Appendix - Figure X. This needs to be consistent.

      The figures are now referred as figure supplement x in both text and figures

      Line 125 - edit spacing.

      We think this issue (long internet link) will be curated later and more efficiently by the journal, during the step of formatting necessary for publication.

      144 - With which to study  with which we studied?

      We made the change as suggested.

      151 - Supp Fig 1 - the text says that the gene is highly transcribed in human and mouse testes, but the information in the figure states that the level in mouse tissues is "medium"

      We have corrected this mistake in the text; See line 176

      165 - The two mutations are most likely deleterious. Please specifically mention what analyses done to predict the deleterious nature to support these claims.

      Both variants, c.1084C>T and c.2112del, are extremely rare in the general population with a reported allele frequency of 6.5x10-5 and 6.5x10-06 respectively in gnomAD v3. Moreover, these variants are annotated with a high impact on the protein structure (MoBiDiC prioritization algorithm (MPA) score = 10, DOI: 10.1016/j.jmoldx.2018.03.009) and predicted to induce each a premature termination codon, p.(Arg362Ter) and p.(Arg704SerfsTer7) respectively, leading to the production of a truncated protein. This information is now given line 164-169

      196-200/Figure 4 - As serum starved cells/basal body (B) are not mentioned in the main text, as is, Fig 4A would be sufficient/is relevant to the text. Please make the text reflect the contents of the whole figure, or re/move to supplement.

      We agree with the reviewer that the full description of the figure should be in the text. We added two sentences to describe figure 4B see lines 217-218.

      224 - spermatozoa (plural) fits better here, not spermatozoon

      OK changed accordingly

      236 - According to the figure legend, 6B is only showing data from the epididymal sperm, not postnatal time points; should be referencing 6C. Alignment of Marker label

      As indicated above, the figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed. The corresponding text was changed accordingly see lines 249-266

      255-256 - Referenced figure 7B3, however, 7B3 only shows tubulin staining, so no CCDC146 can be observed. Did authors mean to reference fig 7B as a whole?

      Sorry for this mistake. We agree and the text is now figure 8B6 (figure 7 and 8 were switched)

      305 - "of tubules" - I presume it is meant to be microtubules?

      Yes; The text was changed as suggested

      317-321 - a diagram of HTCA would be useful here

      We have added a reference where HTCA diagram is available see line 363. Moreover, a TEM view of HTCA is presented figure 12A

      322/Fig 11A - an arrow denoting the damage might be useful, as A1 and A3 look similar. The size of the marker bar is missing. Please update the information on figure legend.

      Concerning, the comparison between A1 and A3, the take home message is that there is a great variability in the morphological damages. This point is now underlined in the corresponding text. We updated the size of the marker bar as suggested (200 nm). See line 365-367

      323 - Please mark where capitulum is in the figure

      Capitulum was changed for nucleus

      Since Fig 11B2 is not referenced in the main text, it does not seem to add anything to the data, and could be removed/moved to supplement.

      We added a sentence to describe figure 11B2 line 370

      342-343 - manchette in step I is not seen clearly - the figure needs to be annotated better. However, DPY19L2 is absent in step I in the KO, but the main text does not reflect that - why is that?

      We do not understand the remark of the reviewer “manchette in step I is not seen clearly”. The figure shows clearly the manchette (red signal) in both WT and KO (Figure 13 D1/D2).

      For steps 13-15 WT spermatids, the size of the manchette decreases and become undetectable. In KO spermatids, the shrinkage of the manchette is hampered and in contrast continue to expand (Figure 13D2). We also provide a new Figure 13-figure supplement 1 for other illustrations of very long manchettes and a statistical analysis. In the meantime, the acrosome is strongly remodeled, as shown in figure 16-new, with detached acrosome (panel H). This morphological defect may induce a loss of the DPY19L2 staining (Figure 13 D2 stage I-III). This explanation is now inserted in the text line 396399

      Figure 15B and 15C only show KO, corresponding images from the WT should be present for comparison.

      WT images are now provided in Figure 1-figure supplement 1 new

      Figure 12 - Figure 12 - JM?.

      JM was removed. It does not mean anything

      Figure 12C and Supplementary Fig 10 - structures need to be labelled, as it is unclear what is where

      Done

      338 - text mentions step III, but only sperm from step VII are shown in Figure 13

      As suggested by reviewer 3, we changed stage by step. The text was modified to take into account this remark see lines 388-396

      360 - This is likely supposed to say Supp Figure 11E-G, not 13??

      Yes, it is a mistake. Corrected

      388 Typo "in a in a".

      Yes, it is a mistake. Corrected

      820 - Fig 3 legend - in KO spermatid nuclei were elongated - could this be labelled by arrows? I am not convinced this phenotype is that different from the WT.

      In fact, the nuclei of elongating KO spermatids are elongated and also very thin, a shape not observed in the WT; We have added arrow heads and modified the text to indicate this point line 200.

      836 - Figure 5 legend says that in yellow is centrin, but that is not true for 5A, where the figure shows labelling for y-tubulin (presumably, according to the figure itself).

      We have modified the text of the legend to take into account the remark

      837- 5A supposedly corresponds to synchronized HEK293T cells, but the reasoning behind using synchronized cells is not mentioned at all in the main text; furthermore, how this synchronization is achieved is not explained in materials and methods (serum starvation? Thymidine block?).

      Yes, figure 5A was obtained with synchronized cells. We have added one paragraph in the MM section. For cell synchronization experiments, cells underwent S-phase blockade with thymidine (5 mM, SigmaAldrich) for 17 h followed by incubation in a control culture medium for 5 h, then a second blockade at the G2-M transition with nocodazole (200 nM, Sigma-Aldrich) for 12 h. Cells were then fixed with cold methanol at different times for IF labelling. See line 224 for changes made in the result section and lines 700-704 for changes made in the MM section.

      845- figure legend says that the RT-PCR was done on CCDC146-HA tagged mice, but the main text does not reflect that.

      We made changes and the description of the KI is now presented before (line 240) the RT-PCR experiment (line 257).

      949 - it is likely supposed to say A2, not B1 (B1 does not exist in Fig 15)

      Yes, it is a mistake. Corrected

      971 - Appendix Fig 3 legend - I believe that the description for B and C are swapped.

      Yes, it is a mistake. Corrected

      Furthermore, some questions to address in A would be: Which cross sections were from which animal/points? How many per animal? Were they always in the same location?

      Yes, we have a protocol for arranging and orienting all testes in the same way during the paraffin embedding phase. The cross-sections are therefore not taken at random, and we can compare sections from the same part of the testis. The number of animals was already indicated in the figure legend (see line 1128)

      Reviewer #3 (Recommendations For The Authors):

      1) There are a number of grammatical and orthographical errors in the text. Careful proofreading should be performed.

      We have sent the manuscript to a professional proofreader

      2) The author should also check for redundancies between the introduction and the discussion.

      The discussion has modified to take into account reviewers’ remarks. Nevertheless, we did our best to avoid redundancies between introduction and discussion.

      3) Can the authors provide a rationale why they have chosen to tag their gene with an HA tag for localisation? One would rather think of fluorescent proteins or a Halo tag.

      Because the functional domains of the protein are unknown, adding a fluorescent protein of 24 KDa may interfere with both the localization and the function of CCDC146. For this reason, we choose a small tag of only 1.1 KDa, to limit as such as possible the risk of interfering with the structure of the protein. This rational is now indicated in the manuscript lines 251-254. It is worth to note, that the tagged-strain shows no sperm defect, demonstrating that the HA-tag does not interfere with CCDC146 function.

      4) In the abstract, line 53, "provide evidence" is not the right term for something that is just suggestive. The term "suggests" would be more appropriate.

      The text was modified to take into account this remark

      5) Line 74: "genetic deficiency" sounds strange here, do the authors mean simply "mutation"?

      Infertility may be due to several genetic deficiency such as chromosomal defects (XXY (Klinefelter syndrome)), microdeletion of the Y chromosome or mutations in a single gene. Therefore, mutation is too restrictive. Nevertheless, we modified the sentence which is now “…or a genetic disorder including chromosomal or single gene deficiencies”

      6) Lines 163-164: the authors describe the mutations (premature stop mutations) and say that they could either lead to complete absence of the gene product, or the expression of a truncated protein. Did they test this, for example, with some immuno blot analyses?

      As stated above, unfortunately, we were unable to verify the presence of RNA-decay in these patients for lack of biological material.

      7) Line 184 and Fig 2E: the sperm head morphologies should be quantitatively assessed.

      We provide now a full statistical analysis of the observed defects: see new panel in Figure 2 F

      8) Fig 3: The annotation should be more precise - KO certainly means CDCC146-KO. The colours of the IH panels is different, which attracts attention but is clearly a colour-adjustment artefact. Colours should be adjusted for the panels to look comparable. It would be also helpful to add arrowheads into the figure to point at the phenotypes that are highlighted in the text.

      We have added Ccdc146 KO in all figures. We have added arrow heads to point out the spermatids showing a thin and elongated nucleus. Concerning adjustment of colors, we attempted to make images of panel B comparable. See new figure 3.

      9) Fig 6A: the authors use RT PCR to determine expression dynamics of their gene of interested, and use actin (apparently) as control. However, actin and CDCC146 expression levels follow the same trend. How is the interpreted?

      The reviewer did not understand the figure. The orange bars do not correspond to actin expression and the grey bars to Ccdc146 expression but both bars represent the mRNA expression levels of Ccdc146 relative to Actb (orange) and Hprt (grey) expression in CCDC146-HA mouse pups’ testes. We tested two housekeeping genes as reference to be sure that our results were not distorted by an unstable expression of a housekeeping gene. We did not see significant difference between both house keeping genes. Actin was not used.

      10) In line 235, the authors suggest posttranslational modifications of their protein as potential cause for a slightly different migration in SDS PAGE as predicted from the theoretical molecular weight. This is not necessarily the case, some proteins do migrate just differently as predicted.

      We have changed the text accordingly and now provide alternative explanation for the slightly different migration. See lines 258-259

      11) The annotation of Fig 6 panels is problematic. First, why do the authors write "Laemmli" as description of the gel? It would be more helpful to write what is loaded on the gel, such as "sperm". Second, in panels B and C it would be helpful to add the antibodies used. It is not clear why there is a signal in the WT lane of panel B, but not in the HA lane (supposing an anti-HA antibody is used: why has WT a specific HA band?). In panel C, it is not clear why the blot that has so beautifully shown a single band in panel B suddenly gives such a bad labelling. Can the authors explain this? Also, they cut off the blot, likely because to too much background, but this is bad practice as full blots should be shown. In the current state, the panel C does not allow any clear conclusion. To make it conclusive, it must be repeated.

      Several mistakes were present in this figure. This figure was recomposed. The WB on testicular extract was suppressed and we now present a new WB allowing to compare the presence of CCDC146 in the flagella and head fractions from WT and HA-CCDC146 sperm. Using an anti-HA Ab, we demonstrate that in epididymal sperm the protein is localized in the flagella only. See new figure 6. The corresponding text was changed accordingly.

      12) The authors have raised an HA-knockin mouse for CDCC146, which they explained by the unavailability of specific antibodies. However, in Fig 7, they use a CDCC146 antibody. Can they clarify?

      The commercial Ab work for HUMAN CCDC146 but not for MOUSE CCDC146. We have added few words to make the situation clearer, we have added the following information “the commercial Ab works for human CCDC146 only”. See line 240

      13) In Fig 7A (line 258), the authors hypothesise that they stain mitochondria - why not test this directly by co-staining with mitochondria markers?

      We chose another solution to resolve this question:

      To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the whole flagellum.

      14) It seems that in both, Fig 7 and 8, the authors use expansion microscopy to localise CDCC146 in sperm tails. However, the staining differs substantially between the two figures. How is this explained?

      In figure 8 we used the commercial Ab in human sperm, whereas in figure 7 we used the anti-HA Abs in mouse sperm. Because the antibodies do not target the same part of the CCDC146 protein (the tag is placed at the N-terminus of the protein, and the HPA020082 Ab targets the last 130 amino acids of the Cter), their accessibility to the antigenic site could be different. However, it is important to note that both antibodies target the flagellum. This explanation is now inserted see lines 304-312

      15) Fig 8D and line 274: the authors do a fractionation, but only show the flagella fraction. Why?

      Showing all fractions of their experiment would have underpinned the specific enrichment of CDCC146 in the flagella fraction, which is what they aim to show. Actually, given the absence of control proteins, the fact that the band in the flagellar fraction appears to be weaker than in total sperm, one could even conclude that there is more CDCC146 in another (not analysed) fraction of this experiment. Thus, the experiment as it stands is incomplete and does not, as the authors claim, confirm the flagellar localisation of the protein.

      We agree with the reviewer’s remark. We provide now new results showing both flagella and nuclei fractions in new figure 6A. This experiment is presented lines 253-256

      16) Line 283, Fig 9D,F: The description of the microtubules in this experiment is not easy to understand. Do the authors mean to say that the labelling shows that the protein is associated with doublet microtubules, but not with the two central microtubules? They should try to find a clearer way to explain their result.

      As suggested by reviewer 2, we have changed the figure to make it clearer. The text was changed accordingly. See new figure 9 and new corresponding legend lines 1006.

      17) Fig 9G - how often could the authors observe this? Why is the axoneme frayed? Does this happen randomly, or did the authors apply a specific treatment?

      Yes, it happens randomly during the fixation process.

      18) Line 300 and Fig 10A - the authors talk about the 90-kDa band, but do say anything about what they think this band is representing.

      We have now added the following sentence lines 340-342: “This band may correspond to proteolytic fragment of CCDC146, the solubilization of microtubules by sarkosyl may have made CCDC146 more accessible to endogenous proteases.”

      19) Fig 11A, lines 321-322: the authors write that the connecting piece is severely damaged. This is not obvious for somebody who does not work in sperm. Perhaps the authors could add some arrow heads to point out the defects, and briefly describe them in the text.

      We realized from your remark that our message was not clear. In fact, there is a great variability in the morphological damages of the HTCA. For instance, the HTCA of Ccdc146 KO sperm presented in figure 10A2 is quite normal, whereas that in figure 10A4 is completely distorted. This point is now underlined in the corresponding text. See lines 367-369

      We also added the size of the marker bar (200 nm), which were missing in the figure’s legend.

      20) Line 323: it will be important to name which tubulin antibody has been used to identify centrioles, as they are heavily posttranslationally modified.

      The different types of anti-tubulin Abs are described in the corresponding figure’s legend

      21) Fig 11B - phenotypes must be quantified to make these observations meaningful.

      We agree that a quantification would improve the message. However, testicular sperm are obtained by enzymatic separation of spermatogenic cells and the number of testicular sperm are very low. Moreover, not all sperm are stained. Taking these two points into account, it seems to us that quantification could be difficult to analyze. For this reason, the quantification was not done; however, it is important to note that these defects were not observed in WT sperm, demonstrating that these defects are cased by the lack of CCDC146. We have added a sentence to underline this point; See lines 374-375

      22) Line 329: Figure 12AB - is this a typo - should it read Figure 12B?

      We have split the panel A in A1 and A2 and changed the text accordingly. See line 378

      23) Why are there not wildtype controls in Fig 12B, C?

      We provide now as Figure 12-figure supplement 1, a control image for fig 12B. For figure 12C, the emergence of the flagellum from the distal centriole in WT is already shown in Fig 12A1

      24) Fig 13: the authors write that the manchette is "clearly longer and wider than in WT cells" (lines 342-343). How can they claim this without quantitative data?

      We now provide a statistical analysis of the length of the manchette. See figure 13-figure supplement 1A. We also provide a new a new image illustrating the length of the manchette in Ccdc146 KO spermatids; See Figure 13-figure supplement 1B.

    1. Reviewer #1 (Public Review):

      Summary: This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

      The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

      Strengths: The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

      This revision is a much improved manuscript and I command the authors for many of their edits.

      I find the last part of the discussion, starting at "It is generally believed that changes in gene expression patterns are the result of the evolution of CREs", to be confusing.<br /> In this section, I believe the authors sequentially:<br /> - emphasize the role of CRE in morphological evolution (I agree)<br /> - emphasize that TF, and in particular their own CRE, are themselves important mutational targets of evolution (I agree, but the phrasing need to insist the authors are here talking about the CRE found at the TF locus, not the CRE bound by the TF).<br /> - use the stickleback Pel enhancer as an example, which I think is a good case study, but the authors also then make an argument about DNA fragility sites, which is hard to connect with the present study.<br /> - then continue on "DNA fragility" using the peppered moth and butterfly cortex locus. There is no evidence of DNA fragility at these loci, so the connection does not work. "The cortex gene locus is frequently mutated in Lepidoptera", the authors say. But a more accurate picture would be that the cortex locus is repeatedly involved in the generation of color pattern variants. Unlike for Pel fragile enhancer, we don't know if the causal mutations at this locus are repeatedly the same, and the haplotypes that have been described could be collateral rather than causal. Overall, it is important to clarify the idea that mutation bias is a possible factor explaining "genetic hotspots of evolution" (or genetic parallelism sensu 10.1038/nrg3483), but it is also possible that many genetic hotspots are repeated mutational targets because of their "optimal pleiotropy" (e.g. hub position in GRNs, such as mamo might be), or because of particularly modular CRE region that allow fine-tuning. Thus, I find the "fragility" argument misleading here. In fact the finding that "bd" and "bdf" alleles are different in nature is against the idea of a fragility bias (unless the authors can show increased mutation rates at this locus in a wild silkmoth species?). These alleles are also artificially-selected ie. they increased in frequency by breeding rather than natural selection in the wild, so while interesting for our understand of the genotype-phenotype map, they are not necessarily representative of the mutations that may underlie evolution in the wild.<br /> - Curiously, the last paragraph ("Some research suggests that common fragile sites...") elaborate on the idea that some sites of the genome are prone to mutation. The connection with mamo and the current article are extremely thin. There is here an attempt to connect meiotic and mitotic breaks to Bm-mamo, but this is confusing : it seems to propose Bm-mamo as a recruiter of epigenetic modulators that may drive higher mutation rates elsewhere. Not only I am not convinced by this argument without actual data, but this would not explain how the mutations at the Bm-mamo itself evolved.

      On a more positive note, I find it fascinating that the authors identified a TF that clearly articulates or orchestrate larval pattern development, and that when it is deleted, can generate healthy individuals. In other words, while it is a TF with many targets, it is not too pleiotropic. This idea, that the genetically causal modulators of developmental evolution are regulatory genes, has been described elsewhere (e.g. Fig 4c in 10.1038/s41576-020-0234-z, and associated refs). To me, the beautiful findings about Bm-mamo make sense in the general, existing framework that developmental processes and regulatory networks "shape" the evolutionary potential and trajectories of organisms. There is a degree of "programmability" in the genomes, because some loci are particularly prone to modulate a given type of trait. Here, Bm-mamo, as a potentially regulator of both CPs and melanin pathway genes, appear to be a potent modulator of epithelial traits. Claiming that there are inherent mutational biases behind this is unwarranted.

    1. Author Response

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      In this manuscript, the authors explore the effects of DNA methylation on the strength of regulatory activity using massively parallel reporter assays in cell lines on a genome-wide level. This is a follow-up of their first paper from 2018 that describes this method for the first time. In addition to adding more indepth information on sequences that are explored by many researchers using two main methods, reduced bisulfite sequencing and sites represented on the Illumina EPIC array, they now show also that DNA methylation can influence changes in regulatory activity following a specific stimulation, even in absence of baseline effects of DNA methylation on activity. In this manuscript, the authors explore the effects of DNA methylation on the response to Interferon alpha (INFA) and a glucocorticoid receptor agonist (dexamethasone). The authors validate their baseline findings using additional datasets, including RNAseq data, and show convergences across two cell lines. The authors then map the methylation x environmental challenge (IFNA and dex) sequences identified in vitro to explore whether their methylation status is also predictive of regulatory activity in vivo. This is very convincingly shown for INFA response sequences, where baseline methylation is predictive of the transcriptional response to flu infection in human macrophages, an infection that triggers the INF pathways.

      Thank you for your strong assessment of our work!

      The extension of the functional validity of the dex-response altering sequences is less convincing.

      We agree. We note that genes close to dex-specific mSTARR-seq enhancers tend to be more strongly upregulated after dex stimulation than those near shared enhancers, which parallels our results for IFNA (lines 341-344). However, there is unfortunately no comparable data set to the human flu data set (i.e., with population-based whole genome-bisulfite sequencing data before and after dex challenge), so we could not perform a parallel in vivo validation step. We have added this caveat to the revised manuscript (lines 555-557).

      Sequences altering the response to glucocorticoids, however, were not enriched in DNA methylation sites associated with exposure to early adversity. The authors interpret that "they are not links on the causal pathway between early life disadvantage and later life health outcomes, but rather passive biomarkers". However, this approach does not seem an optimal model to explore this relationship in vivo. This is because exposure to early adversity and its consequences is not directly correlated with glucocorticoid release and changes in DNA methylation levels following early adversity could be related to many physiological mechanisms, and overall, large datasets and meta-analyses do not show robust associations of exposure to early adversity and DNA methylation changes. Here, other datasets, such as from Cushing patients may be of more interest.

      Thank you for making these important points. We have expanded the set of caveats regarding the lack of enrichment of early adversity-reported sites in the mSTARR-data set (lines 527-533). Specifically, we note that the relationship between early adversity and glucocorticoid physiology is complex (e.g., Eisenberger and Cole, 2012; Koss and Gunnar, 2018) and that dex challenge models one aspect of glucocorticoid signaling but not others (e.g., glucocorticoid resistance). Nevertheless, we also see little evidence for enrichment of early adversity-associated sites in the mSTARR data set at baseline, independently of the dex challenge experiment (lines 483-485; Figure 4).

      We also agree that large data sets (e.g., Houtepen et al., 2018; Marzi et al., 2018) and reviews (e.g., Cecil et al., 2020) of early adversity and DNA methylation in humans show limited evidence of associations between early adversity and DNA methylation levels. However, the idea that early adversity impacts downstream outcomes remains pervasive in the literature and popular science (see Dubois et al., 2019), which we believe makes tests like ours important to pursue. We also hope that our data set (and others generated through these methods) will be useful in interpreting other settings in which differential methylation is of interest as well—in line with your comment below. We have clarified both of these points in the revised manuscript (lines 520-522; 536-539).

      Overall, the authors provide a great resource of DNA methylation-sensitive enhancers that can now be used for functional interpretation of large-scale datasets (that are widely generated in the research community), given the focus on sites included in RBSS and the Illumina EPIC array. In addition, their data lends support that differences in DNA methylation can alter responses to environmental stimuli and thus of the possibility that environmental exposures that alter DNS methylation can also alter the subsequent response to this exposure, in line with the theory of epigenetic embedding of prior stimuli/experiences. The conclusions related to the early adversity data should be reconsidered in light of the comments above.

      Thank you! And yes, we have revised our discussion of early life adversity effects as discussed above.

      Reviewer #1 (Recommendations For The Authors):

      While the paper has a lot of strengths and provides new insight into the epigenomic regulation of enhancers as well as being a great resource, there are some aspects that would benefit from clarification.

      a. It would be great to have a clearer description of how many sequences are actually passing QC in the different datasets and what the respective overlaps are in bps or 600bp windows. Now often only % are given. Maybe a table/Venn diagram for overview of the experiments and assessed sequences would help here. This concern the different experiments in the K652, A549, and Hep2G cell lines, including stimulations.

      We now provide a supplementary figure and supplementary table providing, for each dataset, the number of 600 bp windows passing each filter (Figure 2-figure supplement 1; Supplementary File 9), as well as a supplementary figure providing an upset plot to show the number of assessed sequences shared across the experiments (Figure 2-figure supplement 2).

      b. It would also be helpful to have a brief description of the main differences in assessed sequences and their coverage of the old (2018) and new libraries in the main text to be able better interpret the validation experiments.

      We now provide information on the following characteristics for the 2018 data set versus the data set presented for the first time here: mean (± SD) number of CpGs per fragment; mean (± SD) DNA sequencing depth; and mean (± SD) RNA sequencing depth (lines 169-170 provide values for the new data set; in line 194, we reference Supplementary File 5, which provides the same values for the old data set). Notably, the coverage characteristics of analyzed windows in both data sets are quite high (mean DNA-seq read coverage = 94x and mean RNA-seq read coverage = 165x in the new data set at baseline; mean DNA-seq read coverage = 22x and mean RNA-seq read coverage = 54x in Lea et al. 2018).

      c. Statements of genome-wide analyses in the abstract and discussion should be a bit tempered, as quite a number of tested sites do not pass QC and do not enter the analysis. From the results it seems like from over 4.5 million sequences, only 200,000 are entering the analysis.

      The reason why many of the windows are not taken forward into our formal modeling analysis is that they fail our filter for RNA reads because they are never (or almost never) transcribed—not because there was no opportunity for transcription (i.e., the region was indeed assessed in our DNA library, and did not show output transcription, as now shown in Figure 2-figure supplement 1). We have added a rarefaction analysis (lines 715-722 in Materials and Methods) of the DNA fragment reads to the revised manuscript which supports this point. Specifically, it shows that we are saturated for representation of unique genomic windows (i.e., we are above the stage in the curve where the proportion of active windows would increase with more sequencing: Figure 1figure supplement 4). Similarly, a parallel rarefaction curve for the mSTARR-seq RNA-seq data (Figure 1-figure supplement 4) shows that we would gain minimal additional evidence for regulatory activity with more sequencing depth. We now reference these analyses in revised lines 179-184 and point to the supporting figure in line 182.

      In other words, our analysis is truly genome-wide, based on the input sequences we tested. Most of the genome just doesn’t have regulatory activity in this assay, despite the potential for it to be detected given that the relevant sequences were successfully transfected into the cells.

      d. Could the authors comment on the validity of the analysis if only one copy is present (cut-off for QC)?

      We think this question reflects a misunderstanding of our filtering criteria due to lack of clarity on our part, which we have modified in the revision. We now specify that the mean DNA-seq sequencing depth per sample for the windows we subjected to formal modeling was quite high:

      93.91 ± 10.09 SD (range = 74.5 – 113.5x) (see revised lines 169-170). In other words, we never analyze windows in which there is scant evidence that plasmids containing the relevant sequence were successfully transfected (lines 170-172).

      Our minimal RNA-seq criteria require non-zero counts in at least 3 replicate samples within either the methylated condition or the unmethylated condition, or both (lines 166-168). Because we know that multiple plasmids containing the corresponding sequence are present for all of these windows—even those that just cross the minimal RNA-seq filtering threshold—we believe our results provide valid evidence that all analyzed windows present the opportunity to detect enhancer activity, but many do not act as enhancers (i.e., do not result in transcribed RNA). Notably, we observe a negligible correlation between DNA sequencing depth for a fragment, among analyzed windows, and mSTARR-seq enhancer activity (R2 = 0.029; now reported in lines 183-184). We also now report reproducibility between replicates, in which all replicate pairs have r > 0.89, on par with previously published STARR-seq datasets (e.g., Klein et al., 2020; Figure 1-figure supplement 6, pointed to in line 193).

      e. While the authors state that almost all of the control sequences contain CpGs sites, could the authors also give information on the total number of CpG sites in the different subsets? Was the number of CpGs in a 600 bp window related to the effects of DNA methylation on enhancer activity?

      We now provide the number of CpG sites per window in the different subsets in lines 282-284. As expected, they are higher for EPIC array sites and for RRBS sites because the EPIC array is biased towards CpG-rich promoter regions, and the enzyme typically used in the starting step of RRBS digests DNA at CpG motifs (but control sequences still contain an average of ~13 CpG sites per fragment). We also now model the magnitude of the effects of DNA methylation on regulatory activity as a function of number of CpG sites within the 600 bp windows. Consistent with our previous work in Lea et al., 2018, we find that mSTARR-seq enhancers with more CpGs tend to be repressed by DNA methylation (now reported in lines 216-219 and Figure 1figure supplement 11).

      f. In the discussion, a statement on the underrepresented regions, likely regulatory elements with lower CG content, that nonetheless can be highly relevant for gene regulation would be important to put the data in perspective.

      Thanks for this suggestion. We agree that regulatory regions, independent of CpG methylation, can be highly relevant, and now clarify in the main text that the “unmethylated” condition of mSTARR-seq is essentially akin to a conventional STARR-seq experiment, in that it assesses regulatory activity regardless of CpG content or methylation status (lines 128-130).

      Consequently, our study is well-designed to detect enhancer-like activity, even in windows with low GC content. We now show with additional analyses that we generated adequate DNA-seq coverage on the transfected plasmids to analyze 90.2% of the human genome, including target regions with no or low CpG content (lines 148-149; 153-156; Supplementary file 2). As noted above, we also now clarify that regions dropped out of our formal analysis because we had little to no evidence that any transcription was occurring at those loci, not because sequences for those regions were not successfully transfected into cells (see responses above and new Figure 1-figure supplement 4 and Figure 2-figure supplement 1).

      g. To control for differences in methylation of the two libraries, the authors sequence a single CpGs in the vector. Could the authors look at DNA methylation of the 600 bp windows at the end of the experiment, could DNA methylation of these windows be differently affected according to sequence? 48 hours could be enough for de-methylation or re-methylation.

      We agree that variation in demethylation or remethylation depending on fragment sequence is possible. We now state this caveat in the main text (lines 158-159), and specify that genomic coverage of our bisulfite sequencing data across replicates are (unfortunately) too variable to perform reliable site-by-site analysis of DNA methylation levels before and after the 48 hour experiment (lines 1182-1185). Instead, we focus on a CpG site contained in the adapter sequence (and thus included in all plasmids) to generate a global estimate of per replicate methylation levels. We also now note that any de-methylation or re-methylation would reduce our power to detect methylation-dependent activity, rather than leading to false positives (lines 163-165).

      h. The section on the method for correction for multiple testing should be more detailed as it is very difficult to follow. Why were only 100 permutations used, the empirical p-value could then only be <0.01? The description of a subsample of the N windows with positive Betas is unclear, should the permutation not include the actual values and thus all windows - or were the no negative Betas? Was FDR accounting for all elements and pairs?

      We have now expanded the text in the Materials and Methods section to clarify the FDR calculation (lines 691, 695-699, 702, 706). We clarify that the 100 permutations were used to generate a null distribution of p-values for the data set (e.g., 100 x 17,461 p-values for the baseline data set), which we used to derive a false discovery rate. Because we base our evidence on FDRs, we therefore compare the distribution of observed p-values to the distribution of pvalues obtained via permutation; we do not calculate individual p-values by comparing an observed test statistic against the test statistics for permuted data for that individual window.

      We compare the data to permutations with only positive betas because in the observed data, we observe many negative betas. These correspond to windows which have no regulatory activity (i.e., they have many more input DNA reads than RNA-seq reads) and thus have very small pvalues in a model testing for DNA-RNA abundance differences. However, we are interested in controlling the false discovery rate of windows that do have regulatory activity (positive betas). In the permuted data, by contrast and because of the randomization we impose, test statistics are centered around 0 and essentially symmetrical (approximately equally likely to be positive or negative). Retaining all p-values to construct the null therefore leads to highly miscalibrated false discovery rates because the distribution of observed values is skewed towards smaller values— because of windows with “significantly” no regulatory activity—compared to the permuted data. We address that problem by using only positive betas from the permutations.

      i. The interpretation of the overlap of Dex-response windows with CpGs sites associated with early adversity should be revisited according to the points also mentioned in the public review and the authors may want to consider exploring additional datasets with other challenges.

      Thank you, see our responses to the public review above and our revisions in lines (lines 555559). We agree that comparisons with more data sets and generation of more mSTARR-seq data in other challenge conditions would be of interest. While beyond the scope of this manuscript, we hope the resource we have developed and our methods set the stage for just such analyses.

      Reviewer #2 (Public Review):

      This work presents a remarkably extensive set of experiments, assaying the interaction between methylation and expression across most CpG positions in the genome in two cell types. To this end, the authors use mSTARR-seq, a high-throughput method, which they have previously developed, where sequences are tested for their regulatory activity in two conditions (methylated and unmethylated) using a reporter gene. The authors use these data to study two aspects of DNA methylation:

      1) Its effect on expression, and 2. Its interaction with the environment. Overall, they identify a small number of 600 bp windows that show regulatory potential, and a relatively large fraction of these show an effect of methylation on expression. In addition, the authors find regions exhibiting methylation-dependent responses to two environmental stimuli (interferon alpha and glucocorticoid dexamethasone).

      The questions the authors address represent some of the most central in functional genomics, and the method utilized is currently the best method to do so. The scope of this study is very impressive and I am certain that these data will become an important resource for the community. The authors are also able to report several important findings, including that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures.

      Thank you for this generous summary!

      The main weaknesses of the study are: 1. The large number of regions tested seems to have come at the expense of the depth of coverage per region (1 DNA read per region per replicate). I have not been convinced that the study has sufficient statistical power to detect regulatory activity, and differential regulatory activity to the extent needed. This is likely reflected in the extremely low number of regions showing significant activity.

      We apologize for our lack of clarity in the previous version of the manuscript. Nonzero coverage for half the plasmid-derived DNA-seq replicates is a minimum criterion, but for the baseline dataset, the mean depth of DNA coverage per replicate for windows passing the DNA filter is quite high: 12.723 ± 41.696 s.d. overall, and 93.907 ± 10.091 s.d. in the windows we subjected to full analysis (i.e., windows that also passed the RNA read filter). We now provide these summary statistics in lines 148-149 and 169-170 and Supplementary file 5 (see also our responses to Reviewer 1 above). We also now show, using a rarefaction analysis, that our data set saturates the ability to detect regulatory windows based on DNA and RNA sequencing depth (new Figure 1-figure supplement 4; lines 179-184; 715-722).

      2) Due to the position of the tested sequence at the 3' end of the construct, the mSTARR-seq approach cannot detect the effect of methylation on promoter activity, which is perhaps the most central role of methylation in gene regulation, and where the link between methylation and expression is the strongest. This limitation is evident in Fig. 1C and Figure 1-figure supplement 5C, where even active promoters have activity lower than 1. Considering these two points, I suspect that most effects of methylation on expression have been missed.

      Thank you for pointing this out. We agree that we have not exhaustively detected methylationdependent activity in all promoter regions, given that not all promoter regions are active in STARR-seq. However, there is good evidence that some promoter regions can function like enhancers and thus be detected in STARR-seq-type assays (Klein et al., 2020). This important point is now noted in lines 187-189; an example promoter showing methylation-dependent regulatory activity in our dataset is shown in Figure 3E.

      We also now clarify that Figure 1C shows significant enrichment of regulatory activity in windows that overlap promoter sequence (line 239). The y-axis is not a measure of activity, but rather the log-transformed odds ratio, with positive values corresponding to overrepresentation of promoter sequences in regions of mSTARR-seq regulatory activity. Active promoters are 1.640 times more likely to be detected with regulatory activity than expected by chance (p = 1.560 x 10-18), which we now report in a table that presents enrichment statistics for all ENCODE elements shown in Figure 1C for clarity (Supplementary file 4). Moreover, 74.1% of active promoters that show regulatory activity have methylation-dependent activity, also now reported in Supplementary file 4.

      Overall, the combination of an extensive resource addressing key questions in functional genomics, together with the findings regarding the relationship between methylation and environmental stimuli makes this a key study in the field of DNA methylation.

      Thank you again for the positive assessment!

      Reviewer #2 (Recommendations For The Authors):

      I suggest the authors conduct several tests to estimate and/or increase the power of the study:

      1) To estimate the potential contribution of additional sequencing depth, I suggest the authors conduct a downsampling analysis. If the results are not saturated (e.g., the number of active windows is not saturated or the number of differentially active windows is not saturated), then additional sequencing is called for.

      We appreciate the suggestion. We have now performed a downsampling/rarefaction curve analysis in which we downsampled the number of DNA reads, and separately, the number of RNA reads. We show that for both DNA-seq depth and RNA-seq depth, we are within the range of sequencing depth in which additional sequencing would add minimal new analysis windows in the dataset (Figure 1-figure supplement 4; lines 179-184; 715-722).

      2) Correlation between replicates should be reported and displayed in a figure because low correlations might also point to too few reads. The authors mention: "This difference likely stems from lower variance between replicates in the present study, which increases power", but I couldn't find the data.

      We now report the correlations between RNA and DNA replicates within the current dataset and within the Lea et al., 2018 dataset (Figure 1-figure supplement 6). The between-replicate correlations in both our RNA libraries and DNA libraries are consistently high (r ≥ 0.89).

      3) The correlation between the previous and current K562 datasets is surprisingly low. Given that these datasets were generated in the same cell type, in the same lab, and using the same protocol, I expected a higher correlation, as seen in other massively parallel reporter assays. The fact that the correlations are almost identical for a comparison of the same cell and a comparison of very different cell types is also suspicious.

      Thanks for raising this point. We think it is in reference to our original Figure 1-Figure supplement 6, for which we now provide Pearson correlations in addition to R2 values (now Figure 1-Figure supplement 8). We note that this is not a correlation in raw data, but rather the correlation in estimated effect sizes from a statistical model for methylation-dependent activity. We now provide Pearson correlations for the raw data between replicates within each dataset (Figure 1-Figure supplement 6), which for the baseline dataset are all r > 0.89 for RNA replicates and r > 0.98 for DNA replicates, showing that replicate reproducibility in this study is on par with other published studies (e.g., Klein et al., 2020 report r > 0.89 for RNA replicates and r > 0.91 for DNA replicates).

      We do not know of any comparable reports in other MPRAs for effect size correlations between two separately constructed libraries, so it’s unclear to us what the expectation should be. However, we note that all effect sizes are estimated with uncertainty, so it would be surprising to us to observe a very high correlation for effect sizes in two experiments, with two independently constructed libraries (i.e., with different DNA fragments), run several years apart—especially given the importance of winner’s curse effects and other phenomena that affect point estimates of effect sizes. Nevertheless, we find that regions we identify as regulatory elements in this study are 74-fold more likely to have been identified as regulatory elements in Lea et al., 2018 (p < 1 x10-300).

      4) The authors cite Johnson et al. 2018 to support their finding that merely 0.073% of the human genome shows activity (1.7% of 4.3%), but:

      a. the percent cited is incorrect: this study found that 27,498 out of 560 million regions (0.005%) were active, and not 0.165% as the authors report.

      We have modified the text to clarify the numerator and denominator used for the 0.165% estimate from Johnson et al 2018 (lines 175-176). The numerator is their union set of all basepairs showing regulatory activity in unstimulated cells, which is 5,547,090 basepairs. The denominator is the total length of the hg38 human genome, which is 3,298,912,062 basepairs.

      Notably, the denominator (the total human genome) is not 560 million—while Johnson et al (2018) tested 560 million unique ~400 basepair fragments, these fragments were overlapping, such that the 560 million fragments covered the human genome 59 times (i.e., 59x coverage).

      b. other studies that used massively parallel reporter assays report substantially higher percentages, suggesting that the current study is possibly underpowered. Indeed, the previous mSTARR-seq found a substantially larger percentage of regions showing regulatory activity (8%). The current study should be compared against other studies (preferably those that did not filter for putatively active sequences, or at least to the random genomic sequences used in these studies).

      We appreciate this point and have double checked comparisons to Johnson et al., 2018 and Lea et al., 2018. Our numbers are not unusual relative to Johnson et al., 2018 (0.165%), which surveyed the whole genome. Also, in comparing to the data from Lea et al., 2018, when processed in an identical manner (our criteria are more stringent here), our values of the percent of the tested genome showing significant regulatory activity are also similar: 0.108% in the Lea et al., 2018 dataset versus 0.082% in the baseline dataset. Finally, our rarefaction analyses (see our responses above) indicate that we are not underpowered based on sequencing depth for RNA or DNA samples. We also note that there are several differences in our analysis pipeline from other studies: we use more technical replicates than is typical (compare to 2-5 replicates in Arnold et al., 2013; Johnson et al., 2018; Muerdter et al., 2018), we measure DNA library composition based on DNA extracted from each replicate post-transfection (as opposed to basing it on the pre-transfection library: [Johnson et al., 2018], and we use linear mixed models to identify regulatory activity as opposed to binomial tests [Johnson et al., 2018; Arnold et al., 2013; Muerdter et al., 2018].

      I find it confusing that the four sets of CpG positions used: EPIC, RRBS, NR3C1, and random control loci, add up together to 27.3M CpG positions. Do the 600 bp windows around each of these positions sufficient to result in whole-genome coverage? If so, a clear explanation of how this is achieved should be added.

      Thanks for this comment. Although our sequencing data are enriched for reads that cover these targeted sites, the original capture to create the input library included some off target reads (as is typical of most capture experiments, which are rarely 100% efficient). We then sequenced at such high depth that we ultimately obtained sequencing coverage that encompassed nearly the whole genome. We now clarify in the main text that our protocol assesses 27.3 million CpG sites by assessing 600 bp windows encompassing 93.5% of all genomic CpG sites (line 89), which includes off-target sites (line 149).

      scatter plot showing the RNA to DNA ratios of the methylated (x-axis) vs unmethylated (y-axis) library would be informative. I expect to see a shift up from the x=y diagonal in the unmethylated values.

      We have added a supplementary figure showing this information, which shows the expected shift upwards (Figure 1-figure supplement 9).

      Another important figure missing is a histogram showing the ratios between the unmethylated and methylated libraries for all active windows, with the significantly differentially active windows marked.

      We have added a supplementary figure showing this information (Figure 1-Supplementary Figure 10).

      Perhaps I missed it, but what is the distribution of effect sizes (differential activity) following the various stimuli?

      This information is provided in table form in Supplementary Files 3, 10, and 11, which we now reference in the Figure 2 legend (lines 365-366).

      Minor changes

      It is unclear what the lines connecting the two groups in Fig.3C represent, as these are two separate groups of regions.

      We now clarify in the figure legend that values connected by a line are the same regions, not two different sets of regions. They show the correlation between DNA methylation and gene expression at mSTARR-seq-identified enhancers in individuals before and after IAV stimulation, separately for enhancers that are shared between conditions (left) versus those that are IFNAspecific (right). The two plots therefore do show two different sets of regions, which we have depicted to visualize the contrast in the effect of stimulation on the correlation on IFNA-specific enhancers versus shared enhancers. We have revised the figure legend to clarify these points (line 458-460).

      L235-242 are unclear. Specifically - isn't the same filter mentioned in L241-242 applied to all regions?

      Yes, the same filter for minimal RNA transcription was applied to all regions. We have modified the text (lines 264-265, 271, 275-277) to clarify that the enrichment analyses were performed twice, to test whether the target types were: 1) enriched in the dataset passing the RNA filter (i.e., the dataset showing plasmid-derived RNA reads in at least half the sham or methylated replicates; n = 216,091 windows) and 2) enriched in the set of windows showing significant regulatory activity (at FDR < 1%; n = 3,721 windows).

      To improve cohesiveness, the section about most CpG sites associated with early life adversity not showing regulatory activity in K562s can be moved to the supplementary in my opinion.

      Thank you for this suggestion. Because ELA and the biological embedding hypothesis (via DNA methylation) were major motivations for our analysis (see Introduction lines 42-48; 75-79), and we also discuss these results in the Discussion (lines 518-520), we have respectfully elected to retain this section in the main manuscript. We have added text in the Discussion explaining why we think experimental tests of methylation effects on regulation are relevant to the literature on early life adversity (lines 520-522), and have added discussion on limits to these analyses (lines 527-533).

      References:

      Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A (2013) Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science, 339, 1074-1077.

      Cecil CA, Zhang Y, Nolte T (2020) Childhood maltreatment and DNA methylation: A systematic review. Neuroscience & Biobehavioral Reviews, 112, 392-409.

      Dubois M, Louvel S, Le Goff A, Guaspare C, Allard P (2019) Epigenetics in the public sphere: interdisciplinary perspectives. Environmental Epigenetics, 5, dvz019.

      Eisenberger NI, Cole SW (2012) Social neuroscience and health: neurophysiological mechanisms linking social ties with physical health. Nature neuroscience, 15, 669-674.

      Houtepen L, Hardy R, Maddock J, Kuh D, Anderson E, Relton C, Suderman M, Howe L (2018) Childhood adversity and DNA methylation in two population-based cohorts. Translational Psychiatry, 8, 1-12.

      Johnson GD, Barrera A, McDowell IC, D’Ippolito AM, Majoros WH, Vockley CM, Wang X, Allen AS, Reddy TE (2018) Human genome-wide measurement of drug-responsive regulatory activity. Nature communications, 9, 1-9.

      Klein JC, Agarwal V, Inoue F, Keith A, Martin B, Kircher M, Ahituv N, Shendure J (2020) A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nature Methods, 17, 1083-1091.

      Koss KJ, Gunnar MR (2018) Annual research review: Early adversity, the hypothalamic–pituitary– adrenocortical axis, and child psychopathology. Journal of Child Psychology and Psychiatry, 59, 327-346.

      Marzi SJ, Sugden K, Arseneault L, Belsky DW, Burrage J, Corcoran DL, Danese A, Fisher HL, Hannon E, Moffitt TE (2018) Analysis of DNA methylation in young people: limited evidence for an association between victimization stress and epigenetic variation in blood. American journal of psychiatry, 175, 517-529.

      Muerdter F, Boryń ŁM, Woodfin AR, Neumayr C, Rath M, Zabidi MA, Pagani M, Haberle V, Kazmar T, Catarino RR (2018) Resolving systematic errors in widely used enhancer activity assays in human cells. Nature methods, 15, 141-149.

    1. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions.

      This is my tricky or troubling fact. I think it may just be purely human to wonder about the afterlife and we like to create ideas and follow religion but were blindly following in a sense. As a religious person I myself don't know more about the afterlife than the next person does. And the fact that science cannot prove what is and isn't involved in the afterlife is troubling to say the least.