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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors investigate the unique Mycobacteriaceae cell envelope using cryo-tomography/cryo-electron microscopy with Corynebacterium glutamicum as a model organism. Cryo-EM images of C. glutamicum cells successfully resolved previously observed densities corresponding to the MM, arabinogalactan, peptidoglycan, and inner membrane layers of the cell envelope along with the S-layer. The authors found that the S-layer is patchy in a manner dependent on growth phase (i.e. liquid versus solid growth). Intriguingly, when the S-layer was present, the leaflets of the MM appeared to be disrupted. The authors solved the structure of purified S-layer protein PS2 by cryo-EM, however they could not resolve the C-terminal membrane interaction domain. The authors found that PS2 is hexameric and different hexamers are linked by trimeric interface to create a porous structure. Phylogenetic analysis showed conservation of PS2 within corynebacteria and suggested a signature for MM-association.

Major comments:

1. The S-layer structure is porous and the authors suggest that it may function as a molecular sieve or permeability barrier. This hypothesis should either be tested experimentally, or further discussion is needed regarding what small molecules (chemical features, size) would be able to penetrate.
2. The authors show cryo-EM images of dividing C. glutamicum cells but don't make any statements as to the presence, morphology, and measurements of the different cell envelope layers. This analysis should be included.
3. The authors should include more discussion as to the patchiness or "wavy" MM near sites of PS2 contact. Cryo-EM of cells that express a variant of PS2 that lack the membrane anchoring domain would demonstrate that this is specific to PS2-membrane contacts. Minimally, providing some quantification for this phenotype would strengthen the claim (for instance, does the spacing between the perturbations match the expected scale of distance between S-layer membrane contacts).
4. The authors speculate on complete conservation of certain residues in the C-terminal domain of PS2 and hypothesize that they may be important for maturation or targeting of MM-associated proteins. Two additional examples of proteins with this motif are mentioned as evidence. Authors should search for this motif in pre-existing lists of MM proteins in the literature to test if this hypothesis is robust. Experiments to test if the conserved C-terminal residues of PS2 are required for export or assembly into an S-layer are feasible but optional given the scope of the paper.
5. The authors do not draw the distinction between MM-associated and integral MM proteins (that contain a transmembrane domain). Is the C-terminal membrane anchoring domain of PS2 likely to span the entire bilayer or just be associated by a few amino acids?

Minor comments:

1. The authors comment that the thickness of the MM both with and without the S-layer is the similar and conclude that there is no change in mycolic acid length. The resolution of the technique is not sufficient to make this statement.
2. It would be helpful if the authors could comment if their membrane dimension measurements agree with previously published results in the main text of the manuscript. It is currently only included in the legend of Table S1.

Significance

The manuscript provides compelling images and structures of the C. glutamicum cell envelope and S-layer protein PS2, respectively. These cryo-EM images of the cell envelope appear to agree nicely with pre-existing studies in the field. The introduction of the manuscript was well-written and the data in the manuscript is of broad interest to those who study the Mycobacteriaceae cell envelope. There is a lot of compelling data included in the paper, but the study would be strengthened by further analysis of the data as well as additional experiments to support some of the hypotheses suggested.

Reviewer expertise: bacterial genetics, bacterial cell envelope, protein transport

Замечание: Не очень понимаю, а зачем было объединять категории? 2 и 3 категория были объединены в одну, но зачем? Если делаете такие действия, то описывайте логику, иначе не ясно.

Author response:

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

Reviewer #1:

Reviewer #1 was very appreciative of our results and commented “This is a novel result in ferredoxin and a significant contribution to the field”. We are very honored and pleased.

Reviewer #2:

(1) Changing the nomenclature of the models investigated to include the oxidation state being discussed. As they are now (CM, CMNA, etc), multiple re-reads were required to ascertain which redox state was being discussed for a particular model in a given section of the text. Appending "Ox" or "Red" for oxidized or reduced would be sufficient. 

As you indicated there are several nomenclatures to distinguish the model systems in the text. On the other hand, the main issue discussed in the text is the ionization potential (IP), which is calculated by the difference in energies between oxidized and reduced states for each model. In other words, a discussion of the IP value on each model includes both the “Ox” and “Red” energies. In order to clarify the relationship between the nomenclature of models and redox states, we added sentences below.

“Note that the IP value is obtained for each model by calculating both the Ox and Red state energies of the model.” (lines 195-196).

On the other hand, we must specify the charge state when the geometry optimization is performed for CM and CMH models. Therefore, we revised the sentence as follows.

“The decrease in |IP| value indicates that the relative stability of the Red state is suppressed compared with the CMH but is significantly larger than the CM, suggesting the importance of the protonation of Asp64 (Fig. S2B). 

To consider the effect of the structural change caused by the redox on the IP, geometrical optimization of the 4Fe-4S core was performed for the CM (Red) and CMH (Red) models using the same level of theory to the single-point calculations. The optimized Cartesian coordinates are summarized in Table S3. As illustrated in Fig. S2A, the IP values of CM and CMH change from –3.27 to –2.38 eV (|DIP| = 0.89 eV), and from –1.06 to –0.19 eV (|DIP| = 0.87 eV), respectively, before and after the geometrical optimization.” (lines 224-232)

(2) In addition to the very thorough DFT investigation of the different spin and charge combinations, did the authors try a broken-symmetry calculation to obtain the ground state description of the FeS cluster? Given the ubiquity of this approach in other FeS cluster studies, it was surprising that this approach was not taken here. Granted, the DFT investigation of each possible combination is sufficiently thorough and need not be redone. 

Thank you for your comments. A term “spin-unrestricted method”, which is used in the manuscript in the text is synonym of “broken-symmetry method”. In order to emphasize this, we revised the manuscript as follows. 

“All calculations were performed by using the spin-unrestricted (broken-symmetry) hybrid DFT method with the B3LYP functional set. As the basis set, 6-31G* and 6-31+G* were used for [Fe, C, N, O, H] and [S] atoms, respectively, for the IP calculations.” (Line 451)

(3) Line 161 "an" to "a" 

We corrected the mistake. Thank you so much. (Line 161)

(4) Figure 4A seems a bit odd. Why do the traces eclipse the y-axis? And the traces between 330 and 370 nm are much noisier and appear thicker than the rest of the plot. Is this an issue with the monochromator grating used in wavelength selection? Reducing the thickness of the individual traces may help the data presentation in this figure. Also, the arrows on the plot have an opaque white background. Can this be removed so that the arrows do not eclipse the traces in the plot? 

The spectrum in the Fig.4A seemed to be odd. The spectral figure has been revised to improve its appearance. (We have also corrected E53A in Figure 5B.) This reviewer also pointed out that “the traces between 330 and 370 nm are much noisier”. We are struggling with the noise caused by the grating (or the motor malfunction) of the monochromator as you pointed out. Once the monochromator is repaired and a smooth spectrum is obtained, we will upload further revisions.

(5) Figure S9 is a very nice schematic illustrating the general findings of the study. Can this be moved to the main text?

Thank you for your helpful comment. Accordingly, the Fig.9S and its legend are moved to the main text. (Lines 675-680)

Reviewer #1 (Public review):

This manuscript by Bai et al concerns the expression of Scleraxis (Scx) by muscle satellite cells (SCs) and the role of that gene in regenerative myogenesis. The authors report the expression of this gene associated with tendon development in satellite cells. Genetic deletion of Scx in SCs impairs muscle regeneration, and the authors provide evidence that SCs deficient in Scx are impaired in terms of population growth and cellular differentiation. Overall, this report provides evidence of the role of this gene, unexpectedly, in SC function and adult regenerative myogenesis.

There are a few points of concern.

(1) From the data in Figure 1, it appears that all of the SCs, assessed both in vitro and in vivo, express Scx. The authors refer to a scRNA-seq dataset from their lab and one report from mdx mouse muscle that also reveal this unexpected gene expression pattern. Has this been observed in many other scRNA-seq datasets? If not, it would be important to discuss potential explanations as to why this has not been reported previously.

(2) A major point of the paper, as illustrated in Fig. 3, is that Scx-neg SCs fail to produce normal myofibers and renewed SCs following injury/regeneration. They mention in the text that there was no increased PCD by Caspase staining at 5 DPI. A failure of cell survival during the process of SC activation, proliferation, and cell fate determination (differentiation versus self-renewal) would explain most of the in vivo data. As such, this conclusion that would seem to warrant a more detailed analysis in terms of at least one or two other time points and an independent method for detecting dead/dying cells (the in vitro data in Fig. 4F is also based on assessment of activated Caspase to assess cell death). The in vitro data presented later in Fig. S4G,H do suggest an increase in cell loss during proliferative expansion of Scx-neg SCs. To what extent does cell loss (by whatever mechanism of cell death) explain both the in vivo findings of impaired regeneration and even the in vitro studies showing slower population expansion in the absence of Scx?

(3) I'm not sure I understand the description of the data or the conclusions in the section titled "Basement membrane-myofiber interaction in control and Scx cKO mice". Is there something specific to the regeneration from Scx-neg myogenic progenitors, or would these findings be expected in any experimental condition in which myogenesis was significantly delayed, with much smaller fibers in the experimental group at 5 DPI?

(4) The data presented in Fig. 4B showing differences in the purity of SC populations isolated by FACS depending on the reporter used are interesting and important for the field. The authors offer the explanation of exosomal transfer of Tdt from SCs to non-SCs. The data are consistent with this explanation, but no data are presented to support this. Are there any other explanations that the authors have considered and that could be readily tested?

(5) The Cut&Run data of Fig. 6 certainly provide evidence of direct Scx targets, especially since the authors used a novel knock-in strain for analyses. The enrichment of E-box motifs provides support for the 207 intersecting genes (scRNA-seq and Cut&Run) being direct targets. However, the rationale elaborated in the final paragraph of the Results section proposing how 4 of these genes account for the phenotypes on the Scx-neg cells and tissues is just speculation, however reasonable. These are not data, and these considerations would be more appropriate in the Discussion in the absence of any validation studies.

Comments on revisions:

The authors have adequately addressed all of the concerns I raised regarding the original submission. I have no further issues to be addressed.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This manuscript by Bai et al concerns the expression of Scleraxis (Scx) by muscle satellite cells (SCs) and the role of that gene in regenerative myogenesis. The authors report the expression of this gene associated with tendon development in satellite cells. Genetic deletion of Scx in SCs impairs muscle regeneration, and the authors provide evidence that SCs deficient in Scx are impaired in terms of population growth and cellular differentiation. Overall, this report provides evidence of the role of this gene, unexpectedly, in SC function and adult regenerative myogenesis.

We appreciate the comments and thank her/him for the support.

There are a few minor points of concern.

(1) From the data in Figure 1, it appears that all of the SCs, assessed both in vitro and in vivo, express Scx. The authors refer to a scRNA-seq dataset from their lab and one report from mdx mouse muscle that also reveals this unexpected gene expression pattern. Has this been observed in many other scRNA-seq datasets? If not, it would be important to discuss potential explanations as to why this has not been reported previously.

Thanks for this question regarding data in Fig.1. We did initially use immunofluorescence staining of Pax7 and GFP on muscle sections and primary myoblast cultures prepared from Tg-ScxGFP mice to conclude that Scx was expressed in satellite cells (SCs). In addition to the cited mdx RNA-seq data, we have included a re-analysis of a published scRNA-seq data set in Fig.2E (Dell'Orso et al., Development, 2019), and our own scRNA-seq data (Fig.S5D, F). We have now re-examined an additional scRNA-seq data set of TA muscles at various regeneration time points (De Micheli et al., Cell Rep. 2020), in which Scx expression was detected in MuSC progenitors and mature muscle cells. We have added the De Micheli et al. reference and the re-analysis of that scRNA-seq data set for Scx expression as an additional panel in Fig. 2E, with accompanying text (p. 7, ln. 4-6). Thus, our immunostaining results are consistent with scRNA-seq data from our and two other independent scRNA-seq data sets.

We think that Scx expression in the adult myogenic lineage was not previously reported mainly because its expression level was low, and might be dismissed as spurious detection. Additionally, detecting such low expression levels requires sophisticated detection methods with high capture efficiency. Previous studies have noted limitations in transcript capture or transcription factor dropout in 10x Genomics-based datasets (Lambert et al., Cell, 2018; Pokhilko et al., Genome Res., 2021). The most likely and straightforward reason is that Scx was simply not a focus in prior studies amid so many other genes of interest. We have now added this last explanation in the text (p.7, ln. 8-9), following the re-analyses of Scx expression in published scRNA-seq data sets.

(2) A major point of the paper, as illustrated in Fig. 3, is that Scx-neg SCs fail to produce normal myofibers and renewed SCs following injury/regeneration. They mention in the text that there was no increased PCD by Caspase staining at 5 DPI. A failure of cell survival during the process of SC activation, proliferation, and cell fate determination (differentiation versus self-renewal) would explain most of the in vivo data. As such, this conclusion would seem to warrant a more detailed analysis in terms of at least one or two other time points and an independent method for detecting dead/dying cells (the in vitro data in Fig. 4F is also based on an assessment of activated Caspase to assess cell death). The in vitro data presented later in Fig. S4G, H do suggest an increase in cell loss during proliferative expansion of Scx-neg SCs. To what extent does cell loss (by whatever mechanism of cell death) explain both the in vivo findings of impaired regeneration and even the in vitro studies showing slower population expansion in the absence of Scx?

We appreciate these constructive suggestions. Based on the number of available control and cKO animals, we were limited to one additional time point at 3 dpi to assess PCD by TUNEL in vivo. We were disappointed again to find no appreciable levels of PCD at 3 dpi by TUNEL (new Fig.S4I), thus no quantifications were included. We also re-did the in vitro experiment using purified SCs and monitored PCD by staining for cleaved Caspase-3 using a validated tube of antibodies (positive staining after 6 h of treatment by 1 mM staurosporine of control and ScxcKO cells; included as new Fig. S4J and legend). We were pleased to find an increase of cleaved Caspase3 stained cells, i.e. PCD, of Scx-cKO SCs at day 4 in culture, compared to that of the control. We have now replaced the old Fig. 4F with new Fig.4F and 4G to document PCD. We also provided new text/legend for these new data (p.10. ln. 2-10; new legend for Fig. 4F and 4G).

(3) I'm not sure I understand the description of the data or the conclusions in the section titled "Basement membrane-myofiber interaction in control and Scx cKO mice". Is there something specific to the regeneration from Scx-neg myogenic progenitors, or would these findings be expected in any experimental condition in which myogenesis was significantly delayed, with much smaller fibers in the experimental group at 5 DPI?

We very much appreciate this comment. We agree that there is unlikely anything specific about the regeneration from Scx-negative myogenic progenitors. Unfilled or empty ghost fibers (basement membrane remnant) are expected due to small fiber and poor regeneration in the ScxcKO mice at 5 dpi. We have removed the subtitle and changed the content to an expected consequence rather than something special (p. 8, ln. 19-22).

(4) The data presented in Fig. 4B showing differences in the purity of SC populations isolated by FACS depending on the reporter used are interesting and important for the field. The authors offer the explanation of exosomal transfer of Tdt from SCs to non-SCs. The data are consistent with this explanation, but no data are presented to support this. Are there any other explanations that the authors have considered and that could be readily tested?

Thanks for highlighting this phenomenon. We struggled with the SC purity issue for a long time. The project started with using the R26RtdT reporter for tdT’s paraformaldehyde  resistant strong fluorescence (fixation) to aid visualization in vivo. Later, when we used the tdT signal to purify SCs by FACS, we found that only 80% sorted tdT+ cells are Pax7+. We then switched to the R26RYFP reporter, from which we achieved much higher purity (95%) of SCs (Pax7+) by FACS. As such, we also repeated and confirmed many in vivo experimental results using the R26RYFP reporter (included in the manuscript). Due to the low purity of tdT+SCs by FACS, we discontinued that mouse colony after we confirmed the superior utility of the R26RYFP reporter for SC isolation.

We sincerely apologize for not being able to conduct further testable experiments on this intriguing phenomenon. However, this issue has since been addressed and published by Murach et al., iScience, (2021). Like our experience, they found non-satellite mononuclear cells with tdT fluorescence after TMX treatment when SCs were isolated via FACS. To determine this was not due to off-target recombination or a technical artifact from tissue processing, they conducted extensive analyses. They found that the tdT+ mononuclear cells included fibrogenic cells (fibroblasts and FAPs), immune cells/macrophages, and endothelial cells. Additionally, they confirmed the significant potential of extracellular vesicle (EV)-mediated cargo transfer, which facilitates the transfer of full-length tdT transcript from lineage-marked Pax7+ cells to those mononuclear cells. We have modified the text to emphasize and acknowledge their contribution to this important point, and explained the difference between YFP and tdT reporter alleles in more detail (p.9, ln. 11-17).

(5) The Cut&Run data of Fig. 6 certainly provide evidence of direct Scx targets, especially since the authors used a novel knock-in strain for analyses. The enrichment of E-box motifs provides support for the 207 intersecting genes (scRNA-seq and Cut&Run) being direct targets. However, the rationale elaborated in the final paragraph of the Results section proposing how 4 of these genes account for the phenotypes on the Scx-neg cells and tissues is just speculation, however reasonable. These are not data, and these considerations would be more appropriate in the Discussion in the absence of any validation studies.

We agree with this comment and have moved speculations into the Discussion (p. 15, ln. 4-15, and from p. 18, ln. 4 to p. 19, ln. 4).

Reviewer #2 (Public Review):

Summary:

Scx is a well-established marker for tenocytes, but the expression in myogenic-lineage cells was unexplored. In this study, the authors performed lineage-trace and scRNA-seq analyses and demonstrated that Scx is expressed in activated SCs. Further, the authors showed that Scx is essential for muscle regeneration using conditional KO mice and identified the target genes of Scx in myogenic cells, which differ from those of tendons.

Strengths:

Sometimes, lineage-trace experiments cause mis-expression and do not reflect the endogenous expression of the target gene. In this study, the authors carefully analyzed the unexpected expression of Scx in myogenic cells using some mouse lines and scRNA-seq data.

We appreciate the comments and thank her/him for noting the strengths of our manuscript.

Weaknesses:

Scx protein expression has not been verified.

We are aware of this weakness. We had previously used Western blotting (WB) using cultured SCs from control and ScxcKO mice, but did not detect endogenous Scx protein even in the control. In response to this comment, we have re-done several WB experiments using new lysates from control and ScxcKO SCs and two commercial antibodies: anti-Scx antibody 1 from Abcam (ab58655) and anti-Scx antibody 2 from Invitrogen (PA5-23943). These antibodies have been reported to detect endogenous Scx protein in tendon cells in Spang et al., BMC Musculoskelet Disord (2016) and  Bochon et al., Int J Stem Cells (2021). Despite our best efforts, we were not able to detect a reliable Scx band. We have also conducted immunofluorescence using these two antibodies. Still, we failed to detect a difference of staining signals between control and cKO SCs using these antibodies. Lastly, we conducted immunofluorescence using the ScxTy1 myoblasts and we did not find the staining signal coinciding with the Ty1 signal (by double staining). We have been very frustrated by not knowing what caused this technical difficulty in our hands. Given that these were negative data, we did not include them. However, we do hope that the combined data from scRNA-seq, ScxCreERT2 lineage-tracing, Tg-ScxGFP expression, and ScxTy1 knock-in together are deemed sufficient to make up for the deficiency of data for endogenous Scx protein in regenerative myogenic cells.

Response to Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

p. 8: The text refers to Fig. 3I, but this should be Fig. 3H.

We apologize for the confusion. Please note that by keeping all 14 dpi data in the same row, we placed Fig.3I at an unconventional/unexpected position, i.e., next to 3D &3E, and above 3F-H. We were aware that this unconventional placement could cause confusion, and it did. With that said, we have now re-arranged the subfigures (same data content) so that the updated Fig.3 contains subfigures in the expected and proper spatial order. We double-checked the figure referral in the text (p. 8, ln. 16-17) and the text is correct – just that the original Fig.3I should have been at the original Fig.3H position and that is now corrected.

Reviewer #2 (Recommendations For The Authors):

(1) Given that Scx binds to the E-box and regulates gene expression, it is of interest to know the relevance between MyoD and Scx. If possible, the reviewer recommends to include some discussions.

Thanks for the comment. MyoD1 is a well-known transcript factor regulating myogenesis, whereas Scx is primarily studied in tenocytes and other connective tissues. We agree that our new findings deserve a discussion regarding the relevance between MyoD1 and Scx.  We have added a description of their differences in the discussion and two new references (p.19, ln. 7-17).

(2) Considering that Scx is a transcriptional factor, it is interesting that Scx-GFP was not detected in the nuclei of regenerated myofibers. Could the subcellular localization of Scx-GFP provide some insights into the function of Scx as a transcription factor during muscle regeneration?

Tg-ScxGFP is a transgenic line generated by random insertion into the genome (Pryce et al., 2007; cited). The plasmid used for transgenesis was constructed by replacing most of Scx’s first exon with GFP, and including ~ 9Kb flanking regulatory sequences. As such, the ScxGFP is not a fusion gene, but rather that the GFP expression is regulated by Scx promoter and enhancer(s). This GFP reporter lacks a nuclear localization signal (NLS), hence it is mainly detected in the cytoplasm; some nuclear signal is detected, presumably due to GFP’s small size permitting passive diffusion into the nucleus. Thus, the GFP signal is used as a reporter for Scx expression, but GFP subcellular localization does not provide insight into Scx function per se. Conversely, ScxTy1/Ty1 is a knock-in allele created by fusing a triple-Ty1 tag (3XTy1) to the C-terminus of Scx, and we observed that Ty1 is located in the nucleus by the immunofluorescent staining. We used the Ty1 epitope to carry out CUT&RUN experiments to gain insight to the function of Scx as a transcription factor.

(3) Fig1D The number of arrows in the Merge image is not matched with others. In addition, the star mark in the Pax7 image is likely an error.

Apologies. We have now corrected these errors in the revised Fig.1D.

(4) FigS1A Is there only one myofiber shown in the dashed line in this image? It is unclear why only this myofiber is surrounded by the dashed line.

The dashed line encircles a single fiber because it was not visible in the provided image. However, there are 3 fibers in this image. Because we did not immuno-stain for myofibers here, we circled one fiber for illustration. For clarity, we brightened the background (of the entire original images) so the background signals from myofiber boundaries are discernable without outlines.

(5) FigS1B There was no overlapped DAPI staining in the Myogenin+ cell. DAPI-staining should be present in Myogenin+ cells because myogenin is located in the nucleus.

Fig.S1B is immuno-staining for MyoD , and we marked one MyoD+DAPI+GFP+ cell/nucleus. Fig.S1C is immune-staining for Myogenin, and we also marked one (cell/nucleus) that is triple positive.

(6) The position of the asterisk for the ScxGFP in FigS1D is misaligned. In addition, the position is not matched with Fig1C. Because all myofibers are Scx-positive, it is strange that only one myofiber has an asterisk. The reviewer suggests removing the mark.

Thank you for pointing out these errors. We have now corrected the misalignment and removed the unnecessary asterisk.

Author response:

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

eLife Assessment 

This study presents valuable experimental and numerical results on the motility of a magnetotactic bacterium living in sedimentary environments, particularly in environments of varying magnetic field strengths. The evidence supporting the claims of the authors is solid, although the statistical significance comparing experiments with the numerical work is weak. The study will be of interest to biophysicists interested in bacterial motility. 

We thank the reviewers and editors for their careful reading and the constructive comments. With respect to the statement about weak statistical significance, we think that this statement mixes two separate issues, the significance of the difference between experiments at 0 and 50µT and the comparison of experiments with simulations. We have amended our manuscript to address both points as described below. The difference between the experiments at 0 and 50µT is indeed significant, and the discrepancy between experiments and simulations can be explained by unavoidable differences in the way we quantify bacterial throughput.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors present experimental and numerical results on the motility Magnetospirillum gryphiswaldense MSR-1, a magnetotactic bacterium living in sedimentary environments. The authors manufactured microfluidic chips containing three-dimensional obstacles of irregular shape, that match the statistical features of the grains observed in the sediment via microcomputer tomography. The bacteria are furthermore subject to an external magnetic field, whose intensity can be varied. The key quantity measured in the experiments is the throughput ratio, defined as the ratio between the number of bacteria that reach the end of the microfluidic channel and the number of bacteria entering it. The main result is that the throughput ratio is non-monotonic and exhibits a maximum at magnetic field strength comparable with Earth's magnetic field. The authors rationalize the throughput suppression at large magnetic fields by quantifying the number of bacteria trapped in corners between grains. 

Strengths: 

While magnetotactic bacteria's general motility in bulk has been characterized, we know much less about their dynamics in a realistic setting, such as a disordered porous material. The micro-computer tomography of sediments and their artificial reconstruction in a microfluidic channel is a powerful method that establishes the rigorous methodology of this work. This technique can give access to further characterization of microbial motility. The coupling of experiments and computer simulations lends considerable strength to the claims of the authors, because the model parameters (with one exception) are directly measured in the experiments. 

Weaknesses: 

The main weakness of the manuscript pertains to the discussion of the statistical significance of the experimental throughput ratio. Especially when comparing results at zero and 50 micro Tesla. The simulations seem to predict a stronger effect than seen in the experiments. The authors do not address this discrepancy. 

We thank the reviewer for their positive assessment and the detailed constructive remarks. 

The increase in bacterial throughput between 0 and 50 µT is indeed more pronounced in the simulations than in the experiments, partly due to the fact that there is considerably more variability in the experimental data. We did two things to address this issue: (1) We performed additional statistical test addressing the difference between the experimental results at 0 and 50 µT. Indeed, the difference is only weakly significant (in contrast to the difference of either to 500µT). The increase is however consistent with the observation in the absence of obstacles in the channel, where we see a monotonous increase from 0 to 500 µT (Supp. Figure S5). We have added the test results in the caption of Fig. 3. (2) To address the difference between simulations and experiments, we added a section in Methods on how we determine the throughput and a short discussion in the Results section. The key points are that the initial condition is different in simulations and experiments and that the throughput is therefore quantified differently. This difference is due to experimental limitations: we cannot track bacteria through the whole channel and we wanted to avoid pushing them into the channel with fluid flow to avoid effects of flow on the results. As a consequence, bacteria continue to enter the IN region of the channel from the inlet during the experiment, while in the simulation, they all start at the beginning of the channel simultaneously. We expect this to mostly affect the case with diffusive transport (B=0).

Reviewer #2 (Public Review): 

Summary: 

simulation study of magnetotactic bacteria in microfluidic channels containing sediment-mimicking obstacles. The obstacles were produced based on micro-computer tomography reconstructions of bacteria-rich sediment samples. The swimming of bacteria through these channels is found experimentally to display the highest throughput for physiological magnetic fields. Computer simulations of active Brownian particles, parameterized based on experimental trajectories are used to quantify the swimming throughput in detail. Similar behavior as in experiments is obtained, but also considerable variability between different channel geometries. Swimming at strong field is impeded by the trapping of bacteria in corners, while at weak fields the direction of motion is almost random. The trapping effect is confirmed in the experiments, as well as the escape of bacteria with reducing field strength. 

Strengths: 

This is a very careful and detailed study, which draws its main strength from the fruitful combination of the construction of novel microfluidic devices, their use in motility experiments, and simulations of active Brownian particles adapted to the experiment. Based on their results, the authors hypothesize that magnetotactic bacteria may have evolved to produce magnetic properties that are adapted to the geomagnetic field in order to balance movement and orientation in such crowded environments. They provide strong arguments in favor of such a hypothesis. 

Weaknesses: 

Some of the issues touched upon here have been studied also in other articles. It would be good to extend the list of references accordingly and discuss the relation briefly in the text. 

We thank the reviewer for the constructive comments. We answer to the point concerning previous literature in the response to the recommendations below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Here follows a list of points the authors should address. 

(1) Are additional experiments feasible to decrease the statistical noise present in Fig. 3c? At the very least, the authors should discuss the statistical significance of the results at 50 muT vis-a-vis 0 T. 

See our response to Strengths/Weaknesses above

(2) The experimental setup is not immediately clear. I think that adding a panel from Fig. S1 (or a sketch thereof) would help clarify, especially in relation to the entry zone and end zone. 

We are not sure what you mean. Fig. 3A already contains exactly such a panel. We have however added another supplementary figure that shows an additional detailed view of the setup (Fig. S3). In addition, we revised several figures: We have replaced Fig. S1 with a better version and exchanged the schematic view of the obstacle channel in Fig 1, removing the additional inlets that were not used in this study (also in Fig 3A), Instead we added a comment in Methods explaining their presence. Hopefully this makes the setup clear.

(3) It should be also stated that there is no external flow imposed on the channel. 

We have added such a statement in the description of the experiment (in section 2.2 Swimming of magnetotactic bacteria through sediment-mimicking obstacle channels.  

(4) Fig. 3c and Fig. 6c are seemingly showing the same quantity (or closely related ones). The authors should use the same symbol and give an explicit mathematical definition. 

The two quantities are not exactly the same, as we cannot directly quantify the flux of bacteria through the channel in our experiments. On the one hand, we cannot track bacteria through the whole channel, on the other hand, the initial conditions are not exactly the same as in the simulations. In the simulations all bacteria start at the same time at the entrance to the channel. In the experiments, they enter from the inlet and do so at different times (pushing them in with fluid flow would be possible, but carries the risk of perturbing the results due to induced flow through the channel). We have added a new section in the Methods section that explains this difference and describes the procedure used to obtain the throughput from the experiments in detail. We have also added a corresponding comment in the Result section, where the simulations are compared with the experiments. 

Minor issues: 

- Figures have different styles that should be unified. For example, the panel labels sometimes have round brackets and sometimes they don't.

See above

- Page 6, (muCT) should have the Greek letter mu 

Thanks, corrected.

- Fig. 3a is not very clear; see my point 2 above. 

See above

Reviewer #2 (Recommendations For The Authors): 

I have only a few comments and questions, which the authors should address: 

(1) The observed exponential dependence of decay time on the "well" depth could be related to the exponential density distribution of active particles in a gravitational field, which has been derived previously. Might be interesting to discuss such a possible connection. 

Thank you for the suggestion, the two cases are indeed somewhat analogous with behaviors reminiscent of thermal processes with an effective temperature. Such a description is however not generally possible (even for sedimentation, only some features are described). We plan to address in future work whether it can be made more quantitative in our case of escape from the corner traps. We have included a short discussion of the analogy in the section on trapping and escape. 

(2) The authors should consider the following relevant references, and discuss them briefly in their manuscript:

- Sedimentation, trapping, and rectification of dilute bacteria J Tailleur, ME Cates EPL 86, 60002 (2009) 

- Human spermatozoa migration in microchannels reveals boundary-following navigation P Denissenko, V Kantsler, DJ Smith, J Kirkman-Brown Proc. Natl. Acad. Sci. USA 109, 8007-8010 (2012) 

- Wall accumulation of self-propelled spheres J Elgeti, G Gompper Europhysics Letters 101, 48003 (2013) 

- Wall entrapment of peritrichous bacteria: a mesoscale hydrodynamics simulation study SM Mousavi, G Gompper, RG Winkler Son Maber 16 (20), 4866-4875 (2020) 

- A Geometric Criterion for the Optimal Spreading of Active Polymers in Porous Media C Kurzthaler, S Mandal, T Bhabacharjee, H Löwen, SS Daba, HA Stone Nat. Commun. 12, 7088 (2021) 

- Run-to-Tumble Variability Controls the Surface Residence Times of E. coli Bacteria G Junot, T Darnige, A Lindner, VA Martinez, J Arlt, A Dawson, WCK Poon, H Auradou, E Clement Phys. Rev. Leb. 128, 248101 (2022) 

- Dynamics and phase separation of active Brownian particles on curved surfaces and in porous media P Iyer, RG Winkler, DA Fedosov, G Gompper Phys. Rev. Research 5, 033054 (2023) 

We agree that there is a lot of literature on these aspects, specifically interaction of self-propelled objects with walls and motion of swimmers through porous media. We have slightly extended our overview of previous literature in the introduction and included most of these references.

Reviewer #1 (Public review):

Summary:

In this excellent manuscript by Egan et al., the authors very carefully dissect the roles of inflammasome components in restricting Salmonella Typhimurium (STm) replication in human macrophages. They show that caspase-1 is essential to mediating inflammasome responses and that caspase-4 contributes to bacterial restriction at later time points. The authors show very clear roles for the host proteins that mediate terminal lysis, gasdermin D and ninjurin-1. The unique finding in this study is that in the absence of inflammasome responses, Salmonella hypereplicates within the cytosol of macrophages. These findings suggest that caspase-1 and possibly caspase-4 play roles in restricting the replication of Salmonella in the cytosol as well as in the Salmonella containing vacuole.

Strengths:

(1) The genetic and biochemical approaches have shown for the first time in human macrophages that the caspase-1-GSDMD-NINJ1 axis is very important for restricting intracellular STm replication. In addition, they demonstrate a later role for Casp4 in control of intracellular bacterial replication.

(2) In addition, they show that in macrophages deficient in the caspase-1-GSDMD-NINJ1 axis that STm are found replicating in the cytosol, which is a novel finding. The electron microscopy is convincing that STm are in the cytosol.

(3) The authors go on to use a chloroquine resistance assay to show that inflammasome signaling also restricts STm within SCVs in human macrophages.

(4) Finally, they show that the Type 3 Secretion System encoded on Salmonella Pathogenicity Island 1 contributes to STm's cytosolic access in human macrophages.

Weaknesses:

(1) Their results with human macrophages suggest that there are differences between murine and human macrophages in inflammasome-mediated restriction of STm growth. For example, Thurston et al. showed that in murine macrophages that inflammasome activation controls the replication of mutant STm that aberrantly invades the cytosol, but only slightly limits replication of WT STm. In contrast, here the authors found that primed human macrophages rely on caspase-1, gasdermin D and ninjurin-1 to restrict WT STm. I wonder if the priming of the human macrophages in this study could account for the differences in these studies. Along those lines, do the authors see the same results presented in this study in the absence of priming the macrophages with Pam3CSK4. I think that determining whether the control of intracellular STm replication is dependent on priming is very important. Another difference with the Thurston et al. paper is the way that the STm inoculum was prepared - stationary phase bacteria that were opsonized. Could this also account for differences between the two studies rather than differences between murine and human macrophages in inflammasome-dependent control of STm?

(2) The authors show that the pore-forming proteins GSDMD and Ninj1 contribute to control of STm replication in human macrophages. Is it possible that leakage of gentamicin from the media contributes to this control?

(3) One major question that remains to be answered is whether casp-1 plays a direct role in the intracellular localization of STm. If the authors quantify the percentage of vacuolar vs. cytosolic bacteria at early time points in WT and casp-1 KO macrophages, would that be the same in the presence and absence of casp-1? If so, then this would suggest that there is a basal level of bacterial-dependent lysis of the SCV and in WT macrophages the presence of cytosolic PAMPS trigger cell death and bacteria can't replicate in the cytosol. However, in the inflammasome KO macrophages, the host cell remains alive and bacteria can replicate in the cytosol.

Comments on revisions:

The authors have addressed my previous concerns. The addition of the statements indicating the limitations of the study are an important addition.

Reviewer #2 (Public review):

Summary:

This work addresses the question of how human macrophages restrict intracellular replication of Salmonella.

Strengths:

Through a series of genetic knockouts and using specific inhibitors, Egan et al. demonstrated that the inflammasome components caspase-1, caspase-4, gasdermin D (GSDMD), and the final lytic death effector ninjurin-1 (NINJ1) are required for control of Salmonella replication in human macrophages. Interestingly, caspase-1 proved crucial in restricting Salmonella early during infection, whereas caspase-4 was essential in the later stages of infection. Furthermore, using a chloroquine resistance assay and state-of-the-art microscopy, the authors found that NAIP receptor and caspase-1 mostly regulate replication of cytosolic bacteria, with smaller, yet significant, impact on the vacuolar bacteria.

The finding that inflammasomes are critical in the restriction of replication of intracellular Salmonella in human macrophages contrasts with the published minimal role of inflammasomes in restriction of replication of intracellular Salmonella in murine macrophages. Some of these differences could be due to differences in the methodologies used in the two studies. However, the findings suggest yet another example of interspecies and intercellular differences in regulation of bacterial infections by the immune system.

Comments on revisions:

The authors may wish to comment that the measurements of released cytokines by ELISA do not discriminate between active and full-length forms of the cytokines.

Author response:

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

Reviewer #1: 

(1) Their results with human macrophages suggest that there are differences between murine and human macrophages in inflammasome-mediated restriction of STm growth. For example, Thurston et al. showed that in murine macrophages that inflammasome activation controls the replication of mutant STm that aberrantly invades the cytosol, but only slightly limits replication of WT STm. In contrast, here the authors found that primed human macrophages rely on caspase-1, gasdermin D and ninjurin-1 to restrict WT STm. I wonder if the priming of the human macrophages in this study could account for the differences in these studies. Along those lines, do the authors see the same results presented in this study in the absence of priming the macrophages with Pam3CSK4. I think that determining whether the control of intracellular STm replication is dependent on priming is very important.

We thank the Reviewer for their careful attention to our manuscript and for their thoughtful comments. We have addressed this question about the impact of priming by repeating the bacterial intracellular burden assays in unprimed WT and CASP1-/- THP-1 cells. We have added additional figures to the manuscript to address this: Figure 1 – Figure Supplement 3. Under unprimed conditions, CASP1-/- cells still harbored significantly higher bacterial burdens at 6 hpi and a significant fold-increase in bacterial CFUs compared to WT cells. These results suggest that the caspase-1-mediated restriction of intracellular Salmonella replication in human macrophages is independent of priming. 

(2) Another difference with the Thurston et al. paper is the way that the STm inoculum was prepared - stationary phase bacteria that were opsonized. Could this also account for differences between the two studies rather than differences between murine and human macrophages in inflammasome-dependent control of STm?

We thank the Reviewer for this excellent suggestion. To address this possibility, we repeated the bacterial intracellular burden assays in WT and CASP1-/- THP-1 cells using stationary phase bacteria. We infected WT and CASP1-/- THP-1 cells with stationary phase Salmonella, and we subsequently assayed for intracellular bacterial burdens. These data have now been added to the manuscript in Figure 1 – Figure Supplement 4. Interestingly, we did not observe any fold-change in the bacterial colony forming units in both the WT and CASP1-/- THP-1 cells for the stationary phase Salmonella. These data indicate that by 6 hours postinfection, Salmonella do not replicate efficiently in human macrophages unless grown under SPI-1-inducing conditions. Furthermore, these results suggest that differences in how the Salmonella inoculum is prepared may contribute to the discrepancies between our study and previous studies, as noted by the Reviewer. 

(3) The authors show that the pore-forming proteins GSDMD and Ninj1 contribute to control of STm replication in human macrophages. Is it possible that leakage of gentamicin from the media contributes to this control?

Response: We thank the Reviewer for their insightful comment. We have addressed this question on the impact of gentamicin by repeating the bacterial intracellular burden assays using a lower concentration of gentamicin in combination with extensively washing the cells with RPMI media to remove the gentamicin. WT and CASP1-/- THP-1 cells were infected with WT Salmonella. Then, at 30 minutes post-infection, cells were treated with 25 μg/ml of gentamicin to kill any extracellular bacteria. At 1 hour post-infection (hpi), the cells were washed for a total of five times with fresh RPMI to remove the gentamicin, and then the media was replaced with fresh media containing no gentamicin. In parallel, we also treated cells with 100 μg/ml of gentamicin at 30 minutes post-infection, washed the cells five times with fresh RPMI at 1 hpi to remove the gentamicin, and then replaced the media with fresh media containing 10 μg/ml of gentamicin. This data has now been included in the manuscript as Figure 1 – Figure Supplement 5. We observed similar levels in the intracellular bacterial burdens at 1 hpi and 6 hpi and a fold-increase in bacterial colony forming units in CASP1-/- cells compared to WT cells across both gentamicin conditions, suggesting that gentamicin appears to not contribute to the intracellular control of Salmonella replication in human macrophages. Of note, we also tried repeating the bacterial intracellular burden assays without gentamicin, using only washes to remove extracellular at 1 hpi; however, under these experimental conditions, we observed high levels of extracellular Salmonella. Therefore, we relied on using a lower concentration of gentamicin to kill extracellular Salmonella in conjunction with extensive washing to remove the gentamicin for the remainder of the infection. 

(4) One major question that remains to be answered is whether casp-1 plays a direct role in the intracellular localization of STm. If the authors quantify the percentage of vacuolar vs. cytosolic bacteria at early time points in WT and casp-1 KO macrophages, would that be the same in the presence and absence of casp-1? If so, then this would suggest that there is a basal level of bacterial-dependent lysis of the SCV and in WT macrophages the presence of cytosolic PAMPS trigger cell death and bacteria can't replicate in the cytosol. However, in the inflammasome KO macrophages, the host cell remains alive and bacteria can replicate in the cytosol.

We thank this Reviewer for raising this important point. We have addressed this experimentally by quantifying the percentage of vacuolar vs. cytosolic Salmonella at 2 hpi in WT, NAIP-/-, and CASP1-/- THP-1 cells using a chloroquine (CHQ) resistance assay. This data has now been included in the manuscript in the new Figure 5A. The original subfigures of Figure 5 have consequently been rearranged. We did not observe any significant differences in vacuolar and cytosolic bacterial burdens at this early time point in WT, NAIP-/-, and CASP1-/- THP-1 cells. As noted by the Reviewer, these results suggest that the basal level of bacterialdependent lysis of the SCV in human macrophages is not dependent on caspase-1 or NAIP. 

Reviewer #3: 

(1) The main weaknesses of the study are the inherent limitations of tissue culture models. For example, to study interaction of Salmonella with host cells in vitro, it is necessary to kill extracellular bacteria using gentamicin. However, since Salmonella-induced macrophage cell death damages the cytosolic membrane, gentamicin can reach intracellular bacteria and contribute to changes in CFU observed in tissue culture models (major point 1). This can result in tissue culture "artefacts" (i.e., observations/conclusions that cannot be recapitulated in vivo). For example, intracellular replication of Salmonella in murine macrophages requires T3SS-2 in vitro, but T3SS-2 is dispensable for replication in macrophages of the spleen in vivo (Grant et al., 2012).  

We thank the Reviewer for their helpful comments and insightful suggestions. We have addressed some of the concerns about gentamicin in our response to Reviewer #1 above. To address the Reviewer’s concerns further, we have included language to acknowledge the limitations of our study based on the artefacts of tissue culture models in our Discussion section: “In this study, we utilized tissue culture models to examine intracellular Salmonella replication in human macrophages. These in vitro systems allow for precise control of experimental conditions and, therefore, serve as powerful tools to interrogate the molecular mechanisms underlying inflammasome responses and Salmonella replication in both immortalized and primary human cells. Still, there are limitations of tissue culture models, as they lack the inherent complexity of tissues and organs in vivo. To assess whether our findings reflect Salmonella dynamics in the mammalian host, it will be important to complement our studies and extend the implications of our work using approaches that model more complex systems, such as organoids or organ explant models co-cultured with immune cells, and in vivo techniques, such as humanized mouse models.”

(2) In Figure 1: are increased CFU in WT vs CASP1-deficient THP-1 cells due to Caspase 1 restricting intracellular replication or due to Caspase-1 causing pore formation to allow gentamicin to enter the cytosol thereby restricting bacterial replication? The same question arises about Caspase-4 in Figure 2, where differences in CFU are observed only at 24h when differences in cell death also become apparent. The idea that gentamicin entering the cytosol through pores is responsible for controlling intracellular Salmonella replication is also consistent with the finding that GSDMD-mediated pore formation is required for restricting intracellular Salmonella replication (Figure 3). Similarly, the finding that inflammasome responses primarily control Salmonella replication in the cytosol could be explained by an intact SCV membrane protecting Salmonella from gentamicin (Figure 5). 

We thank the Reviewer for highlighting this important point regarding gentamicin.

We have addressed this question in our response above to Review #1 and in Figure 1 – Figure Supplement 5. We observed caspase-1-mediated restriction of Salmonella in human macrophages even when cells were treated with a lower concentration of gentamicin (25 μg/ml) for 30 minutes and then extensively washed with RPMI media to remove any gentamicin for the remainder of the infection. These data suggest that gentamicin is likely not responsible for controlling intracellular Salmonella in human macrophages.

Sommaire du webinaire FCPE avec timestamps

https://vimeo.com/1012776402

Ce sommaire couvre les points principaux abordés lors du webinaire FCPE sur l'équipement d'un enfant avec son premier smartphone.

Introduction et contexte (0:00:00 - 0:03:24)

• Le webinaire, animé par Axelle Desaint, directrice générale d'Internet sans crainte, est organisé par la FCPE Nationale et s'adresse à tous les parents. [1, 2]
• L'objectif est d'aider les parents à accompagner leurs enfants dans l'utilisation de leur premier smartphone et de discuter des tensions que cela peut engendrer. [2, 3]
• Internet sans crainte propose plus de 200 outils gratuits pour aider les parents à accompagner leurs enfants dans leur usage du numérique. [4]
• L'association organise également le Safer Internet Day en France. [5]

Premier équipement et points de vigilance (0:07:43 - 0:08:11)

• Le premier téléphone est souvent un appareil récupéré de la famille. Il est crucial de supprimer toutes les données (photos, contenus) et applications inadaptées à l'enfant avant de le lui confier. [6, 7]

Le contrôle parental : un allié pour la protection (0:12:08 - 0:13:37)

• Le contrôle parental n'est pas de l'espionnage mais un outil pour encadrer et protéger l'enfant. [8]
• Il permet de définir le temps d'écran, de limiter l'accès à certains contenus et de gérer les achats potentiels. [9-11]

Le temps d'écran : un sujet de préoccupation majeur (0:15:04 - 0:17:59)

• Le temps d'écran est la principale source de tension entre parents et enfants. [12, 13]
• Il est important de discuter avec l'enfant des moments d'écran plutôt que du temps d'écran. [14, 15]
• Les applications et plateformes sont conçues pour capter l'attention et inciter à rester connecté le plus longtemps possible. [16, 17]
• Il est crucial d'identifier ces mécanismes avec l'enfant (scroll infini, vidéos enchaînées, notifications) pour mieux les gérer. [17-21]

Poser un cadre et favoriser l'auto-régulation (0:19:43 - 0:24:23)

• Plutôt que de parler de temps d'écran, il est plus constructif de discuter des moments d'écran avec l'enfant. [14]
• Les enfants ont besoin d'un cadre pour s'auto-réguler. [22]
• Il est important d'éviter les écrans avant le coucher pour garantir un sommeil de qualité. [23]
• Le site "fa nu" propose un outil pour créer une charte numérique en famille. [24]
• Les paramètres des smartphones proposent des fonctionnalités pour visualiser et gérer le temps d'écran (temps passé, temps par application, notifications, mode silencieux). [25-27]

Impacts du temps d'écran sur la santé (0:25:11 - 0:30:05)

• L'utilisation excessive des écrans peut avoir des conséquences négatives sur le sommeil, la santé physique et mentale. [28-30]
• La surexposition à des contenus négatifs ("Doomscrolling" ou "obésité de scrolling") peut engendrer anxiété et stress. [31-33]
• Il est important de veiller à ce que les écrans ne remplacent pas d'autres activités essentielles (interactions sociales, activités physiques, lecture, etc.). [34]

Âge d'accès aux réseaux sociaux et aux messageries (0:31:48 - 0:38:41)

• Il existe un âge légal d'accès aux réseaux sociaux et aux messageries, généralement 13 ans. [35, 36]
• WhatsApp est le premier réseau social des enfants, offrant des fonctionnalités proches des réseaux sociaux classiques. [37-39]

Conseils pour une utilisation responsable (0:40:41 - 0:53:00)

• Privilégier les comptes privés, activer les messages de rappel pour faire des pauses, désactiver les notifications. [40]
• Gérer ses contacts et avoir conscience avec qui on parle. [41, 42]
• Définir quel est le bon cadre pour communiquer et éviter de partager sa vie privée sur les groupes familiaux. [43, 44]
• Ne pas partager de photos intimes et oser parler d'intimité et de consentement avec son enfant. [45, 46]
• Réfléchir avant de partager tout type de contenu. [47]

Cyber-harcèlement et contenus choquants (0:53:58 - 1:12:45)

• Les enfants sont exposés à des contenus choquants, violents et à caractère sexuel, notamment sur Telegram. [48-54]
• Il est crucial de les sensibiliser aux risques du cyber-harcèlement et de leur apprendre à réagir face à ces situations. [55-59]
• En cas de cyber-harcèlement, il est important de conserver des preuves (copies d'écran), bloquer l'agresseur, signaler le compte et demander le retrait des publications. [60-63]
• Le site internet-signalement.gouv.fr permet de signaler les contenus illégaux. [64]

Ressources et outils d'Internet sans crainte (1:12:48 - 1:31:50)

• Le guide "Équiper son enfant d'un smartphone" [65, 66]
• Guides pratiques sur les principaux réseaux sociaux [67, 68]
• Dossier thématique sur le cyber-harcèlement, les réseaux sociaux, la parentalité numérique, etc. sur le site internet sans crainte [69-71]
• Compte Instagram d'Internet sans crainte pour des conseils et informations [72, 73]

Questions des participants et réponses d'Axelle Desaint (1:17:35 - 1:31:50)

• Le danger de l'espionnage des enfants et l'importance de la confiance. [74]
• La nécessité de dialoguer et d'explorer les réseaux sociaux avec son enfant. [75-78]
• L'utilisation des applications de contrôle parental. [78-80]
• La géolocalisation et ses impacts sur les jeunes. [81-83]
• La méconnaissance de Telegram par les parents. [84]

Ce sommaire n'inclut pas les remarques des participants qui n'apportent pas d'éléments clés à la compréhension du sujet principal.

Sommaire du webinaire FCPE avec timestamps

Ce sommaire couvre les points principaux abordés lors du webinaire FCPE sur l'équipement d'un enfant avec son premier smartphone.

Introduction et contexte (0:00:00 - 0:03:24)

• Le webinaire, animé par Axelle Desaint, directrice générale d'Internet sans crainte, est organisé par la FCPE Nationale et s'adresse à tous les parents. [1, 2]
• L'objectif est d'aider les parents à accompagner leurs enfants dans l'utilisation de leur premier smartphone et de discuter des tensions que cela peut engendrer. [2, 3]
• Internet sans crainte propose plus de 200 outils gratuits pour aider les parents à accompagner leurs enfants dans leur usage du numérique. [4]
• L'association organise également le Safer Internet Day en France. [5]

Premier équipement et points de vigilance (0:07:43 - 0:08:11)

• Le premier téléphone est souvent un appareil récupéré de la famille. Il est crucial de supprimer toutes les données (photos, contenus) et applications inadaptées à l'enfant avant de le lui confier. [6, 7]

Le contrôle parental : un allié pour la protection (0:12:08 - 0:13:37)

• Le contrôle parental n'est pas de l'espionnage mais un outil pour encadrer et protéger l'enfant. [8]
• Il permet de définir le temps d'écran, de limiter l'accès à certains contenus et de gérer les achats potentiels. [9-11]

Le temps d'écran : un sujet de préoccupation majeur (0:15:04 - 0:17:59)

• Le temps d'écran est la principale source de tension entre parents et enfants. [12, 13]
• Il est important de discuter avec l'enfant des moments d'écran plutôt que du temps d'écran. [14, 15]
• Les applications et plateformes sont conçues pour capter l'attention et inciter à rester connecté le plus longtemps possible. [16, 17]
• Il est crucial d'identifier ces mécanismes avec l'enfant (scroll infini, vidéos enchaînées, notifications) pour mieux les gérer. [17-21]

Poser un cadre et favoriser l'auto-régulation (0:19:43 - 0:24:23)

• Plutôt que de parler de temps d'écran, il est plus constructif de discuter des moments d'écran avec l'enfant. [14]
• Les enfants ont besoin d'un cadre pour s'auto-réguler. [22]
• Il est important d'éviter les écrans avant le coucher pour garantir un sommeil de qualité. [23]
• Le site "fa nu" propose un outil pour créer une charte numérique en famille. [24]
• Les paramètres des smartphones proposent des fonctionnalités pour visualiser et gérer le temps d'écran (temps passé, temps par application, notifications, mode silencieux). [25-27]

Impacts du temps d'écran sur la santé (0:25:11 - 0:30:05)

• L'utilisation excessive des écrans peut avoir des conséquences négatives sur le sommeil, la santé physique et mentale. [28-30]
• La surexposition à des contenus négatifs ("Doomscrolling" ou "obésité de scrolling") peut engendrer anxiété et stress. [31-33]
• Il est important de veiller à ce que les écrans ne remplacent pas d'autres activités essentielles (interactions sociales, activités physiques, lecture, etc.). [34]

Âge d'accès aux réseaux sociaux et aux messageries (0:31:48 - 0:38:41)

• Il existe un âge légal d'accès aux réseaux sociaux et aux messageries, généralement 13 ans. [35, 36]
• WhatsApp est le premier réseau social des enfants, offrant des fonctionnalités proches des réseaux sociaux classiques. [37-39]

Conseils pour une utilisation responsable (0:40:41 - 0:53:00)

• Privilégier les comptes privés, activer les messages de rappel pour faire des pauses, désactiver les notifications. [40]
• Gérer ses contacts et avoir conscience avec qui on parle. [41, 42]
• Définir quel est le bon cadre pour communiquer et éviter de partager sa vie privée sur les groupes familiaux. [43, 44]
• Ne pas partager de photos intimes et oser parler d'intimité et de consentement avec son enfant. [45, 46]
• Réfléchir avant de partager tout type de contenu. [47]

Cyber-harcèlement et contenus choquants (0:53:58 - 1:12:45)

• Les enfants sont exposés à des contenus choquants, violents et à caractère sexuel, notamment sur Telegram. [48-54]
• Il est crucial de les sensibiliser aux risques du cyber-harcèlement et de leur apprendre à réagir face à ces situations. [55-59]
• En cas de cyber-harcèlement, il est important de conserver des preuves (copies d'écran), bloquer l'agresseur, signaler le compte et demander le retrait des publications. [60-63]
• Le site internet-signalement.gouv.fr permet de signaler les contenus illégaux. [64]

Ressources et outils d'Internet sans crainte (1:12:48 - 1:31:50)

• Le guide "Équiper son enfant d'un smartphone" [65, 66]
• Guides pratiques sur les principaux réseaux sociaux [67, 68]
• Dossier thématique sur le cyber-harcèlement, les réseaux sociaux, la parentalité numérique, etc. sur le site internet sans crainte [69-71]
• Compte Instagram d'Internet sans crainte pour des conseils et informations [72, 73]

Questions des participants et réponses d'Axelle Desaint (1:17:35 - 1:31:50)

• Le danger de l'espionnage des enfants et l'importance de la confiance. [74]
• La nécessité de dialoguer et d'explorer les réseaux sociaux avec son enfant. [75-78]
• L'utilisation des applications de contrôle parental. [78-80]
• La géolocalisation et ses impacts sur les jeunes. [81-83]
• La méconnaissance de Telegram par les parents. [84]

Ce sommaire n'inclut pas les remarques des participants qui n'apportent pas d'éléments clés à la compréhension du sujet principal.

Reviewer #1 (Public review):

Summary:

This study introduces a novel therapeutic strategy for patients with high-risk HER2-positive breast cancer and demonstrates that the incorporation of pyrotinib into adjuvant trastuzumab therapy can improve invasive disease-free survival.

Strengths:

The study features robust logic and high-quality data. Data from 141 patients across 23 centers were analyzed, thereby effectively mitigating regional biases and endowing the research findings with high applicability.

Weaknesses:

(1) Introduction and Discussion: Update the literature regarding the efficacy of pyrotinib combined with trastuzumab in treating HER2-positive advanced breast cancer.

(2) Did all the data have a normal distribution? Expand the description of statistical analysis.

(3) The novelty and innovative potential of your manuscript compared to the published literature should be described in more detail in the abstract and discussion section.

(4) Figure legend should provide a bit more detail about what readers should focus on.

(5) P-values should be clarified for the analysis.

(6) The order (A, B, and C) in Figure 3 should be labeled in the upper left corner of the Figure.

Reviewer #2 (Public review):

In this manuscript, Cao et al. evaluated the efficacy and safety of 12 months pyrotinib after trastuzumab-based adjuvant therapy in patients with high-risk, HER2-positive early or locally advanced breast cancer. Notably, the 2-year iDFS rate reached 94.59% (95% CI: 88.97-97.38) in all patients, and 94.90% (95% CI: 86.97-98.06) in patients who completed 1-year treatment of pyrotinib. This is an interesting and uplifting results, given that in ExteNET study, the 2-year iDFS rate was 93.9% (95% CI 92·4-95·2) in the 1-year neratinib group, and the 5-year iDFS survival was 90.2%, and 1-year treatment of neratinib in ExteNET study did not translate into OS benefit after 8-year follow-up. In this case, readers will be eagerly anticipating the long-term follow-up results of the current PERSIST study, as well as the results of the phase III clinical trial (NCT03980054).

I have the following comments:

(1) The introduction of the differences between pyrotinib and neratinib in terms of mechanism, efficacy, resistance, etc. is supposed to be included in the text so that authors could better highlight the clinical significance of the current trial.

(2) Please make sure that a total of 141 patients were enrolled in the study, 38 patients had a treatment duration of less than or equal to 6 months, and a total of 92 and 31 patients completed 1-year and 6-month treatment of extended adjuvant pyrotinib, respectively, which means 7 patients had a treatment duration of fewer than 6 months.

(3) The previous surgery history should be provided, and how many patients received lumpectomy, and mastectomy.

Reviewer #1 (Public review):

Summary:

This study adapts a previously published model of the cat spinal locomotor network to make predictions of how phase durations of swing and stance at different treadmill speeds in tied-belt and split-belt conditions would be altered following a lateral hemisection. The simulations make several predictions that are replicated in experimental settings.

Strengths:

(1) Despite only altering the connections in the model, the model is able to replicate very well several experimental findings. This provides strong validation for the model and highlights its utility as a tool to investigate the operations of mammalian spinal locomotor networks.

(2) The study provides insights about interactions between the left and right sides of the spinal locomotor networks, and how these interactions depend on the mode of operation, as determined by speed and state of the nervous system.

(3) The writing is logical, clear, and easy to follow.

Weaknesses:

(1) Could the authors provide a statement in the methods or results to clarify whether there were any changes in synaptic weight or other model parameters of the intact model to ensure locomotor activity in the hemisected model?

(2) The authors should remind the reader what the main differences are between state-machine, flexor-driven, and classical half-center regimes (lines 77-79).

(3) There may be changes in the wiring of spinal locomotor networks after the hemisection. Yet, without applying any sort of plasticity, the model is able to replicate many of the experimental data. Based on what was experimentally replicated or not, what does the model tell us about possible sites of plasticity after hemisection?

(4) Why are the durations on the right hemisected (fast) side similar to results in the full spinal transected model (Rybak et al. 2024)? Is it because the left is in slow mode and so there is not much drive from the left side to the right side even though the latter is still receiving supraspinal drive, as opposed to in the full transection model? (lines 202-203).

(5) There is an error with probability (line 280).

Reviewer #1 (Public review):

Summary:

This manuscript uses the eye lens as a model to investigate basic mechanisms in the Fgf signaling pathway. Understanding Fgf signaling is of broad importance to biologists as it is involved in the regulation of various developmental processes in different tissues/organs and is often misregulated in disease states. The Fgf pathway has been studied in embryonic lens development, namely with regards to its involvement in controlling events such as tissue invagination, vesicle formation, epithelium proliferation, and cellular differentiation, thus making the lens a good system to uncover the mechanistic basis of how the modulation of this pathway drives specific outcomes. Previous work has suggested that proteins, other than the ones currently known (e.g., the adaptor protein Frs2), are likely involved in Fgfr signaling. The present study focuses on the role of Shp2 and Shc1 proteins in the recruitment of Grb2 in the events downstream of Fgfr activation.

Strengths:

The findings reveal that the juxtamembrane region of the Fgf receptor is necessary for proper control of downstream events such as facilitating key changes in transcription and cytoskeleton during tissue morphogenesis. The authors conditionally deleted all four Fgfrs in the mouse lens that resulted in molecular and morphological lens defects, most importantly, preventing the upregulation of the lens induction markers Sox2 and Foxe3 and the apical localization of F-actin, thus demonstrating the importance of Fgfrs in early lens development, i.e. during lens induction. They also examined the impact of deleting Fgfr1 and 2, on the following stage, i.e. lens vesicle development, which could be rescued by expressing constitutively active KrasG12D. By using specific mutations (e.g. Fgfr1ΔFrs lacking the Frs2 binding domain and Fgfr2LR harboring mutations that prevent binding of Frs2), it is demonstrated that the Frs2 binding site on Fgfr is necessary for specific events such as morphogenesis of lens vesicle. Further, by studying Shp2 mutations and deletions, the authors present a case for Shp2 protein to function in a context-specific manner in the role of an adaptor protein and a phosphatase enzyme. Finally, the key surprising finding from this study is that downstream of Fgfr signaling, Shc1 is an important alternative pathway - in addition to Shp2 - involved in the recruitment of Grb2 and in the subsequent activation of Ras. The methodologies, namely, mouse genetics and state-of-the-art cell/molecular/biochemical assays are appropriately used to collect the data, which are soundly interpreted to reach these important conclusions. Overall, these findings reveal the flexibility of the Fgf signaling pathway and its downstream mediators in regulating cellular events. This work is expected to be of broad interest to molecular and developmental biologists.

Weaknesses:

A weakness that needs to be discussed is that Le-Cre depends on Pax6 activation, and hence its use in specific gene deletion will not allow evaluation of the requirement of Fgfrs in the expression of Pax6 itself. But since this is the earliest Cre available for deletion in the lens, mentioning this in the discussion would make the readers aware of this issue. Referring to Jag1 among "lens-specific markers" (page 5) is debatable, suggesting changing to the lines of "the expected upregulation of Jag1 in lens vesicle". The Abstract could be modified to clearly convey the existing knowledge gap and the key findings of the present study. As it stands now, it is a bit all over the place. Some typos in the manuscript need to be fixed, e.g. "...yet its molecular mechanism remains largely resolved" - unresolved? "...in the development lens" - in the developing lens? In Figure 4 legend, "(B) Grb2 mutants Grb2 mutants displayed...", etc.

Reviewer #2 (Public review):

Summary:

I have reviewed a manuscript submitted by Wang et al., which is entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development". In this paper, the authors first examined lens phenotypes in mice with Le-Cre-mediated knockdown (KD) of all four FGFR (FGFR1-4), and found that pERK signals, Jag1, and foxe3 expression are absent or drastically reduced, indicating that FGF signaling is essential for lens induction. Next, the authors examined lens phenotypes of FGFR1/2-KD mice and found that lens fiber differentiation is compromised and that proliferative activity and cell survival are also compromised in lens epithelium. Interestingly, Kras activation rescues defects in lens growth and lens fiber differentiation in FGFR1/2-KD mice, indicating that Ras activation is a key step for lens development. Next, the authors examined the role of Frs2, Shp2, and Grb2 in FGF signaling for lens development. They confirmed that lens fiber differentiation is compromised in FGFR1/3-KD mice combined with Frs2-dysfunctional FGFR2 mutants, which is similar to lens phenotypes of Grb2-KD mice. However, lens defects are milder in mice with Shp2YF/YF and Shp2CS mutant alleles, indicating that the involvement of Shp2 is limited for the Grb2 recruitment for lens fiber differentiation. Lastly, the authors showed new evidence on the possibility that another adapter protein, Shc1, promotes Grb2 recruitment independent of Frs2/Shp2-mediated Grb2 recruitment.

Strengths:

Overall, the manuscript provides valuable data on how FGFR activation leads to Ras activation through the adapter platform of Frs2/Shp2/Grb2, which advances our understanding of complex modification of the FGF signaling pathway. The authors applied a genetic approach using mice, whose methods and results are valid to support the conclusion. The discussion also well summarizes the significance of their findings.

Weaknesses:

The authors eventually found that the new adaptor protein Shc1 is involved in Grb2 recruitments in response to FGF receptor activation. however, the main data for Shc1 are histological sections and statistical evaluation of lens size. So, my major concern is that the authors need to provide more detailed data to support the involvement of Shc1 in Grb2 recruitment of FGF signaling for lens development.

Reviewer #3 (Public review):

Summary:

The manuscript entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development" by Wang et al., investigates the molecular mechanism used by FGFR signaling to support lens development. The lens has long been known to depend on FGFR signaling for proper development. Previous investigations have demonstrated that FGFR signaling is required for embryonic lens cell survival and for lens fiber cell differentiation. The requirement of FGFR signaling for lens induction has remained more controversial as deletion of both Fgfr1 and Fgfr2 during lens placode formation does not prevent the induction of definitive lens markers such as FOXE3 or αA-crystallin. Here the authors have used the Le-Cre driver to delete all four FGFR genes from the developing lens placode demonstrating a definitive failure of lens induction in the absence of FGFR signaling. The authors focused on FGFR1 and FGFR2, the two primary FGFRs present during early lens development, and demonstrated that lens development could be significantly rescued in lenses lacking both FGFR1 and FGFR2 by expressing a constitutively active allele of KRAS. They also showed that the removal of pro-apoptotic genes Bax and Bak could also lead to a substantial rescue of lens development in lenses lacking both FGFR1 and FGFR2. In both cases, the lens rescue included both increased lens size and the expression of genes characteristic of lens cells.

Significantly the authors concentrated on the juxtamembrane domain, a portion of the FGFRs associated with FRS2. Previous investigations have demonstrated the importance of FRS2 activation for mediating a sustained level of ERK activation. FRS2 is known to associate both with GRB2 and SHP2 to activate RAS. The authors utilized a mutant allele of Fgfr1, lacking the entire juxtamembrane domain (Fgfr1ΔFrs), and an allele of Fgfr2 containing two-point mutations essential for Frs2 binding (Fgfr2LR). When combining three floxed alleles and leaving only one functional allele (Fgfr1ΔFrs or Fgfr2LR) the authors got strikingly different phenotypes. When only the Fgfr1ΔFrs allele was retained, the lens phenotype matched that of deleting both Fgfr1 and Fgfr2. However, when only the Fgfr2LR allele was retained the phenotype was significantly milder, primarily affecting lens fiber cell differentiation, suggesting that something other than FRS2 might be interacting with the juxtamembrane domain to support FGFR signaling in the lens. The authors also deleted Grb2 in the lens and showed that the phenotype was similar to that of the lenses only retaining the Fgfr2LR allele, resulting in a failure of lens fiber cell differentiation and decreased lens cell survival. However, mutating the major tyrosine phosphorylation site of GRB2 did not affect lens development. The author additionally investigated the role of SHP2 lens development by making by either deleting SHP2 or by making mutations in the SHP2 catalytic domain. The deletion of the SHP2 phosphatase activity did not affect lens development as severely as the total loss of SHP2 protein, suggesting a function for SHP2 outside of its catalytic activity. Although the loss of Shc1 alone has only a slight effect on lens size and pERK activation in the lens, the authors showed that the loss of Shc1 exacerbated the lens phenotype in lenses lacking both Frs2 and Shp2. The authors suggest that SHC1 binds to the FGFR juxtamembrane domain allowing for the recruitment of GRB2 independently of FRS2.

Strengths:

(1) The authors used a variety of genetic tools to carefully dissect the essential signals downstream of FGFR signaling during lens development.

(2) The authors made a convincing case that something other than FRS2 binding mediates FGFR signaling in the juxtamembrane domain.

(3) The authors demonstrated that despite the requirement of both the adaptor function and phosphatase activity of SHP2 are required for embryonic survival, neither of these activities is absolutely required for lens development.

(4) The authors provide more information as to why FGFR loss has a phenotype much more severe than the loss of FRS2 alone during lens development.

(5) The authors followed up their work analyzing various signaling molecules in the context of lens development with biochemical analyses of FGF-induced phosphorylation in murine embryonic fibroblasts (MEFs).

(6) In general, this manuscript represents a Herculean effort to dissect FGFR signaling in vivo with biochemical backing with cell culture experiments in vitro.

Weaknesses:

(1) The authors demonstrate that the loss of FGFR1 and FGFR2 can be compensated by a constitutive active KRAS allele in the lens and suggest that FGFRs largely support lens development only by driving ERK activation. However, the authors also saw that lens development was substantially rescued by preventing apoptosis through the deletion of BAK and BAX. To my knowledge, the deletion of BAK and BAX should not independently activate ERK. The authors do not show whether ERK activation is restored in the BAK/BAX deficient lenses. Do the authors suggest the FGFR3 and/or FGFR4 provide sufficient RAS and ERK activation for lens development when apoptosis is suppressed? Alternatively, is it the survival function of FGFR-signaling as much as a direct effect on lens differentiation?

(2) The authors make the argument that deleting all four FGFRs prevented lens induction but that the deletion of only FGFR1 and FGFR2 did not. Part of this argument is the retention of FOXE3 expression, αA-crystallin expression, and PROX1 expression in the FGFR1/2 double mutants. However, in Figure 1E, and Figure 1F, the staining of the double mutant lens tissue with FOXE3, αA-crystallin, and PROX1 is unconvincing. However, the retention of FOXE3 expression in the FGFR1/FGFR2 double mutants was previously demonstrated in Garcia et al 2011. Also, there needs to be an enlargement or inset to demonstrate the retention of pSMAD in the quadruple FGFR mutants in Figure 1D.

(3) Do the authors suggest that GRB2 is required for RAS activation and ultimately ERK activation? If so, do the authors suggest that ERK activation is not required for FGFR-signaling to mediate lens induction? This would follow considering that the GRB2 deficient lenses lack a problem with lens induction.

(4) The increase in p-Shc is only slightly higher in the Cre FGFR1f/f FGFR2r/LR than in the FGFR1f/Δfrs FGFR2f/f. Can the authors provide quantification?

(5) The authors have not shown directly that Shc1 binds to the juxtamembrane region of either Fgfr1 or Fgfr2.

(6) The authors have used the Le-Cre strain for all of their lens deletion experiments. Previous work has documented that the Le-Cre transgene can cause lens defects independent of any floxed alleles in both homozygous and hemizygous states on some genetic backgrounds (Dora et al., 2014 PLoS One 9:e109193 and Lam et al., Human Genomics 2019 13(1):10. Are the controls used in these experiments Le-Cre hemizygotes?

Reviewer #3 (Public review):

This important study provides insights into the functional diversification of RIP family kinase proteins in vertebrate animals. The provided results, which combine bioinformatic and experimental analyses, will be of interest to specialists in both immunology and evolutionary biology. However, the computational part of the methodology is insufficiently covered in the paper and the experimental results would benefit from including data for additional species.

(1) In the Methods section concerning gene loss analysis, the authors refer to the 'Phylogenetic analysis' section for details of RIPK sequence acquisition and alignment procedure. This section is missing from the manuscript as provided. In its absence, it is hard for the reviewer to provide relevant comments on gene presence/absence analysis.

(2) In the same section, the authors state that gene sequences were filtered and grouped based on the initial gene tree pattern (lines 448-449). How exactly did the authors filter the non-RIP kinases and other irrelevant homologs from the gene trees? Did they consider the reciprocal best (BLAST) hit approach or similar approaches for orthology inference? Did they also encounter potential pseudogenes of genes marked as missing in Figure 1C? Will the gene trees mentioned be available as supplementary files?

(3) The authors state the presence of additional RIPK2 paralog in non-therian vertebrates. The ramifications of this paralog loss in therians are not discussed in the text, although RIPK2 is also involved in NF-kB activation. In addition, the RIPK2B gene loss pattern is shunned from Figure 1C to Supplementary Figure 4, despite posing comparable interest to the reader.

(4) The authors present evidence for (repeated) positive selection in both RIPK1 and RIPK3 in bats; however, neither bat RIPK1/3 orthologs nor bat-specific RHIM tetrad variants (IQFG, IQLG) are considered in the experimental part of the work.

(5) The authors present gene presence/absence patterns for zebra mussels as an outgroup of vertebrate species analyzed. From the evolutionary perspective, adding results for a closer invertebrate group, such as lancelets, tunicates, or echinoderms, would be beneficial for reconstructing the evolutionary progression of RIPK-mediated immune functions in animals.

(6) In the broader sense, the list of non-mammalian species included in the study is not explained or substantiated in the text. What was the rationale behind selecting lizards, turtles, and lampreys for experimental assays? Why was turtle RIPK3 but not turtle RIPK1CT protein used for functional tests? Which results do the authors expect to observe if amphibian or teleost RIPK1/3 are included in the analysis, especially those with divergent tetrad variants?

(7) For lamprey RIPK3, the observed NF-kB activity levels still remain lower than those of mammalian and reptilian orthologs even after catalytic tetrad modification. In the same way, switching human RIPK3 catalytic tetrad to that of lamprey does not result in NF-kB activation. What are the potential reasons for the observed difference? Does it mean that lamprey's RIPK3 functions in NF-kB activation are, at least partially, delegated to RIPK1?

(8) In lines 386-388, the authors state that 'only non-mammalian RIPK1CT proteins required the RHIM for maximal NF-kB activation', which is corroborated by results in Figure 4B. The authors further associate this finding with a lack of ZBP1 in the respective species (lines 388-389). However, non-squamate reptiles seem to retain ZBP1, as suggested by Supplementary Table 1. Given that, do the authors expect to observe RHIM-independent (maximal) NF-kB activation in turtles and crocodilians or respective RIPK1CT-transfected cells?

What even is the largest number that has real world use what would be the point of bigger numbers if we cant use the big numbers we have now for real world calculations?

Probably interesting for background:

Eklund, A., Frank, J., Nilsson, L., Zetterberg, A., & Mansson, J. 2024. Times of trouble - Seasonal variation in number and severity of attacks on sheep caused by large carnivores and eagles in Sweden. European Journal of Wildlife Research, 70(9): 2-11. DOI: https://doi.org/10.1007/s10344-023-01761-4

Kvalshaug, O.J. 2013. Inter-specific patterns of depredation on domestic sheep and semi-domestic reindeer in Norway, by a large predator guild. Master Thesis, Norwegian University of Life Sciences, 36.

Linnell, J.D.C., Nilsen, E.B., Lande, U., Herfindal, I., Odden, J., & Skogen, K. 2005. Zoning as a means of mitigating conflicts with large carnivores: Principles and reality. Conservation Biology Series-Cambridge, 9: 163-175. DOI: https://doi.org/10.1017/cbo9780511614774.011

Mabille, G., Stien, A., Tveraa, T., Mysterud, A., Brøseth, H., & Linnell, J.D.C. 2015. Sheep farming and large carnivores: What are the factors influencing claimed losses? Ecosphere, 6(5): 1-17. DOI: https://doi.org/10.1890/es14-00444.1

Strand, G., Hansen, I., De Boon, A., & Sandström, C. 2019. Carnivore Management Zones and their Impact on Sheep Farming in Norway. Environmental Management, 64: 537-552. DOI: https://doi.org/10.1007/s00267-019-01212-4

Strand, G. 2020. The combined effects of centralization and carnivore management on sheep farmers and sheep farming in Norway. Human Dimensions of Wildlife, 26(4): 321-336. DOI: https://doi.org/10.1080/10871209.2020.1818895

More detail is needed here, include how you determined the limit of detection of your assay. State that you used standard curves to estimate limit of detection (LOD), but see Klymus et al. (2020). Given that your assays are for qualitative purposes, the limit of quantification (LOQ) is likely not relevant in your case. Please verify this to clarify in the main text why the qPCR efficiency may be irrelevant for your assays, but the LOD is.

Depending on who you will get as an examiner, it may be worthwhile to also mention that you did the testing according to the MIQE guidelines, which I think were incorporated into this paper (see thier Appendix S1 for the checklist):

• Thalinger, B., Deiner, K., Harper, L. R., Rees, H. C., Blackman, R. C., Sint, D., ... & Bruce, K. (2021). A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environmental DNA, 3(4), 823-836.

• Bustin, S. A. (2024). Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE. Molecular aspects of medicine, 96, 101249.

• Klymus, K. E., Merkes, C. M., Allison, M. J., Goldberg, C. S., Helbing, C. C., Hunter, M. E., Jackson, C. A., Lance, R. F., Mangan, A. M., Monroe, E. M., Piaggio, A. J., Stokdyk, J. P., Wilson, C. C., & Richter, C. A. (2020). Reporting the limits of detection and quantification for environmental DNA assays. Environmental DNA, 2, 271–282. https://doi.org/10.1002/edn3.29

Số lượng chỉ dẫn được tạo ra ở mỗi bước: Việc tính toán một mini-batch của đạo hàm làm giảm phương sai của việc xuống đồi đạo hàm ngẫu nhiên. Tương tự như vậy, việc tạo ra nhiều chỉ dẫn dúng làm tăng khả năng tối ưu của LLM. Mặt khác

Bảng 4 tổng hợp các chỉ dẫn tốt nhất trên bài toán GSM8K với các scorer và optimizer LLM khác nhau. Nhận xét: - Các phong cách tạo chỉ dẫn của các LLM có sự khác nhau lớn: PaLM 2-L IT và text-bison thường ngắn gọn, còn GPT thì dài và nhiều chi tiết. - Mặc dù một số chỉ dẫn tốt nhất chứ cụm từ "step-by-step", tất cả các cụm khác đều đạt kết quả tương tự hoặc cao hơn với các cụm có ý nghĩa khác.

Với mỗi số lượng n điểm, 5 tập điểm khác nhau sẽ được tạo ngẫu nhiên. Ngoài việc đánh giá dựa trên khoảng cách tối ưu, bài báo còn đánh giá dựa trên số lượng bước tối ưu cần thực hiện để đạt được tối ưu toàn cục. Nhận xét: - gpt-4 tốt hơn nhiều so với 2 mô hình còn lại ở tất cả các kích thước bài toán. Cụ thể, với các bài toán có kích thước nhỏ, gpt-4 đạt tối ưu toàn cục nhanh gấp 4 lần so với các mô hình khác.

Cài đặt của bài toán: - Lấy mẫu ngẫu nhiên n điểm với 2 giá trị tọa độ x và y đều nằm trong khoảng [-100, 100]. - Gurobi solver được sử dụng để tạo giải pháp ground-truth. Điểm đánh giá được sử dụng cho các giải pháp được sinh ra là khoảng cách tối ưu (optimality gap). Trong đó, khoảng cách tối ưu được định nghĩa là hiệu giữa giải pháp được tạo sinh và giải pháp ground-truth sau đó chia cho giá trị của giải pháp ground-truth.

Các mô hình được sử dụng trong thực nghiệm: - Optimizer LLM: pretrained PaLM 2-L, instruction-tuned PaLM 2-L, text-bison. gpt-3.5 turbo và gpt-4 - Scorer LLM: pretrained PaLM 2-L và text-bison

Với pretrained PaLM 2-L là scorer LLM, các optimizer LLM sẽ tạo sinh các chỉ dẫn A_begin, Còn text-bison là mô hình đã dược instruction-tuned, các optimizer LLM sẽ tạo sinh các chỉ dẫn Q_begin và Q_end khi text-bison làm scorer LLM

Dominic system (after Dominic O'Brien is a Person-Action image association system for numbers in specific order. Uses it to turn two digit numbers into famous people. Also associates an action with a famous person, representing the same two digit number. Now you can imagine a 4 digit number as a person doing an action A mobile number would be 2 person action combinations in sequence. The upfront work is remembering the persons and actions as images for each of the 00-99 two digit numbers. Then putting a four digit number together requires putting them in sequence e.g. in a one of your preselected [[Memory palaces 20201007192310]], The act of remembering is constructing the images and placing them in the memory palace of choice.

There is also a Person-Action-Object system PAO, which allows you to do three pairs of digits in one image. Allowing 1 million numbers to remember.

Tim Ferris posting a text by Gabriel Wyner from 2014 on learning a new language in several steps 1) hear the novel sounds in the language and how to spell them 2) learn a list of basic words by connecting them to their image not their translatiojn 3) learn (simplified) grammar 4) continue the game (adding focused vocab, reading, listening speaking etc)

Stamp

Probably interesting for background:

Eklund, A., Frank, J., Nilsson, L., Zetterberg, A., & Mansson, J. 2024. Times of trouble - Seasonal variation in number and severity of attacks on sheep caused by large carnivores and eagles in Sweden. European Journal of Wildlife Research, 70(9): 2-11. DOI: https://doi.org/10.1007/s10344-023-01761-4

Kvalshaug, O.J. 2013. Inter-specific patterns of depredation on domestic sheep and semi-domestic reindeer in Norway, by a large predator guild. Master Thesis, Norwegian University of Life Sciences, 36.

Linnell, J.D.C., Nilsen, E.B., Lande, U., Herfindal, I., Odden, J., & Skogen, K. 2005. Zoning as a means of mitigating conflicts with large carnivores: Principles and reality. Conservation Biology Series-Cambridge, 9: 163-175. DOI: https://doi.org/10.1017/cbo9780511614774.011

Mabille, G., Stien, A., Tveraa, T., Mysterud, A., Brøseth, H., & Linnell, J.D.C. 2015. Sheep farming and large carnivores: What are the factors influencing claimed losses? Ecosphere, 6(5): 1-17. DOI: https://doi.org/10.1890/es14-00444.1

Strand, G., Hansen, I., De Boon, A., & Sandström, C. 2019. Carnivore Management Zones and their Impact on Sheep Farming in Norway. Environmental Management, 64: 537-552. DOI: https://doi.org/10.1007/s00267-019-01212-4

Strand, G. 2020. The combined effects of centralization and carnivore management on sheep farmers and sheep farming in Norway. Human Dimensions of Wildlife, 26(4): 321-336. DOI: https://doi.org/10.1080/10871209.2020.1818895

More detail is needed here, include how you determined the limit of detection of your assay. State that you used standard curves to estimate limit of detection (LOD), but see Klymus et al. (2020). Given that your assays are for qualitative purposes, the limit of quantification (LOQ) is likely not relevant in your case. Please verify this to clarify in the main text why the qPCR efficiency may be irrelevant for your assays, but the LOD is.

Depending on who you will get as an examiner, it may be worthwhile to also mention that you did the testing according to the MIQE guidelines, which I think were incorporated into this paper (see thier Appendix S1 for the checklist):

• Thalinger, B., Deiner, K., Harper, L. R., Rees, H. C., Blackman, R. C., Sint, D., ... & Bruce, K. (2021). A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environmental DNA, 3(4), 823-836.

• Bustin, S. A. (2024). Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE. Molecular aspects of medicine, 96, 101249.

• Klymus, K. E., Merkes, C. M., Allison, M. J., Goldberg, C. S., Helbing, C. C., Hunter, M. E., Jackson, C. A., Lance, R. F., Mangan, A. M., Monroe, E. M., Piaggio, A. J., Stokdyk, J. P., Wilson, C. C., & Richter, C. A. (2020). Reporting the limits of detection and quantification for environmental DNA assays. Environmental DNA, 2, 271–282. https://doi.org/10.1002/edn3.29

Методика Фейнмана: 1. Определиться с тем, что хочешь изучить 2. Выучить это, либо научить кого то другого 3. Если есть пробелы в понимании - вернутся к материалу и найти их. 4. Упростить понятное и привести аналогии, примеры

Marx, Grundrisse, Heft 4

Categorías:

1. Diseño inclusivo e innovación democrática

2. Participación significativa en la gobernanza de la IA

3. Transparencia y rendición de cuentas para la prevención de daños

4. Acceso efectivo a la justicia

Convection

convection

From what I gather, people with this mutation have to go this lifelong treatment. My question is how intense is this treatment? I am not too familiar with the intake of these chemicals, are they harmful, or are they similar to simple vitamins? Can people with this condition live "normal" lives or not? I think these questions are good to know if the researchers are trying to find more effective treatment methods.

Author response:

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

Reviewer #1:

- Summary: 

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities. 

We agree with this summary.

- Strengths: 

A difficult decision-making task was examined in two monkeys.

We agree with this statement.

- Weaknesses: 

One of the monkeys did not fully learn the task. The manuscript lacked a coherent hypothesis to be tested, and no attempt was made to consider the possibility that this part of the brain may have little to do with the task that was being studied. 

We understand the reviewers concern. It is correct that one of the monkeys (Mi) did not perform at a high level, but it should be noted that both monkeys learned significantly above chance level. Therefore, we would argue that both monkeys in fact did learn the task but Mi’s performance was suboptimal. This difference in the performance levels gave us a rare opportunity to dive deeper into the reasons why some animals perform better than the others and we show that Mi (the lower performing monkey) paid more attention to the outcome of the previous trial – this is evident from our behavioural and decoding models.

We tested the overall hypothesis that neurons of the nucleus dentate can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components. Many neurons in the dentate are multimodal (Figure 3C-D) which was something that was theorized. One of the specific hypotheses that we tested is that the dentate cells can be direction-selective for both the sensorimotor and cognitive component. Given that many of the recorded cells showed direction-selectivity in their firing rate modulation for gap directions and/or stimulus directions, we provide strong evidence that this hypothesis is correct. We have now spelled out this hypothesis more explicitly in the introduction of the revised version. We now also explain better why we tested this specific hypothesis. Indeed, earlier studies in primates such as those by Herzfeld and colleagues (2018, Nat. Neuro.) and van Es and colleagues (2019, Current Biol) have indicated that direction-selectivity of cerebellar activity may occur in various sensorimotor domains.

We also appreciate the comment of this Reviewer that in our original submission we did not show our attempt to consider the possibility that this part of the brain may have little to do with the task that was being studied. We in fact did consider this possibility in that we successfully injected 3 ml of muscimol (5 μg/ml, Sigma Aldrich) into the dentate nucleus in vivo in one of the monkeys (Mo). This application resulted in a reduction of more than 10% in correct responses of the covert attention task after 45 minutes, whereas the performance remained the same following saline injections. Unfortunately, due to the timing of the experiments and Covid19-related laboratory restrictions we were unable to perform these experiments in the other monkey or repeat them in Mo. We aim to replicate this in future experiments and publish it when we have full datasets of at least two monkeys available. For this paper we have prioritized our tracing experiments, highlighting the connections of the dentate nucleus with attention related areas in brainstem and cortex in both monkeys, following perfusion.

- Perhaps the large differences in performance between the two subjects can be used as a way to interpret the neural data's relationship to behavior, as it provided a source of variance. This is what we would hypothesize if we believed that this area of the brain is playing a significant role in the task. If one animal learns much more poorly, and this region of the brain is important for that behavior, then shouldn't there be clear, interpretable differences in the neural data? 

We thank the Reviewer for this comment. We have added a new Supplementary Figure 2, in which we present the data for both monkeys separately in the revised manuscript. Comparing the two datasets however, we see more commonalities related to the significant learning in both monkeys than differences that might be related to their different levels of learning. We have therefore decided to show the different datasets transparently in the new Supplementary Figure 2, but to stay on the conservative side in our interpretations.

- How should we look for these differences? A number of recent papers in mice have uncovered a large body of data showing that during the deliberation period, when the animal is interpreting a sensory stimulus (often using the whisker system), there is ramping activity in a principal component space among neurons that contribute to the decision. This ramping activity is present (in the PCA space) in the motor areas of the cortex, as well as in the medial and lateral cerebellar nuclei. Perhaps a similar computational approach would benefit the current manuscript. 

We also appreciate this point. We have done the principal component analysis accordingly, and we indeed do find the ramping activity in several components of the dentate activity of both monkeys (Mi and Mo). We have now added a new Supplementary Figure 3 with the first three components of both correct and incorrect trials for Mi and Mo, highlighting their potential contribution.

- What is the hypothesis that is being tested? That is, what do you think might be the function of this region of the cerebellum in this task? It seems to me that we are not entirely in the dark, as previous literature on mice decision-making tasks has produced a reasonable framework: the deliberation period coincides with ramping activity in many regions of the frontal lobe and the cerebellum. Indeed, the ramp in the cerebellum appears to be a necessary condition for the ramp to be present in the frontal lobe. Thus, we should see such ramping activity in this task in the dentate. When the monkey makes the wrong choice, the ramp should predict it. If you don't see the ramping activity, then it is possible that the hypothesis is wrong, or that you are not recording from the right place. 

It is indeed one of our specific hypotheses that the dentate cells can be direction-selective for the preparing cognitive component and/or sensorimotor response. We provide evidence that this hypothesis may be correct when we analyze the regular time response curves (see Figure 2 and the new Supplementary Figure 2 where the data of both monkeys are now presented separately). Moreover, we have now verified this by analysing the ramping curves of PCA space (new Supplementary Figure 3) and firing frequency of DN neurons that modulated upon presentation of the C-stimulus (new Supplementary Figure 4). These figures and findings are now referred to in the main text.

- As this is a difficult task that depends on the ability of the animals to understand the meaning of the cues, it is quite concerning that one of the monkeys performed poorly, particularly in the early sessions. Notably, the disparity between the two subjects is rather large: one monkey at the start of the recordings achieved a performance that was much better than the second monkey did at the end of the recording sessions. You highlighted the differences in performance in Figure 1D and mentioned that you started recording once the animals reached 60% performance. However, this did not make sense to me as the performance of Mi even after the final day of recording did not reach the performance of Mo on the first day of recording. Thus, in contrast to Mo, Mi appeared to be not ready for the task when the recording began.

We understand this point. However, please note that the learning performance of the monkeys concerned retraining sessions after they had had several weeks of vacation. So, even though it is correct that one of the two monkeys had a very good consolidation and started already at a relatively high level on the first retraining session, the other one also started and ended at a level above chance level (the y-axis starts at 0.5). We now highlight this point better in the Results section.

- One objective of having two monkeys is to illustrate that what is true in one animal is also true in the other. In some figures, you show that the neural data are significantly different, while in others you combine them into one. Thus, are you confident that the neural data across the animals should be combined, as you have done in Figure 2? Perhaps you can use the large differences in performance as a source of variance to find meaning in the neural data. 

This is a valid question; as highlighted above, we have now addressed this point in the new Supplementary Figure 2, where the data for both monkeys are presented separately. Given the sample sizes and level of variances, it is in general difficult to draw conclusions about the potential differences and contributions, but the data are sufficiently transparent to observe common trends. With regard to linking differences in the neural data to the differences in performance level, please also consider Figure 4, the new Supplementary Figure 3 (with the ramping PCA component) and new Supplementary Figure 4 (with the additional analysis of the ramping activity of DN neurons that modulated upon presentation of the C-stimulus), which suggests that the ramping stage of Mo starts before that of Mi. This difference highlights the possibility that injecting accelerations of the simple spike modulations of Purkinje cells in the cerebellar hemispheres into the complex of cerebellar nuclei may be instrumental in improving the performance of responses to covert attention, akin to what has been shown for the impact of Purkinje cells of the vestibulocerebellum on eye movement responses to vestibular stimulation (De Zeeuw et al. 1995, J Neurophysiol). This possibility is now also raised in the Discussion.

- How do we know that these neurons, or even this region of the brain, contribute to this task? When a new task is introduced, the contributions of the region of the brain that is being studied are usually established via some form of manipulation. This question is particularly relevant here because the two subjects differed markedly in their performance, yet in Figure 3 you find that a similar percentage of neurons are responding to the various elements of the task.

We appreciate this question. As highlighted above, we are refraining from showing our muscimol manipulation (3 ml of 5 μg/ml muscimol, Sigma Aldrich), as it only concerns 1 successful dataset and 1 control experiment. We hope to replicate this reversible lesion experiment in the future and publish it when we have full new datasets of at least two monkeys available. As explained above, for this paper we have sacrificed both monkeys following a timed perfusion, so as to have similar survival times for the transport of the neuro-anatomical tracer involved.  

- Behavior in both animals was better when the gap direction was up/down vs. left/right. Is this difference in behavior encoded during the time that the animal is making a decision? Are the dentate neurons better at differentiating the direction of the cue when the gap direction is up/right vs. left/right? 

These data have now been included in the new Supplementary Figure 2; we did not observe any significant differences in this respect.

Reviewer #2:

- The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory. 

We thank the Reviewer for this positive and constructive feedback.

- The study is solid and I do not have major concerns, but only points for possible improvement. 

We thank the Reviewer for this positive feedback.

- A key feature of this data is the extended changes/ramps in DN output across epochs (Figure 2). Crudely, this presents a challenge for the view that DN output mainly drives motor effectors, as the saccade itself lasts only a tiny fraction of the overall task. Some discussion of this dichotomy in thinking about the function(s) of the cerebellum, vis a vis the multifarious DN targets the authors demonstrate here, etc., would be helpful. 

We agree with the Reviewer and we have expanded our Discussion on this point, also now highlighting the outcome of the new PCA analysis recommended by Reviewer 1 (see the new Supplementary figure Figure 3).

- A high-level suggestion on the data: the presentation of the data focuses (sensibly) on the representation of the stimulus and response epochs (Figures 2-3). Yet, the authors then show that from decoding, it is, in fact, a trial outcome that is best represented in the population (Figure 4). While there is nothing 'wrong' with this, it reads slightly incongruously, and the reader does a bit of a "double take" back to the previous figures to see if they missed examples of the trial-outcome signals, but the previous presentations only show correct trials. Consider adding somewhere in the first 3 main figures some neural data showing comparisons with incorrect trials. This way, the reader develops prior expectations for the outcome decoding result and frame of reference for interpreting it. On a related note, the text contains an earlier introduction of this issue (p24 last sentence) and p25 paragraph 1 cites Figure 3D and 3E for signals "related to the absence of reward" - but the caption says this includes only correct trials? 

We thank the Reviewer for bringing up these points. We have addressed the textual suggestions. Moreover, we have done the PCA analysis suggested by Reviewer 1 for both the correct and incorrect trials (see Supplementary material).

- P29: The discrepancy in retrograde labeling between monkeys (2 orders of magnitude): I realize the authors can't really do anything about this, but the difference is large enough to warrant concerns in the interpretation (how did the tracer spread over the drastically larger area? Isotropically? Could it cross more "hard boundaries" and incorporate qualitatively different inputs/outputs?). A small discussion of possible caveats in interpreting the outcomes would be helpful. 

We fully agree with this comment. As highlighted in the text, in both monkeys we first identified the optimal points for injection in the dentate nucleus electrophysiologically and we used the same pump with the same settings to carry out the injections, but even so the differences are substantial. We suspect that the larger injection might have been caused by an air bubble trapped in the syringe or a deviation in the stock solution, but we can never be sure of that. We have added a potential explanation for the caveat that might have played a role.

- And a list of quick points: 

We have addressed all points listed below; we want to thank the Reviewer for bringing them up.

P3 paragraph 2 needs comma "in daily life,". 

P4 paragraph 2 "C-gap" terminology not previously defined. 

P4 paragraph 2 "animals employed different behavioral strategies". Grammatically, you should probably say "each animal employed a different behavioral strategy," but also scientifically the paragraph doesn't connect this claim to anything about the DN (whereas, e.g., the abstract does make this connection clear). 

P5 paragraph 1 "theca" should be "the". 

P6 paragraph 1 problem with ignashenkova citation insert. 

P10 paragraph 1 I think the spike rate "difference between highest and lowest" is not exactly the same as "variance," you might want to change the terminology. 

P10 paragraph 1 should probably say "To determine if a cell preferentially modulated". 

P10 paragraph 1 last sentence the last clause could be clearer. 

P17 paragraph 2 should be something like "as well as those by Carpenter and..."? 

P20 caption: consider "...directionality in the task: only one C-stim...". 

P20 caption: consider "to the left and right in the [L/R] task...to the top/bottom in the [U/D] task". 

Fig1E and S1 - is there a physical meaning of the "weight" unit, and if none, can this be transformed into a more meaningful unit? 

P21 paragraph 1 consider "activity was recorded for 304 DN neurons...". 

P21 paragraph 1 "correlations with the temporal windows" it's not clear how activity can "correlate" with a time window, consider rephrasing (activity levels changed during these time epochs, depending on stimulus identity). 

P21 paragraph 1 should be "by comparing the number of spikes in a bin...". 

P22 paragraph 2 "when we aligned the neurons to the time of maximum change" needs clarification. The maximum change of what? And per neuron? Across the population? 

P22 paragraph 2 "than that of the facilitating" should be "than did the facilitating units". 

P24 paragraph 1 needs a comma and rewording "Within each direction, trials are sorted by the time of saccade onset". 

P24 paragraph 1 should probably say "Same as in G, but for suppressed cells". 

P24 paragraph 2 should say "more than one task event" not "events". 

P24 paragraph 2 needs a comma "To fully characterize the neural responses, we fitted". 

P25 paragraph 1 should probably say "we sampled from similar populations of DN". 

P34 paragraph 3 consider rephrasing the sentence that contains both "dissociation" and "dissociate". 

P37 last line: consider "coordination of cerebellum and cerebral cortex *in* higher order mental..."? 

P38 paragraph 1 citation needed for "kinematics of goal-directed hand actions of others"? 

P38 paragraph 1 commas probably not needed "map visual input, from high-level visual regions, onto..." 

References

- Herzfeld D.J., Kojima Y, Soetedjo R, Shadmehr R (2018) Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat Neurosci 21:736–743.

- van Es, D.M., van der Zwaag W., and Knapen T. (2019) Topographic Maps of Visual Space in the Human Cerebellum. Current Biol Volume 29, Issue 10p1689-1694.e3May 20.

- De Zeeuw CI, Wylie DR, Stahl JS, Simpson JI. (1995) Phase relations of Purkinje cells in the rabbit flocculus during compensatory eye movements. J Neurophysiol. Nov;74(5):2051-64. doi: 10.1152/jn.1995.74.5.2051.

Probably interesting for background:

Eklund, A., Frank, J., Nilsson, L., Zetterberg, A., & Mansson, J. 2024. Times of trouble - Seasonal variation in number and severity of attacks on sheep caused by large carnivores and eagles in Sweden. European Journal of Wildlife Research, 70(9): 2-11. DOI: https://doi.org/10.1007/s10344-023-01761-4

Kvalshaug, O.J. 2013. Inter-specific patterns of depredation on domestic sheep and semi-domestic reindeer in Norway, by a large predator guild. Master Thesis, Norwegian University of Life Sciences, 36.

Linnell, J.D.C., Nilsen, E.B., Lande, U., Herfindal, I., Odden, J., & Skogen, K. 2005. Zoning as a means of mitigating conflicts with large carnivores: Principles and reality. Conservation Biology Series-Cambridge, 9: 163-175. DOI: https://doi.org/10.1017/cbo9780511614774.011

Mabille, G., Stien, A., Tveraa, T., Mysterud, A., Brøseth, H., & Linnell, J.D.C. 2015. Sheep farming and large carnivores: What are the factors influencing claimed losses? Ecosphere, 6(5): 1-17. DOI: https://doi.org/10.1890/es14-00444.1

Strand, G., Hansen, I., De Boon, A., & Sandström, C. 2019. Carnivore Management Zones and their Impact on Sheep Farming in Norway. Environmental Management, 64: 537-552. DOI: https://doi.org/10.1007/s00267-019-01212-4

Strand, G. 2020. The combined effects of centralization and carnivore management on sheep farmers and sheep farming in Norway. Human Dimensions of Wildlife, 26(4): 321-336. DOI: https://doi.org/10.1080/10871209.2020.1818895

More detail is needed here, include how you determined the limit of detection of your assay. State that you used standard curves to estimate limit of detection (LOD), but see Klymus et al. (2020). Given that your assays are for qualitative purposes, the limit of quantification (LOQ) is likely not relevant in your case. Please verify this to clarify in the main text why the qPCR efficiency may be irrelevant for your assays, but the LOD is.

Depending on who you will get as an examiner, it may be worthwhile to also mention that you did the testing according to the MIQE guidelines, which I think were incorporated into this paper (see thier Appendix S1 for the checklist):

• Thalinger, B., Deiner, K., Harper, L. R., Rees, H. C., Blackman, R. C., Sint, D., ... & Bruce, K. (2021). A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environmental DNA, 3(4), 823-836.

• Bustin, S. A. (2024). Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE. Molecular aspects of medicine, 96, 101249.

• Klymus, K. E., Merkes, C. M., Allison, M. J., Goldberg, C. S., Helbing, C. C., Hunter, M. E., Jackson, C. A., Lance, R. F., Mangan, A. M., Monroe, E. M., Piaggio, A. J., Stokdyk, J. P., Wilson, C. C., & Richter, C. A. (2020). Reporting the limits of detection and quantification for environmental DNA assays. Environmental DNA, 2, 271–282. https://doi.org/10.1002/edn3.29

Author response:

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

Reviewer #1 (Public Review):

Summary:

The pituitary gonadotropins, FSH and LH, are critical regulators of reproduction. In mammals, synthesis and secretion of FSH and LH by gonadotrope cells are controlled by the hypothalamic peptide, GnRH. As FSH and LH are made in the same cells in mammals, variation in the nature of GnRH secretion is thought to contribute to the differential regulation of the two hormones. In contrast, in fish, FSH and LH are produced in distinct gonadotrope populations and may be less (or differently) dependent on GnRH than in mammals. In the present manuscript, the authors endeavored to determine whether FSH may be independently controlled by a distinct peptide, cholecystokinin (CCK), in zebrafish.

Strengths:

The authors demonstrated that the CCK receptor is enriched in FSH-producing relative to LH-producing gonadotropes, and that genetic deletion of the receptor leads to dramatic decreases in gonadotropin production and gonadal development in zebrafish. Also, using innovative in vivo and ex vivo calcium imaging approaches, they show that LH- and FSH-producing gonadotropes preferentially respond to GnRH and CCK, respectively. Exogenous CCK also preferentially stimulated FSH secretion ex vivo and in vivo.

Weaknesses:

The concept that there may be a distinct FSH-releasing hormone (FSHRH) has been debated for decades. As the authors suggest that CCK is the long-sought FSHRH (at least in fish), they must provide data that convincingly leads to such a conclusion. In my estimation, they have not yet met this burden. In particular, they show that CCK is sufficient to activate FSH-producing cells, but have not yet demonstrated its necessity. Their one attempt to do so was using fish in which they inactivated the CCK receptor using CRISPR-Cas9. While this manipulation led to a reduction in FSH, LH was affected to a similar extent. As a result, they have not shown that CCK is a selective regulator of FSH.

Our conclusion regarding the necessity of CCK signaling for FSH secretion is based on the following evidence:

(1) CCK-like receptors are expressed in the pituitary gland predominantly on FSH cells.

(2) Application of CCK to pituitaries elicits FSH cell activation and to a much lesser degree activation of LH cells.  (calcium imaging assays)

(3) Application of CCK to pituitaries and by injections in-vivo significantly increased only FSH release.

(4) Mutating the FSH-specific CCK receptor in a different species of fish (medaka) also causes a complete shutdown of FSH production and phenocopies a fsh-mutant phenotype (Uehara, Nishiike et al. 2023).

Taken together, we believe that this data strongly supports the conclusion that CCK is necessary for FSH production and release from the fish pituitary. Admittedly, the overlapping effects of CCK on both FSH and LH cells in zebrafish (evident in both our calcium imaging experiments and especially in the KO phenotype) complicates the interpretation of the phenotype. We speculate that the effect of CCK on LH cells in zebrafish can be caused either by paracrine signaling within the gland or by the effects of CCK on GnRH neurons that were shown to express CCK receptors .

In the current version, we emphasize that CCK also induces LH secretion. Although it does not affect LH to the same extent as FSH, an overlap does exist. This is mentioned in the abstract and discussion.

Moreover, they do not yet demonstrate that the effects observed reflect the loss of the receptor's function in gonadotropes, as opposed to other cell types.

Although there is evidence for the expression of CCK receptor in other tissues, we do show a direct decrease of FSH and LH expression in the gonadotrophs of the pituitary of the mutant fish; taken together with its significant expression in FSH cells compared to the rest of the cells of the pituitary in the cell specific transcriptomic, it is the most reasonable explanation for the mutant phenotype.

Unfortunately, unlike in mice, technologies for conditional knockout of genes in specific cell types are not yet available for our model and cell types. Additional tissue distribution of the three receptors types of CCK was added in supplementary figure 1, from this tissue distribution it can be appreciated how in the pituitary only CCKBRA (our identified CCK receptor) is expressed, while in other tissues it is either not expressed or expressed with the additional CCK receptors that can compensate its activity.

It also is not clear whether the phenotypes of the fish reflect perturbations in pituitary development vs. a loss of CCK receptor function in the pituitary later in life. Ideally, the authors would attempt to block CCK signaling in adult fish that develop normally. For example, if CCK receptor antagonists are available, they could be used to treat fish and see whether and how this affects FSH vs. LH secretion.

While the observed gonadal phenotype of the KO (sex inversed fish) should have a developmental origin since it requires a long time to manifest, the effect of the KO on FSH and LH cells is probably more acute. Unfortunately a specific antagonist that affect only CCKRBA and not the other CCK receptors wasn’t identified yet.

In the Discussion, the authors suggest that CCK, as a satiety factor, may provide a link between metabolism and reproduction. This is an interesting idea, but it is not supported by the data presented. That is, none of the results shown link metabolic state to CCK regulation of FSH and fertility. Absent such data, the lengthy Discussion of the link is speculative and not fully merited.

In the revised manuscript, we provided data to link cck with metabolic status in supplementary figure 1 and modified the discussion to tone down the link between metabolic status to and reproductive state.

Also in the Discussion, the authors argue that "CCK directly controls FSH cells by innervating the pituitary gland and binding to specific receptors that are particularly abundant in FSH gonadotrophs." However, their imaging does not demonstrate innervation of FSH cells by CCK terminals (e.g., at the EM level).

Innervation of the fish pituitary does not imply a synaptic-like connection between axon terminals and endocrine cells. In fact, such connections are extremely rare, and their functionality is unclear. Instead, the mode of regulation between hypothalamic terminals and endocrine cells in the fish pituitary is more similar to "volume transmission" in the CNS, i.e. peptides are released into the tissue and carried to their endocrine cell targets by the circulation or via diffusion. A short explanation was added in lines 395-398 in the discussion

Moreover, they have not demonstrated the binding of CCK to these cells. Indeed, no CCK receptor protein data are shown.

Our revised manuscript  includes detailed experiments showing the activation of the receptor by its homologous ligand, supplementary Figure 1 includes a transactivation  assay of CCK to its receptor and the effect of the different mutants on the activation of the receptor. Unfortunately, no antibody is available against this fish specific receptor (one of the caveats of working with fish models); therefore, we cannot present receptor protein data.

The calcium responses of FSH cells to exogenous CCK certainly suggest the presence of functional CCK receptors therein; but, the nature of the preparations (with all pituitary cell types present) does not demonstrate that CCK is acting directly in these cells.

We agree with the reviewer that there are some disadvantages in choosing to work with a whole-tissue preparation. However, we believe that the advantages of working in a more physiological context far outweigh the drawbacks as it reflects the natural dynamics more precisely. Since our transcriptome data, as well as our ISH staining, show that the CCK receptor is exclusively expressed in FSH cells, it is improbable that the observed calcium response is mediated via a different pituitary cell type.

Indeed, the asynchrony in responses of individual FSH cells to CCK (Figure 4) suggests that not all cells may be activated in the same way. Contrast the response of LH cells to GnRH, where the onset of calcium signaling is similar across cells (Figure 3).

The difference between the synchronization levels of LH and FSH cells activity stems from the gap-junction mediated coupling between LH cells that does not exist between FSH cells(Golan, Martin et al. 2016). Therefore, the onset of calcium response in FSH cells is dependent on the irregular diffusion rate of the peptide within the preparation, whereas the tight homotypic coupling between LH cells generates a strong and synchronized calcium rise that propagates quickly throughout the entire population

The differences in connectivity between LH and FSH cells is mentioned in lines 194-195

Finally, as the authors note in the Discussion, the data presented do not enable them to conclude that the endogenous CCK regulating FSH (assuming it does) is from the brain as opposed to other sources (e.g., the gut).

We agree with the reviewer that, for now, we are unable to determine whether hypothalamic or peripheral CCK are the main drivers of FSH cells. While the strong innervation of the gland by CCK-secreting hypothalamic neurons strengthens the notion of a hypothalamic-releasing hormone and also fits with the dogma of the neural control of the pituitary gland in fish (Ball 1981), more experiments are required to resolve this question.

Reviewer #2 (Public Review):

Summary:

This manuscript builds on previous work suggesting that the CCK peptide is the releasing hormone for FSH in fishes, which is different than that observed in mammals where both LH and FSH release are under the control of GnRH. Based on data using calcium imaging as a readout for stimulation of the gonadotrophs, the researchers present data supporting the hypothesis that CCK stimulates FSH-containing cells in the pituitary. In contrast, LH-containing cells show a weak and variable response to CCK but are highly responsive to GnRH. Data are presented that support the role of CCK in the release of FSH. Researchers also state that functional overlap exists in the potency of GnRH to activate FSH cells, thus the two signalling pathways are not separate. The results are of interest to the field because for many years the assumption has been that fishes use the same signalling mechanism. These data present an intriguing variation where a hormone involved in satiation acts in the control of reproduction.

Strengths:

The strengths of the manuscript are that researchers have shed light on different pathways controlling reproduction in fishes.

Weaknesses:

Weaknesses are that it is not clear if multiple ligand/receptors are involved (more than one CCK and more than one receptor?). The imaging of the CCK terminals and CCK receptors needs to be reinforced.

Reviewer consultation summary: 

The data presented establish sufficiency, but not necessity of CCK in FSH regulation. The paper did not show that CCK endogenously regulates FSH in fish. This has not been established yet.

This is a very important comment, also raised by reviewer 1. To avoid repetition, please see our detailed response to the comment above.

The paper presents the pharmacological effects of CCK on ex vivo preparations but does not establish the in vivo physiological function of the peptide. The current evidence for a novel physiological regulatory mechanism is incomplete and would require further physiological experiments. These could include the use of a CCK receptor antagonist in adult fish to see the effects on FSH and LH release, the generation of a CCK knockout, or cell-specific genetic manipulations.

As detailed in the responses to the first reviewer, we cannot conduct conditional, cellspecific gene knockout in our model. However we did conducted KO and show the direct effect on FSH and LH secretion together with physiological characterisation of the mutant.

Zebrafish have two CCK ligands: ccka, cckb and also multiple receptors: cckar, cckbra and cckbrb. There is ambiguity about which CCK receptor and ligand are expressed and which gene was knocked out.

In the revised manuscript, we clarified which of the receptors are expressed (CCKRBA) and which receptor is targeted. We also provided data showing the specificity of the receptors (both WT and mutant) to the ligands. Supplementary 1 shows receptor cross-activation. The method also specifies the exact NCBI ID numbers of the targeted receptor and the antibody used for the immunostaining.

Blocking CCK action in fish (with receptor KO) affects FSH and LH. Therefore, the work did not demonstrate a selective role for CCK in FSH regulation in vivo and any claims to have discovered FSHRH need to be more conservative.

We agree with the reviewer that the overlap in the effect of CCK measured in the calcium activation of cells and in the KO model does not allow us to conclude selectivity. In this context, it is crucial to highlight that CCKRBA exhibits high expression on FSH cells but not on LH cells. Therefore, the effect of CCK on LH cells is likely paracrine or through GnRH neurons that were shown to express CCK receptors. In the current version, we emphasize that CCK also induces LH secretion. Although it does not affect LH to the same extent as FSH, an overlap does exist. This is mentioned in the abstract and discussion.

The labelling of the terminals with anti-CCK looks a lot like the background and the authors did not show a specificity control (e.g. anti-CCK antibody pre-absorbed with the peptide or anti-CCK in morphant/KO animals).

Figures colours had been updated to better visualise the specific staining of the antibody. Also, The same antibody had been previously used to mark CCK-positive cells in the gut of the red drum fish(Webb, Khan et al. 2010) , where a control (pre-absorbed with the peptide) experiment had been conducted.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Abstract:

The authors have not yet established that CCK is the primary regulator of FSH in vivo.

In the new version, we highlight the leading effect of CCK on the reproductive axis, which includes FSH and LH.

Introduction:

The authors need to make clear earlier in the Introduction that fish have two types of gonadotropes. This information comes too late (last paragraph) currently.

Added in line 42

They should discuss relevant data on the differential regulation of FSH and LH in fish, as a rationale for looking for different releasing factors.

This has been discussed in the first paragraph of the introduction

In the last sentence of the penultimate paragraph, the authors assume that it must be a hypothalamic factor that regulates FSH. Why is this necessarily the case? Are there data indicating that a hypothalamic factor is required for FSH production in fish?

This has been mentioned in the discussion, we do not deny that circulating CCK or CCK from other brain areas might affect FSH secretion in the pituitary (line 402-404). However, as the hypothalamus serves as the main gateway from the brain to the pituitary and contains hypophysiotropic CCK neurons it is the most reasonable assumption.

Results:

In the first paragraph, the authors reference three types of CCK receptors, only one of which is expressed in the pituitary. The specific receptor should be named here.

The receptor name and NCBI id had been added in this paragraph.

Figure 1: What specificity controls were used for the ISH in Figure 1?

HCR- The method used to identify RNA expression and developed by Molecular Instruments (https://www.molecularinstruments.com/hcr-rnafish-protocols), do not require specific control as had been previously done with older ISH methods. The use of multiple short probes assure the specificity to the RNA.More over the expression is specific to the targeted cells.

In Figure 1D, the red square is missing in the KO fish (at low magnification).

This was fixed in the updated version.

In Figure 1G, the number of dots does not correspond to the number of animals described in the figure legend. Does each point represent an animal?

Each dot represent a fish. The order of the numbers in the legend didn’t match the order in the graph, this had been fixed in the last version

Figure 2A: It is not clear that all FSH (GFP) cells are double-labeled. Should all double-labeled cells appear white? Many appear as green. Some quantification of the proportion of co-labeling is needed. Also, the scale bars are too small to read. Perhaps add the size of the scale bars to the legend.

They are all double-labeled, as can be seen by the single-color images, since GFP fluorescence is stronger than RCaMP fluorescence, the double-labelling might be seen a green cells; a scale bar was added.

Figure 2C: Is the synchronous activity of LH cells here dependent on endogenous GnRH? Can these events be blocked with a GnRH receptor antagonist?

We currently do not have enough data to support this hypothesis and the in vivo 2 photon system is not optimal to answer these questions since these are spontaneous events which are difficult to predict. This is the main reason we moved to an ex vivo system. The similar response we receive when applying GnRH in the ex vivo system support it is GnRH activation.

Figure 4C: As some LH cells respond to CCK, can the authors really claim that CCK is a selective regulator of FSH? What explains the heterogeneity in the response of LH cells to CCK?

In this version, we highlight that CCK directly activates FSH but it is also affecting LH to some extent. However it is clear that the effect on FSH cells is more significant.

Figures 5A and B: With larger Ns, some of the trends might be significant (e.g., GnRH stimulated FSH release and CCK stimulated LH release).

Though there is a trend, the values in the Y axis reveal that the trend of response of FSH to GnRH and LH to CCK is lower then the distribution of the basal response (the before) in all of the graphs. Hence we do not believe a larger N will affect those results. We added the range of the secreted hormones concentrations in the result description to emphasize the difference in values,

Figures 5C and D: What explains the lack of an increase in LH secretion following GnRH treatment?

We did not measure LH Secretion in the plasma as we didn’t have enough blood, we do see an increase in LH transcription (see supplementary figure 5 – figure supplement 1)

Also, as mRNA levels were measured (in C), reference should be made to expression rather than transcription. Not all changes in mRNA levels reflect changes in transcription.Also, remove transcription from the legend. Reference to supplementary Figure 4 in the legend should be supplementary Figure 6. Finally, in C and D, distinguish males from females (as in 5A and B).

Modifications had been done according to the reviewer suggestions.

Figure legends:

The figure legends are very long. One way to shorten them is to remove descriptions of the results. The legends should indicate what is in each figure, not the results of the experiments.

Modifications had been done according to the reviewer suggestions.

Sample sizes should be spelled out in the legends, as they are not in the M&M.

We made sure all sample sizes are mentioned in the legend

Materials and Methods:

Section 1.1 can be removed as it repeats content presented elsewhere.

This section was removed

Section 1.5: It is unclear what this means: "blinding was not applied to ensure tractability" Please clarify.

This section was removed

Reviewer #2 (Recommendations For The Authors):

It appears that zebrafish have two ligands: ccka, cckb. Also multiple receptors: cckar, cckbra and cckbrb. Authors need to discuss this and clearly state which ligand and which receptor they are referring to in the manuscript.

We discussed the receptor type in the first paragraph of the results, the exact synthetic peptide used is described in the methods. The 8 amino acids of the mature CCK peptide are the same between CCKa and CCKb. A sentence regarding the specificity of the antibody to the mature CCK peptide was added in line 101.

"to GnRH puff application (300 μl of 30 μg/μl)"; (250 μl of 30 μg/ml CCK)

Please give the final concentration to make it easy on the readers of the data.

The molarity of the final concentration was added.

(2.4) Differential calcium response underlies differential hormone. This section is a bit confusing to read, for example:

"For that, we collected the medium perfused through our ex vivo system (Fig. 2a) and measured LH and FSH levels using a specific ELISA validated for zebrafish [31] while monitoring the calcium activity of the cells."

So the authors did the ELISA while monitoring the activity (?). This sentence does not make sense: please rewrite it.

We modified this sentence  in line 308-311

To functionally validate the importance of CCK signalling we used CRISPR-cas9 to generate loss-of-function (LOF) mutations in the pituitary- CCK receptor gene.

The authors need to clearly state WHICH gene they inactivated: Zebrafish have three CCK-receptors, so "the pituitary receptor gene" needs to be defined.

Was added again in line 107, and is mentioned in the methods

Figure 3 is a crucial figure!

Figure 3B: The data are not very convincing. Please state how thick the sections are in the figure legend (assuming these are adult pituitaries),

Added in the legend (figure 1C in the new version), slice thickness and adult fish.

Please show at least the merged image a high magnification view of the co-localization of the receptor with the cells.

This is figure 1 in the new revision, a magnified figure was added

Please give the scale bar size for 3B.

Scales for all images were added

Figure 3C: the co-localization of the terminals of the CCK and FSH cells shows very few cells expressing close to terminals.

Important: Because the labelling of the terminals with anti-CCK looks a lot like the background, it is very important to show the control (anti-CCK antibody pre-absorbed with the peptide). The authors should have these data. The photo needs to have been taken at the same gain (contrast) and the photo showing the terminals.

This is  a commercial antibody that had been previously validated for CCK in fish. The co-localization pattern resembles GnRH innervation in the pituitary. In fish when hypothalamic neurons innervate the pituitary they do not innervate all the cells, as this is an endocrine system, the peptide can travel to neighbouring cells via diffusion or aided blood flow (Golan, Zelinger et al. 2015) ).  The images reveal the direct innervation of CCK in the pituitary and its proximity to FSH cells.

Figure 4c, on right. The text seems to be stretched as if the photo was adjusted without locking the aspect ratio. Please check the original images.

This has been fixed

Can the authors use different pseudo colours? Differentiating a double label of white versus yellow is very difficult, and thus the photo is not very convincing.

This had been changed to green and magenta

What is meant by "CCK-AB" antibody? Perhaps anti-CCK would be a better label

This has been fixed

Figure 5A: increase the magnification of the insets; the structure of the gonads is very difficult to see with clarity in these low mag images. The most obvious way to improve this figure is to reduce or eliminate the pie graph (not really necessary) and show a high magnification (and larger) image of the gonadal structure.

This is figure 1 in the new version, with magnification of the gonad next to each body section.

Discussion:

" Moreover, in the zebrafish, as well as in other species, the functional overlap in gonadotropin signalling pathways is not limited to the pituitary but is also present in the gonad, through the promiscuity of the two gonadotropin receptors"<br /> The reasoning of this sentence is not clear: zebrafish do not use GnRH to control reproduction: they lack GnRH1 through genomic rearrangement (see Whitlock, Postlethwait and Ewer 2019) and KO of GnRH2/GnRH3 does not affect reproduction.

While GnRH KO model indicate a redundancy of GnRH in this axis in zebrafish, there is also ample evidence for its importance in regulating reproduction such as its effect on gonadotropin (Golan, Martin et al. 2016) and its use in spawning inductions in fish (Mizrahi and Levavi-Sivan 2023). We believe it is currently too soon to conclude that GnRH signalling is completely non relevant to reproduction in cyprinids.  

Reviewing Editor (Recommendations For The Authors):

It would be interesting to see calcium imaging experiments in the CCKR receptor mutants to establish a more direct connection between peptide action and activity.

We added a receptor assay that reflect the non-activation of the mutated receptors by CCK (supplementary figure 1) , and compared it to the wild type that is activated. This show that: 1) CCK directly activate our identified receptor in FSH cells. 2) the mutated receptors are non-active.

"all homozygous fish (CCKR+12/+7/-1/ CCKR+12/+7/-1, n=12)"

It may be better to write the genotype of fish separately as CCKR+12/+12, CCKR+7/+7 and CCKR-1/-1, n=12) otherwise it seems as if all alleles occurred together in the same fish.

Modified according to the reviewer request

In Figure 1 scale bar legends are very small. 

Description of the scale bars were added to the all the legends

Figure 1 legend "On the top right of each panel is the gender distribution" - fish have no gender but sex.

Modified according to the reviewer request

The authors should endeavour to improve the presentation of the figures. They should use a sans-serif font and check that text is not cut at the edge of figure panels, that scale bars are uniform and clearly labelled and fonts are of similar size and clearly legible. E.g. labels of the fish brain of Fig3A are very small.

We modified all the figures to adapt the font and the scales, we increased the size of the image in Figure 3a to make the labels clearer.

Please use the elife format to name supplementary figures, as Figure X - Figure Supplement Y (each supplement associated with one of the main figures).

Fixed

Peptide concentrations in the ex vivo experiments should also be given as molar concentrations not only as '250 μl of 30 μg/ml CCK'.

Fixed

"In contrast, FSH cells responded with a very low calcium rise in hormonal secretion in response to GnRH" - a very low rise in hormonal secretion

Fixed

Please clarify why you used a GnRH synthetic agonist and not the native peptide.

It is commonly used for spawning induction in fish (line 245); it has also been shown to directly affect the secretion of LH and FSH (Biran, Golan et al. 2014, Biran, Golan et al. 2014, Mizrahi, Gilon et al. 2019) , added to line 245.

References

Ball, J. (1981). "Hypothalamic control of the pars distalis in fishes, amphibians, and reptiles." General and comparative endocrinology 44(2): 135-170.

Biran, J., M. Golan, N. Mizrahi, S. Ogawa, I. S. Parhar and B. Levavi-Sivan (2014). "Direct regulation of gonadotropin release by neurokinin B in tilapia (Oreochromis niloticus)." Endocrinology 155(12): 4831-4842.

Biran, J., M. Golan, N. Mizrahi, S. Ogawa, I. S. Parhar and B. Levavi-Sivan (2014). "LPXRFa, the Piscine Ortholog of GnIH, and LPXRF Receptor Positively Regulate Gonadotropin Secretion in Tilapia (Oreochromis niloticus)." Endocrinology 155(11): 4391-4401.

Golan, M., A. O. Martin, P. Mollard and B. Levavi-Sivan (2016). "Anatomical and functional gonadotrope networks in the teleost pituitary." Scientific Reports 6: 23777.

Golan, M., E. Zelinger, Y. Zohar and B. Levavi-Sivan (2015). "Architecture of GnRH-Gonadotrope-Vasculature Reveals a Dual Mode of Gonadotropin Regulation in Fish." Endocrinology 156(11): 4163-4173.

Mizrahi, N., C. Gilon, I. Atre, S. Ogawa, I. S. Parhar and B. Levavi-Sivan (2019). "Deciphering Direct and Indirect Effects of Neurokinin B and GnRH in the Brain-Pituitary Axis of Tilapia." Front Endocrinol (Lausanne) 10: 469.

Mizrahi, N. and B. Levavi-Sivan (2023). "A novel agent for induced spawning using a combination of GnRH analog and an FDA-approved dopamine receptor antagonist." Aquaculture 565: 739095.

Uehara, S. K., Y. Nishiike, K. Maeda, T. Karigo, S. Kuraku, K. Okubo and S. Kanda (2023). "Cholecystokinin is the follicle-stimulating hormone (FSH)-releasing hormone." bioRxiv: 2023.2005.2026.542428.

Webb, K. A., Jr., I. A. Khan, B. S. Nunez, I. Rønnestad and G. J. Holt (2010). "Cholecystokinin: molecular cloning and immunohistochemical localization in the gastrointestinal tract of larval red drum, Sciaenops ocellatus (L.)." Gen Comp Endocrinol 166(1): 152-159.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

The authors introduce a computational model that simulates the dendrites of developing neurons in a 2D plane, subject to constraints inspired by known biological mechanisms such as diffusing trophic factors, trafficked resources, and an activity-dependent pruning rule. The resulting arbors are analyzed in terms of their structure, dynamics, and responses to certain manipulations. The authors conclude that 1) their model recapitulates a stereotyped timecourse of neuronal development: outgrowth, overshoot, and pruning 2) Neurons achieve near-optimal wiring lengths, and Such models can be useful to test proposed biological mechanisms- for example, to ask whether a given set of growth rules can explain a given observed phenomenon - as developmental neuroscientists are working to understand the factors that give rise to the intricate structures and functions of the many cell types of our nervous system. 

Overall, my reaction to this work is that this is just one instantiation of many models that the author could have built, given their stated goals. Would other models behave similarly? This question is not well explored, and as a result, claims about interpreting these models and using them to make experimental predictions should be taken warily. I give more detailed and specific comments below.  

We thank the reviewer for the summary of the work. But the criticism “that this is one instantiation of many models [we] could have built” is unfair as it can apply to any model. We chose one of the most minimalistic models which implements known biological mechanisms including activity-independent and -dependent phases of dendritic growth, and constrained parameters based on experimental data. We compare the proposed model to other alternatives in the Discussion section. In the revised manuscript, we additionally investigate the sensitivity of model output to variations of specific parameters, as explained below.

Point 1.1. Line 109. After reading the rest of the manuscript, I worry about the conclusion voiced here, which implies that the model will extrapolate well to manipulations of all the model components. How were the values of model parameters selected? The text implies that these were selected to be biologically plausible, but many seem far off. The density of potential synapses, for example, seems very low in the simulations compared to the density of axons/boutons in the cortex; what constitutes a potential synapse? The perfect correlations between synapses in the activity groups is flawed, even for synapses belonging to the same presynaptic cell. The density of postsynaptic cells is also orders of magnitude of, etc. Ideally, every claim made about the model's output should be supported by a parameter sensitivity study. The authors performed few explorations of parameter sensitivity and many of the choices made seem ad hoc.  

We have performed detailed sensitivity analysis on the model parameters mentioned by the reviewer, including (I) the density of postsynaptic cells (somatas), (II) the density of potential synapses, and (III) the level of correlations between synapses. 

(I) While the density of postsynaptic cells in our baseline model seems a bit low, at least when compared to densities observed in adulthood (Keller et al., 2018), we explored how altering this value affects the model dynamics. We found that the postsynaptic cell density does not affect the timing of dendritic outgrowth, overshoot and synaptic pruning. It only changes the final size of the dendritic arbor and the resulting number of connected synapses. This analysis is now included in Supplementary Figure 3-2.

(II) The density of potential synapses and the density of connected synapses that we used in the manuscript are already in the range of densities that can be found in the literature (Leighton et al., 2024; Ultanir et al., 2007; Glynn et al., 2011; Yang et al., 2014), some of which we already cited in the original submission.

A potential concern might be that the rapid slowing down of growth in the model could be due to a depletion of potential synapses. To illustrate that this is not the case, we showed that the number of available potential synapses over the time course of the simulations remains high (Figure 3, new panel e). Therefore, the initial density of potential synapses is sufficient and does not affect the final density of connected synapses.

To further illustrate the robustness of our model dynamics to longer simulation times, we added a new supplementary figure (Supplementary Figure 3-1).

These new figure additions (Figure 3e, Supplementary Figure 3-1, and Supplementary Figure 3-2) and their implications for the model dynamics are discussed in the Results section of the revised paper:

p.9 line 198, “After the initial overshoot and pruning, dendritic branches in the model stay stable, with mainly small subbranches continuing to be refined (Figure 3-Figure Supplement 1). This stability in the model is achieved despite the number of potential synaptic partners remaining high (Figure 3e), indicating a balance between activity-independent and activitydependent mechanisms. The dendritic growth and synaptic refinement dynamics are independent of the postsynaptic somata densities used in our simulations (Figure 3-Figure Supplement 2). Only the final arbor size and the number of connected synapses decrease with an increase in the density of the somata, while the timing of synaptic growth, overshoot and pruning remains the same (Figure 3-Figure Supplement 2).”

We also added more details to the description of our model in the Methods section:

p.24 line 615, “For all simulations in this study, we distributed nine postsynaptic somata at regular distances in a grid formation on a 2-dimensional 185 × 185 pixel area, representing a cortical sheet (where 1 pixel = 1 micron, Figure 4). This yields a density of around 300 neurons per 𝑚𝑚2 (translating to around 5,000 per 𝑚𝑚3, where for 25 neurons in Figure 3Figure Supplement 2 this would be around 750 neurons per 𝑚𝑚2 or 20,000 per 𝑚𝑚3). The explored densities are a bit lower than compared to neuron densities observed in adulthood (Keller et al., 2018). In the same grid, we randomly distributed 1,500 potential synapses, yielding an initial density of 0.044 potential synapses per 𝜇𝑚2 (Figure 3e). At the end of the simulation time, around 1,000 potential synapses remain, showing that the density of potential synapses is sufficient and does not significantly affect the final density of connected synapses. Thus, the rapid slowing down of growth in our model is not due to a depletion of potential synaptic partners. The resulting density of stably connected synapses is approximately 0.015 synapses per 𝜇𝑚2 (around 60 synapses stabilized per dendritic tree, Figure 3b). This density compares well to experimental findings, where, especially during early development, synaptic densities are described to be within a range similar to the one observed in our model (Leighton et al., 2024; Ultanir et al., 2007; Glynn et al., 2011; Yang et al., 2014; Koshimizu et al., 2009; Tyler and Pozzo-Miller, 2001).”

(III) Lastly, we investigated how the correlation between synapses of the same activity group might affect our conclusions. As correlations in our model mainly arise from patterns of spontaneous activity which are abundant in early postnatal development (retinal waves (Ackman et al., 2012) or endogenous activity in the form of highly synchronized events involving a large fraction of the cells (Siegel et al., 2012), we explored varying the correlations within each activity group, across activity groups and combinations of both. While this analysis supported our previously described intuition on how competition between synaptic activities should drive activity-dependent refinement, recently a study found direct evidence for such subcellular refinement of synaptic inputs specifically dependent on spontaneous activity between retinal ganglion cell axons and retinal waves in the superior colliculus (Matsumoto et al., 2024). The new analysis confirmed our earlier results that the competition between activity groups leads to activity-dependent refinement and yielded further insight into how the studied activity correlations can affect the competition. Those results are presented in a completely new figure (new Figure 5, supported by the Supplementary Figure 5-1 and 5-2) and discussed in the Results section:

p.11 line 249, “Group activity correlations shape synaptic overshoot and selectivity competition across synaptic groups.

Since correlations between synapses emerge from correlated patterns of spontaneous activity abundant during postnatal development (Ackman et al., 2012; Siegel et al., 2012), we explored a wide range of within-group correlations in our model (Figure 5a). Although a change in correlations within the group has only a minor effect on the resulting dendritic lengths (Figure 5b) and overall dynamics, it can change the density of connected synapses and thus also affect the number of connected synapses to which each dendrite converges throughout the simulations (Figure 5c,e). This is due to the change in specific selectivity of each dendrite which is a result of the change in within-group correlations (Figure 5d). While it is easier for perfectly correlated activity groups to coexist within one dendrite (Figure 5-Figure Supplement 1a, 100%), decreasing within-group correlations increases the competition between groups, producing dendrites that are selective for one specific activity group (60%, Figure 5d, Figure 5-Figure Supplement 1a). This selectivity for a particular activity group is maximized at intermediate (approximately 60%) within-group correlations, while the contribution of the second most abundant group generally remains just above random chance levels (Figure 5-Figure Supplement 1a). Further reducing within-group correlations (20%, Figure 5a) causes dendrites to lose their selectivity for specific activity groups due to the increased noise in the activity patterns (20%, Figure 5a). Overall, reducing within-group correlations increases synapse pruning (Figure 5f, bottom), also found experimentally (Matsumoto et al., 2024) as dendrites require an extended period to fine-tune connections aligned with their selectivity biases. This phenomenon accounts for the observed reduction in both the density and number of synapses connected to each dendrite.

In addition to the within-group correlations, developmental spontaneous activity patterns can also change correlations between groups as for example retinal waves propagated in different domains (Feller et al., 1997) (Figure 5-Figure Supplement 2). An increase in between-group correlations in our model intuitively decreases competition between the groups since fully correlated global events synchronize the activity of all groups (Figure 5-Figure Supplement 2). The reduction in competition reduces pruning in the model, which can be recovered by combining cross-group correlations with decreased within-group correlations (Figure 5-Figure Supplement 2). Our simulations show that altering the correlations within activity groups increases competition (by lowering the within-group correlations) or decreases competition (by raising the across-group correlations). Hence, in our model, competition between activity groups due to non-trivially structured correlations is necessary to generate realistic dynamics between activity-independent growth and activity-dependent refinement or pruning.

In sum, our simulations demonstrate that our model can operate under various correlations in the spike trains. We find that the level of competition between synaptic groups is crucial for the activity-dependent mechanisms to either potentiate or depress synapses and is fully consistent with recent experimental evidence showing that the correlation between spontaneous activity in retinal ganglion cells axons and retinal waves in the superior colliculus governs branch addition vs. elimination (Matsumoto et al., 2024)."

Precise details on the implementation of the changed activity correlations were added to the Methods section:

p. 25 line 638, “Within-group and across-group activity correlations. For the decreased withingroup correlations, we generated parent spike trains for each individual group with the firing rate 𝑟𝑖𝑛 = 𝑟𝑡𝑜𝑡𝑎𝑙 ∗ 𝑃𝑖𝑛 (e.g., 𝑃𝑖𝑛 = 100%; 60%; 20%, Figure 5). All the synapses of the same group share the same parent spike train and the remaining spikes for each synapse are uniquely generated with the firing rate 𝑟𝑟𝑒𝑠𝑡 = 𝑟𝑡𝑜𝑡𝑎𝑙 ∗ (1 − 𝑃𝑖𝑛) (e.g., (1 − 𝑃𝑖𝑛) = 0%; 40%; 80%), resulting in the desired firing rate 𝑟𝑡𝑜𝑡𝑎𝑙 (see Table 1). For the increase in across-group correlations, we generated one master spike train with the firing rate 𝑟𝑐𝑟𝑜𝑠𝑠 = 𝑟𝑡𝑜𝑡𝑎𝑙 ∗ 𝑃𝑐𝑟𝑜𝑠𝑠 for all the synapses of all groups (e.g., 𝑃𝑐𝑟𝑜𝑠𝑠 = 5%; 10%; 20%, Figure 5-Figure Supplement 2). This master spike train is shared across all groups and then filled up according to the within-group correlation (if not specified differently 𝑃𝑖𝑛 = 1 − 𝑃𝑐𝑟𝑜𝑠𝑠 to maintain the rate 𝑟𝑡𝑜𝑡𝑎𝑙). In all the cases, also in those where the change in across-group correlations is combined with the change in within-group correlations, the remaining spikes for each synapse are generated with a firing rate 𝑟𝑟𝑒𝑠𝑡 = 𝑟𝑡𝑜𝑡𝑎𝑙 ∗ (1 − 𝑃𝑖𝑛 − 𝑃𝑐𝑟𝑜𝑠𝑠) to obtain an overall desired firing rate of 𝑟𝑡𝑜𝑡𝑎𝑙.”

Point 1.2. Many potentially important phenomena seem to be excluded. I realize that no model can be complete, but the choice of which phenomena to include or exclude from this model could bias studies that make use of it and is worth serious discussion. The development of axons is concurrent with dendrite outgrowth, is highly dynamic, and perhaps better understood mechanistically. In this model, the inputs are essentially static. Growing dendrites acquire and lose growth cones that are associated with rapid extension, but these do not seem to be modeled. Postsynaptic firing does not appear to be modeled, which may be critical to activity-dependent plasticity. For example, changes in firing are a potential explanation for the global changes in dendritic pruning that occur following the outgrowth phase.  

Thanks to the reviewer for bringing up these important considerations. We do indeed write in the Introduction (e.g. lines 36-76) which phenomena we include in the model and why. The Discussion also compares our model to others (lines 433-490), pointing out that most models either focus on activity-independent or activity-dependent phases. We include both, combining the influence of both molecular gradients and growth factors as well as activity-dependent connectivity refinements instructed by spontaneous activity. We consider our model a tractable, minimalist mechanistic model which includes both activity-independent and activity-dependent aspects. 

Regarding postsynaptic firing, this is indeed super relevant and an important point to consider. In one of our recent publications (Kirchner and Gjorgjieva, 2021), we studied only an activity-dependent model for the organization of synaptic inputs on non-growing dendrites which have a fixed length. There, we considered the effect of postsynaptic firing (via a back-propagating action potential) and demonstrated that it plays an important role in establishing a global organization of synapses on the entire dendritic tree of the neuron. For example, we showed that it could lead to the emergence of retinotopic maps on the dendritic tree which have been found experimentally (Iacaruso et al., 2017). Since we use the same activity-dependent plasticity model in this paper, we expect that the somatic firing will have the same effect on establishing synaptic distributions on the entire dendritic tree. This is now also discussed in the Discussion section of the revised manuscript:

p. 21 line 491, “Although we did not explicitly model postsynaptic firing, our previous work with static dendrites has shown that it can play an important role in establishing a global organization of synapses on the entire dendritic tree of the neuron (Kirchner and Gjorgjieva, 2021). For example, we showed that it could lead to the emergence of retinotopic maps on the dendritic tree which have been found experimentally (Iacaruso et al., 2017). Since we use the same activity-dependent plasticity model in this paper, we expect that the somatic firing will have the same effect on establishing synaptic distributions on the entire dendritic tree.”

Including the concurrent development of axons in the model is indeed very interesting. In fact, a recent tour-de-force techniques paper found similar to what we assume. Hebbian activity-dependent dynamics of axonal branches of retinal ganglion cells experiencing spontaneous activity in relation to retinal waves in the superior colliculus (Matsumoto et al., 2024). New branches tend to be added at the locations where spontaneous activity of individual branches is more correlated with retinal waves, whereas asynchronous activity is associated with branch elimination. We suspect the same Hebbian activity-dependent dynamics to apply also to dendritic growth. 

To address simultaneous dynamic axons to our growing dendrites, in the revised version of the manuscript, we included a simplified form of axonal dynamics by allowing changes in the lifetime and location of potential synapses, which come from axons of presynaptic partners. We explored different median lifetimes of synapses in combination with several distances with which a synapse can move in the simulated space (new Supplementary Figure 3-3). Our results show that dynamically moving synapses only affect the dynamics and stability of our model when the rate of moving synapses combined with the distance of moving synapses is faster than the dendritic growth. In scenarios in which synapses can move across large distances, dendrites get further destabilized due to synapses transferring from one dendrite to another, perturbing the attractor fields of the potential synapses even in late phases of the simulations. Besides such non-biological scenarios, dynamically moving synapses do not affect the model dynamics too much. Thus, they mostly add additional noise and variability to the growth and pruning without changing the timing and amplitude of the dynamics. These results are discussed in the results section of the revised manuscript:

p.9 line 207, “The development of axons is concurrent with dendritic growth and highly dynamic Matsumoto et al. (2024). To address the impact of simultaneously growing axons, we implemented a simple form of axonal dynamics by allowing changes in the lifetime and location of potential synapses, originating from the axons of presynaptic partners (Figure 3-Figure Supplement 3). When potential synapses can move rapidly (median lifetime of 1.8 hours), the model dynamics are perturbed quite substantially, making it difficult for the dendrites to stabilize completely (Figure 3–Figure Supplement 3c). However, slowly moving potential synapses (median lifetime of 18 hours) still yield comparable results (Figure 3-Figure Supplement 3). The distance of movement significantly influenced results only when potential synaptic lifetimes were short. For extended lifetimes, the moving distance had a minor impact on the dynamics, predominantly affecting the time required for dendrites to stabilize. This was the result of synapses being able to transfer from one dendrite to another, potentially forming new long-lasting connections even at advanced stages of synaptic refinement. In sum, our results show that potential axonal dynamics only affect the stability of our model when these dynamics are much faster than dendritic growth.”

Precise details on the implementation of the dynamically moving synapses and their synaptic lifetimes are now in the Methods section:

p. 25 line 650, “Dynamically moving synapses. For the moving synapses we introduced lifetimes for each synapse, randomly sampled from a log-normal distribution with median 1.8h (for when they move frequently), 4.5h or 18h (for when they move rarely) and variance equal to 1 (Figure 3-Figure Supplement 3b). The lifetime of a synapse decreases only when the synapse is not connected to any of the dendrites (i.e., is a potential synapse). When the lifetime of a synapse expires, the synapse moves to a new location with a new lifetime sampled from the same log-normal distribution. This enables synapses to move multiple times throughout a simulation. The exact locations and distances to which each synapse can move are determined by a binary matrix (dimensions: 𝑝𝑖𝑥𝑒𝑙𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 × 𝑝𝑖𝑥𝑒𝑙𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒) representing a ring (annulus) with the inner radius 𝑑/4 and outer radius 𝑑/2 , where the synapse location is at the center of the matrix. All the locations of the matrix within the ring boundaries (between the inner radius and outer radius) are potential locations to which the synapse can move. The synapse then moves randomly to one of the possible locations where no other synapse or dendrite is located. For the movement distances, we chose the ring dimensions 3 × 3, 25 × 25 and 101 × 101, yielding the moving distances (radii) of 1 pixel per movement, 12 pixels per movement and 50 pixels per movement (𝑟 = (𝑑−1)/2). These pixel distances represent small movements, as much as a dendrite can grow in one step (1 micron), and larger movements which are far enough so that the synapse will not attract the same branches again (12 microns) or far enough so that it might attract a completely different dendrite (50 microns, Figure 3-Figure Supplement 3a).”

Point 1.3. Line 167. There are many ways to include activity -independent and -dependent components into a model and not every such model shows stability. A key feature seems to be that larger arbors result in reduced growth and/or increased retraction, but this could be achieved in many ways (whether activity dependent or not). It's not clear that this result is due to the combination of activity-dependent and independent components in the model, or conceptually why that should be the case.

We never argued for model uniqueness. There are always going to be many different models (at different spatial and temporal scales, at different levels of abstraction). We can never study all of them and like any modeling study in systems neuroscience we have chosen one model approach and investigated this approach. We do compare the current model to others in the Discussion. If the reviewers have a specific implementation that we should compare our model to as an alternative, we could try, but not if this means doing a completely separate project.

Point 1.4. Line 183. The explanation of overshoot in terms of the different timescales of synaptic additions versus activity-dependent retractions was not something I had previously encountered and is an interesting proposal. Have these timescales been measured experimentally? To what extent is this a result of fine-tuning of simulation parameters?  

We found that varying the amount of BDNF controls the timescale of the activity-dependent plasticity (see our Figure 6c). Hence, changing the balance between synaptic additions vs. retractions is already explored in Figure 6e and f. Here we show that the overshoot and retraction does not have to be fine-tuned but may be abolished if there is too much activity-dependent plasticity. 

Regarding the relative timescales of synaptic additions vs. retractions: since the first is mainly due to activity-independent factors, and the second due to activity-dependent plasticity, the questions is really about the timescales of the latter two. As we write in the Introduction (lines 61-63), manipulating activity-dependent synaptic transmission has been found to not affect morphology but rather the density and specificity of synaptic connections (Ultanir et al. 2007), supporting the sequential model we have (although we do not impose the sequence, as both activity-independent and activitydependent mechanisms are always “on”; but note that activity-dependent plasticity can only operate on synapses that have already formed).

The described results are robust to parameter variations (performed on the postsynaptic density, potential synapse density, and within- and across-group correlations) as described in the reply to reviewer #1 point 1.1.

Point 1.5. Line 203. This result seems at odds with results that show only a very weak bias in the tuning distribution of inputs to strongly tuned cortical neurons (e.g. work by Arthur Konnerth's group). This discrepancy should be discussed.  

First, we note that the correlated activity experienced by our modeled synapses (and resulting synaptic organization) does not necessarily correspond to visual orientation, or any stimulus feature, for that matter, but is rather a property of correlated spontaneous activity. 

Nonetheless, there is some variability in what the experimental data show. Many studies have shown that synapses on dendrites are organized into functional synaptic clusters: across brain regions, developmental ages and diverse species from rodent to primate (Kleindienst et al., 2011; Takahashi et al., 2012; Winnubst et al., 2015; Gökçe et al., 2016; Wilson et al., 2016; Iacaruso et al., 2017; Scholl et al., 2017; Niculescu et al., 2018; Kerlin et al., 2019; Ju et al., 2020, Hedrick et al., 2022, Hedrick et al., 2024). Interestingly, some in vivo studies have reported lack of fine-scale synaptic organization (Varga et al., 2011; X. Chen et al., 2011; T.-W. Chen et al., 2013; Jia et al., 2010; Jia et al., 2014), while others reported clustering for different stimulus features in different species. For example, dendritic branches in the ferret visual cortex exhibit local clustering of orientation selectivity but do not exhibit global organization of inputs according to spatial location and receptive field properties (Wilson et al. 2016; Scholl et al., 2017). In contrast, synaptic inputs in mouse visual cortex do not cluster locally by orientation, but only by receptive field overlap, and exhibit a global retinotopic organization along the proximal-distal axis (Iacaruso et al., 2017). We proposed a theoretical framework to reconcile these data: combining activity-dependent plasticity similar to the BDNF-proBDNF model that we used in the current work, and a receptive field model for the different species (Kirchner and Gjorgjieva, 2021). This is now also discussed in the Discussion section of the revised manuscript:

p. 20 line 471, “The correlated activity experienced by our modeled synapses (and resulting synaptic organization) does not necessarily correspond to visual orientation, or any stimulus feature, for that matter, but is rather a property of spontaneous activity. Nonetheless, there is some variability in what the experimental data show. Many have shown that synapses on dendrites are organized into functional synaptic clusters: across brain regions, developmental ages and diverse species from rodent to primate (Kleindienst et al., 2011; Winnubst et al., 2015; Iacaruso et al., 2017; Scholl et al., 2017; Niculescu et al., 2018; Takahashi et al., 2012; Gökçe et al., 2016; Wilson et al., 2016; Kerlin et al., 2019; Ju et al., 2020; Hedrick et al., 2022, 2024). Other studies have reported lack of fine-scale synaptic organization (Chen et al., 2013; Varga et al., 2011; Chen et al., 2011; Jia et al., 2010, 2014). Interestingly, some of these discrepancies might be explained by different species showing clustering with respect to different stimulus features (orientation or receptive field overlap) (Scholl et al., 2017; Wilson et al., 2016; Iacaruso et al., 2017). Our prior work proposed a theoretical framework to reconcile these data: combining activity-dependent plasticity as we used in the current work, and a receptive field model for the different species (Kirchner and Gjorgjieva, 2021).”

Point 1.6. Line 268. How does the large variability in the size of the simulated arbors relate to the relatively consistent size of arbors of cortical cells of a given cell type? This variability suggests to me that these simulations could be sensitive to small changes in parameters (e.g. to the density or layout of presynapses).  

We again thank the reviewer for the detailed explanation and feedback on parameters that should be tested in more detail. We have explored several of the suggested model parameters and believe that we have managed to explain and illustrate their effects on the model's dynamics clearly. The precise changes are explained in the reply to point 1.1 and are now available in the revised version of the manuscript.

Point 1.7. The modeling of dendrites as two-dimensional will likely limit the usefulness of this model. Many phenomena- such as diffusion, random walks, topological properties, etc - fundamentally differ between two and three dimensions.  

Indeed, there are many differences between two and three dimensions. We have ongoing work that extends the current model to 3D but is beyond the scope of the current paper. In systems neuroscience, people have found very interesting results making such simplified geometric assumptions about networks, for instance the one-dimensional ring model has been used to uncover fundamental insights about computations even though highly simplified and abstracted. We are convinced that our model, especially with the new sensitivity analysis, makes interesting and novel contributions and predictions.

Point 1.8. The description of wiring lengths as 'approximately optimal' in this text is problematic. The plotted data show that the wiring lengths are several deviations away from optimal, and the random model is not a valid instantiation of the 2D non-overlapping constraints the authors imposed. A more appropriate null should be considered.  

We appreciate the reviewer’s feedback regarding the use of the term “approximately optimal” in describing wiring lengths. We acknowledge that our initial terminology was imprecise and could be misleading. We had previously referred to the minimal wiring length as the optimal wiring length, which does not fully capture the nuances of neuronal wiring optimization. As noted in prior literature, such as the work by Hermann Cuntz (Cuntz et al., 2010 & 2012), neurons can optimize their wiring beyond simply minimizing dendritic length.

To address this issue, to better capture the balance between wiring minimization and functional constraints, such as conduction delays, we have developed a new modeling approach based on minimum spanning trees with a balancing factor (Cuntz et al., 2010 & 2012). This factor modulates the trade-off between minimizing wiring length and accounting for conduction delays from synapses to the soma. Specifically, the model assumes a balance between minimizing the total dendritic length and minimizing the tree distance between synapses and the site of input integration, typically the soma. This balance is illustrated in Figure 8 (Figure 7 in the original manuscript), where we demonstrate that the deviation from the theoretical minimum length arises because direct paths to synapses often require longer dendrites in our models.

Together with the new result, which we added as the new panels f, g and h to Figure 8 (originally Figure 7), we also adjusted panel a of Figure 8, to now illustrate the difference between random wiring, minimal wiring and minimal conductance delay. The updated Figure 8 and its new findings are discussed in the results section of the revised manuscript:

p.17 line 387, “This deviation is expected given that real dendrites need to balance their growth processes between minimizing wire while reducing conduction delays. The interplay between these two factors emerges from the need to reduce conduction delays, which requires a direct path length from a given synapse to the soma, consequently increasing the total length of the dendritic cable. (Cuntz et al., 2010, 2012; Ferreira Castro et al., 2020).

To investigate this further, we compared the scaling relations of the final morphologies of our models with other synthetic dendritic morphologies generated using a previously described minimum spanning tree (MST) based model. The MST model balances the minimization of total dendritic length and the minimization of conduction delays between synapses and the soma. This balance results in deviations from the theoretical minimum length because direct paths to synapses often require longer dendrites (Cuntz et al., 2008, 2010). The balance in the model is modulated by a balancing factor (𝑏𝑓 ). If 𝑏𝑓 is zero, dendritic trees minimize the cable only, and if 𝑏𝑓 is one, they will try to minimize the conduction delays as much as possible. It is important to note that the MST model does not simulate the developmental process of dendritic growth; it is a phenomenological model designed to generate static morphologies that resemble real cells.

To facilitate the comparison of total lengths between our simulated and MST morphologies, we generated MST models under the same initial conditions (synaptic spatial distribution) as our models and simulated them to match several morphometrics (total length, number of terminals, and surface area) of our grown morphologies. This allowed us to create a corresponding MST tree for each of our synthetic trees. Consequently, we could evaluate whether the branching structures of our models were accurately predicted by minimum spanning trees based on optimal wiring constraints. We found that the best match occurred with a trade-off parameter 𝑏𝑓 = 0.9250 (Figure 8f). Using the morphologies generated by the MST model with the specified trade-off parameter (𝑏𝑓 ), we showed that the square root of the synapse count and the total length (𝐿) in both our model generated trees and the MST trees exhibit a linear scaling relationship (Figure 8g; 𝑅2 = 0.65). The same linear relationship can be observed for the square root of the surface area and the total length 𝐿 of our model trees and the MST trees (Figure 8h; 𝑅2 = 0.73). Overall, these results indicate that our model generate trees are wellfitted by the MST model and follow wire optimization constraints.

We acknowledge that the value of the balancing factor 𝑏𝑓 in our model is higher than the range of balancing factors that is typically observed in the biological dendritic counterparts, which generally ranges between 0.2 and 0.4 (Cuntz et al., 2012; Ferreira Castro et al., 2020; Baltruschat et al., 2020). However, it is still remarkable that our model, which does not explicitly address these two conservation laws, achieves approximately optimal wiring. Why do we observe such a high 𝑏𝑓 value? We reason that two factors may contribute to this. First, in our models, local branches grow directly to the nearest potential synapse, potentially taking longer routes instead of optimally branching to minimize wiring length (Wen and Chklovskii, 2008). Second, the growth process in our models does not explicitly address the tortuosity of the branches, which can increase the total length of the branches used to connect synapses. In the future, it will be interesting to add constraints that take these factors into account. Taken together, combining activity-independent and -dependent dendrite growth produces morphologies that approximate optimal wiring.”

Further details on the fitted MST model and the corresponding analysis were added to the methods section:

p.26 line 669, “Comparison with wiring optimization MST models. To evaluate the wire minimization properties of our model morphologies (n=288), we examined whether the number of connected synapses (N), total length (L), and surface area of the spanning field (S) conformed to the scaling law 𝐿 ≈ 𝜋−1/2 ⋅ 𝑆1/2 ⋅ 𝑁1/2 (Cuntz et al., 2012). Furthermore, to validate that our model dendritic morphologies scale according to optimal wiring principles, we created simplified models of dendritic trees using the MST algorithm with a balancing factor (bf). This balancing factor adjusts between minimizing the total dendritic length and minimizing the tree distance between synapses and the soma (Cost = 𝐿 + 𝑏𝑓 ⋅ 𝑃 𝐿) (MST_tree; best bf = 0.925) (Cuntz et al., 2010); TREES Toolbox http://www.treestoolbox.org).

Initially, we generated MSTs to connect the same distributed synapses as our models. We performed MST simulations that vary the balancing factor between 𝑏𝑓 = 0 and 𝑏𝑓 = 1 in steps of 0.025 while calculating the morphometric agreement by computing the error (Euclidean distance) between the morphologies of our models and those generated by the MST models. The morphometrics used were total length, number of terminals, and surface area occupied by the synthetic morphologies.”

Point 1.9. It's not clear to me what the authors are trying to convey by repeatedly labeling this model as 'mechanistic'. The mechanisms implemented in the model are inspired by biological phenomena, but the implementations have little resemblance to the underlying biophysical mechanisms. Overall my impression is that this is a phenomenological model intended to show under what conditions particular patterns are possible. Line 363, describing another model as computational but not mechanistic, was especially unclear to me in this context.  

What we mean by mechanistic is that we implement equations that model specific mechanisms i.e. we have a set of equations that implement the activity-independent attraction to potential synapses (with parameters such as the density of synapses, their spatial influence, etc) and the activitydependent refinement of synapses (with parameters such as the ratio of BDNF and proBDNF to induce potentiation vs depression, the activity-dependent conversion of one factor to the other, etc). This is a bottom-up approach where we combine multiple elements together to get to neuronal growth and synaptic organization. This approach is in stark contrast to the so-called top-down or normative approaches where the method would involve defining an objective function (e.g. minimal dendritic length) which depends on a set of parameters and then applying a gradient descent or other mathematical optimization technique to get at the parameters that optimize the objective function. This latter approach we would not call mechanistic because it involves an abstract objective function (who could say what a neuron or a circuit should be trying to optimize?) and a mathematical technique for how to optimize the function (we don’t know if neurons can compute gradients of abstract objective functions). 

Hence our model is mechanistic, but it does operate at a particular level of abstraction/simplification. We don’t model individual ion channels, or biophysics of synaptic plasticity (opening and closing of NMDA channels, accumulation of proteins at synapses, protein synthesis). We do, however, provide a biophysical implementation of the plasticity mechanism through the BDNF/proBDNF model which is more than most models of plasticity achieve, because they typically model a phenomenological STDP or Hebbian rule that just uses activity patterns to potentiate or depress synaptic weights, disregarding how it could be implemented. To the best of our understanding, this is what is normally considered mechanistic in the field (in contrast to, for example, biophysical).

Reviewer #2 (Public Review): 

This work combines a model of two-dimensional dendritic growth with attraction and stabilisation by synaptic activity. The authors find that constraining growth models with competition for synaptic inputs produces artificial dendrites that match some key features of real neurons both over development and in terms of final structure. In particular, incorporating distance-dependent competition between synapses of the same dendrite naturally produces distinct phases of dendritic growth (overshoot, pruning, and stabilisation) that are observed biologically and leads to local synaptic organisation with functional relevance. The approach is elegant and well-explained, but makes some significant modelling assumptions that might impact the biological relevance of the results. 

Strengths: 

The main strength of the work is the general concept of combining morphological models of growth with synaptic plasticity and stabilisation. This is an interesting way to bridge two distinct areas of neuroscience in a manner that leads to findings that could be significant for both. The modelling of both dendritic growth and distance-dependent synaptic competition is carefully done, constrained by reasonable biological mechanisms, and well-described in the text. The paper also links its findings, for example in terms of phases of dendritic growth or final morphological structure, to known data well. 

Weaknesses: 

The major weaknesses of the paper are the simplifying modelling assumptions that are likely to have an impact on the results. These assumptions are not discussed in enough detail in the current version of the paper. 

(1) Axonal dynamics. 

A major, and lightly acknowledged, assumption of this paper is that potential synapses, which must come from axons, are fixed in space. This is not realistic for many neural systems, as multiple undifferentiated neurites typically grow from the soma before an axon is specified (Polleux & Snider, 2010). Further, axons are also dynamic structures in early development and, at least in some systems, undergo activity-dependent morphological changes too (O'Leary, 1987; Hall 2000). This paper does not consider the implications of joint pre- and post-synaptic growth and stabilisation.  

We thank the reviewer for the summary of the strengths and weaknesses of the work. While we feel that including a full model of axonal dynamics is beyond the scope of the current manuscript, some aspects of axonal dynamics can be included and are now implemented and tested in the revised manuscript. Since this feedback covers similar aspects of the model that were also pointed out by reviewer #1, we refer here to our detailed reply to their comments 1.1 and 1.2, where we list and discuss all the analyses performed to address the raised issues.

(2) Activity correlations 

On a related note, the synapses in the manuscript display correlated activity, but there is no relationship between the distance between synapses and their correlation. In reality, nearby synapses are far more likely to share the same axon and so display correlated activity. If the input activity is spatially correlated and synaptic plasticity displays distance-dependent competition in the dendrites, there is likely to be a non-trivial interaction between these two features with a major impact on the organisation of synaptic contacts onto each neuron.  

We have explored the amount of correlation (between and within correlated groups) in the revised manuscript (see also our reply to reviewer comment 1.1).

However, previous experimental work, (e.g. Kleindienst et al., 2011) has provided anatomical and functional analyses that it is unlikely that the functional synaptic clustering on dendritic branches is the result of individual axons making more than one synapse (see pg. 1019).

(3) BDNF dynamics 

The models are quite sensitive to the ratio of BDNF to proBDNF (eg Figure 5c). This ratio is also activity-dependent as synaptic activation converts proBDNF into BDNF. The models assume a fixed ratio that is not affected by synaptic activity. There should at least be more justification for this assumption, as there is likely to be a positive feedback relationship between levels of BDNF and synaptic activation.  

The reviewer is correct. We used the BDNF-proBDNF model for synaptic plasticity based on our previous work (Kirchner and Gjorgjieva, 2021).  

There, we explored only the emergence of functionally clustered synapses on static dendrites which do not grow. In the Methods section (Parameters and data fitting) we justify the choice of the ratio of BDNF to proBDNF from published experimental work. We also performed sensitivity analysis (Supplementary Fig. 1) and perturbation simulations (Supplementary Fig. 3), which showed that the ratio is crucial in regulating the overall amount of potentiation and depression of synaptic efficacy, and therefore has a strong impact on the emergence and maintenance of synaptic organization. Since we already performed all this analysis, we expect that the same results will also apply to the current model which includes dendritic growth, as it involves the same activity-dependent mechanism.

A further weakness is in the discussion of how the final morphologies conform to principles of optimal wiring, which is quite imprecise. 'Optimal wiring' in the sense of dendrites and axons (Cajal, 1895; Chklovskii, 2004; Cuntz et al, 2007, Budd et al, 2010) is not usually synonymous with 'shortest wiring' as implied here. Instead, there is assumed to be a balance between minimising total dendritic length and minimising the tree distance (ie Figure 4c here) between synapses and the site of input integration, typically the soma. The level of this balance gives the deviation from the theoretical minimum length as direct paths to synapses typically require longer dendrites. In the model this is generated by the guidance of dendritic growth directly towards the synaptic targets. The interpretation of the deviation in this results section discussing optimal wiring, with hampered diffusion of signalling molecules, does not seem to be correct. 

We agree with this comment. We had wrongly used the term “optimal wiring” as neurons can optimize their wiring not only by minimizing their dendritic length but other factors as noted by the reviewer. In the revised manuscript we replaced the term “optimal wiring” with “minimal wiring” wherever it was incorrectly used. On top of that, we performed further analysis and discussed these differences, as pointed out in the reply to reviewer #1 point 1.8.

To summarize, we want to again thank the reviewer for their in-depth review and all the suggestions that helped us improve the analysis and implementation of our model.

Reviewer #3 (Public Review): 

The authors propose a mechanistic model of how the interplay between activity-independent growth and an activity-dependent synaptic strengthening/weaken model influences the dendrite shape, complexity and distribution of synapses. The authors focus on a model for stellate cells, which have multiple dendrites emerging from a soma. The activity independent component is provided by a random pool of presynaptic sites that represent potential synapses and that release a diffusible signal that promotes dendritic growth. Then a spontaneous activity pattern with some correlation structure is imposed at those presynaptic sites. The strength of these synapses follow a learning rule previously proposed by the lab: synapses strengthen when there is correlated firing across multiple sites, and synapses weaken if there is uncorrelated firing with the relative strength of these processes controlled by available levels of BDNF/proBDNF. Once a synapse is weakened below a threshold, the dendrite branch at that site retracts and loses its sensitivity to the growth signal 

The authors run the simulation and map out how dendrites and synapses evolve and stabilize. They show that dendritic trees growing rapidly and then stabilize by balancing growth and retraction (Figure 2). They also that there is an initial bout of synaptogenesis followed by loss of synapses, reflecting the longer amount of time it takes to weaken a synapse (Figure 3). They analyze how this evolution of dendrites and synapses depends on the correlated firing of synapses (i.e. defined as being in the same "activity group"). They show that in the stabilized phase, synapses that remain connected to a given dendritic branch are likely to be from same activity group (Figure 4). The authors systemically alter the learning rule by changing the available concentration of BDNF, which alters the relative amount of synaptic strengthening, which in turn affects stabilization, density of synapses and interestingly how selective for an activity group one dendrite is (Figure 5). In addition the authors look at how altering the activity-independent factors influences outgrowth (Figure 6). Finally, one of the interesting outcomes is that the resulting dendritic trees represent "optimal wiring" solutions in the sense that dendrites use the shortest distance given the distribution of synapses. They compare this distribute to one published data to see how the model compared to what has been observed experimentally.  

There are many strengths to this study. The consequence of adding the activity-dependent contribution to models of synapto- and dendritogenesis is novel. There is some exploration of parameters space with the motivation of keeping the parameters as well as the generated outcomes close to anatomical data of real dendrites. The paper is also scholarly in its comparison of this approach to previous generative models. This work represented an important advance to our understanding of how learning rules can contribute to dendrite morphogenesis.

We thank the reviewer for the positive evaluation of the work and the suggestions below.

To improve the clarity of the manuscript, we adjusted and fixed some figures and corresponding paragraphs as follows:

(1) We increased the number of ticks and their corresponding numbers in all the figures to make them easier to read and interpret.

(2) In Figure 3 panel d, showing the evolution of synaptic weight, we corrected the upper limit at the yaxis to 1 (from previously 2).

(3) Due to a typo in the implementation of the BDNF concentration, we had to correct the used BDNF concentrations from 49%, 45% and 40%, to 49%, 46.5% and 43% respectively.

(4) The y-axis labels of Figure 6 (old Figure 5) panel e and f were changed to make the plots clearer (e: “morphology change explained (%)” to "effect on morphology (%)", and f: “synapse connection explained (%)” to "effect on connected synapses (%)").

(5) The values for the eta and tau-w in the supplementary Table were corrected. Previously tau-w was falsely 6000 time steps which was corrected to 3000 time steps, and eta was 45% and is now 46.5%.

We believe that all the changes to the manuscript will address the reviewer’s concerns and enhance the clarity and accuracy of the findings described in the manuscript.

Analyse des angles morts de la feuille de route

La feuille de route de la santé mentale et de la psychiatrie, bien qu'ambitieuse et dotée de moyens importants, présente quelques points de vigilance, que l'on pourrait qualifier d'angles morts.

Difficultés de recrutement: Malgré les efforts financiers consentis pour renforcer les équipes soignantes, notamment en pédopsychiatrie, la mise en œuvre de certaines mesures se heurte à la pénurie de professionnels [1, 2].

Ce manque de personnel qualifié limite l'efficacité des actions et retarde l'amélioration de l'accès aux soins, en particulier dans certaines zones géographiques [1].

Adaptation aux spécificités des personnes âgées: Si la feuille de route prévoit des actions dédiées aux personnes âgées, l'adaptation des dispositifs aux particularités de cette population, notamment en matière de suicide, reste à approfondir [3].

L'inclusion dans VigilanS des personnes âgées sur des critères autres qu'une tentative de suicide est envisagée, mais sa mise en œuvre effective n'est pas encore actée [3].

Prise en charge de la souffrance psychique au travail:

Bien que la feuille de route aborde la question de la souffrance psychique au travail, les actions mises en place se concentrent principalement sur les professionnels de santé [4].

Il serait pertinent d'élargir ces actions à l'ensemble des secteurs d'activité pour mieux répondre aux besoins de la population active.

Efficacité de MonSoutienPsy:

Le dispositif MonSoutienPsy, bien qu'ayant enregistré un nombre important de bénéficiaires, soulève des interrogations quant à son efficacité réelle [5].

L'adhésion des psychologues reste à consolider et une évaluation du dispositif est en cours [5].

Manque d'indicateurs d'impact: Pour certaines actions, l'absence d'indicateurs d'impact précis rend difficile l'évaluation de leur efficacité [6, 7].

La mise en place d'un système de suivi et d'évaluation plus robuste permettrait de mieux mesurer les progrès réalisés et d'ajuster les actions si nécessaire.

En plus de ces angles morts, il est important de souligner que la réussite de la feuille de route dépendra également de facteurs externes:

Évolution des déterminants de la santé mentale:

La feuille de route se focalise sur l'accès aux soins et l'accompagnement des personnes souffrant de troubles psychiques, mais accorde une importance moindre aux déterminants de la santé mentale, tels que la précarité, les violences, la discrimination, etc. [8].

Une action plus forte sur ces déterminants est essentielle pour améliorer durablement la santé mentale des Français.

Mobilisation de l'ensemble des acteurs:

La mise en œuvre de la feuille de route nécessite la collaboration de nombreux acteurs, notamment les ministères, les agences régionales de santé, les professionnels de santé, les associations, etc. [9, 10].

Une coordination efficace et une mobilisation de tous les acteurs sont cruciales pour assurer la réussite des actions.

La scolarité dans la feuille de route de la santé mentale et de la psychiatrie

La feuille de route met l'accent sur le rôle crucial de la scolarité dans la promotion de la santé mentale des enfants et des jeunes.

Plusieurs actions spécifiques ciblent le milieu scolaire:

Renforcement des compétences psychosociales (CPS):

L'action 1 de la feuille de route et la mesure 11 des Assises visent à diffuser le plus largement possible les interventions renforçant les CPS.

Ces compétences sont considérées essentielles pour la promotion du bien-être mental et peuvent être mises en place dans tous les milieux de vie, y compris l'école. [1]

Une stratégie intersectorielle de déploiement 2022-2027, co-portée par la Direction Générale de la Santé (DGS) et la Direction générale de l’enseignement scolaire (DGESCO) est en cours. [2]

L'objectif est de créer un environnement continu de soutien au développement des CPS pour les enfants nés en 2037. [3]

Prévention de la souffrance psychique chez les étudiants: La population étudiante est exposée à de nombreux stress et doit bénéficier de repérage et d'interventions précoces. [4]

Le déploiement du secourisme en santé mentale dans les milieux étudiants vise à former 150 000 secouristes d'ici fin 2025. [5]

En 2023, 2 646 étudiants ont été formés aux premiers secours en santé mentale. [6]

Adressage par les services de médecine scolaire pour MonSoutienPsy:

Le dispositif MonSoutienPsy permet aux personnes souffrant de troubles psychiques d’intensité légère à modérée de bénéficier de séances d’accompagnement psychologique. [7]

La loi de financement de la sécurité sociale (LFSS) pour 2024 prévoit la possibilité d’adressage par les services de médecine scolaire, facilitant l'accès des élèves à ce dispositif. [8]

Sensibilisation des médecins scolaires :

Des conseils pour les parents sur le bon usage des écrans et le sommeil ont été ajoutés au carnet de santé de l’enfant, ainsi que des questions dans le cadre des examens obligatoires. [9, 10]

Ces ajouts visent à inciter les médecins, y compris les médecins scolaires, à échanger avec les familles sur ces sujets importants pour la santé mentale des enfants.

En plus de ces actions spécifiques, la feuille de route aborde des thématiques qui ont un impact direct sur le milieu scolaire:

Lutte contre la stigmatisation:

La lutte contre la stigmatisation des troubles psychiques est un élément important de la feuille de route.

Des actions de sensibilisation et d'information du grand public sont prévues, ce qui peut contribuer à créer un environnement plus inclusif et bienveillant dans les écoles. [11]

Amélioration de l'accès aux soins en pédopsychiatrie:

La feuille de route prévoit des mesures pour renforcer l'offre de soins en pédopsychiatrie, notamment par le financement de projets dans les territoires sous-dotés. [12]

Un meilleur accès aux soins spécialisés peut permettre une prise en charge plus précoce et plus efficace des troubles psychiques chez les enfants et les adolescents scolarisés.

Il est important de noter que la feuille de route ne mentionne pas explicitement de mesures pour :

**Former les enseignants à la détection des troubles psychiques: **

La formation des enseignants à la détection des signes de souffrance psychique chez les élèves pourrait permettre une intervention plus précoce et une meilleure orientation vers les professionnels de santé.

Adapter les programmes scolaires pour inclure des modules d'éducation à la santé mentale :

L'inclusion de modules d'éducation à la santé mentale dans les programmes scolaires pourrait contribuer à sensibiliser les élèves dès le plus jeune âge à l'importance du bien-être mental et à la prévention des troubles psychiques.

En conclusion, la feuille de route de la santé mentale et de la psychiatrie accorde une place importante à la scolarité, en reconnaissant son rôle clé dans la promotion de la santé mentale des enfants et des jeunes.

Toutefois, certains aspects pourraient être renforcés, notamment la formation des enseignants et l'adaptation des programmes scolaires, pour une prise en compte plus globale de la santé mentale dans le milieu éducatif.

Reviewer #1 (Public review):

Summary:

This work uses a novel, ethologically relevant behavioral task to explore decision-making paradigms in C. elegans foraging behavior. By rigorously quantifying multiple features of animal behavior as they navigate in a patch food environment, the authors provide strong evidence that worms exhibit one of three qualitatively distinct behavioral responses upon encountering a patch:<br /> (1) "search", in which the encountered patch is below the detection threshold;<br /> (2) "sample", in which animals detect a patch encounter and reduce their motor speed, but do not stay to exploit the resource and are therefore considered to have "rejected" it; and<br /> (3) "exploit", in which animals "accept" the patch and exploit the resource for tens of minutes.<br /> Interestingly, the probability of these outcomes varies with the density of the patch as well as the prior experience of the animal. Together, these experiments provide an interesting new framework for understanding the ability of the C. elegans nervous system to use sensory information and internal state to implement behavioral state decisions.

Strengths:

(1) The work uses a novel, neuroethologically-inspired approach to studying foraging behavior.

(2) The studies are carried out with an exceptional level of quantitative rigor and attention to detail.

(3) Powerful quantitative modeling approaches including GLMs are used to study the behavioral states that worms enter upon encountering food, and the parameters that govern the decision about which state to enter.

(4) The work provides strong evidence that C. elegans can make 'accept-reject' decisions upon encountering a food resource.

(5) Accept-reject decisions depend on the quality of the food resource encountered as well as on internally represented features that provide measurements of multiple dimensions of internal state, including feeding status and time.

Weaknesses:

(1) The authors repeatedly assert that an individual's behavior in the foraging assay depends on its prior history (particularly cultivation conditions). While this seems like a reasonable expectation, it is not fully fleshed out. The work would benefit from studies in which animals are raised on more or less abundant food before the behavioral task.

(2) The authors convincingly show that the probability of particular behavioral outcomes occurring upon patch encounter depends on time-associated parameters (time since last patch encounter, time since last patch exploitation). There are two concerns here. First, it is not clear how these values are initialized - i.e., what values are used for the first occurrence of each behavioral state? More importantly, the authors don't seem to consider the simplest time parameter, the time since the start of the assay (or time since worm transfer). Transferring animals to a new environment can be associated with significant mechanical stimulus, and it seems quite possible that transferring animals causes them to enter a state of arousal. This arousal, which certainly could alter sensory function or decision-making, would likely decay with time. It would be interesting to know how well the model performs using time since assay starts as the only time-dependent parameter.

(3) Similarly, Figures 2L and M clearly show that the probability of a search event occurring upon a patch encounter decreases markedly with time. Because search events are interpreted as a failure to detect a patch, this implies that the detection of (dilute) patches becomes more efficient with time. It would be useful for the authors to consider this possibility as well as potential explanations, which might be related to the point above.

(4) Based on their results with mec-4 and osm-6 mutants, the authors assert that chemosensation, rather than mechanosensation, likely accounts for animals' ability to measure patch density. This argument is not well-supported: mec-4 is required only for the function of the six non-ciliated light-touch neurons (AVM, PVM, ALML/R, PLML/R). In contrast, osm-6 is expected to disrupt the function of the ciliated dopaminergic mechanosensory neurons CEP, ADE, and PDE, which have previously been shown to detect the presence of bacteria (Sawin et al 2000). Thus, the paper's results are entirely consistent with an important role of mechanosensation in detecting bacterial abundance. Along these lines, it would be useful for the authors to speculate on why osm-6 mutants are more, rather than less, likely to "accept" when encountering a patch.

(5) While the evidence for the accept-reject framework is strong, it would be useful for the authors to provide a bit more discussion about the null hypothesis and associated expectations. In other words, what would worm behavior in this assay look like if animals were not able to make accept-reject decisions, relying only on exploit-explore decisions that depend on modulation of food-leaving probability?

Reviewer #3 (Public review):

Summary:

In this study by Haley et al, the authors investigated explore-exploit foraging using C. elegans as a model system. Through an elegant set of patchy environment assays, the authors built a GLM based on past experience that predicts whether an animal will decide to stay on a patch to feed and exploit that resource, instead of choosing to leave and explore other patches.

Strengths:

I really enjoyed reading this paper. The experiments are simple and elegant, and address fundamental questions of foraging theory in a well-defined system. The experimental design is thoroughly vetted, and the authors provide a considerable volume of data to prove their points. My only criticisms have to do with the data interpretation, which I think is easily addressable.

Weaknesses:

(1) Sensing vs. non-sensing

The authors claim that when animals encounter dilute food patches, they do not sense them, as evidenced by the shallow deceleration that occurs when animals encounter these patches. This seems ethologically inaccurate. There is a critical difference between not sensing a stimulus, and not reacting to it. Animals sense numerous stimuli from their environment, but often only behaviorally respond to a fraction of them, depending on their attention and arousal state. With regard to C. elegans, it is well-established that their amphid chemosensory neurons are capable of detecting very dilute concentrations of odors. In addition, the authors provide evidence that osm-6 animals have altered exploit behaviors, further supporting the importance of amphid chemosensory neurons in this behavior.

(2) Search vs. sample & sensing vs. non-sensing

In Figures 2H and 2I, the authors claim that there are three behavioral states based on quantifying average velocity, encounter duration, and acceleration, but I only see three. Based on density distributions alone, there really only seem to be 2 distributions, not 3. The authors claim there are three, but to come to this conclusion, they used a QDA, which inherently is based on the authors training the model to detect three states based on prior annotations. Did the authors perform a model test, such as the Bayesian Information Criterion, to confirm whether 2 vs. 3 Gaussians is statistically significant? It seems like the authors are trying to impose two states on a phenomenon with a broad distribution. This seems very similar to the results observed for roaming vs. dwelling experiments, which again, are essentially two behavioral states.

(4) History-dependence of the GLM

The logistic GLM seems like a logical way to model a binary choice, and I think the parameters you chose are certainly important. However, the framing of them seems odd to me. I do not doubt the animals are assessing the current state of the patch with an assessment of past experience; that makes perfect logical sense. However, it seems odd to reduce past experience to the categories of recently exploited patch, recently encountered patch, and time since last exploitation. This implies the animals have some way of discriminating these past patch experiences and committing them to memory. Also, it seems logical that the time on these patches, not just their density, should also matter, just as the time without food matters. Time is inherent to memory. This model also imposes a prior categorization in trying to distinguish between sensed vs. not-sensed patches, which I criticized earlier. Only "sensed" patches are used in the model, but it is questionable whether worms genuinely do not "sense" these patches.

(5) osm-6

The osm-6 results are interesting. This seems to indicate that the worms are still sensing the food, but are unable to assess quality, therefore the default response is to exploit. How do you think the worms are sensing the food? Clearly, they sense it, but without the amphid sensory neurons, and not mechanosensation. Perhaps feeding is important? Could you speculate on this?

(7) Impact:

I think this work will have a solid impact on the field, as it provides tangible variables to test how animals assess their environment and decide to exploit resources. I think the strength of this research could be strengthened by a reassessment of their model that would both simplify it and provide testable timescales of satiety/starvation memory.

Author response:

We thank the reviewers for their thoughtful comments. We are working to revise our manuscript and address each of the reviewers comments. A summary of our planned revisions and responses to some of the reviewers’ major concerns are included below.

Cultivation Density: Reviewers #1 and #2 suggested that additional studies testing the effects of varying bacterial density during animal development (cultivation) would strengthen our findings. While we agree with the reviewers that this is a very interesting experiment, it is not feasible. Indeed, we attempted this experiment but found it nontrivial to maintain stable bacterial density conditions over long timescales as this requires matching the rate of bacterial growth with the rate of bacterial consumption. Despite our best efforts, we have not been able to identify conditions that satisfy these requirements. We will focus our revised manuscript to include only assertions about the effects of recent experiences.

Transfer Method: Reviewers #1 and #2 expressed concern that the stress of transferring animals to a new plate may have resulted in an increased arousal state and thus a greater probability of rejecting patches. We thank the reviewers for this thoughtful remark and plan to conduct additional analyses to address this hypothesis. We did, however, anticipate this possibility and, to mitigate the stress of moving, we used an agar plug method where animals were transferred using the flat surface of small cylinders of agar. Importantly, the use of agar as a medium to transfer animals provides minimal disruption to their environment as all physical properties (e.g. temperature, humidity, surface tension) are maintained. Qualitatively, we observe no marked change in behavior from before to after transfer with the agar plug method, especially as compared to the often drastic changes observed when using a metal or eyelash pick.

Time Parameter: Related to the transfer method, Reviewer #1 expressed concern that the simplest time parameter (time since start of the assay) might better predict animal behavior. We thank the reviewer for pointing out the need to specifically test whether the time-dependent change in explore-exploit decision-making corresponds better with satiety (time off patch) or arousal (time since transfer/start of assay) state. We will conduct additional analyses to address these alternative hypotheses.

Parameter Initialization: Reviewer #1 pointed out an oversight in our methods section regarding the model parameter values used for the first encounter. We plan to clarify the initialization of parameters in the manuscript. In short, for the first patch encounter where k = 1:

● ρk is the relative density of the first patch.

● τs is the duration of time spent off food since the beginning of the recorded experiment. For the first patch, this is equivalent to the total time elapsed.

● ρh is the approximated relative density of the bacterial patch on the acclimation plates (see Assay preparation and recording in Methods). Acclimation plates contained one large 200 µL patch seeded with OD600 = 1 and grown for a total of ~48 hours. As with all patches, the relative density was estimated from experiments using fluorescent bacteria OP50-GFP as described in Bacterial patch density estimation in Methods.

● ρe is equivalent to ρh.

Sensing vs. non-sensing: Reviewer #3 suggested that the term “non-sensing” may not be ethologically accurate. We thank the reviewer for their comment and agree that we do not know for certain whether the animals sensed these patches or were merely non-responsive to them. We are, however, confident that these encounters lack evidence of sensing. Specifically, we note that our analyses used to classify events as sensing or non-sensing examined whether an animal’s slow-down upon patch entry could be distinguished from either that of events where animals exploited or that of encounters with patches lacking bacteria. We found that  “non-sensing” encounters are indeed indistinguishable from encounters with bacteria-free patches where there are no bacteria to be sensed (see Figure 2 - Supplement 7C-D and Patch encounter classification as sensing or non-sensing in Methods). Regardless, we agree with the reviewer that all that can be asserted for certain about these events is that animals do not respond to the bacterial patch in any way that we measured. Therefore, we will replace the term “non-sensing” with “non-responding” to better indicate the ethological interpretation of these events.

Time-dependent changes in sensing vs. non-sensing: Reviewer #1 remarked that the sensation of dilute patches increases with time. We agree with the reviewer that we observe increased responsiveness to dilute patches with time. Although this is interesting, our primary focus was on what decision an animal made given that they clearly sensed the presence of the bacterial patch. Nonetheless, we will add this observation to the discussion as an area of future work to investigate the sensory mechanisms behind this effect.

Classification of sensing vs. non-sensing: Reviewers #2 and #3 expressed concerns about the validity of the two clusters identified using the semi-supervised QDA approach described. We are grateful to the reviewers for pointing out the difficulty in visualizing the clusters and the need for additional clarity in explaining the supervised labeling. We will use additional visualizations and methods to validate the clusters we have discovered. Specifically, we aim to provide additional evidence that the sensing vs. nonsensing data is bi-modal (i.e. a two-cluster classification method fits best). Further, it seems that there may be some confusion as to how we arrived at 3 encounter types (i.e. search, sample, exploit) that we plan to clarify in the manuscript. Specifically, it’s important to note that two methods were used on two different (albeit related) sets of parameters. We first used a two-cluster GMM to classify encounters as explore or exploit. We then used a two-cluster semi-supervised QDA to classify encounters as sensing or non-sensing (to be changed to “non-responding”, see above response) using a different set of parameters. We thus separated the explore cluster into two (sensing and non-sensing exploratory events) resulting in three total encounter types: exploit, sample (explore/sensing), and search (explore/non-sensing). We will clarify this in the text. Additionally, we will clarify the labelling used for “supervising” QDA. Specifically, we made two simple assumptions: 1) animals must have sensed the patch if they exploited it and 2) animals must not have sensed the patch if there were no bacteria to sense. Thus, we labeled encounters as sensing if they were found to be exploitatory as we assume that sensation is prerequisite to exploitation; and we labeled encounters as non-sensing for events where animals encountered patches lacking bacteria (OD600 = 0). All other points were non-labeled prior to learning the model. In this way, our labels were based on the experimental design and results of the GMM, an unsupervised method; rather than any expectations we had about what sensing should look like. The semi-supervised QDA method then used these initial labels to iteratively fit a paraboloid that best separated these clusters, by minimizing the posterior variance of classification.

Accept-reject vs. stay-switch: Reviewers #1 and #2 ask for additional discussion on how the accept-reject decision-making framework differs from the stay-switch framework. We thank the reviewers for alerting us to this gap in our discussion. We intend to clarify that these frameworks ask two different types of questions (i.e. “Do you want to eat it?” versus “If so, how long do you want to eat it for?”). These concepts are well described in canonical foraging theory literature (see Pyke, Pulliam & Charnov 1977 for a review on the subject) and are easily distinguishable for animals that forage using the following framework: 1) search for prey, 2) encounter prey from a distance, 3) identify prey type, 4) decide to pursue (accept-reject decision), 5) pursue and capture the prey, 6) exploit prey, and 7) decide to stop exploiting and start searching again (stay-switch decision). In this case, it is easy to see the distinction between accept-reject and stay-switch decisions. However, in some scenarios, animals must physically encounter prey prior to identification and then must make an accept-reject decision. In these cases where pursuit and capture are not visualized, it is harder to distinguish between accept-reject and stay-switch decisions. In our experiments, we find significant bimodality in encounter duration (see Figure 2H) where short duration (exploratory) encounters appear to represent a lower bound where animals spend the minimum amount of time possible on a patch (less than 2 minutes), which we interpret as a rejection of the patch. On the other hand, exploitatory encounters span a large range of durations from 2 to 60+ minutes which we interpret as an initial acceptance of the patch followed by a series of stay-switch decisions which determine the overall duration of the encounter. While one could certainly model our data using only stay-switch decision-making, we ascertain that an encounter of minimal duration is better interpreted ethologically as a rejection than as an immediate switch decision. We will revise the text to further extrapolate upon our point of view on this somewhat philosophical distinction and what it predicts about C. elegans behavior.

Sensory mutant behavior: Reviewers #1 and #3 ask for further speculation on the observed behavior of osm-6 and mec-4 animals. We will further elaborate on our findings, how they relate to previous studies, and what they suggest about the mechanisms behind these foraging decisions.

Model design: Reviewer #3 suggested several alterations to the behavioral model. While the proposed model seems entirely reasonable and could aid in elucidating the time component of how prior experience affects decision-making, we chose the present model based on our experience with model selection using these data. Indeed, as the reviewer suggested, we did a great number of analyses involving model selection including model selection criteria (AIC, BIC) and optimization with regularization techniques (LASSO and elastic nets). We found that the problem of model selection was compounded by the enormous array of highly correlated variables we had to choose from. Additionally, we found that both interaction terms and non-linear terms of our task variables could be predictive of accept-reject decisions but that the precise set of terms selected depended sensitively on which model selection technique was used and generally made rather small contributions to prediction. The diverse array of results and combinatorial number of predictors to possibly include failed to add anything of interpretable value. We therefore chose to take a different approach to this problem. Rather than trying to determine what the “best” model was we instead asked whether a minimal model could be used to answer a set of core questions. Indeed, our goal was not maximal predictive performance but rather to distinguish between the effects of different influences enough to determine if encounter history had a significant, independent effect on decision making. We thus chose to only include task variables that spanned the most basic components of behavioral mechanisms to ask very specific questions. For example, we selected a time variable that we thought best encapsulated satiety. While we could have included many additional terms, or made different choices about which terms to include, based on our analyses these choices would not have qualitatively changed our results. Further, we sought to validate the parameters we chose with additional studies (i.e. food-deprived and sensory mutant animals). We regard our study as an initial foray into demonstrating accept-reject decision-making in nematodes. The exact mechanisms and, consequently, the best model design is therefore beyond the scope of this study. Lastly, Reviewer #3 criticized the use of only sensed patches in the model. While we acknowledge that we are not certain as to whether the “non-sensing” encounters are truly not sensed, we find qualitatively similar results when including all exploratory patches in our analyses. In fact, when all encounters are used, we find stronger correlations between our task variables and the accept-reject decision. However, we take the position that sensation is necessary for decision-making and thus believe that while our model’s predictive performance may be better using all encounters, the interpretation of our findings is stronger when we only include sensing events.

Reviewer #2 (Public review):

Summary:

The manuscript investigates the role of the Mid1 gene in hippocampal (HPC) development and its contribution to Opitz G/BBB syndrome (OS), which is characterized by neurological deficits and structural abnormalities. The authors use a knockout mouse model (Mid1-/y) to elucidate the underlying molecular mechanisms that contribute to learning and memory impairments. They demonstrate that Mid1 gene deletion leads to reduced synaptic plasticity, abnormal neural rhythms, and decreased cognitive functions, providing a mechanistic explanation for the neurological deficits seen in OS patients. This study addresses an important gap in understanding the neural mechanisms underlying Opitz G/BBB syndrome and provides substantial evidence that the Mid1 gene plays a critical role in hippocampal function and cognition.

Strengths:

Understanding the role of Mid1 in HPC development could have broader implications for neurodevelopmental disorders beyond OS, particularly in conditions associated with synaptic dysfunction or memory impairments. The study's focus on the impact of Mid1 on the cAMP signaling pathway, BDNF expression, and synaptic plasticity offers novel mechanisms relevant to both neurodevelopment and neurodegeneration. Moreover, the combination of RNA-seq, electrophysiological measurements, and histological staining provides a multidimensional approach to understanding how Mid1 influences neuronal function and structure.

Weaknesses:

(1) The introduction is insufficient, and the number of references is too low. With only nine references, there isn't enough context to adequately explain the background and previous evidence.

(2) The specificity of behavioral deficits is lacking. The authors indicate learning and memory dysfunction, yet the Y-maze and Morris water maze primarily assess spatial memory. Additional behavioral tests, such as the novel object recognition test for recognition memory or fear conditioning for associative learning, should be included to provide a more comprehensive assessment.

(3) The manuscript mentions decreased synaptic plasticity but lacks thorough investigation; a more detailed analysis of long-term potentiation (LTP) or depression (LTD) would strengthen the claims. Additionally, while spine morphology is analyzed, incorporating electrophysiological measurements of synaptic strength would better correlate structural changes with functional outcomes.

(4) The authors performed H&E staining to count the number of hippocampal pyramidal neurons; however, H&E lacks specificity for identifying pyramidal neurons. Neuronal-specific IHC staining would be more appropriate for this quantification. Additionally, the manuscript does not mention the counting method used, which should be clarified.

(5) Information on the knockout mice used in the study is missing from the Methods section. Additionally, the sex of the mice should be specified, as exploring potential sex-specific differences in the impact of Mid1 deletion could significantly enhance the study's findings.

More detail is needed here, include how you determined the limit of detection of your assay. State that you used standard curves to estimate limit of detection (LOD), but see Klymus et al. (2020). Given that your assays are for qualitative purposes, the limit of quantification (LOQ) is likely not relevant in your case. Please verify this to clarify in the main text why the qPCR efficiency may be irrelevant for your assays, but the LOD is.

Depending on who you will get as an examiner, it may be worthwhile to also mention that you did the testing according to the MIQE guidelines, which I think were incorporated into this paper (see thier Appendix S1 for the checklist):

• Thalinger, B., Deiner, K., Harper, L. R., Rees, H. C., Blackman, R. C., Sint, D., ... & Bruce, K. (2021). A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environmental DNA, 3(4), 823-836.

• Bustin, S. A. (2024). Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE. Molecular aspects of medicine, 96, 101249.

• Klymus, K. E., Merkes, C. M., Allison, M. J., Goldberg, C. S., Helbing, C. C., Hunter, M. E., Jackson, C. A., Lance, R. F., Mangan, A. M., Monroe, E. M., Piaggio, A. J., Stokdyk, J. P., Wilson, C. C., & Richter, C. A. (2020). Reporting the limits of detection and quantification for environmental DNA assays. Environmental DNA, 2, 271–282. https://doi.org/10.1002/edn3.29

Probably interesting for background:

Eklund, A., Frank, J., Nilsson, L., Zetterberg, A., & Mansson, J. 2024. Times of trouble - Seasonal variation in number and severity of attacks on sheep caused by large carnivores and eagles in Sweden. European Journal of Wildlife Research, 70(9): 2-11. DOI: https://doi.org/10.1007/s10344-023-01761-4

Kvalshaug, O.J. 2013. Inter-specific patterns of depredation on domestic sheep and semi-domestic reindeer in Norway, by a large predator guild. Master Thesis, Norwegian University of Life Sciences, 36.

Linnell, J.D.C., Nilsen, E.B., Lande, U., Herfindal, I., Odden, J., & Skogen, K. 2005. Zoning as a means of mitigating conflicts with large carnivores: Principles and reality. Conservation Biology Series-Cambridge, 9: 163-175. DOI: https://doi.org/10.1017/cbo9780511614774.011

Mabille, G., Stien, A., Tveraa, T., Mysterud, A., Brøseth, H., & Linnell, J.D.C. 2015. Sheep farming and large carnivores: What are the factors influencing claimed losses? Ecosphere, 6(5): 1-17. DOI: https://doi.org/10.1890/es14-00444.1

Strand, G., Hansen, I., De Boon, A., & Sandström, C. 2019. Carnivore Management Zones and their Impact on Sheep Farming in Norway. Environmental Management, 64: 537-552. DOI: https://doi.org/10.1007/s00267-019-01212-4

Strand, G. 2020. The combined effects of centralization and carnivore management on sheep farmers and sheep farming in Norway. Human Dimensions of Wildlife, 26(4): 321-336. DOI: https://doi.org/10.1080/10871209.2020.1818895

More detail is needed here, include how you determined the limit of detection of your assay. State that you used standard curves to estimate limit of detection (LOD), but see Klymus et al. (2020). Given that your assays are for qualitative purposes, the limit of quantification (LOQ) is likely not relevant in your case. Please verify this to clarify in the main text why the qPCR efficiency may be irrelevant for your assays, but the LOD is.

Depending on who you will get as an examiner, it may be worthwhile to also mention that you did the testing according to the MIQE guidelines, which I think were incorporated into this paper (see thier Appendix S1 for the checklist):

• Thalinger, B., Deiner, K., Harper, L. R., Rees, H. C., Blackman, R. C., Sint, D., ... & Bruce, K. (2021). A validation scale to determine the readiness of environmental DNA assays for routine species monitoring. Environmental DNA, 3(4), 823-836.

• Bustin, S. A. (2024). Improving the quality of quantitative polymerase chain reaction experiments: 15 years of MIQE. Molecular aspects of medicine, 96, 101249.

• Klymus, K. E., Merkes, C. M., Allison, M. J., Goldberg, C. S., Helbing, C. C., Hunter, M. E., Jackson, C. A., Lance, R. F., Mangan, A. M., Monroe, E. M., Piaggio, A. J., Stokdyk, J. P., Wilson, C. C., & Richter, C. A. (2020). Reporting the limits of detection and quantification for environmental DNA assays. Environmental DNA, 2, 271–282. https://doi.org/10.1002/edn3.29

Reviewer #2 (Public review):

Summary:

The Flower protein is expressed in various cell types, including neurons. Previous studies in flies have proposed that Flower plays a role in neuronal endocytosis by functioning as a Ca2+ channel. However, its precise physiological roles and molecular mechanisms in neurons remain largely unclear. This study employs C. elegans as a model to explore the function and mechanism of FLWR-1, the C. elegans homolog of Flower. This study offers intriguing observations that could potentially challenge or expand our current understanding of the Flower protein. Nevertheless, further clarification or additional experiments are required to substantiate the study's conclusions.

Strengths:

A range of approaches was employed, including the use of a flwr-1 knockout strain, assessment of cholinergic synaptic activity via analyzing aldicarb (a cholinesterase inhibitor) sensitivity, imaging Ca2+ dynamics with GCaMP3, analyzing pHluorin fluorescence, examination of presynaptic ultrastructure by EM, and recording postsynaptic currents at the neuromuscular junction. The findings include notable observations on the effects of flwr-1 knockout, such as increased Ca2+ levels in motor neurons, changes in endosome numbers in motor neurons, altered aldicarb sensitivity, and potential involvement of a Ca2+-ATPase and PIP2 binding in FLWR-1's function.

Weaknesses:

(1) The observation that flwr-1 knockout increases Ca2+ levels in motor neurons is notable, especially as it contrasts with prior findings in flies. The authors propose that elevated Ca2+ levels in flwr-1 knockout motor neurons may stem from "deregulation of MCA-3" (a Ca2+ ATPase in the plasma membrane) due to FLWR-1 loss. However, this conclusion relies on limited and somewhat inconclusive data (Figure 7). Additional experiments could clarify FLWR-1's role in MCA-3 regulation. For instance, it would be informative to investigate whether mutations in other genes that cause elevated cytosolic Ca2+ produce similar effects, whether MCA-3 physically interacts with FLWR-1, and whether MCA-3 expression is reduced in the flwr-1 knockout.

(2) In silico analysis identified residues R27 and K31 as potential PIP2 binding sites in FLWR-1. The authors observed that FLWR-1(R27A/K31A) was less effective than wild-type FLWR-1 in rescuing the aldicarb sensitivity phenotype of the flwr-1 knockout, suggesting that FLWR-1 function may depend on PIP2 binding at these two residues. Given that mutations in various residues can impair protein function non-specifically, additional studies may be needed to confirm the significance of these residues for PIP2 binding and FLWR-1 function. In addition, the authors might consider explicitly discussing how this finding aligns or contrasts with the results of a previous study in flies, where alanine substitutions at K29 and R33 impaired a Flower-related function (Li et al., eLife 2020).

(3) A primary conclusion from the EM data was that FLWR-1 participates in the breakdown, rather than the formation, of bulk endosomes (lines 20-22). However, the reasoning behind this conclusion is somewhat unclear. Adding more explicit explanations in the Results section would help clarify and strengthen this interpretation.

(4) The aldicarb assay results in Figure 3 are intriguing, indicating that reduced GABAergic neuron activity alone accounts for the flwr-1 mutant's hyposensitivity to aldicarb. Given that cholinergic motor neurons also showed increased activity in the flwr-1 mutant, one might expect the flwr-1 mutant to display hypersensitivity to aldicarb in the unc-47 knockout background. However, this was not observed. The authors might consider validating their conclusion with an alternative approach or, at the minimum, providing a plausible explanation for the unexpected result. Since aldicarb-induced paralysis can be influenced by factors beyond acetylcholine release from cholinergic motor neurons, interpreting aldicarb assay results with caution may be advisable. This is especially relevant here, as FLWR-1 function in muscle cells also impacts aldicarb sensitivity (Figure S3B). Previous electrophysiological studies have suggested that aldicarb sensitivity assays may sometimes yield misleading conclusions regarding protein roles in acetylcholine release.

(5) Previous studies have suggested that the Flower protein functions as a Ca²⁺ channel, with a conserved glutamate residue at the putative selectivity filter being essential for this role. However, mutating this conserved residue (E74Q) in C. elegans FLWR-1 altered aldicarb sensitivity in a direction opposite to what would be expected for a Ca²⁺ channel function. Moreover, the authors observed that E74 of FLWR-1 is not located near a potential conduction pathway in the FLWR-1 tetramer, as predicted by Alphafold3. These findings raise the possibility that Flower may not function as a Ca2+ channel. While this is a potentially significant discovery, further experiments are needed to confirm and expand upon these results.

(6) Phrases like "increased excitability" and "increased Ca2+ influx" are used throughout the manuscript. However, there is no direct evidence that motor neurons exhibit increased excitability or Ca2+ influx. The authors appear to interpret the elevated Ca2+ signal in motor neurons as indicative of both increased excitability and Ca2+ influx. However, this elevated Ca2+ signal in the flwr-1 mutant could occur independently of changes in excitability or Ca2+ influx, such as in cases of reduced MCA-3 activity. The authors may wish to consider alternative terminology that more accurately reflects their findings.

Reviewer #1 (Public review):

Summary:

This paper is a relevant overview of the currently published literature on low-intensity focussed ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems nevertheless relevant to summarize the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data-free permutation of the parameter space.<br /> The meta-analysis concludes that there are, at best, weak trends toward specific parameters predicting the direction of the stimulation effects. The data have been incorporated into an open database, that will ideally continue to be populated by the community and thereby become a helpful resource as the field moves forward.

Strengths:

The current state of human TUS is concisely and well summarized. The methods of the meta-analysis are appropriate. The database is a valuable resource.

Weaknesses:

These are not so much weaknesses but rather comments and suggestions that the authors may want to consider.

(1) I may have missed this, but how will the database be curated going forward? The resource will only be as useful as the quality of data entry, which, given the complexity of TUS can easily be done incorrectly.

(2) It would be helpful to report the full statistics and effect sizes for all analyses. At times, only p-values are given. The meta-analysis only provides weak evidence (judged by the p-values) for two parameters having a predictive effect on the direction of neuromodulation. This reviewer thinks a stronger statement is warranted that there is currently no good evidence for duty cycle or sonication direction predicting outcome (though I caveat this given the full stats aren't reported). The concern here is that some readers may gallop away with the impression that the evidence is compelling because the p-value is on the correct side of 0.05.

(3) This reviewer thinks the issue of (independent) replication should be more forcefully discussed and highlighted. The overall motivation for the present paper is clearly and thoughtfully articulated, but perhaps the authors agree that the role that replication has to play in a nascent field such as TUS is worth considering.

(4) A related point is that many of the results come from the same groups (the so-called theta-TUS protocol being a clear example). The analysis could factor this in, but it may be helpful to either signpost independent replications, which studies come from the same groups, or both.

(5) The recent study by Bao et al 2024 J Phys might be worth including, not least because it fails to replicate the results on theta TUS that had been limited to the same group so far (by reporting, in essence, the opposite result).

(6) The summary of TUS effects is useful and concise. Two aspects may warrant highlighting, if anything to safeguard against overly simplistic heuristics for the application of TUS from less experienced users. First, could the effects of sonication (enhancing vs suppressing) depend on the targeted structure? Across the cortex, this may be similar, but for subcortical structures such as the basal ganglia, thalamus, etc, the idiosyncratic anatomy, connectivity, and composition of neurons may well lead to different net outcomes. Do the models mentioned in this paper account for that or allow for exploring this? And is it worth highlighting that simple heuristics that assume the effects of a given TUS protocol are uniform across the entire brain risk oversimplification or could be plain wrong? Second, and related, there seems to be the implicit assumption (not necessarily made by the authors) that the effects of a given protocol in a healthy population transfer like for like to a patient population (if TUS protocol X is enhancing in healthy subjects, I can use it for enhancement in patient group Y). This reviewer does not know to which degree this is valid or not, but it seems simplistic or risky. Many neurological and psychiatric disorders alter neurotransmission, and/or lead to morphological and structural changes that would seem capable of influencing the impact of TUS. If the authors agree, this issue might be worth highlighting.

Reviewer #2 (Public review):

Park et al. set out to test two competing hypotheses about the role of the medial prefrontal cortex (PFC) in cognitive control, the ability to use task-relevant cues and ignore task-irrelevant cues to guide behavior. The "central computation" hypothesis assumes that cognitive control relies on computations performed by the PFC, which then interacts with other brain regions to accomplish the task. Alternatively, the "local computation" hypothesis suggests that computations necessary for cognitive control are carried out by other brain regions that have been shown to be essential for cognitive control tasks, such as the dorsal hippocampus and the thalamus. If the central computation hypothesis is correct, PFC lesions should disrupt cognitive control. Alternatively, if the local computation hypothesis is correct, cognitive control would be spared after PFC lesions. The task used to assess cognitive control is the active place avoidance task in which rats must avoid a section of a rotating arena using the stationary room cues and ignoring the local olfactory cues on the rotating platform. Performance on this task has previously been shown to be disrupted by hippocampal lesions and hippocampal ensembles dynamically represent the room and arena depending on the animal's proximity to the shock zone. They found no group (lesion vs. sham) differences in the three behavioral parameters tested: distance traveled, latency to enter the shock zone, and number of shock zone entries for both the standard task and the "conflict" task in which the shock zone was rotated by 180 degrees. The only significant difference was the savings index; the lesion group entered the new shock zone more often than the sham group during the first 5 minutes of the second conflict session. This deficit was interpreted as a cognitive flexibility deficit rather than a cognitive control failure. Next, the authors compared cytochrome oxidase activity between sham and lesion groups in 14 brain regions and found that only the amygdala showed significant elevation in the lesion vs. sham group. Pairwise correlation analysis revealed a striking difference between groups, with many correlations between regions lost in the lesion group (between reuniens and hippocampus, reuniens and amygdala and a correlation between dorsal CA1 and central amygdala that appeared in the lesion group and were absent in the sham group. Finally, the authors assessed dorsal hippocampal representations of the spatial frame (arena vs. room) and found no differences between lesion and sham groups. The only difference in hippocampal activity was reduced overdispersion in the lesion group compared to the sham group on the pretraining session only and this difference disappeared after the task began. Collectively, the authors interpret their findings as supporting the local computation hypothesis; computations necessary for cognitive control occur in brain regions other than the PFC.

Strengths:

(1) The data were collected in a rigorous way with experimental blinding and appropriate statistical analyses.

(2) Multiple approaches were used to assess differences between lesion and sham groups, including behavior, metabolic activity in multiple brain regions, and hippocampal single-unit recording.

Weaknesses:

(1) Only male rats were used with no justification provided for excluding females from the sample.

(2) The conceptual framework used to interpret the findings was to present two competing hypotheses with mutually exclusive predictions about the impact of PFC lesions on cognitive control. The authors then use mainly null findings as evidence in support of the local computation hypothesis. They acknowledge that some people may question the notion that the active place avoidance task indeed requires cognitive control, but then call the argument "circular" because PFC has to be involved in cognitive control. This assertion does not address the possibility that the active place avoidance task simply does not require cognitive control.

(3) The authors did not link the CO activity with the behavioral parameters even though the CO imaging was done on a subset of the animals that ran the behavioral task nor did they make any attempt to interpret these findings in light of the two competing hypotheses posed in the introduction. Moreover, the discussion lacks any mechanistic interpretations of the findings. For example, there are no attempts to explain why amygdala activity and its correlation with dCA1 activity might be higher in the PFC lesioned group.

(4) Publishing null results is important to avoid wasting animals, time, and money. This study's results will have a significant impact on how the field views the role of the PFC in cognitive control. Whether or not some people reject the notion that the active place avoidance task measures cognitive control, the findings are solid and can serve as a starting point for generating hypotheses about how brain networks change when deprived of PFC input.

Reviewer #1 (Public review):

Summary:

The study by Xu and colleagues provides a useful study of brainstem circuits involved in evoked respiratory reflexes that they define to be cough or cough-like in nature. The study is conducted in mice which has the benefit of allowing for the use of modern transgenic tools, although many of the experiments end up using viral vector-based approaches that could be deployed in any species. The disadvantage of the mouse model is understanding the true identity of the respiratory event that is defined as cough. This limitation requires careful interrogation in order to understand the biology of the circuit under investigation. In this respect, the authors provide an incomplete description of a putative cough pathway linking the caudal spinal trigeminal nucleus with the ventral respiratory group. Neurons assigned as CaMKII+ with putative inputs from the paratrigeminal nucleus are central to this circuit, although the evidence for each of these claims is relatively weak or non-existent. Overall, the study employs interesting methods but limitations in methods and details of methods reduce interpretation of the study outcomes.

Strengths:

The use of modern methods to investigate brainstem circuits involved in an essential respiratory reflex.

Weaknesses:

(1) The most significant issue that needs careful consideration is the exact respiratory response, which is called a cough. The authors show a trace from their plethysmography recordings and superimpose the 3 phases of cough (inspiration, compression, expiration) with confidence, yet the parameters used to delineate these phases are unclear. Of more concern, an identical respiratory trace was reported recently as a sneeze in Jiang et al Cell 2024 (PMID 39243765). Comparing Figure 1 in the Xu study with Figure 5 in the Jiang study, it is impossible to see any difference in the respiratory trace that would allow the assignment of one as cough and the other as sneeze. The audio signals also look remarkably similar and the purported cough signal in the Jiang study is quite different. Gannot et al Nat Neurosci 2024 (PMID 38977887) seems to agree with Xu in the identity of a cough signal, but Li et al Cell 2021 (PMID 34133943) again labels these as sneezes. One of the older studies that tried to classify respiratory signals in mice (Chen et al PlosONE 2013) labeled the Jiang cough trace as a deep inspiration, while sneeze looks different again. To add further confusion, Zhang et al AJP 2017 (PMID 28228416 ) provide yet another respiratory plethysmography trace that they define as a cough, and label responses discussed above as expiration reflexes. This begs the question - who, if anyone, is correct? Interpreting the circuits underlying these peculiar mouse responses depends on accuracy in defining the response in the first instance.

(2) The involvement of the causal nSp5 in cough is an unexpected finding. Some understanding of if and how vagal afferent inputs reach this location would help strengthen the manuscript. The authors claim in the discussion that the nucleus of the solitary tract is not the source of inputs, but rather they may arise from the paratrigeminal nucleus (although no data is presented to support this claim). This could fit with the known jugular vagal afferent pathway, which is embryologically distinct and terminates in trigeminal regions, rather than the NTS. But if this is correct, what does this finding then say about the purported involvement of NTS neurons in cough in mice, for example, the recent study by Gannot et al Nat Neurosci where Tac1-expressing NTS neurons were integral for what they call cough in mice? Xu and colleagues are encouraged to resolve their input circuitry so that we can better understand the pathway under investigation and how it relates to the NTS pathway. Related to this, and the issues differentiating cough-like responses from sneeze, the authors will need to consider how to differentiate their cough-like circuitry from the sneeze pathway from the caudal nSp5 to the cVRG as reported by Li et al Cell 2021. It seems highly possible that the two groups are studying the same circuitry, yet the interpretation is confounded by an inability to agree on the identity of the evoked response.

(3) Injection volumes and titres for AAV transductions are not stated anywhere. The methods (line 484) indicate that different volumes were used for different purposes, but nowhere is this information stated properly. Looking at representative images suggests that volumes were very large, with most of the brainstem often transduced. As single slices are only ever shown it becomes a concern as to how extensive transductions truly are. The authors need to provide complete maps of viral transduction so that readers can understand exactly what regions could contribute to responses, thereby confounding interpretation.

(4) The authors do not provide any data to explore the impacts of manipulations on basal breathing. This is important as impacts on the respiratory patterning will likely have profound effects on evoked responses that are not related to the specific pathway under investigation. For example, in Figure 2b. breathing looks to be severely compromised in the TKO animals and disrupted in the M4 DREADD animals. Figure 3 also shows the effects of optical stimulation on breathing patterns, which appear like apnea with several breakthrough augmented breaths (some labeled as cough?), although hard to see properly in the traces provided. Figure 5, one would expect VRG inhibition to have impacts on breathing, and the traces supplied appear to suggest this is the case. Please include data showing breathing effects and consider how these may confound your study interpretation.

Reviewer #2 (Public review):

Summary:

This study employs a combination of state-of-the-art experimental approaches in mice to identify components of the brainstem circuits involved in the cough reflex in a freely behaving mouse model. The cough reflex is an important respiratory airway defense mechanism, and there has been longstanding interest in defining the neural circuits involved in the mammalian brainstem. Consistent with other recent studies, the present results provide multiple lines of evidence indicating that mice are a suitable model for studying neural mechanisms generating cough behavior. The main novel finding of this study is the authors' results indicating that the caudal spinal trigeminal nucleus (SP5C) nucleus plays a role in generating cough-like behaviors in response to inhaled tussigen. The supporting data presented for this role includes the authors' findings that: (1) neural activity in the SP5C is strongly correlated with tussigen-evoked cough-like behaviors, (2) impairing synaptic outputs or chemogenetic inhibition of SP5C neurons effectively abolished these cough-like reflexes, (3) optogenetically activating a specific subpopulation of excitatory neurons in the SP5C triggers cough-like behaviors, (4) SP5C neurons project monosynaptically to ventral medullary regions containing respiratory circuits that exhibit cough-related neural activity, and (5) specific activation of the SP5C-ventral respiratory circuitry induces robust cough-like behavior without tussive stimuli. This study will be valuable to respiratory neurobiologists studying mechanosensory control of breathing in mammals.

Strengths:

(1) The authors developed an experimental paradigm in mice that combines whole-body plethysmography (WBP), audio, and video tracking to assess breathing and putative cough-like behaviors in conscious animals.

(2) The mouse model enables optogenetic, chemogenetic, virus-based circuit tracing and manipulation, and in vivo fiber photometry to analyze neural activity and define circuity in the medulla-producing cough-like behavior.

(3) Multiple lines of evidence from these experimental approaches support the conclusion that the SP5C nucleus plays a role in the respiratory reflex behaviors studied in mice, but there is uncertainty that these behaviors are definitively cough.

Weaknesses:

(1) This paper lacks essential quantitative details about the number of animals studied explicitly for many of the experimental paradigms presented and for statistical analyses as well as to verify replication of the neuroanatomical data presented.

(2) The authors' evidence is incomplete that the reflex behavior produced in their mouse model is definitively cough, limiting functional interpretation of the putative circuit identified and requiring more thorough experimental interrogation of the behavior studied.

(3) The medullary circuit described conveys afferent sensorimotor signals to downstream respiratory circuits to coordinate cough-like motor behavior, but how the circuits that typically mediate the cough reflex, which involve airway-related vagal sensory neurons, operate in conjunction or parallel with the SP5C circuit described has not been determined, which is a significant gap in understanding how the present results fit into the neural control of the cough reflex.

Reviewer #3 (Public review):

Summary:

This paper points out an inconsistency of the roles of the striatal spiny neurons projecting to the indirect pathway (iSPN) and the synaptic plasticity rule of those neurons expressing dopamine D2 receptors and proposes a novel, intriguing mechanisms that iSPNs are activated by the efference copy of the chosen action that they are supposed to inhibit.

The proposed model was supported by simulations and analysis of the neural recording data during spontaneous behaviors.

Strengths:

Previous models suggested that the striatal neurons learn action-value functions, but how the information about the chosen action is fed back to the striatum for learning was not clear. The author pointed out that this is a fundamental problem for iSPNs that are supposed to inhibit specific actions and its synaptic inputs are potentiated with dopamine dips.

The authors propose a novel hypothesis that iSPNs are activated by efference copy of the selected action which they are supposed to inhibit during action selection. Even though intriguing and seemingly unnatural, the authors demonstrated that the model based on the hypothesis can circumvent the problem of iSPNs learning to disinhibit the actions associated with negative reward errors. They further showed by analyzing the cell-type specific neural recording data by Markowitz et al. (2018) that iSPN activities tend to be anti-correlated before and after action selection.

Weaknesses:

(1) It is not correct to call the action value learning using the externally-selected action as "off-policy." Both off-policy algorithm Q-learning and on-policy algorithm SARSA update the action value of the chosen action, which can be different from the greedy action implicated by the present action values. In standard reinforcement learning terminology, on-policy or off-policy is regarding the actions in the subsequent state, whether to use the next action value of (to be) chosen action or that of greedy choice as in equation (7).

It is worth noting that this paper suggested that dopamine neurons encode on-policy TD errors:<br /> Morris G, Nevet A, Arkadir D, Vaadia E, Bergman H (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9, 1057-63. https://doi.org/10.1038/nn1743

(2) It is also confusing to contract TD learning and Q-learning, as the latter is considered as one type of TD learning. In the TD error signal by state value function (6) is dependent on the chosen action a_{t-1} implicitly in r_t and s_t based on the reward and state transition function.

(3) It is not clear why interferences of the activities for action selection and learning can be avoided, especially when actions are taken with short intervals or even temporal overlaps. How can the efference copy activation for the previous action be dissociated with the sensory cued activation for the next action selection?

(4) Although it may be difficult to single out the neural pathway that carries the efference copy signal to the striatum, it is desired to consider their requirements and difference possibilities. A major issue is that the time delay from actions to reward feedback can be highly variable.

An interesting candidate is the long-latency neurons in the CM thalamus projecting to striatal cholinergic interneurons, which are activated following low-reward actions:<br /> Minamimoto T, Hori Y, Kimura M (2005). Complementary process to response bias in the centromedian nucleus of the thalamus. Science, 308, 1798-801. https://doi.org/10.1126/science.1109154

(5) In the paragraph before Eq. (3), Eq. (1) should be Eq. (2) for the iSPN.

Reviewer #2 (Public review):

Summary:

The authors analyze parameters related to anisotropy and gyrification in the developing human brain and describe an increase in tissue fraction (TF) across development. They correlate TF and sulcal depth in the CP and SP across local neighborhoods, describing a negative correlation. Also, they perform age-mismatched correlation of tissue fraction at early stages with sulcal depth at later ones and show correlation inside sulci, which they interpret as indicating the presence of minor structural changes in the brain that precede the development of sulci.

Strengths:

The study compiles a large cohort of cases through different developmental ages and performs sophisticated data analysis. Overall, the work is interesting.

Weaknesses:

I have some questions. What is the potential meaning of TF? It seems to be an estimator of anisotropy highly related to fractional anisotropy (FA), but it behaves in a complementary manner, increasing along gestation, in sharp contrast with the decrease observed in FA in this study (suppl. fig 3) and by others. Please clarify how it is calculated, what is the potential biological meaning of TF and how it differs from FA.

The correlations between TF and sulcal depth do not seem to provide much novelty, since as mentioned by the authors, previous evidence has pointed in that direction. The other concept in the paper relates to detecting structural changes in prospective sulcal areas in the cortex, which the authors analyze through the age-mismatched correlation of TF and subsequent sulcation. However, the results do not show a robust correlation as detailed below and do not seem particularly useful, as they require the inclusion of post-hoc information in the model, limiting the strength of the relationship and the predictive value. My main point of criticism is that if TF is a good marker of the structural modifications that will favor the development of sulci later in development, TF should show a map predictive of those sulci (e.g. at GW 25), that is however not the case. It is not necessary to correlate with future sulcal depth, as we know exactly where the primary sulci will develop. Conversely, it seems that TF decreases along the gyrification process, and it might just be a measure of the structural changes accompanying it.

In Figure 2 it illustrates the increase in TF across GA, but no R values or significance values are provided. Please add them to evaluate the robustness of the correlation.

In previous work of the authors, the subplate is not clearly distinguished from the subcortical white matter after 31 GW, as it starts to disintegrate (Kostovic et al., 2002; Calixto et al., 2024). However, in this manuscript, the SP is differentiated at those later ages. The methods section describes a 2 mm thick compartment below the cortical plate. However, if that is the case, it seems quite arbitrary (to coincide with the resolution of the diffusion imaging) and risks analyzing a compartment that is no longer present. Please explain the criteria followed for such distinction and more importantly, how such distinction is reliable considering the low detectability described in previous studies. In this regard, the discussion described that a rapid increase in TF was only seen in the SP after 30 GW, but maybe this increase would reflect the dissipation of the SP and the transformation of that space in subcortical white matter, with a much more expected anisotropy. The authors should review this.

The analysis describes a negative correlation between tissue fraction and sulcal depth when gyrification proceeds and the authors find that an age-mismatched correlation between tissue fraction in young embryos and sulcal depth in older embryos also shows a negative correlation in future sites of sulcation. However, for the correlation to exist, the tissue fraction in young individuals should already show low values in the prospective sulci, but no clear changes can be seen at GW 25 or 27 in lissencephalic areas that will bear sulci later on, as is the case of the central sulcus at GW 25 or the STS at GW 27, the latter showing very high tissue fraction (instead of the expected low).

Another question refers to Figures 3b and c. The graphs represent specific neighborhoods in the central sulcus at 27 and 35 GW. It can be argued that those neighborhoods might not be representative of the brain or of the whole sulcus. Please show the graph with all neighborhoods, which will provide more definitive evidence of the existence of the correlation. In this regard, the average graphs represented in Figure 3F seem to show a clear correlation at 27 GW in the subplate, but the correlation seems to fade at later stages (in both SP and CP), with both sulci and gyri exhibiting a negative correlation while other sulcal areas do not exhibit correlation. I think all points should be included in the correlation to better support the hypothesis.

Figure 4 shows the age-mismatched correlations, but they do not seem convincing particularly when they should be more useful, at 25 GW. Indeed, as seen in both Figures A and C, the central sulcus shows a negative correlation only in a few spots on one hemisphere, while (in C) most of the prospective sulcus shows a positive correlation, contrary to the hypothesis.

Lastly, the authors performed an age-mismatched correlation between TF at different ages and the sulcal depth at 35W, when it is maximal. This maximal depth might be "pushing" the correlation to significant territory. The authors should provide correlation also with the sulcal depth at other GAs, such as P29, P31, or P33, and analyze how the correlations hold.

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Reply to the reviewers

Response to Reviewers

We thank all three reviewers for their time and engagement, for their generally supportive comments, and for raising some important concerns. We are pleased to submit a significantly revised manuscript where we tried to accommodate all suggested changes and extensions. Importantly, we have included additional experiments that support the relevance of FACT for the overall stability of the inner kinetochore. Below is a detailed point-to-point response. Changes to the manuscript relative to the original submission have been highlighted at the end of this response.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

Summary: The authors investigated molecular interactions between CCAN and FACT complexes. They revealed contact domains in FACT and the cognate subcomplexes of CCAN by in vitro reconstitution from recombinant proteins followed by SEC and pull-down assay.

They also revealed a couple of potential means to control interactions between FACT and the CCAN. They conclude that phosphorylation of FACT by CK2 is essential for binding to the CCAN; and CENP-A nucleosomes or DNA prevent CCAN from interacting with FACT.

Major comments:

The authors show that phosphorylation of FACT is essential for interaction with CCAN.

They argue that this phosphorylation is partly catalysed by CK2.

My concerns are:

-1- The authors assume that the sites phosphorylated in insect cell are also phosphorylated in human cells. However, it is not demonstrated which residues are phosphorylated in human cells and whether they match those from insect cells. Whether phosphorylation of recombinant proteins in insect cells is physiologically relevant to mammalian is uncertain. Kinetochore components are not very well conserved evolutionarily, thus their regulation may be different.

We thank the reviewer for these remarks, which we answer together with point 2 below.

-2- They identify several residues which are phosphorylated by CK2 in vitro. However, these are not necessarily the same sites as those phosphorylated in insect cells or more importantly in human cells. The in vitro phosphorylation by CK2 did not restore binding affinity in full, suggesting phosphorylation at other sites may be critical for interaction with CCAN. Further evidence is required to support the claim that those sites are phosphorylated in vivo and important for integrity of kinetochores in mitosis.

Our analysis of FACT phosphorylation represents a relatively small part of a very data-rich paper, and was not meant to be exhaustive. Nonetheless, the reviewer's comments are important and well received. We agree that we have no definitive evidence that the same sites are phosphorylated in insect cells, in vitro, and in human cells. However, it is quite remarkable, and supports specificity, that the interaction with FACT, lost after dephosphorylation in vitro, is restored with CK2 and not with three additional mitotic kinases (CDK1, Aurora B, and PLK1 - Figure S8D). We also note that S437, S444 and S667 of SSRP1, which were phosphorylated by CK2 in vitro, were also detected as phosphorylated sites on recombinant FACT purified from insect cells (Table S1). So collectively, while we agree with the reviewer that the analysis of FACT phosphorylation is not complete, it does significantly add to the manuscript and more generally to the FACT field.

Minor comments:

Figure 1H

I am confused with 4 stars shown at the top of the right plot. If the 4 stars are meant to show a significant difference, then the statement in the text (line 123) is not correct.

"SSRP1 localization was also largely unaffected ..."

Similar discrepancies are found in Figures 3H (line 212), Figures S2 (line 122), S5I (line 197), and S6I (line209). Figure S6H is not referred to anywhere.

There is no description for the numbers at the top. Are they mean values? Do red bars represent S.D.?

We thank the reviewer for these comments. In this revised version of the manuscript, we have substantially improved the quantification and statistical analysis. The main problem with the previous automated analysis is that the non-circular shape of the CREST-staining led to inconsistencies with the statistical analysis and the statement. In contrast, the same analysis works well when the CENP-C signal was used for KT identification (e.g. in Figure 3), as CENP-C staining yields well separated circular signals ideally suited for our automated identification of individual KTs and subsequent retrieval of fluorescence intensities. We have therefore modified our analysis macro for all experiments where CREST was used as a reference. We used Othsu-thresholding of the DAPI signal for generating a segmentation mask per each cell. Then, integrated cell intensities were calculated for each fluorescence channel based on the DAPI reference mask. With these adjustments, the statistical analyses (Figures 1, S2, S3) support the claim presented. We have updated the Methods and Results sections to reflect the revised analysis.

The numbers on top of the graphs are median values, bars represent interquartile ranges. We have now included the description in figure legends.

We appreciate your feedback, which prompted us to clarify and enhance the rigor of our approach.

We are now referring to Fig. S6H in the text.

Figure 1D

There is no description of R* to the right of gels.

We have added a description of R* to the relevant figure legend.

Figure S2

A 4 hour nocodazole treatment is too short to drive all cells into mitosis. Is the data taken from mitotic cells only?

Yes, the data are taken only from the mitotic population. We have now clarified this in the figure legend.

Reviewer #1 (Significance (Required)):

The interaction of FACT with kinetochore components has been known for several years. However how FACT contributes to architecture or function of kinetochore is not very well understood. How the FACT complex, which is known for its established role as a histone chaperone, is involved in kinetochore assembly/architecture will attract interest in several fields of basic research including epigenetics, mitosis, structural biology.

We are grateful to the reviewer for this supportive statement that recognizes the broad potential interest of the manuscript.

Identification of CCAN subunits that interact with FACT is important for future analysis to understand the kinetochore function of FACT. The authors identified OPQRU and CHIKM subcomplex in addition to TW as FACT-interacting domains. These subcomplexes are geographically scattered in a 3D model of CCAN holocomplex. Stoichiometry of CCAN and FACT might be informative whether a single or multiple FACT binds to the multiple sites of CCAN. The authors do not address whether these multiple sites are occupied simultaneously, separately or sequentially.

We thank the reviewer for raising this point. As mentioned in the discussion, we have not yet been able to perform a structural analysis of the FACT/CCAN complex to determine its stoichiometry. However, we have now added a newexperiment (Figure S1B,C) where we quantified in-gel tryptophan fluorescence after analytical size-exclusion chromatography. This strongly suggests that FACT and CCAN form a complex with a 1:1 stoichiometry. Nevertheless, we cannot comment on which sites are occupied.

The statement at the end of Abstract (lines 23-25) is a speculative hypothesis without evidence for "a pool of CCAN that is not stably integrated into chromatin", "chaperoning CCAN", and "stabilisation of CCAN".

We agree with the reviewer that this is speculative, and have therefore modified the Abstract to clearly indicate this point.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

FACT is a histone chaperone and is involved in various events on chromatin such as transcription and replication. In addition, FACT interacts with various kinetochore components, suggesting potential functions at the kinetochore. However, it is largely unclear how FACT functions in the kinetochore. Authors of this MS took the biochemical approach to understand roles of FACT in the kinetochore.

Authors demonstrated that FACT forms a complex with the constitutive centromere associated network (CCAN), which contains 16 subunits on centromeric chromatin, using multiple binding sites. They also showed that casein kinase II (CK2) phosphorylated FACT and dephosphorylated FACT did not bind to CCAN. Furthermore, they displayed that DNA addition disrupt the stable FACT-CCAN complex.

Overall, while authors have done solid and high-quality biochemical analyses (these are elegant), it is still unclear how FACT plays its roles in the kinetochore. Simple knockout or knockdown study on FACT might be complicated, because FACT has multi-functions. If authors can identify specific regions of FACT for interaction with CCAN, they would put specific mutations into FACT to analyze phenotype. Although they did not reach a high-resolution structure for the FACT-CCAN complex, they can utilize AlphaFold and test specific interaction regions, biochemically. Then, using such information, significance of FACT-CCAN interaction might be testable in cells. Such a kind of study would be expected. In summary, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.

We thank the reviewer for praising the biochemical work in our manuscript. The reviewer, however, also underscored the limits of our functional analysis. The reviewer proposes generating separation-of-function mutants in a minimal kinetochore-binding region. Indeed, we have identified the minimal domain for the interaction of FACT with kinetochores. However, this information is insufficient for a reliable functional analysis at this stage, as the region we identified encompasses the AIDs and the phosphorylation-rich region, both of which have been previously shown to be important for transcription and other functions. Furthermore, any suitable mutant should be tested in cells devoid of endogenous FACT, raising the concern that the resulting phenotype may be indirect.

Nonetheless, as we wanted to provide at least an initial answer to the reviewer's concern, we enriched the manuscript by adding experiments in a recently published cell line (K562-SSRP1-dTAG) where FACT levels can be controlled with a small molecule (Žumer et al. Mol Cell., 2024) and that the authors generously shared with us. In this line, which grows in suspension and that we had to adapt to grow on a substrate for imaging, we were able to deplete FACT while cells were arrested in mitosis. We are glad to report that we found a significant reduction in the kinetochore levels of CENP-TW after this treatment, which is consistent with other conclusions from our study. These experiments add an initial functional characterization of the interaction of FACT with kinetochores, and extend the significance of the manuscript. We refer to these results again below in response to specific point 5.

Specific point

Authors showed nice mitotic localization of FACT. Can they observe this localization by a usual IF? Using GFP fusion, do they observe kinetochore localization like IF experiments?

The localization of FACT was observed using pre-extraction and fixation followed by antibody staining. We have now added a panel demonstrating mitotic localization of GFP-SSRP1 at the kinetochore in transiently transfected RPE-1 cells (Fig. S2A).

On page 7, authors tested CENP-C binding to FACT and they conclude that C-teminal region of CENP-C preferentially binds to FACT. However, they used N-terminal region of CENP-C (2-545) for CCAN-FACT complex formation in entire MS. therefore, this is complicated, and story on CENP-C N-terminal region can be removed from this MS.

We were only able to purify full-length CENP-C with tags at the N- and C-terminus, including an MBP tag with a stabilizing effect. At the time of our first successful purification of full-length CENP-C, we had already established the solid phase assay using MBPFACT as a bait on amylose beads and CENP-C2-545HIKM as one of the preys. As we cannot obtain stable full-length CENP-C without MBP, this form of CENP-C is incompatible with our pull-down assay. Nevertheless, CENP-C2-545 still has low affinity for FACT, influencing the FACT/CCAN interaction independent of the PEST-rich region. We, therefore, opted for keeping this information in the manuscript.

On page 9, authors suddenly focus on N-terminal tails of CENP-Q and CENP-U. Why did they focus on this region. They should explain this. If they perform a structural prediction, they should describe this point.

Thanks for raising this point. We focused on the N-terminal tails of CENP-QU because they are known interaction hubs. We have now added a sentence to introduce this concept and citing the appropriate literature.

I agree the fact that FACT phosphorylation is required for FACT-CCAN interaction. They may explain how the phosphorylation contributes to stable FACT-CCAN interaction.

We have added a sentence explaining that FACT is known to mimic DNA, and negative charges due to phosphorylation could drive this effect. A more detailed mechanistic understanding will require identifying specific phosphorylation sites required for the interaction.

Readers really want to know phenotype, if FACT-CCAN interaction was compromised without disruption pf CCAN assembly in cells. Although I agree that FACT has some functions in the kinetochore, it is still unclear what FACT does in the kinetochore.

We wholeheartedly agree with the reviewer. As depletion of FACT by RNAi required 48 h, an unreasonably long time for this multifunctional protein. We therefore turned to engineering RPE-1 cells for rapid degradation of SSRP1. While these attempts were unsucessful, earlier this year, Žumer et al. Mol Cell., 2024 reported generating a K562-SSRP1-dTAG cell line growing in suspension. As already reported, this cell line now allowed to demonstrate a significant effect on the kinetochore stability of CENP-TW upon mitotic depletion of FACT.

Reviewer #2 (Significance (Required)):

As mentioned above, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.

We thank the reviewer for this positive assessment. In this revision, we have obtained initial evidence that FACT contributes to kinetochore stability.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

Main findings:

The major findings of this paper are:

Detailed dissection of CCAN subunit interactions and requirements to bind the FACT complex using in vitro reconstituted components Binding of FACT and nucleosomes to CENP-C are mutually exclusive FACT phosphorylation by CK2 enhances interaction with CCAN FACT localization in mitosis depends on the CCAN CCAN binding to FACT is outcompeted by DNA and CENP-A nucleosomes The claims and conclusions of the paper are supported by the data and do not require additional experiments. All experiments include biological replicates and appropriate controls.

We are thankful to the reviewer for this very positive assessment of our work.

Minor comments

Intro: • Line 81: In humans [...], here it is worth mentioning that in Drosophila, FACT subunits have been shown to interact directly with the CENP-A assembly factor CAL1 (Ref 61). This paper is perfunctorily cited once in the context of its implication of FACT in CENP-A deposition, but it merits more consideration when setting up the foundational context for the present work.

We have extended the Introduction and discuss the specified paper more thoroughly.

Figure 1:

1F: Add insets.

Done.

1G and all other figures containing IFs: Avoid red/green color scheme (red-green colorblindness is fairly common, affecting about 8% of men).

Done.

1E: Please add a table summarizing interactions.

We have included this table as Fig. S1E.

Results: • It's fine to direct readers to previous work in which you reconstituted the CCAN, but the text should mention how proteins are exogenously expressed and purified (as done for FACT in line 247).

Done.

Line 113: FACT has been shown to localize to the mitotic kinetochore also in Drosophila (Ref 61).

We have included this information now.

Line 132: The authors should cite work from the Drosophila system as well when they mention centromere transcriptional activity in mitosis (e.g. https://doi.org/10.1083/jcb.201404097; https://doi.org/10.1083/jcb.201611087; and Ref 61).

We have added these citations.

Figure 2F: The authors could use a line to mark the region interacting with FACT and that interacting with CENP-A to help summarize the data in this diagram.

Done.

Figure 4: Highlight constructs n.2 (FACT^TRUNC) since these are sufficient for interaction (e.g., use a box around them).

Done.

Line 276: "CCAN decodes CENP-A^NCP..." What do the authors mean by "decodes"? This whole sentence would benefit from clearer language.

We thank the reviewer for this suggestion and have aimed for clearer language.

Figure 6: There's a lot of information in these experiments that would benefit from two schematics, one showing the mechanism of FACT + CCAN binding with DNA and one with CENP-A nucleosomes.

Done.

Discussion: The authors discuss FACT localization at kinetochores in mitosis. In Drosophila Schneider cells, FACT is observed enriched at the centromeres in both mitosis and interphase (Ref 61). The authors mention their inability to detect FACT in interphase in the discussion, but I did not find this mentioned in the results. The authors state that FACT "redistributes to the entire chromosome" upon entry into interphase. They cite Figure 1F in reference to this statement, but the staining in the early G1 panel is difficult to interpret with the low signal/noise scaling of CENP-C and the lack of zoom insets. Their protocol uses a pre-extraction step with Triton prior to fixation. Apparently, this was not enough to reveal FACT in interphase, but better images and a brief description are warranted.

We have now added a staining of SSRP1 in interphase in the panel.

It is unlikely that FACT would change its localization pattern in mitosis. A more likely possibility is that in mitosis FACT is not redistributed, but rather more tightly bound (and thus less easily extracted by Triton treatment) at kinetochores, while along the arms FACT is more readily removed by extraction because at this time transcription is repressed and FACT is likely less engaged in transcription-mediated histone destabilization.

We thank the reviewer for these remarks and have updated the Discussion.

Given the well-known function of FACT in transcription and the many studies linking transcription to centromere maintenance, including with the involvement of FACT, the model that "the localization of FACT at the kinetochore coincides with active centromeric transcription in mitosis and interphase" is very tempting. A speculative model would go a long way to help the reader visualize all these complex aspects of FACT's interactions and possible functions.

We agree with the reviewer that such a model is tempting. However, we also feel that it would be rather speculative at this stage and we feel that the manuscript does not provide sufficient data to support the model.

Reviewer #3 (Significance (Required)):

The strongest aspect of the study is the detailed characterization of protein-protein interactions, as well as competition with DNA and CENP-A nucleosomes. The siRNA experiments in cells complement this largely in vitro study. However, a limitation of the study is that it does not shed light on what FACT might be doing at the centromere. Additionally, it does not sufficiently provide context for these findings in relation to previous studies that have demonstrated the roles of FACT at the centromere in budding yeast, fission yeast, and Drosophila. Nonetheless, this study provides valuable insights into the details of FACT interactions at the kinetochore and will be of interest to readers interested in centromeres and kinetochore. I am a centromere biologist with molecular and cell biology expertise.

We are very grateful to the reviewer for his/her support.

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Referee #3

Evidence, reproducibility and clarity

Main findings:

The major findings of this paper are:

• Detailed dissection of CCAN subunit interactions and requirements to bind the FACT complex using in vitro reconstituted components
• Binding of FACT and nucleosomes to CENP-C are mutually exclusive
• FACT phosphorylation by CK2 enhances interaction with CCAN
• FACT localization in mitosis depends on the CCAN
• CCAN binding to FACT is outcompeted by DNA and CENP-A nucleosomes The claims and conclusions of the paper are supported by the data and do not require additional experiments. All experiments include biological replicates and appropriate controls.

Minor comments

Intro:

• Line 81: In humans [...], here it is worth mentioning that in Drosophila, FACT subunits have been shown to interact directly with the CENP-A assembly factor CAL1 (Ref 61). This paper is perfunctorily cited once in the context of its implication of FACT in CENP-A deposition, but it merits more consideration when setting up the foundational context for the present work.

Figure 1:

• 1F: Add insets.
• 1G and all other figures containing IFs: Avoid red/green color scheme (red-green colorblindness is fairly common, affecting about 8% of men).
• 1E: Please add a table summarizing interactions.

Results:

• It's fine to direct readers to previous work in which you reconstituted the CCAN, but the text should mention how proteins are exogenously expressed and purified (as done for FACT in line 247).
• Line 113: FACT has been shown to localize to the mitotic kinetochore also in Drosophila (Ref 61).
• Line 132: The authors should cite work from the Drosophila system as well when they mention centromere transcriptional activity in mitosis (e.g., https://doi.org/10.1083/jcb.201404097; https://doi.org/10.1083/jcb.201611087; and Ref 61).
• Figure 2F: The authors could use a line to mark the region interacting with FACT and that interacting with CENP-A to help summarize the data in this diagram.
• Figure 4: Highlight constructs n.2 (FACT^TRUNC) since these are sufficient for interaction (e.g., use a box around them).
• Line 276: "CCAN decodes CENP-A^NCP..." What do the authors mean by "decodes"? This whole sentence would benefit from clearer language.
• Figure 6: There's a lot of information in these experiments that would benefit from two schematics, one showing the mechanism of FACT + CCAN binding with DNA and one with CENP-A nucleosomes.

Discussion:

The authors discuss FACT localization at kinetochores in mitosis. In Drosophila Schneider cells, FACT is observed enriched at the centromeres in both mitosis and interphase (Ref 61). The authors mention their inability to detect FACT in interphase in the discussion, but I did not find this mentioned in the results. The authors state that FACT "redistributes to the entire chromosome" upon entry into interphase. They cite Figure 1F in reference to this statement, but the staining in the early G1 panel is difficult to interpret with the low signal/noise scaling of CENP-C and the lack of zoom insets. Their protocol uses a pre-extraction step with Triton prior to fixation. Apparently, this was not enough to reveal FACT in interphase, but better images and a brief description are warranted. It is unlikely that FACT would change its localization pattern in mitosis. A more likely possibility is that in mitosis FACT is not redistributed, but rather more tightly bound (and thus less easily extracted by Triton treatment) at kinetochores, while along the arms FACT is more readily removed by extraction because at this time transcription is repressed and FACT is likely less engaged in transcription-mediated histone destabilization. Given the well-known function of FACT in transcription and the many studies linking transcription to centromere maintenance, including with the involvement of FACT, the model that "the localization of FACT at the kinetochore coincides with active centromeric transcription in mitosis and interphase" is very tempting. A speculative model would go a long way to help the reader visualize all these complex aspects of FACT's interactions and possible functions.

Significance

The strongest aspect of the study is the detailed characterization of protein-protein interactions, as well as competition with DNA and CENP-A nucleosomes. The siRNA experiments in cells complement this largely in vitro study. However, a limitation of the study is that it does not shed light on what FACT might be doing at the centromere. Additionally, it does not sufficiently provide context for these findings in relation to previous studies that have demonstrated the roles of FACT at the centromere in budding yeast, fission yeast, and Drosophila. Nonetheless, this study provides valuable insights into the details of FACT interactions at the kinetochore and will be of interest to readers interested in centromeres and kinetochore.

I am a centromere biologist with molecular and cell biology expertise.

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

FACT is a histone chaperone and is involved in various events on chromatin such as transcription and replication. In addition, FACT interacts with various kinetochore components, suggesting potential functions at the kinetochore. However, it is largely unclear how FACT functions in the kinetochore. Authors of this MS took the biochemical approach to understand roles of FACT in the kinetochore.

Authors demonstrated that FACT forms a complex with the constitutive centromere associated network (CCAN), which contains 16 subunits on centromeric chromatin, using multiple binding sites. They also showed that casein kinase II (CK2) phosphorylated FACT and dephosphorylated FACT did not bind to CCAN. Furthermore, they displayed that DNA addition disrupt the stable FACT-CCAN complex.

Overall, while authors have done solid and high-quality biochemical analyses (these are elegant), it is still unclear how FACT plays its roles in the kinetochore. Simple knockout or knockdown study on FACT might be complicated, because FACT has multi-functions. If authors can identify specific regions of FACT for interaction with CCAN, they would put specific mutations into FACT to analyze phenotype. Although they did not reach a high-resolution structure for the FACT-CCAN complex, they can utilize AlphaFold and test specific interaction regions, biochemically. Then, using such information, significance of FACT-CCAN interaction might be testable in cells. Such a kind of study would be expected. In summary, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.

Specific point

1. Authors showed nice mitotic localization of FACT. Can they observe this localization by a usual IF? Using GFP fusion, do they observe kinetochore localization like IF experiments?
2. On page 7, authors tested CENP-C binding to FACT and they conclude that C-teminal region of CENP-C preferentially binds to FACT. However, they used N-terminal region of CENP-C (2-545) for CCAN-FACT complex formation in entire MS. therefore, this is complicated, and story on CENP-C N-terminal region can be removed from this MS.
3. On page 9, authors suddenly focus on N-terminal tails of CENP-Q and CENP-U. Why did they focus on this region. They should explain this. If they perform a structural prediction, they should describe this point.
4. I agree the fact that FACT phosphorylation is required for FACT-CCAN interaction. They may explain how the phosphorylation contributes to stable FACT-CCAN interaction.
5. Readers really want to know phenotype, if FACT-CCAN interaction was compromised without disruption pf CCAN assembly in cells. Although I agree that FACT has some functions in the kinetochore, it is still unclear what FACT does in the kinetochore.

Significance

As mentioned above, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors investigated molecular interactions between CCAN and FACT complexes. They revealed contact domains in FACT and the cognate subcomplexes of CCAN by in vitro reconstitution from recombinant proteins followed by SEC and pull-down assay.

They also revealed a couple of potential means to control interactions between FACT and the CCAN. They conclude that phosphorylation of FACT by CK2 is essential for binding to the CCAN; and CENP-A nucleosomes or DNA prevent CCAN from interacting with FACT.

Major comments:

The authors show that phosphorylation of FACT is essential for interaction with CCAN. They argue that this phosphorylation is partly catalysed by CK2.

My concerns are:

1. The authors assume that the sites phosphorylated in insect cell are also phosphorylated in human cells. However, it is not demonstrated which residues are phosphorylated in human cells and whether they match those from insect cells. Whether phosphorylation of recombinant proteins in insect cells is physiologically relevant to mammalian is uncertain. Kinetochore components are not very well conserved evolutionarily, thus their regulation may be different.
2. They identify several residues which are phosphorylated by CK2 in vitro. However, these are not necessarily the same sites as those phosphorylated in insect cells or more importantly in human cells. The in vitro phosphorylation by CK2 did not restore binding affinity in full, suggesting phosphorylation at other sites may be critical for interaction with CCAN. Further evidence is required to support the claim that those sites are phosphorylated in vivo and important for integrity of kinetochores in mitosis.

Minor comments:

Figure 1H

I am confused with 4 stars shown at the top of the right plot. If the 4 stars are meant to show a significant difference, then the statement in the text (line 123) is not correct. "SSRP1 localization was also largely unaffected ..." Similar discrepancies are found in Figures 3H (line 212), Figures S2 (line 122), S5I (line 197), and S6I (line209). Figure S6H is not referred to anywhere. There is no description for the numbers at the top. Are they mean values? Do red bars represent S.D.?

Figure 1D

There is no description of R* to the right of gels.

Figure S2

A 4 hour nocodazole treatment is too short to drive all cells into mitosis. Is the data taken from mitotic cells only?

Significance

The interaction of FACT with kinetochore components has been known for several years. However how FACT contributes to architecture or function of kinetochore is not very well understood. How the FACT complex, which is known for its established role as a histone chaperone, is involved in kinetochore assembly/architecture will attract interest in several fields of basic research including epigenetics, mitosis, structural biology.

Identification of CCAN subunits that interact with FACT is important for future analysis to understand the kinetochore function of FACT. The authors identified OPQRU and CHIKM subcomplex in addition to TW as FACT-interacting domains. These subcomplexes are geographically scattered in a 3D model of CCAN holocomplex. Stoichiometry of CCAN and FACT might be informative whether a single or multiple FACT binds to the multiple sites of CCAN. The authors do not address whether these multiple sites are occupied simultaneously, separately or sequentially.

The statement at the end of Abstract (lines 23-25) is a speculative hypothesis without evidence for "a pool of CCAN that is not stably integrated into chromatin", "chaperoning CCAN", and "stabilisation of CCAN".

Author response:

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

Reviewer #1 (Public Review):

In this revision, the authors significantly improved the manuscript. They now address some of my concerns. Specifically, they show the contribution of end-effects on spreading the inputs between dendrites. This analysis reveals greater applicability of their findings to cortical cells, with long, unbranching dendrites than other neuronal types, such as Purkinje cells in the cerebellum.

They now explain better the interactions between calcium and voltage signals, which I believe improve the take-away message of their manuscript. They modified and added new figures that helped to provide more information about their simulations.

However, some of my points remain valid. Figure 6 shows depolarization of ~5mV from -75. This weak depolarization would not effectively recruit nonlinear activation of NMDARs. In their paper, Branco and Hausser (2010) showed depolarizations of ~10-15mV.

More importantly, the signature of NMDAR activation is the prolonged plateau potential and activation at more depolarized resting membrane potentials (their Figure 4). Thus, despite including NMDARs in the simulation, the authors do not model functional recruitment of these channels. Their simulation is thus equivalent to AMPA only drive, which can indeed summate somewhat nonlinearly.

In the current study, we used short sequences of 5 inputs, since the convergence of longer sequences is extremely unlikely in the network configurations we have examined. This resulted in smaller EPSP amplitudes of ~5mV (Figure 6 - Supplement 2A, B). Longer sequences containing 9 inputs resulted in larger somatic depolarizations of ~10mV (Figure 6 - Supplement 2E, F). Although we had modified the (Branco, Clark, and Häusser 2010) model to remove the jitter in the timing of arrival of inputs and made slight modifications to the location of stimulus delivery on the dendrite, we saw similar amplitudes when we tested a 9-length sequence using (Branco, Clark, and Häusser 2010)’s published code (Figure 6 - Supplement 2I, J). In all the cases we tested (5 input sequence, 9 input sequence, 9 input sequence with (Branco, Clark, and Häusser 2010) code repository), removal of NMDA synapses lowered both the somatic EPSPs (Figure 6 - Supplement 2C,D,G,H,K,L) as well as the selectivity (measured as the difference between the EPSPs generated for inward and outward stimulus delivery) (Figure 6 Supplement 2M,N,O). Further, monitoring the voltage along the dendrite for a sequence of 5 inputs showed dendritic EPSPs in the range of 20-45 mV (Figure 6 - Supplement 2P, Q), which came down notably (10-25mV) when NMDA synapses were abolished (Figure 6 - Supplement 2R, S). Thus, even sequences containing as few as 5 inputs were capable of engaging the NMDA-mediated nonlinearity to show sequence selectivity, although the selectivity was not as strong as in the case of 9 inputs.

Reviewer #1 (Recommendations for the authors):

Minor points:

Figure 8, what does the scale in A represent? I assume it is voltage, but there are no units. Figure 8, C, E, G, these are unconventional units for synaptic weights, usually, these are given in nS / per input.

We have corrected these. The scalebar in 8A represents membrane potential in mV. The units of 8C,E,G are now in nS.

Reviewer #2 (Public Review):

Summary:

If synaptic input is functionally clustered on dendrites, nonlinear integration could increase the computational power of neural networks. But this requires the right synapses to be located in the right places. This paper aims to address the question of whether such synaptic arrangements could arise by chance (i.e. without special rules for axon guidance or structural plasticity), and could therefore be exploited even in randomly connected networks. This is important, particularly for the dendrites and biological computation communities, where there is a pressing need to integrate decades of work at the single-neuron level with contemporary ideas about network function.

Using an abstract model where ensembles of neurons project randomly to a postsynaptic population, back-of-envelope calculations are presented that predict the probability of finding clustered synapses and spatiotemporal sequences. Using data-constrained parameters, the authors conclude that clustering and sequences are indeed likely to occur by chance (for large enough ensembles), but require strong dendritic nonlinearities and low background noise to be useful.

Strengths:

(1) The back-of-envelope reasoning presented can provide fast and valuable intuition. The authors have also made the effort to connect the model parameters with measured values. Even an approximate understanding of cluster probability can direct theory and experiments towards promising directions, or away from lost causes.

(2) I found the general approach to be refreshingly transparent and objective. Assumptions are stated clearly about the model and statistics of different circuits. Along with some positive results, many of the computed cluster probabilities are vanishingly small, and noise is found to be quite detrimental in several cases. This is important to know, and I was happy to see the authors take a balanced look at conditions that help/hinder clustering, rather than to just focus on a particular regime that works.

(3) This paper is also a timely reminder that synaptic clusters and sequences can exist on multiple spatial and temporal scales. The authors present results pertaining to the standard `electrical' regime (~50-100 µm, <50 ms), as well as two modes of chemical signaling (~10 µm, 100-1000 ms). The senior author is indeed an authority on the latter, and the simulations in Figure 5, extending those from Bhalla (2017), are unique in this area. In my view, the role of chemical signaling in neural computation is understudied theoretically, but research will be increasingly important as experimental technologies continue to develop.

Weaknesses:

(1) The paper is mostly let down by the presentation. In the current form, some patience is needed to grasp the main questions and results, and it is hard to keep track of the many abbreviations and definitions. A paper like this can be impactful, but the writing needs to be crisp, and the logic of the derivation accessible to non-experts. See, for instance, Stepanyants, Hof & Chklovskii (2002) for a relevant example.

It would be good to see a restructure that communicates the main points clearly and concisely, perhaps leaving other observations to an optional appendix. For the interested but time-pressed reader, I recommend starting with the last paragraph of the introduction, working through the main derivation on page 7, and writing out the full expression with key parameters exposed. Next, look at Table 1 and Figure 2J to see where different circuits and mechanisms fit in this scheme. Beyond this, the sequence derivation on page 15 and biophysical simulations in Figures 5 and 6 are also highlights.

We appreciate the reviewers' suggestions. We have tightened the flow of the introduction. We understand that the abbreviations and definitions are challenging and have therefore provided intuitions and summaries of the equations discussed in the main text.

Clusters calculations

Our approach is to ask how likely it is that a given set of inputs lands on a short segment of dendrite, and then scale it up to all segments on the entire dendritic length of the cell.

Thus, the probability of occurrence of groups that receive connections from each of the M ensembles (PcFMG) is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative zone-length with respect to the total dendritic arbor (Z/L) and the number of ensembles (M).

Sequence calculations

Here we estimate the likelihood of the first ensemble input arriving anywhere on the dendrite, and ask how likely it is that succeeding inputs of the sequence would arrive within a set spacing.

Thus, the probability of occurrence of sequences that receive sequential connections (PcPOSS) from each of the M ensembles is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative window size with respect to the total dendritic arbor (Δ/L) and the number of ensembles (M).

(2) I wonder if the authors are being overly conservative at times. The result highlighted in the abstract is that 10/100000 postsynaptic neurons are expected to exhibit synaptic clustering. This seems like a very small number, especially if circuits are to rely on such a mechanism. However, this figure assumes the convergence of 3-5 distinct ensembles. Convergence of inputs from just 2 ense mbles would be much more prevalent, but still advantageous computationally. There has been excitement in the field about experiments showing the clustering of synapses encoding even a single feature.

We agree that short clusters of two inputs would be far more likely. We focused our analysis on clusters with three of more ensembles because of the following reasons:

(1) The signal to noise in these clusters was very poor as the likelihood of noise clusters is high.

(2) It is difficult to trigger nonlinearities with very few synaptic inputs.

(3) At the ensemble sizes we considered (100 for clusters, 1000 for sequences), clusters arising from just two ensembles would result in high probability of occurrence on all neurons in a network (~50% in cortex, see p_CMFG in figures below.). These dense neural representations make it difficult for downstream networks to decode (Foldiak 2003).

However, in the presence of ensembles containing fewer neurons or when the connection probability between the layers is low, short clusters can result in sparse representations (Figure 2 - Supplement 2). Arguments 1 and 2 hold for short sequences as well.

(3) The analysis supporting the claim that strong nonlinearities are needed for cluster/sequence detection is unconvincing. In the analysis, different synapse distributions on a single long dendrite are convolved with a sigmoid function and then the sum is taken to reflect the somatic response. In reality, dendritic nonlinearities influence the soma in a complex and dynamic manner. It may be that the abstract approach the authors use captures some of this, but it needs to be validated with simulations to be trusted (in line with previous work, e.g. Poirazi, Brannon & Mel, (2003)).

We agree that multiple factors might affect the influence of nonlinearities on the soma. The key goal of our study was to understand the role played by random connectivity in giving rise to clustered computation. Since simulating a wide range of connectivity and activity patterns in a detailed biophysical model was computationally expensive, we analyzed the exemplar detailed models for nonlinearity separately (Figures 5, 6, and new figure 8), and then used our abstract models as a proxy for understanding population dynamics. A complete analysis of the role played by morphology, channel kinetics and the effect of branching requires an in-depth study of its own, and some of these questions have already been tackled by (Poirazi, Brannon, and Mel 2003; Branco, Clark, and Häusser 2010; Bhalla 2017). However, in the revision, we have implemented a single model which incorporates the range of ion-channel, synaptic and biochemical signaling nonlinearities which we discuss in the paper (Figure 8, and Figure 8 Supplement 1, 2,3). We use this to demonstrate all three forms of sequence and grouped computation we use in the study, where the only difference is in the stimulus pattern and the separation of time-scales inherent in the stimuli.

(4) It is unclear whether some of the conclusions would hold in the presence of learning. In the signal-to-noise analysis, all synaptic strengths are assumed equal. But if synapses involved in salient clusters or sequences were potentiated, presumably detection would become easier? Similarly, if presynaptic tuning and/or timing were reorganized through learning, the conditions for synaptic arrangements to be useful could be relaxed. Answering these questions is beyond the scope of the study, but there is a caveat there nonetheless.

We agree with the reviewer. If synapses receiving connectivity from ensembles had stronger weights, this would make detection easier. Dendritic spikes arising from clustered inputs have been implicated in local cooperative plasticity (Golding, Staff, and Spruston 2002; Losonczy, Makara, and Magee 2008). Further, plasticity related proteins synthesized at a synapse undergoing L-LTP can diffuse to neighboring weakly co-active synapses, and thereby mediate cooperative plasticity (Harvey et al. 2008; Govindarajan, Kelleher, and Tonegawa 2006; Govindarajan et al. 2011). Thus if clusters of synapses were likely to be co-active, they could further engage these local plasticity mechanisms which could potentiate them while not potentiating synapses that are activated by background activity. This would depend on the activity correlation between synapses receiving ensemble inputs within a cluster vs those activated by background activity. We have mentioned some of these ideas in a published opinion paper (Pulikkottil, Somashekar, and Bhalla 2021). In the current study, we wanted to understand whether even in the absence of specialized connection rules, interesting computations could still emerge. Thus, we focused on asking whether clustered or sequential convergence could arise even in a purely randomly connected network, with the most basic set of assumptions. We agree that an analysis of how selectivity evolves with learning would be an interesting topic for further work.

References

Bhalla, Upinder S. 2017. “Synaptic Input Sequence Discrimination on Behavioral Timescales Mediated by Reaction-Diffusion Chemistry in Dendrites.” Edited by Frances K Skinner. eLife 6 (April):e25827. https://doi.org/10.7554/eLife.25827.

Branco, Tiago, Beverley A. Clark, and Michael Häusser. 2010. “Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons.” Science (New York, N.Y.) 329 (5999): 1671–75. https://doi.org/10.1126/science.1189664.

Foldiak, Peter. 2003. “Sparse Coding in the Primate Cortex.” The Handbook of Brain Theory and Neural Networks. https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/2994/FoldiakSparse HBTNN2e02.pdf?sequence=1.

Golding, Nace L., Nathan P. Staff, and Nelson Spruston. 2002. “Dendritic Spikes as a Mechanism for Cooperative Long-Term Potentiation.” Nature 418 (6895): 326–31. https://doi.org/10.1038/nature00854.

Govindarajan, Arvind, Inbal Israely, Shu-Ying Huang, and Susumu Tonegawa. 2011. “The Dendritic Branch Is the Preferred Integrative Unit for Protein Synthesis-Dependent LTP.” Neuron 69 (1): 132–46. https://doi.org/10.1016/j.neuron.2010.12.008.

Govindarajan, Arvind, Raymond J. Kelleher, and Susumu Tonegawa. 2006. “A Clustered Plasticity Model of Long-Term Memory Engrams.” Nature Reviews Neuroscience 7 (7): 575–83. https://doi.org/10.1038/nrn1937.

Harvey, Christopher D., Ryohei Yasuda, Haining Zhong, and Karel Svoboda. 2008. “The Spread of Ras Activity Triggered by Activation of a Single Dendritic Spine.” Science (New York, N.Y.) 321 (5885): 136–40. https://doi.org/10.1126/science.1159675.

Losonczy, Attila, Judit K. Makara, and Jeffrey C. Magee. 2008. “Compartmentalized Dendritic Plasticity and Input Feature Storage in Neurons.” Nature 452 (7186): 436–41. https://doi.org/10.1038/nature06725.

Poirazi, Panayiota, Terrence Brannon, and Bartlett W. Mel. 2003. “Pyramidal Neuron as Two-Layer Neural Network.” Neuron 37 (6): 989–99. https://doi.org/10.1016/S0896-6273(03)00149-1.

Pulikkottil, Vinu Varghese, Bhanu Priya Somashekar, and Upinder S. Bhalla. 2021. “Computation, Wiring, and Plasticity in Synaptic Clusters.” Current Opinion in Neurobiology, Computational Neuroscience, 70 (October):101–12. https://doi.org/10.1016/j.conb.2021.08.001.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

(1) The technology requires a halo-tagged derivation of the active compound, and the linked position will have a huge impact on the potential "target hits" of the molecules. Given the fact that most of the active molecules lack of structure-activity relationship information, it is very challenging to identify the optimal position of the halo tag linkage.

We appreciate your insightful comment. While finding the optimal position to attach a chemical linker to a small molecule of interest is indeed a challenging but necessary step, this is a common difficulty across all target-ID methods, except for those that are modification-free, as we described in Discussion. However, modification-free approaches such as DARTS, CETSA, and TPP have their own limitations, such as low sensitivity and a high false-positive rate. Additionally, DARTS and SPROX are limited to use with cell lysates. Please refer to the introduction in our manuscript for more details on these approaches. On the other hand, synthesizing HTL derivatives is relatively straightforward compared to other modifications, and we provide helpful guidelines for chemical linker design, provided the optimal chemical moiety has been identified, which is crucial for target identification. We selected dasatinib and HCQ/CQ as model compounds because previous studies offered insights into their derivative synthesis. Our data also show that DH5 retains strong kinase inhibitory activity (Figure 4—figure supplement 2), and DC661-H1 demonstrates potent inhibition of autophagy (Figure 6—figure supplement 1). For novel compounds, conducting a thorough structure-activity relationship (SAR) study is essential to determine the optimal position for HTL derivative synthesis.

(2) Although POST-IT works in zebrafish embryos, there is still a long way to go for the broad application of the technology in other animal models.

Thank you for your constructive comment. Yes, there is still a long way to go in developing the POST-IT system for broader applications in other animal models, especially in mice. However, we hope that our study provides valuable insights and inspiration to scientists and experts for applying the POST-IT system in various models. We are also committed to further improving its applicability.

(3) The authors identified SEPHS2 as a new potential target of dasatinib and further validated the direct binding of dasatinib with this protein. However, considering the super strong activity of dasatinib against c-Src (sub nanomolar IC50 value), it is hard to conclude the contribution of SEPHS2 binding (micromolar potency) to its antitumor activity.

Thank you for your insightful comment. We agree that the anticancer activity of dasatinib primarily results from inhibiting tyrosine kinases such as SRC and ABL. However, SEPHS2 contains an “opal" termination codon, UGA, at the 60th amino acid residue, which codes for selenocysteine. Due to the technical challenge of expressing selenoproteins in E. coli, we mutated it to cysteine for expression in E. coli to avoid premature translation termination, as described in the Materials and Methods section. Although the purified recombinant SEPHS2 shows a Kd of about 10 µM for dasatinib, the binding affinity to endogenous SEPHS2 may be higher since selenocysteine is larger and more electronegative than cysteine. This presents an interesting area for future investigation. Furthermore, our study of dasatinib’s binding to SEPHS2 could help facilitate the development of new SEPHS2 inhibitors, potentially targeting the active site of SEPHS2.

Reviewer #3 (Public review):

(1) Target Specificity: It is crucial for the authors to differentiate between the primary targets of the POST-IT system and those identified as side effects. This distinction is essential for assessing the specificity and utility of the technology.

Thank you for your insightful comment. Drugs inevitably bind to various proteins with differing affinities, which can contribute to both side effects and beneficial outcomes. Typically, the primary targets exhibit high affinities. In this manuscript, we ranked the identified protein targets of DH5 based on affinity from mass spectrometry and p-values (Fig. 5A), and for DC661-H1, we used the SILAC ratio (Fig. 6A). We also individually assessed many drug-protein binding affinities using the MST assay, as well as in vitro and in cellulo assays, demonstrating their specificity. Moreover, we believe it is essential to identify as many protein targets as possible at physiological drug concentrations to better understand the drug’s side effects. Of course, further investigation is required to assess the roles and effects of these target proteins.

(2) In Vivo Target Identification: The manuscript lacks detailed clarity on which specific targets were successfully identified in the in vivo experiments. Expanding on this information would provide a clearer view of the system's effectiveness and scope in complex biological settings.

Thank you for your insightful comment regarding in vivo target identification. In this manuscript, we utilized a cell line as the primary method for in vivo target identification and validation after optimizing our system in test tubes. We successfully validated many of the targets identified using our POST-IT system (Figure 6—figure supplement 3). To demonstrate the proof of principle for in vivo application, we employed zebrafish embryos as an in vivo model, showing that endogenous SRC can be effectively pulled down by DH5 treatment (Fig. 7). While we could have explored the entire proteome to identify endogenous target proteins in zebrafish that bind to DH5 or dasatinib, we felt this would extend beyond our original scope, given that we have already demonstrated POST-IT’s ability to identify target proteins for dasatinib. Specific target identification and validation are crucial when using zebrafish for drug discovery. Additionally, we acknowledge that drugs likely interact with a range of protein targets in living organisms and may undergo metabolism and interactions within the circulatory system, which we address in our discussion.

(3) Reproducibility and Scalability: Discussion on the reproducibility of the POST-IT system across various experimental setups and biological models, as well as its scalability for larger-scale drug discovery programs, would be beneficial.

Thank you for the suggestion. While our system has shown  high reproducibility in our experiments, further improving both reproducibility and scalability would be advantageous. One potential approach to address this is through the generation of stable-expressing cell lines and transgenic zebrafish lines, which we have discussed in the revised manuscript. Establishing stable cell lines with robust POST-IT expression could enhance scalability for drug discovery applications.

(4) Quantitative Analysis: A more detailed quantitative analysis of the protein interactions identified by POST-IT, including statistical significance and comparative data against other technologies, would enhance the manuscript.

Thank you for your suggestion. In our assessment of drug-protein affinity, we included Kd values as quantitative measures using MST assays. The protein targets of dasatinib identified through mass spectrometry are also accompanied by p-values for quantitative analysis (Fig. 5A), and the detailed procedures are described in the Material and methods section. While it is challenging to provide direct comparative data against other technologies, our system successfully identified many known target proteins for dasatinib, as well as SEPHS2 and VPS37C as new targets for dasatinib and for HCQ/CQ, respectively, which were not detected by other methods.

(5) Technological Limitations: The authors should discuss any limitations or potential pitfalls of the POST-IT system, which would be crucial for future users and for guiding subsequent improvements.

Thank you for your insightful suggestion We agree that clearly defining the technological limitations is important. Therefore, we have expanded our original discussion on the limitations of our POST-IT system (Discussion section, paragraph 6).

(6) Long-Term Stability and Activity: Information on the long-term stability and activity of the POST-IT components in different biological environments would ensure the reliability of the system in prolonged experiments.

Yes, this is an important question. We did not notice any stability or toxicity issues with Halo-PafA and Pup substrates in HEK293T cells or zebrafish, which is an important factor for stable cell lines and transgenic zebrafish lines. However, HTL derivatives of the drug could be toxic or unstable due to the nature of the drug or its metabolism, which needs to be taken into account when designing experiments, and we have included this in the Discussion.

(7) Comparison with Existing Technologies: A detailed comparison with existing proximity tagging and target identification technologies would help position POST-IT within the current landscape, highlighting its unique advantages and potential drawbacks.

We appreciate your valuable feedback and agree that such comparisons are crucial. We have included a detailed overview and comparison of existing proximity-tagging systems and their related target identification technologies in the Introduction (lines 78-100) and Discussion (lines 391-412), highlighting their respective pros and cons. Additionally, we have expanded the discussion to further compare these technologies with our POST-IT system, addressing its advantages and limitations (lines 378-390, lines 448-467). We hope this provides sufficient context and information to effectively position POST-IT among the landscape of proximity-tagging target identification technologies.

(8) Concerns Regarding Overexposed Bands: Several figures in the manuscript, specifically Figure 3A, 3B, 3C, 3F, 3G, Figure 4D, and the second panels in Figure 7C as well as some figures in the supplementary file, exhibit overexposed bands.

We appreciate your astute observation regarding the overexposed bands and apologize for any confusion. The “overexposed” bands represent the unpupylated proteins, while the bands above them correspond to the pupylated proteins. We intended to clearly show both pupylated and unpupylated bands, although the latter are generally much weaker. We are currently working on further improving our POST-IT system to enhance pupylation efficiency.

(9) Innovation Concern: There is a previous paper describing a similar approach: Liu Q, Zheng J, Sun W, Huo Y, Zhang L, Hao P, Wang H, Zhuang M. A proximity-tagging system to identify membrane protein-protein interactions. Nat Methods. 2018 Sep;15(9):715-722. doi: 10.1038/s41592-018-0100-5. Epub 2018 Aug 13. PMID: 30104635. It is crucial to explicitly address the novel aspects of POST-IT in contrast to this earlier work.

Thank you for bringing this to our attention. Proximity-tagging systems like BioID, TurboID, NEDDylator, and PafA (Lui Q et al., Nat Methods 2018) were initially developed to study protein-protein interactions or identify protein interactomes, as these applications are of broader interest and generally easier to implement. However, applying proximity-tagging systems for small molecule target identification requires significant optimization. As described in the introduction (lines 78-100), target protein identification systems have since been developed using TurboID and NEDDylator (Tao AJ et al., Nat Commun 2023; Hill ZB et al., J Am Chem Soc 2016). It is conceivable that a PafA-based proximity-tagging system could also be adapted for target-ID, and other groups may pursue this approach in the future. Although the PafA-Pup system shows great promise for target-ID applications, extensive optimization was needed to enable its use for this purpose. Finally, we demonstrate that POST-IT offers distinct advantages over other proximity-tagging-based target-ID systems. For more details, please refer to the introduction and discussion sections.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Figure 1- Figure Supplement 1A: The Pup substrate "HB-Pup" is mentioned, but the main text or figure legend provides no introduction or description.

We appreciate your astute observation. We have added a description in the main text and figure legend as follows: “…and used HB-Pup as a control, which contains 6´His and BCCP at the N terminus of Pup” in the main text (line 142) and “HB, TS, and SBP refer to 6´His and BCCP, twin-STII (Strep-tag II), and streptavidin binding peptide, respectively.” in the Figure 1-figure supplement 1A.

(2) Figure 1 - Figure Supplement 3B: The authors used TS-sPupK61R as a substrate but did not explain why. The main text mentions that mutating sPup alone did not affect polypupylation, raising the question of why TS-sPupK61R was used in this figure. Furthermore, while the authors state that polypupylation becomes evident after 1 hour of incubation (more pronounced after 2 or 3 hours), the reactions here were conducted for only 30 minutes.

Thank you for your question. Figure 1 - Figure Supplement 3B was conducted to test self-pupylation levels in the different Halo-PafA derivatives. For this purpose, we could use any Pup substrate such as SBP-sPup and SBPK4R-sPupK61R, instead of Ts-sPup and TS-sPupK61R, as they do not show any differences in pupylation activity. We chose Ts-sPup and TS-sPupK61R simply because any Pup substrates could be used for this purpose. Similarly, we did not need to incubate the reaction for a longer time to detect polypupylation, as our intention was to test “self-pupylation”. We demonstrated in Figure 1 – figure supplement 2 that polypupylation is dependent on the number or position of lysine residues in Pup substrate or tags. The results clearly showed that self-pupylation was almost completely abolished by the Halo8KR mutation. To clarify this, we added the following description in lines 168-169: “Ts-sPup and TS-sPupK61R were chosen as sPup substrates for this experiment, although any Pup substrates could have been used. The levels of self-pupylation were assessed.”

(3) Line 156: The statement that "the TS-tag completely abolished polypupylation in TS-sPup" is inaccurate. Using TSK8R-sPupK61R as the substrate, several bands appear, which likely represent Halo-PafA with varying degrees of polypupylation. Some bands also appear to correspond to those seen when using TS-sPup as a substrate. The authors should clarify how they distinguish between multipupylation and polypupylation in this case.

We sincerely appreciate your insight into clarifying the distinction between multipupylation and polypupylation. Polypupylation refers to the addition of a new Pup onto a previously linked Pup on the target protein, akin to polyubiquitination. In contrast, multipupylation involves multiple single pupylations at different positions on the target proteins. Since pupylation occurs exclusively at lysine residues in tag-Pup substrates, mutating all lysine residues to arginine, as in TSK48R-sPupK61R, prevents the mutant tag-Pup from linking to another Pup. This means that only single pupylation can proceed with this type of mutant Pup substrate. If multiple pupylated bands are observed with this mutant substrate, it indicates “multipupylation” rather than “polypupylation”, as shown in Figure 1-figure supplement 2D. The same applies to the pupylation bands in Figure 1-figure supplement 2E and F, as sSBP-sPupK61R and SBPK4R-sPupK61R lack lysine residues. By comparing these multipupylation bands, it is also possible to distinguish them from polypupylation bands, which are marked by yellow arrows. However, after 2-3 pupylation bands, higher-order bands become increasingly difficult to distinguish.

To clarify the mutation in the TS-tag, we revised the sentence in line 156 from “However, further mutations within the TS-tag completely abolished polypupylation in TS-sPup” to “However, further mutations of two lysine residues within the TS-tag, creating TSK8R-sPupK61R, completely abolished polypupylation in TS-sPup”. Additionally, we have inserted sentences in line 152 to define polypupylation and multipupylation, as described here.

(4) Line 160: Similar to the above concern about line 156, the claim that SBPK4R and sSBP completely prevented polypupylation is unconvincing and requires more supporting evidence.

Thank you for raising this concern. As mentioned above, both SBPK4R and sSBP lack lysine residues required for pupylation. As a result, these mutants can only undergo multiple single pupylations on the lysine residues of the target protein, which leads to “multipupylation”. In Figure 1-figure supplement 2E and F, pupylation bands by sSBP-sPupK61R or SBPK4R-sPupK61R do not display doublet bands (one from multipupylation and the other from polypupylation), as seen with SBP-sPup, marked by yellow arrows. Notably, Halo-PafA containing polypupylated branches migrates more slowly than one with an equal number of multipupylation events. To clarify this point, we have added the phrase “as shown in sSBP-sPupK61R and SBP4KR-sPupK61R” at the end of the sentence in line 160.

(5) Lines 176-177: The authors claim that PafAS126A exhibited reduced polypupylation compared to PafA, but given that PafAS126A may reduce depupylase activity, how could it reduce polypupylation levels? Moreover, it is hard to find any data supporting this conclusion in Figure 1 - Figure Supplement 3B.

We appreciate your insightful comment. At this point, we do not fully understand how the mutation that reduces depupylase activity also decreases polypupylation. It is possible that PafAS126A has a lower preference for pupylated Pup as a prey, which is required for polypupylation, since depupylase activity depends on recognizing pupylated Pup as a prey to remove it. Nonetheless, Halo-PafAS126A shows reduced levels of higher molecular weight bands compared to Halo-PafA, as shown in Figure 1-figure supplement 3B, while exhibiting increased pupylation in lower molecular weight bands, which represent either multipupylation or low-degree polypupylation. Since higher molecular weight bands (> 150 kD) are likely due to polypupylation, this result suggests reduced polypupylation and increased multipupylation in Halo-PafAS126A. To clarify this in the main text, we have added the following description in line 177: “as evidenced by the decreased levels of high molecular weight bands and an increase in low molecular weight bands”

(6) POST-IT system in cellulo validation: The system was developed using the Halo-tag, yet the in-cell validation uses FRB and FKBP instead, without explaining this switch. This inconsistency makes the logic of the experiment unclear.

We appreciate your insightful comment. The interaction between rapamycin and FRB or FKBP is known to be highly specific and robust, making this system useful in various biological contexts. Due to this property, rapamycin can induce interaction between two proteins when one is fused with FRB and the other with FKBP. Before testing or optimizing the POST-IT system in cells, we hypothesized that using the rapamycin-induced interaction between FRB and FKBP could introduce pupylation of the target protein, provided that PafA is fused with FRB or FKBP and the target protein is fused with the other. The results demonstrate that PafA can introduce pupylation of the target protein in a proximity-dependent manner via this chemically induced interaction. To further clarify this in the main text, we modified the original sentence in lines 214-216 as follows: “To mimic drug-target interaction-induced pupylation in live cells and assess the potential of PafA as a proximity-tagging system for target-ID, we incorporated the rapamycin-induced interaction between FRB and FKBP into our PL system, as this interaction between a small molecule and a protein is known to be highly specific and robust (Figure 3—figure supplement 1A).”

(7) Line 209: The authors decided to use the SBP-tag for further studies due to better performance, but in Figure 3 - Figure supplement 1, they still used the unintroduced HB-Pup as the substrate, which is confusing and lacks explanation.

Thank you for raising your question. The SBP-tag is not superior to the TS-tag in terms of pupylation activity. However, the TSK8R mutant cannot bind to Strep-Tactin beads, while the SBP mutants, SBPK4R and sSBP, can bind to streptavidin. Therefore, we chose the SBP-tag instead of the TS-tag for further studies as a Pup substrate in POST-IT system, as we needed to pull down the target proteins. HB-Pup is consistently used as a control throughout various experiments, as it is the original Pup substrate. In Figure 3-figure supplement 1B and C, HB-Pup was used to test chemically induced pupylation by PafA. In these cases, it was not so critical which Pup substrate was chosen. Furthermore, we compared HB-Pup and different SBP-sPup substrates in Figure 3-figure supplement 1D, where HB-Pup was used as a control or for comparison. Although pupylation bands with HB-Pup appear more robust, this substrate contains multiple lysine residues, leading to high levels of polypupylation. To make it clear, we modified the sentence in line 209 to “Therefore, we decided to use the SBP-tag as a Pup substrate in the POST-IT system for further studies.”.

(8) Line 220: Both SBP-sPup and SBPK4R-sPupK61R are described as exhibiting efficient pupylation, but the data show mostly self-pupylation and little to no pupylation of the target protein.

Thank you for your concern. However, pupylation of the target protein is actually quite substantial, as the intensities of the free form and pupylated proteins are relatively similar, as shown in the upper panel of Figure 3-figure supplement 1D. Self-pupylation is always much higher than target pupylation, because PafA constantly pupylates itself, whereas pupylation of the target protein occurs only through interaction. Furthermore, V5-FRB-mKate2-PafA contains many lysine residues, which increases the levels of self-pupylation.

(9) Lines 222-224: The authors chose SBPK4R-sPupK61R to avoid polypupylation, although SBP-sPup did not cause detectable polypupylation. Neither substrate caused pupylation of the target protein, so the rationale behind this choice is unclear.

Thank you for raising your question. Similar to the above comment (#8), please refer to the pupylation bands of the target protein, as shown in the upper panel of Figure 3-figure supplement 1D. The pupylation band of the target protein is quite remarkable, as the intensities of the free form and pupylated proteins are comparable. Additionally, there are no multiple pupylation bands in either case, except for one additional weak multipupylation band, indicating no polypupylation by SBP-sPup, which does not have K-to-R mutations. Of course, SBPK4R-sPupK61R can only undergo single pupylation, as it does not contain lysine residues. Although we did not observe polypupylation by SBP-sPup in this experimental condition, it is possible that SBP-sPup may cause polypupylation under different experimental conditions or with other target proteins. Since SBPK4R-sPupK61R exhibits comparable pupylation of the target protein at least in this experiment setting as SBP-sPup, we selected SBPK4R-sPupK61R as the Pup substrate for POST-IT system to avoid any potential polypupylation that could be caused by SBP-sPup in other cases. We believe that polypupylation can introduce bias into the analysis and hinder the comprehensive discovery of additional target proteins for small molecules.

(10) Line 224: The authors conclude that rapamycin greatly reduced self-pupylation, but the supporting data are unclear.

Thank you for your constructive comments on our manuscript. Please refer to the lower panel of Figure 3-figure supplement 1D. When using either SBPK4R-sPupK61R or SBP-sPup, rapamycin treatment results in reduced levels of self-pupylation compared to the no-treatment control. However, we did not observe this reduction with HB-Pup and do not know the reason. To clarify this in the main text, we added the following description to the end of the sentence: “when using either SBPK4R-sPupK61R or SBP-sPup, as shown in the lower panel of Figure 3—figure supplement 1D”

(11) Line 234: The authors selected an 18-amino acid linker, but given that linkers longer than 10 amino acids enhance labeling, this choice should be explained.

Thank you for raising your question. In fact, a linker of 10 amino acids (aa) or longer is likely to behave similarly. We chose an 18 aa linker instead of a 40 aa linker primarily for the convenience of cloning and to reduce the potential for DNA sequence recombination associated with longer repeats. Additionally, a longer, flexible linker may behave like an intrinsically disordered protein (Harmon et al., 2017), which can lead to unwanted protein-protein interactions or phase separation. To elaborate on this, we added the following sentences after the sentence in line 233-235: “We chose the 18-amino acid linker instead of the 40-amino acid linker for easier cloning and to lower the risk of DNA recombination from longer repeats. Additionally, a longer, flexible linker may behave like an intrinsically disordered protein (Harmon et al., 2017), an unwanted feature for target-ID.”

(12) S126A and K172R mutations: The authors claim that these mutations additively enhanced pupylation under cellular conditions, but in Figure 3B, the band intensities appear similar for the wild-type and mutant versions.

Thank you for raising your concern. Although a single pupylation band appears similar among the three different Halo-PafA proteins, multipupylation bands are slightly but noticeably increased by the S126A and K172R mutations compared to Halo8KR-PafA. Since we used SBPK4R-sPupK61R as a Pup substrate, all higher molecular weight bands result from multipupylation rather than polypupylation. This illustrates why it is preferable to use SBPK4R-sPupK61R over SBP-sPup, as the pupylation bands with SBP-sPup are mixtures of poly- and multipupylation, making it difficult to assess levels of target labeling. To clarify this in the main text, we added the following description after the sentence in line 236: “as the higher molecular weight multipupylation bands are slightly but noticeably increased with these mutations compared to Halo8KR-PafA”

(13) Line 263: The authors selected DH5 for further experiments due to its efficiency, but the data suggest that the performance of DH1 to DH5 is similar.

We appreciate your question about the different dasatinib HTL derivatives. However, our data clearly show that DH2-5 derivatives bind significantly more effectively to Halo-PafA in vitro and in live cells compared to DH1 (Figure 4A and B). Additionally, the DH2-5 derivatives result in dramatically increased pupylation of the target protein in vitro and noticeable enhancement in live cells (Figure 4C and D). Among DH2 to DH5, there is no obvious difference in binding to Halo-PafA or pupylation of the target protein. Therefore, we chose DH5, as we believe that the longer linker in DH5 may facilitate the binding of a more diverse range of target proteins to dasatinib, enabling the discovery of additional target proteins.

(14) Line 309: The authors introduce HCQ and CQ as important drugs but then investigate the mechanism using DC661 without introducing or justifying the choice of this compound.

Thank you for your point. We explained the reason to choose DC661, a dimer form of CQ, instead of CQ for the synthesis of an HTL derivative in line 310. “assuming that a dimer would enhance binding affinity as previously described.” As the dimer forms of a drug or a small molecule such as testosterone dimers, estrogen dimers, and numerous anticancer drug dimers have been often developed to enhance drug effects (Paquin A et., Molecules 2021). Similarly, dimer forms of HCQ/CQ have been introduced and shown to be more potent (Hrycyna CA et al., ACS Chem Biol 2014; Rebecca VW et al., Cancer Discovery 2019). We expected that using a dimer form might offer higher probability to identify target proteins for HCQ/CQ.

(15) The authors suggest that multipupylation levels were enhanced but do not explain whether this might benefit the system or introduce other issues. Clarifying this point would provide valuable insight for potential users of this system.

Thank you for your thoughtful suggestion. Polypupylation likely leads to biased enrichment of a limited set of target proteins, and its levels may not correlate with the binding affinity of target proteins to the small molecule of interest, features that can negatively impact target-ID. In contrast, multipupylation may be correlated with binding affinity or interaction frequency, as we observed increased levels of multipupylation with higher Pup concentrations and longer incubation times. This suggests that target proteins with multiple lysines in proximity to PafA can be sequentially pupylated, starting with the most accessible lysine. However, if a target protein has only one accessible lysine, pupylation will occur only once, regardless of the protein’s affinity to the small molecule. In summary, while polypupylation may be a drawback for target-ID, multipupylation could be useful for both target-ID and understanding binding mode. To elaborate on this, we added the following additional explanation after the sentence in line 152: “, whereas multipupylation is more likely correlated with binding affinity or interaction frequency.”

(16) The author should address whether the Halotag ligand modification of the drug alters the binding properties between the drug and targets. That may be causing artifact binding of the drug and other proteins.

Thank you for your insightful comment. Yes, it is true that chemical modifications of the small molecule of interest, such as linker derivatization (e.g., HTL) or photo-affinity labeling, generally lead to reduced activity or affinity compared to the original molecule. Synthesizing a derivative is a common challenge across all target-ID methods, except for modification-free approaches, as we mentioned in the Discussion. However, modification-free methods like DARTS, CETSA, and TPP have their own limitations, including low sensitivity or high false positive rates. Identifying the optimal position for chemical modification on the small molecule of interest is critical. We chose dasatinib and HCQ/CQ as model compounds, because previous studies provided insights into their derivative synthesis. In addition, our data show that DH5 retains robust kinase inhibitory activity (Figure 4-figure supplement 2), and DC661-H1 exhibits potent autophagy inhibition (Figure 6-figure supplement 1). For novel compounds, a thorough structure-activity relationship study is essential to identify the optimal position for HTL derivative synthesis.

(17) The author stated there is no observable toxicity in zebrafish without providing a detailed analysis or enough data. Further analysis of the expression of Halo-PafA and its substrate sPup influence on toxicity or side effects to the living cells or animals would be needed. It is important for in vivo applications.

Thank you for your constructive suggestion. We have now included additional experimental data in Figure 7-figure supplement 1, showing no toxicity in zebrafish embryos expressing the POST-IT system. We assessed toxicity in two ways: by injecting the POST-IT DNA plasmid into one-cell-stage embryos for acute expression, and by using embryos from transgenic zebrafish expressing POST-IT under a heat-shock inducible promoter. Neither the injection nor the heat-shock activation of POST-IT expression resulted in any noticeable toxicity.

Reviewer #3 (Public review):

Gatt et al. present a novel take on single-cell RNA-sequencing from complex planktonic samples, introducing an approach they aptly named Ukiyo-e-Seq. This work combines environmental sampling with cell picking, microscopic imaging, and Smart-seq2 single-cell RNA sequencing to profile uncultured eukaryotic plankton. Developing single-cell approaches for such ecosystems is critical, given the poor representation of many planktonic species in cultures and reference databases. This work could help bridge existing technological gaps between morphological and molecular studies of aquatic microeukaryotes

The authors argue that microscopy does not provide information on the biochemistry of species under consideration. At best, it provides taxonomic labeling of species within a sample, yet imaging fails to assess their metabolic state or to disentangle cryptic species. In a standard metatranscriptomic setup, the sequence pool is described by aligning assembled contigs with reference databases to obtain functional and taxonomic information. This complex community-level data is impossible to parse at the single-organism level. Moreover, by relying on reference datasets, a lot of potential information can be missed. The aim of the approach is to combine the strengths of both methods, generating single-cell transcriptomic data linked to individual plankton images.

Strengths:

Ukiyo-e-Seq generated a valuable dataset by combining imaging and transcriptomics for individual planktonic organisms from environmental samples. This multimodal approach has the potential to improve taxonomic predictions and functional insights at the single-organism level. This manuscript demonstrates the technical feasibility of such an approach. Data of this type is rare and thus represents a valuable resource to further advance single-cell sequencing of planktonic species from environmental samples.

Weaknesses:

(1) The merge-split strategy, where single-cell reads are pooled prior to assembly, is counterintuitive. Pooling obscures the single-organism resolution that single-cell methods aim to achieve. The approach might be useful for assembling low-coverage contigs, but risks masking unique expression profiles for transcripts unique to a given well. As an alternative, the authors could assemble each well independently to obtain well-specific transcriptomic bins. Assemblies could then be clustered based on sequence similarity, thereby imposing strict clustering parameters to maintain resolution, to create a common reference for downstream analysis if needed. In my opinion, better results would be obtained by implementing a per-well assembly and read mapping.

(2) The focus on the top five most expressed contigs throughout the manuscripts' data analysis is a limiting choice, as it excludes most contigs. In the preprint, we are presented with a very narrow view of the data. Visualising the entire range of assembled contigs would provide a better picture of the transcriptomic composition and diversity per well. It would be interesting to assess if the full information could be used to preliminary bin transcriptomic sequences from individual wells, for example, by gathering all 'private' contigs with high read coverage in a single well. Does such a set represent a single complete eukaryotic transcriptome?

(3) I missed a verification with (broad-scale) taxonomic assessments based on the associated microscopic images. In their goals, the authors state that a joint approach has the potential to discover new taxonomic biodiversity. I agree, and to me, this is what is exciting about the preprint, yet I miss an example or the right bioinformatic implementation to drive home this claim. Are there organisms in wells where poor taxonomic annotations, based on alignment to a reference database or the LCA approach implemented in Kraken2, would usually result in ignoring the species in classic metatranscriptomics? Can you advance the taxonomic annotation by referring back to the organisms' picture? Can manual assessment of taxonomy advance the results from the LCA approach?

(4) The current use of AlphaFold to predict protein structures does not convincingly add to the study's core objectives.

Overall, Ukiyo-e-Seq presents a promising method for studying single-cell diversity in environmental samples, though the bioinformatic pipeline requires refinement to support some of the claims made by the authors. Additionally, the manuscript would benefit from clarity and additional details in its methods and a more consistent approach to presenting results and summary statistics across all assembled contigs and all sampled wells, rather than focusing on selected wells.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors aim to elucidate the diversity and gene expression patterns of marine plankton using innovative collection and sequencing methodologies. Their work investigates the taxonomic and functional profiles of planktonic communities, providing insights into their ecological roles and responses to environmental changes.

Strengths:

The methodology utilized in this study, particularly the combination of single-cell sequencing and advanced bioinformatics techniques, represents a significant advancement in the field of plankton research. The application of the Smart-seq2 protocol for cDNA synthesis, followed by rigorous quality control measures, ensures high-quality data generation. This comprehensive approach not only enhances the resolution of the obtained genetic information but also allows for a more detailed exploration of the diversity and functional potential of the phytoplankton community.

One of the major strengths of this study is the rigorous methodological approach, including precise sampling techniques and robust data analysis protocols, which enhance the reliability of the results. The use of advanced sequencing technologies allows for a comprehensive assessment of gene expression, significantly contributing to our understanding of plankton diversity and its implications for marine ecosystems.

Weaknesses:

While the evidence presented is solid, there are areas where the analysis could be expanded. The authors could further explore the ecological interactions within plankton communities, which would provide a more holistic view of their functional roles. Additionally, a broader discussion of the implications of their findings for marine conservation efforts could enhance the manuscript's impact.

The choice of both the plankton net and filter pore size during the plankton collection process is critical, as these factors directly impact the types of phytoplankton collected. The use of a 25 μm filter paper, in particular, may result in the omission of many eukaryotic phytoplankton species. This limitation, combined with the characteristics of the plankton net, could affect the comprehensiveness and accuracy of the results, potentially influencing the study's conclusions regarding phytoplankton diversity.

The timing of fixation is crucial, as it directly affects whether the measured transcriptome accurately represents the organisms' actual transcriptional state in their native water environment. If fixation occurred a significant time after sample collection, the transcriptomic data may not reflect their true in situ transcriptional activity, which greatly reduces the relevance of this method.

Thank you for your time, effort, and expertise.

We agree that additional analyses could improve our understanding of the plankton communities sampled. We have conducted an array of alternative analyses that were not included in the current manuscript and plan to perform new analyses over the next few months as part of a deeper revision of the manuscript. We are especially interested in “providing a more holistic view of the functions” of individual plankton within the community.

As for the protocol details, the pore size of the filter paper was chosen to focus on ~100 micron-sized organisms as a starting point: they are likely to contain more RNA than smaller organisms, making them well suited for an initial proof of concept of the methodology. That choice, however, is not particularly tightly constrained, therefore smaller plankton could be captured. This is supported by the lack of correlation, in our data, between organismal size and number of detected sequencing reads.

Timing to cell death/fixation is a common question we receive not just in this manuscript but any RNA-Seq from primary samples. In this case, plankton were seen swimming until picking, and after picking each organism was deposited within two seconds into a lysis buffer for fixation. Therefore, we do not have reason to believe that the transcriptional activity sampled in the sequencing reads differs in any major way from the one in living plankton. Nonetheless, a study specifically testing the effect of time between ocean sampling and reverse transcription would provide more quantitative information on this point.

Reviewer #2 (Public review):

Summary:

The paper introduces Ukiyo-e-Seq, a novel method integrating microscopy with single-cell transcriptomics to study individual, uncultured eukaryotic plankton cells. By combining microscopic imaging with transcriptomic analysis, the approach links plankton morphology to gene expression, enabling taxonomic identification and functional protein exploration. Ukiyo-e-Seq was tested on 66 microbial eukaryotic cells, revealing taxonomic diversity across four superkingdoms and allowing analysis of protein complexes and developmental genes in individual species. According to the authors, this method has the potential to advance single-cell marine biodiversity studies by addressing limitations in traditional taxonomy and metatranscriptomics, especially for rare or uncultured organisms.

However, the study's conclusions are often weakly supported by data, particularly given that this is not the first study to combine microscopy and single-cell transcriptomics of eukaryotic plankton using Smart-seq2.

Strengths:

A notable strength is the authors' generation of several single-cell transcriptomes for the diatom Chaetoceros, which could benefit from greater focus rather than broadly addressing eukaryotic single cells.

Weaknesses:

The study lacks comparison with other single-cell transcriptomics studies and it was presented as the first study that combines imaging and single-cell transcriptomics (smart-seq2) of eukaryotic plankton while in fact it is not. The sampling methodology is not replicable as the authors used a tea strainer instead of standard plankton collection equipment to filter larger cells. Terminology throughout the paper is unconventional, such as "public and private contigs," "single-organism genomics," "highly expressed contigs," and "optical methods." Additionally, the authors did not specify which database was used for taxonomic assignments. These issues may stem from the authors' limited background in microbial ecology. Overall, the study has many drawbacks and it could benefit from complete rewriting and focusing mainly on single-cell transcriptomics of diatoms.

Thank you for your time, effort, and expertise.

There might be a bit of confusion between single-cell and single-organism sequencing, likely due to lack of clarity in our initial submission. In particular, in this manuscript no effort was spent trying to dissociate oligocellular plankton into individual cells before sequencing. While probably feasible, we expect that to be technically much harder than single-organism sequencing as performed here. The reviewer does not reference a published paper where combined imaging and RNA-Seq of individual uncultured plankton has been achieved, and we were unable to find one in the scientific literature. As stated in the manuscript, others have already performed some work on cultured plankton and single-organism sequencing (without matching images) of uncultured environmental microorganisms.

The suggestion to focus on a smaller biological niche such as diatoms and adopt language more familiar to that specific community is well received. Indeed, given that organisms as diverse as fish larvae and diatoms could be profiled with Ukiyo-e-Seq, future studies could use the same method to address specific questions with a deeper and more narrow scope. However, this manuscript is demonstrating the feasibility of Ukiyo-e-Seq and its ability to produce usable data for a broad spectrum of organisms: part of the scientific audience might not have a specific interest in diatoms.

The tea strainer was used for coarse pre-filtering: the exact pore size, geometry and factory tolerance on those measurements are inconsequential because each organism is later chosen (or not) based on a high-resolution microscopy image (or multiple, if fluorescence is considered). This really is a strength of Ukiyo-e-Seq over FACS or droplet-based sorters, which can only collect coarse optical information from each organism for (typically) less than 1 millisecond. In Ukiyo-q-Seq, while the actual decision to pick an individual is currently manual (by the operator of the picker), it can be automated in principle. For instance, one could build a machine learning model of plankton taxonomy based on a large collection of labelled images and use predictions from such a model to automatically drive the picker (e.g. focussing on diatoms), increasing throughput. Even in that case, however, the initial filtering stages using tea strainers, plankton nets, filter paper etc. would not be critical for the final selection of individuals as long as they are not too restrictive.

The database used for taxonomic assignment was the NCBI non-redundant nucleotide database, accessed through the reference library provided by Kraken2 (nt).

Reviewer #3 (Public review):

Gatt et al. present a novel take on single-cell RNA-sequencing from complex planktonic samples, introducing an approach they aptly named Ukiyo-e-Seq. This work combines environmental sampling with cell picking, microscopic imaging, and Smart-seq2 single-cell RNA sequencing to profile uncultured eukaryotic plankton. Developing single-cell approaches for such ecosystems is critical, given the poor representation of many planktonic species in cultures and reference databases. This work could help bridge existing technological gaps between morphological and molecular studies of aquatic microeukaryotes

The authors argue that microscopy does not provide information on the biochemistry of species under consideration. At best, it provides taxonomic labeling of species within a sample, yet imaging fails to assess their metabolic state or to disentangle cryptic species. In a standard metatranscriptomic setup, the sequence pool is described by aligning assembled contigs with reference databases to obtain functional and taxonomic information. This complex community-level data is impossible to parse at the single-organism level. Moreover, by relying on reference datasets, a lot of potential information can be missed. The aim of the approach is to combine the strengths of both methods, generating single-cell transcriptomic data linked to individual plankton images.

Strengths:

Ukiyo-e-Seq generated a valuable dataset by combining imaging and transcriptomics for individual planktonic organisms from environmental samples. This multimodal approach has the potential to improve taxonomic predictions and functional insights at the single-organism level. This manuscript demonstrates the technical feasibility of such an approach. Data of this type is rare and thus represents a valuable resource to further advance single-cell sequencing of planktonic species from environmental samples.

Weaknesses:

(1) The merge-split strategy, where single-cell reads are pooled prior to assembly, is counterintuitive. Pooling obscures the single-organism resolution that single-cell methods aim to achieve. The approach might be useful for assembling low-coverage contigs, but risks masking unique expression profiles for transcripts unique to a given well. As an alternative, the authors could assemble each well independently to obtain well-specific transcriptomic bins. Assemblies could then be clustered based on sequence similarity, thereby imposing strict clustering parameters to maintain resolution, to create a common reference for downstream analysis if needed. In my opinion, better results would be obtained by implementing a per-well assembly and read mapping.

(2) The focus on the top five most expressed contigs throughout the manuscripts' data analysis is a limiting choice, as it excludes most contigs. In the preprint, we are presented with a very narrow view of the data. Visualising the entire range of assembled contigs would provide a better picture of the transcriptomic composition and diversity per well. It would be interesting to assess if the full information could be used to preliminary bin transcriptomic sequences from individual wells, for example, by gathering all 'private' contigs with high read coverage in a single well. Does such a set represent a single complete eukaryotic transcriptome?

(3) I missed a verification with (broad-scale) taxonomic assessments based on the associated microscopic images. In their goals, the authors state that a joint approach has the potential to discover new taxonomic biodiversity. I agree, and to me, this is what is exciting about the preprint, yet I miss an example or the right bioinformatic implementation to drive home this claim. Are there organisms in wells where poor taxonomic annotations, based on alignment to a reference database or the LCA approach implemented in Kraken2, would usually result in ignoring the species in classic metatranscriptomics? Can you advance the taxonomic annotation by referring back to the organisms' picture? Can manual assessment of taxonomy advance the results from the LCA approach?

(4) The current use of AlphaFold to predict protein structures does not convincingly add to the study's core objectives.

Overall, Ukiyo-e-Seq presents a promising method for studying single-cell diversity in environmental samples, though the bioinformatic pipeline requires refinement to support some of the claims made by the authors. Additionally, the manuscript would benefit from clarity and additional details in its methods and a more consistent approach to presenting results and summary statistics across all assembled contigs and all sampled wells, rather than focusing on selected wells.

Thank you for your time and effort, and for your expertise on the matter.

The suggestions to conduct additional bioinformatic analyses to explore more fully the criticality and potential of various design choices (e.g. meta-assembly) are well received. We have tried some of those ideas already (e.g. assembling individual wells) and we have considered but not yet conducted or polished others (e.g. a more thorough taxonomic verification). We will endeavour to carry out as many of those analyses as possible during the deeper revision process in the coming months.

AlphaFold 3’s use was designed to demonstrate the ability to investigate protein-protein interactions from individual species. When two peptide sequences are detected within the same well, they are more likely to be potential interacting partners than in a metatranscriptomic study, because the compartmentalisation of reads into tens or hundreds of wells greatly reduces the search space of potential interaction partners (which has a baseline runtime complexity of n squared, where n is the number of peptide sequences identified).

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Reviewer #3 (Public review):

This work brings a computational approach to the study of promoters and transcription. The paper is improved but there are still factual errors and implausible explanations. I am not convinced by the response from the authors, concerning the promoter -35 element, in their rebuttal.

Comments on author rebuttal:

- We respectfully but strongly disagree that our analysis has misrepresented the true nature of -35 boxes. First, accounting for more A's at position 5 in the PWM is not going to lead to a "critical error." This is because positions 4-6 of the motif barely have any information content (bits) compared to positions 1-3 (see Fig 1A).

The analysis does misrepresent the consensus -35 element, which is, unequivocally, TTGACA. I agree that positions 4-6 of the element are less well-conserved.

- This assertion is not just based on our own PWM, but based on ample precedent in the literature. In PMID 14529615, TTG is present in 38% of all -35 boxes, but ACA only in 8%.

This does not mean that TTGACA is not the consensus, or that "ACA" is not important at promoters where it's present.

- In PMID 29388765, with the -10 instance TATAAT, the -35 instance TTGCAA yields stronger promoters compared to the -35 instance TTGACA (See their Figure 3B).

This is a known phenomenon and results from "perfect" promoters being limited at the point of RNA polymerase promoter escape (because the RNAP struggles to "let go" of perfect promoters). This does not mean the TTGACA is not the consensus. Indeed, and this is a key point, it is evident in the figure the authors refer to that TTGACA stimulates more transcription than alternative -35 sequences when -10 elements are not perfect.

- In PMID 29745856 (Figure 2), the most information content lies in positions 1-3, with the A and C at position 5 both nearly equally represented, as in our PWM.

The motif shown in this paper suffers from exactly the same issue as the paper under review; the variable spacing between the -35 hexamer and -10 element isn't taken into account by MEME.

- In PMID 33958766 (Figure 1) an experimentally-derived -35 box is even reduced to a "partial" -35 box which only includes positions 1 and 2, with consensus: TTnnnn.

This paper does not show an "experimentally-derived -35 box" in Figure 1 (or anywhere else, as far as I can see).

- In addition, we did not derive the PWMs as the reviewer describes. The PWMs we use are based on computational predictions that are in excellent agreement with experimental results. Specifically, the PWMs we use are from PMID 29728462, which acquired 145 -10 and -35 box sequences from the top 3.3% of computationally predicted boxes from Regulon DB.

The paper mentioned states "for the genomic RNAP logo, sequences were taken from computationally predicted RNAP binding sites on RegulonDB" so these are not experimentally defined promoters? It's not obvious from the paper, or regulon DB, which sequences these are or how they were predicted.

- Thank you for pointing out that our original submission was incomplete in this regard. We address these concerns by new analyses, including some new experiments. First, Rho dependent termination is associated with the RUT motif, which is very rich in Cytosines (PMID: 30845912). Given that our sequences confer between 65%-78% of AT-content, canonical rho dependent termination is unlikely. However, we computationally searched for rho-dependent terminators using the available code from PMID: 30845912, but the algorithm did not identify any putative RUTs. Because this analysis was not informative, we did not include it in the paper.

I don't believe it is the case that Rho absolutely requires a RUT sequence. My understanding is that, if an RNA is not translated, Rho will intervene (e.g. see PMID: 18487194).

- We respectfully disagree that the reviewer's point is pertinent because what the reviewer is referring to is the likelihood that the sequence is a promoter, which indeed increases with AT content, but we are focused on the likelihood that a sequence becomes a promoter through DNA mutation

I disagree that this distinction is relevant. An AT-rich sequence will much more closely resemble a promoter by chance than a GC rich sequence. As an extreme example, the sequence TTTTTT can be converted into a reasonable -10 element by one change (to TATTTT) but the sequence GGGGGG can't.

Reviewer #1 (Public review):

Summary:

This study from Abssy et al. aims to determine if different non-invasive peripheral stimulation techniques - such as magnetic and electrical stimulations - may influence pain intensity, unpleasantness, and secondary hyperalgesia using a 4-arm parallel-group study. They observed no effect on pain intensity and unpleasantness. Also, they reported that only the TENS (electrical stimulation) did not impact secondary hyperalgesia. They hypothesized that the effects were probably due to the sound emitted by RPMS (magnetic stimulation). In a follow-up study, they tried to determine if covering the sound of RPMS would abolish the effect on secondary hyperalgesia using a single-arm design. They observed no effect of RPMS.

Strengths:

(1) The research team recruited a relatively large sample size for this type of study.

(2) The phasic heat pain protocol appears rigorous and well-described.

(3) The Figures are helpful in facilitating the understanding of the study design and results.

(4) The statistical analyses appear sound.

Weaknesses:

(1) The proposed design is not sufficient to answer the research question. The rationale of the study proposed in the introduction is that auditory stimulation may explain the analgesic effects of RPMS. To answer this question, the authors should have used a factorial design using 4 groups (active RPMS + sound; active RPMS + no sound; sham RPMS + sound; sham RPMS + no sound). Using this design, it would have been possible to determine if the sound, the afferent stimulation, or both are necessary to produce analgesia. Rather, they tested two types of RPMS (iTBS, cTBS) without real rationale, one electrical stimulation and a placebo.

(2) There are multiple ways that the current design could have introduced biases. The study was not randomized but pseudo-randomised. What does that mean? Was their allocation concealment? Was the assessor and data analyst blinded to group allocation? Did an intention to treat analyses were performed? Did the participants were adequately blinded (was it measured)?

(3) The TENS parameters used were not optimal and are not those commonly used in clinical practice. This could have explained the lack of TENS effects. The lack of TENS effects has not been discussed and it is concerning. If TENS had been effective (as expected), the story about the auditory effects would not have been presented as the primary mechanisms underlying the current results.

(4) No primary outcome has been identified. It is important to mention that the interpretation of results is based on the presence of only one statistically significant result. Pain intensity and pain unpleasantness are not affected. This was not properly addressed in the Discussion. What does that mean that secondary hyperalgesia is affected but not pain?

(5) The use of secondary hyperalgesia as a variable requires further clarification. How is it possible to measure secondary hyperalgesia if there is no lesioned tissue? If heat creates secondary hyperalgesia without lesion, what does that mean physiologically? Is it a valid and reliable "pain" variable?

(6) The follow-up study has been designed to cover the RPMS sound using pink noise. However, the pink noise was also present during the PHP measurement. How can we determine whether the absence of change is due to the pink noise during the RPMS or the presence of pink noise during PHP? I don't think this is possible to discriminate.

Appraisal:

(7) Despite all these potential issues, authors interpret their data with high confidence and with several overstatements in the Title, Abstract, and Discussion. The results do not support their conclusions. The fact that auditory stimulation may produce an analgesic effect is a hypothesis, but the current study cannot ascertain it.

Reviewer #2 (Public review):

Summary:

In this article, Abssy, Osokin, Osborne, et al. aimed to demonstrate the effect of Peripheral Magnetic Stimulation (PMS) as a pain relief tool, studying its effects in an experimentally induced pain paradigm applied over healthy subjects. This is a relevant objective, as it will give a proxy indication of its utility as a clinical intervention to treat pain. Shockingly, in the first experiment, the authors found that this effect existed, not only in the active PMS groups but also in the sham PMS. With a clever second experiment, the authors used pink noise to mask the clicking sound and the PMS: this modification abolished the hypoalgesic effect of PMS.

Strengths:

This study presents an adequately calculated sample size (n = 100 for study 1 and n = 32 for study 2). This gives trustability to the results and allows for a correct disaggregated analysis to assess gender effects, which correct execution does not often occur. Nuisance variables are adequately addressed, figures and writing are clear, and I especially liked figures 4 and 5 for their easiness of interpretation. They explore two different stimulation protocols for the PMS, extending their results beyond parametrization. Secondary hyperalgesia is a particularly relevant measurement, as it is a common symptom in many relevant painful conditions. Pseudorandomization and counterbalanced design are also appreciated, as well as reinforcement of the results through Bayesian statistical approaches. Regarding the scientific content, the main result (auditory modulation of pain in PMS) is exciting and very interesting by itself and will be relevant for the pain community, granting further research, both from a fundamental and clinical perspective. Personally, I respect that they recognize that results did not match their a priori hypothesis, instead of committing HARKing. And it is a very thrilling mismatch for sure!

It will be especially interesting for those among us dedicated to neural stimulation for pain treatment.

Weaknesses:

Although the study presents solid results, some specific concerns make me reluctant to accept the interpretations that the authors take from said results. I list the most important here.

(1) My biggest concern in this paper is that the stimulation protocols are not applied after pain was induced in the subjects, but before. This is not bad in itself, but as the paper presents the stimulations as potential "treatments" it generates a severe mismatch between the objective, context (introduction), and impact (discussion) presented for the experiments, and how they are actually designed. This adds to the fact that healthy volunteers are used here to generate a study with low translational capability, that aims to be translational and provide an indication for clinics (maybe this is why the reduction in pain intensity caused by PMS when applied in patients, reported in references [29, 35 and 39], is not observed here).

(2) TENS treatment duration is simply too short (90s) to be considered a therapeutic TENS intervention. I get that this duration was chosen to match the one of PMS, but TENS is never applied like this in the clinics, in which the duration varies from 10 minutes to an hour (or more). This specific study comparing different durations recommends 40 minutes for knee osteoarthritis pain relief (PMID: 12691335). Under these conditions, this stimulation is more similar to a sham TENS than to a real TENS treatment: I would suggest interpreting it as such. As the paper is right now, it could give the impression that PMS could produce clinical effects not observed in TENS, but while the PMS application resembles a clinical one, the TENS application does not (due to its extremely short duration). As an example, giving paracetamol at a dose 10 times below its effective dose is a placebo, not a paracetamol treatment.

(3) This study measured pain, not central sensitization. Specifically, the effects refer to the area of secondary hyperalgesia. The IASP definition for central sensitization is "Increased responsiveness of nociceptive neurons in the central nervous system to their normal or subthreshold afferent input." (PMID: 32694387). No neuronal results are reported in this article. Therefore, central sensitization is not measured here, and we do not know if it is reduced by sound. This frontally clashes with the title of the article and with many interpretations of the results. For a deep review on this topic, I recommend PMID: 39278607 and the short article PMID: 30416715.

(4) There is no mention of blinding/masking/concealing in this manuscript. Was the therapist blind to whether they applied one protocol, another, or a placebo? Were the evaluators blind, as this can heavily influence their measurements? And the volunteers? Was allocation concealed? Was this blinding measured afterwards? Blinding is, together with randomization, the most important methodological feature for those interventional studies. For example, not introducing blinding and concealing directly makes a study lose 4 out of 10 points in the PEDro scale, failing to fulfill criteria 3, 5, 6, and 7 (https://pedro.org.au/english/resources/pedro-scale/). Continuing with methodological considerations, the dropout percentage is high (18% for the first and 25% for the second study), both above the 15% cutoff for criterion 8 of the PEDro, losing another point. It is not mentioned whether the statistical analysis was intention-to-treat or per-protocol. Assuming the second, criterion 9 is failed too. Also, although between-group comparisons are done for study 1, they are not for study 2. Criterion 10 depends on this, so I would recommend doing it to avoid failing it. As it is right now, the study will be a 3/10 on the PEDro scale, being therefore considered "low-quality level evidence". As some of these criteria can be fulfilled in this study, I will recommend doing so to increase its quality level to medium (more in "recommendations for authors").

(5) Data reporting and statistical treatment can be improved, as only differences are reported and regression to the mean is not accounted for in this study. Moreover, baseline levels for the dependent variables (control session) are not accessible for evaluation and they are not compared statistically, making it impossible to know if the groups were similar at baseline. This will imply failing criterion 3 of the PEDro, for a total of 2/10 points.

Author response:

Reviewer 1 (Public Review)

(1) The proposed design is not sufficient to answer the research question. The rationale of the study proposed in the introduction is that auditory stimulation may explain the analgesic effects of RPMS. To answer this question, the authors should have used a factorial design using 4 groups (active RPMS + sound; active RPMS + no sound; sham RPMS + sound; sham RPMS + no sound). Using this design, it would have been possible to determine if the sound, the afferent stimulation, or both are necessary to produce analgesia. Rather, they tested two types of RPMS (iTBS, cTBS) without real rationale, one electrical stimulation and a placebo.

We will clarify that the study design employed was originally designed to determine whether iTBS or cTBS would be more effective to reduce pain. We included TENS as a positive control, and sham as a negative control. We were indeed surprised by the findings, and present them herein. Future RCTs should be performed to reproduce these findings.

(2) There are multiple ways that the current design could have introduced biases. The study was not randomized but pseudo-randomised. What does that mean? Was their allocation concealment? Was the assessor and data analyst blinded to group allocation? Did an intention to treat analyses were performed? Did the participants were adequately blinded (was it measured)?

This study was not designed as an RCT, but rather as experimental study. The study was pseudo-randomized to ensure that the groups had equal allocation and distribution of sexes.

The groups were blinded to the other stimulations (they were not informed of the various arms of the study, through different consent forms).

It was not possible to blind the experimenter as the iTBS and cTBS protocols are very different: iTBS has multiple bursts separated by brief intervals, whereas cTBS is continuous). The data were masked for analysis, and only unblinded at the final stage. We will update the manuscript to reflect these changes.

(3) The TENS parameters used were not optimal and are not those commonly used in clinical practice. This could have explained the lack of TENS effects. The lack of TENS effects has not been discussed and it is concerning. If TENS had been effective (as expected), the story about the auditory effects would not have been presented as the primary mechanisms underlying the current results.

We acknowledge that this is a limitation of the study. A future study should address this. However, we will not remove the arm for transparency.

(4) No primary outcome has been identified. It is important to mention that the interpretation of results is based on the presence of only one statistically significant result. Pain intensity and pain unpleasantness are not affected. This was not properly addressed in the Discussion. What does that mean that secondary hyperalgesia is affected but not pain?

We reiterate that this study was not designed as an RCT, but rather an experimental study with The primary outcomes measures that capture change in  were measures of pain sensitivity (pain intensity NRS, pain unpleasantness NRS, and secondary hyperalgesia). We will clarify this in the revised manuscript.

We will now include discussion of the effects being solely on secondary hyperalgesia, and not on pain intensity and unpleasantness.

(5a) The use of secondary hyperalgesia variable is concerning. How is it possible to measure secondary hyperalgesia if there is no lesioned tissue?

Secondary hyperalgesia refers to hyperalgesia assessed in an area adjacent to or remote of the site of stimulation. In general, it is not required to lesion a tissue to activate the nociceptive system or to induce pain. We have cited other studies that have employed secondary hyperalgesia as a pain outcome measure without inducing a lesion.

Hyperalgesia reflects increased pain on suprathreshold stimulation. Then, one measures the subjective response to a painful (i.e. suprathreshold) stimulation, then applies a conditioning stimulation (e.g. heat), and measures the subjective response to the same original stimulus. If the response after conditioning is higher than the baseline measure, hyperalgesia has been induced. Secondary hyperalgesia just refers to hyperalgesia assessed in an area adjacent to or remote of the site of stimulation. In general, it is not required to lesion a tissue to activate the nociceptive system or to induce pain.

(5b) If heat creates secondary hyperalgesia without lesion, what does that mean physiologically?

Secondary hyperalgesia is normally interpreted as a perceptual correlate of central sensitization.

(5c) Is it a valid and reliable "pain" variable?

Yes and yes. A noxious heat stimulus can reliably elicit secondary hyperalgesia (see section 3.2 from Quesada et al. 2021). We also cite several studies that have used secondary hyperalgesia as an outcome measure of central sensitization in pain.

(6) The follow-up study has been designed to cover the RPMS sound using pink noise. However, the pink noise was also present during the PHP measurement. How can we determine whether the absence of change is due to the pink noise during the RPMS or the presence of pink noise during PHP? I don't think this is possible to discriminate.

We will add a third study that performs the control analysis with the sound of the rPMS masked, but no pink noise otherwise. The study will be performed in two groups: one with pink noise, and one without pink noise.

Appraisal

(7) Despite all these potential issues, authors interpret their data with high confidence and with several overstatements in the Title, Abstract, and Discussion. The results do not support their conclusions. The fact that auditory stimulation may produce an analgesic effect is a hypothesis, but the current study cannot ascertain it.

We believe that the chief concern with the interpretation lies with concerns with the second study. The proposed third experiment will address these concerns.

Reviewer 2 (Public Review):

(1) My biggest concern in this paper is that the stimulation protocols are not applied after pain was induced in the subjects, but before. This is not bad in itself, but as the paper presents the stimulations as potential "treatments" it generates a severe mismatch between the objective, context (introduction), and impact (discussion) presented for the experiments, and how they are actually designed. This adds to the fact that healthy volunteers are used here to generate a study with low translational capability, that aims to be translational and provide an indication for clinics (maybe this is why the reduction in pain intensity caused by PMS when applied in patients, reported in references [29, 35 and 39], is not observed here).

We will reframe these as prophylaxis, rather than treatment. This study was an experimental study originally designed to determine which stimulation parameters (cTBS or iTBS) would be better suited to modulate pain. We performed the study in healthy individuals undergoing acute pain, akin to a person undergoing painful procedure, which could lead to central sensitization and pain persistence (e.g., post-surgical pain). However, before testing this in individuals undergoing actual procedures, it is essential to determine efficacy in people before translation.

Khan et al [29] is a case study with neuropathic pain, whereas our study uses a nociceptive pain model. Lim et al [35] employed 10 sessions of rPMS stimulation in patients with acute low back pain. Similar to our study, the change in VAS driven by rPMS was no different than the sham stimulation. We notice that there is no reference 39, and will correct this.

(2) TENS treatment duration is simply too short (90s) to be considered a therapeutic TENS intervention. I get that this duration was chosen to match the one of PMS, but TENS is never applied like this in the clinics, in which the duration varies from 10 minutes to an hour (or more). This specific study comparing different durations recommends 40 minutes for knee osteoarthritis pain relief (PMID: 12691335). Under these conditions, this stimulation is more similar to a sham TENS than to a real TENS treatment: I would suggest interpreting it as such. As the paper is right now, it could give the impression that PMS could produce clinical effects not observed in TENS, but while the PMS application resembles a clinical one, the TENS application does not (due to its extremely short duration). As an example, giving paracetamol at a dose 10 times below its effective dose is a placebo, not a paracetamol treatment.

We acknowledge that this is a limitation, and will address this in the Discussion of the revised manuscript.

(3) This study measured pain, not central sensitization. Specifically, the effects refer to the area of secondary hyperalgesia. The IASP definition for central sensitization is "Increased responsiveness of nociceptive neurons in the central nervous system to their normal or subthreshold afferent input." (PMID: 32694387). No neuronal results are reported in this article. Therefore, central sensitization is not measured here, and we do not know if it is reduced by sound. This frontally clashes with the title of the article and with many interpretations of the results. For a deep review on this topic, I recommend PMID: 39278607 and the short article PMID: 30416715.

It is widely accepted that central sensitization is the neurophysiological basis of secondary hyperalgesia (see PMID: 11313449; PMID: 10581220).

The reviewer is conflating secondary hyperalgesia due to central sensitization and chronic pain. Whether chronic pain is driven or maintained by central sensitization is not the goal of our study. However, there is ample evidence that nociceptive drive can induce plasticity in the CNS, which alters pain sensitivity, and that these changes facilitate pain.

(4a) There is no mention of blinding/masking/concealing in this manuscript. Was the therapist blind to whether they applied one protocol, another, or a placebo? Were the evaluators blind, as this can heavily influence their measurements? And the volunteers? Was allocation concealed? Was this blinding measured afterwards? Blinding is, together with randomization, the most important methodological feature for those interventional studies. For example, not introducing blinding and concealing directly makes a study lose 4 out of 10 points in the PEDro scale, failing to fulfill criteria 3, 5, 6, and 7 (https://pedro.org.au/english/resources/pedro-scale/).

This study was not designed as an RCT, but rather as experimental study. The study was pseudo-randomized to ensure that the groups had equal allocation and distribution of sexes.

The groups were blinded to the other stimulations (they were not informed of the various arms of the study, through different consent forms). However, blinding was not measured afterwards (again, this was not meant to be an RCT).

It was not possible to blind the experimenter as the iTBS and cTBS protocols are very different: iTBS has multiple bursts separated by brief intervals, whereas cTBS is continuous). The data were masked for analysis, and only unblinded at the final stage. We will update the manuscript to reflect these changes.

(4b) Continuing with methodological considerations, the dropout percentage is high (18% for the first and 25% for the second study), both above the 15% cutoff for criterion 8 of the PEDro, losing another point.

In the study, only 2 withdrew after feeling the heat, 2 were lost to follow up, and 2 had incomplete data. That totals 6/123 in Study 1. In study 2, none of the participants that met inclusion/exclusion criteria, and who were ‘allocated’ to the study were included (0% dropout/data loss).

We are unsure how to address this point, as we had clear inclusion/exclusion criteria, and these could only be measured after consenting. As this is an experimental study performed on healthy individuals in a university setting, we are not able to collect any study related data prior to consent.

We openly reported individuals who did not meet the criteria, and thus were excluded. These criteria are a combination of what is required to collect good quality data, and what we are ethically permitted to do. We understand that in an interventional trial where >15% drop out due to intolerance, or adverse events would indeed be concerning.

(5) Data reporting and statistical treatment can be improved, as only differences are reported and regression to the mean is not accounted for in this study. Moreover, baseline levels for the dependent variables (control session) are not accessible for evaluation and they are not compared statistically, making it impossible to know if the groups were similar at baseline. This will imply failing criterion 3 of the PEDro, for a total of 2/10 points.

This only concerns study 1, as study 2 is a within subject study design. Study 1 provides the raw data in Figure 4. We will provide the raw data for each of the primary outcome measures in a supplemental table in the revision.

Reviewer 1: Lei Li Reviewer Comments: Lynch et. al developed demuxSNP, a supervised demultiplexing approach for single-cell cell hashing data in a multi-modal (hashtag expression and SNP profiles) fashion. They utilized a probabilistic method to infer sample identities of cells using cell hashing modality, and then build a KNN model using SNPs of high cofinance ones from previous step. They then use this KNN model to predict cell identities for cells assigned as uncertain or negative by cell hashing.They have demonstrated the performance through a comparison with existing single-modal methods using both real data and simulated data. They have published an R package for the research community. It is interesting and encouraging to see another study focuses on multi-modal demultiplexing for cell hashing data. Below are some major and minor points from my side:1. I am not surprised that a multi-modal demultiplexing beats single-modal methods across both real and simulated datasets. To my knowledge, there are at least two groups proposed multi-modal demultiplexing approach for cell hashing data. Both were uploaded to bioRxiv last year and get published recently. One called hadge (https://link.springer.com/article/10.1186/s13059-024-03249-z ), and another called HTOreader hybrid (https://academic.oup.com/bib/article/25/4/bbae254/7686601), which is discussed by this study. Hadge is a comprehensive framework that integrated popular cell hashing-based and SNP-based methods, allowing for a joint deconvolution by combining best method from each modality. HTOreader hybrid proposed an improved demultiplexing method for cell hashing signals, and then also integrates demultiplexing results from both modality for a better deconvolution in a hybrid fashion. Indeed, this work has implemented different method for the same purpose. I tried both methods, and there're some major updates between bioRxiv version and published version. Thus, even one of them has been discussed, I think it's still necessary to include these two published methods into comparison, to reveal pros and cons of different methods, therefore provide useful information for users to select the method according to their specific experiment configurations.2. demuxSNP method picked top N commonly expressed genes for SNP calculation. In the tutorial on Github, the N was set to 100. I am wondering in a more heterozygous dataset, the N = 100 still sufficient or not. Is there a way for users to determine the N for their specific dataset more systematically? Or the authors can show some data to demonstrate that N = 100 is robust across different datasets?3. The dataset GSE267835 is private. Please provide reviewer token in the Data Availability statement during submission process.4. Color of uncertain cells in Fig1-B is a bit misleading cause in Fig1-A the same color was used to represent "background staining". Even A and B and different panels, however, a big black arrow makes readers thought they're the same data. Therefore, change the color of uncertain cells into another color would be good to avoid confusions.5. In Fig2-A and B, what are the units for the X axis? Are they log2 or log2 hashtag counts? Please add that information to the figure and legend.6. For Fig-2 C and D, please use the formal spell of names of existing methods like you did in Fig2E.7. Please add line numbers to the draft for reviewers' convenience8. Some minor format issues exist. For example, the "Result" section should a header format instead of normal text.

Reviewer 2: Hongzhi Wen Reviewer Comments: The paper introduces stMMR, a multi-modal graph learning method designed to integrate gene expression, spatial location, and histological information for accurate spatial domain identification from spatially resolved transcriptomics (SRT) data. The method employs graph convolutional networks (GCN) and self-attention modules, along with cross-modal contrastive learning, to enhance feature integration and representation.Strengths:1. Using GCN to capture local spatial dependency is natural and effective. Introducing attention mechanism for capturing global relations intuitively make senses, however, need more justification. Contrastive learning for cross-modal feature fusion is also a natural choice in multimodal learning. Overall, the methodology is novel and solid.2. Extensive benchmark analysis across various types of spatial data and tissues demonstrates superior performance of the method in spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. The empirical evidence is very convincing.3. The method's application to chicken heart development, breast cancer, and lung cancer showcases its potential in reconstructing spatiotemporal lineage structures and delineating tumor microenvironments, highlighting its value in clinical research.Weaknesses:1. In Figure 4, SpaceFlow is the only baseline for the case study. However, the performance of SpaceFlow is not topranked in other experiments. There should be a justification for why SpaceFlow is highlighted here.2. The contribution of the global attention mechanism to the whole framework is not very clear. The authors may provide more intuition and empirical justification (e.g., ablation study) if they would like to highlight this design.3. By introducing the hyperparameters $\alpha$, $\beta$ and $\gamma$ in Eq. (11), the method has a significantly larger search space than other methods. It is important to note how these hyperparameters are chosen in practice, more importantly, whether the test performance is referred when adjusting these hyperparameters. This might result in an unfair evaluation.

Reviewer 1: Shihua Zhang Reviewer Comments: In this paper, the authors developed a multi-modal deep learning method for identifying spatial domains from ST data by integrating gene expression, spatial location and histological information. This method adopts the graphconvolutional networks and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. They did several typical analysis to valid this this method. Generally, the wiring of this paper is OK. More specific comments:1. Spatial domain has been overwhelmingly studied recently. The authors need to pay more attention to why it is needed to introduce a new method. The novelty of the current method should be carefully clarified. For example, how the histological information help to improve the performance? Does the "geometric" deep learning really help?2. This method has been applied to some stereotypical data. The authors should applied it to some recently generated data by some new ST techniques.3. Figure 3 stMMR enhances spatial gene expression profiles. It is hard to see how the method enhance the spatial gene expression (e.g., LPL).4. With the accumulation of multi-slice spatial transcriptome data, the integration and alignment of spatial transcriptome data will be essential. Can this method be extended for this situation like STAGATE (Nat Comput Sci.2023 Oct; 3(10):894-906)? This will be valuable for ST analysis.5. The scalability of this method should be carefully explored.6. The authors should provide a detailed tutorial for users.

Reviewer 2: Changwei Shao Reviewer Comments: The authors reported the M. macrocephalus reference genome with a highly degenerated ZW sex chromosome and analyzed the expression pattern of sex chromosomes. In a word, this work extends our understanding of the mechanisms of sex chromosome evolution of fish species. The interpretation of the results is sound for the most part, and gives enough proof to verify their results. I just have few concerns as followed.1.On line 54, please confirm it. In the tongue-sole, the size of Z chromosome (21.91Mb) is larger than the W chromosome(16.45Mb).2.On line 88, 89 and 116, the numbers mentioned do not correspond with the results in Figure 1A. Please confirm it.3.In the section on "Gene Prediction and Annotation", a more comprehensive prediction of gene structure can be achieved by combining three methods: de novo prediction, transcriptome prediction, and homology prediction. The results obtained from these three approaches can be integrated using the EVM software, followed by annotation assessment with BUSCO. The method section is somewhat vague and lacks clear logic. For protein prediction, it is advisable to utilize multiple databases, such as SwissProt, InterPro, and Nr, to corroborate evidence from various sources.4.On line 210, there is an error in the caption of Figure 3. Figure 3B should be a colinearity map of the linkage groups and chromosomes.5.The SNP sites identified in females may include those from the Z chromosome, linkage group 23 (LG23) will contain SNP information from both the Z and W chromosomes. This could potentially affect the demarcation of the region of sex conflict.6.On the sex chromosomes, are there candidate genes related to sex differentiation in regions with a high enrichment of specific SNPs? please provide a detailed explanation.7.What is the distribution of genes in the Z and W chromosome-specific regions, and what is the gene loss rate?

Reviewer1: Yusuke Takehana Reviewer Comments: The authors assembled a chromosome-level genomic sequence and identified the sex chromosomes of the fish Megaleporinus macrocephalus. This manuscript is potentially interesting because evolution of sex chromosomes and sex-determining genes are one of the most fundamental and popular topics in the evolutionary biology. However, the conceptual advance and the novelty of this study are quite limited. It is another paper adding now one more species to the list of assembled genomes in this fish family. In addition, there is nothing new about the description of the sex chromosomes such as their degenerative signature. Such studies have already been conducted many times and similar conclusions have been reported. Furthermore, the experimental evidence presented appears rather preliminary and is not sufficient to support the claims and interpretations presented in discussion. I am therefore afraid that I have to say that the manuscript does not provide new insights into evolution of sex chromosomes, and thus will not be of sufficient interest to the readers of Gigascience.1. Overall, the paper was very difficult to read due to a lack of logic structure and many errors, such as confusing between males and females, between chromosomes and linkage groups, and so on.2. The introduction is not logically written. It is unclear what is known and to what extent, and why the genome of this species is being determined.3. I did not understand why the authors concluded that Chr13 is the W chromosome and not the Z chromosome. They should assemble the Z and W chromosomes separately and confirm them from different information. It is also unclear how they rule out the possibility that the sequences are chimeric. If they really want to reveal the evolutionary process of sex chromosomes, they should use all the data (Hi-C, linkage analysis, Pool-seq, gene information) to compare the structure of Z and W in detail, including synteny with closely related species.4. The analysis on sex chromosome gene candidates is too poor. Basic analyses have not been conducted on whether these genes are W-specific, whether they are in both Z and W, whether they have paralogs or not on autosomes, how much sequence variation there is, when and in which cells they are expressed, etc.5. All of the discussions are superficial and lacking in logic, and it is unclear what they want to discuss.6. The figures legends are poorly explained, and contain incorrect information, so I don't understand the meaning of the data at all.7. This manuscript contained many grammatical errors leading to many confusing statements, and some sentences that were grammatically correct but awkward meaning. I strongly recommend that the authors seek advice of someone with a good knowledge of English, preferably a native speaker.

Reviewer2: JITENDRA KUMAR Barupal Reviewer Comments: To the authors,Thank you for the opportunity to review the manuscript GIGA-D-24-00083. The authors created a tool to predict association between genes and metabolites using various algorithms. The authors provide the tool as a web application, and as a python package. To get the reciprocal relationship between gene and metabolites, i.e. which metabolites can change which gene or vice versa, this tool can be a toolkit for the biologist or bioinformatician.The tool has application specially the relationship between changes in genes and metabolites is not direct, many complex mechanisms exist e.g. epigenetic or polymorphism. So the tool can be alternate to other available tools.Also, the manuscript brings the community focus on causal relationships instead of just correlation based relationships. The tool used temporal causality algorithms for predicting relationships between genes and metabolites.However, I recommend major revisions before publication. Here are my reasons and comments for the revisions:General issues with web accessibility and package installation :1. There are concerns about web accessibility, as indicated by web browsers flagging the connection as insecure. This may stem from geographical restrictions or the absence of HTTPS certification. Addressing these issues would ensure secure access to the server.2. Despite successful initiation of the client application from the git repository as a python module, no results were generated upon launching. It is suggested that the authors distribute the tool as a Docker image to facilitate seamless usage, eliminating concerns regarding dependencies and version compatibility.Other comments :1. There are inconsistencies regarding data preprocessing. While the manuscript mentions that the tool will handle preprocessing, it also indicates that users need to provide processed files. Clarification is needed on whether preprocessing is required. It seems, the tool required preprocessed data.2. For clarity use "causality and correlation" instead of "causality/correlation" algorithms.3.Can the tool process any new temporal numerical data series, or does it specifically filter for genes? For instance, if I provide a list of proteins along with a list of genes, will I receive the association between them? It is suggested to include this in the discussion section.4.Does the tool offer the capability to generate a causal diagram or network from these vectors, thereby providing visual support for their assertion regarding the causal relationship between metabolites and genes? If the author is working in this direction, it is suggested that information can be added in the discussion section.5. What definition of causal relationship did the author use, and could they provide a citation for their definition. Predictability or any other criteria were used for causal relationships. Please include the definition or criteria in the introduction and method section.6. What are the minimum or maximum time points (interval) for input files? e.g. will the tool work if I provide only two times points or If I provide 48 times points. Please include the information in the method section.7. What is the influence of the number of time points on the vector relationship presented in the paper? Have any studies by the authors addressed this question? Please include the results and discussion.8. Could the authors clarify which heuristic algorithm was employed for ranking the genes? Additionally, can they elaborate on how their approach to gene ranking is heuristic rather than relying on mathematical optimization or algorithmic methods? Clarification on the term "heuristic" would be beneficial.9. Could the authors offer an example from studies conducted on yeast, E. coli, or other simple organisms, demonstrating how changes in gene sequences have readily been observed to affect metabolite levels? Please include that in the results section.10. Does the tool generate a vector indicating many-to-many relationships or one-to-one relationships? In other words, does it reveal whether one gene is associated with many metabolites, and vice versa, or if it establishes a single genemetabolite relationship? Please include this in the results section. Also, in the discussion section please include examples of application of these relationships in various fields e.g. metabolic engineering or cancer metabolism.11. Table 1 compares the features of CAT Bridge with other available methods. It should encompass features provided by other tools that are not available in the author's tool, such as knowledge-driven integration or integration with a third-party database. Additionally, it should address the limitation posed by the requirement of time series data, which is not just a strength but also a challenge, particularly for epidemiology studies where multiple time series for gene expression may not be feasible.12. Please use alternative phrases to "Self-generated data," such as "experimentally obtained data," to clarify that the author is utilizing data acquired in the lab to validate the tool. (e.g. line 42, 223, and 492).

Reviewer 1: Tara Eicher Reviewer Comments: The authors introduce a useful tool (CAT Bridge) for integrating multiple causal and correlative analyses for multi-omics integration, which also includes a visualization and LLM component. The authors further provide two case studies (human and plant) illustrating the utility of CAT Bridge. I believe that this work should be published, as it contributes to the field of multi-omics analysis.However, I am very concerned about the lack of description regarding the LLM. As explained by Mittelstadt et al (https://www.nature.com/articles/s41562-023-01744-0), LLMs do not always provide factual answers. The authors need to justify the use of the LLM to determine the relevance of a gene-metabolite association. In particular, the authors should add to the main text (or at least the supplementary) a detailed description of the prompt construction and should justify why this prompt is expected to result in factual information. Furthermore, the authors should discuss the caveats of using LLMs in this context, starting with the linked article above. I believe that the manuscript will only be publishable once this concern is addressed.In addition, the authors are recommended to address the following more minor concerns:Implementation:1. Your "example file" links at https://catbridge.work are broken. Please fix this.Abstract:1. Line 32: "Nevertheless, substantial challenges remain in determining the association between gene-metabolite pairs due to the complexity of cellular networks." This is not a clear statement. What about the complexity of cellular networks presents challenges in determining the associations?2. Make sure you are using present tense consistently, not past tense (Line 39).3. Please use the scientific name with the common name in parentheses as follows: Capsicum chinense (chili pepper). Use only the scientific name throughout the rest of the document (Line 41).Background:1. Line 56: "Background" should not be plural.2. Lines 59-60: More comprehensive than what? Please elaborate here.3. In Line 60, please include and familiarize yourself with the following reference: Eicher, T., G. Kinnebrew, A. Patt, K. Spencer, K. Ying, Q. Ma, R. Machiraju and E. A. Mathé (2020). "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources." Metabolites 10: 202.4. Lines 67-68: Citation needed.5. Line 72: Please use the scientific name with the common name in parentheses.6. Lines 74-77: Citations needed.7. Lines 77-78: Give an example of biologically naïve conclusions from purely data-driven strategies.8. Line 78: Discuss how the machine learning models could address the drawbacks of the correlation models and vice-versa.Materials and Methods:1. It seems that CAT Bridge needs to be run on one metabolite at a time. In this case, I would not use the term "gene-metabolite pair association" in Line 104, but rather "associations between genes and the target metabolite".2. Line 115: Clearly state which of these methods are non-linear and which address the lag issue.3. Line 136: Your figures are out of order (Figure 1B comes after Figure 2B).4. Please take a look at the Minimum Standards Reporting Checklist (https://academic.oup.com/gigascience/pages/Minimum_Standards_of_Reporting_Checklist). In particular:a. In the section starting at Line 153, list the number of seedlings used.b. Were all timepoints collected from all seedlings? List the total number of samples.c. How many mg were collected per sample (can use a range here)?d. 3 biological replicates per seedling? Give more detail here.e. What machine was used for the ultrasonic process? If frequency settings are permitted by the machine, list the settings used.f. How many of the 28 younger and 54 older adults had both transcriptome and metabolome data?5. Line 209: "Younger" and "older" are better terms.Results:1. Line 248: How does the AI agent analyze the functional annotations?2. Lines 281-282: "This illustrates the advantage of causal relationship modeling methods over traditional methods".3. Line 290: Please also include the updated IntLIM paper (IntLIM 2.0): Eicher, T., K. D. Spencer, J. K. Siddiqui, R. Machiraju and E. A. Mathe (2023). "IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements." Bioinformatics Advances 3(1): vbad009.4. Make sure the colors are consistent in Table 1.5. Line 156: The scientific name of the pepper species is inconsistent with other areas of the text.Figures:1. S1 should be provided as a table, not a figure.2. Please make S2 larger. It is difficult to read.3. S3 needs labels (x axis, y axis, legend).

score για ένα χωροδιακό πολυφωνικό (ένα ένα τα γραμματικά είδη) choir 1: διαβάζει τις ρίζες των λέξεων choir 2: διαβάζει τις καταλήξεις των λέξεων choir 3: διαβάζει τα ρήματα choir 4: διαβάζει τις λέξεις από ένα μέχρι 3 γράμματα διαβάζει τα άρθρα/αντωνυμίες κλπ

Reviewer #3 (Public review):

Summary:

Chen et al. develop and characterize a new approach for screening drugs for epilepsy. The idea is to increase the ability to study seizures in animals with epilepsy because most animal models have rare seizures. Thus, the authors use the existing intrahippocampal kainic acid (IHKA) mouse model, which can have very unpredictable seizures with long periods of time between seizures. The authors employ an additional method to trigger seizures in the IHKA model. This method is closed-loop optogenetic stimulation of area CA1. There are several assumptions: area CA1 is the best location, triggered seizures are the same as spontaneous seizures, and this method will be useful despite requiring a great deal of effort. Regarding the latter, using a mouse model with numerous seizures (such as the pilocarpine model) might be more efficient than using a modified IHKA protocol that requires viral injection for optogenetics, fiber insertion requiring additional surgery, and accurate targeting to reliably trigger seizures on-demand. Aside from these caveats, the authors do succeed in studying seizures more readily in a mouse model of rare seizures. However, the seizures are evoked, not spontaneous. As currently presented, it is not clear how the triggered seizures can be used to investigate if antiseizure medication can reduce seizure burden as measured by seizure severity and seizures per day.

The authors modified the IHKA model to inject KA into CA3 instead of CA1 in order to preserve the CA1 pyramidal cells that they will later stimulate. To express the excitatory opsin channelrhodopsin (ChR2) in area CA1, they use a virus that expresses ChR2 in cells that express the Thy-1 promoter. The authors demonstrate that CA3 delivery of KA can induce a very similar chronic epilepsy phenotype to the injection of KA in CA1 and show that optical excitation of CA1 can reliably induce seizures. These are the strengths of the study.

While the authors show that electrophysiological signatures of induced vs spontaneous seizures are similar in many ways, the authors also show several differences and it is not clear if these differences are meaningful. Notably, the induced seizures are robustly inhibited by the antiseizure medication levetiracetam and variably but significantly inhibited by diazepam, similar to many mouse models with chronic recurrent seizure activity. I agree with the authors that this modified IHKA model will be of most value for higher throughput screening of potential antiseizure therapies, but with the caveat that the data may not generalize to other epilepsy models or humans. The authors evaluate the impact of repeated stimulation on the reliability of seizure induction and show that seizures can be reliably induced by CA1 stimulation for as long as 16 days, but the utility of the model would be better demonstrated if seizures could be shown to be inducible over the range of weeks to months.

Strengths:

(1) The authors show that the IHKA model of chronic epilepsy can be modified to preserve CA1 pyramidal cells (but at a cost of CA3 cells), allowing on-demand optogenetic stimulation of CA1 that appears to lower seizure threshold and thus trigger a seizure event.

(2) The authors show that repeated reactivation of CA1 even in untreated mice can promote kindling and induction of seizure activity, indeed generating two mouse models in total.

(3) Many electrophysiological signatures are similar between the induced and spontaneous seizures, and induced seizures reliably respond to treatment with antiseizure medications.

(4) Given that more seizures can be observed per mouse using on-demand optogenetics, this model enhances the utility of each individual mouse.

Weaknesses:

(1) Evaluation of seizure similarity using the SVM modeling and clustering is not sufficiently explained to show if there are meaningful differences between induced and spontaneous seizures. SVM modeling did not include analysis to assess the overfitting of each classifier since mice were modeled individually for classification.

(2) The difference between seizures and epileptiform discharges or trains of spikes (which are not seizures) is not made clear.

(3) The utility of increasing the number of seizures for enhancing statistical power is limited unless the sample size under evaluation is the number of seizures. However, the standard practice is for the sample size to be the number of mice.

(4) Seizure burden is not easily tested.

(5) It is unlikely that long-term adaptation to CA1-stimulated seizure induction is absent in these mice. A duration of evaluation longer than 16 days is warranted in light of the downward slope at days 13-16 for induced seizures in Figure 4C.

(6) Human epilepsy is extensively heterogeneous in both etiology and individual phenotype, and it may be hard to generalize the approach.

(7) No mention or assessment of mouse sex as a biological variable.

Author response:

In this initial response to the public review, we outline our plan to address the major concerns raised. Below, we provide a general categorization of the suggestions and our corresponding responses

Weakness #1: Statistical Concerns - using the number of seizures (rather than the number of animals) may identify small effects that could be insignificant. Effect size should be taken into consideration.

Reviewer 1:

“While the data generally supports the authors' conclusions, a weakness of this manuscript lies in their analytical approach where EEG feature-space comparisons used the number of spontaneous or evoked seizures as their replicates as opposed to the number of IHK mice; these large data sets tend to identify relatively small effects of uncertain biological significance as being highly statistically significant.”

Reviewer 2:

“In several sections of the paper, the authors argue that two different groups are similar on the basis that no statistical difference was found between the two groups (i.e., p > 0.05); however, the failure to find a statistically significant difference, particularly with relatively small sample sizes, is not rigorous evidence that the two groups are actually similar - they are just "not significantly different.”

Reviewer 3:

“(3) The utility of increasing the number of seizures for enhancing statistical power is limited unless the sample size under evaluation is the number of seizures. However, the standard practice is for the sample size to be the number of mice.”

Reviewer 3:

“(1) Evaluation of seizure similarity using the SVM modeling and clustering is not sufficiently explained to show if there are meaningful differences between induced and spontaneous seizures. SVM modeling did not include analysis to assess the overfitting of each classifier since mice were modeled individually for classification.”

We understand the reviewers’ concerns. In this work, we used linear mixed effect model to address two levels of variability –between animals and within animals. The interactive linear mixed effect model shows that most (~90%) of the variability in our data comes from within animals (Residual), the random effect that the model accounts for, rather than between animals. Since variability between animals are low, the model identifies common changes in seizure propagation across animals, while accounting for the variability in seizures within each animal. Therefore, the results we find are of changes that happen across animals, not of individual seizures. We will make text edits to enhance understanding of the linear mixed effect model.

To address the point raised about similarity, we will explain how the SVM classifier was trained. The purpose of the SVM is not to identify meaningful differences between induced and spontaneous seizures. Rather, it is to classify EEG sections as “seizures” or non-seizures, demonstrating the gross similarity between induced and spontaneous seizures despite minor differences. We will make text clarifications for the SVM model.

Weakness #2: Clinical and biological significance is unclear.

Reviewer 1:

“Furthermore, the clinical relevance of similarly small differences in EEG feature space measurements between seizure-naïve and epileptic mice is also uncertain.”

Reviewer 2:

“While the paper may be relevant for the ETSP and contract research organizations (CROs), the paper was not written to attract the interest of biological scientists, even those in this specific area of epilepsy research. It may be of low interest to other neuroscientists… The key issue the authors aim to address is the 30-40% of patients with DRE, but the real problem with DRE patients is not that these people have seizures with no effect of the ASDs; rather, although ASD may reduce seizure burden, these patients continue to have some remaining seizures even after high doses of ASDs, which often leads to adverse effects from the particular ASDs… It remains unclear that the optogenetically induced seizures in this model are better than similarly induced seizures in a naïve animal, and there is no evidence that the model will be useful for finding new ASDs to treat DRE.”

Reviewer 3:

“(6) Human epilepsy is extensively heterogeneous in both etiology and individual phenotype, and it may be hard to generalize the approach.”

Reviewer 2:

“The authors state that this approach should be used to test for and discover new ASDs for DRE, and also used for various open/closed loop protocols with deep-brain stimulation; however, the paper does not actually discuss rigorously or critically the background literature on other published studies in these areas or how this approach will improve future research for a broader audience than the ETSP and CROs. Thus, it is not clear whether the utility will apply more widely and how extensive a readership will be attracted to this work.”

We appreciate the reviewer’s concerns. We will revise the manuscript to better emphasize the potential significance of our approach. The on-demand seizure model can be applied to address biologically and clinically relevant questions beyond its utility in drug screening. For example, crossing the Thy1-ChR2 mouse line with genetic epilepsy models, such as Scn1a mutants, could reveal how optogenetic stimulation differentially induces seizures in mutant versus non-mutant mice, providing insights into seizure generation and propagation in Dravet Syndrome. Due to the cellular specificity of optogenetics, we also envision this approach being used to study circuit-specific mechanisms of seizure generation and propagation. Regarding drug-resistant epilepsy (DRE) and anti-seizure drug (ASD) screening, we agree with the reviewer that probing new classes of ASDs for DRE represents the critical goal. However, we believe a full exploration of additional ASD classes and/or modeling DRE lies outside the scope of this manuscript.

Weakness #3: Definition of Seizure is unclear

Reviewer 2:

“Although the figures provide excellent examples of individual electrographic seizures and compare induced seizures in epileptic and naïve animals, it is unclear which criteria were used to identify an actual seizure induced by the optogenetic stimulus, versus a hippocampal paroxysmal discharge (HPD), an "afterdischarge", an "electrophysiological epileptiform event" (EEE, Ref #36, D'Ambrosio et al., 2010 Epilepsy Currents), or a so-called "spike-wave-discharge" (SWD). Were HPDs or these other non-seizure events ever induced using stimulation in animals with IH-KA? A critical issue is that these other electrical events are not actual seizures, and it is unclear whether they were included in the column showing data on "electrographic afterdischarges" in Figure 5 for the studies on ASDs”

Reviewer 3:

“(2) The difference between seizures and epileptiform discharges or trains of spikes (which are not seizures) is not made clear.”

Reviewer 2:

“The differences between the optogenetically evoked seizures in IH-KA vs naïve mice are interpreted to be due to the "epileptogenesis" that had occurred, but the lesion from the KA-induced injury would be expected to cause differences in the electrically and behaviorally recorded seizures - even if epileptogenesis had not occurred. This is not adequately addressed.”

Thank you for pointing out the unclear definition of the seizures analyzed. We agree and will revise the text to clarify this issue. In this manuscript, we focused on tonic-clonic seizures. We analyzed animal behavior during evoked events, and a high percentage of induced electrographic events were accompanied by behavioral seizures with a Racine scale of three or above. Regarding epileptogenesis, our model is based on the IHK model, in which spontaneous tonic-clonic seizures occur a few to several days after KA injection. These mice are, by definition, epileptogenic. We will further clarify this methodology in the text.

Weakness #4: Similarity/Difference with Kindling Not Clear

Reviewer 2:

“The authors did not test whether an apparent "kindling" effect, apparently seen in naïve controls, also occurred in animals micro-injected with kainic acid (KA). This effect could cause model instability that might result in variability in response to ASDs. It is not clear whether the number of optogenetically induced seizures in epileptic animals would affect the response to drugs. It is also unclear how much of an improvement the animal model in the present work is over other similar models of TLE, where electrically triggered seizures could simply be applied to one of them.”

Reviewer 3:

“(5) It is unlikely that long-term adaptation to CA1-stimulated seizure induction is absent in these mice. A duration of evaluation longer than 16 days is warranted in light of the downward slope at days 13-16 for induced seizures in Figure 4C.”

We appreciate the reviewer’s comments regarding the “kindling effect” as well as its similarity to the kindling model. We will carefully assess the data and address this in the revised manuscript. In electrical kindling, the activated cellular population is non-specific, including both excitatory and inhibitory neurons. In our model, we specifically activate predominantly excitatory neurons (Thy1-positive neurons), which we observed to participate in convulsant-induced seizures (as demonstrated in Thy1-GCaMP experiments). We consider this specificity an improvement over the kindling model, making our approach more biologically relevant.

Weakness #5: Time needed to generate model is significant. Unclear if animals were pre-selected

Reviewer 1:

“Finally, the multiple surgeries and long timetable to generate these mice may limit the value compared to existing models in drug-testing paradigms.

Reviewer 2:

“The authors offer little mention of other research using animal models of TLE to screen ASDs, of which there are many published studies - many of them with other strengths and/or weaknesses. For example, although Grabenstatter and Dudek (2019, Epilepsia) used a version of the systemic KA model to obtain dose-response data on the effects of carbamazepine on spontaneous seizures, that work required use of KA-treated rats selected to have very high rates of spontaneous seizures, which requires careful and tedious selection of animals. The ETSP has published studies with an intra-amygdala kainic acid (IA-KA) model (West et al., 2022, Exp Neurol), where the authors claim that they can use spontaneous seizures to identify ASDs for DRE; however, their lack of a drug effect of carbamazepine may have been a false negative secondary to low seizure rates. The approach described in this paper may help with confounds caused by low or variable seizure rates. These types of issues should be discussed, along with others.”

We appreciate the reviewer’s insights. In an existing model investigating spontaneous tonic-clonic seizures (such as the intra-amygdala kainate injection model), the time investment is back-loaded, requiring two to three weeks per condition while counting spontaneous seizures, which may occur only once a day. In contrast, our model requires a front-loaded time investment. Once the animals are set up, we can test multiple drugs within a few weeks, providing significant time savings. Additionally, we did not pre-screen animals in our study. Existing models often pre-select mice with high rates of spontaneous seizures, whereas in our model, seizures can be induced even in animals with few spontaneous seizures. We believe that bypassing the need for pre-screening is a key advantage of our induced seizure model.

Reviewer 3:

“(7) No mention or assessment of mouse sex as a biological variable.”

Thank you for pointing this out. Both female and male animals were included in this study: Epileptic cohort: 7 males, 3 females; Naïve cohort: 3 males, 4 females

Author response:

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

Reviewer #1 (Public Review):

Summary:

Wilson's Disease (WD) is an inherited rare pathological condition due to a mutation in ATP7B that alters mitochondrial structure and dysfunction. Additionally, WD results in dysregulated copper metabolism in patients. These metabolic abnormalities affect the functions of the liver and can result in cholecystitis. Understanding the immune component and its contribution to WD and cholecystitis has been challenging. In this work, the authors have performed single-cell RNA sequencing of mesenchymal tissue from three WD patients and three liver hemangioma patients.

Strengths:

The authors describe the transcriptomic alterations in myeloid and lymphoid compartments.

Weaknesses:

In brief, this manuscript lacks a clear focus, and the writing needs vast improvement. Figures lack details (or are misrepresented), the results section only catalogs observations, and the discussion needs to focus on their findings' mechanistic and functional relevance. The major weakness of this manuscript is that the authors do not provide a mechanistic link between the absence of ATP7B and NK cells' impaired/altered functions. While the work is of high clinical relevance, there are various areas that could be improved.

In this study, we reported for the first time that ATP7B mutation and the resulting metabolic abnormalities in hepatocytes cause functional alteration of immune cells in WD patients. We dissected the transcriptional profiles of liver mesenchymal cells and delineated the functional differences of main immune cells in WD patients through scRNA-seq. The NK cell exhaustion and its clinical significance were further demonstrated.

The mechanism study is of our concern. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication rather than through the direct action of ATP7B protein. How ATP7B mutation disturbs the metabolic homeostasis in hepatocyte, how metabolic pathways regulate the release of signal substances, and how signal substances act on the NK cells need to be explained. These contents, together with this manuscript, are beyond the scope of a single article, so we put the novelty in this manuscript.

We sincerely appreciate the comments. We have improved the manuscript based on your valuable suggestions. The mechanism study is our subsequent research topic. We are actively promoting it and have found that ATP7B mutation rewires a certain metabolism pathway in hepatocyte, and that a critical metabolite functions as the mediator causing NK cell exhaustion.

Reviewer #2 (Public Review):

Summary:

Wilson's disease is a rare genetic disorder caused by mutations in the ATP7B gene. Previous studies have documented that ATP7B mutations can disrupt copper metabolism, affecting brain and liver function. In this paper, the authors performed a retrospective clinical study and found that Wilson's disease has a high incidence of cholecystitis. Single-cell RNA-seq analysis revealed changes in the immune microenvironment, including the activation of immune responses and the exhaustion of natural killer cells.

Strengths:

A key finding of this study is that the predominant ATP7B gene mutation in the Chinese population is the 2333G>T (p. R778L) mutation. The authors reported associations between Wilson's disease and cholecystitis, as well as the exhaustion of natural killer cells.

Weaknesses:

The underlying mechanisms linking ATP7B mutations to cholecystitis and natural killer cell exhaustion remain unclear. Specifically, it is not yet determined whether copper metabolism alterations directly cause cholecystitis and natural killer cell exhaustion, or if these effects are secondary to liver dysfunction.

In this study, we reported for the first time that ATP7B mutation and the resulting metabolic abnormalities in hepatocytes cause functional alteration of immune cells in WD patients. We dissected the transcriptional profiles of liver mesenchymal cells and delineated the functional differences of main immune cells in WD patients through scRNA-seq, focusing on the NK cell exhaustion and its clinical significance.

The mechanism study is of our concern. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication, so we prioritize the studying of this aspect. How ATP7B mutation disturbs the metabolic homeostasis in hepatocyte, how metabolic pathways regulate the release of signal substances, and how signal substances act on the NK cells need to be explained. These contents, together with this manuscript, are beyond the scope of a single article, so we put the novelty in this manuscript.

We sincerely appreciate the comments. The mechanism study is the topic of our follow-up study. We are actively promoting the research and we have found that ATP7B mutation rewires a certain metabolism pathway in hepatocyte, and that a critical metabolite functions as the mediator causing NK cell exhaustion.

Reviewer #1 (Recommendations For The Authors):

Major:

(1) Abstract. A major portion of this manuscript focuses on non-NK cells. Data that describes NK cell exhaustion is only minimal. Therefore, the authors should modify the abstract.

Thank you for your valuable suggestion. We have supplemented the description of functional changes in other immune cells, and have modified the abstract (line 31-35).

(2) Introduction. There are three paragraphs. The first paragraph discusses cholecystitis. However, there are too many repetitions, and the information is unclear. In the second part, the authors discuss NK cells and their exhaustion. The authors do not establish a clear rationale or logic linking NK cells to WD or cholecystitis. In the last paragraph, the authors describe their findings. Their correlation between NK cell exhaustion and the poor healing process of cholecystitis has no direct experimental proof.

Thank you for your comments. We have deleted the repetitions and rephrased some sentences (line 72-74). Briefly, in the first paragraph, we proposed the significant prognostic value of immune cell dysfunction for cholecystitis. In the second paragraph, we introduced NK cell exhaustion and its potential to predict prognosis of certain diseases. In the third paragraph, we introduced that the liver is a central organ involved in metabolism and immunity, holding a large number of NK cells. Liver pathologies commonly impact the development and outcome of inflammation-associated diseases such as cholecystitis. WD was selected as a research model. In the last paragraph, we introduced our findings from clinical study, scRNA-seq, clinical samples, and bioinformatics analysis, and concluded at the end.

(3) Results. Overall, the results section lacks clarity and a clear focus. Figure legends need to be significantly detailed. The authors make too many broad statements without any support. The authors also make too many overstatements.

Thank you for your valuable suggestion. We have improved the inaccurate statements and made detailed refinement of figure legends. All the changes are marked in the manuscript, and related responses are described below.

Figure 1: No information is provided about the functional impairment of ATP7B protein due to the mutation found in the cohort of Chinese patients. What does 'immune abnormalities' (line 127) mean? What is the relevance of showing liver fibrosis and copper accumulation in the eye in Figure 1c and d, respectively? Total cholesterol concentrations are still within the range in the plasma of WD patients, but the authors call it higher. ECAR has not changed in WD patients, but the authors claim it has (line 117).

(1) All these gene mutations in WD disable the protein function and cause the same outcome. (2) We have deleted the inappropriate statement. (3) In clinical observation, we found that WD not only causes copper accumulation in hepatocytes, but also leads to a variety of diseases, including liver fibrosis, Kayser-Fleischer Ring, and lower risk of hyperglycemia. We showed these together with the data of cholecystitis incidence. We think these might suggest the significance of intercellular communication between hepatocytes and other cells in microenvironment. (4) We have deleted the inappropriate statement (line 108-110, 112-113).

Figure 2: Did the authors use the liver mesenchymal tissue or mesenchymal cells? Figure 2 states that they used mesenchymal cells, different from liver mesenchymal tissue. Numbers within Figure 2b UMAP are not visible. Were the initial T and NK cells annotated as indicated in Figure S2 (CD3D, CD#E, CD3G)? If so, that does not include NK cells.

(1) The liver mesenchymal cells were used for scRNA-seq. (2) It is possible that the image resolution was reduced due to the compression of files by the submission system during merging process. We confirm that the image resolution of all figures meets publishing requirements, and that all characters on the figures are visible. You can download figure files to view details. (3) It was our negligence that the incomplete cell markers were shown in Figure S2. We have updated the markers (CD3D, CD3E, NKG7), references (Ref #53, #55, and #56), and related figures (Figure 2e, and Figure S2c).

Figure 3: The authors should change 'Case' to 'WD patients' both in the text and figures. DEGs in Figure 3C indicate a transcriptomic alteration in the B cell compartment, which the authors do not delineate. Also, the rationale and explanation for the CellChat analyses are minimal. Concluding that a change occurred within the TME with minimal data and explanations is unfair.

Thank you for your comments. (1) We apologize for the confusion caused by the use of nomenclatures and abbreviations in the text and figures. In all scRNA-seq data analysis, presentation, and description, we used specific terms (CASE and CON) to refer to the group of WD patients and controls, as well as their cell population. We have now unified the use of nomenclature in full text and defined them when first appeared (line 126-127), avoiding using lowercase form to prevent confusion. (2) We have now compared the expression of key genes of B cell between the two group in the next section “The dysfunction of main immune cells in WD patients” (line 230-235, Figure 4e, Figure S4e). (3) We have described the results of cellular communication in more detail (line 188-194). (4) We have modified the conclusion and all the related statement in full text (line 29-31, 82-84, 149, 194-195).

Figure 4: This section deals with multiple cell types with minimal explanations. This section discusses various cell types, but it lacks focus. In particular, the T cell section should be separated and elaborated more in detail.

(1) In this section, we intended to show the comparison in function of main immune cells that account for a considerable proportion, instead of just showing differently expressed genes that provide minimal information. The evaluation of functional signature, based on the integration of multiple gene expression, allows a direct understanding of the final outcome owing to transcriptional changes. (2) Given that the main functions of T cells did not change significantly and there were more significant changes in innate immunity, the T cell section is relatively short and unsuitable as a separated part.

Figure 5: What are the distinct subsets of NK cells authors have found in the WD patients and controls? How do these subsets differ between the two groups in numbers and their transcriptomes? The presentation and labeling of Figure 5 and Supplementary Figure 5 need to be vastly improved. The pseudotime presentation in Figure 5b should be presented separately for the patients and the controls. Are the changes in gene expression presented in Figure 5a due to the change in the subset compositions? Figure 5c immuno-staining is not at all visible. A clear explanation should be given for the differences between Figure 5c and Figure 5e, where NKG2A expressions are shown. A better explanation for Figure 5d is required. Did the authors use all the antibodies with the same fluorochrome? If so, what color is that? Can the authors include the individual samples in the bar diagram in Figure 5e? Again, the data in Figure 5 is insufficient to conclude that NK cells are exhausted in WD patients. While the role of changes in the expression of T-BET and EOMES can be related to dysfunction and cellular exhaustion of NK cells, the statement made by the authors needs to be toned down as they do not test with independent experiments.

(1) The subsets of NK cell were clustered by gene expression profile and labeled by the characteristically expressed gene, using certain algorithm in the routine procedure. They cannot be distinguished in clinical samples by one or several genes or other sorting methods. Thus, we were not able to analyze these subsets in clinical samples. (2) We have supplemented the comparison of numbers and transcriptomes of three NK subtypes between the two groups (line 268-273). (3) We have checked the figures and confirmed that all characters on the figures are visible. (4) We have separately presented the plot in Figure S5d. (5) We compared the expression level of genes presented in Figure 5a between the two groups in three NK subtypes and supplemented this part (line 264-268). The results were very consistent across the three subtypes, suggesting that the results in total NK population were contributed by all three subtypes and not affected by a single composition. (6) KLRC1 is also known as NKG2A. We are sorry for not making a clear explanation, and now we use KLRC1 only in all text to avoid confusion. We have made a more clear and detailed description for Figure 5c, 5d, and 5e (now labeled as Figure 5b, 5c, and 5d), and have included the fluorochrome in Figure 5d (now labeled as Figure 5c) and the individual value in Figure 5e (now labeled as Figure 5d) (line 293-299). (7) In this section, we found the upregulated expression of inhibitory receptors, downregulated expression of effector molecules, and the impaired NK cell-mediated cytotoxicity in NK cell of WD patients from scRNA-seq. Then we validated the findings in clinical liver section samples and clinical blood samples by mIHC and flow cytometry, respectively. According to the recent articles, exhausted NK cells are characterized by decreased production of effector cytokines (e.g., IFNγ), as well as by impaired cytolytic activity, and downregulate expression of certain activating receptors and upregulate expression of inhibitory receptors (e.g., 10.3389/fimmu.2017.00760, 10.1038/s41590-018-0132-0, 10.1038/s41467-019-09212-y, 10.1080/2162402X.2016.1264562). Therefore, we concluded NK cell exhaustion in WD patients. (8) In the part about transcription factors, we kept the description of objective data and deleted the statement of the contribution of transcription factors to NK exhaustion.

Figure 6: Data presented in Figure 6 and the conclusion made in this manuscript are predictive. There is no direct testing of ATP7B in NK cells to show the functions of this gene. Extension of this to patient survival is purely speculative. As long as authors state these facts clearly in their text, it can be acceptable. However, they do not extend their conclusions to similar liver diseases.

ATP7B mutation is hepatocyte-specific, and it does not occur in any immune cells. The function of ATP7B in NK cell was not studied. We found the NK exhaustion and poor prognosis of cholecystitis in WD patients. Given that there were researches demonstrating that NK exhaustion is correlated with poor liver cancer prognosis, we hypothesized that NK exhaustion contributes to the poor prognosis of cholecystitis. Bioinformatics studies confirmed our hypothesis and supported the extension of this result to other inflammatory diseases. We had no experimental data, but this result was reliable in bioinformatics method.

(4) Discussion: While the authors analyzed multiple cell types, the discussion is primarily focused on NK cells. There is no clear link between copper utilization, NK cell function, and exhaustion that the authors articulate.

Thank you for your comments. The focus of our study is NK cell exhaustion, which is experimentally proven, so we discussed this aspect. We prioritize the effect of intercellular communication and metabolic alteration on the NK cell exhaustion in our follow-up study. Excess copper is released into the circulation in some circumstances in WD patients, but generally they receive long-term de-coppering therapy to maintain intracellular copper at a non-lethal level. Thus, we do not tend to consider copper as a critical factor in this study. In original manuscript, we mentioned the cuproptosis and its potential as a novel target. It is likely to lead to ambiguity and misunderstanding, so we deleted this part to put our point of view clearly.

(5) Supplementary Figures: The presentation and labeling of these figures need to be changed.

Thank you for your suggestions. We have modified the figures and confirmed that all characters on the figures are visible.

Reviewer #2 (Recommendations For The Authors):

It is better to test whether ATP7B mutation can directly affect immune functions.

Thank you for your suggestions. Given that the ATP7B mutation is hepatocyte-specific, its effect on immune cells is most probably through intercellular communication. Thus, we prioritize the effect of intercellular communication on the NK cell exhaustion and we are actively promoting the research.

Author response:

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

Public reviews:

Reviewer 1

We would like to express our gratitude to Reviewer 1 for providing a thorough summary of our work and highlighting its strengths. With regards to the weaknesses, we are committed to improve the manuscript by performing the necessary changes. First, we will specify the exact p-value in all cases.

Regarding the discussion section, we acknowledge the feedback regarding its potential confusion. In line with the reviewer's suggestion, we will reduce the literature review and highlight our findings.

Finally, for the preprint we did not include cofounders such as HIV infection and ethnicity as our study population did not exhibit viral infections and comprised only Hispanic individuals. We will make a more thorough description of the population of study and address these characteristics explicitly in both the methods section and the initial part of the results.

Reviewer 2

We appreciate and thank reviewer 2 for the commentaries. Although it is true that several papers have described the role of microbiome in COVID-19 severity, we firmly believe that our current work stands out. There is not much information related to this association in Mediterranean countries, especially in the south of Spain. In addition, most of the studies only describe microbiota composition in stool or nasopharyngeal samples separately, without investigating any potential relationships between them as we do.

(1) We agree with the reviewer idea of a limited sample size. We faced the challenge of collecting the samples during the peak of COVID-19 pandemia. Thus, doctors and nurses were overwhelmed and not always available for carrying out patient recruitment following the inclusion criteria. Despite these constraints, we ensured that all included samples met our specified inclusion criteria and were from subjects with confirmed symptomatology.

In addition, our main goal was to identify whether severity of the disease could be assessed through microbiota composition. Therefore we did not include a healthy group. Despite not having a large N, our results should be reproducible as they are supported by statistical analysis.

(2) We thank reviewer commentary, and since our original sentence may have lacked clarity, we intend to modify it to ensure it conveys the intended meaning more effectively.

Nonetheless, we remain confident in the significance of our findings. Not only have we found correlation between microbiota and COVID severity, but we have also described how specific bacteria from each condition is associated with key biochemical parameters of clinical COVID infection.

(3) We appreciate the feedback provided by the reviewer. In this case, we have performed 16S analysis due to its cost-effectiveness compared to metagenomic approaches. Furthermore, 16S analysis has undergone refinements that ensure comprehensive coverage and depth, along with standardized analysis protocols. Unlike 16S, metagenomic approaches lack software tools such as QIIME that facilitate standardization of analysis and, thus, reduce reproducibility of results.

(4) We sincerely appreciate this insightful suggestion. simply listing associations between both microbiomes and COVID-19 severity could not be enough, we intend to discuss how microbiota composition may be linked to the mechanisms underlying COVID-19 pathogenesis in our discussion.

(5) We are grateful for the constructive criticism and intend to rewrite our abstract to enhance clarity. Additionally, we will thoroughly review all figures and their descriptions to ensure accuracy and comprehensibility.

Reviewer 3

We acknowledge the annotations made by reviewer 3 and are committed to addressing all identified weaknesses to enhance the quality of our work. Our idea is to modify the methods section and figures to make them easier to understand.

Specifically, in the case of Figure 1, we recognize an error in the description of the Bray-Curtis test. We appreciate the commentary and we will make the necessary changes. Moreover, there is another observation related to Figure 1 description. We are going to modify it in order to gain accuracy.

For figure 2 we are planning to add a supplementary table showing the abundance of detected genus. Nevermind, we will also update the manuscript text to provide clarification on how we obtained this result.

Regarding the clarification about "1% abundance," we want to emphasize that we are referring to relative abundance, where 1 represents 100%. To avoid confusion, we will explicitly state this in both the methods section and figure descriptions. Besides, it is true that the statistical test employed for the analysis is not mentioned in the figure description and we recognize that the image may be difficult to interpret. Therefore, we will modify the text and a supplementary table displaying the abundance and p values is going to be added.

Furthermore, we agree with the reviewer's suggestion to investigate whether the bacteria identified as potential biomarkers for each condition are specific to their respective severity index or if there is a threshold. Thus, we will reanalyze the data and include a supplementary table with the abundance of each biomarker for each condition. We will also place greater emphasis on these results in our discussion.

Finally, in response to the reviewer's suggestion, we are going to go through the nasopharyngeal-fecal axis part in the discussion. It is well described that COVID-19 induces a dysbiosis in both microbiomes. Consequently, we understand that the ratio we have described could be an interesting tool for assessing COVID severity development as it considers alterations in both environments. However, we acknowledge that there may be room for improvement in clarifying the significance of this intriguing finding and its implications.

O enquadramento na faixa de isenção de imposto de renda NÃO DEVE SER UTILIZADO COMO CRITÉRIO para o deferimento do benefício da assistência judiciária gratuita.

AgInt no AREsp 2.441.809-RS , Rel. Ministro Herman Benjamin, Segunda Turma, por unanimidade, julgado em 8/4/2024, DJe 2/5/2024.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

This manuscript from Schwintek and coworkers describes a system in which gas flow across a small channel (10^-4-10^-3 m scale) enables the accumulation of reactants and convective flow. The authors go on to show that this can be used to perform PCR as a model of prebiotic replication.

Strengths:

The manuscript nicely extends the authors' prior work in thermophoresis and convection to gas flows. The demonstration of nucleic acid replication is an exciting one, and an enzyme-catalyzed proof-of-concept is a great first step towards a novel geochemical scenario for prebiotic replication reactions and other prebiotic chemistry.

The manuscript nicely combines theory and experiment, which generally agree well with one another, and it convincingly shows that accumulation can be achieved with gas flows and that it can also be utilized in the same system for what one hopes is a precursor to a model prebiotic reaction. This continues efforts from Braun and Mast over the last 10-15 years extending a phenomenon that was appreciated by physicists and perhaps underappreciated in prebiotic chemistry to increasingly chemically relevant systems and, here, a pilot experiment with a simple biochemical system as a prebiotic model.

I think this is exciting work and will be of broad interest to the prebiotic chemistry community.

Weaknesses:

The manuscript states: "The micro scale gas-water evaporation interface consisted of a 1.5 mm wide and 250 µm thick channel that carried an upward pure water flow of 4 nl/s ≈ 10 µm/s perpendicular to an air flow of about 250 ml/min ≈ 10 m/s." This was a bit confusing on first read because Figure 2 appears to show a larger channel - based on the scale bar, it appears to be about 2 mm across on the short axis and 5 mm across on the long axis. From reading the methods, one understands the thickness is associated with the Teflon, but the 1.5 mm dimension is still a bit confusing (and what is the dimension in the long axis?) It is a little hard to tell which portion (perhaps all?) of the image is the channel. This is because discontinuities are present on the left and right sides of the experimental panels (consistent with the image showing material beyond the channel), but not the simulated panels. Based on the authors' description of the apparatus (sapphire/CNC machined Teflon/sapphire) it sounds like the geometry is well-known to them. Clarifying what is going on here (and perhaps supplying the source images for the machined Teflon) would be helpful.

We understand. We will update the figures to better show dimensions of the experimental chamber. We will also add a more complete Figure in the supplementary information. Part of the complexity of the chamber however stems from the fact that the same chamber design has also been used to create defined temperature gradients which are not necessary and thus the chamber is much more complex than necessary.

We added the scheme of the whole PTFE Chip to Figure 2 in the top left corner, indicating the ROI shown in the fluorescence micrographs. Additionally, the channel walls are now clearly indicated by white dotted lines. The dimensions of the setup are now shown clearer, by showing the total width of the channel as well as its height until the gas flux channel, as well as its depth. Changed caption of the figure accordingly and it now reads: “[…] The PTFE chip cutout in the top left corner shows the ROI used for the micrographs. The color scale is equal for both simulation and experiment and Channel dimensions are 4 x 1.5 x 0.25 mm as indicated. Dotted lines visualize the location of the channel walls. […]“

The data shown in Figure 2d nicely shows nonrandom residuals (for experimental values vs. simulated) that are most pronounced at t~12 m and t~40-60m. It seems like this is (1) because some symmetry-breaking occurs that isn't accounted for by the model, and perhaps (2) because of the fact that these data are n=1. I think discussing what's going on with (1) would greatly improve the paper, and performing additional replicates to address (2) would be very informative and enhance the paper. Perhaps the negative and positive residuals would change sign in some, but not all, additional replicates?

To address this, we will show two more replicates of the experiment and include them in Figure 2.

We are seeing two effects when we compare fluorescence measurements of the experiments.

Firstly, degassing of water causes the formation of air-bubbles, which are then transported upwards to the interface, disrupting fluorescence measurements. This, however, mostly occurs in experiments with elevated temperatures for PCR reactions, such as displayed in Figure 4.

Secondly, due to the high surface tension of water, the interface is quite flexible. As the inflow and evaporation work to balance each other, the shape of the interface adjusts, leading to alterations in the circular flow fields below.

Thus the conditions, while overall being in steady state, show some fluctuations. The strong dependence on interface shape is also seen in the simulation. However, modeling a dynamic interface shape is not so easy to accomplish, so we had to stick to one geometry setting. Again here, the added movies of two more experiments should clarify this issue.

We performed three more replicates of the experiment and included the averaged data points together with their respective standard deviation as error bars in Figure 2d. Additionally, the videos of each individual repeat are now added to the supplementary files for the reader to better understand where the strong fluctuations around half an hour come from. The Figure caption was adjusted to “ […] The maximum relative concentration of DNA increased within an hour to ~30 X the initial concentration, with the trend following the simulation. Error bars are the standard deviation from four independent measurements. […].

The main text was also changed to better explain how the fluctuations impact the measurements: […] Water continuously evaporated at the interface, but nucleic acids remained in the aqueous phase accumulating near the interface. They could only escape downward either by diffusion or by the vortex induced by the gas flowing across the interface, pushing the molecules back deeper into the bulk (See the flow lines in Fig2(b) taken from the simulation).  As the gas flow continuously removed excess vapor, the evaporation rate remained constant. Thus, except for fluctuations, a stable interface shape should be expected. However, due to the high surface tension of water, the interface is very flexible. As the inflow and evaporation work to balance each other, the shape of the interface adjusts, likely in response to small fluctuations in gas pressure and spatial variations in water surface tension. This is leading to alterations in the circular flow fields below (Supplementary Movie 2).

As these fluctuations are difficult to simulate, we decided to stick with one interface shape, matching evaporation and inflow speeds. The evaporation rate at the interface was therefore set to be proportional to the vapor concentration gradient and varied spatially along the interface between 5 and 10.5 µm/s (See Suppl. Fig. VI.1(d)). Using the known diffusion coefficient of 95 µm²/s for the 63mer[9]}, the simulation closely matched the experimental results. In both cases, DNA accumulated in regions with circular flow patterns driven by the gas flux (Fig.2(b), right panel).

5 minutes after starting the experiment, the maximum DNA accumulation was 3-fold, while after one hour of evaporation, around 30-fold accumulation was observed. Due to molecules residing in very shallow volumes when directly at the interface, the fluorescence signal can vary drastically compared to measurements deeper in the bulk. This can be seen in the fluctuations between independent measurements (See Supplementary Movies 2b,2b,2c), especially around 0.5~h shown in Figure 2(d). The simulated maximum accumulation followed the experimental results and starts saturating after about one hour (Fig.2(d)). […]”

The authors will most likely be familiar with the work of Victor Ugaz and colleagues, in which they demonstrated Rayleigh-Bénard-driven PCR in convection cells (10.1126/science.298.5594.793, 10.1002/anie.200700306). Not including some discussion of this work is an unfortunate oversight, and addressing it would significantly improve the manuscript and provide some valuable context to readers. Something of particular interest would be their observation that wide circular cells gave chaotic temperature profiles relative to narrow ones and that these improved PCR amplification (10.1002/anie.201004217). I think contextualizing the results shown here in light of this paper would be helpful.

Thanks for pointing this out and reminding us. We apologize. We agree that the chaotic trajectories within Rayleigh-Bénard convection cells lead to temperature oscillations similar to the salt variations in our gas-flux system. Although the convection-driven PCR in Rayleigh-Bénard is not isothermal like our system, it provides a useful point of comparison and context for understanding environments that can support full replication cycles. We will add a section comparing approaches and giving some comparison into the history of convective PCR and how these relate to the new isothermal implementation.

We added a main text paragraph after the last paragraph in section “Strand Separation Dynamics”: “[…]Rayleigh-Bénard convection cells generate similar patterns to those seen in Fig. 3(c) The oscillations in salt concentration resemble the temperature fluctuations observed in convection-based PCR reactions from earlier studies [32,33], which showed that chaotic temperature variations, compared to periodic ones, enhanced the efficiency of the PCR reaction.[…]

Again, it appears n=1 is shown for Figure 4a-c - the source of the title claim of the paper - and showing some replicates and perhaps discussing them in the context of prior work would enhance the manuscript.

We appreciate the reviewer for bringing this to our attention. We will now include the two additional repeats for the data shown in Figure 4c, while the repeats of the PAGE measurements are already displayed in Supplementary Fig. IX.2. Initially, we chose not to show the repeats in Figure 4c due to the dynamic and variable nature of the system. These variations are primarily caused by differences at the water-air interface, attributed to the high surface tension of water. Additionally, the stochastic formation of air bubbles in the inflow—despite our best efforts to avoid them—led to fluctuations in the fluorescence measurements across experiments. These bubbles cause a significant drop in fluorescence in a region of interest (ROI) until the area is refilled with the sample.

Unlike our RNA-focused experiments, PCR requires high temperatures and degassing a PCR master mix effectively is challenging in this context. While we believe our chamber design is sufficiently gas-tight to prevent air from diffusing in, the high surface-to-volume ratio in microfluidics makes degassing highly effective, particularly at elevated temperatures. We anticipate that switching to RNA experiments at lower temperatures will mitigate this issue, which is also relevant in a prebiotic context.

The reviewer’s comments are valid and prompt us to fully display these aspects of the system. We will now include these repeats in Figure 4c to give readers a deeper understanding of the experiment's dynamics. Additionally, we will provide videos of all three repeats, allowing readers to better grasp the nature of the fluctuations in SYBR Green fluorescence depicted in Figure 4c.

The data from the triplicates are now added to Figure 4c, showing how air bubbles, forming through degassing at the high temperatures required for Taq polymerase, disrupt the measurement, as they momentarily dry off the channel and stop the reaction until the channel fills again. Figure caption has been adapted and now reads: “[…] Dotted lines show the data from independent repeats. Air bubbles formed through degassing can momentarily disrupt the reaction. […]”

We additionally changed the main text to explain the reader the experimental difficulties: “[…] In other repetitions of the reaction, this increase was sometimes even observed earlier, around the one-hour mark (dotted lines). However, air bubbles nucleated by degassing events rise and temporarily dry out the channel, interrupting the reaction until the liquid refills the channel (Supplementary Movies 4,4b,4c\&5). Despite our best efforts, we were unable to fully prevent this, especially given the high temperatures required for Taq polymerase activity. In an identical setting when the gas- and water flux were switched off, no fluorescence increase was found (See Fig. 4(c) red lines). Fluorescence variations are additionally caused by fluctuations in the position of the gas-water interface, as discussed earlier. […]”

I think some caution is warranted in interpreting the PCR results because a primer-dimer would be of essentially the same length as the product. It appears as though the experiment has worked as described, but it's very difficult to be certain of this given this limitation. Doing the PCR with a significantly longer amplicon would be ideal, or alternately discussing this possible limitation would be helpful to the readers in managing expectations.

This is a good point and should be discussed more in the manuscript. Our gel electrophoresis is capable of distinguishing between replicate and primer dimers. We know this since we were optimizing the primers and template sequences to minimize primer dimers, making it distinguishable from the desired 61mer product. That said, all of the experiments performed without a template strand added did not show any band in the vicinity of the product band after 4h of reaction, in contrast to the experiments with template, presenting a strong argument against the presence of primer dimers.

We added a main text section explaining this to the reader: “[…]Suppl. Fig. IX.2 shows all independent repeats of the corresponding experiments. No product was detected in any of these cases, ruling out reaction limitations such as primer dimer formation. Primer dimers would form even in the absence of a template strand and would be identifiable through gel electrophoresis. As Taq polymerase requires a significant overlap between the two dimers to bind, this would result in a shorter product compared to the 61mer used here.  […]”

Reviewer #2 (Public review):

Schwintek et al. investigated whether a geological setting of a rock pore with water inflow on one end and gas passing over the opening of the pore on the other end could create a non-equilibrium system that sustains nucleic acid reactions under mild conditions. The evaporation of water as the gas passes over it concentrates the solutes at the boundary of evaporation, while the gas flux induces momentum transfer that creates currents in the water that push the concentrated molecules back into the bulk solution. This leads to the creation of steady-state regions of differential salt and macromolecule concentrations that can be used to manipulate nucleic acids. First, the authors showed that fluorescent bead behavior in this system closely matched their fluid dynamic simulations. With that validation in hand, the authors next showed that fluorescently labeled DNA behaved according to their theory as well. Using these insights, the authors performed a FRET experiment that clearly demonstrated the hybridization of two DNA strands as they passed through the high Mg++ concentration zone, and, conversely, the dissociation of the strands as they passed through the low Mg++ concentration zone. This isothermal hybridization and dissociation of DNA strands allowed the authors to perform an isothermal DNA amplification using a DNA polymerase enzyme. Crucially, the isothermal DNA amplification required the presence of the gas flux and could not be recapitulated using a system that was at equilibrium. These experiments advance our understanding of the geological settings that could support nucleic acid reactions that were key to the origin of life.

The presented data compellingly supports the conclusions made by the authors. To increase the relevance of the work for the origin of life field, the following experiments are suggested:

(1) While the central premise of this work is that RNA degradation presents a risk for strand separation strategies relying on elevated temperatures, all of the work is performed using DNA as the nucleic acid model. I understand the convenience of using DNA, especially in the latter replication experiment, but I think that at least the FRET experiments could be performed using RNA instead of DNA.

We understand the request only partially. The modification brought about by the two dye molecules in the FRET probe to be able to probe salt concentrations by melting is of course much larger than the change of the backbone from RNA to DNA. This was the reason why we rather used the much more stable DNA construct which is also manufactured at a lower cost and in much higher purity also with the modifications. But we think the melting temperature characteristics of RNA and DNA in this range is enough known that we can use DNA instead of RNA for probing the salt concentration in our flow cycling.

Only at extreme conditions of pH and salt, RNA degradation through transesterification, especially under alkaline conditions is at least several orders of magnitude faster than spontaneous degradative mechanisms acting upon DNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.]. The work presented in this article is however focussed on hybridization dynamics of nucleic acids. Here, RNA and DNA share similar properties regarding the formation of double strands and their respective melting temperatures. While RNA has been shown to form more stable duplex structures exhibiting higher melting temperatures compared to DNA [Dimitrov, R. A., & Zuker, M. (2004). Prediction of hybridization and melting for double-stranded nucleic acids. Biophysical Journal, 87(1), 215-226.], the general impact of changes in salt, temperature and pH [Mariani, A., Bonfio, C., Johnson, C. M., & Sutherland, J. D. (2018). pH-Driven RNA strand separation under prebiotically plausible conditions. Biochemistry, 57(45), 6382-6386.] on respective melting temperatures follows the same trend for both nucleic acid types. Also the diffusive properties of RNA and DNA are very similar [Baaske, P., Weinert, F. M., Duhr, S., Lemke, K. H., Russell, M. J., & Braun, D. (2007). Extreme accumulation of nucleotides in simulated hydrothermal pore systems. Proceedings of the National Academy of Sciences, 104(22), 9346-9351.].

Since this work is a proof of principle for the discussed environment being able to host nucleic acid replication, we aimed to avoid second order effects such as degradation by hydrolysis by using DNA as a proxy polymer. This enabled us to focus on the physical effects of the environment on local salt and nucleic acid concentration. The experiments performed with FRET are used to visualize local salt concentration changes and their impact on the melting temperature of dissolved nucleic acids.  While performing these experiments with RNA would without doubt cover a broader application within the field of origin of life, we aimed at a step-by-step / proof of principle approach, especially since the environmental phenomena studied here have not been previously investigated in the OOL context. Incorporating RNA-related complexity into this system should however be addressed in future studies. This will likely require modifications to the experimental boundary conditions, such as adjusting pH, temperature, and salt concentration, to account for the greater duplex stability of RNA. For instance, lowering the pH would reduce the RNA melting temperature [Ianeselli, A., Atienza, M., Kudella, P. W., Gerland, U., Mast, C. B., & Braun, D. (2022). Water cycles in a Hadean CO2 atmosphere drive the evolution of long DNA. Nature Physics, 18(5), 579-585.].

(2) Additionally, showing that RNA does not degrade under the conditions employed by the authors (I am particularly worried about the high Mg++ zones created by the flux) would further strengthen the already very strong and compelling work.

Based on literature values for hydrolysis rates of RNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.], we estimate RNA to have a half-life of multiple months under the deployed conditions in the FRET experiment (High concentration zones contain <1mM of Mg2+). Additionally, dsRNA is multiple orders of magnitude more stable than ssRNA with regards to degradation through hydrolysis [Zhang, K., Hodge, J., Chatterjee, A., Moon, T. S., & Parker, K. M. (2021). Duplex structure of double-stranded RNA provides stability against hydrolysis relative to single-stranded RNA. Environmental Science & Technology, 55(12), 8045-8053.], improving RNA stability especially in zones of high FRET signal. Furthermore, at the neutral pH deployed in this work, RNA does not readily degrade. In previous work from our lab [Salditt, A., Karr, L., Salibi, E., Le Vay, K., Braun, D., & Mutschler, H. (2023). Ribozyme-mediated RNA synthesis and replication in a model Hadean microenvironment. Nature Communications, 14(1), 1495.], we showed that the lifetime of RNA under conditions reaching 40mM Mg2+ at the air-water interface at 45°C was sufficient to support ribozymatically mediated ligation reactions in experiments lasting multiple hours.

With that in mind, gaining insight into the median Mg2+ concentration across multiple averaged nucleic acid trajectories in our system (see Fig. 3c&d) and numerically convoluting this with hydrolysis dynamics from literature would be highly valuable. We anticipate that longer residence times in trajectories distant from the interface will improve RNA stability compared to a system with uniformly high Mg2+ concentrations.

Added a new Supplementary section for this. We used the trace from Figure 3(c) and calculated the hydrolysis rate for each timestep by using literature values from RNA [Li, Y., & Breaker, R. R. (1999). Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2 ‘-hydroxyl group. Journal of the American Chemical Society, 121(23), 5364-5372.]. We conclude that the conditions deployed for the experiment are not harsh on RNA, with hydrolysis rates in the E-6 1/min regime. The figure below (also now in the supplementary information) shows the hydrolysis of RNA deployed under the conditions of the experiment in Figure 3. RNA is not expected to hydrolyze under these conditions and timescales, in which a replication reaction would occur. With a half life of around 83 days, even a prebiotically plausible – very slow – replication reaction would not be constrained by hydrolysis boundary conditions in this scenario.

Referenced to this section in the supplementary information in the maintext: […] In the experimental conditions used here, RNA would also not readily degrade, even if the strand enters the high salt regimes (See Suppl. Sec. IX). Using literature values for hydrolysis rates under the deployed conditions, we estimate dissolved RNA to have a half life of around 83 days. […]

(3) Finally, I am curious whether the authors have considered designing a simulation or experiment that uses the imidazole- or 2′,3′-cyclic phosphate-activated ribonucleotides. For instance, a fully paired RNA duplex and a fluorescently-labeled primer could be incubated in the presence of activated ribonucleotides +/- flux and subsequently analyzed by gel electrophoresis to determine how much primer extension has occurred. The reason for this suggestion is that, due to the slow kinetics of chemical primer extension, the reannealing of the fully complementary strands as they pass through the high Mg++ zone, which is required for primer extension, may outcompete the primer extension reaction. In the case of the DNA polymerase, the enzymatic catalysis likely outcompetes the reannealing, but this may not recapitulate the uncatalyzed chemical reaction.

This is certainly on our to-do list for future experiments in this setting. Our current focus is on templated ligation rather than templated polymerization and we are working hard to implement RNA-only enzyme-free ligation chain reaction, based on more optimized parameters for the templated ligation from 2’3’-cyclic phosphate activation that was just published [High-Fidelity RNA Copying via 2′,3′-Cyclic Phosphate Ligation, Adriana C. Serrão, Sreekar Wunnava, Avinash V. Dass, Lennard Ufer, Philipp Schwintek, Christof B. Mast, and Dieter Braun, JACS doi.org/10.1021/jacs.3c10813 (2024)]. But we first would try this at an air-water interface which was shown to work with RNA in a temperature gradient [Ribozyme-mediated RNA synthesis and replication in a model Hadean microenvironment, Annalena Salditt, Leonie Karr, Elia Salibi, Kristian Le Vay, Dieter Braun & Hannes Mutschler, Nature Communications doi.org/10.1038/s41467-023-37206-4 (2023)] before making the jump to the isothermal setting we describe here. So we can understand the question, but it was good practice also in the past to first get to know the setting with PCR, then jump to RNA.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Could the authors comment on the likelihood of the geological environments where the water inflow velocity equals the evaporation velocity?

This is an important point to mention in the manuscript, thank you for pointing that out. To produce a defined experiment, we were pushing the water out with a syringe pump, but regulated in a way that the evaporation was matching our flow rate. We imagine that a real system will self-regulate the inflow of the water column on the one hand side by a more complex geometry of the gas flow, matching the evaporation with the reflow of water automatically. The interface would either recede or move closer to the gas flux, depending on whether the inflow exceeds or falls short of the evaporation rate. As the interface moves closer, evaporation speeds up, while moving away slows it down. This dynamic process stabilizes the system, with surface tension ultimately fixing the interface in place.

We have seen a bit of this dynamic already in the experiments, could however so far not yet find a good geometry within our 2-dimensional constant thickness geometry to make it work for a longer time. Very likely having a 3-dimensional reservoir of water with less frictional forces would be able to do this, but this would require a full redesign of a multi-thickness microfluidics. The more we think about it, the more we envisage to make the next implementation of the experiment with a real porous volcanic rock inside a humidity chamber that simulates a full 6h prebiotic day. But then we would lose the whole reproducibility of the experiment, but likely gain a way that recondensation of water by dew in a cold morning is refilling the water reservoirs in the rocks again. Sorry that I am regressing towards experiments in the future.

We added a paragraph after the second paragraph in Results and Discussion.

It now reads: […] For a real early Earth environment we envision a system that self-regulates the water column's inflow by automatically balancing evaporation with capillary flows. The interface adjusts its position relative to the gas flux, moving closer if the inflow is less than the evaporation rate, or receding if it exceeds it. When the interface nears the gas flux, evaporation accelerates, while moving it away slows evaporation. This dynamic process stabilizes the system, with surface tension ultimately fixing the interface's position. […]

(2) Could the authors speculate on using gases other than ambient air to provide the flux and possibly even chemical energy? For example, using carbonyl sulfide or vaporized methyl isocyanide could drive amino acid and nucleotide activation, respectively, at the gas-water interface.

This is an interesting prospect for future work with this system. We thought also about introducing ammonia for pH control and possible reactions. We were amazed in the past that having CO2 instead of air had a profound impact on the replication and the strand separation [Water cycles in a Hadean CO2 atmosphere drive the evolution of long DNA, Alan Ianeselli, Miguel Atienza, Patrick Kudella, Ulrich Gerland, Christof Mast & Dieter Braun, Nature Physics doi.org/10.1038/s41567-022-01516-z (2022)]. So going more in this direction absolutely makes sense and as it acts mostly on the length-selectively accumulated molecules at the interface, only the selected molecules will be affected, which adds to the selection pressure of early evolutionary scenarios.

Of course, in the manuscript, we use ambient air as a proxy for any gas, focusing primarily on the energy introduced through momentum transfer and evaporation. We speculate that soluble gasses could establish chemical gradients, such as pH or redox potential, from the bulk solution to the interface, similar to the Mg2+ accumulation shown in Figure 3c. The nature of these gradients would depend on each gas's solubility and diffusivity. We have already observed such effects in thermal gradients [Keil, L. M., Möller, F. M., Kieß, M., Kudella, P. W., & Mast, C. B. (2017). Proton gradients and pH oscillations emerge from heat flow at the microscale. Nature communications, 8(1), 1897.] and finding similar behavior in an isothermal environment would be a significant discovery.

Added a paragraph in the Conclusion to showcase this: [… ] Furthermore we expect that other gases, such as CO2, could establish chemical gradients in this environment. Such gradients have been observed in thermal gradients before [23] and finding similar behaviour in an isothermal environment would be a significant discovery.[…]

(3) Line 162: Instead of "risk," I suggest using "rate".

Thanks for pointing this out! Will be changed.

Fixed.

(4) Using FRET of a DNA duplex as an indicator of salt concentration is a decent proxy, but a more direct measurement of salt concentration would provide further merit to the explicit statement that it is the salt concentration that is changing in the system and not another hidden parameter.

Directly observing salt concentration using microscopy is a difficult task. While there are dyes that change their fluorescence depending on the local Na+ or Mg2+ concentration, they are not operating differentially, i.e. by making a ratio between two color channels. Only then we are not running into artifacts from the dye molecules being accumulated by the non-equilibrium settings. We were able to do this for pH in the past, but did not find comparable optical salt sensors. This is the reason we ended up with a FRET pair, with the advantage that we actually probe the strand separation that we are interested in anyhow. Using such a dye in future work would however without a doubt enhance the understanding of not only this system, but also our thermal gradient environments.

(5) Figure 3a: Could the authors add information on "Dried DNA" to the caption? I am assuming this is the DNA that dried off on the sides of the vessel but cannot be sure.

Thanks to the reviewer for pointing this out. This is correct and we will describe this better in the revised manuscript.

Added a sentence in the caption to address this: […] Fluctuations in interface position can dry and redissolve DNA repeatedly (see “Dried DNA” in right panel). […]

(6) Figure 4b and c: How reproducible is this data? Have the authors performed this reaction multiple independent times? If so, this data should be added to the manuscript.

The data from the gel electrophoresis was performed in triplicates and is shown in full in supplementary information. The data in c is hard to reproduce, as the interface is not static and thus ROI measurements are difficult to perform as an average of repeats. Including the data from the independent repeats will however give the reader insight into some of the experimental difficulties, such as air bubbles, which form from degassing as the liquid heats up, that travel upwards to the interface, disrupting the ongoing fluorescence measurements.

This was also pointed out by reviewer 1 and addressed there.

(7) Line 256: "shielding from harmful UV" statement only applies to RNA oligomers as UV light may actually be beneficial for earlier steps during ribonucleoside synthesis. I suggest rephrasing to "shielding nucleic acid oligomers from UV damage.".

Will be adjusted as mentioned.

Fixed.

(8) The final paragraph in the Results and Discussion section would flow better if placed in the Conclusion section.

This is a good point and we will merge results and discussion closer together.

Fixed.

(9) Line 262, "...of early Life" is slightly overstating the conclusions of the study. I suggest rephrasing to "...of nucleic acids that could have supported early life."

This is a fair comment. We thank the reviewer for his detailed analysis of the manuscript!

Changed the phrase to: […]In this work we investigated a prebiotically plausible and abundant geological environment to support the replication of nucleic acids. […]

(10) In references, some of the journal names are in sentence case while others are in title case (see references 23 and 26 for example).

Thanks - this will be fixed.

Fixed.

Author response:

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

Reviewer #1 (Public Review):

This study provides compelling evidence that RAR, rather than its obligate dimerization partner RXR, is functionally limiting for chromatin binding. This manuscript provides a paradigm for how to dissect the complicated regulatory networks formed by dimerizing transcription factor families.

Dahal and colleagues use advanced SMT techniques to revisit the role of RXR in DNA-binding of the type-2 nuclear receptor (T2NR) RAR. The dominant consensus model for regulated DNA binding of T2NRs posits that they compete for a limited pool of RXR to form an obligate T2NR-RXR dimer. Using advanced SMT and proximity-assisted photoactivation technologies, Dahal et al. now test the effect of manipulating the endogenous pool size of RAR and RXR on heterodimerization and DNA-binding in live U2OS cells. Surprisingly, it turns out that RAR, rather than RXR, is functionally limiting for heterodimerization and chromatin binding. By inference, the relative pool size of various T2NRs expressed in a given cell, rather than RXR, is likely to determine chromatin binding and transcriptional output.

The conclusions of this study are well supported by the experimental results and provide unexpected novel insights into the functioning of the clinically important class of T2NR TFs. Moreover, the presented results show how the use of novel technologies can put long-standing theories on how transcription factors work upside down. This manuscript provides a paradigm for how to further dissect the complicated regulatory networks formed by T2NRs or other dimerizing TFs. I found this to be a complete story that does not require additional experimental work. However, I do have some suggestions for the authors to consider.

Reviewer #1 (Recommendations For The Authors):

(1) Does the increased chromatin binding measured when the RAR levels are increased reflect a higher occupancy of a similar set of loci, or are additional loci bound? The authors could discuss this issue in the context of the published literature. Obviously, this could be addressed experimentally by ChIP-seq or a similar analysis, but this would extend beyond the main topic of this manuscript.

We attempted to explore this experimentally using ChIP-seq with multiple RAR- and RXR-specific antibodies. Unfortunately, our results were inconclusive, as the antibody enrichment relative to the IgG control was insufficient for reliable interpretation. Specifically, our ChIP-seq enrichment levels were only around 1.5fold, while the accepted standard for meaningful ChIP enrichment is typically at least 2-fold. Due to these technical limitations, we decided to defer these experiments for now.

However, we agree with the reviewer that understanding whether the increased chromatin binding of RAR reflects higher occupancy at the same set of loci or binding to additional loci is a key question. In similar experiments involving the transcription factor TFEB (Esbin et al., 2024, Genes Dev, doi: 10.1101/gad.351633.124) where an increase in the SMT bound fraction occurred, both scenarios—higher occupancy at known loci and binding to additional loci in ChIP-seq was observed. So, addressing this intriguing possibility in future studies focused on RAR and RXR would be interesting.

(2) The results presented suggest convincingly that endogenous RXR is normally in excess to its binding partners (in U2OS cells). This point could be strengthened further by reducing RXR levels, e.g., by knocking out 1 allele or the use of shRNAs (although the latter method might be too hard to control). Overexpression of another T2NR might also help determine the buffer capacity of RXR.

We appreciate the reviewers’ acknowledgment that our results convincingly demonstrate that endogenous RXR is typically in excess relative to its binding partners in U2OS cells. We agree that this conclusion could be further reinforced by experiments such as overexpression of another T2NR to test RXR's buffering capacity. We are actively pursuing follow-up experiments involving overexpression of additional T2NRs to address this question in more detail. These studies are ongoing, and we plan to explore the buffer capacity of RXR more extensively in a future manuscript.

(3) The ~10% difference in fbound of RAR and RXR (in Figs 1 and 2), while they should be 1:1 dimers, is explained by invoking the expression of RXR isoforms. Can the authors be more specific concerning the nature of these isoforms?

We have provided detailed information about different T2NRs expressed in U2OS cells according to the Expression Atlas and the Human Protein Atlas Database in Supplementary Table S1. Table S1 specifically shows that both isoforms of RXRα and RXRβ are expressed in U2OS cells. Additionally, the caption of Table S1 explicitly notes the presence of isoform RXRβ in U2OS cells. In the main text, we reference Table S1 when discussing the 10% difference in fbound between RARα and RXRα, and we have now suggested that the expression of RXRβ likely accounts for the observed discrepancy.

Reviewer #2 (Public Review):

Summary:

In the manuscript "Surprising Features of Nuclear Receptor Interaction Networks Revealed by Live Cell Single Molecule Imaging", Dahal et al combine fast single molecule tracking (SMT) with proximity-assisted photoactivation (PAPA) to study the interaction between RARa and RXRa. The prevalent model in the nuclear receptor field suggests that type II nuclear receptors compete for a limiting pool of their partner RXRa. Contrary to this, the authors find that over-expression of RARa but not RXRa increases the fraction of RXRa molecules bound to chromatin, which leads them to conclude that the limiting factor is the abundance of RARa and not RXRa. The authors also perform experiments with a known RARa agonist, all trans retinoic acid (atRA) which has little effect on the bound fraction. Using PAPA, they show that chromatin binding increases upon dimerization of RARa and RXRa.

Strengths:

In my view, the biggest strength of this study is the use of endogenously tagged RARa and RXRa cell lines. As the authors point out, most previous studies used either in vitro assays or over-expression. I commend the authors on the generation of single-cell clones of knock-in RARa-Halo and Halo-RXRa. The authors then carefully measure the abundance of each protein using FACS, which is very helpful when comparing across conditions. The manuscript is generally well written and figures are easy to follow. The consistent color-scheme used throughout the manuscript is very helpful.

Weaknesses:

(1) Agonist treatment:

The authors test the effect of all trans retinoic acid (atRA) on the bound fraction of RARa and RXRa and find that "These results are consistent with the classic model in which dimerization and chromatin binding of T2NRs are ligand independent." However, all the agonist treatments are done in media containing FBS. FBS is not chemically defined and has been found to have between 10 and 50 nM atRA (see references in PMID 32359651 for example). The addition of 1 nM or 100 nM atRA is unlikely to result in a strong effect since the medium already contains comparable or higher levels of agonist. To test their hypothesis of ligand-independent dimerization, the authors should deplete the media of atRA by growing the cells in a medium containing charcoal-stripped FBS for at least 24 hours before adding agonist.

We acknowledge the reviewer's concern regarding the presence of atRA in FBS and agree that it may introduce baseline levels of agonist. However, in our experiments, both the 1 nM and 100 nM atRA treatments resulted in observable changes in RAR expression levels (Figure S3C). Additionally, the luciferase assays demonstrated that 100 nM atRA significantly increased retinoic acid-responsive promoter activity (Figure S1C). Given these clear responses to atRA, we believe the observed lack of effect on the chromatin-bound fraction cannot be attributed to the presence of comparable or higher levels of atRA in the FBS, as the reviewer suggests. Moreover, since our results align with the established literature and do not impact the core findings of our study, we decided not to pursue the suggested experiments with charcoal-stripped FBS in this manuscript.  

(2) Photobleaching and its effect on bound fraction measurements:

The authors discard the first 500 to 1000 frames due to the high localization density in the initial frames. This will preferentially discard bound molecules that will bleach in the initial frames of the movie and lead to an over-estimation of the unbound fraction.

For experiments with over-expression of RAR-Halo and Halo-RXR, the authors state that the cells were pre-bleached and that these frames were used to calculate the mean intensity of the nuclei. When pre-bleaching, bound molecules will preferentially bleach before the diffusing population. This will again lead to an over-representation of the unbound fraction since this is the population that will remain relatively unaffected by the pre-bleaching. Indeed, the bound fraction for over-expressed RARa and RXRa is significantly lower than that for the corresponding knock in lines. To confirm whether this is a biological result, I suggest that the authors either reduce the amount of dye they use so that this pre-bleaching is not necessary or use the direct reactivation strategy they use for their PAPA experiments to eliminate the pre-bleaching step.

As for the measurement of the nuclear intensity, since the authors have access to multiple HaloTag dyes, they can saturate the HaloTagged proteins with a high concentration of JF646 or JFX650 to measure the mean intensity of the protein while still using the PA-JFX549 for SMT. Together, these will eliminate the need to prebleach or discard any frames.

The Janelia Fluor dyes used in our experiments are known for their high photostability (Grimm et al., 2021, JACS Au, doi: 10.1021/jacsau.1c00006). During the initial 80 ms imaging to calculate the mean nuclear intensity, the laser power was kept at very low intensity (~3%) for a brief duration (~10 seconds), in contrast to the high-intensity (~100%) used during the tracking experiments, which span around 3 minutes. This low-power illumination does not induce significant photobleaching but merely puts the dyes in a temporary dark state. Therefore, this pre-bleaching step closely resembles the direct reactivation strategy employed in our PAPA experiments.

To further address the reviewer's concern, we performed a frame cut-off analysis for our SMT movies of endogenous RARα-Halo and over-expressed RARα-Halo (Figure S9B). The analysis shows no significant change in the bound fraction of either endogenous or over-expressed RARα-Halo when discarding the initial 1000 frames. Based on these results, we conclude that the pre-bleaching does not lead to an overestimation of the unbound fraction, and that our experimental approach is robust.

(3) Heterogeneous expression of the SNAP fusion proteins:

The cell lines expressing SNAP tagged transgenes shown in Fig S6 have very heterogeneous expression of the SNAP proteins. While the bulk measurements done by Western blotting are useful, while doing single-cell experiments (especially with small numbers - ~20 - of cells), it is important to control for expression levels. Since these transgenic stable lines were not FACS sorted, it would be helpful for the reader to know the spread in the distribution of mean intensities of the SNAP proteins for the cells that the SMT data are presented for. This step is crucial while claiming the absence of an effect upon over-expression and can easily be done with a SNAPTag ligand such as SF650 using the procedure outlined for the over-expressed HaloTag proteins.

We agree with the reviewer that there is heterogeneity in SNAP protein expression across the transgenic lines. In response to the reviewer’s suggestion, we performed the proposed experiment to assess the distribution of mean intensities for two key experimental conditions: Halo-RXRα with overexpressed RARα-SNAP and HaloRXRα with overexpressed RARαRR-SNAP. These results again confirm that the increase in chromatin-bound fraction of Halo-RXRα is observed only in the presence of RARα capable of heterodimerizing with RXRα, supporting our main conclusion (Figure S9).

For these experiments, we followed the same labelling procedure described in the methods section for tracking endogenous Halo-tagged proteins alongside transgenic SNAP proteins. As shown in Figure S9, for ~ 70 cell nuclei, the distribution of mean intensities is similar for both conditions, with the bound fraction of Halo-RXRα significantly increasing in the presence of RARα-SNAP compared to RARαRR-SNAP. This analysis underscores that the observed effects are indeed due to the functional differences between the two RARα variants rather than variability in expression levels.

(4) Definition of bound molecules:

The authors state that molecules with a diffusion coefficient less than 0.15 um2/s are considered bound and those between 1-15 um2/s are considered unbound. Clarification is needed on how this threshold was determined. In previous publications using saSPT, the authors have used a cutoff of 0.1 um2/s (for example, PMID 36066004, 36322456). Do the results rely on a specific cutoff? A diffusion coefficient by itself is only a useful measure of normal diffusion. Bound molecules are unlikely to be undergoing Brownian motion, but the state array method implemented here does not seem to account for non-normal diffusive modes. How valid is this assumption here?

We acknowledge the inconsistency in the diffusion coefficient thresholds for defining the chromatin-bound fraction used across our group’s publications. The choice of threshold or cutoff (0.1 µm²/s vs 0.15 µm²/s) is largely arbitrary and does not significantly impact the results. To validate this, we tested the effect of different cutoffs on fbound (%) for endogenously expressed Halo-tagged RARα and RXRα (Figure S10). As shown in Figure S10, there was no substantial difference in fbound (%) calculated using a 0.1 µm²/s versus 0.15 µm²/s cutoff (e.g., RARα clone c156: 47±1% vs 49±1%; RXRα clone D6: 34±1% vs 35±1%). 

Since we have consistently applied the 0.15 µm²/s cutoff throughout this manuscript across all experimental conditions, the comparative analysis of fbound (%) remains valid. While we agree that a Brownian diffusion model may not fully capture the motion of bound molecules, our state array model accounts for localization error, which likely incorporates some of the chromatin motion features. Moreover, the distinction between bound (<0.15 µm²/s) and unbound (1-15 µm²/s) populations is sufficiently large that using a normal diffusion model is reasonable for our analysis.

(5) Movies:

Since this is an imaging manuscript, I request the authors to provide representative movies for all the presented conditions. This is an essential component for a reader to evaluate the data and for them to benchmark their own images if they are to try to reproduce these findings.

We have now included representative movies for all the SMT experimental conditions presented in the manuscript. Please see data availability section of the manuscript.

(6) Definition of an ROI:

The authors state that "ROI of random size but with maximum possible area was selected to fit into the interior of the nuclei" while imaging. However, the readout speed of the Andor iXon Ultra 897 depends on the size of the defined ROI. If the ROI was variable for every movie, how do the authors ensure the same sampling rate?

We used the frame transfer mode on the Andor iXon Ultra 897 camera for our acquisitions, which allows for fast frame rate measurements without altering the exposure time between frames. Additionally, we verified the metadata of all our movies to ensure a consistent frame interval of 7.4 ms across all conditions. This confirms that the sampling rate was maintained uniformly, despite the variability in ROI size. 

Reviewer #2 (Recommendations For The Authors):

(1) 'Hoechst' is mis-spelled.

We have now corrected this typo in the manuscript.

(2) Cos7 appears in several places throughout the text. I assume this is a typo. If so, please correct it. If not, please explain if some experiments were done in Cos7 cells and kindly provide a justification for that.

The use of Cos7 cells is intentional and not a typo. Cos7 cells have been previously utilized in studies investigating the interaction between T2NRs (Kliewer et al., 1992, Nature, doi: 10.1038/355446a0). In our study, due to technical issues with antibodies for coIP in U2OS cells, we initially used Cos7 cells for control experiments to verify that Halo-tagging of RARα and RXRα did not disrupt their interaction, by transiently expressing the constructs in Cos7 cells. Following these control experiments, we confirmed the direct interaction of endogenously expressed RAR and RXR in U2OS cells with their respective binding partners using the SMT-PAPA assay. Since these results confirmed that Halo-tagging did not interfere with RAR-RXR interactions, we chose not to repeat the coIP experiments in U2OS cells.

Reviewer #3 (Public Review):

Summary:

This study aims to investigate the stoichiometric effect between core factors and partners forming the heterodimeric transcription factor network in living cells at endogenous expression levels. Using state-of-the-art single-molecule analysis techniques, the authors tracked individual RARα and RXRα molecules labeled by HALO-tag knock-in. They discovered an asymmetric response to the overexpression of counter-partners. Specifically, the fact that an increase in RARα did not lead to an increase in RXRα chromatin binding is incompatible with the previous competitive core model. Furthermore, by using a technique that visualizes only molecules proximal to partners, they directly linked transcription factor heterodimerization to chromatin binding.

Strengths:

The carefully designed experiments, from knock-in cell constructions to singlemolecule imaging analysis, strengthen the evidence of the stoichiometric perturbation response of endogenous proteins. The novel finding that RXR, previously thought to be a target of competition among partners, is in excess provides new insight into key factors in dimerization network regulation. By combining the cutting-edge single-molecule imaging analysis with the technique for detecting interactions developed by the authors' group, they have directly illustrated the relationship between the physical interactions of dimeric transcription factors and chromatin binding. This has enabled interaction analysis in live cells that was challenging in single-molecule imaging, proving it is a powerful tool for studying endogenous proteins.

Weaknesses:

As the authors have mentioned, they have not investigated the effects of other T2NRs or RXR isoforms. These invisible factors leave room for interpretation regarding the origin of chromatin binding of endogenous proteins (Recommendations 4). In the PAPA experiments, overexpressed factors are visualized, but changes in chromatin binding of endogenous proteins due to interactions with the overexpressed proteins have not been investigated. This might be tested by reversing the fluorescent ligands for the Sender and Receiver. Additionally, the PAPA experiments are likely to be strengthened by control experiments (Recommendations 5).

We agree that this would be an interesting experiment. However, there are three technical challenges that complicate its implementation: First, as demonstrated in our original PAPA paper, dark state formation is less efficient when dyes are conjugated to Halo compared to SNAPf, making the reverse configuration less optimal. Second, SNAPf-tagged proteins have slower labeling kinetics than Halotagged proteins, often resulting in under-labeling of SNAPf. Third, our SNAPf transgenes were integrated polyclonally. Since background PAPA scales with the concentration of the sender-labeled protein, variable concentrations of the senderlabeled SNAPf proteins would introduce significant variability, complicating the interpretation of the background PAPA signal. Due to these concerns, we believe that performing reciprocal measurements with reversed fluorescent ligands may not yield reliable results. 

Reviewer #3 (Recommendations For The Authors):

(1) The term "Surprising features" in the title is ambiguous and may force readers to search for what it specifically refers to. Including a word that evokes specific features might be helpful.

Our findings contradict previous work, which suggested that chromatin binding of T2NRs is regulated by competition for a limited pool of RXR. In contrast, we found that RAR expression can limit RXR chromatin binding, but not the other way around, which challenges the existing model. This unexpected result is what we refer to as a "surprising feature" in our title, and we believe it accurately reflects the novel insights our study provides. We also think that this is clearly conveyed in our manuscript abstract, supporting the use of "Surprising features" in the title. 

(2) p.3, line 11 - The threshold of 0.15 μm2s-1 seems to be a crucial value directly linked to the value of fbound. What is the rationale for choosing this specific value? If consistent conclusions can be obtained using threshold values that are similar but different, it would strengthen the robustness of the results.

Please refer to our response to Reviewer #2’s Public Review point 4. The threshold choice is arbitrary and doesn’t affect the overall conclusions. To test this, we compared fbound (%) values calculated using both 0.1 μm²s-1 and 0.15 μm²s-1 cutoffs. For example, with endogenously expressed Halo-tagged RARα (clone c156), we observed fbound values of 47±1% vs 49±1%, and for RXRα (clone D6), 34±1% vs 35±1%, respectively (Figure S10). Since we have consistently applied the 0.15 μm²s-1 cutoff across all experimental conditions in this manuscript, the comparisons of fbound (%) between different conditions are robust and valid.

(3) p.4, line 13 - "the fbound of endogenous RARα-Halo (47{plus minus}1%) was largely unchanged upon expression of SNAP (47{plus minus}1%)" part of the sentence is not surprising. It would make more sense if it were expressed as "the fbound of endogenous RARα-Halo (47{plus minus}1%) was largely unchanged upon expression of RXRα-SNAP (49{plus minus}1%), consistent with the control SNAP (47{plus minus}1%).".

We understand how the original phrasing may be confusing to the readers and have restructured the sentence as suggested by the reviewer for clarity.

(4) p.6, line 26 - The discussion that "most chromatin binding of endogenous RXRα in U2OS cells depends on heterodimerization partners other than RARα" seems to contradict the top right figure in Figure 4. If that's the case, the binding partner for the bound red molecule might be yellow rather than blue. Given a decrease in the number of RARα molecules with an unchanged binding ratio, the total number of binding molecules has decreased. Could it be interpreted that the potential reduction in RXRα chromatin binding, accompanying the decrease in binding RARα, is compensated for by other partners?

We agree with the reviewer that both the yellow and blue molecules in Figure 4 represent T2NRs that can heterodimerize with RXR. For simplicity, we chose to omit the depiction of RXR dimerization with other T2NRs (represented in yellow) in Figure 4. We have now included a note in the figure caption to clarify this. We plan to follow up on the buffer capacity of RXR with other T2NRs in a separate manuscript and will discuss this aspect in more detail once we have data from those experiments.

(5) Fig. 3 - I expected that DR localizations always appear more frequently than PAPA localizations by the difference in the number of distal molecules. Why does the linear line for SNAP-RXRα in Fig. 3 B have a slope exceeding 1? Also, although the sublinearity is attributed to binding saturation, is there any possibility that this sublinearity originates from the PAPA system like the saturation of PAPA reactivation? Control samples like Halo-SNAPf-3xNLS might address these concerns.

The number of DR and PAPA localizations depends on the arbitrarily chosen intensity and duration of green and violet light pulses. For any given protein pair, different experimental settings can result in PAPA localizations being greater than, less than, or equal to the number of DR localizations. Therefore, the informative metric is not the absolute number of DR and PAPA localizations, but rather how the ratio of PAPA to DR localizations changes between different conditions—such as between interacting pairs and non-interacting controls.

Regarding the sublinearity, we agree that it is essential to consider whether the observed sublinearity might stem from saturation of the PAPA signal. We know of two ways in which this could occur:

First, PAPA can be saturated as the duration of the green light pulse increases and dark-state complexes are depleted. However, this cannot explain the nonlinearity that we observe, because the duration of the green light pulse is constant, and thus the probability that a given complex is reactivated by PAPA is also constant. Likewise, holding the violet pulse duration constant yields a constant probability that a given molecule is reactivated by DR. PAPA localizations are expected to scale linearly with the number of complexes, while DR localizations are expected to scale linearly with the total number of molecules. Sublinear scaling of PAPA localizations with DR localizations thus implies that the number of complexes scales sublinearly with the total concentration of the protein.

Second, saturation could occur if PAPA localizations are undercounted compared to DR localizations. While this is a valid concern, we consider it unlikely in this case because 1) our localization density is below the level at which our tracking algorithm typically undercounts localizations, and 2) we observe sublinearity for RXR → RAR PAPA even though the number of PAPA localizations is lower than the DR localizations; undercounting due to excessive localization density would be expected to introduce the opposite bias in this case.

(6) Fig. 4 - The differences between A, B, and C on the right side of the model are subtle, making it difficult to discern where to see. Emphasizing the difference in molecule numbers or grouping free molecules at the top might help clarify these distinctions.

We appreciate the reviewer’s feedback. In response, we have revised Figure 4 by grouping the free molecules on the top right side for panels A, B and C, as suggested.

(7) While the main results are obtained through single-molecule imaging, no singlemolecule fluorescence images or trajectory plots are provided. Even just for representative conditions, these could serve as a guide for readers trying to reproduce the experiments with different custom-build microscope setups. Also, considering data availability, depositing the source data might be necessary, at least for the diffusion spectra.

We have now included representative movies for all the presented SMT conditions as source data. Please see data availability section of the manuscript.

(8) Tick lines are not visible on many of the graph axes. 

We have revised the figures to ensure that the tick lines are now clearly visible on all graph axes.

(9) Inconsistencies in the formatting are present in the methods, such as "hrs" vs. "hours", spacing between numbers and units, and "MgCl2". "u" should be "μ" and "x" should be "×". 

We have corrected the formatting errors.

(10) Table S4, rows 16 and 17 - Are "RAR"s typos for "RXR"s? 

We have corrected this in the manuscript.

(11) p.10~12 - Are three "Hoestch"s typos for "Hoechst"s? 

This is now corrected in the manuscript.

(12) p.11, line 17 - According to the referenced paper, the abbreviation should be "HILO" in all capital letters, not "HiLO". 

This is now corrected in the manuscript.

(13) "%" on p.3, line 18, and "." on p.6, line 27 are missing. 

This missing “%”  and “.” are now added.

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.<br /> (2) This binding is specific to promoter regions rather than broad spreading.<br /> (3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.<br /> (4) Xist targeting results in the attenuation of transcription at autosomal regions.<br /> (5) The B-repeat region is important for autosomal Xist binding and gene repression.<br /> (6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses:

The revised manuscript now includes many additional bioinformatic analyses to support the premise that Xist RNA targets a specific set of about 100 promoters and attenuates their expression in the early stages of differentiation. I have previously raised significant concerns about the bioinformatic analyses and the robustness of the data, especially those linked to CHART-seq datasets. Despite some improvements, fundamental problems with the analysis remain, precluding a conclusion on whether Xist RNA binds specific autosomal promoters. The main concerns include:

(1) The authors nicely explain the use of biological replicates; however, they still fail to provide the sufficient analysis I requested on d0 and sense probes. While some quantification is presented in Figures 1E and 1F, the peak calling I asked for has still not been performed. In the response document, the authors report that about 600 peaks were identified in d0 female ESCs compared to about 100 in differentiated conditions. They explain this by the well-known phenomenon of having a background of differentiated cells in d0. In my opinion, this reasoning is flawed. With 98% of cells not inducing Xist in the culture, it is unimaginable why 600 peaks would be detected in the peak calling analysis. Rather, this demonstrates a high background in the CHART peak calling. To assess this further, I have reanalyzed d7 CHART datasets and found robust enrichment of the sense probe on promoters of genes, even stronger than the antisense probe. MACS peak calling also identifies a robust number of peaks on the sense probe. Indeed, even though Figure 1F shows low sense probe enrichment, this is because it focuses on the anti-sense peaks only. An opposite effect is observed when focusing on all genes or on sense-peaks. Thefore it is tough to decide which of the signal is truelly due to Xist binding and what is an inherent problem with the CHART signal. These results cast serious doubts on the biological conclusions of this work and point to a very high background level of promoter signal in both sense and antisense samples.

(2) The authors do not address the conundrum of their results: how is it possible to have a genome-wide autosomal accumulation of Xist signal at promoters (see Figures 1A and 1B), while simultaneously specifically affecting only 100 promoters in the genome? The signal is either general (as Figures 1A and 1B suggest) or specific (as implied by the peak calling), but it cannot be both. Current data points to the fact that CHART has a bias for the most open parts of the chromatin.

(3) The text is still very confusing when it comes to Polycomb. Some experiments point to the fact that there are few PRC1/2 marks at putative Xist autosomal binding sites (Figure 3C), while the use of X1 induces the loss of PRC2 marks. I still find this internally contradictory. The authors sadly do not address my concerns with additional analysis. Their current data indicate that upon Xist upregulation, Xist-RNA binds to autosomal regions that are highly expressed and devoid of Polycomb. These loci then become transcriptionally attenuated and gain some (but low) level of PRC2 in a Xist-dependent fashion. If this model is true, then all these regions should not have Xist in d0 of differentiation and should also have slightly lower levels of PRC2. The argument that there is a low level of Xist in 2-5% of cells should not be a problem because most of the signal will come from the 98% of cells not expressing Xist (as seen in Figure 1A). Without timepoint 0, the whole premise of the paper is difficult to interpret. Either the d0 samples are good enough, or the system is so leaky that it is nearly impossible to identify Xist-specific effects. Males are a useful control but are obviously a genetically very different line with distinct epigenetic and signaling statuses. It is crucial to compare the timing of repression/PRC accumulation to conclude if and how Xist is functional on these loci.

(4) The authors did not address my concerns about the transcriptional analysis. I belive that the control genes are not selected properly. This analysis should not have been performed on just 100 randomly selected regions/genes. Instead, bootstrapping of 100 randomly selected regions/genes should be done, e.g., 1000 times. Additionally, one should only sample from expressed genes to have a comparable control gene set. For example, in Figures 4D and 4E, the distribution of control regions is entirely different. To stress again, relying on a set of 100 randomly selected genes/regions is not statistically robust; controls have to be matched, and bootstrapping has to be performed. Finally, each timepoint uses a different set of autosomal targets. There is a need to visualize the same set of genes across all timepoints (including d0). For example, are genes bound by Xist at d7 highly expressed at d0 and then attenuated only at d7? What happens to them at d14 (see points from 3)? The arguments about d0 heterogeneity are again not convincing (nor is Figure 3H, which shows a different set of genes).

(5) Transcriptional analysis is often shown only as tracks however the reads for key example genes have to be quantified properly and not just visualized or amalgamated in a violin plot.

Reviewer #2 (Public review):

Summary:

To follow-up on recent reports of Xist-autosome interaction the authors examine female (and male transgenic) mESCs and MEFs by CHARTseq. Upon finding that only 10% of reads map to X, they sought to identify reproducible alternative sites of Xist-binding, and identify ~100 autosomal Xist-binding sites in active chromatin regions. They demonstrate a transient down-regulation of autosomal expression. They utilize published male transgenic inducible Xist mESC data to support their findings. In their system, inhibition of Xist reduces autosomal impact.

Strengths:

The authors address a topical and interesting question with a series of models including developmental timepoints and utilize unbiased approaches (CHARTseq, RNAseq). For the CHARTseq they have controls of both sense probes and male cells; and indeed do detect considerable background with their controls. The use of 'metagene' plots provides a visual summation of genic impact. They compare with published data.

Weaknesses:

The revised text and rebuttal clarified my confusion of the 'follow-up' analyses (Figure 4) compared to published datasets. Further, the figure legends have been improved.

While the controls were a strength, it appears that when focussed on bound regions, the background (from sense probes) is now also substantially higher than global background (compare 1E to 1A/B). Thus, why do these autosomal targets enrich for the sense probes, and how to distinguish from such background for the ∆B experiments? If male and sense are both controls, then why is sense lower for males than females, doesn't this suggest Xist impact? While authors note d0 might detect Tsix, the signal is only slightly reduced by d14 and never equivalent. Indeed, the new PCA (S1C) does show as noted that female Xist interactions are distinct from sense and male, but the male signal is even more distinct from sense probes.

It would have been preferable to see the dispersion of the Xist RNA cloud in these ∆B cells, rather than a reference.

Only 2 replicates were used, but there were multiple time-points: D0, D4, d7, d14; further, the correlation analysis showed good reproducibility, and in response to reviews they note that 2 replicates are standard of practice.

The conclusion that RepB is "required for localization to the ~100 genes" is based on density (panel 2E); however, these autosomal targets retain enrichment at TSSs (panel 2A) and indeed the text suggests they are the same sites, suggesting that in fact the choice of autosomal region binding is not RepB dependent. Thus, this remains unresolved for me.

The introduction is clear, and the senior author is a leader in the field; however, by this reviewer's count 19 of the 52 references include the senior author.

Better descriptors for the supplemental Excel files would be helpful.

Aim achievement: The authors do identify autosomal sites with enrichment of chromatin marks and evidence of silencing. Their revised text clarifies many issues, although this reviewer still remains unconvinced that the autosomal targeting is repB-dependent.

The impact of Xist on autosomes is important for consideration of impact of changes in Xist expression with disease (notably cancers). Knowing the targets (if consistent) would enable assessment of such impact.

Author response:

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

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.

(2) This binding is specific to promoter regions rather than broad spreading.

(3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.

(4) Xist targeting results in the attenuation of transcription at autosomal regions.

(5) The B-repeat region is important for autosomal Xist binding and gene repression.

(6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses: The claims reported in this paper are largely unsubstantiated by the data, with multiple misinterpretations, lacking controls, and inadequate statistics. Fundamental flaws in the experimental design/analysis preclude the validity of the findings. Major concerns are listed below: (1) The entire paper is based on the CHART observation that Xist is specifically targeted to autosomal promoters. Overall, the data analysis is flawed and does not support such conclusions. Importantly the sense WT and the 0h controls are not used, nor are the biological replicates. 

We respectfully disagree with Rev1 but nevertheless thank the reviewer for making some suggestions that helped to strengthen our manuscript.  We have provided new experiments and analyses in the revised manuscript. Please see responses below.

Rev1 seems to have missed or misunderstood some key experiments. In fact, the sense WT and 0h controls were shown. Furthermore, we included at least two biological replicates for each experiment.

We used both male ES cells (which do not express Xist) and sense probes as key negative controls, as outlined in Figure S1. Crucially, we only analyzed peaks that were reproducible between biological replicates. The Xist CHART peaks in differentiating female ES cells were significantly enriched above the “background” defined by the sense probe and male controls. Specifically, in comparison to undifferentiated female ES cells (day 0) where both X chromosomes are active and Xist is not induced, Xist CHART robustly pulled down the X chromosome during cell differentiation (day 4, day 7, and day 14). In contrast, male ES cells showed no significant pull-down of the X chromosome, and the sense group also exhibited markedly reduced binding (new Figure S1B). Furthermore, Principal Component Analysis (PCA) of CHART-seq reads (day 4 as an example) include Xist, sense, and input in WT and ΔRepB female, further confirmed that the sense probe CHART was clearly distinguishable from Xist CHART signals. Please see revised Figure S1C. Together, these findings underscore the specificity and robustness of our CHART results.

Data is typically visualized without quantification, and when quantified, control loci/gene sets are erroneously selected. Firstly, CHART validation on the X in FigS1 is misleading and not based on any quantifications (e.g., see the scale on Kdm6a (0-190) compared to Cdkl5 (0-40)). If scaled appropriately, there is Xist signal on the escapee. 

Rev1 may have misread the presented data. In the example raised by Rev1, Fig. S1 is inherently quantitative: e.g., a ratio is a number in Fig. S1A (now Fig. S1B) and all gene tracks in Fig. 1B-E are shown with scales. We showed X-linked genes in Fig. S1 (now Fig. S2) as a control to demonstrate that the CHART worked and that Xist accumulated over time from day 0 to day 14. Our new Figure 1B demonstrates the Xist accumulation in graph format. 

Our paper focuses on Xist autosomal binding sites. Thus, the X-linked examples were placed in the supplement. Escapee genes do in fact accumulate Xist at their promoter regions and this finding is consistent with data published by Simon et al. (2013, Nature). It was therefore not desirable in this paper to reanalyze X-linked genes, including escapees. Nevertheless, to address the reviewer’s concerns, we present new data in new Figure S3A. Here we analyzed the density of Xist binding across X-linked genes, including both active and inactive genes, as well as escapee genes. From this quantitative analysis, it should be clear that escapees do bind Xist. However, from the metagene plots in Figure S3B, we confirm the previous conclusion that escapees bind Xist at high levels just upstream of the promoter and that there is a depletion of Xist in the escapee gene body, consistent with a barrier preventing Xist from moving into the active gene. 

All X-linked loci should have been quantified and classified based on escape status; sense control should also be quantified, and biological replicates should be shown separately. 

Please see above response.

Additionally, in the revised manuscript, we have examined the Irreproducible Discovery Rate (IDR) to validate the reproducibility of peaks between the two replicates in the revised version, and we included a representative example from female WT ES cells at day 4 (revised Figure S4A). The results showed a strong correlation between the replicates, with an IDR threshold of 0.05 (red point > 0.05). As described in the Methods section, to ensure reliable and robust peak identification, we performed peak calling (MACS2) separately on each replicate, and then used bedtools intersect to identify peaks that overlapped between the two replicates. This stringent process, including strict q-value settings in MACS2, ensures the reliability and reproducibility of the peaks presented in this study.

Secondly, and most importantly, Figure 1 does not convincingly show specific Xist autosomal binding. Panel A quantification is on extremely variable y-scales and actually shows that Xist is recruited globally to nearly all autosomal genes, likely indicating an unspecific signal. Again, the sense and 0h controls should have been quantified along with biological replicates. 

Figure 1 shows heatmaps and corresponding metagenes for d0, d4, d7, and d14 female ES cells. Two biological replicates are analyzed. In our revised manuscript, we have used Pearson and Spearman correlation coefficients to measure the strength and direction of a relationship between two biological replicates and shown that the two replicates have high reproducibility (new Figure S1A). On d0, the Xist coverage on autosomes and X chromosome is low, but there is a clear increase on d4, d7, and d14, particularly at the TSS of autosomal genes, as shown by the metagene plots on in Figure 1A-B and the CHART density maps in new Figure 1E-F. We also show relative depletion of Xist signals in the male and sense negative controls.

Upon inspecting genome browser tracks of all regions reported in the manuscript (Rbm14, Srp9, Brf1, Cand2, Thra, Kmt2c, Kmt2e, Stau2, and Bcl7b), the signal is unspecific on all sites with the possible exception of Kmt2e. On all other loci, there is either a strong signal in the 0h ESC controls or more signal in some of the sense controls. This implies that peak calling is picking up false positive regions. How many peaks would have been picked up if the sense or the 0h controls were used for peak calling? It is likely that there would be a lot since there are also possible "peaks" (e.g., Fzd9) in control tracks. 

The analysis cannot be performed by visual inspection. A statistical analysis must be performed to call signal above noise. This is why we performed peak-calling on two biological replicates and identified overlapping peaks using bedtools intersect to improve reliability. Significant peaks are noted as black bars under each track. As mentioned above, for our analysis, we focused on the top 100 peaks based on peak scores to ensure robustness. Xist has significantly higher signal compared to the sense probe in the Xist-autosomal peak regions (revised Figure 1E-F). Additionally, we conducted peak calling on undifferentiated ES cells (d0) and detected a significantly higher number of peaks (~600) compared to the differentiated states (d4 or d7) (~100).

Single-cell sequencing studies have shown that about 2% of undifferentiated mESCs express detectable Xist (Pacini et al., Nat Commun, 2021). The Xist peaks in “day 0” cells may be due to the differentiating population.

Further inspection of the data was not possible as the authors did not provide access to the raw fastq files. When inspecting results from past published experiments {Engreitz, 2013 #1839} reported regions were not bound by Xist. 

On the contrary, we deposited the raw data files to GEO prior to the submission of the paper and included the reviewer link to access them. As of August 24, 2024, GEO publicly released these files, allowing for full inspection of the data. 

Regarding the Engreitz publication, it is not recommended to compare our current study to their analysis for the crucial reason that the Engreitz study was not conducted under physiological conditions. The authors overexpressed the Xist gene in male ES cells. Because Xist RNA can silence genes in male cells as well, this ectopic overexpression normally leads to cell death — thus forcing examination of effects in a narrow time window before Xist can fully spread and act across the genome. Comparing our experiments (endogenous Xist expression in female ES cells) to the ectopic overexpression in male ES cells of Engreitz et al. should therefore not be undertaken.

Thirdly, contrary to the authors' claim, deleting the B repeat does not lead to a loss of autosomal signal. Indeed, comparing Fig1A and Fig2B side by side clearly shows no difference in the autosomal signal, likely because the autosomal signal is CHART background. Properly quantifying the signal with separate replicates as well as the sense and 0h controls is vital. Overall current data together with published results indicate that CHART peak calling on autosomes is due to technical noise or artefacts.

In our revised manuscript, we have included the quantitative results as mentioned above in the main and supplementary figure (new Figure 1E-F, Figure 2E-F, and S3A). The data clearly show an enrichment in the Xist CHART samples in differentiating female ES cells.

We believe the reviewer may be comparing the original Figure 1A and Figure 2A (not Figure 2B). As mentioned above, the analysis cannot be performed by visual inspection. Please see new Figure 2E and 2F. From these data, it should be clear that deleting RepB causes a decrease in Xist targeting to autosomal loci.

(2) The RNA-seq analysis is also flawed and precludes strong statements. Firstly, the analysis frequently lacks statistical analysis (Fig3B, FigS2B-C) and is often based on visualizations (Fig 3D-G) without quantifications. Day 4 B-repeat deletion does not lead to a significant change in the expression of genes close to Xist signal (Fig3H, d14 does not fully show). 

Please see new revised Figure 3B and Figures S2B-C (now revised as Figures S6A and S6B). 

Secondly, for all transcriptional analysis, it is important to show autosomal non-target genes, which is not always done. 

In the revised manuscript, we included non-target genes for each analysis (new Figure 4E-F, 5D and 5F, 7C and 7E, S7F, S8).

Indeed, both males and B repeat deletion will lead to transcriptional changes on autosomes as a secondary effect from different X inactivation status. The control set, if used, is inappropriate as it compares one randomly selected set of ~100 genes. This introduces sampling error and compares different classes of genes. Since Xist signal targets more active genes, it is important to always compare autosomal target genes to all other autosomal genes with similar basal expression patterns.

Please see new Figure S8. We included 100 randomly selected non-target sites on autosomes for this comparative analysis. For consistency, we applied the same flanking regions (10 kb) in the analysis of both target and non-target genes. We believe that this selection method for nontargets is appropriate for two reasons: first, it allows us to control for Xist binding and non-binding; second, it ensures a similar number of genes in both groups, providing a robust foundation for statistical analysis. 

(3) The ChIP-seq analysis also has some problems. The authors claim that there is no positive correlation between genes close to Xist autosomal binding (10kb) compared to those 50kb away (Fig 3C, S2D); however, this analysis is based entirely on metagene visualization. Signal within the Xist binding sites should be quantified (not genes close by) and compared to other types of genomic loci and promoters. Focusing on the 50kb group only as controls is misleading.

We believe the reviewer may have misunderstood our conclusions. As stated in the paper, we observed lower coverage of the histone marks H3K27me3 and H2AK119ub, associated with PRC2 and PRC1, respectively. Our conclusions regarding PRC1/2 support the RNA-seq results, indicating that Xist tends to bind to actively expressed genes. In other words, these genes exhibit lower levels of PRC-mediated silencing signals. This observation underscores the relationship between Xist binding and gene activity, highlighting that Xist preferentially associates with regions that are less subject to silencing by polycomb repressive complexes.

Secondly, the authors only look at PRC mark signal upon differentiation; what about the 0h timepoint, i.e., is there pre-marking? 

Day 0 is not an appropriate timepoint for this analysis because Xist is not yet induced. There is also a small fraction of cells (<5%) that spontaneously differentiate and start to undergo XCI. Because of these reasons, the day 0 timepoint is considered somewhat heterogeneous and it would be difficult to make conclusions regarding Xist peaks in these samples.

Most worryingly, the data analysis is not consistent between figures (see Fig3C vs 5H-I). In Fig5, the group of Xist targets was chosen as those within 100kb of Xist binding, which would encompass all the control regions from Fig3C. In this analysis, the authors report that there is Xist-dependent H3K27me3 deposition, and in fact, here the Xist autosomal targets have more of it than the controls. Overall, all of this analysis is misleading, and clear conclusions cannot be made.

We believe that the reviewer may have also misunderstood the analysis in Figure 5. Figure 5 shows the effect of the Xist inhibitor, X1, on H3K27me3 and gene expression. X1 blocks reduces PRC2 targeting and gene silencing — consistent with X1’s effect on RepA as published in Aguilar et al. 2022. 

All in all, because the fundamental observation is not robust (see point 1), all subsequent analyses are also affected. There are also multiple other inconsistencies within the analysis; however, they have not been included here for brevity.

We again respectfully disagree with Rev1 but thank the reviewer for making suggestions that helped to strengthen our manuscript.  We believe that the revised manuscript with new analyses is improved in part because of the reviewer’s critical comments.

Reviewer #2 (Public review):

Summary:

To follow-up on recent reports of Xist-autosome interaction the authors examine female (and male transgenic) mESCs and MEFs by CHARTseq. Upon finding that only 10% of reads map to X, they sought to identify reproducible alternative sites of Xist-binding, and identify ~100 autosomal Xistbinding sites and show a transient impact on expression.

Strengths:

The authors address a topical and interesting question with a series of models including developmental timepoints and utilize unbiased approaches (CHARTseq, RNAseq). For the CHARTseq they have controls of both sense probes and male cells; and indeed do detect considerable background with their controls. The use of deletions emphasizes that intact functional Xist is involved. The use of 'metagene' plots provides a visual summation of genic impact.

Reviewer 2 has made some excellent suggestions. We have revised the manuscript accordingly and are grateful to the reviewer for the recommendations.

Weaknesses:

Overall, the result presentation has many 'sample' gene presentations (in contrast to the stronger 'metagene' summation of all genes). The manuscript often relies on discussion of prior X chromosomal studies, while the data generated would allow assessment of the X within this study to confirm concordance with prior results using the current methodology/cell lines. 

Many of the 'follow-up' analyses are in fact reprocessing and comparison of published datasets. The figure legends are limited, and sample size and/or source of control is not always clear. While similar numbers of autosomal Xist-binding sites were often observed, the presented data did not clarify how many were consistent across time-points/cell types. While there were multiple time points/lines assessed, only 2 replicates were generally done.

We apologize for the deficiencies in the legend.  The revised manuscript has corrected them.

We generated many new datasets with deep sequencing, with at least two biological replicates for each. Such experiments are extremely expensive by nature. Thus, two biological replicates are typically considered acceptable.

Additionally, we performed reanalysis of published datasets to test whether — in the hands of other investigators — cell lines expressing Xist also supported autosomal targeting. Figure 4 is a case in point. Here we examined Tg1 and Tg2, which respond to doxycycline to overexpress Xist from an ectopic site. Transcriptomic analysis showed significant downregulation of autosomal Xist targets, as exemplified by Rbm14 and Bcl7b (new Figure 4C, S9B). In contrast, non-targets of Xist such as Stau1 did not demonstrate significant changes in gene expression (new Figure 4E and 4G). Looking across all autosomal target genes, we observed a significant decrease in mean expression in the Xist overexpressing cell lines (new Figure 4D). The fact that the autosomal changes were also observed in datasets generated by other investigators greatly strengthen our conclusions. 

Aim achievement:

The authors do identify autosomal sites with enrichment of chromatin marks and evidence of silencing. More details regarding sample size and controls (both treatment, and most importantly choice of 'non-targets' - discussed in comments to authors) are required to determine if the results support the conclusions.

Specific scenarios for which I am concerned about the strength of evidence underlying the conclusion:

I found the conclusion "Thus, RepB is required not only for Xist to localize to the X- chromosome but also for its localization to the ~100 autosomal genes " (p5) in constrast to the statement 2 lines prior: "A similar number of Xist peaks across autosomes in ΔRepB cells was observed and the autosomal targets remained similar". Some quantitative statistics would assist in determining impact, both on autosomes and also X; perhaps similar to the quintile analysis done for expression.

We have added the Xist coverage panel for day 4 and 7 in the identified Xist-autosomal peak regions (new Figure 1E-F, Figure 2E-F), as mentioned above. The results clearly demonstrate that the deletion of RepB decreases Xist binding to autosomes. Also, we showed that ΔRepB increased X-linked genes expression in our revised Figure 3D. 

It is stated that there is a significant suppression of X-linked genes with the autosomal transgenes; however, only an example is shown in Figure 4B. To support this statement, a full X chromosomal geneset should be shown in panels F and G, which should also list the number of replicates. 

Please see new Figure 4B.

As these are hybrid cells, perhaps allelic suppression could be monitored? Is Med14 usually subject to X inactivation in the Ctrl cells, and is the expression reduced from both X chromosomes or preferentially the active (or inactive) X chromosome?

If Rev2 is referring to Figure 4, the dataset used in Figure 4 comes from another research group and was previously published (Loda, A. et al. Nat Commun, 2017).

If Rev2 is referring to our ES cells, they are N2 cell lines.  The X chromosomes are fully hybridized (Cas/Mus), but the autosomes are not fully hybridized (Ogawa et al., Science, 2008). Med14 is subject to XCI and is expressed from the Xa, silenced on the Xi. 

The expression change for autosomes after transgene induction is barely significant; and it was not clear what was used as the Ctrl? This is a critical comparator as doxycycline alone can change expression patterns.

We agree that there was a modest change in expression after transgene induction, but it is a significant change. Again, the dataset is from a published study where the authors generated doxycycline-responsive Xist transgenes (see above). The control in this case is Dox-treated wildtype cells. We now clarify these points.

In the discussion there is the statement. "Genetic analysis coupled to transcriptomic analysis showed that Xist down-regulates the target autosomal genes without silencing them. This effect leads to clear sex difference - where female cells express the ~100 or so autosomal genes at a lower level than male cells (Figure 7H)." This sweeping statement fails to include that in MEFs there is no significant expression difference, in transgenics only borderline significance, and at d14 no significant expression difference. The down-regulation overall seems to be transient during development while targeting is ongoing?

Indeed, the Xist effects on autosomes seem to occur during cell differentiation in ES cells. While there is no apparent effect in MEFs, we cannot exclude effects on other somatic cells. Regardless of whether the effects are in early development or throughout life, the sex differences may have life-long effects in mammals. The study conducted in human cells by the Plath lab also concluded that the differences primarily affect stem cells.

Finally, I would have liked to see discussion of the consistency of the identified genes to support the conclusion that the autosomal sites are not merely the results of Xist diffusion.

We address this in the third paragraph of the Discussion. Our main argument is that if autosomal binding were caused by diffusion, then RepB deletion or X1 treatment would have led to increased binding at autosomal sites, as Xist would bind less to the X chromosome. However, as demonstrated in our study, both treatments resulted in reduced Xist binding on both the X chromosome and autosomes. This finding suggests that the binding is specific and reliant on Xist's RepA and RepB domains, rather than being a passive diffusion process.

To examine overlap between the conditions (days of differentiation and WT/RepB cells), we generated Venn Diagrams as now shown in Figure S4E.

The impact of Xist on autosomes is important for consideration of impact of changes in Xist expression with disease (notably cancers). Knowing the targets (if consistent) would enable assessment of such impact.

We thank Rev2 for the very helpful review and for the forward-looking experiments. Indeed, the physiological changes brought on by autosomal targeting will be of future interest.

Reviewer #3 (Public review):

Summary:

Yao et al use CHART to identify chromatin associated with Xist in female mouse ESCs, and, as control, male ESCs at various timepoints of differentiation. Besides binding of Xist to X chromosome regions they found significant binding to autosomes, concentrating mostly on promoter regions of around 100 autosomal genes, as elucidated by MACS. The authors went on to show that the RepB repeat is mostly responsible for these autosomal interactions using a female ESC line in which RepB is deleted. Evidence is provided that Xist interacts with active autosomal genes containing lower coverage of repressive marks H3K27me3 and H2AK119ub and that RepB dependent Xist binding leads to dampening of expression, but not silencing of autosomal genes. These results were confirmed by overexpression studies using transgenic ESCs with doxycycline-inducible Xist as well as via a small molecule inhibitor of Xist (X1), inducing/inhibiting the dampening of autosomal genes, respectively. Finally, using MEFs and Xist mutants RepB or RepE the authors provide evidence that Xist is bound to autosomal genes in cells after the XCI process but appears not to affect gene expression. The data presented appear generally clear and consistent and indicate some differences between human and mouse autosomal regulation by Xist. Thus, these results are timely and should be published.

We thank Rev3 for the positive remarks and great suggestions.  We have amended the manuscript per below. 

Strengths:

Regulation of autosomal gene expression by Xist is a "big deal" as misregulation of this lncRNA causes developmental defects and human disease. Moreover, this finding may explain sexspecific developmental differences between the sexes. The results in this manuscript identify specific mouse autosomal genes bound by Xist and decipher critical Xist regions that mediate this binding and gene dampening. The methods used in this study are appropriate, and the overall data presented appear convincing and are consistent, indicating some differences between human and mouse autosomal regulation by Xist.

Weaknesses:

(1) The figure legends and/or descriptions of data are often very short lacking detail, and this unnecessarily impedes the reading of the manuscript, in particular the figures would benefit not only from more detailed descriptions/explanations of what has been done but also what is shown. 

We have included more detailed descriptions in the figure legends and throughout the manuscript.

This will facilitate the reading and overall comprehension by the reader. One out of many examples: In Fig S1B in the CHART data at d4 and d7 there is not only signal in female WT Xist antisense but also in female sense control. For a reader that is not an expert in XCI it would be helpful to point out in the legend that this signal corresponds to the lncRNA Tsix (I suppose), that is transcribed on the other strand.

We thank the reviewer for this excellent point.  We have amended the Results section accordingly.

(2) Different scales are used in the lower panels of Figures 1A and 2A, which makes it difficult to directly compare signals between the different differentiation stages.

We have included a figure combining all timepoints — d0, d4, d7, and d14 WT female Xist CHART signals  — on the X chromosome and autosomes to support our thesis. Please see new Figure 1B.

(3) In this study some of the findings on mouse cells contrast previously published results in human ESCs: 1) Xist binding occurs preferentially to promoters in mice, not in human. 2) Binding of Xist is mostly detected in polycomb-depleted regions in mice but there is a positive correlation between Xist RNA and PRC2 marks in human ESCs. These differences are surprising but may be very interesting and relevant. While I am aware that this might be a difficult task, it would be helpful to experimentally address this issue in order to distinguish whether species specific and/or methodological differences between the studies are responsible for these differences.

Indeed, our findings in mouse cells contrast with those observed in humans. As discussed in the manuscript, this discrepancy may be attributed to factors such as cell type, differentiation methods, and the Xist pull-down technique employed (our CHART method utilizes a 20 nt oligo library, whereas RAP uses long oligos). We agree that future work should investigate the underlying causes of these differences between mouse and human systems.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

For Figure 2: labelling ∆B on the panel A timeline (e.g. d0-∆B) would make the results clearer for the audience. Panel B makes most sense beside panel E of Figure 1, so combine here and skip in Figure 1?

We have modified Figure 2A and thank Rev2 for this suggestion. As for the embedded tables: since we performed peak calling for WT and ∆B separately, we believe that showing both the peak numbers and their corresponding peak patterns provides a clearer representation of the data.

I agree that at day 7 there appears to be a difference in X; but by day 14 this looks much more minimal - is it just time-shifted rather than altered? Perhaps this could be discussed. Autosomal binding sites show no change in number.

Day 7 exhibits the strongest Xist binding on the X chromosome, consistent with the de novo establishment phase of XCI when Xist is expressed at the highest levels (300 copies/cell during de novo XCI versus ~100 copies/cell during maintenance [Sunwoo et al., 2015 as cited]. Per our RNA-seq analysis here, we also observed highest Xist expression on day 7 and reduced levels on day 14 (Fig. S5A). This expression difference explains the reduced Xist CHART levels on day 14 compared to day 7. 

While the X has previously been examined, it would seem beneficial to conduct the same expression analyses (Figure 3) for the X (perhaps supplemental), as the authors have the data 'in hand'. I feel comparison to X in the main figure for panels A and B would fit, while a similar analysis for the X for panel C could be supplemental, presumably supporting the published data to which this data is currently compared. 

This is a good suggestion. Please find the new data in Figures 2E-F and 3D, which demonstrate that the RepB deletion inhibits Xist binding on the X chromosome, resulting in increased X-linked gene expression, as previously mentioned. Since Xist binds across the X chromosome, we did not perform peak calling as we did for the autosomes. Therefore, applying a similar analysis as in Figures 3A-B may not be appropriate in this case.

Such a direct comparison to X-data from the same study would be important. For panel H: How many replicates (2)? This should be in the legend. What is the change in median expression? Again, a supplemental figure showing impact on X-linked targets would be useful. Do male and female ESCs show an expression difference prior to differentiation (ie d0)? The data underlying this Figure should be in one of the supplementary tables, showing the full statistical tests and average change. The supplementary tables 8-12 list the WT target genes, not expression differences with the deletion. Again, given that the difference appears transient, might the ∆B cells be altered in rate of differentiation?

Panel H (revised Figure 3G) includes two replicates, and this has been added to the legends. We have provided a supplementary figure demonstrating that RepB increases the expression levels of X-linked genes on days 4, 7, and 14 (revised Figure 3D). Male and female ESCs show differences in the expression of X-linked genes, as both X chromosomes are active in females at this stage prior to differentiation (revised Figure S5C). 

A supplementary table with statistical tests and average change information has been included in our revised version (Table S11).

On the other hand, these Xist-autosomal target genes displayed no significant differences between WT male, female, or ∆B female cells on day 0 — prior to onset of XCI and Xist expression. Please see new Figure 3H. 

As for whether ∆B cells are altered in their rate of differentiation, the analysis by Colognori et al. 2019 indicates that ∆B cells differentiate similarly to WT cells. (In Figure 6 of Colognori et al. 2019, autosomal genes expressed similarly in WT and ∆B cells, whereas XCI is affected only in ∆B cells)

We have also modified the legends for our supplementary tables.

Why were the transgene lines examined upon neuronal differentiation rather than the same approach as in Figures 1-3? I would have thought neuronal differentiation might be more similar to d14, where limited changes remain? Could the authors clarify and discuss?

We apologize for the confusion. The Tg lines in Figure 4 came from a previously published study. We performed reanalysis of published datasets because we wanted to test whether — in the hands of other investigators — cell lines expressing Xist also supported autosomal targeting. Here we examined Tg1 and Tg2, which respond to doxycycline to overexpress Xist from an ectopic site. Transcriptomic analysis showed significant downregulation of autosomal Xist targets, as exemplified by Bcl7b and Rbm14 (Figure 4C and S9B). In contrast, non-targets of Xist such as Stau1 did not demonstrate significant changes in gene expression (Figure 4E and 4F). Looking across all autosomal target genes, we observed a significant decrease in mean expression in the Xist overexpressing cell lines (Figure 4D). The fact that the autosomal changes were also observed in datasets generated by other investigators greatly strengthen our conclusions. We have clarified this in the Results section.

Figure 5 - the legend should specify the number of replicates and clarify the blue/green (intuitive, but not specified). Are the 'target' / 'non-target' genes from d4 Chart (but the RNA from d5)? How are 'non-targets' defined - do they match the 'targets' in certain criteria (expression level, chromatin features, GC content)? Do they change per differentiation protocol?

We have modified the legends to clarify that the 'target' and 'non-target' genes are derived from the day 4 CHART-seq data, while the RNA data is from day 5, as that study sequenced day 5 and not day 4. Non-targets were randomly chosen based on (i) the absence of Xist binding and (ii) similar expression levels. Please see revised Figure S8.

It would be helpful to compare Xist expression levels across the various models, and the MEF model could be better described - are they polyploid as often happens?

We have included the Xist expression levels of ES cells and MEF cells in the revised version (revised Figure S5A, 6D). The transformed MEFs are indeed tetraploid, as is typical.

For 6A to be informative, one needs to know % mapping to X in ES timeline, which is in supplemental, so perhaps 6A should also be supplemental?

We have moved 6A to the supplemental figure.

It is odd that ∆B seems to have had more impact in MEFs, and I would like more discussion - but I also think I am missing something: "We observed that Xist signals were more substantially reduced on both the Xi and autosomal regions in ΔRepE MEFs compared to ΔRepB cells", yet in lower panel 6 G it looks like ∆B is LOWER than ∆E? Am I misinterpreting?

We apologize for the confusing writing.  The revised text now reads:  “To investigate, we utilized a deletion of Xist’s Repeat E (∆RepE), which was previously demonstrated to severely abrogate localization of Xist to the Xi 41,42. We reasoned that the severe loss of Xist binding might unmask a transcriptomic difference. As expected, we observed that Xist signals were somewhat more reduced on the Xi in ΔRepE MEFs compared to ΔRepB cells (Figure 6E-6F). Despite this reduction, peak coverages in autosomal target genes did not increase in ΔRepE MEFs (Figure 6E-6F). However, there was an overall decrease in the number of significant autosomal peaks in ∆RepE MEFs relative to WT cells (Figure 6A). Regardless, we observed no significant transcriptomic differences in ∆RepE MEFs relative to WT MEFs (Figure 7A-7E). Additionally, further examination of RNA sequencing data from male and female MEF cells in two published studies 43,44 corroborated that the expression levels of these autosomal Xist targets did not exhibit significant changes (Figure 7F and 7G). Altogether, the analysis in MEFs demonstrates that Xist continues to bind autosomal genes in post-XCI somatic cells. However, autosomal binding of Xist in post-XCI cells does not overtly impact expression of the associated autosomal genes. Nonetheless, we cannot exclude more subtle changes that do not meet the significance cut-off.”

Overall, I would like to see how consistent these autosomal peaks are - I shudder to suggest Venn diagrams, but something to show whether there are day/lineage specific peaks and/or ∆repeat B/E resistant peaks. 

We now present Venn diagrams comparing MEF, ES_d4, and ES_d7, showing approximately 50% overlap between MEF and ES cells (revised Figure S10B). This may be expected, as each timepoint is a different developmental stage of XCI, with expected gene expression differences.

Very minor comments:

It would be easier if the supplemental tables were tabs in 1 file!

We will defer to the editor on how best to format the supplemental tables.

Similar to the text, could gene names be included in the supplemental?

We have provided gene names in the supplemental files.

Figure 3 legend: should 'representing' be representative?

We have modified it.

"Xist patterns identified in human cells" p 5; it is challenging to follow human versus mouse, so specify or ensure correct use of XIST/Xist Indeed, we edited the manuscript accordingly.

Gene names should be italicized.

We have italicized gene names in our manuscript.

Ref. 38 lacks details (...).

We have updated the reference.

Peak-like characters - perhaps characteristics? P8

We have modified this.

Reviewer #3 (Recommendations for the authors):

On page 6, the 6th sentence in the first paragraph needs correction. "Consistent with Xist's behavior on the X chromosome."

We have modified the sentence. Thank you.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The study by Longhurst et al. investigates the mechanisms of chemoresistance and chemosensitivity towards three compounds that inhibit cell cycle progression: camptothecin, colchicine, and palbociclib. Genome-wide genetic screens were conducted using the HAP1 Cas9 cell line, revealing compound-specific and shared pathways of resistance and sensitivity. The researchers then focused on novel mechanisms that confer resistance to palbociclib, identifying PRC2.1. Genetic and pharmacological disruption of PRC2.1 function, but not related PRC2.2, leads to resistance to palbociclib. The researchers then show that disruption of PRC2.1 function (for example, by MTF2 deletion), results in locus-specific changes in H3K27 methylation and increases in D-type cyclin expression. It is suggested that increased expression of D-type cyclins results in palbociclib resistance.

Strengths:

The results of this study are interesting and contribute insights into the molecular mechanisms of CDK4/6 inhibitors. Importantly, while CDK4/6 inhibitors are effective in the clinic, tumour recurrence is very high due to acquired resistance.

Weaknesses:

A key resistance mechanism is Rb loss, so it is important to understand if resistance conferred by PRC2.1 loss is mediated by Rb, and whether restoration of PRC2.1 function in Rb-deplete cells results in renewed palbociclib sensitivity. It is also important to understand the clinical implications of the results presented. The inclusion of these data would significantly improve the paper. However, besides some presentation issues and typos as described below, it is my opinion that the results are robust and of broad interest.

Major questions:

(1) Is the resistance to CDK4/6 inhibition conferred by mutation of MTF2 mediated by Rb?

(2) Are mutations in PRC2.1 found in genetic analyses of tumour samples in patients with acquired resistance?

We thank the reviewer for their editing and experimental suggestions, and have integrated their responses into our re-submitted manuscript.

We also agree that understanding the role of RB1 in mediating palbociclib resistance to the proposed resistance mechanism is of particular interest. However, as there are three RB proteins expressed in human cells, this is a technically difficult question to probe genetically. Despite this technical challenge, we have provided multiple lines of evidence in our resubmitted manuscript that the resistance to palbociclib observed in our PRC2.1-deficent cells is mediated through the canonical CDK4/6-RB1 pathway. First, disruption of RB1 in HAP1 cells results in palbociclib resistance to a level comparable level to PRC2.1 disruption (Fig. 4E). Second, inactivation of SUZ12 or MTF2 increases the number of cells entering S-phase in palbociclib treatment (Fig. 4G) with no increase in basal rates of apoptosis (Fig. S2D), suggesting that any proliferation advantage observed in PRC2.1-defective cells is due to resistance to  palbociclib-induced cell cycle arrest. Third, we show that over expression of CCND1 and CCND2 is sufficient to drive resistance to palbociclib in wild-type HAP1 cells (Fig. S5F).  And finally, increased levels of CCND1 and CCND2 observed in cells lacking PRC2.1 activity results in higher CDK4/6 activity as measured by RB1 phosphorylation, despite palbociclib blockade (Fig. 6F). All these lines of evidence strongly suggest that MTF2-containing PRC2.1 regulates G1 progression in through the canonical CDK4/6RB1 pathway by repressing CCND1 and CCND2 expression. 

Whether or not MTF2 deletion leads to palbociclib resistance in clinical samples is also of a question of particular interest. Currently, we are unaware of any reports that specifically mention MTF2 deletion as leading to palbociclib resistance, and we were unable to find another example in our own cancer database review. However, we have included references to other examples of MTF2 mutation resulting in chemotherapeutic resistance in our discussion. Additionally, although MTF2 is rarely observed to be mutated in cancers (Ngubo et al. 2023), it is highly differentially expressed and investigating decreased MTF2 transcription in palbociclib resistant tumors, though challenging, might prove fruitful.  However, as mechanisms of palbociclib resistance is an area of active investigation, we speculate that future studies might uncover additional examples of MTF2 mediating resistance to this clinically important chemotherapeutic.  

Reviewer #2 (Public Review):

Summary:

Longhurst et al. assessed cell cycle regulators using a chemogenetic CRISPR-Cas9 screen in haploid human cell line HAP1. Besides known cell cycle regulators they identified the PRC2.1 subcomplex to be specifically involved in G1 progression, given that the absence of members of the complex makes the cells resistant to Palbociclib. They further showed that in HAP1 cells the PRC2.1, but not the PRC2.2 complex is important to repress the cyclins CCND1 and CCND2. This can explain the enhanced resistance to Palbociclib, a CDK4/6Inhibitor, after PRC2.1 deletion.

Strengths:

The initial CRISPR screen is very interesting because it uses three distinct chemicals that disturb the cell cycle at various stages. This screen mostly identified known cell cycle regulators, which demonstrates the validity of the approach. The results can be used as a resource for future research.

The most interesting outcome of the experiment is the finding that knockouts of the PRC2.1 complex make the cell resistant to Palbociclib. In a further experiment, the authors focused on MTF2 and JARID2 as the main components of PRC2.1 and PRC2.2, respectively. Via extensive analyses, including genome-wide experiments, they confirmed that MTF2 is particularly important to repress the cyclins CCND1 and CCND2. The absence of MTF2 therefore leads to increased expression of these genes, sufficient to make the cell resistant to palociclib. This result will likely be of wide interest to the community.

Weaknesses:

The main weakness of the manuscript is that the experiments were performed in only one cell line. To draw more general conclusions, it would be essential to confirm some of the results in other cell lines.

In addition, some of the findings, such as the results from the CRISPR screen as well as the stronger impact of the MTF2 KO on H3K27me3 and gene expression (compared to JARID2 KO), are not unexpected, given that similar results were already obtained before by other labs.

We thank the reviewer for their suggestions and we believe that we have addressed their main concern about the generality of the MTF2 regulation of D-type cyclin expression in our resubmitted manuscript. We have now shown through shRNA knockdown that MTF2 represses CCND1 in two additional cell lines, the breast cancer MDA-MB-231 and immortalized monkey COS7 cell line (Fig. 6E). However, it is important to note that MTF2 did not control CCND1 expression in every cell line tested (Fig. 6D), underscoring the context-dependent nature of this regulation. Future studies will illuminate what cell or tumor types in which this regulation is observed.

Additionally, while MTF2 has previously been shown to exert a greater effect on H3K27me3 levels in some circumstances (Loh et al. 2021, Rothberg et al. 2018), a number of notable reports in ES cell lines have concluded that PRC2 localization and H3K27me3 at the majority of genomic sites are dependent on both PRC2.1 and PRC2.2 activity (Healy et al. 2019, Højfeldt et al. 2019, Perino et al. 2020, Oksuz et al. 2018). Therefore, we think it is important to highlight the greater dependence on MTF2 for promoter proximal H3K27me3 levels in our transformed cell line context.  

Reviewer #3 (Public Review):

This study begins with a chemogenetic screen to discover previously unrecognized regulators of the cell cycle. Using a CRISPR-Cas9 library in HAP1 cells and an assay that scores cell fitness, the authors identify genes that sensitize or desensitize cells to the presence of palbociclib, colchicine, and camptothecin. These three drugs inhibit proliferation through different mechanisms, and with each treatment, expected and unexpected pathways were found to affect drug sensitivity. The authors focus the rest of the experiments and analysis on the polycomb complex PRC2, as the deletion of several of its subunits in the screen conferred palbociclib resistance. The authors find that PRC2, specifically a complex dependent on the MTF2 subunit, methylates histone 3 lysine 27 (H3K27) in promoters of genes associated with various processes including cell-cycle control. Further experiments demonstrate that Cyclin D expression increases upon loss of PRC2 subunits, providing a potential mechanism for palbociclib resistance.

The strengths of the paper are the design and execution of the chemogenetic screen, which provides a wealth of potentially useful information. The data convincingly demonstrate in the HAP1 cell line that the MTF2-PRC2 complex sustains the effects of palbociclib (Figure 4), methylates H3K27 in CpG-rich promoters (Figure 5), and represses Cyclin D expression (Figure 6). These results could be of great interest to those studying cell-cycle control, resistance mechanisms to therapeutic cell-cycle inhibitors, and chromatin regulation and gene expression.

There are several weaknesses that limit the overall quality and potential impact of the study. First, none of the results from the colchicine and camptothecin screens (Figures 1 and 2) are experimentally validated, which lessens the rigor of those data and conclusions. Second, all experiments validating and further exploring results from the palbociclib screen are restricted to the Hap1 cell line, so the reproducibility and generality of the results are not established. While it is reasonable to perform the initial screen to generate hypotheses in the Hap1 line, other cancer and non-transformed lines should be used to test further the validity of conclusions from data in Figures 4-6. Third, conclusions drawn from data in Figures 3D and 4D are not fully supported by the experimental design or results. Finally, there have been other similar chemogenetic screens performed with palbociclib, most notably the study described by Chaikovsky et al. (PMID: 33854239). Results here should be compared and contrasted to other similar studies.

We thank the reviewer for their suggestions regarding our manuscript. While the genes recovered as mediating cellular responses to camptothecin and colchicine was never confirmed following our chemogenetic screens, we felt our primary findings were in the area of palbociclib resistance and decided focus our follow-up investigations on genes. We included the results camptothecin and colchicine chemogenetic screens as confirmation of the specificity of PRC2 mutation resulting in resistance to palbociclib (Fig. 4C) and for others in the community to use as a resource for future investigations. We have also clarified our results for Figure 3D and 4D in our revised manuscript, as well as included additional plots of these results (Fig. S1DS1F). And, with our resubmitted manuscript, we believe we have addressed their concern of the generality of our results by demonstrating our primary finding that MTF2 regulates D-type cyclins in additional cell lines other than HAP1. We feel these results indicate that while not “general”, there are additional cellular contexts that our main result holds true. In line with this, and to address how our chemogenetic screens fits into the landscape of previous studies, including Chaikosvsky et al., we have included the following lines to our discussion:  “Additionally, other chemogenetic screens utilizing palbociclib and have not identified that inactivation of PRC2 components as either enhancing or reducing palbociclib-induced proliferation defects, suggesting that PRC2 mutation is neutral in the cell lines studied. These observations not only underscore the context-dependent ramifications of mutation of these PRC2 complex members, but also may help inform the context in which CDK4/6 inhibitors are most efficacious.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) "We found that only thirteen and twenty genes resulted in sensitivity or resistance, respectively, in every conditions tested and were deemed non-specific and excluded from any further analysis (see Table S2)." It's unclear to me why these genes were deemed 'nonspecific'. Are these genes functionally important for the general exclusion of xenobiotic molecules?

By this, we simply meant that these effects were not specific to one condition. Such genes could affect drug half-life or a general stress response, but are less likely to have functions directly tied to the pathway targeted by a drug than are genes whose loss affects only one condition.  

(2) "Given that increased CCND1 levels is sufficient to drive increased CDK4/6 kinase activity, upregulation of these D-type cyclins is likely to be a significant contributor to the palbociclib resistance in MTF2∆ cells." It's unclear to me what is the basis for this statement. This is only true if there is free CDK4/6. If CDK4/6 is already fully occupied by D-type cyclins, then increased CCND1 levels would not be expected to have an effect. 

While we anticipated that increased levels of CCND1 would result in more CDK4/6-Dtype association, we now demonstrate in the new Figure S5F that there is more CCND1 in complex with CDK6 in both SUZ12∆ and MTF2∆ cell lines. Furthermore, we able to show in Figure S5G that overexpression of D-type cyclins results in resistant to palbociclib-induced proliferation defects in HAP1 cells.

(3) The description of the results is very confusing in places, especially regarding "resistance" versus "sensitivity" genes. For example: "CCNE1, CDK6, CDK2, CCND2 and CCND1, all of which are integral to promoting the G1/S phase transition, ranked as the 2nd, 24th, 27th, 29th and 46th most important genes for palbociclib resistance, respectively (Figures 1F and 1G). CCND1 and CCND2 bind either CDK4 or CDK6, the molecular targets of palbociclib, whereas CDK2 and CCNE1 form a related CDK kinase that promotes the G1/S transition.

Similarly, cells with sgRNAs targeting RB1, whose phosphorylation by CDK4/6 is a critical step in G1 progression, displayed substantial resistance to palbociclib." My reading of this paragraph suggests that disruption of the CDK6 locus is associated with palbociclib resistance - surely this is a typo and instead should have been sensitivity? Please explain.

We thank the reviewer for pointing this out and have corrected this typo  

(4) Sensitivity to palbociclib was enhanced in cells expressing sgRNAs targeting H4 acetylation, positive regulators of Pol II transcription, and regulators of the DNA Damage Response pathway (Figures 3A and 3B), although this sensitivity was much weaker than that seen with DNA damaging agents. This observation is consistent with long-term treatment with palbociclib inducing DNA damage, as has been suggested by a number of recent publications 65,66." This is also consistent with recent work on Cdk7 inhibitors (Wilson et al. Mol Cell 2023), as Cdk7 inhibition is expected to affect both CDK1/2/4/6 activities and Pol II transcription.

We thank the reviewer for bringing this observation to our attention and we have added this citation to this passage in our manuscript.

(5) Figure 3D - would it not make sense to plot the data such that palbo concentration is on the x-axis? It is also difficult to interpret since the data are normalized to starting "% proliferation" at the indicated palbo treatment, when it is likely that % proliferation changes significantly with palbo concentration. Indeed, this is the graphing format used for a later figure (Figure 4D). The data with rotenone suggests palbo antagonizes rotenone-mediated reduction in proliferation. But it's unclear to me whether the graph shows the converse - that rotenone treatment modulates palbo-induced cell cycle arrest.

This reviewer is correct about the fact that increasing doses of palbociclib in the absence of oxidative phosphorylation do indeed have an effect on proliferation. However, it is helpful to normalize proliferation values to each initial dose of palbociclib and then compare this to the different oxidative phosphorylation inhibitors treatment combinations. To illustrate that the oxidative phosphorylation inhibitors do indeed antagonize palbociclib-induced proliferation defects, we have now included the data graphed as each oxidative phosphorylation inhibitor vs palbociclib as Supplemental Figures S1D-S1F.

• The highest concentration of GSK126 tested (5µM) does not appear to confer resistance, but perhaps this is due to off-target effects or cytotoxicity?

We agree with the reviewer that at the highest doses of dose of GSK126, low doses of palbociclib do not confer resistance to palbociclib. However, higher doses do appear to have this effect. We have included a statement in our results section to address this reviewer’s observations. 

• Disruption of Emi1 leads to resistance (Figure 1F, FZR1), yet overexpression induces resistance (Mouery et al. bioRxiv 2023). Explain.

We do not understand why EMI1 responds in this way, and therefore we cannot comment on this in the text. 

Typos/stylistic comments:

• Typo "However, the net result of these opposing effects on cell cycle progression, and the contribution of the individual subcomplexes to this regulation, rained unclear."

We thank the reviewer for pointing this out, and we have corrected it.  

• Use of the word "growth" - I think the authors should be more precise. Is "proliferation" meant here?

We thank the reviewer for pointing this out, and we have corrected it.

• n Figure 4G, two of the panels have 8.42%. Is this correct, or may it be a copy/paste error?

This was an error, but is no longer relevant as we have reconducted and reanalyzed this experiment.

Reviewer #2 (Recommendations For The Authors):

Major Points

(1) Some of the conclusions should be confirmed in additional cell lines. I would suggest testing the resistance to Palbociclib in several additional cell lines, where MTF2 and JARID2 are deleted. If the conclusion can be generalized, one would expect that the differential role of MTF2 versus JARID2 can be confirmed in more cell lines.

While the PRC2.1-dependent repression of D-type cyclins does not appear to be general, we have now demonstrated in Figures 5SE and 6F that there are multiple different cellular contexts in which our observations are consistent. Specifically, we demonstrate that GSK126 causes upregulation of CCND1 in both immortalized nontumor cells (COS7 cells) and in the breast cancer cell line MDA-MB-231. Moreover, in both cases we showed that this effect is PRC2.1-dependent, as shRNA knockdown of MTF2 increases expression of CCND1.

(2) In addition, it may be attractive to make use of publicly available RNA-seq data of MTF2 and JARID2 knockout/down cells, to investigate the generality of the finding that PRC2.1 regulates CCND1 and CCND2.

While it would be useful to address this issue, Figure S5E demonstrates that the repression of D-type cyclin expression by PRC2.1 is context dependent. Furthermore, prior to identifying the lines shown in Figure 6F and 5SE, we were not aware of which lines to focus our investigations on. However, we have now demonstrated a few cellular contexts in which either chemical inhibition of PRC2 or knockdown of MTF2 results in de-repression of CCND1 expression.

(3) At a bare minimum the authors should strongly discuss the limitations of the study, and tone down the conclusions.

We would agree with this based upon the data in the original submitted manuscript, however, now that we have shown that this effect is more general, this is less critical. That said, we do not see this effect in all cell lines, and we have made this apparent in the final version of the manuscript.

Minor point

(1) In my view, Figures 1-3 should be shortened to the most essential points, and some data/figures should be moved to the supplementary figures. Especially the STING genenetwork graphs are in my view not particularly meaningful.

While we understand the opinion of this reviewer, we feel that these data will be of significant interest to some readers.  

(2) Figure 6E and 6F/G appear to be largely redundant. This can perhaps be made more concise.

This has been addressed in the new version of Figure 6

(3) Figure 5D should be enlarged. 

We thank the reviewer for this suggestion and have enlarged the image.

Reviewer #3 (Recommendations For The Authors):

The manuscript could be edited to improve clarity. In several places, the scientific logic motivating an experiment is confusing, and there are several hypotheses and conclusions that seem opposite from what the data are suggesting. Some aspects of the figures were also unclear. Specific examples include the following:

(1) Last sentence of abstract : "Our results demonstrate a role for PRC2.1, but not PRC2.2, in promoting G1 progression." Data show that knockout of PRC2.1 components promotes G1 progression through upregulation of CycD, so the conclusion here is the opposite.

We thank the reviewer for catching this error. We have now changed this to “in antagonizing G1 progression”.

(2) In the second paragraph of the results, CCNE1, CDK2, etc are described as scoring high for palbociclib resistance, but those genes scored as sensitizing. Also, in that paragraph, it is described that a drug is sensitizing cells to loss of a gene, which seems like incorrect logic. It should be clarified that knock-out of a gene either sensitizes or desensitizes cells to the drug.

We thank the reviewer for catching this error. We have now corrected it.  

(3) In the motivation for the experiment in Figure 3D, it is written: "we asked whether chemical inhibition of oxidative phosphorylation could rescue sensitivity to palbociclib". Considering that knock-out of genes that mediate oxidative phosphorylation confer resistance to palbociclib, it is confusing why it was expected that chemical inhibitors would restore sensitivity.

We are sorry if the original wording was confusing. We have now changed this to “combined inhibition of oxidative phosphorylation and CDK4/6 activity mutually rescue the proliferation defect imposed by agents targeting the other process”.  

(4) If the intention of Figure 3D is to test the hypothesis that chemical inhibition of oxidative phosphorylation modulates sensitivity to palbociclib, the clarity of Figure 3D would be improved if data were shown such that palbociclib concentration is on the x-axis and the different curves are different drug concentrations.

It appears that there is some mutual suppression, which inhibition of each process rescues cells partly from inhibition of the other. In fact, with these drugs the stronger of the two is seen as the rescue of mitochondrial poisons by palbociclib. We have now discussed this in the text.  

(5) The authors should check the units on the x-axis in Figure 4D, should they be log[uM Palbo] or log [nM Palbo]?

We thank the reviewer for catching this error. We have now corrected it

(6) It should be clarified which data are summarized in the graph to the right in Figure 4G, are these experiments with palbociclib?

This is currently included in the figure legends.

(7) The text suggests that the control CCNE1 knockout is shown in Figure 4E, but those data are missing.

This has been corrected in Figure 4E.

Several conclusions are not well supported by the data and should be revised or more data and analysis should be added.

(1) The titular conclusion that the "PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2" has only been demonstrated in the context of a Cdk4/6 inhibitor in HAP1 cells. There is little evidence supporting this claim that is broadly applicable. For example, data in Figure 4G show small and not demonstrable significant differences in G1 and S phase populations in the mock experiments. Also, experiments in other cells are needed to support the rigor and generality of the conclusion.

Our chemogenetic screen and competitive proliferation assay data in Figure 4A, 4C and 4E support the conclusion that PRC2.1 and PRC2.2 play opposing roles in G1 progression. Furthermore, we have repeated the initial BrdU incorporation experiments shown in Figure 4G and have been able to demonstrate that JARID2∆ cells do indeed display a significant decrease of cells entering into S-phase when treated with palbociclib. Most importantly, in the Figures 6D and 6E we show additional cell lines where this is the case.  Therefore, we feel that this title is valid in the current version of the manuscript, where we have shown it to be the case in multiple tumor-derived human cell lines as well as immortalized non-human primate cells.  

(2) It is unclear how the data in Figure 3D support the conclusion that the administered inhibitors of oxidative phosphorylation influence response to palbociclib.

As noted in the response to point 4, we have now discussed this mutual rescue more thoroughly in the text.  

(3) In Figure 4D, the IC50 values should be calculated and statistical significance based on biological replicates should be determined. Also, the conclusion that "increasing doses of GSK126 withstood palbociclib-induced growth suppression" is overstated, as ultimately all drug conditions succumb to palbocilib suppression of proliferation, although there may be differences in sensitivity.

We have now  included a statical analysis of each data point in Figure 4D.  

Editorial comments:

(1) The title does not seem to optimally capture the content of the paper. Please consider changing it, e.g. focusing on palbociclib resistance. 

While we used this particular drug to make the original observation, we feel it is more general to discuss the underlying biology (cyclin gene control) than the pharmacological methodology. Moreover, we have now extended our findings about the regulation of D-type cyclins by PRC2.1 to several cell lines, derived from both cancers and primary cells, re-enforcing the fact that this effect is observed more broadly.   

(2) Please indicate the biological system (haploid human HAP1 cells) in either title or abstract.

The abstract now indicates that we have observed this in CML, breast cancer and immortalized primary cells.

Reviewer #3 (Public review):

This study begins with a chemogenetic screen to discover previously unrecognized regulators of the cell cycle. Using a CRISPR-Cas9 library in HAP1 cells and an assay that scores cell fitness, the authors identify genes that sensitize or desensitize cells to the presence of palbociclib, colchicine, and camptothecin. The results suggest that these three drugs inhibit proliferation through different mechanisms, and with each treatment, expected and unexpected pathways were found to affect drug sensitivity. The authors focus the rest of the experiments and analysis on the polycomb complex PRC2, as deletion of several of its subunits in the screen conferred palbociclib resistance. The authors find that PRC2, specifically a complex dependent on the MTF2 subunit, methylates histone 3 lysine 27 (H3K27) in promoters of genes associated with various processes including cell-cycle control. Further experiments demonstrate that Cyclin D expression increases upon loss of PRC2 subunits, providing a potential mechanism for palbociclib resistance.

The strengths of the paper are the design and execution of the chemogenetic screen, which provides a wealth of potentially useful information. The data convincingly demonstrate in the HAP1 cell line that the MTF2-PRC2 complex sustains the effects of palbociclib (Fig. 4), methylates H3K27 in CpG-rich promoters (Fig. 5), and represses Cyclin D expression (Fig. 6). The correlation between MTF2-PRC2 inhibition and increased Cyclin D levels is shown in multiple cell lines using both genetic and chemical approaches. These results could be of great interest to those studying cell-cycle control, resistance mechanisms to therapeutic cell-cycle inhibitors, and chromatin regulation and gene expression.

There are a few weaknesses that somewhat temper the overall quality and potential impact of the study. First, the results from the colchicine and camptothecin screens (Fig. 1 and 2) are not experimentally validated, which lessens the rigor of those data and conclusions. Second, some experiments validating and further exploring results from the palbociclib screen (Figs. 4 and 5) are restricted to the Hap1 cell line, so the generality of some conclusions is not established. Third, conclusions drawn from data in Fig. 4D are not fully supported by proper use of biological replicates and analysis of the results.

Comments on revisions:

Proper statistical analysis considering biological replicates is still not applied to determine whether differences in palbociclib IC50 values at different GSK126 concentrations are significant.

Reviewer #2 (Public review):

Summary:

In this manuscript, Dr. Yao Li et al. documented the metabolomic profile of the aorta from OVX rats and that from OVX plus E2. These conditions mimic post-menopause hypertension and hormonal replacement therapy.

Strengths:

The authors state that this is probably the first study to examine the metabolic changes in the aorta of post-menopause hypertension.

Weaknesses:

There are several weaknesses, and a few of them are quite serious.

(1) The aorta is not a resistant artery and has little to do with hypertension. The authors should have used resistant arteries for this study. The expression of several adrenergic receptors and cholinergic receptors in the aorta and resistant arteries are different. It is unknown whether the aorta metabolomic profile has any relevance to BP and whether they are similar to that of the resistant arteries. I understand the logistics issue of obtaining enough tissues from resistant arteries. At least, once some leads are discovered in the aorta, the authors should validate it in resistant arteries. This should be feasible.

(2) The aorta and all the arteries have three layers. It is critically important to know whether the metabolic changes occur in the intima or in the media, while the adventitia probably has little to do with vasoconstriction and hypertension. If the authors want to use the aorta to conduct the preliminary study, they should completely remove the adventitia and then use samples with and without their endothelium stripped and then assess their metabolomic profiles. After the leads are obtained from this preliminary profiling, they should be validated in endothelium and smooth muscles of the resistant artery. The current experiments are not appropriately designed.

(3) The tail-cuff BP measurement is a technique of the last century. The current gold standard of BP measurement is by telemetry. The tail-cuff method is particularly problematic in this study because the 1-2 h restraining of the rats for more than 10 times BP measurement will cause significant stress in the animal, and their stress hormone secretion might cause biased metabolomic profiles in the OVX versus shames operated mice. The problem can be totally avoided by using telemetry.

(4) Although the L-AABA showed a high p-value (10^-4) of a decrease in the OVX rats, the fold change is small (2-3 folds). Such a small change should be validated using a different method to be convincing.

(5) The authors claim (or hypothesize) that the reduced AABA level in OVX can cause vascular remodeling. This can be easily validated by the histology of the OVX-resistant artery, and they should do that during the revision. The authors should also examine the M1 macrophage function from the OVX mice to validate their claimed link of AABA to M1.

(6) As mentioned above, the authors need to pinpoint the changes of AABA to target cells, i.e., endothelial cells, SMC, or M1, and then use in vitro or in vivo cell biology approaches to assess whether these cells in the OVX rat indeed have an abnormality in function and, indeed, such functional changes are responsible for the BP phenotype.

(7) The results of the current study can be condensed into 1 or 2 figures that can serve as a base or a starting point for a deeper scientific study.

Summary

The experimental design of this manuscript is inappropriate, and the methods are not up to the current standards. The whole study is descriptive and rudimentary. It lacks validation and mechanism. The data from this manuscript might be of some value and can serve as the first step for more investigation of the mechanism of post-menopause hypertension.

Author response:

Reviewer #1 (Public review):

Summary:

The authors aim to investigate the relationship between low estrogen levels, postmenopausal hypertension, and the potential role of the molecule L-AABA as a biomarker for hypertension. By employing metabolomic analysis and various statistical methods, the study seeks to understand how estrogen deficiency affects blood pressure and identify key metabolites involved in this process, with a particular focus on L-AABA.

Strengths:

The study addresses a relevant and understudied area: the role of estrogen and metabolites in postmenopausal hypertension. It presents a novel hypothesis that L-AABA may serve as a protective factor against hypertension, which could have significant clinical implications if proven.

We appreciate the acknowledgment of our study’s focus on an important and understudied area. Our hypothesis regarding L-AABA’s role as a possible protective factor against hypertension indeed holds promise for advancing clinical implications.

Weaknesses:

The evidence linking L-AABA to hypertension is largely correlative, lacking experimental validation or mechanistic proof. Key limitations, such as the inadequacy of the ovariectomy model in replicating human menopause, are acknowledged but not addressed with alternative approaches. In summary, while the study offers an intriguing hypothesis, its conclusions are premature and require further experimental validation and human data to substantiate the claims.

We recognize the limitations regarding the correlative nature of our findings and the inadequacy of the OVX model in replicating human menopause. Future research will prioritize experimental validation and incorporate human studies to solidify our conclusions.

Reviewer #2 (Public review):

Summary:

In this manuscript, Dr. Yao Li et al. documented the metabolomic profile of the aorta from OVX rats and that from OVX plus E2. These conditions mimic post-menopause hypertension and hormonal replacement therapy.

Strengths:

The authors state that this is probably the first study to examine the metabolic changes in the aorta of post-menopause hypertension.

As pointed out by the reviewer, our study may be the first to investigate changes in aortic metabolism in postmenopausal hypertension. As an exploratory study, our goal is to depict the overall characteristics and explore possible research directions.

Weaknesses:

There are several weaknesses, and a few of them are quite serious.

(1) The aorta is not a resistant artery and has little to do with hypertension. The authors should have used resistant arteries for this study. The expression of several adrenergic receptors and cholinergic receptors in the aorta and resistant arteries are different. It is unknown whether the aorta metabolomic profile has any relevance to BP and whether they are similar to that of the resistant arteries. I understand the logistics issue of obtaining enough tissues from resistant arteries. At least, once some leads are discovered in the aorta, the authors should validate it in resistant arteries. This should be feasible.

We acknowledge the limitation of using the aorta and will aim to include studies on resistant arteries to validate our metabolomic findings.

(2) The aorta and all the arteries have three layers. It is critically important to know whether the metabolic changes occur in the intima or in the media, while the adventitia probably has little to do with vasoconstriction and hypertension. If the authors want to use the aorta to conduct the preliminary study, they should completely remove the adventitia and then use samples with and without their endothelium stripped and then assess their metabolomic profiles. After the leads are obtained from this preliminary profiling, they should be validated in endothelium and smooth muscles of the resistant artery. The current experiments are not appropriately designed.

Future studies will involve detailed profiling of specific arterial layers, focusing on the intima and media to enhance the relevance of our findings related to hypertension.

(3) The tail-cuff BP measurement is a technique of the last century. The current gold standard of BP measurement is by telemetry. The tail-cuff method is particularly problematic in this study because the 1-2 h restraining of the rats for more than 10 times BP measurement will cause significant stress in the animal, and their stress hormone secretion might cause biased metabolomic profiles in the OVX versus shames operated mice. The problem can be totally avoided by using telemetry.

We appreciate the suggestion and will consider telemetry for more accurate blood pressure measurements in future experiments to minimize stress-related bias.

(4) Although the L-AABA showed a high p-value (10^-4) of a decrease in the OVX rats, the fold change is small (2-3 folds). Such a small change should be validated using a different method to be convincing.

We plan to employ additional methods to validate the observed changes in L-AABA levels in the following research, ensuring robustness of our findings.

(5) The authors claim (or hypothesize) that the reduced AABA level in OVX can cause vascular remodeling. This can be easily validated by the histology of the OVX-resistant artery, and they should do that during the revision. The authors should also examine the M1 macrophage function from the OVX mice to validate their claimed link of AABA to M1.

We intend to conduct histological analyses and examine M1 macrophage function in OVX-resistant arteries to validate our hypothesis in the following research.

(6) As mentioned above, the authors need to pinpoint the changes of AABA to target cells, i.e., endothelial cells, SMC, or M1, and then use in vitro or in vivo cell biology approaches to assess whether these cells in the OVX rat indeed have an abnormality in function and, indeed, such functional changes are responsible for the BP phenotype.

Addressing these points, we aim to pinpoint specific cell types affected by AABA variations and conduct in vitro and in vivo studies to examine their physiological impacts in the following research.

(7) The results of the current study can be condensed into 1 or 2 figures that can serve as a base or a starting point for a deeper scientific study.

Thank you for your suggestion. As a omics research, our research approach may differ from traditional mechanism studies.

Summary

The experimental design of this manuscript is inappropriate, and the methods are not up to the current standards. The whole study is descriptive and rudimentary. It lacks validation and mechanism. The data from this manuscript might be of some value and can serve as the first step for more investigation of the mechanism of post-menopause hypertension.

Reviewer #3 (Public review):

Summary:

The decrease in estrogen levels is strongly associated with postmenopausal hypertension. Dr. Yao Li and colleagues aimed to investigate the metabolomic mechanisms of underlying postmenopausal hypertension using OVX and OVX+E2 rat models. They successfully established a correlation between reduced estrogen levels and the development of hypertension in rats. They identified L-alpha-aminobutyric acid (AABA) as a potential marker for postmenopausal hypertension. The research explored the metabolic alterations in aortic tissues and proposed several potential mechanisms contributing to postmenopausal hypertension.

Strengths:

The group performed a comprehensive enrichment analysis and various statistical analyses of the metabolomics data.

As summarized by the reviewer, our current study conducted a comprehensive analysis of metabolomics data. It is also a reliable foundation for further mechanism research.

Weaknesses:

(1) The manuscript is descriptive in nature, although they mentioned their primary objective is to explore the potential mechanisms linking low estrogen levels with postmenopausal hypertension. No mechanism insights have been interrogated in this study, which has been mentioned by the authors in the discussion. The connection between E2, AABA, and macrophage needs to be validated in endothelial cells, vascular smooth muscle cells, and other aortic tissue cells. Without such verification, the manuscript predominantly raises hypotheses only based on metabolomic data.

We have proposed research hypotheses based on detailed omics data. Further research on the mechanisms involving endothelial and vascular smooth muscle cells to validate the pathway connections between E2, AABA, and macrophages is undoubtedly the future direction of this study.

(2) The serum contains three forms of estrogen: Estradiol, Estrone, and Estriol. The authors used the Rat E2 ELISA kit. Ideally, all three forms of estrogen should be measured.

Future assays will aim to measure Estradiol, Estrone, and Estriol to capture a more comprehensive picture of estrogen’s role in postmenopausal hypertension.

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Referee #2

Evidence, reproducibility and clarity

Corynebacterium glutamicum is an important organism with industrial applications, and it constitutes a model organism for the study of other Corynebacteriales, which include important pathogens such as Mycobacterium tuberculosis and Corynebacterium diphtheriae. This work provides with a thorough structural and functional characterization of the S-layer structure of C. glutamicum based on solid data acquired through protein engineering, structural biology, and cell microscopy/imaging studies, and phylogenetic analysis. The authors have determined an atomic structural model of the S-layer from C. glutamicum formed by the protein PS2 exhibiting a different degree of conservation between external and mycomembrane facing surfaces; and they show evidence in vivo of how the S-layer assemble at the cell poles in this organism in line with the actinobacterial elongasome. The authors also show that the presence of the S-layer provides resistance to lysozyme, elaborating several hypotheses that may explain this observation, and demonstrate PS2 S-layer as a feasible platform for covalent surface display both in vitro and in vivo.

The conclusions are well supported by the data provided. When required, experiments have been performed with an adequate number of replicates and their statistical analysis is properly provided. The information provided in the data and methods´ section are sufficient for experimental reproducibility.

No major comments

Minor comments. Text and figures are clear but the following aspects/questions should be addressed/clarified:

• While the authors indicate that "S-layers are two-dimensional monolayered crystals typically composed of a single (glyco)protein ..." (lines 23-24), I haven´t found a reference to whether PS2 is glycosylated or not in the text. If PS2 is glycosylated and its glycosylation sites are known, where would they locate in the glycosylated structure? Would glycosylation affect the size of the pores observed with the recombinant protein?
• Related to the permeability of lysozyme through the S-layer. Have the authors considered the IP of the lysozyme, which (assuming it is hen white egg lysozyme) it´s > 9. Could the negatively charged PS2 nonspecifically capture/retain at the outward-facing surface? Could glycosylation have something to do with it too?
• Clarify which AF version was used for predicting the structural model of PS2, AF2 (as described in the main text, lines 160 and 656) or AF3 (supplementary Fig. 2). If it was AF3, the corresponding reference should be updated.
• Line 205. Replace "Analysis of its surface electrostatics reveals that PS2..." by "Analysis of the electrostatic potential surface of PS2 reveals that..."
• Figure 2 - panels (e), (g), (i), (j). Cartoons and specially ball-and-stick representations colored in white are very hard to visualize or not visible. Please use a darker color or darken the edges to improve visibility. Labels in panel (g) are very small and difficult to see, please increase their size (panels (i) and (j) seem okay).
• Line 215. "vivo and recombinant PS2AD" remove "and".
• Line 1011-12. Replace "...positive in blue to negative in red" by "...positive and negative charges are colored in blue and red, respectively"
• Supplementary figure 3. Reduce the label size in the why axis (probability)
• Supplementary figure 5. It is labeled as "4" instead of "5". The view captured in the figure does not allow to visualize the interface clearly. Please consider another orientation or an alternative representation such an open-book view.
• Supplementary table 1. Replace "BL21(DH3)" by "BL21 (DE3)"
• Review abbreviations, italics, spaces between number and units... (page 517, C. glutamicum in italic; choose either cryoEM or cryo-EM abbreviation for consistency...)

Significance

From my point of view as structural biologist, the present work uncovers key aspects of PS2 S-layer architecture and assemble over the OM of C. glutamicum, together with evidences in vivo of how the S-layer incorporated at cell poles alongside with the bacterial elongasome, and data supporting an implication of the PS2 S-layer in cell envelop stability. These findings constitute an important advance in the structural and functional understanding of S-layer in corynebacteriales, at the time it opens new questions regarding the role of the S-layer in cell integrity and as an interaction interface with external factors. Moreover, the authors present data supporting the feasibility of the PS2 S-layer of C. glutamicum as a protein-based platform to anchor with potential application in bioengineering materials and synthetic biology, and which has the potential to expand the biotechnological/industrial applications of C. glutamicum. Thus, this work has a significant relevance for the scientific community investigating the structure, function and biogenesis of the S-layer and the cell envelop, in particular. But also in a more general outlook, for the scientific community working in the fields of host-pathogen interactions and bioengineering materials.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study, the authors investigate the structure of the surface layer, or S-layer, in the Corynebacteriales organism Corynebacterium glutamicum. Studies of the S-layer in Corynebacteriales are lacking and both the function and assembly of the S-layer is unclear in the context of the unique cell envelope of these microbes. In other bacterial systems, the S-layer has been implicated in many critical biological processes. The authors report the ex vivo cryo-EM structure of the S-layer protein PS2 from C. glutamicum, which shows hexameric symmetry with additional trimeric interfaces. The authors show that the C-terminal membrane anchor domain, which is not resolved in the reported structure, is important for lipid binding based on heterologous expression experiments in E. coli. Used the Spytag/Spycatcher tagging system and fluorescence microscopy, the authors determine that S-layer assembly occurs at the cell poles and is likely coordinated the polar growth mechanism of C. glutamicum.

Major comments:

1. The description of the different types of symmetry within the PS2 structure was confusing and difficult to correlate with the structure as depicted in Fig. 2. Authors should clarify labeling and coloring in Fig. 2e, g-j.
2. Investigation of the function of the S-layer as a permeability barrier (Fig. 3e) would be strengthened by testing susceptibility of cells +/- S-layer to different classes of antibiotics or osmotic shock (optional).
3. Due to the probable importance of the membrane-anchoring domain on S-layer function, can the authors comment on potential predicted structure of the regions of the membrane anchoring domain that was not resolved in their structure? How does this region differ between different Corynebacteriales species (or in S-layer proteins in mycobacterial species) that have different mycomembrane dimensions?
4. The authors need to clarify if the version of PS2 used in the live cell imaging experiments detailed in Fig. 4d-f are PS2AD or PS2FL. While they show that PS2AD-Spytag is able to self-assemble, it is possible that the dynamics of PS2AD assembly in vivo are very different from PS2FL due to the absence of the membrane-anchoring domain. Comparison of dynamics between these two constructs would also be a nice addition to the paper.
5. The pulse labeling experiments using Spycatcher would be strengthened by including fluorescent D-amino acids within the same cells to show true co-localization of S-layer assembly and PG synthesis. This could also shed light on the timescale of S-layer assembly in relation to biogenesis of other layers of the cell envelope.

Minor comments:

1. line 71: authors should elaborate on terminology "P6 symmetry"
2. In Fig. 1g, it is not immediately clear that there is lattice formation. Authors should consider including an inset zoomed in box to make this clearer.
3. line 165, 171, 194: do the authors mean "protomers"? If not, use of the word "promoter" is confusing
4. citation on line 374 is incorrect. The cited paper focuses on inner membrane proteins that transport mycolic acid. Also, many of the mycomembrane porins, especially in C. glutamicum do not have a beta-barrel structure (see Ziegler et al, JMC, 2008)
5. Citations detailing polar assembly of other envelope layers would provide additional support for generalized polar assembly of Corynebacteriales cell envelope (arabinogalactan- Marando et al, JACS, 2022) (mycolic acid biosynthesis proteins- Thouvenel et al, Sci Reports, 2023)
6. Some of the supplementary figures are not referenced in the text

Significance

This study is of broad interest to both the Corynebacteriales and S-layer fields. The study is thorough and detailed but could be strengthened by some clarification in how the structure is presented and further discussion of the biological implications of the membrane anchoring domain. There is a long-held interest in understanding how the unique Corynebacteriales cell envelope is assembled, and the work contributes substantially to the field.

Reviewer expertise: bacterial genetics, bacterial cell envelope, protein transport

Author response:

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

eLife Assessment

This useful study reports on the discovery of an antimicrobial agent that kills Neisseria gonorrhoeae. Sensitivity is attributed to a combination of DedA assisted uptake of oxydifficidin into the cytoplasm and the presence of a oxydifficidin-sensitive RplL ribosomal protein. Due to the narrow scope, the broader antibacterial spectrum remains unclear and therefore the evidence supporting the conclusions is incomplete with key methods and data lacking. This work will be of interest to microbiologists and synthetic biologists.

General comment about narrow scope: The broader antibacterial spectrum of oxydifficidin has been reported previously (S B Zimmerman et al., 1987). The main focus of this study is on its previously unreported potent anti-gonococcal activity and mode of action. While it is true that broad-spectrum antibiotics have historically played a role in effectively controlling a wide range of infections, we and others believe that narrow-spectrum antibiotics have an overlooked importance in addressing bacterial infections. Their advantage lies in their ability to target specific pathogens without markedly disrupting the human microbiota.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Kan et al. report the serendipitous discovery of a Bacillus amyloliquefaciens strain that kills N. gonorrhoeae. They use TnSeq to identify that the anti-gonococcal agent is oxydifficidin and show that it acts at the ribosome and that one of the dedA gene products in N. gonorrhoeae MS11 is important for moving the oxydifficidin across the membrane.

Strengths:

This is an impressive amount of work, moving from a serendipitous observation through TnSeq to characterize the mechanism by which Oxydifficidin works.

Weaknesses:

(1) There are important gaps in the manuscript's methods.

The requested additions to the method describing bacterial sequencing and anti-gonococcal activity screening will be made. However, we do not think the absence of these generic methods reduces the significance of our findings.

(2) The work should evaluate antibiotics relevant to N. gonorrhoeae.

(1) It is not clear to us why reevaluating the activity of well characterized antibiotics against known gonorrhoeae clinical strains would add value to this manuscript. The activity of clinically relevant antibiotics against antibiotic-resistant N. gonorrhoeae clinical isolates is well described in the literature. Our use of antibiotics in this study was intended to aid in the identification of oxydifficidin’s mode of action. This is true for both Tables 1 and 2.

(2) If the reviewer insists, we would be happy to include MIC data for the following clinically relevant antibiotics: ceftriaxone (cephalosporin/beta-lactam), gentamicin (aminoglycoside), azithromycin (macrolide), and ciprofloxacin (fluoroquinolone).

(3) The genetic diversity of dedA and rplL in N. gonorrhoeae is not clear, neither is it clear whether oxydifficidin is active against more relevant strains and species than tested so far.

(1) We thank the reviewer for this suggestion. We aligned the DedA sequence from strain MS11 with DedA proteins from 220 N. gonorrhoeae strains that have high-quality assemblies in NCBI. The result showed that there are no amino acid changes in this protein. Using the same method, we observed several single amino acid changes in RplL. This included changes at A64, G25 and S82 in 4 strains with one change per strain. These sites differ from R76 and K84, where we identified changes that provide resistance to oxydifficidin. Notably, in a similar search of representative Escherichia, Chlamydia, Vibrio, and Pseudomonas NCBI deposited genomes, we did not identify changes in RplL at position R76 or K84.

(2) While the usefulness of screening more clinically relevant antibiotics against clinical isolates as suggested in comment 2 was not clear to us, we agree that screening these strains for oxydifficidin activity would be beneficial. We have ordered Neisseria gonorrhoeae strain AR1280, AR1281 (CDC), and Neisseria meningitidis ATCC 13090. They will be tested when they arrive.

Reviewer #2 (Public Review):

Summary:

Kan et al. present the discovery of oxydifficidin as a potential antimicrobial against N. gonorrhoeae, including multi-drug resistant strains. The authors show the role of DedA flippase-assisted uptake and the specificity of RplL in the mechanism of action for oxydifficidin. This novel mode of action could potentially offer a new therapeutic avenue, providing a critical addition to the limited arsenal of antibiotics effective against gonorrhea.

Strengths:

This study underscores the potential of revisiting natural products for antibiotic discovery of modern-day-concerning pathogens and highlights a new target mechanism that could inform future drug development. Indeed there is a recent growing body of research utilizing AI and predictive computational informatics to revisit potential antimicrobial agents and metabolites from cultured bacterial species. The discovery of oxydifficidin interaction with RplL and its DedA-assisted uptake mechanism opens new research directions in understanding and combating antibiotic-resistant N. gonorrhoeae. Methodologically, the study is rigorous employing various experimental techniques such as genome sequencing, bioassay-guided fractionation, LCMS, NMR, and Tn-mutagenesis.

Weaknesses:

The scope is somewhat narrow, focusing primarily on N. gonorrhoeae. This limits the generalizability of the findings and leaves questions about its broader antibacterial spectrum. Moreover, while the study demonstrates the in vitro effectiveness of oxydifficidin, there is a lack of in vivo validation (i.e., animal models) for assessing pre-clinical potential of oxydifficidin. Potential SNPs within dedA or RplL raise concerns about how quickly resistance could emerge in clinical settings.

(1) Spectrum/narrow scope: The broader antibacterial spectrum of oxydifficidin has been reported previously (S B Zimmerman et al., 1987). The focus of this study is on its previously unreported potent anti-gonococcal activity and its mode of action. While it is true that broad-spectrum antibiotics have historically played a role in effectively controlling a wide range of infections, we and others believe that narrow-spectrum antibiotics have an overlooked importance in addressing bacterial infections. Their advantage lies in their ability to target specific pathogens without markedly disrupting the human microbiota.

(2) Animal models: We acknowledge the reviewer’s insight regarding the importance of in vivo validation to enhance oxydifficidin’s pre-clinical potential. However, due to the labor-intensive process needed to isolate oxydifficidin, obtaining a sufficient quantity for animal studies is beyond the scope of this study. Our future work will focus on optimizing the yield of oxydifficidin and developing a topical mouse model for subsequent investigations.

(3) Potential SNPs: Please see our response to Reviewer #1’s comment 3. We acknowledge that potential SNPs within dedA and rplL raise concerns regarding clinical resistance, which is a common issue for protein-targeting antibiotics. Yet, as pointed out in the manuscript, obtaining mutants in the lab was a very low yield endeavor.

Reviewer #3 (Public Review):

Summary:

The authors have shown that oxydifficidin is a potent inhibitor of Neisseria gonorrhoeae. They were able to identify the target of action to rplL and showed that resistance could occur via mutation in the DedA flippase and RplL.

Strengths:

This was a very thorough and clearly argued set of experiments that supported their conclusions.

Weaknesses:

There was no obvious weakness in the experimental design. Although it is promising that the DedA mutations resulted in attenuation of fitness, it remains an open question whether secondary rounds of mutation could overcome this selective disadvantage which was untried in this study.

We thank the reviewer for the positive comment. We agree that investigating factors that could compensate for the fitness attenuation caused by DedA mutation would enhance our understanding of the role of DedA.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The use of the term "N. gonorrhoeae wildtype" should not be used. It is uninformative, as the species contains a large amount of diversity. Instead, please name the strain. From Figure 1, it looks like the authors used MS11. Since MS11 is a longstanding lab strain and likely does not reflect circulating N. gonorrhoeae, and since H041 is no longer in circulation, the authors should ideally test the compound against more representative strains of N. gonorrhoeae. This includes panels of isolates available through the CDC, for example (https://www.cdc.gov/drugresistance/resistance-bank/index.html). I encourage the authors to include FC428 or another recently identified isolate with the penA 60 allele to demonstrate oxydifficidin's activity against contemporary concerning isolates/lineages.

(1) “N. gonorrhoeae MS11” is now used instead of “N. gonorrhoeae WT” in this manuscript.

(2) In our revised manuscript, we have added MIC data for recently identified Neisseria gonorrhoeae isolates AR#1280 and AR#1281 which contain the penA 60 allele (Table 1). The data shows oxydifficidin maintains its potent activity against these multidrug-resistant strains. We also added a description of this data to the results section as shown below.

Original text: “Oxydifficidin was more potent against N. gonorrhoeae MS11 than almost all other antibiotics we tested. In fact, it was only slightly less active than the highly optimized third-generation cephalosporin, ceftazidime.([18]) However, unlike third-generation cephalosporins, oxydifficidin retained activity against the multidrug resistant H041 clinical isolate (Table 1).([4]) H041 is resistant to the “standard of care” cephalosporin ceftriaxone (2 µg/mL) as well as a number of other antibiotics that are normally active against N. gonorrhoeae (penicillin G, 4 µg/mL; cefixime, 8 µg/mL; levofloxacin, 32 µg/mL).”

Changed to: “Oxydifficidin was more potent against N. gonorrhoeae MS11 than most other antibiotics we tested. Notably, unlike clinically used antibiotics such as ceftriaxone, azithromycin, and ciprofloxacin, oxydifficidin retained activity against all multidrug-resistant clinical isolates we examined (Table 1).” (Line 77-79)

(2) Does oxydifficidin have activity against N. meningitidis? It is the species most closely related to N. gonorrhoeae and the other pathogenic Neisseria.

Oxydifficidin has potent activity against N. meningitidis ATCC 13090. In our revised manuscript, we have included its MIC data in Figure 1c.

(3) Given claims that oxydifficidin activity in N. gonorrhoeae as compared to other Neisseria reflects N. gonorrhoeae's dedA and sensitive rplL, it would be good to assess the allelic diversity of these genes in N. gonorrhoeae. There are over 20,000 genomes from clinical isolates of N. gonorrhoeae in databases. It should be straightforward to check whether dedA and rplL allelic variants already exist in the population. Should variants be observed, oxydifficidin should be tested against the associated strains of N. gonorrhoeae.

Response: We thank the reviewer for this suggestion. We aligned the DedA sequence from strain MS11 with DedA proteins from 220 N. gonorrhoeae strains that have high-quality assemblies in NCBI. The result showed that there are no amino acid changes in this protein. Using the same method, we observed several single amino acid changes in RplL. This included changes at A64, G25 and S82 in 4 strains with one change per strain. These sites differ from R76 and K84, where we identified changes that provide resistance to oxydifficidin. Notably, in a similar search of representative Escherichia, Chlamydia, Vibrio, and Pseudomonas NCBI deposited genomes, we did not identify changes in RplL at position R76 or K84.

New text: “A survey of 220 N. gonorrhoeae strains with high-quality assemblies in NCBI found no mutations in the DedA protein.” (Line 104-105)

“These two mutations were not found in the survey of the same collection of N. gonorrhoeae strains used to look for DedA mutations.” (Line 143-144)

(4) Clinically relevant antibiotics for N. gonorrhoeae are penicillin, tetracycline, spectinomycin, gentamicin, ciprofloxacin, azithromycin, ceftriaxone; moreover, zoliflodacin and gepotidacin have reportedly successfully completed phase 3 trials. The authors should redo their MIC testing with these antibiotics (e.g., for Figures 1 and 2 and Tables 1 and 2), both because this will enable direct comparison with the many clinical isolates that have undergone testing and because these are the drugs most pertinent to clinical practice. Ampicillin, ceftazidime, chloramphenicol, bacitracin, and daptomycin are not relevant. Could the authors explain why they tested vancomycin, polymyxin B, irgasan, melittin, avilamycin, and thiostrepton?

Our use of antibiotics with diverse modes of action (e.g. vancomycin, polymyxin B, irgasan, melittin, avilamycin, and thiostrepton) in this study was intended to aid in the identification of oxydifficidin’s mode of action. This is true for both Tables 1 and 2.

To address the reviewer’s concern, in our revised manuscript, we have added MIC data for the following clinically relevant antibiotics: ceftriaxone (cephalosporin/beta-lactam), gentamicin (aminoglycoside), azithromycin (macrolide), and ciprofloxacin (fluoroquinolone) to Table 1.

(5) Please describe the characteristics of the transposon library (finding four transposons in a single strain does seem unexpected, given how most transposon libraries aim for one transposon insertion per strain).

We understand that one transposon insertion per strain is ideal for transposon libraries. This Bacillus strain proved to be recalcitrant to genetic manipulation. In the rare cases where we obtained resistance colonies upon electroporation with the transposon, all colonies contained multiple (≥ 4) transposon insertions. This made it impractical to build a library with one transposon insertion per library member.

We assumed that the anti-N. gonorrhoeae activity most likely originated from a natural product BGC, which typically range from 10-100 kb in size.

Based on the average of 50 kb per BGC, ~80 transposon insertions would be required to fully search the 4.2 Mb genome of Bacillus amyloliquefaciens BK for a BGC. At 4 mutations per transformant, 1x coverage of the genome would require only 20 library members.

After extensive electroporation of transposon into Bacillus amyloliquefaciens BK, we were able to obtain a library of 50 members, including one mutant (Tn5-3) that lacked anti-N. gonorrhoeae activity.

New text added to the methods section:

“A library containing 50 transposon mutants was obtained. In the mutants examined, each strain contained ≥4 transposon insertions” (Line 337-339)

(6) Please describe in the methods how you sequenced and annotated the genome of Bacillus amyloliquefaciens BK.

The sequencing method is now described in “Genomic Sequencing and annotation of Bacillus amyloliquefaciens” section. The genome of Bacillus amyloliquefaciens BK was not fully annotated. Mutations were identified as described in the updated methods section below.

New text:

“Genomic Sequencing and annotation of Bacillus amyloliquefaciens

Genomic DNA from Bacillus amyloliquefaciens BK WT and transposon mutant Tn5-3 was isolated using PureLink Microbiome DNA purification kit (Invitrogen) according to the manufacturer’s instructions.

The Bacillus amyloliquefaciens BK WT genome was assembled by mapping its sequencing data onto the annotated genome of Bacillus amyloliquefaciens FZB42 using Geneious Prime. Differences in the mutant strain Tn5-3 were identified by mapping its sequencing data onto the assembled Bacillus amyloliquefaciens BK WT genome. The mutated genes were then annotated using NCBI BLAST. The oxydifficidin BGC was annotated using the antiSMASH online server.” (Line 253-260)

(7) Please describe in the methods how you screened the library for strains that lacked anti-gonococcal activity.

The method is added to our revised manuscript as section “Screening of Bacillus Strains Lacking Anti-N. gonorrhoeae Activity”.

New text:

“Screening of Bacillus Strains Lacking Anti-N. gonorrhoeae Activity

The transposon mutants of Bacillus amyloliquefaciens BK were grown overnight in LB medium at 30 °C. Each overnight culture was then diluted 1:5000, and 1 μl of the diluted culture was spotted onto a GCB agar plate swabbed with N. gonorrhoeae cells. The plate was then incubated overnight at 37 °C with 5% CO2. The mutant strain (Tn5-3) lacking anti-N. gonorrhoeae activity was identified due to its failure to produce a zone of growth inhibition in the resulting N. gonorrhoeae lawn.” (Line 341-346)

(8) Was only one strain found that was a 'non-producer' of anti-N. gonorrhoeae activity? Line 68 suggests that this was only one of multiple non-producers. Is that correct? If so, did you work up the others, and did they also have disruptions in the same biosynthetic gene cluster?

Only one strain was identified as a “non-producer” of anti-N. gonorrhoeae activity. We have modified the text to clarify this point.

Original text: “The sequencing of one non-producer strain revealed that it surprisingly contained four transposon insertions and one frame shift mutation.”

Changed to: “The sequencing of the non-producer strain revealed that it surprisingly contained four transposon insertions and one frame shift mutation.” (Line 53-54 )

(9) All sequences (including Bacillus amyloliquefaciens BK) must be deposited in a public database (e.g., NCBI) and the accession numbers reported in the manuscript.

Genomic sequence data of Bacillus amyloliquefaciens BK has been deposited in GenBank, and its accession number (GCA_019093835.1) now appears in figure legend of Figure S1a.

Figure S1a legend:

“Genome-based phylogenetic tree containing Bacillus amyloliquefaciens BK and closely related Bacillus spp. The tree was built by Genome Clustering of MicroScope using neighbor-joining method. The NCBI accession numbers of Bacillus strains used in the tree are GCA_000196735.1, GCA_000204275.1, GCA_000015785.2, GCA_019093835.1, GCA_000009045.1, GCA_000011645.1, GCA_000172815.1, GCA_000008005.1, and GCA_000007845.1 (from top to bottom).”

Minor

(10) Statements in the article would benefit from fact-checking. For example:

- gonorrhea is not the second most prevalent sexually transmitted infection worldwide; it is the second most reported bacterial sexually transmitted infection.

- Treatment is ceftriaxone 500mg IM x1 in the US, but 1g IM x1 in the UK and Europe. The UK guidelines also permit ciprofloxacin, should sequencing indicate gyrA 91S. I suggest reviewing / specifying which treatment guidelines you're referring to.

We appreciate the reviewer’s corrections. The word “prevalent” is now changed to “reported”.

Original text: “Gonorrhea, which is caused by Neisseria gonorrhoeae, is the second most prevalent sexually transmitted infection worldwide.”

Changed to: “Gonorrhea, which is caused by Neisseria gonorrhoeae, is the second most reported sexually transmitted infection worldwide.” (Line 2-3)

Original text: “Gonorrhea is the second most prevalent sexually transmitted infection worldwide, its causative agent is the bacterium Neisseria gonorrhoeae.”

Changed to: “Gonorrhea is the second most reported sexually transmitted infection worldwide, its causative agent is the bacterium Neisseria gonorrhoeae.” (Line 18-19)

“In the USA” is now added to the sentence stating gonorrhea treatment.

Original text: “The high dose (500 mg) of the cephalosporin ceftriaxone is currently the only recommended therapy for treating gonorrhea infections.”

Changed to: “The high dose (500 mg) of the cephalosporin ceftriaxone is currently the only recommended therapy for treating gonorrhea infections in the USA.” (Line 20-22)

(11) Please make sure all results are in the results section. The report of cell morphology, for example, should be in the results, not the discussion.

In our revised manuscript, we have included the cell morphology data in the results section with the text changes below.

Original text: “Interestingly, not only was dedA deficient N. gonorrhoeae less susceptible to oxydifficidin, oxydifficidin also kills this mutant more slowly (Figure 2b) than WT N. gonorrhoeae MS11.”

Changed to: “Interestingly, not only was dedA deficient N. gonorrhoeae less susceptible to oxydifficidin, oxydifficidin also kills this mutant more slowly (Figure 2b) than WT N. gonorrhoeae MS11. The dedA deletion mutant also showed an altered cell morphology with reduced membrane integrity and lower formation of micro-colonies (Figure S4). (Line 100-104)

Original text: “The dedA deletion mutant also showed an altered cell morphology with reduced membrane integrity and lower formation of micro-colonies (Figure S4), indicating that it should show reduced pathogenesis and fitness, and, as a result, not accumulate in a clinical setting, which adds to the therapeutic appeal of oxydifficidin.”

Changed to: “The dedA deletion mutant exhibited altered cell morphology, characterized by diminished membrane integrity and reduced micro-colony formation, indicating that it should show reduced pathogenesis and fitness, and, as a result, not accumulate in a clinical setting, which adds to the therapeutic appeal of oxydifficidin” (Line 206-210)

(12) Tables 1 and 2 should be combined and should address the most relevant antibiotics

The MIC data of additional relevant antibiotics are now included in Table 1. However, we still believe that keeping Tables 1 and 2 separate enhances the clarity of the manuscript. Table 2 specifically focuses on diverse ribosomal targeting antibiotics, which highlights the unique binding site of oxydifficidin.

(13) Supplemental Figure 1a. The tree could be better resolved, and there are four entries with the identical listing of "Bacillus amyloliquefaciens subsp. plantarum" on different branches. In the methods or the legend, please indicate the accession numbers for these genomes. Also please specify how this tree was made-is it a maximum likelihood tree? Something else?

The tree is now better resolved and includes new entries. The requested information regarding accession numbers and tree construction method has been included in the figure legend.

New supplemental Figure 1a legend:

“a. Genome-based phylogenetic tree containing Bacillus amyloliquefaciens BK and closely related Bacillus spp. The tree was built by Genome Clustering of MicroScope using neighbor-joining method. The NCBI accession numbers of Bacillus strains used in the tree are GCA_000196735.1, GCA_000204275.1, GCA_000015785.2, GCA_019093835.1, GCA_000009045.1, GCA_000011645.1, GCA_000172815.1, GCA_000008005.1, and GCA_000007845.1 (from top to bottom).”

Reviewer #2 (Recommendations For The Authors):

The conclusions drawn in the manuscript are well-supported by the experimental data presented.

I have the below minor comments:

(1) "serendipitously identified" - I feel this wording should be avoided throughout the manuscript. The point of a research paper is to communicate methodology and experimental detail, and this language portrays the opposite.

While we agree that methodology and experimental procedures are paramount in scientific reporting, we believe it is equally important to convey, particularly to younger generations, that a part of the scientific process is often unplanned and can benefit from chance observations. Therefore, we would like to keep this wording.

(2) The introduction should include the biological roles/function of DedA proteins in bacteria.

DedA proteins perform a wide array of biological roles and functions in bacteria. In the results section (Line 107-116), we have described the most well-established of these functions, particularly the flippase activity, which appears to be directly related to oxydifficidin sensitivity. We believe that introducing this information in the results section enhances the manuscript’s clarity and flow.

(3) "When we screened this contaminant for antibacterial activity against lawns of other Gram-negative bacteria it did not produce a zone of growth of inhibition against any of the bacteria we tested (e.g., Escherichia coli, Vibrio cholerae, Caulobacter crescentus)." Can these data Figures be included in the Supplements?

This result was recorded in the lead author’s notebook, but no image was saved.

(4) Line 52: Was any base analyses performed on the Tn-mutants i.e., how many insertion-sites? Depth of mutants? Was a library constructed in this study or previously? Why were only BGC assessed?

Please see our response to Reviewer #1’s comment (5). We focused on BGCs because we believed the anti-N. gonorrhoeae activity most likely resulted from a molecule encoded by a natural product BGC.

(5) Line 98: Do the other 2 predicted DedA-like proteins also have a role in uptake of oxydifficidin? Is there some redundancy in uptake?

We generated knockout mutants for two other predicted DedA-like proteins in N. gonorrhoeae MS11, and the MIC of oxydifficidin for these mutants remained the same as for the N. gonorrhoeae MS11 wild type strain. Therefore, we believe that the DedA protein discussed in this manuscript is the primary transporter of oxydifficidin. However, we cannot completely rule out the possibility of redundancy in oxydifficidin uptake by other DedA-like proteins.

New text: “We also generated deletion mutants for two other predicted dedA-like genes, and the MIC of oxydifficidin for these mutants remained the same as for the N. gonorrhoeae MS11 wild type strain.” (Line 98-100)

Reviewer #3 (Recommendations For The Authors):

This is a well presented manuscript and I could not immediately see any issues with it.

We appreciate the reviewer’s positive feedback.

Author response:

We are submitting a revised manuscript with major additions that address the main concerns in the initial reviews. At the highest level, this revision provides i) orthogonal biochemical measurements that yield concrete evidence of lysosomal protein aggregates, and ii) a plausible mechanism linking lysosomal lipid handling and protein aggregation through disruption of ESCRT function. We believe these additions significantly improve the completeness of this study and the conclusions that can be drawn from the data.

Below are more specific highlights on the addition in this revision:

-       We included orthogonal techniques (thioflavin-T staining and Lyso-IP followed by differential extraction) and confirmed the accumulation of RIPA-insoluble protein aggregates at the lysosomes in cells under lipid perturbation (Figure 3).

-       We performed TMT-Proteomics and identified accumulation of insoluble ESCRT components at the lysosomes under lipid perturbation (Figure 4). Two new authors involved in this effort are added onto the manuscript.

-       The ESCRT result prompted us to revisit lysosomal membrane integrity. With improved imaging conditions and analysis we were able to see increased membrane permeabilization under lipid perturbation. VPS4A overexpression partially rescued this phenotype, suggesting that lipid accumulation impairs ESCRT disassembly (Figure 5).

-       Together, the results suggest that lipid perturbation impairs ESCRT function, compromising both lysosomal membrane repair and microautophagy, resulting in the accumulation of endogenous protein aggregates at the lysosomes (Graphical Abstract).

Reviewer #1 (Recommendations For The Authors):

(1) Perhaps the most prominent limitation of this work is the unilateral focus on native cells (i.e. cells under no endogenous or exogenous stress) as the model for protein aggregate formation. Furthermore, although the ProteoStat stain has been utilized by many investigators before, the sole reliance on this stain as the read-out for their assays is concerning. To compound the concern, the ProteoStat-positive puncta co-localize with lysosmal markers which was surprising even to the authors. All in all, it behooves the authors to test proteostasis in multiple parallel ways to actually define what they are studying. How is it possible that protein aggregates under native conditions are only co-localized with lysosomes? Are we really studying protein aggregates which should predominantly be cytoplasmic insoluble aggregates?

(a) They need to get away from a simple stain like ProteoStat and conduct co-stainings with other markers such as poly-ubiquitin antibodies and other chaperones to define what and where else exactly are these aggregates.

Co-staining with poly-ubiquitin was included in the original manuscript. We added orthogonal staining with another widely used amyloid dye, Thioflavin-T, and provided fine-grained quantification of lysosomal vs cytosolic localization of various signals (Figures S4A-C & 3A-B).

(b) They need to do Immunoblots with and without triton insolubility to see if these aggregates are insoluble as most would predict. They can do lysosomal isolation vs cytoplasmic to see if the insoluble aggregates are really lysosomal.

We performed Lyso-IP followed by differential detergent extraction to confirm the accumulation of insoluble proteins at the lysosomes (Figure 3C). Proteomic analysis identified some of these insoluble proteins as ESCRT subunits (Figure 4).

(c) They should compare aggregate formation in the native state versus cells with lysosomal inhibition via Bafilomycin or chloroquine versus cells with proteosomal inhibition. The lysosomal inhibition experiments are particularly informative given the lysosomal relevance they have uncovered.

We included other small molecule inhibitors and at different time points to compare the effect of different modes of proteostasis challenge (Figure S4A-D). Together with the ESCRT finding, our results suggest the role of microautophagy in our system, and provide a model of how ProteoStat- and/or ubiquitin- positive substrates become partitioned between the cytoplasm and lysosomes under different perturbations.

(d) Many protein aggregates which are too bulky for proteosome degradation will traditionally be dealt with by aggrephagy. Why is this not observed?

Knockdown of core macroautophagy components did not impact Proteostat intensity in our CRISPRi screen, suggesting that basal macroautophagy plays a negligible role in clearing endogenous amyloid-like structures in our experimental system. We provide an alternative model that these aggregates instead arrive at the lysosomes via microautophagy.

(2) After addressing #1, they can validate if the genes they identified by CRISPR screens are also important in modulation of protein aggregate burden in other systems. For example, if they inhibit lysosomes by Bafilo or Chloroquine to obtain protein aggregates and then Knockdown the identified genes in the CRISPR screens, will they get the same results?

We addressed the effect of different modes of proteostasis challenge as recommended above. Deacidifying the lysosomes alone causes intense protein aggregation (Figure S4A-D) and eventually cell death, and was thus not combined with other perturbations.

(3) They identify lysosomal lipid metabolism genes/pathways as the culprit for inducing proteostasis. In particular sphingolipid and cholesteryl ester species appear to be operational here. However, there are no specific lipids species or specific lipid metabolism gene that is causative. Rather, you have to knockdown entire processes to have an effect. This suggests that the focus on lysosome health (i.e. permeability, proteolysis, etc) is rudimentary. When you have to knockdown entire classes of lipids, this would indicate more broad effects on cellular lipids (including membrane lipids beyond the lysosome) and related cellular health?

We included data on the effect of knocking down MYLIP, PSAP, and as a comparison PSMD2 on the growth rate of K562 cells (Figure S5A). MYLIP and PSAP KDs, which cause predominantly an accumulation of lipids, do not impede cell growth. Increasing lipid uptake by MYLIP KD increases cell proliferation under our culture conditions, suggesting a general negative impact on cell health was not required for the association between lipid levels and protein aggregates.

(a) They conduct a superficial methyl-beta-cyclodextrin experiment with equivocal results. The use of MBCD for different time-courses to deplete various membrane cholesterol pools including the plasma membrane pool is important to ascertain what aspect of the cellular cholesterol is affecting proteostasis. MBCD +/- cholesterol reintroduction time-courses for rescue will also be key to determine the culprit cellular cholesterol pool.

The MBCD / Filipin experiment helped us determine that ProteoStat doesn’t directly stain cholesterol, nor any major plasma membrane components. Free cholesterol was implicated in neither the screen nor the lipidomics and was not the subject of targeted experiments.

(b) The same concept can be applied to sphingolipids. There are sphingolipids in abundance in multiple membrane compartments. Which ones are causal here? More nuanced evaluation of this with sphingolipid staining/tracking can be conducted.

We attempted experiments where sphingolipids were added back to cells grown in FBS-depleted media. Nevertheless, we were not able to consistently deliver these lipid species and doing so while ensuring the correct subcellular localization at physiologically relevant level would require substantial methods development.

(c) As part of this, are lipid rafts and/or caveolae being affected by the perturbations in cholesterol and sphingolipids? Lipid rafts are highly enriched in these 2 lipids which could link to their preteostasis observation.

Indeed, ceramides released from SM hydrolysis are proposed to self-assembled into microdomains with negative curvature that can promote the formation of intralumenal vesicles (Alonso and Goni, 2018; Niekamp et al 2022). We propose that SM accumulation may hinder this process by counteracting the negative membrane curvature and impede microautophagy.

(d) How about ER membrane lipids? The UPR and subsequent effects on proteostasis are intricately involved with ER lipid bilayer composition.

We did not perform lipidomics on ER membranes in this study, though we note that at steady state, sphingolipids and cholesterol esters are not expected to be enriched at the ER (Ikonen and Zhou, 2021). We checked whether lipid-related genetic perturbations induced the UPR in published perturb-seq data in K562 cells. Neither MYLIP nor PSAP knockdown induced a UPR.

In conclusion, the manuscript is interesting but the excitement over a link between lysosome-related lipid metabolism and proteostasis needs to be tamped until a more robust experimental approach is employed to generate supportive and corroborating results.

Reviewer #2 (Recommendations For The Authors):

- The paper has a number of grammatically awkward sentences. Editing these would enhance clarity.

- It is important to show the co-localization of aggregates with the lysosome. This is shown in supplements but should be in a main figure. Here the authors cite previous work indicating that ProteoStat puncta co-localize with ubiquitinated proteins and state that they do not see this, then essentially just move on. Is there an explanation for this discrepancy and can it be resolved? What do they think is really going on? What happens to levels of ubiquitinated proteins when lipid metabolism is perturbed as in these experiments?

We have included the lipid-induced lysosomal protein aggregation data in the main text (Figure 3A-B), and provided fine-grained quantification of the cytosolic-vs-lysosomal ProteoStat / Ub / ThT signals under different aggregate-inducing conditions (Figure S4A-D). We discuss these results in the main text and propose a model involving ESCRT-mediated microautophagy in the main text. This is supported further by the LysoIP-proteomics and LMP analysis.

- Please add an indicator of amino acid numbers to Fig. 3C.

These annotations are now included (now Figure S3C).

- The legend for 3D is mislabelled.

We have corrected the legend (now Figure S3D).

Reviewer #3 (Recommendations For The Authors):

Protein homeostasis and lipid homeostasis are both are important for maintaining cellular functions. However, the crosstalk remains largely unknown. The manuscript entitled as "Impairment of lipid homoeostasis causes accumulation of protein aggregates in the lysosome" deals with this interesting topic. An important link between lysosomal protein aggregation and sphingolipids/cholesterol esters metabolism were discovered. The topic belonging to the Cell Biology domain also falls into the aims and scope of eLife. Here are the revisions I recommend:

(1) From lipidomics analysis, a remarkable correlation between levels of sphingomyelin and cholesterol ester and ProteoStat staining was found. Could the authors explain how sphingomyelin and cholesterol ester are quantified? The two lipids are not included as internal standards from the lipidomics experiment.

Sphingomyelin and cholesterol ester internal standards are included in the Avanti 330707 SPLASH® LIPIDOMIX® Mass Spec Standard, which was supplied at 3% v/v to the MeOH/H2O cell lysis buffer. We have amended the Methods section to clarify this.

(2) Could the authors perhaps delete Figure 1B and show it on Figure 2A only? There is no need to show the same figure two times. The threshold of both False Discovery Rate and Median Enrichment needs to be added. From Figure 2A, the Lysosomal hydrolases (GBA, LIPA, GALC) seems located in statistically insignificant region. Based on previous studies, the GBA could have an effect on sphingolipid levels, then how to explain that sphingomyelin was highly correlated with ProteoSate staining?

We have combined the two volcano plots into a single figure (now Figure 1D), and added a line to help visualize the gene effects while considering the combined contribution of FDR and enrichment. Individual lysosomal hydrolases indeed have insignificant effects on ProteoStat and this is discussed in the main text as having relatively constrained impacts on the general lipidome. For example, while GBA and GALC KDs can lead to accumulation of their immediate substrates (glucosylceramide and galactosylceramide, respectively), they do not directly impinge on sphingomyelin.

(3) The authors show the corelation between ProteoState staining and different lipids/lipid classes in Figure 3B and Figure S3A. It is not necessary to show the corelation with individual lipids (such as sphingomyelin(d18:1/24:0) and cholesterol ester(18:2). The corelation with full collection of lipid classes would be more representative, which is only list in Figure 3B and Figure S3A. It is suggested to add the information of how many individual lipids in each chass are used for the correlation analysis. Replace Figure 3A to Figure S3A, and put Figure 3A as supplementary figure are suggested.

We decided to retain the correlation of two individual lipids (a sphingomyelin and a cholesterol ester species) with ProteoStat as examples to illustrate with clarity how we obtained the class-wide comparison. The number of individual lipids included in each class for correlation analysis is now included in Figures 2F and S3A.

(4) The authors state that lipid uptake and metabolism modulate proteostasis. However, only cholesterol and LDL were tested. It would be more precise to state as cholesterol uptake and metabolism modulate proteostasis. In addition, sphingolipids and cholesterol esters accumulate with increased lysosomal protein aggregation. It would be interesting to see the effects of sphingolipids uptake, since sphingolipids are correlated with proteostasis better than cholesterol.

We attempted to add back specific sphingolipids to assess sufficiency. However, we found it challenging to ensure that these lipids were distributed to the correct subcellular locations at physiologically relevant levels. Without this crucial information, it was difficult to draw any conclusions about the sufficiency of the sphingolipids we tested to impair proteostasis.

Alonso A, Goñi FM. 2018. The Physical Properties of Ceramides in Membranes. Annu Rev Biophys 47:633–654. doi:10.1146/annurev-biophys-070317-033309

Ikonen E, Zhou X. 2021. Cholesterol transport between cellular membranes: A balancing act between interconnected lipid fluxes. Dev Cell 56:1430–1436. doi:10.1016/j.devcel.2021.04.025

Niekamp P, Scharte F, Sokoya T, Vittadello L, Kim Y, Deng Y, Südhoff E, Hilderink A, Imlau M, Clarke CJ, Hensel M, Burd CG, Holthuis JCM. 2022. Ca2+-activated sphingomyelin scrambling and turnover mediate ESCRT-independent lysosomal repair. Nat Commun 13:1875. doi:10.1038/s41467-022-29481-4

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Anne Schreuder et al studied the genetic vulnerabilities of two previously described hypomorphic BRCA1 missense mutations- I26A and R1699Q, and compare these to a BRCA1-depleted setting to identify novel vulnerabilities of the two hypomorphic BRCA1 alleles. The authors showed that BRCA1I26A mutated cells have very similar vulnerabilities to BRCA1 wildtype cells, while the BRCA1R1699Q mutation induced a more similar phenotype to BRCA1-deficient cells. Then the authors unveiled a unique vulnerability to the loss of NDE1 with increased genomic instability specifically in BRCA1R1699Q mutated cells, but not BRCA1-proficient or -deficient cells. While the experiment design strategy is quite reasonable and the data are quite solid, some of the interpretations look not that convincing.

Major concerns:

1. According to your data, RAD51 IRIF were reduced to similar levels in BRCA1 depleted cells reconstituted with either BRCA1R1699Q or BRCA1I26A mutants，suggesting that both the two mutants have defects in HR (Line 110-117). And when you tested the Olaparib sensitivity, your results showed that unlike wildtype BRCA1, re-expression of BRCA1I26A only partially rescued the sensitivity, suggesting that BRCA1I26A still have the capacity to perform HR (Line 120-125). Since the function of BRCA1I26A is quite controversial in the field, the authors should explain in your experiment why RAD51 IRIF of BRCA1I26A is not correlated with its HR level, these two data should be consistent.

Moreover, in line 194-195, you mentioned that this finding correlates with research showing that the I26A mutation does not affect tumour suppression and HR by BRCA1. It is hard to tell whether BRCA1I26A is defective or functional in HR, what's your opinion about it? If you agree that BRCA1I26A is functional in HR, then why it affects RAD51 IRIF. <br /> 2. In line 175, the authors validated the synthetic lethal interaction between BRCA1 and CSA in BRCA1-depleted RPE1 cells and BRCA1-mutated HCC1937 cells (Figure 2D, Supplemental figure 2A, B, C). Actually, HCC1937 is a both BRCA1 and FAM35A-mutated cell line (DOI: 10.15252/embj.201899543), it is a HR functional cell line that does not response to PARPi. According to your CRSIPR data in Table1 and others publications, loss of 53BP1 or its downstream factors such as C20ORF196, FAM35A are synthetic survival with BRCA1 loss. If CSA is synthetic lethal with BRCA1 loss in HCC1937, suggesting that CSA is not simply synthetic lethal with BRCA1 loss of function, at least not only synthetic lethal with HR deficiency. CSA maybe a promising drug target for treating with the PARPi resistant or the PARPi non-response patients. The authors should mention it in the manuscript. 3. RPE1 hTERT P53-/- BRCA1-/- cells have very severe cell growth defects compared with RPE1 hTERT P53-/- BRCA1+/+ cells. Did you see a growth defect or a certain cell death when you induce acute BRCA1 depletion? In Figure 1C, you only showed the survival rate compared with PARPi non-treatment group. Can you also show the growth curve of all these cell lines? 4. Based on your CRISPR screen results from Table 1,2,3, you made the conclusion that BRCA1I26A exhibits vulnerabilities similar to BRCA1-proficient cells and BRCA1R1699Q exhibits vulnerabilities similar to BRCA1-deficient cells. However, when looked at the data carefully, XRCC1 and several FA genes were all found as synthetic lethality hits with BRCA1-deficient, BRCA1I26A, BRCA1R1699Q. And the known genes such as TP53BP1 and ATMIN were found beneficial for survival in the all three screens. If BRCA1I26A exhibits vulnerabilities similar to BRCA1-proficient cells, then why the known hits in the screen are same with BRCA1-deficient cells. Loss of NDE1 is specifically toxic to cells expressing BRCA1R1699Q. Did you find any target that specifically toxic to cells expressing BRCA1I26A?

It is hard to tell whether your conclusion is correct. Of course, the three cells have some same and also different genetic background, you may consider how to separate the difference. Separate the difference will benefit the treatment for patients with different BRCA1 mutation background.

Minor concerns:

1. RPE1 hTERT P53-/- BRCA1-/- cell line has very clear BRCA1 depletion background. Why at the beginning, you did not choose to use RPE1 hTERT P53-/- BRCA1-/- cells reconstituted with BRCA1 wildtype, BRCA1R1699Q or BRCA1I26A mutants to perform the CRISPR screens? What are the advantages of your strategy by first depletion of BRCA1 with auxin and then inducible express BRCA1 constructs? It looks much more complicated.
2. In Supplemental Figure 2C, a blot to detect BRCA1 should be included.

Significance

If the conclusions are correct, the new findings will tell the importance to differentiate between patient-derived mutations when assessing novel treatment targets.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Exploiting synthetic lethality based on functional correlations has the potential to significantly improve the survival of cancer patients by reducing resistance to targeted therapies and increasing anti-tumour efficacy when combined with other treatment modalities. Schreuder et al., aim to identifying novel vulnerabilities of patient-derived mutations that could improve patient stratification based on a specific genetic background. Precisely, they established a model system to perform a genome-wide CRISPR-Cas9 KO screen to identify genomic vulnerabilities of BRCA1 variants with reported hypomorphic phenotypes, namely BRCA1 R1699Q and BRCA1 I26A in engineered RPE1 hTERT cells with AID tag. Using this system the authors were able to confirm known synthetic lethal genes reported in literature (e.g. APEX2, PARP1, POLQ) comparing cells with acute BRCA loss and BRCA1 deficiency. Moreover, the screen identified two genes, CSA and GPX4 that were not previously described as synthetic lethal with BRCA1 loss. What is potentially interesting, but marginally explored, is the identification of a unique synthetic vulnerability of cells expressing BRCA1 R1699Q mutant and NDE1 gene encoding for a dynamic scaffold protein essential in neocortical neurogenesis and heterochromatin patterning by H4K20me3, whose loss of function results in nuclear architecture aberrations and DNA double-strand breaks (Chomiak et al., iScience 2022). Accordingly, cells ablated of NDE1 and expressing BRCA1 R1699Q mutant show less proliferation of cells expressing either BRCA1 WT or BRCA1-depleted. Furthermore, cells lacking NDE1 show increased genomic instability by means of increased micronuclei and anaphase bridges compared to BRCA1 proficient and BRCA1 R1699Q mutant.

Major comments:

1. The authors claim that cells expressing BRCA1-I26A are largely HR-proficient, based on a milder effect on Olaparib sensitivity compared to cells expressing BRCA1-R1699A (Fig. 1C). However, I26A mutant cells are defective in RAD51 IRIF (Fig. 1B), indicative of an HR defect. Recently it has been shown that BRCA1 RING mutations that do not impact BARD1 binding, including I26A, render BRCA1 unable to accumulate to DNA damage sites and unable to form RAD51 foci when such mutation is combined with mutations that disable RAP80-BRCA1 interaction (Sherker et al., 2021). How do the authors explain this discrepancy with the literature?
2. The reduction in survival following CSA depletion in BRCA1-proficient vs. -deficient cells is only 20% (Figure 2 and S2B). In my opinion, such a minor difference is not supporting the notion of a SL interaction between BRCA1 and CSA. To substantiate CSA as synthetic lethal hit, I would recommend the authors comparing the effect of CSA loss to that of EXO1 or BLM loss, both genes recently identified by the same group as SL partners of BRCA1 using the same experimental screening set up (van de Kooij et al, 2024). Moreover, validation data for GPX4 is missing.
3. Similar to the minor effect observed for CSA, DOT1L and OTUD5 depletions caused rather mild and/or divergent phenotypes between the two sgRNAs used (Figure 4B), rather arguing against robust SL interactions between those genes and BRCA1 deficiency that could be therapeutically exploited.
4. To strengthen their conclusion in Figure 4C the authors should perform complementation experiments with NDE1 WT and, ideally, with NDE1 mutant(s). On a related note, are NDE1 knock-out cells expressing BRCA1-R1699A more sensitive to PARPi?
5. Graphs shown in Fig. 1A-C, Fig. 4B, S2D, S3B, S3E and S3F are lacking proper statistical analysis of the differences. Some experiments have only been repeated twice (e.g. Figure 1C), precluding running statistical tests.

Minor comments:

1. The authors should include representative images for results shown in Fig.1 A-C
2. The authors should add immunoblots for BRCA1 in Fig. S2C to indicate successful BRCA1 cDNA complementation in HCC1937 cells.
3. Most numbers in the Venn diagram shown in Figure 3A cannot be read when printed.
4. In the western blots shown in Supplemental Figure 1A, the electrophoretic mobility of BRCA1 variants expressed in RPE1 is quite variable. Could the authors indicate in the Figure (e.g. with arrowheads) which bands represent endogenous and which transgenic BRCA1. Moreover, in BRCA-wt complemented cells there are two bands following auxin/DOX addition, whereas there is one band observed in cells expressing BRCA1 hypomorphic variants
5. Line 229 please correct "BRCA1-proficient" to "BRCA1-depleted".

Significance

General assessment:

This manuscript starts with an attractive hypothesis, which is the generation of a cellular system to study patient-derived hypomorphic BRCA1 missense mutations rather than using BRCA1 knockout cells. Performing CRISPR/Cas9-mediated genome-wide synthetic lethal screens in this system allowed uncovering genetic vulnerabilities of cells expressing BRCA1-R1699A, a pathogenic mutant identified in several cancer patients. The data are of good quality and the manuscript is coherent and generally well written (few typos). However, some data describe mainly negative results (e.g. BRCA1-I26A mutant) or weak phenotypes while other more interesting aspects are not rigorously exploited (e.g. NDE1 SL) and therefore need to be interpreted with more caution and extended by additional experiments.

Advance:

BRCA1-R1699Q is classified as a pathogenic variant despite its low penetrance and intermediate cancer risk in breast and ovarian cancer compared to other variants. A recent case report highlighted the unique clinical outcome of a patient with the BRCA1 R1699Q variant, suggesting that this variant may differ from others in terms of cancer risk and drug response (Saito et al., Cancer Treatment and Research Communications 2022). These findings underscore the need for further studies to confirm these observations and to elucidate the specific mechanisms underlying the response to platinum agents and PARP inhibitors in patients with the BRCA1 R1699Q variant. The manuscript proposed by the authors has the potential to help understanding how BRCA1 missense mutations can contribute to determine treatment sensitivity and pave the way to patient stratification.

Audience:

This manuscript is suitable for a specialized, basic research audience.

Reviewer #1 (Public review):

Summary:

This manuscript constitutes further analysis of a dataset generated for a previously-published study from the same group. The experiments in the previous work use an RNA-DNA proximity assay to capture RNAs that interact with chromatin, especially beyond their site of transcription, by crosslinking-and proximity ligation. The previous work added one novel feature to this treatment, compared to other studies by the same group, where they treated the nuclei with RNase A prior to crosslinking. The initial study concluded that long-range chromatin interaction via chromatin looping is affected by RNase treatment. In the current manuscript, the group analyze the data from this experiment in more detail. They describe some notable features of RNAs that remain after RNase treatment and where they are associated within the genome. Overall, the further analyses are somewhat useful, with some exceptions for specific analyses that are not clear in the current manuscript. The work is very complementary to the previously published original study, to the point that it is surprising it was not included in that study.

Strengths:

(1) The analyses are a useful complement that fill in gaps from the Calandrelli et al paper. Some of the findings are suggestive of RNA-protein networks that operate at long distances to regulate promoters.

Weaknesses:

(1) The beginning of the Results section, and elsewhere, describes steps that likely were performed in the previous publication from which the data are being further analyzed and possibly partially reanalyzed. The current manuscript should more clearly describe if there are any aspects of the pipeline that have been modified from the Calandrelli study (which does not have much detail regarding iMARGI parameters in the published paper) for the further analysis in this manuscript.

(2) The RNase treatment approach is similar to that addressed in recent papers from the Jenner and Davidovich groups (https://doi.org/10.1016/j.celrep.2024.113856; https://doi.org/10.1016/j.celrep.2024.113858) where these groups found RNase treatment significantly affected solubility of chromatin, causing aggregation. The authors should address this work and place it in light of their current study.

(3) Figure 1f: it is not clear what it means for genes to be "non-differentially expressed" in this context. Isn't this also RNase-insensitive? And how is the "Ctrl specific" RNA set determined? This is confusing, since RNase is assumed to degrade most of the RNA in these samples.

(4) Figure 2a: The results are somewhat surprising, given that protein-coding genes are depleted more in the RNase treatment. Is the Ctrl set the same as in 1f? This emphasizes the importance of defining that population better.

(5) Figure 3a: The text references this figure in ways that do not match the figure, referencing at least nine column clusters when there are only six. Heatmaps of certain TFs and "RAH explained" percentages don't seem to match the Results section description, either. The authors claim EZH2 binding sites are the top TF overlap with RAHs and yet do not include EZH2 in Figure 3a. Suz12 (EZH2 binding partner) and H3K27me3 (EZH2 product) are also referenced in the text for this figure, but not included in the figure itself.

(6) The manuscript uses the term "non-diffusive RNA-chromatin interactome" which is not directly supported by data. The authors use the term initially to describe the RNase-resistant species in their previous work, but through the current study, they support a model where the RNase resistance is simply due to protection by protein binding, not by any constraints on diffusion in particular chromatin environments.

Reviewer #3 (Public review):

Summary:

The study investigated stable RNA-chromatin interactions by applying RNase treatment before the iMARGI (in situ mapping of RNA-genome interactome) procedure to remove promiscuous, unprotected RNA transcripts and selectively enrich for RNA-inaccessible, potentially functional RNA-chromatin interactions (RNA-Transcription factor and RNA-histone). The researchers found that short-range interactions (<1kb) are RNase resistant, possibly due to the protection from RNA polymerases. They noticed that long-range RNA-chromatin interactions (>2Mbp or interchromosomal) were also enriched after RNase treatment, hypothesizing that these interactions are stabilized by chromatin-binding proteins. They found that genic caRNAs were sensitive, while repeat-derived caRNAs, such as rRNA and satellite repeats, were resistant to RNase. Long non-coding RNAs (lncRNAs), particularly those associated with diseases, were over-represented among RNase-insensitive transcripts, indicating their potential regulatory significance. Additionally, RNase-insensitive caRNAs exhibited higher evolutionary conservation, implying that they are protected by protein complexes, especially in long-range interactions. RNA Attachment Hot Zones (RAHs) enriched post-RNase treatment were found to localize in functional genomic regions such as promoters, transcription factor binding sites (TFBS), and histone modification sites. Importantly, RNase treatment amplified specific RNA-transcription factor interactions, with caRNA signals being preserved at TFBS for factors with RNA-binding capabilities, suggesting that direct RNA-protein binding helps protect caRNAs from degradation. They also found that different TFs are enriched with specific caRNA species, distinguishing them from their genomic footprints. In addition, transcripts with higher abundance tend to enrich at more TFBS. Overall, the study highlights the role of RNase-inaccessible caRNAs in chromatin regulation and provides insight into their functional significance in genome organization.

Strengths:

This study involves rigorous and comprehensive data analysis involving datasets with very high sequencing depth and appropriate statistical tests (e.g., chi-square tests to validate the association between caRNAs and TFBS statistically). This analysis was further strengthened by comparing their results with orthogonal datasets, such as RedChIP and fRIP-seq, providing robust, cross-validated evidence for the caRNA-TFBS associations. In addition to examining broad interactions, the authors identified specific long-range RNA-chromatin interactions and pinpointed specific transcription factors and histone modification markers that are associated with these interactions. The authors explored the evolutionary implications of RNase-insensitive caRNAs and their potential medical relevance, particularly by identifying caRNAs linked to disease-associated genes and long non-coding RNAs (lncRNAs). This combination of detailed analysis, along with functional relevance, broadens the scope of the research, making it a significant contribution to chromatin biology.

Weaknesses:

However, I have the following concerns regarding the studies:<br /> (1) I don't understand the logic behind calling promoters, enhancers, and similar regions "functionally important regions" when describing the enrichment of RNase-insensitive interactions. Genic regions that are RNase-sensitive are also functionally relevant. So, what makes promoters, enhancers, etc, unique in terms of functionality?<br /> (2) First, while the study offers strong evidence for associations between caRNAs, transcription factors, and chromatin markers, it lacks direct functional validation experiments such as RNA knockdown or CRISPR interference, to confirm the specific roles of these RNAs in gene regulation or chromatin structure modifications.<br /> (3) Another limitation is the incomplete investigation of caRNAs with short-range interactions (<1kb). The authors hypothesized that these are protected by RNA polymerases but did not provide supporting experimental evidence or references to previous studies. Offering either experimental validation or a rationale for excluding these short-range interactions would strengthen this hypothesis. The conclusion that authors drew on that "chromatin-associated RNAs (caRNAs) involved in short- to middle-range interactions are more susceptible to RNase treatment" was unclear for the specific "short-range" distance. The data shown in Supplementary Figure 2a contradicted the conclusion in the discussion that "long-distance RNA-chromatin interactions are preferentially preserved after RNase treatment, while short-range interactions are depleted." as well as the suggestion made linking RNase inaccessibility to evolutionarily conserved in the paper.<br /> (4) The study heavily relies on RNase treatment to isolate stable RNA-chromatin interactions, which might neglect important transient or weak interactions and overlook the functional relevance of RNase-sensitive interactions, hence missing the dynamic nature of RNA-chromatin interactions.<br /> (5) Tthe analysis is limited to human embryonic stem cells (H1 cells), which might restrict the generalizability of the findings. Expanding the study to include a cell type that represents a broader range of cell types or tissues will strengthen the conclusions.<br /> (6) The term "RNase A treatment" in the methods section could be clearer if specified as "RNase-treated iMARGI," which encompasses the standard iMARGI protocol.<br /> (7) There is some ambiguity regarding whether the researchers generated new data or reanalyzed existing datasets. While it is mentioned early on that previously published RNase-treated iMARGI datasets were reanalyzed, the text later states that "three biological replicates were generated for the RNase-treated samples." Clarifying whether the data were newly generated in this study or obtained from public datasets would improve the clarity.<br /> (8) The color scheme should be the same for heatmaps for control, and RNase-treated samples in Figure 4.

Author response:

We thank the editors and reviewers for their thorough evaluation of our manuscript. We appreciate the constructive feedback and insights provided. 

We acknowledge that some of our conclusions would benefit from more measured statements and additional computational controls. We will revise the manuscript to better reflect the scope and limitations of our analytical approach. While we cannot add new experimental validations at this stage, we will strengthen our computational analyses and clarify our methodology.

Below, we outline our planned revisions to address the major points raised in the public reviews:

Clarification of Terms and Definitions:

(1) We will make it clearer in our manuscript to emphasize that we reuse the same raw datasets from our previous study as described in Calendrilli et al, 2023, and there is no modification to the experimental methods or data. 

(2) We will provide clear definitions for:

- "Non-differentially expressed" genes

- "Ctrl specific" RNA sets

- The composition of control populations in different analyses

(3) We will revise the use of "non-diffusive RNA-chromatin interactome" and “RNase-resistant” terminology to better reflect our actual findings.

(4) We will also improve clarity regarding:

- The rationale for focusing on specific genomic regions

- The interpretation of evolutionary conservation data

(5) We will provide additional rationale on the exclusion of short-range interactions.

Figure Revisions:

(1) Figure 3a: We will correct any discrepancy between text references and figure content.

(2) Figure 4: We will standardize the color scheme between control and RNase-treated samples.

(3) We will follow the reviewer's suggestion to move figure 1g to the supplementary file. 

Additional Computational Analyses:

(1) We will consider adding controls for RNA length effects and integrate any existing knowledge on the protection extent variation across different RBP.

Discussions:

(1) We will carefully rephrase our conclusions to more accurately reflect the scope and limitations of our computational findings, ensuring we do not overstate the implications.

(2) We will expand the discussion of limitations, including:

- The focus on RNase-resistant interactions only

- The cell-type specificity of our findings

- The lack of functional validation

- The limited ability to discern and study the transient or weak RNA-chromatin interactions using the current dataset

(3) Regarding the recent papers from Jenner and Davidovich groups about RNase treatment effects on chromatin solubility:

- We will discuss these findings in our revised manuscript

- We will address potential limitations this may impose on our interpretations