Author response:

General Statements:

The formation of three-dimensional tubes is a fundamental process in the development of organs and aberrant tube size leads to common diseases and congenital disorders, such as polycystic kidney disease, asthma, and lung hypoplasia. The apical (luminal) extracellular matrix (ECM) plays a critical role in epithelial tube morphogenesis during organ formation, but its composition and organization remain poorly understood. Using the Drosophila embryonic salivary gland as a model, we reveal a critical role for the PAPS Synthetase (Papss), an enzyme that synthesizes the universal sulfate donor PAPS, as a critical regulator of tube lumen expansion. Additionally, we identify two zona pellucida (ZP) domain proteins, Piopio (Pio) and Dumpy (Dpy) as key apical ECM components that provide mechanical support to maintain a uniform tube diameter.

The apical ECM has a distinct composition compared to the basal ECM, featuring a diverse array of components. Many studies of the apical ECM have focused on the role of chitin and its modification, but the composition of the non-chitinous apical ECM and its role, and how modification of the apical ECM affects organogenesis remain elusive. The main findings of this manuscript are listed below.

(1) Through a deficiency screen targeting ECM-modifying enzymes, we identify Papss as a key enzyme regulating luminal expansion during salivary gland morphogenesis. 

(2) Our confocal and transmission electron microscopy analyses reveal that Papss mutants exhibit a disorganized apical membrane and condensed aECM, which are at least partially linked to disruptions in Golgi structures and intracellular trafficking. Papss is also essential for cell survival and basal ECM integrity, highlighting the role of sulfation in regulating both apical and basal ECM.

(3) Salivary gland-specific overexpression of wild-type Papss rescues all defects in Papss mutants, but the catalytically inactive mutant form does not, suggesting that defects in sulfation are the underlying cause of the phenotypes.

(4) We identify two ZP domain proteins, Piopio (Pio) and Dumpy (Dpy), as key components of the salivary gland aECM. In the absence of Papss, Pio is progressively lost from the aECM, while the Dpy-positive aECM structure is condensed and detaches from the apical membrane, resulting in a narrowed lumen. 

(5) Mutations in pio or dpy, or in Notopleural (Np), which encodes a matriptase that cleaves Pio, cause the salivary gland lumen to develop alternating bulges and constrictions. Additionally, loss of pio results in loss of Dpy in the salivary gland lumen, suggesting that the Dpycontaining filamentous structures of the aECM is critical for maintaining luminal diameter, with Pio playing an essential role in organizing this structure.

(6) We further reveal that the cleavage of the ZP domain of Pio by Np is critical for the role of Pio in organizing the aECM structure.

Overall, our findings underscore the essential role of sulfation in organizing the aECM during tubular organ formation and highlight the mechanical support provided by ZP domain proteins in maintaining tube diameter. Mammals have two isoforms of Papss, Papss1 and Papss2. Papss1 shows ubiquitous expression, with higher levels in glandular cells and salivary duct cells, suggesting a high requirement for sulfation in these cell types. Papss2 shows a more restricted expression, such as in cartilage, and mutations in Papss2 have been associated with skeletal dysplasia in humans. Our analysis of the Drosophila Papss gene, a single ortholog of human Papss1 and Papss2, reveals its multiple roles during salivary gland development. We expect that these findings will provide valuable insights into the function of these enzymes in normal development and disease in humans. Our findings on the key role of two ZP proteins, Pio and Dpy, as major components of the salivary gland aECM also provide valuable information on the organization of the non-chitinous aECM during organ formation.

We believe that our results will be of broad interest to many cell and developmental biologists studying organogenesis and the ECM, as well as those investigating the mechanisms underlying human diseases associated with conserved mutations.

Point-by-point description of the revisions:

We are delighted that all three reviewers were enthusiastic about the work. Their comments and suggestions have improved the paper. The details of the changes we have made in response to each reviewer’s comments are included in italicized text below.

Reviewer #1 (Evidence, reproducibility and clarity):

PAPS is required for all sulfotransferase reactions in which a sulfate group is covalently attached to amino acid residues of proteins or to side chains of proteoglycans. This sulfation is crucial for properly organizing the apical extracellular matrix (aECM) and expanding the lumen in the Drosophila salivary gland. Loss of Papss potentially leads to decreased sulfation, disorganizing the aECM, and defects in lumen formation. In addition, Papss loss destabilizes the Golgi structures.

In Papss mutants, several changes occur in the salivary gland lumen of Drosophila. The tube lumen is very thin and shows irregular apical protrusions. There is a disorganization of the apical membrane and a compaction of the apical extracellular matrix (aECM). The Golgi structures and intracellular transport are disturbed. In addition, the ZP domain proteins Piopio (Pio) and Dumpy (Dpy) lose their normal distribution in the lumen, which leads to condensation and dissociation of the Dpy-positive aECM structure from the apical membrane. This results in a thin and irregularly dilated lumen.

(1) The authors describe various changes in the lumen in mutants, from thin lumen to irregular expansion. I would like to know the correct lumen diameter, and length, besides the total area, by which one can recognize thin and irregular.

We have included quantification of the length and diameter of the salivary gland lumen in the stage 16 salivary glands of control, Papss mutant, and salivary gland-specific rescue embryos (Figure 1J, K). As described, Papss mutant embryos have two distinct phenotypes, one group with a thin lumen along the entire lumen and the other group with irregular lumen shapes. Therefore, we separated the two groups for quantification of lumen diameter. Additionally, we have analyzed the degree of variability for the lumen diameter to better capture the range of phenotypes observed (Figure 1K’). These quantifications enable a more precise assessment of lumen morphology, allowing readers to distinguish between thin and irregular lumen phenotypes.

(2) The rescue is about 30%, which is not as good as expected. Maybe the wrong isoform was taken. Is it possible to find out which isoform is expressed in the salivary glands, e.g., by RNA in situ Hyb? This could then be used to analyze a more focused rescue beyond the paper.

Thank you for this point, but we do not agree that the rescue is about 30%. In Papss mutants, about 50% of the embryos show the thin lumen phenotype whereas the other 50% show irregular lumen shapes. In the rescue embryos with a WT Papss, few embryos showed thin lumen phenotypes. About 40% of the rescue embryos showed “normal, fully expanded” lumen shapes, and the remaining 60% showed either irregular (thin+expanded) or slightly overexpanded lumen. It is not uncommon that rescue with the Gal4/UAS system results in a partial rescue because it is often not easy to achieve the balance of the proper amount of the protein with the overexpression system. 

To address the possibility that the wrong isoform was used, we performed in situ hybridization to examine the expression of different Papss spice forms in the salivary gland. We used probes that detect subsets of splice forms: A/B/C/F/G, D/H, and E/F/H, and found that all probes showed expression in the salivary gland, with varying intensities. The original probe, which detects all splice forms, showed the strongest signals in the salivary gland compared to the new probes which detect only a subset. However, the difference in the signal intensity may be due to the longer length of the original probe (>800 bp) compared to other probes that were made with much smaller regions (~200 bp). Digoxigenin in the DIG labeling kit for mRNA detection labels the uridine nucleotide in the transcript, and the probes with weaker signals contain fewer uridines (all: 147; ABCFG, 29; D, 36; EFH, 66). We also used the Papss-PD isoform, for a salivary gland-specific rescue experiment and obtained similar results to those with Papss-PE (Figure 1I-L, Figure 4D and E). 

Furthermore, we performed additional experiments to validate our findings. We performed a rescue experiment with a mutant form of Papss that has mutations in the critical rescues of the catalytic domains of the enzyme, which failed to rescue any phenotypes, including the thin lumen phenotype (Figure 1H, J-L), the number and intensity of WGA puncta (Figure 3I, I’), and cell death (Figure 4D, E). These results provide strong evidence that the defects observed in Papss mutants are due to the lack of sulfation.  

(3) Crb is a transmembrane protein on the apicolateral side of the membrane. Accordingly, the apicolateral distribution can be seen in the control and the mutant. I believe there are no apparent differences here, not even in the amount of expression. However, the view of the cells (frame) shows possible differences. To be sure, a more in-depth analysis of the images is required. Confocal Z-stack images, with 3D visualization and orthogonal projections to analyze the membranes showing Crb staining together with a suitable membrane marker (e.g. SAS or Uif). This is the only way to show whether Crb is incorrectly distributed. Statistics of several papas mutants would also be desirable and not just a single representative image. When do the observed changes in Crb distribution occur in the development of the tubes, only during stage 16? Is papss only involved in the maintenance of the apical membrane? This is particularly important when considering the SJ and AJ, because the latter show no change in the mutants.

We appreciate your suggestion more thoroughly analyze Crb distribution. We adapted a method from a previous study (Olivares-Castiñeira and Llimargas, 2017) to quantify Crb signals in the subapical region and apical free region of salivary gland cells. Using E-Cad signals as a reference, we marked the apical cell boundaries of individual cells and calculated the intensity of Crb signals in the subapical region (along the cell membrane) and in the apical free region. We focused on the expanded region of the SG lumen in Papss mutants for quantification, as the thin lumen region was challenging to analyze. This quantification is included in Figure 2D. Statistical analysis shows that Crb signals were more dispersed in SG cells in Papss mutants compared to WT.

(4) A change in the ECM is only inferred based on the WGA localization. This is too few to make a clear statement. WGA is only an indirect marker of the cell surface and glycosylated proteins, but it does not indicate whether the ECM is altered in its composition and expression. Other important factors are missing here. In addition, only a single observation is shown, and statistics are missing.

We understand your concern that WGA localization alone may not be sufficient to conclude changes in the ECM. However, we observed that luminal WGA signals colocalize with Dpy-YFP in the WT SG (Figure 5-figure supplement 2C), suggesting that WGA detects the aECM structure containing Dpy. The similar behavior of WGA and Dpy-YFP signals in multiple genotypes further supports this idea. In Papss mutants with a thin lumen phenotype, both WGA and Dpy-YFP signals are condensed (Figure 5E-H), and in pio mutants, both are absent from the lumen (Figure 6B, D). We analyzed WGA signals in over 25 samples of WT and Papss mutants, observing consistent phenotypes. We have included the number of samples in the text. While we acknowledge that WGA is an indirect marker, our data suggest that it is a reliable indicator of the aECM structure containing Dpy. 

(5) Reduced WGA staining is seen in papss mutants, but this could be due to other circumstances. To be sure, a statistic with the number of dots must be shown, as well as an intensity blot on several independent samples. The images are from single confocal sections. It could be that the dots appear in a different Z-plane. Therefore, a 3D visualization of the voxels must be shown to identify and, at best, quantify the dots in the organ.

We have quantified cytoplasmic punctate WGA signals. Using spinning disk microscopy with super-resolution technology (Olympus SpinSR10 Sora), we obtained high-resolution images of cytoplasmic punctate signals of WGA in WT, Papss mutant, and rescue SGs with the WT and mutant forms of Papss-PD. We then generated 3D reconstructed images of these signals using Imaris software (Figure 3E-H) and quantified the number and intensity of puncta. Statistical analysis of these data confirms the reduction of the number and intensity of WGA puncta in Papss mutants (Figure 3I, I’). The number of WGA puncta was restored by expressing WT Papss but not the mutant form. By using 3D visualization and quantification, we have ensured that our results are not limited to a single confocal section and account for potential variations in Z-plane localization of the dots.

(6) A colocalization analysis (statistics) should be shown for the overlap of WGA with ManII-GFP.

Since WGA labels multiple structures, including the nuclear envelope and ECM structures, we focused on assessing the colocalization of the cytoplasmic WGA punctate signals and ManIIGFP signals. Standard colocalization analysis methods, such as Pearson’s correlation coefficient or Mander’s overlap coefficient, would be confounded by WGA signals in other tissues. Therefore, we used a fluorescent intensity line profile to examine the spatial relationship between WGA and ManII-GFP signals in WT and Papss mutants (Figure 3L, L’). 

(7) I do not understand how the authors describe "statistics of secretory vesicles" as an axis in Figure 3p. The TEM images do not show labeled secretory vesicles but empty structures that could be vesicles.

Previous studies have analyzed “filled” electron-dense secretory vesicles in TEM images of SG cells (Myat and Andrew, 2002, Cell; Fox et al., 2010, J Cell Biol; Chung and Andrew, 2014, Development). Consistent with these studies, our WT TEM images show these vesicles. In contrast, Papss mutants show a mix of filled and empty structures. For quantification, we specifically counted the filled electron-dense vesicles (now Figure 3W). A clear description of our analysis is provided in the figure legend.

(8) The quality of the presented TEM images is too low to judge any difference between control and mutants. Therefore, the supplement must present them in better detail (higher pixel number?).

We disagree that the quality of the presented TEM images is too low. Our TEM images have sufficient resolution to reveal details of many subcellular structures, such as mitochondrial cisternae. The pdf file of the original submission may not have been high resolution. To address this concern, we have provided several original high-quality TEM images of both WT and Papss mutants at various magnifications in Figure 2-figure supplement 2. Additionally, we have included low-magnification TEM images of WT and Papss mutants in Figure 2H and I to provide a clearer view of the overall SG lumen morphology. 

(9) Line 266: the conclusion that apical trafficking is "significantly impaired" does not hold. This implies that Papss is essential for apical trafficking, but the analyzed ECM proteins (Pio, Dumpy) are found apically enriched in the mutants, and Dumpy is even secreted. Moreover, they analyze only one marker, Sec15, and don't provide data about the quantification of the secretion of proteins.

We agree and have revised our statement to “defective sulfation affects Golgi structures and multiple routes of intracellular trafficking”. 

(10) DCP-1 was used to detect apoptosis in the glands to analyze acellular regions. However, the authors compare ST16 control with ST15 mutant salivary glands, which is problematic. Further, it is not commented on how many embryos were analyzed and how often they detect the dying cells in control and mutant embryos. This part must be improved.

Thank you for the comment. We agree and have included quantification. We used stage 16 samples from WT and Papss mutants to quantify acellular regions. Since DCP-1 signals are only present at a specific stage of apoptosis, some acellular regions do not show DCP-1 signals. Therefore, we counted acellular regions regardless of DCP-1 signals. We also quantified this in rescue embryos with WT and mutant forms of Papss, which show complete rescue with WT and no rescue with the mutant form, respectively. The graph with a statistical analysis is included (Figure 4D, E).

(11) WGA and Dumpy show similar condensed patterns within the tube lumen. The authors show that dumpy is enriched from stage 14 onwards. How is it with WGA? Does it show the same pattern from stage 14 to 16? Papss mutants can suffer from a developmental delay in organizing the ECM or lack of internalization of luminal proteins during/after tube expansion, which is the case in the trachea.

Dpy-YFP and WGA show overlapping signals in the SG lumen throughout morphogenesis. DpyYFP is SG enriched in the lumen from stage 11, not stage 14 (Figure 5-figure supplement 2). WGA is also detected in the lumen throughout SG morphogenesis, similar to Dpy. In the original supplemental figure, only a stage 16 SG image was shown for co-localization of Dpy-YFP and WGA signals in the SG lumen. We have now included images from stage 14 and 15 in Figure 5figure supplement 2C. 

Given that luminal Pio signals are lost at stage 16 only and that Dpy signals appear as condensed structures in the lumen of Papss mutants, it suggests that the internalization of luminal proteins is not impaired in Papss mutants. Rather, these proteins are secreted but fail to organize properly. 

(12) Line 366. Luminal morphology is characterized by bulging and constrictions. In the trachea, bulges indicate the deformation of the apical membrane and the detachment from the aECM. I can see constrictions and the collapsed tube lumen in Fig. 6C, but I don't find the bulges of the apical membrane in pio and Np mutants. Maybe showing it more clearly and with better quality will be helpful.

Since the bulging phenotype appears to vary from sample to sample, we have revised the description of the phenotype to “constrictions” to more accurately reflect the consistent observations. We quantified the number of constrictions along the entire lumen in pio and Np mutants and included the graph in Figure 6F.

(13) The authors state that Papss controls luminal secretion of Pio and Dumpy, as they observe reduced luminal staining of both in papss mutants. However, the mCh-Pio and Dumpy-YFP are secreted towards the lumen. Does papss overexpression change Pio and Dumpy secretion towards the lumen, and could this be another explanation for the multiple phenotypes? 

Thank you for the comment. To clarify, we did not observe reduced luminal staining of Pio and Dpy in Papss mutants, nor did we state that Papss controls luminal secretion of Pio and Dpy. In Papss mutants, Pio luminal signals are absent specifically at stage 16 (Figure 5H), whereas strong luminal Pio signals are present until stage 15 (Figure 5G). For Dpy-YFP, the signals are not reduced but condensed in Papss mutants from stages 14-16 (Figure 5D, H). 

It remains unclear whether the apparent loss of Pio signals is due to a loss of Pio protein in the lumen or due to epitope masking resulting from protein aggregation or condensation. As noted in our response to Comment 11 internalization of luminal proteins seems unaffected in Papss mutants; proteins like Pio and Dpy are secreted into the lumen but fail to properly organize. Therefore, we have not tested whether Papss overexpression alters the secretion of Pio or Dpy.

In our original submission, we incorrectly stated that uniform luminal mCh-Pio signals were unchanged in Papss mutants. Upon closer examination, we found these signals are absent in the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

Regulation of luminal ZP protein level is essential to modulate the tube expansion; therefore, Np releases Pio and Dumpy in a controlled manner during st15/16. Thus, the analysis of Pio and Dumpy in NP overexpression embryos will be critical to this manuscript to understand more about the control of luminal ZP matrix proteins.

Thanks for the insightful suggestion. We overexpressed both the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. It is important to note that these overexpression experiments were done in the presence of the endogenous WT Np. 

Overexpression of Np.WT led to increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. In contrast, overexpression of Np.S990A resulted in a near complete loss of luminal mCh-Pio signals. Pio antibody signals remained strong at the apical membrane but was weaker in the luminal filamentous structures compared to WT. 

Due to the GFP tag present in the UAS-Np.S990A line, we could not reliably analyze Dpy-YFP signals because of overlapping fluorescent signals in the same channel. However, the filamentous Pio signals in the lumen co-localized with GFP signals, suggesting that these structures might also include Dpy-YFP, although this cannot be confirmed definitively. 

These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

(14) Minor:

Fig. 5 C': mChe-Pio and Dumpy-YFP are mixed up at the top of the images.

Thanks for catching this error.  It has been corrected.

Sup. Fig7. A shows Pio in purple but B in green. Please indicate it correctly.

It has been corrected.

Reviewer #1 (Significance):

In 2023, the functions of Pio, Dumpy, and Np in the tracheal tubes of Drosophila were published. The study here shows similar results, with the difference that the salivary glands do not possess chitin, but the two ZP proteins Pio and Dumpy take over its function. It is, therefore, a significant and exciting extension of the known function of the three proteins to another tube system. In addition, the authors identify papss as a new protein and show its essential function in forming the luminal matrix in the salivary glands. Considering the high degree of conservation of these proteins in other species, the results presented are crucial for future analyses and will have further implications for tubular development, including humans.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:

There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

Minor comments

(1) Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

Thanks for the comment. Dpy-YFP is a knock-in line in which YFP is inserted into the middle of the dpy locus (Lye et al., 2014; the insertion site is available on Flybase). mCh-Qsm is also a knock-in line, with mCh inserted near the N-terminus of the qsm gene using phi-mediated recombination using the qsm^MI07716 line (Chu and Hayashi, 2021; insertion site available on Flybase). Based on this, we have updated the nomenclature from Qsm-mCh to mCh-Qsm throughout the manuscript to accurately reflect the tag position. To our knowledge, no western blot has been performed on Dpy-YFP or mCh-Qsm lines. We have mentioned this explicitly in the Discussion.  

(2) The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

Thanks for raising this important point. We have added a caveat in the Discussion noting this limitation and the need for additional tools, such as an antibody or a functional fusion protein, to confirm the localization of Dpy.

(3) TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

We agree that HPF is a better method and intent to use the HPF system in future studies. We acknowledge that chemical fixation contributes to the appearance of a gap between the apical membrane and the aECM, which we did not observe in the HPF/FS method (Chung and Andrew, 2014). Despite this, the TEM images still clearly reveal that Papss mutants show a much thinner and more electron-dense aECM compared to WT (Figure 2H, I), consistent to the condensed WGA, Dpy, and Pio signals in our confocal analyses. As the reviewer mentioned, we believe that the current TEM data are sufficient to support the conclusion of severe aECM disorganization and Golgi defects in Papss mutants.

(4) The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

Thank you for the suggestion. Pioneering studies in C. elegans have highlighted the key role of sulfation in diverse developmental processes, including neuronal organization, reproductive tissue development, and phenotypic plasticity. We have now cited several works.  

Reviewer #2 (Significance):

This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

My expertise: I am a developmental geneticist with interests in apical ECM

Reviewer #3 (Evidence, reproducibility and clarity):

In this work Woodward et al focus on the apical extracellular matrix (aECM) in the tubular salivary gland (SG) of Drosophila. They provide new insights into the composition of this aECM, formed by ZP proteins, in particular Pio and Dumpy. They also describe the functional requirements of PAPSS, a critical enzyme involved in sulfation, in regulating the expansion of the lumen of the SG. A detailed cellular analysis of Papss mutants indicate defects in the apical membrane, the aECM and in Golgi organization. They also find that Papss control the proper organization of the Pio-Dpy matrix in the lumen. The work is well presented and the results are consistent.

Main comments

- This work provides a detailed description of the defects produced by the absence of Papss. In addition, it provides many interesting observations at the cellular and tissular level. However, this work lacks a clear connection between these observations and the role of sulfation. Thus, the mechanisms underlying the phenotypes observed are elusive. Efforts directed to strengthen this connection (ideally experimentally) would greatly increase the interest and relevance of this work.

Thank you for this thoughtful comment. To directly test whether the phenotypes observed in Papss mutants are due to the loss of sulfation activity, we generated transgenic lines expressing catalytically inactive forms of Papss, UAS-PapssK193A, F593P, in which key residues in the APS kinase and ATP sulfurylase domains are mutated. Unlike WT UAS-Papss (both the Papss-PD or Papss-PE isoforms), the catalytically inactive UAS-Papssmut failed to rescue any of the phenotypes, including the thin lumen phenotype (Figure 1I-L), altered WGA signals (Figure I, I’) and the cell death phenotype (Figure 4D, E). These findings strongly support the conclusion that the enzymatic sulfation activity of Papss is essential for the developmental processes described in this study.  

- A main issue that arises from this work is the role of Papss at the cellular level. The results presented convincingly indicate defects in Golgi organization in Papss mutants. Therefore, the defects observed could stem from general defects in the secretion pathway rather than from specific defects on sulfation. This could even underly general/catastrophic cellular defects and lead to cell death (as observed).

This observation has different implications. Is this effect observed in SGs also observed in other cells in the embryo? If Papss has a general role in Golgi organization this would be expected, as Papss encodes the only PAPs synthatase in Drosophila.

Can the authors test any other mutant that specifically affect Golgi organization and investigate whether this produces a similar phenotype to that of Papss?

Thank you for the comment. To address whether the defects observed in Papss mutants stem from general disruption of the secretory pathway due to Golgi disorganization, we examined mutants of two key Golgi components: Grasp65 and GM130. 

In Grasp65 mutants, we observed significant defects in SG lumen morpholgy, including highly irregular SG lumen shape and multiple constrictions (100%; n=10/10). However, the lumen was not uniformly thin as in Papss mutants. In contrast, GM130 mutants–although this line was very sick and difficult to grow–showed relatively normal salivary glands morphology in the few embryos that survived to stage 16 (n=5/5). It is possible that only embryos with mild phenotypes progressed to this stages, limiting interpretation. These data have now been included in Figure 3-figure supplement 2. Overall, while Golgi disruption can affect SG morphology, the specific phenotypes seen in Papss mutants are not fully recapitulated by Grasp65 or GM130 loss. 

- A model that conveys the different observations and that proposes a function for Papss in sulfation and Golgi organization (independent or interdependent?) would help to better present the proposed conclusions. In particular, the paper would be more informative if it proposed a mechanism or hypothesis of how sulfation affects SG lumen expansion. Is sulfation regulating a factor that in turn regulates Pio-Dpy matrix? Is it regulating Pio-Dpy directly? Is it regulating a

product recognized by WGA?

For instance, investigating Alcian blue or sulfotyrosine staining in pio, dpy mutants could help to understand whether Pio, Dpy are targets of sulfation.

Thank you for the comment. We’re also very interested in learning whether the regulation of the Pio-Dpy matrix is a direct or indirect consequence of the loss of sulfation on these proteins. One possible scenario is that sulfation directly regulates the Pio-Dpy matrix by regulating protein stability through the formation of disulfide bonds between the conserved Cys residues responsible for ZP module polymerization. Additionally, the Dpy protein contains hundreds of EGF modules that are highly susceptible to O-glycosylation. Sulfation of the glycan groups attached to Dpy may be critical for its ability to form a filamentous structure. Without sulfation, the glycan groups on Dpy may not interact properly with the surrounding materials in the lumen, resulting in an aggregated and condensed structure. These possibilities are discussed in the Discussion.

We have not analyzed sulfation levels in pio or dpy mutants because sulfation levels in mutants of single ZP domain proteins may not provide much information. A substantial number of proteoglycans, glycoproteins, and proteins (with up to 1% of all tyrosine residues in an organism’s proteins estimated to be sulfated) are modified by sulfation, so changes in sulfation levels in a single mutant may be subtle. Especially, the existing dpy mutant line is an insertion mutant of a transposable element; therefore, the sulfation sites would still remain in this mutant. 

- Interpretation of Papss effects on Pio and Dpy would be desired. The results presented indicate loss of Pio antibody staining but normal presence of cherry-Pio. This is difficult to interpret. How are these results of Pio antibody and cherry-Pio correlating with the results in the trachea described recently (Drees et al. 2023)?

In our original submission, we stated that the uniform luminal mCh-Pio signals were not changed in Papss mutants, but after re-analysis, we found that these signals were actually absent from the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

After cleavages by Np and furin, the Pio protein should have three fragments. The Nterminal region contains the N-terminal half of the ZP domain, and mCh-Pio signals show this fragment. The very C-terminal region should localize to the membrane as it contains the transmembrane domain. We think the middle piece, the C-terminal ZP domain, is recognized by the Pio antibody. The mCh-Pio and Pio antibody signals in the WT trachea (Drees et al., 2023) are similar to those in the SG. mCh-Pio signals are detected in the tracheal lumen as uniform signals, at the apical membrane, and in cytoplasmic puncta. Pio antibody signals are exclusively in the tracheal lumen and show more heterogenous filamentous signals. 

In Papss mutants, the middle fragment (the C-terminal ZP domain) seems to be most affected because the Pio antibody signals are absent from the lumen. The loss of Pio antibody signals could be due to protein degradation or epitope masking caused by aECM condensation and protein misfolding. This fragment seems to be key for interacting with Dpy, since Pio antibody signals always colocalize with Dpy-YFP. The N-terminal mCh-Pio fragment does not appear to play a significant role in forming a complex with Dpy in WT (but still aggregated together in Papss mutants), and this can be tested in future studies.

In response to Reviewer 1’s comment, we performed an additional experiment to test the role of Np in cleaving Pio to help organize the SG aECM. In this experiment, we overexpressed the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. Np.WT overexpression resulted in increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. However, overexpression of Np.S990A resulted in the absence of luminal mCh-Pio signals. Pio antibody signals were strong at the apical membrane but rather weak in the luminal filamentous structures. Since the UAS-Np.S990A line has the GFP tag, we could not reliably analyze Dpy-YFP signals due to overlapping Np.S990A.GFP signals in the same channel. However, the luminal filamentous Pio signals co-localized with GFP signals, and we assume that these overlapping signals could be Dpy-YFP signals. 

These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I. 

A proposed model of the Pio-Dpy aECM in WT, Papss, pio, and Np mutants has now been included in Figure 7.

-  What does the WGA staining in the lumen reveal? This staining seems to be affected differently in pio and dpy mutants: in pio mutants it disappears from the lumen (as dpy-YFP does), but in dpy mutants it seems to be maintained. How do the authors interpret these findings? How does the WGA matrix relate to sulfated products (using Alcian blue or sulfotyrosine)?

WGA binds to sialic acid and N-acetylglucosamine (GlcNAc) residues on glycoproteins and glycolipids. GlcNAc is a key component of the glycosaminoglycan (GAG) chains that are covalently attached to the core protein of a proteoglycan, which is abundant in the ECM. We think WGA detects GlcNAc residues in the components of the aECM, including Dpy as a core component, based on the following data. 1) WGA and Dpy colocalize in the lumen, both in WT (as thin filamentous structures) and Papss mutant background (as condensed rod-like structures), and 2) are absent in pio mutants. WGA signals are still present in a highly condensed form in dpy mutants. That’s probably because the dpy mutant allele (dpyov1) has an insertion of a transposable element (blood element) into intron 11 and this insertion may have caused the Dpy protein to misfold and condense. We added the information about the dpy allele to the Results section and discussed it in the Discussion.

Minor points:

- The morphological phenotypic analysis of Papss mutants (homozygous and transheterozygous) is a bit confusing. The general defects are higher in Papss homozygous than in transheterozygotes over a deficiency. Maybe quantifying the defects in the heterozygote embryos in the Papss mutant collection could help to figure out whether these defects relate to Papss mutation.

We analyzed the morphology of heterozygous Papss mutant embryos. They were all normal. The data and quantifications have now been added to Figure 1-figure supplement 3. 

- The conclusion that the apical membrane is affected in Papss mutants is not strongly supported by the results presented with the pattern of Crb (Fig 2). Further evidences should be provided. Maybe the TEM analysis could help to support this conclusion

We quantified Crb levels in the sub-apical and medial regions of the cell and included this new quantification in Figure 2D. TEM images showed variation in the irregularity of the apical membrane, even in WT, and we could not draw a solid conclusion from these images.

- It is difficult to understand why in Papss mutants the levels of WGA increase. Can the authors elaborate on this?

We think that when Dpy (and many other aECM components) are condensed and aggregated into the thin, rod-like structure in Papss mutants, the sugar residues attached to them must also be concentrated and shown as increased WGA signals.   

- The explanation about why Pio antibody and mcherry-Pio show different patterns is not clear. If the antibody recognizes the C-t region, shouldn't it be clearly found at the membrane rather than the lumen?

The Pio protein is also cleaved by furin protease (Figure 5B). We think the Pio fragment recognized by the antibody should be a “C-terminal ZP domain”, which is a middle piece after furin + Np cleavages. 

- The qsm information does not seem to provide any relevant information to the aECM, or sulfation.

Since Qsm has been shown to bind to Dpy and remodel Dpy filaments in the muscle tendon (Chu and Hayashi, 2021), we believe that the different behavior of Qsm in the SG is still informative. As mentioned briefly in the Discussion, the cleaved Qsm fragment may localize differently, like Pio, and future work will need to test this. We have shortened the description of the Qsm localization in the manuscript and moved the details to the figure legend of Figure 5-figure supplement 3.

Reviewer #3 (Significance):

Previous reports already indicated a role for Papss in sulfation in SG (Zhu et al 2005). Now this work provides a more detailed description of the defects produced by the absence of Papss. In addition, it provides relevant data related to the nature and requirements of the aECM in the SG. Understanding the composition and requirements of aECM during organ formation is an important question. Therefore, this work may be relevant in the fields of cell biology and morphogenesis.

eLife Assessment

This valuable study combines anatomical tracing and slice physiology to examine how anterior thalamic and retrosplenial inputs converge in the presubiculum, a key region in the navigation circuit. The authors show that near-simultaneous co-activation of retrosplenial and thalamic inputs drives supra-linear presubiculum responses, revealing a potential cellular mechanism for anchoring the brain's head direction system to external visual landmarks. Their thorough experimental approach and analyses provide convincing evidence for the cellular basis of how the brain's internal compass may be anchored to the external world, laying the groundwork for future experimental testing in vivo.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors use anatomical tracing and slice physiology to investigate the integration of thalamic (ATN) and retrosplenial cortical (RSC) signals in the dorsal presubiculum (PrS). This work will be of interest to the field, as postsubiculum is thought to be a key region for integrating internal head direction representations with external landmarks. The main result is that ATN and RSC inputs drive the same L3 PrS neurons, which exhibit superlinear summation to near-coincident inputs. Moreover, this activity can induce bursting in L4 PrS neurons, which can pass the signals LMN (perhaps gated by cholinergic input).

Strengths:

The slice physiology experiments are carefully done. The analyses are clear and convincing, and the figures and results are well composed. Overall, these results will be a welcome addition to the field.

Weaknesses:

The conclusions about the circuit-level function of L3 PrS neurons sometimes outstrip the data, and their model of the integration of these inputs is unclear. I would recommend some revision of the introduction and discussion. I also had some minor comments about the experimental details and analysis.

Specific major comments:

(1) I found that the authors' claims sometimes outstrip their data, given that there were no in vivo recordings during behavior. For example, in the abstract that their results indicate "that layer 3 neurons can transmit a visually matched HD signal to medial entorhinal cortex", and in the conclusion they state "[...] cortical RSC projections that carry visual landmark information converge on layer 3 pyramidal cells of the dorsal presubiculum". However, they never measured the nature of the signals coming from ATN and RSC to L3 PrS (or signals sent to downstream regions). Their claim is somewhat reasonable with respect to ATN, where the majority of neurons encode HD, but neurons in RSC encode a vast array of spatial and non-spatial variables other than landmark information (e.g., head direction, egocentric boundaries, allocentric position, spatial context, task history to name a few), so making strong claims about the nature of the incoming signals is unwarranted.

(2) Related to the first point, the authors hint at, but never explain, how coincident firing of ATN and RSC inputs would help anchor HD signals to visual landmarks. Although the lesion data (Yoder et al. 2011 and 2015) support their claims, it would be helpful if the proposed circuit mechanism was stated explicitly (a schematic of their model would be helpful in understanding the logic). For example, how do neurons integrate the "right" sets of landmarks and HD signals to ensure a stable anchoring? Moreover, it would be helpful to discuss alternative models of HD-to-landmark anchoring, including several studies that have proposed that the integration may (also?) occur in RSC (Page & Jeffrey, 2018; Yan, Burgess, Bicanski, 2021; Sit & Goard, 2023). Currently, much of the Discussion simply summarizes the results of the study, this space could be better used in mapping the findings to the existing literature on the overarching question of how HD signals are anchored to landmarks.

Comments on revised version:

The authors addressed all of my major points and most of my minor points in the revised submission.

Reviewer #2 (Public review):

Richevaux et al investigate how anterior thalamic (AD) and retrosplenial (RSC) inputs are integrated by single presubicular (PrS) layer 3 neurons. They show that these two inputs converge onto single PrS layer 3 principal cells. By performing dual wavelength photostimulation of these two inputs in horizontal slices, the authors show that in most layer 3 cells, these inputs summate supra-linearly. They extend the experiments by focusing on putative layer 4 PrS neurons and show that they do not receive direct anterior thalamic nor retrosplenial inputs; rather, they are (indirectly) driven to burst firing in response to strong activation of the PrS network.

This is a valuable study, which investigates an important question - how visual landmark information (possibly mediated by retrosplenial inputs) converges and integrates with HD information (conveyed by the AD nucleus of the thalamus) within PrS circuitry. The data indicate that near-coincident activation of retrosplenial and thalamic inputs leads to non-linear integration in target layer 3 neurons, thereby offering a potential biological basis for landmark + HD binding.

Main limitations relate to the anatomical annotation of 'putative' PrS L4 neurons, and to the presentation of retrosplenial / thalamic input modularity. Specifically, more evidence should be provided to convincingly demonstrate that the 'putative L4 neurons' of the PrS are not distal subicular neurons (as the authors' anatomy and physiology experiments seem to indicate). The modularity of thalamic and retrosplenial inputs could be better clarified in relation to the known PrS modularity.

Reviewer #3 (Public review):

Summary:

The authors sought to determine, at the level of individual presubiculum pyramidal cells, how allocentric spatial information from retrosplenial cortex was integrated with egocentric information from the anterior thalamic nuclei. Employing a dual opsin optogenetic approach with patch clamp electrophysiology, Richevaux and colleagues found that around three quarters of layer 3 pyramidal cells in presubiculum receive monosynaptic input from both brain regions. While some interesting questions remain (e.g. the role of inhibitory interneurons in gating the information flow and through different layers of presubiculum, this paper provides valuable insights into the microcircuitry of this brain region and the role that it may play in spatial navigation.

Strengths:

One of the main strengths of this manuscript was that the dual opsin approach allowed the direct comparison of different inputs within an individual neuron, helping to control for what might otherwise have been an important source of variation. The experiments were well-executed and the data rigorously analysed. The conclusions were appropriate to the experimental questions and were well-supported by the results. These data will help to inform in vivo experiments aimed at understanding the contribution of different brain regions in spatial navigation and could be valuable for computational modelling.

Weaknesses:

Some attempts were made to gain mechanistic insights into how inhibitory neurotransmission may affect processing in presubiuclum (e.g. figure 5) but these experiments were a little underpowered and the analysis carried out could have been more comprehensively undertaken, as was done for other experiments in the manuscript.

Comments on revised version:

The authors have addressed all of my comments and I have nothing further to add. Well done for an interesting and valuable contribution!

eLife Assessment

This study presents a useful set of experiments to explore how a salivary protein might facilitate planthopper-transmitted rice stripe virus infection by interfering with callose deposition and if fully validated, these findings would significantly advance our understanding of tripartite virus-vector-plant interactions and could be of broad interest to plant science research. The authors provide additional data supporting protein-protein interactions and clarify the transient presence of LssaCA in plants. However, the mechanistic framework remains incomplete, particularly regarding the temporal dynamics of callose function and the sustained effect of LssaCA after virus inoculation. Evidence for the tripartite interaction's functional relevance is still limited, and several critical phenotypic and biochemical details require further substantiation.

Reviewer #1 (Public review):

In this study, the authors identified an insect salivary protein LssaCA participating viral initial infection in plant host. LssaCA directly bond to RSV nucleocapsid protein and then interacted with a rice OsTLP that possessed endo-β-1,3-glucanase activity to enhance OsTLP enzymatic activity and degrade callose caused by insects feeding. The manuscript suffers from fundamental logical issues, making its central narrative highly unconvincing.

(1) These results suggested that LssaCA promoted RSV infection through a mechanism occurring not in insects or during early stages of viral entry in plants, but in planta after viral inoculation. As we all know that callose deposition affects the feeding of piercing-sucking insects and viral entry, this is contradictory to the results in Fig. S4 and Fig 2. It is difficult to understand callose functioned in virus reproduction in 3 days post virus inoculation. And authors also avoided to explain this mechanism.

(2) Missing significant data. For example, the phenotypes of the transgenic plants, the RSV titers in the transgenic plants (OsTLP OE, ostlp). The staining of callose deposition were also hard to convince. The evidence about RSV NP-LssaCA-OsTLP tripartite interaction to enhance OsTLP enzymatic activity is not enough.

(3) Figure 4a, there was the LssaCA signal in the fourth lane of pull-down data. Did MBP also bind LsssCA? The characterization of pull-down methods was rough a little bit. The method of GST pull-down and MBP pull-down should be characterized more in more detail.

Author response:

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

Reviewer #1 (Public Review):

In this study, the authors identify an insect salivary protein participating viral initiate infection in plant host. They found a salivary LssaCA promoting RSV infection by interacting with OsTLP that could degrade callose in plants. Furthermore, RSV NP bond to LssaCA in salivary glands to form a complex, which then bond to OsTLP to promote degradation of callose.

The story focus on tripartite virus-insect vector-plant interaction and is interesting. However, the study is too simple and poor-conducted. The conclusion is also overstated due to unsolid findings.

We thank the reviewer for their constructive feedback. We have conducted additional experiments to strengthen our results and conclusions as detailed below:

(1) The comparison between vector inoculation and microinjection involves multiple confounding factors that could affect the experimental results, including salivary components, RSV inoculation titers, and the precision of viral deposition. The differential outcomes could be attributed to these various factors rather than definitively demonstrating the necessity of salivary factors. Therefore, we have removed this comparison from the revised manuscript and instead focused on elucidating the specific mechanisms by which LssaCA facilitates viral infection.

(2) We conducted new experiments to assess the function of LssaCA enzymatic activity in mediating RSV infection. Additional experiments revealed that OsTLP enzymatic activity is highly pH-dependent, with increased activity as pH decreases from 7.5 to 5.0 (Fig. 3H). However, the LssaCA-OsTLP interaction at pH 7.4 significantly enhanced OsTLP enzymatic activity without requiring pH changes. These results demonstrate that LssaCA-OsTLP protein interactions are crucial for mediating RSV infection. In contrast to pH-dependent mechanisms, our study demonstrated that LssaCA's biological function in mediating RSV infection is at least partially, if not completely, independent of its enzymatic activity. We have added these new resulted into the revised manuscript (Lines 220-227). We have also added a comprehensive discussion comparing the aphid CA mechanism described by Guo et al. (2023 doi.org/10.1073/pnas.2222040120) with our findings in the revised manuscript (Lines 350-371).

(3) We have repeated majority of callose deposition experiments, providing clearer images (Figures 5-6). In addition to aniline blue staining, we quantified callose concentrations using a plant callose ELISA kit to provide more precise measurements (Figure 5A, I, 6A, C and S8A). We utilized RT-qPCR to measure callose synthase expression in both feeding and non-feeding areas, confirming that callose synthesis was induced specifically in feeding regions, leading to localized callose deposition (Figures 5D-G and S8B-E). For sieve plate visualization, we examined longitudinal sections, which revealed callose deposition in sieve plates during SBPH feeding and RSV infection (Figure S7).

(4) We generated OsTLP mutant rice seedlings (ostlp) and use this mutant to directly demonstrate that LssaCA mediates callose degradation in planta through enhancement of OsTLP enzymatic activity (Lines 288-302 and Figure 6).

(5) We produced LssaCA recombinant proteins in sf9 cells to ensure full enzymatic activity and constructed a comprehensive CA mutant protein, in which all seven residues constituting the enzymatic active center mutated (LssaCA^H111D,LssaCA^N139H,LssaCA^H141D, LssaCA^H143D, LssaCA^E153H, LssaCA^H166D, LssaCA^T253E) (Fig. S1B). This LssaCA mutant protein demonstrated complete loss of enzymatic activity (Fig. 1C).

Major comments:

(1) The key problem is that how long the LssCA functioned for in rice plant. Author declared that LssCA had no effect on viral initial infection, but on infection after viral inoculation. It is unreasonable to conclude that LssCA promoted viral infection based on the data that insect inoculated plant just for 2 days, but viral titer could be increased at 14 days post-feeding. How could saliva proteins, which reached phloem 12-14 days before, induce enough TLP to degrade callose to promote virus infection? It was unbelievable.

We appreciate your insightful comment and acknowledge that our initial description may have been unclear. We agree that salivary proteins would not present in plant tissues for two weeks post-feeding or post-injection. Our intention was to clarify that when salivary proteins enhance RSV infection, this initial enhancement leads to sustained high viral loads. We measured viral burden at 14 days post-feeding or post-injection because this is the common measurement time point when viral titers are sufficiently high for reliable detection by qRT-PCR or western blotting. We have clarified this rationale in the revised manuscript (Lines 155-157).

To determine the actual persistence of LssaCA in plant tissues, we conducted additional experiments where insects were allowed to feed on a defined aera of rice seedlings for two days. We then monitored LssaCA protein levels at 1 and 3 days after removing the insects. Western blotting analysis revealed that LssaCA protein levels decreased post-feeding and remained detectable at 3 days post-feeding. These results are presented in Figure 2H and described in detail in Lines 184-193.

(2) Lines 110-116 and Fig. 1, the results of viruliferous insect feeding and microinjection with purified virus could not conclude the saliva factor necessary of RSV infection, because these two tests are not in parallel and comparable. Microinjection with salivary proteins combined with purified virus is comparable with microinjection with purified virus.

We thank the reviewer’s insightful comment. We agree that “the results of viruliferous insect feeding and microinjection with the purified virus could not conclude the saliva factor necessary of RSV infection”. However, due to the technical difficulty in collecting sufficient quantities of salivary proteins to conduct the microinjection experiment, we have removed these results from the revised manuscript.

(3) The second problem is how many days post viruliferous insect feeding and microinjection with purified virus did author detect viral titers? in Method section, authors declared that viral titers was detected at 7-14 days post microinjection. Please demonstrate the days exactly.

We thank the reviewer’s insightful comment. We typically measured RSV infection levels at both 7- and 14-days post-microinjection. However, since the midrib microinjection experiments have been removed from the revised manuscript, this methodology has also been removed accordingly.

(4) The last problem is that how author made sure that the viral titers in salivary glands of insects between two experiments was equal, causing different phenotype of rice plant. If not, different viral titers in salivary glands of insects between two experiments of course caused different phenotype of rice plant.

We thank the reviewer’s comment. When we compared the effects of LssaCA deficiency on RSV infection of rice plants, we have compared the viral titers in the insect saliva and salivary glands. The results indicated that the virus titers in both tissues have not changed by LssaCA deficiency, suggesting that the viruses inoculated into rice phloem by insects of different treatments were comparable. Please refer to the revised manuscript Figures 2D-G and Lines 161-173.

(5) The callose deposition in phloem can be induced by insect feeding. In Fig. 5H, why was the callose deposition increased in the whole vascular bundle, but not phloem? Could the transgenic rice plant directional express protein in the phloem? In Fig. 5, why was callose deposition detected at 24 h after insect feeding? In Fig. 6A, why was callose deposition decreased in the phloem, but not all the cells of the of TLP OE plant? Also in Fig.6A and B, expression of callose synthase genes was required.

We thank the reviewer for these insightful comments.

(1) Figure 5. The callose deposition increased in multiple cells within the vascular bundle, including sieve tubes, parenchymatic cells, and companion cells. While callose deposition was detected in other parts of the vascular bundle, no significant differences were observed between treatments in these regions, indicating that in response to RSV infection and other treatments, altered callose deposition mainly occurred in phloem cells. Please refer to the revised 5B, 5J, 6B, and 6D.

(2) Transgenic plant expression. The OsTLP-overexpressing transgenic rice plants express TLP proteins in various cells under the control of CaMV 35S promoter, rather than being directionally expressed in the phloem. However, since TLP proteins are secreted, they are potentially transported and concentrated in the phloem where they can degrade callose.

(3) Figure 5. The 24-hour time point for callose deposition detection was selected based on established protocols from previous studies. According to Hao et al. (Plant Physiology 2008), callose deposition increased during the first 3 days of planthopper infestation and decreased after 4 days. Additionally, Ellinger and Voigt (Ann Bot 2014) demonstrated that callose visualization typically begins 18-24 hours after treatment, making 24 hours an optimal detection time point.

(4) Figure 6, Phloem-specific changes. Similar to Figure 5, while callose deposition was detected in other parts of vascular bundle, significant differences between treatments were mainly observed in phloem cells, indicating that RSV infection specifically affects callose deposition in phloem tissue.

(5) Callose synthase gene expression. We performed RT-qPCR analysis to measure the expression levels of callose synthase genes. The results indicated that OsTLP overexpression did not significantly alter the mRNA levels of these genes, regardless of RSV infection status in SBPH.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants.

(1) Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

We appreciate the reviewer’s insightful comment and have carefully examined the cited publication. The study by Guo et al. (2023) elucidates a distinct mechanism for aphid-mediated transmission of non-persistent, non-circulative viruses (turnip mosaic virus and cucumber mosaic virus). In their model, aphid-secreted CA-II in the plant cell apoplast leads to H⁺ accumulation and localized acidification. This trigger enhanced vesicle trafficking as a plant defense response, inadvertently facilitating virus translocation from the endomembrane system to the apoplast.

In contrast to these pH-dependent mechanisms, our study demonstrated that LssaCA’s biological function in mediating RSV infection is, if not completely, at least partially independent of its enzymatic activity. We performed additional experiments to reveal that OsTLP enzymatic activity is highly pH-dependent and exhibits increased enzymatic activity as pH decreases from 7.5 to 5.0 (Fig. 3H); however, the LssaCA-OsTLP interaction occurring at pH 7.4 significantly enhanced OsTLP enzymatic activity without any change in buffer pH (Fig. 3G). These results demonstrate the crucial importance of LssaCA-OsTLP protein interactions, rather than enzymatic activity alone, in mediating RSV infection.

We have incorporated these new experimental results and added a comprehensive discussion comparing the aphid CA mechanism described by Guo et al. (2023) with our findings in the revised manuscript. Please refer to Figures 3G-H, Lines 220-227 and 350-371 for detailed information.

(2) While this is an interesting work, there are, in my opinion, some weak points. The microinjection experiments result in much lower virus accumulation in rice than infection by vector inoculation, so their interpretation is difficult.

We acknowledge the reviewer's concern regarding the lower virus accumulation observed in microinjection experiments compared to vector-mediated inoculation. We have removed these experiments from the revised manuscript. To address the core question raised by these experiments, we have conducted new experiments that directly demonstrate the importance of LssaCA-OsTLP protein-protein interactions in mediating RSV infection. These results demonstrate the crucial importance of LssaCA-OsTLP protein interactions, rather than enzymatic activity alone, in mediating RSV infection. Additionally, we have incorporated a comprehensive discussion examining carbonic anhydrase activity, pH homeostasis, and viral infection. Please refer to the detailed experimental results and discussion in the sections mentioned in our previous response (Figures 3G-H, Lines 220-227 and 350-371).

(3) Also, the effect of injected recombinant CA protein might fade over time because of degradation or dilution.

We appreciate the reviewer’s insightful comment. This is indeed a valid concern that could affect the interpretation of microinjection results. To address the temporal dynamics of CA protein presence in planta, we conducted time-course experiments to monitor the retention of naturally SBPH-secreted CA proteins in rice plants. Our analysis at 1- and 3- days post-feeding (dpf) revealed that CA protein levels decreased progressively following SBPH feeding, but could also been detected at 3dpf (Fig. 2H). Please refer to Figures 2H and lines 184-193 for detailed information.

(4) The authors claim that enzymatic activity of CA is not required for its proviral activity. However, this is difficult to assess because all CA mutants used for the corresponding experiments possess residual activity.

We appreciate the reviewer’s insightful comment. We constructed a comprehensive CA mutant protein in which all seven residues constituting the enzymatic active center mutated (LssaCA^H111D, LssaCA^N139H, LssaCA^H141D, LssaCA^H143D, LssaCA^E153H, LssaCA^H166D, LssaCA^T253E) (Fig. S1B). This LssaCA mutant protein demonstrated complete loss of enzymatic activity (Fig. 1C). However, since we have removed the recombinant CA protein microinjection experiments from the revised manuscript, we lack sufficient direct evidence to definitively demonstrate that CA enzymatic activity is dispensable for its proviral function. To address the core question raised by these experiments, we have conducted new experiments that provide direct evidence for the importance of LssaCA-OsTLP protein-protein interactions in mediating RSV infection. Additionally, we have incorporated a comprehensive discussion examining carbonic anhydrase activity, pH homeostasis, and viral infection. Please refer to the detailed experimental results and discussion in the sections mentioned in our previous response (Figures 3G-H, Lines 220-227 and 350-371).

(5) It remains also unclear whether viral infection deregulates CA expression in planthoppers and TLP expression in plants. However, increased CA and TLP levels could alone contribute to reduced callose deposition.

We have compared LssaCA mRNA levels in RSV-free and RSV-infected L.striatellus salivary glands, which indicated that RSV infection does not significantly affect LssaCA expression (Figure 1J). By using RSV-free and RSV-infected L.striatellus to feed on rice seedlings, we clarified that RSV infection does not affect TLP expression in plants (Figure 5H).

Reviewer #1: (Recommendations For The Authors):

Other comments:

(1) Most data proving viral infection and LssaCA expression were derived from qPCR assays. Western blot data are strongly required to prove the change at the protein level.

We agree that western blot data are required to prove the change at the protein level. In the revised manuscript, we have added western-blotting results (Figures 1F, 1I, 2C, 2J, and S6).

(2) Line 145, data that LssaCA was significantly downregulated should be shown.

Thank you and the data has been added to the revised manuscript. Please refer to Line 165 and Figure 2D.

(3) Lines 159-161, how did authors assure that the dose of recombinant LssCA was closed to the release level of insect feeding, but not was excessive? How did author exclude the possibility of upregulated RSV titer caused by excessive recombinant LssCA?

We appreciate this important concern regarding dosage controls. While microinjection of recombinant proteins typically yields viral infection levels significantly lower than those achieved through natural insect feeding, higher protein concentrations are often required to achieve high viral infection levels. In this experiment, we compared RSV infection levels following microinjection of BSA+RSV versus LssaCA+RSV, with the expectation that any observed upregulation in RSV titer would be specifically attributable to recombinant LssaCA rather than excessive protein dosing. However, given the low RSV infection levels observed with viral microinjection, we have removed their corresponding results from the revised manuscript.

(4) Lines 124-125, recombinantly expressed LssaCA protein should be underlined, but not the LssaCA protein itself.

We have clearly distinguished recombinantly expressed LssaCA from endogenous LssaCA protein throughout the manuscript, ensuring that all references to recombinant proteins are properly labeled as such.

(5) LssaCA expression in salivary glands of viruliferous and nonviruliferous insects is required. LssaCA accumulation in rice plant exposed to viruliferous and nonviruliferous insects is also required.

We have measured LssaCA mRNA levels in salivary glands of viruliferous and nonviruliferous insects (Figure 1J), and protein levels in rice plant exposed to viruliferous and nonviruliferous insects (Figure 1I).

(6) Fig. 4G, the enzymatic activities of OsTLP were too low compared with that in Fig. 4E and Fig. 7E. Why did the enzymatic activities of the same protein show so obvious difference?

We apologize for the error in Fig. 4G. The original data presented relative fold changes between OsTLP+BSA and OsTLP+LssaCA treatment, with OsTLP+BSA normalized to 1.0 and OsTLP+LssaCA values expressed as fold changes relative to this baseline. However, the Y-axis was incorrectly labeled as “β-1,3-glucanase (units mg^-1)”, which suggested absolute enzymatic activity values. We have now corrected the figure (revised Figure 3G) to display the actual absolute enzymatic activity values with the appropriate Y-axis label “β-1,3-glucanase (units mg^-1)”.

(7) Fig. 7E, was the LssaCA + NP and LssaCA + GST quantified?

Yes, all proteins were quantified, and enzymatic activity values were calculated and expressed as units per milligram of proteins (units mg^-1).

Minor comments:

(1) The keywords: In fact, the LssaCA functioned during initial viral infection in plant, but not viral horizontal transmission.

We appreciate the reviewer’s insightful comment. We have revised the manuscript title to “Rice stripe virus utilizes an Laodelphax striatellus salivary carbonic anhydrase to facilitate plant infection by direct molecular interaction” and changed the keyword from “viral horizontal transmission” to “viral infection of plant”.

(2) Fig. 2A, how about testes? Was this data derived from female insects? Fig. 2C, is the saliva collected from nonviruliferous insects? Fig. 2E, what is the control?

We appreciate the reviewer’s insightful comments.

(1) Fig. 2A: The data present mean and SD calculated from three independent experiments, with 5 tissue samples per experiment. Since 3^rd instar nymphs were used for feeding experiments in this study, we also used 3^rd instar RSV-free nymphs to measure gene expression in guts, salivary glands and fat bodies. R-body represents the remaining body after removing these tissues. Female insects were used to measure gene expression in ovaries, and gene expression in testes was also added. We have added this necessary information to the revised manuscript (please refer to new Figure 1F and Lines 402-403).

(2) Fig. 2C: Yes, saliva was collected from nonviruliferous insects.

(3) Fig. 2E: The control consisted of 100 mM PBS, as described in the experimental section (Lines 643-644): “A blank control consisted of 2 mL of 100 mM PBS (pH 7.0) mixed with 1 mL of 3 mM p-NPA.” In the revised manuscript, we recombinantly expressed LssaCA and its mutant proteins in both sf9 cells and E.coli. Therefore, we have used the mutant proteins as controls to demonstrate specific enzymatic activity. Please refer to Figure 1C, Lines 115-122 and 621-635 for detailed information.

(3) Some figure labeling appeared unprofessional. For example, "a-RSV", "loading" in Fig. 1, "W-saliva", "G-saliva" in Fig. 2, and so on, the related explanations were absent.

We appreciate the reviewer’s insightful comments. We have thoroughly reviewed all figures to ensure professional labels. Specifically, we have:

(1) Used proper protein names to label western blots and clearly explained the antibodies used for protein detection.

(2) Provided comprehensive explanations for all abbreviations used in figures within the corresponding figure legends.

(3) Ensured consistent and clear labeling throughout all figures.

Please refer to the revised Figures 1-3 for these corrections.

(4) Lines 83-84, please cite references on callose preventing viral movement. I do not think the present references were relevant.

We have added a more relevant reference (Yue et al., 2022, Line 82), which revealed that palmitoylated γb promotes virus cell-to-cell movement by interacting with NbREM1 to inhibit callose deposition at plasmodesmata.

(5) The background of transgenic plants of OsTLP OE should be characterized. And the overexpression of OsTLP should be shown. Which generation of OsTLP OE did authors use?

The background of transgenic plants of OsTLP OE and its generation used have been shown in the “Materials and methods” section (Line 782-786) and has been mentioned in the main text (Line 214). T² lines have been selected for further analysis (Line 789).

(6) Fig. 5A, the blank, which derived from plants without exposure to insect, was absent.

We appreciate the reviewer’s insightful comments. We have added the non- fed control in the revised Figure 5A-C.

(7) Fig. 7A, the nonviruruliferous insects were required to serve as a control.

Immunofluorescence localization of RSV and LssaCA in uninfected L. striatellus salivary glands have been added to the revised manuscript (Figure S2).

(8) The manuscript needs English language edit.

The manuscript has undergone comprehensive English language editing to improve clarity, grammar, and overall readability.

Reviewer #2 (Recommendations For The Authors):

(1) The first experiment compares vector inoculation vs microinjection of RSV in tissue. I am not sure that your claim (saliva factors are necessary for inoculation) holds, because the vector injects RSV directly into the phloem, whereas microinjection is less precise and you cannot control where exactly the virus is deposed. However, virus deposited in other tissues than the phloem might not replicate, and indeed you observe, compared to natural vector inoculation, highly reduced virus titers.

We appreciate the reviewer’s insightful comments. We agree that the comparison between vector inoculation and microinjection involves multiple confounding factors that could affect the experimental results, including salivary components, RSV inoculation titers, and the precision of viral deposition. As the reviewer correctly points out, the differential outcomes could be attributed to these various factors rather than definitively demonstrating the necessity of salivary factors. Therefore, we have removed this comparison from the revised manuscript and instead focused on elucidating the specific mechanisms by which LssaCA facilitates viral infection.

(2) Next the authors show that a carbonic anhydrase (CA) that they previously detected in saliva is functional and secreted into rice. I assume this is done with non-infected insects, but I did not find the information. Silencing the CA reduces virus titers in inoculated plants at 14 dpi, but not in infected planthoppers. At 1 dpi, there is no difference in RSV titer in plants inoculated with CA silenced planthoppers or control hoppers. To see a direct effect of CA in virus infection, purified virus is injected together with a control protein or recombinant CA into plants. At 14 dpi, there is about double as much virus in the CA-injected plants, but compared to authentic SBPH inoculation, titers are 20,000 times lower. Actually, I believe it is not very likely that the recombinant CA is active or present so long after initial injection.

We appreciate the reviewer’s insightful comments.

(1) Our previous study identified the CA proteins from RSV-free insects. We have added this information to the revised manuscript (Line 110).

(2) We acknowledge the reviewer's concern regarding the lower virus accumulation observed in microinjection experiments compared to vector-mediated inoculation. We have removed these experiments from the revised manuscript and instead focused on elucidating the specific mechanisms by which LssaCA facilitates viral infection.

(3) We didn’t intend to suggest that LssaCA proteins presented for 14 days post-injection. We measured viral titers at 14 days post-feeding or post-injection because this is the common measurement time point when viral titers are sufficiently high for reliable detection by RT-qPCR or western blotting. We have clarified this rationale in the revised manuscript (Lines 155-157). To determine the actual persistence of LssaCA in plant tissues, we monitored LssaCA protein levels at 1 and 3 dpf. Western blotting analysis revealed that LssaCA protein levels decreased post-feeding and remained detectable at 3 dpf. These results are presented in Figure 2H and described in detail in Lines 184-193.

(3) Then the authors want to know whether CA activity is required for its proviral action and single amino acid mutants covering the putative active CA site are created. The recombinant mutant proteins have 30-70 % reduced activity, but none of them has zero activity. When microinjected together with RSV into plants, RSV replication is similar as injection with wild type CA. Since no knock-out mutant with zero activity is used, it is difficult to judge whether CA activity is unimportant for viral replication, as claim the authors.

We appreciate the reviewer’s insightful comment. We constructed a comprehensive CA mutant protein in which all seven residues constituting the enzymatic active center mutated (LssaCA^H111D, LssaCA^N139H, LssaCA^H141D, LssaCA^H143D, LssaCA^E153H, LssaCA^H166D, LssaCA^T253E) (Fig. S1B). This LssaCA mutant protein demonstrated complete loss of enzymatic activity (Fig. 1C). However, since we have removed the recombinant CA proteins microinjection experiments from the revised manuscript, we lack sufficient direct evidence to definitively demonstrate that CA enzymatic activity is dispensable for its proviral function. To address the core question raised by these experiments, we have conducted new experiments that provide direct evidence for the importance of LssaCA-OsTLP protein-protein interactions in mediating RSV infection. Additionally, we have incorporated a comprehensive discussion examining carbonic anhydrase activity, pH homeostasis, and viral infection. Please refer to the detailed experimental results and discussion in the sections mentioned in our previous response (Figures 3G-H, Lines 220-227 and 350-371).

(4) Next a yeast two hybrid assay reveals interaction with a thaumatin-like rice protein (TLP). It would be nice to know whether you detected other interacting proteins as well. The interaction is confirmed by pulldown and binding affinity assay using recombinant proteins. The kD is in favor of a rather weak interaction between the two proteins.

We have added a list of rice proteins that potentially interact with LssaCA (Table S1) and have measured interactions with additional proteins (unpublished data). Despite the relatively weak binding affinity, the functional significance of the LssaCA-OsTLP interaction in enhancing TLP enzymatic activity is substantial.

(5) Then the glucanase activity of TLP is measured using recombinant TLP-MBP or in vivo expressed TLP. It is not clear to me which TLP is used in Fig. 4G (plant-expressed or bacteria-expressed). If it is plant-expressed TLP, why is its basic activity 10 times lower than in Fig. 4F?

Fig. 4G is the Fig. 3G in the revised manuscript. A E. coli-expressed TLP protein has been used. We apologize for the error in our original Fig. 4G. The original data presented relative fold changes between OsTLP+BSA and OsTLP+LssaCA treatment, with OsTLP+BSA normalized to 1.0 and OsTLP+LssaCA values expressed as fold changes relative to this baseline. However, the Y-axis was incorrectly labeled as “β-1,3-glucanase (units mg^-1)”, which suggested absolute enzymatic activity values. We have now corrected the figure to display the actual absolute enzymatic activity values with the appropriate Y-axis label “β-1,3-glucanase (units mg^-1)”.

(6) There is also a discrepancy in the construction of the transgenic rice plants: did you use TLP without signal peptide or full length TLP? If you used TLP without signal peptide, you should explain why, because the wild type TLP contains a signal peptide.

We cloned the full-length OsTLP gene including the signal peptide sequence (Line 782 in the revised manuscript).

(7) The authors find that CA increases glucanase activity of TLP. Next the authors test callose deposition by aniline blue staining. Feeding activity of RSV-infected planthoppers induces more callose deposition than does feeding by uninfected insects. In the image (Fig. 5A) I see blue stain all over the cell walls of xylem and phloem cells. Is this what the authors expect? I would have expected rather a patchy pattern of callose deposition on cell walls. Concerning sieve plates, I cannot discern any in the image; they are easier to visualize in longitudinal sections than in transversal section as presented here.

We appreciate the reviewer’s insightful comment.

(1) Callose deposition pattern: While callose deposition was detected in other parts of the vascular bundle, significant differences between treatments were mainly observed in phloem cells, indicating that phloem-specific callose deposition is the primary response to RSV infection and SBPH feeding (Figures 5B and 5J).

(2) Sieve plate visualization: We have examined longitudinal sections to visualize sieve plates, which revealed callose deposition in sieve plates during SBPH feeding and RSV infection (Figure S7).

(3) Quantitative analysis: In addition to aniline blue staining, we quantified callose concentrations using a plant callose ELISA kit to provide more precise measurements (Figure 5A, 5I and S8A).

(4) Gene expression analysis: We utilized RT-qPCR to measure callose synthase expression in both feeding and non-feeding areas, confirming that callose synthesis was induced specifically in feeding regions, leading to localized callose deposition (Figures 5D-H).

These experimental results collectively demonstrate that RSV infection induces enhanced callose synthesis and deposition, with this response occurring primarily in phloem cells, including sieve plates, within feeding sites and their immediate vicinity.

(8) I do not quite understand how you quantified callose deposition (arbitrary areas?) with ImageJ. Please indicate in detail the analysis method.

We have added more detailed information for the methods to quantify callose deposition (Lines 673-678).

(9) More callose content is also observed by a callose ELISA assay of tissue extracts and supported by increased expression of glucanase synthase genes. Did you look whether expression of TLP is changed by feeding activity and RSV infection? Silencing CA in planthoppers increases callose deposition, which is inline with the observation that CA increases TLP activity.

We measured OsTLP expression following feeding by RSV-free or RSV-infected SBPH and found that gene expression was not significantly affected by either insect feeding or RSV infection. These results have been added to the revised manuscript (Lines 275-277 and Figure 5H).

(10) Next, callose is measured after feeding of RSV-infected insects on wild type or TLP-overexpressing rice. Less callose deposition (after 2 days) and more virus (after 14 days) is observed in TLP overexpressors. I am missing a control in this experiment, that is feeding of uninfected insects on wild type or TLP overexpressing rice, where I would expect intermediate callose levels.

We appreciate the reviewer’s insightful comment and fully agree with the prediction. In the revised manuscript, we have constructed ostlp mutant plants and conducted additional experiments to further clarify how callose deposition is regulated by insect feeding, RSV infection, LssaCA levels, and OsTLP expression. Specifically: 

(1) Both SBPH feeding and RSV infection induce callose deposition, with RSV-infected insect feeding resulting in significantly higher callose levels compared to RSV-free insect feeding (Fig. 5A-C).

(2) LssaCA enhances OsTLP enzymatic activity, thereby promoting callose degradation (Fig. 5I-K).

(3) OsTLP-overexpressing (OE) plants exhibit lower callose levels than wild-type (WT) plants, while ostlp mutant plants show higher callose levels than WT (Fig. 6A-B).

(4) In ostlp knockout plants, LssaCA no longer affects callose levels, indicating that OsTLP is required for LssaCA-mediated regulation of callose (Fig. 6C-D).

These additional data address the reviewer’s concern and support the conclusion that OsTLP plays a central role in modulating callose levels in response to RSV infection and insect feeding.

(11) Next the authors test for interaction between virions and CA. Immunofluorescence shows that RSV and CA colocalize in salivary glands; in my opinion, there is partial and not complete colocalization (Fig. 7A).

We agree with the reviewer’s observation. CA is primarily produced in the small lobules of the principal salivary glands, while RSV infects nearly all parts of the salivary glands. In regions where RSV and CA colocalize within the principal glands, the CA signal appears sharper than that of RSV, likely due to the relatively higher abundance of CA compared to RSV in these areas. This may explain the partial, rather than complete, colocalization observed in our original Figure 7A. In the revised manuscript, please refer to Figure 1A.

(12) Pulldown experiments with recombinant RSV NP capsid protein and CA confirm interaction, binding affinity assays indicate rather weak interaction between CA and NP. Likewise in pull-down experiments, interaction between NP, CA and TLP is shown. Finally, in vitro activity assays show that activity of preformed TLP-CA complexes can be increased by adding NP; activity of TLP alone is not shown.

We performed two independent experiments to confirm the influence on TLP enzymatic activity by LssaCA or by the LssaCA-RSV NP complex. In the first experiment, we compared the enhancement of TLP activity by LssaCA using TLP alone as a control (Figure 3G). In the second experiment examining the LssaCA-RSV NP complex effect on TLP activity, we used the LssaCA-TLP combination as the baseline control rather than TLP alone (Figure 4B), since we had already established the LssaCA enhancement effect in the previous experiment.

(13) For all microscopic acquisitions, you should indicate the exact acquisition conditions, especially excitation and emission filter settings, kind of camera used and objectives. Use of inadequate filters or of a black & white camera could for example be the reason why you observe a homogeneous cell wall label in the aniline blue staining assays. Counterstaining cell walls with propidium iodide might help distinguish between cell wall and callose label.

Thank you for your insightful suggestions. We have added the detailed information to the revised manuscript (Lines 656-659 and 673-678).

(14) You should provide information whether CA is deregulated in infected planthoppers, as this could also modify its mode of action.\

We have compared LssaCA mRNA levels in RSV-free and RSV-infected L.striatellus salivary glands. The results indicated that RSV infection does not significantly affect LssaCA expression (Figure 1J).

(15) You should show purity of the proteins used for affinity binding measurements.

We have included SDS-PAGE results of purified proteins in the revised manuscript (Figure S3).

(16) L 39: Not all arboviruses are inoculated into the phloem.

Thank you. We have revised this description (Lines 40, 73, 95 and 97).

(17) L 76: Watery saliva is also injected in epidermis and mesophyll cells.

Thank you. We have revised this description (Line 73).

(18) L 79: What do you mean by "avirulent gene"?

Thank you for your valuable comments. We have revised this description as “certain salivary effectors may be recognized by plant resistance proteins to induce effector-triggered immunity”. Please refer to Lines 76-77 for detail.

(19) L 128: Please add delivery method.

Thank you. We have added the delivery methods (Line 134).

(20) L 195: Please explain "MST".

Explained (Line 124). Thank you.

(21) L 203: Please add the plant species overexpressing TLP.

Added (Line 214). Thank you.

(22) L 213: Callose deposition has also a role against phloem-feeding insects.

We appreciate the reviewer’s insight comment. We have added this information to the revised manuscript (Line 252).

(23) L 626: What is a "mutein"?

"mutein" is an abbreviation for mutant proteins. Since the recombinant protein microinjection experiments have been removed from the revised manuscript, the term “mutein” has also been removed. For all other instances, we now use the full term “mutant proteins”.

(24) Fig. 1E: what is "loading"? You should rather show here and elsewhere (or add to supplement) complete protein gels and Western blot membranes and not only bands of interest.

Thank you for your valuable suggestion. Although Figure 1E has been removed from the revised manuscript, we have carefully reviewed all figures to ensure that the term “loading” has been replaced with the specific protein names where appropriate.

(25) Fig. 2C: Please indicate which is the blot and which is the silver stained gel and add mass markers in kDa to the silver stained gel.

Thank you for your suggestion. We have revised figure to include labeled silver-stained gels with indicated molecular weight markers (Figure 1H in the revised manuscript).

eLife Assessment

This paper presents an analysis of demography and selection from whole-genome sequencing of 40 Faroese, with data that are useful beyond the study region. Much of the analysis is solid, but a more fine-scale analysis of demographic history could have led to more interesting findings. In addition, there are concerns about the selection analyses, given the special nature of the studied population and sampling scheme. Finally, lack of data availability limits the broader value of the paper.

Reviewer #1 (Public review):

Summary:

The paper reports an analysis of whole-genome sequence data from 40 Faroese. The authors investigate aspects of demographic history and natural selection in this population. The key findings are that the Faroese (as expected) have a small population size and are broadly of Northwest European ancestry. Accordingly, selection signatures are largely shared with other Northwest European populations, although the authors identify signals that may be specific to the Faroes. Finally, they identify a few predicted deleterious coding variants that may be enriched in the Faroes.

Strengths:

The data are appropriately quality-controlled and appear to be of high quality. Some aspects of the Faroese population history are characterized, in particular, by the relatively (compared to other European populations) high proportion of long runs of homozygosity, which may be relevant for disease mapping of recessive variants. The selection analysis is presented reasonably, although as the authors point out, many aspects, for example differences in iHS, can reflect differences in demographic history or population-specific drift and thus can't reliably be interpreted in terms of differences in the strength of selection.

Weaknesses:

The main limitations of the paper are as follows:

(1) The data are not available. I appreciate that (even de-identified) genotype data cannot be shared; however, that does substantially reduce the value of the paper. Minimally, I think the authors should share summary statistics for the selection scans, in line with the standard of the field.

(2) The insight into the population history of the Faroes is limited, relative to what is already known (i.e., they were settled around 1200 years ago, by people with a mixture of Scandinavian and British ancestry, have a small effective population size, and any admixture since then comes from substantially similar populations). It's obvious, for example, that the Faroese population has a smaller bottleneck than, say, GBR.

More sophisticated analyses (for example, ARG-based methods, or IBD or rare variant sharing) would be able to reveal more detailed and fine-scale information about the history of the populations that is not already known. PCA, ADMIXTURE, and HaplotNet analysis are broad summaries, but the interesting questions here would be more specific to the Faroes, for example, what are the proportions of Scandinavian vs Celtic ancestry? What is the date and extent of sex bias (as suggested by the uniparental data) in this admixture? I think that it is a bit of a missed opportunity not to address these questions.

(3) I don't really understand the rationale for looking at HLA-B allele frequencies. The authors write that "ankylosing spondylitis (AS) may be at a higher prevalence in the Faroe Islands (unpublished data), however, this has not been confirmed by follow-up epidemiological studies". So there's no evidence (certainly no published evidence) that AS is more prevalent, and hence nothing to explain with the HLA allele frequencies?

Reviewer #2 (Public review):

In this paper, Hamid et al present 40 genomes from the Faroe Islands. They use these data (a pilot study for an anticipated larger-scale sequencing effort) to discuss the population genetic diversity and history of the sample, and the Faroes population. I think this is an overall solid paper; it is overall well-polished and well-written. It is somewhat descriptive (as might be expected for an explorative pilot study), but does make good use of the data.

The data processing and annotation follows a state-of-the-art protocol, and at least I could not find any evidence in the results that would pinpoint towards bioinformatic issues having substantially biased some of the results, and at least preliminary results lead to the identification of some candidate disease alleles, showing that small, isolated cohorts can be an efficient way to find populations with locally common, but globally rare disease alleles.

I also enjoyed the population structure analysis in the context of ancient samples, which gives some context to the genetic ancestry of Faroese, although it would have been nice if that could have been quantified, and it is unfortunate that the sampling scheme effectively precludes within-Faroes analyses.

I am unfortunately quite critical of the selection analysis, both on a statistical level and, more importantly, I do not believe it measures what the authors think it does.

Major comments:

(1) Admixture timing/genomic scaling/localization:<br /> As the authors lay out, the Faroes were likely colonized in the last 1,000-1,500 years, i.e., 40-60 generations ago. That means most genomic processes that have happened on the Faroese should have signatures that are on the order of ~1-2cM, whereas more local patterns likely indicate genetic history predating the colonization of the islands. Yet, the paper seems to be oblivious to this (to me) fascinating and somewhat unique premise. Maybe this thought is wrong, but I think the authors miss a chance here to explain why the reader should care beyond the fact that the small populations might have high-frequency risk alleles and the Faroes are intrinsically interesting, but more importantly, it also makes me think it leads to some misinterpretations in the selection analysis

(2) ROH:<br /> Would the sampling scheme impact ROH? How would it deal with individuals with known parental coancestry? As an example of what I mean by my previous comment, 1MB is short enough in that I would expect most/many 1MB ROH-tracts to come from pedigree loops predating the colonization of the Faroes. (i.e, I am actually quite surprised that there isn't much more long ROH, which makes me wonder if that would be impacted by the sampling scheme).

(3) Selection scan:

We are talking about a bottlenecked population that is recently admixed (Faroese), compared to a population (GBR) putatively more closely related to one of its sources. My guess would be that selection in such a scenario would be possibly very hard to detect, and even then, selection signals might not differentiate selection in Faroese vs. GBR, but rather selection/allele frequency differences between different source populations. I think it would be good to spell out why XP-EHH/iHS measures selection at the correct time scale, and how/if these statistics are expected to behave differently in an admixed population.

(4) Similarly, for the discussion of LCT, I am not convinced that the haplotypes depicted here are on the right scale to reflect processes happening on the Faroes. Given the admixture/population history, it at the very least should be discussed in the context of whether the 13910 allele frequency on the Faroes is at odds with what would be expected based on the admixture sources.

(5) I am lacking information to evaluate the procedure for turning the outliers into p-values. Both iHS and XP-EHH are ratio statistics, meaning they might be heavy-tailed if one is not careful, and the central limit theorem may not apply. It would be much easier (and probably sufficient for the points being made here) to reframe this analysis in terms of empirical outliers.

(6) Oldest individual predating gene flow: It seems impossible to make any statements based on a single individual. Why is it implausible that this person (or their parents), e.g., moved to the Faroes within their lifetime and died there?

eLife Assessment

This valuable study identifies and characterizes probe binding errors in a widely used commercial platform for visualizing gene activity in tissue samples, discovering that at least 21 out of 280 genes in a human breast cancer panel are not accurately detected. The authors provide convincing evidence for their findings validated against multiple independent sequencing technologies and reference datasets. Given the broad adoption of this spatial gene detection platform in biomedical research, this work provides an essential quality control resource that will improve data interpretation across numerous studies.

Reviewer #1 (Public review):

Summary:

In the manuscript, Hallinan et al. describe off-target probe binding in the 10x Genomics Xenium platform, which results in invalid profiling of some genes in a spatial context. This was validated by comparing the Xenium results with Visium and scRNA-seq using human breast tissue, which are comprehensive and convincing. The authors also provide a dedicated tool to predict such off-target binding, Off-target Probe Tracker (OPT), which could be widely adopted in the field by researchers who are interested in validating the previously published results.

Strengths:

(1) This is the first study to suggest off-target binding of probes in the gene panels of the Xenium platform, which could be easily overlooked.

(2) The results were rigorously validated with two different methods.

(3) This paper will be a helpful resource for properly interpreting the results of previously published papers based on the Xenium platform (the signals could be mixed).

Weaknesses:

(1) The results were only tested with one tissue (human breast). However, this is not a major weakness, as one can easily extrapolate that this should be the case for any other tissue.

(2) Once the 10X Genomics corrects their gene panels according to this finding, the tool (OPT) will not be useful for most people. Still, it can be used by those who want to design de novo probes from scratch.

Reviewer #2 (Public review):

This paper describes an analysis of a commercially available panel for a spatial transcriptomic approach and introduces a computational tool to predict potential off-target binding sites for the type of probe used in the aforementioned panel. The performance of the prediction tool was validated by examining a dataset that profiled the same cancer tissue with multiple modalities. Finally, a detailed analysis of the potential pitfalls in a published study communicated by the company that commercialized the spatial transcriptomic platform in question is provided, along with best practice guidelines for future studies to follow.

Strengths:

The manuscript is clearly written and easy to follow.

The authors provide clean, organized, and well-documented code in the associated GitHub repository.

Weaknesses:

The manuscript section on the software tool feels underdeveloped.

Reviewer #3 (Public review):

Summary:

The authors present a new computational method (OPT) for predicting off-target probe binding in the commercial 10X Xenium spatial transcriptomics platform. They identified 28 genes in the 10x xenium human breast cancer gene panel (280 genes) that are not accurately detected at the single-molecule level. They validated the predicted off-target binding using reference data from single-cell RNA-seq and 3'-sequencing-based Visium RNA-seq. This work provides a practical resource and will serve as a valuable reference for future data interpretation.

Strengths:

(1) Provides a toolbox for the community to identify off-target probes.

(2) Validates the predictions using single-cell RNA-seq and sequencing-based Visium RNA-seq datasets.

Weaknesses:

(1) Does not apply the OPT method to the most widely used Xenium gene panels (e.g., pan-Human, pan-Mouse panels with ~5,000 genes each).

(2) Lacks clarity on how the confidence level of off-target predictions is calculated.

Author response:

We sincerely thank the editors and the reviewers for their feedback in helping us improve this manuscript. During the time this work has been under review, 10x Genomics has updated the probe sequences of their gene panels. We therefore plan to update these findings as well as further expand to incorporate reviewer recommendations.

eLife Assessment

This valuable study reveals the pro-locomotor effects of activating a deep brain region containing diverse range of neurons in both healthy and Parkinson's disease mouse models. While the findings are solid, mechanistic insights remain limited due to the small sample size. This research is relevant to motor control researchers and offers clinical perspectives.

Reviewer #1 (Public review):

Summary:

This study aimed to investigate the effects of optically stimulating the A13 region in healthy mice and a unilateral 6-OHDA mouse model of Parkinson's disease (PD). The primary objectives were to assess changes in locomotion, motor behaviors, and the neural connectome. For this, the authors examined the dopaminergic loss induced by 6-OHDA lesioning. They found a significant loss of tyrosine hydroxylase (TH+) neurons in the substantia nigra pars compacta (SNc) while the dopaminergic cells in the A13 region were largely preserved. Then, they optically stimulated the A13 region using a viral vector to deliver the channelrhodopsine (CamKII promoter). In both sham and PD model mice, optogenetic stimulation of the A13 region induced pro-locomotor effects, including increased locomotion, more locomotion bouts, longer durations of locomotion, and higher movement speeds. Additionally, PD model mice exhibited increased ipsilesional turning during A13 region photoactivation. Lastly, the authors used whole-brain imaging to explore changes in the A13 region's connectome after 6-OHDA lesions. These alterations involved a complex rewiring of neural circuits, impacting both afferent and efferent projections. In summary, this study unveiled the pro-locomotor effects of A13 region photoactivation in both healthy and PD model mice. The study also indicates the preservation of A13 dopaminergic cells and the anatomical changes in neural circuitry following PD-like lesions that represent the anatomical substrate for a parallel motor pathway.

Strengths:

These findings hold significant relevance for the field of motor control, providing valuable insights into the organization of the motor system in mammals. Additionally, they offer potential avenues for addressing motor deficits in Parkinson's disease (PD). The study fills a crucial knowledge gap, underscoring its importance, and the results bolster its clinical relevance and overall strength.

The authors adeptly set the stage for their research by framing the central questions in the introduction, and they provide thoughtful interpretations of the data in the discussion section. The results section, while straightforward, effectively supports the study's primary conclusion-the pro-locomotor effects of A13 region stimulation, both in normal motor control and in the 6-OHDA model of brain damage.

Weaknesses:

(1) Anatomical investigation. I have a major concern regarding the anatomical investigation of plastic changes in the A13 connectome (Figures 4 and 5). While the methodology employed to assess the connectome is technically advanced and powerful, the results lack mechanistic insight at the cell or circuit level into the pro-locomotor effects of A13 region stimulation in both physiological and pathological conditions. This concern is exacerbated by a textual description of results that doesn't pinpoint precise brain areas or subareas but instead references large brain portions like the cortical plate, making it challenging to discern the implications for A13 stimulation. Lastly, the study is generally well-written with a smooth and straightforward style, but the connectome section presents challenges in readability and comprehension. The presentation of results, particularly the correlation matrices and correlation strength, doesn't facilitate biological understanding. It would be beneficial to explore specific pathways responsible for driving the locomotor effects of A13 stimulation, including examining the strength of connections to well-known locomotor-associated regions like the Pedunculopontine nucleus, Cuneiformis nucleus, LPGi, and others in the diencephalon, midbrain, pons, and medulla. Additionally, identifying the primary inputs to A13 associated with motor function would enhance the study's clarity and relevance.

The study raises intriguing questions about compensatory mechanisms in Parkinson's disease a new perspective with the preservation of dopaminergic cells in A13, despite the SNc degeneration, and the plastic changes to input/output matrices. To gain inspiration for a more straightforward reanalysis and discussion of the results, I recommend the authors refer to the paper titled "Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon from the David Kleinfeld laboratory." This could guide the authors in investigating motor pathways across different brain regions.

(2) Description of locomotor performance. Figure 3 provides valuable data on the locomotor effects of A13 region photoactivation in both control and 6-OHDA mice. However, a more detailed analysis of the changes in locomotion during stimulation would enhance our understanding of the pro-locomotor effects, especially in the context of 6-OHDA lesions. For example, it would be informative to explore whether the probability of locomotion changes during stimulation in the control and 6-OHDA groups. Investigating reaction time, speed, total distance, and even kinematic aspects during stimulation could reveal how A13 is influencing locomotion, particularly after 6-OHDA lesions. The laboratory of Whelan has a deep knowledge of locomotion and the neural circuits driving it so these features may be instructive to infer insights on the neural circuits driving movement. On the same line, examining features like the frequency or power of stimulation related to walking patterns may help elucidate whether A13 is engaging with the Mesencephalic Locomotor Region (MLR) to drive the pro-locomotor effects. These insights would provide a more comprehensive understanding of the mechanisms underlying A13-mediated locomotor changes in both healthy and pathological conditions.

(3) Figure 2 indeed presents valuable information regarding the effects of A13 region photoactivation. To enhance the comprehensiveness of this figure and gain a deeper understanding of the neurons driving the pro-locomotor effect of stimulation, it would be beneficial to include quantifications of various cell types:

• cFos-Positive Cells/TH-Positive Cells: it can help determine the impact of A13 stimulation on dopaminergic neurons and the associated pro-locomotor effect in healthy condition and especially in the context of Parkinson's disease (PD) modeling.

• cFos-Positive Cells /TH-Negative Cells: Investigating the number of TH-negative cells activated by stimulation is also important, as it may reveal non-dopaminergic neurons that play a role in locomotor responses. Identifying the location and characteristics of these TH-negative cells can provide insights into their functional significance.<br /> Incorporating these quantifications into Figure 2 would enhance the figure's informativeness and provide a more comprehensive view of the neuronal populations involved in the locomotor effects of A13 stimulation.

(4) Referred to Figure 3. In the main text (page 5) when describing the animal with 6-OHDA the wrong panels are indicated. It is indicated in Figure 2A-E but it should be replaced with 3A-E. Please do that.

Summary of the Study after revision

The revised manuscript reflects significant efforts to improve clarity, organization, and data interpretation. The refinements in anatomical descriptions, behavioral analyses, and contextual framing have strengthened the manuscript considerably. However, the study still lacks direct causal evidence linking anatomical remodeling to behavioral improvements, and the small sample size in the anatomical analyses remains a concern. The authors have addressed many points raised in the initial review, but further acknowledgement of the exploratory nature of these findings would enhance the scientific rigor of the work.

Key Improvements in the Revision

The revised manuscript demonstrates considerable progress in clarifying data presentation, refining behavioral analyses, and improving the contextualization of anatomical findings. The restructuring of the anatomical section now provides greater precision in describing motor-related pathways, integrating terminology from the Allen Brain Atlas. The addition of new figures (Figures 4 and 5) strengthens the accessibility of these findings by illustrating key connectivity patterns more effectively. Furthermore, the correlation matrices have been adjusted to improve interpretability, ensuring that the presented data contribute meaningfully to the overall narrative of the study.

The authors have also made significant improvements in their behavioral analyses, particularly in the organization and presentation of locomotor data. Figure 3 has been revised to distinctly separate results from 6-OHDA and sham animals, providing a clearer comparison of locomotor outcomes. Additional metrics, such as reaction time, locomotion bouts, and movement speed, further enhance the granularity of the analysis, making the results more informative.

The discussion surrounding anatomical connectivity has also been strengthened. The revised manuscript now places greater emphasis on motor-related pathways and refines its analysis of A13 efferents and afferents. A newly introduced figure provides a concise summary of these connections, improving the contextualization of the anatomical data within the study's broader scope. Moreover, the authors have addressed the translational relevance of their findings by acknowledging the differences between optogenetic stimulation and deep brain stimulation (DBS). Their discussion now better situates the findings within existing literature on PD-related motor circuits, providing a more balanced perspective on the potential implications of A13 stimulation.

Remaining Concerns

Despite these substantial improvements, a number of critical concerns remain. The anatomical findings, though insightful, remain largely correlative and do not establish a causal link between structural remodeling and locomotor recovery. While the authors argue that these data will serve as a reference for future investigations, their necessity for the core conclusions of the study is not entirely clear. Additionally, while the anatomical data offer an interesting perspective on A13 connectivity, their direct relevance to the study's primary goal-demonstrating the role of A13 in locomotor recovery-remains uncertain. The authors emphasize that these data will be valuable for future research, yet their integration into the study's main narrative feels somewhat supplementary. Based on this last thought of the authors it is even more relevant another key limitation lying in the small sample size used for connectivity analyses. With only two sham and three 6-OHDA animals included, the statistical confidence in the findings is inherently limited. The absence of direct statistical comparisons between ipsilesional and contralesional projections further weakens the conclusions drawn from these anatomical studies. The authors have acknowledged that obtaining the necessary samples, acquiring the data, and analyzing them is a prolonged and resource-intensive process. While this may be a valid practical limitation, it does not justify the lack of a robust statistical approach. A more rigorous statistical framework should be employed to reinforce the findings, or alternative techniques should be considered to provide additional validation. Given these constraints, it remains unclear why the authors have not opted for standard immunohistochemistry, which could provide a complementary and more statistically accessible approach to validate the anatomical findings. Employing such an approach would not only increase the robustness of the results but also strengthen the study's impact by providing an independent confirmation of the observed structural changes.

Reviewer #2 (Public review):

Summary:

The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection, thus suggesting a remodeling of the A13 connectome. Whether this remodelling contributes to pro-locomotor effects of the photostimulation of the A13 region remains unknown as causality was not addressed.

Strengths:

Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients. The study also provides a description of the A13 region connectome pertaining to motor behaviors and how it changes after a dopaminergic lesion. Although there is no causal link between anatomical and behavioral data, it raises interesting questions for further studies.

Weaknesses:

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, some uncertainty remains regarding the phenotype of neurons underlying recovery of akinesia and improvement of bradykinesia.

Figure 4 is improved, but the results from the correlation analyses remain difficult to interpret, as they may reflect changes in various impaired brain regions independently of the A13 region. While the analysis offers a snapshot of correlated changes within the connectome, it does not identify which specific cell or axonal populations are actually increasing or decreasing. Although functional MRI connectome analyses are well-established, anatomical data seem less suitable for this purpose. How can one interpret correlated changes in anatomical inputs or outputs between two distinct regions?

Figure 5 is also improved, but there is room for further enhancement. As currently presented, it is difficult to distinguish the differences between the sham and 6-OHDA groups. The first column could compare afferents, while the second column could compare efferents. Given the small sample size, it would be more appropriate to present individual data rather than the mean and standard deviation.

Appraisal and impact

Although the behavioral experiments are convincing, the low number of animals in the anatomical studies is insufficient to make any relevant statistical conclusions due to extremely low statistical power.

Reviewer #3 (Public review):

Kim, Lognon et al. present an important finding on pro-locomotor effects of optogenetic activation of the A13 region, which they identify as a dopamine-containing area of the medial zona incerta that undergoes profound remodeling in terms of afferent and efferent connectivity after administration of 6-OHDA to the MFB. The authors claim to address a model of PD-related gait dysfunction, a contentious problem that can be difficult to treat by dopaminergic medication or DBS in conventional targets. They make use of an impressive array of technologies to gain insight into the role of A13 remodeling in the 6-OHDA model of PD. The evidence provided is solid and the paper is well written, but there are several general issues that reduce the value of the paper in its current form, and a number of specific, more minor ones. Also some suggestions, that may improve the paper compared to its recent form, come to mind.

The most fundamental issue that needs to be addressed is the relation of the structural to the behavioral findings. It would be very interesting to see whether the structural heterogeneity in afferent/effects projections induced by 6-OHDA is related to the degree of symptom severity and motor improvement during A13 stimulation.

The authors provide extensive interrogation of large-scale changes in the organization of the A13 region afferent and efferent distributions. It remains unclear how many animals were included to produce Fig 4 and 5. Fig S5 suggests that only 3 animals were used, is that correct? Please provide details about the heterogeneity between animals. Please provide a table detailing how many animals were used for which experiment. Were the same animals used for several experiments?

While the authors provide evidence that photoactivation of the A13 is sufficient in driving locomotion in the OFT, this pro-locomotor effect seems to be independent of 6-OHDA induced pathophysiology. Only in the pole test do they find that there seems to be a difference between Sham vs 6-OHDA concerning effects of photoactivation of the A13. Because of these behavioral findings, optogenic activation of A13 may represent a gain of function rather than disease-specific rescue. This needs to be highlighted more explicitly in the title, abstract and conclusion.

The authors claim that A13 may be a possible target for DBS to treat gait dysfunction. However, the experimental evidence provided (imparticular lack of disease-specific changes in the OFT) seem insufficient to draw such conclusions. It needs to be highlighted that optogenetic activation does not necessarily have the same effects as DBS (see the recent review from Neumann et al. in Brain: https://pubmed.ncbi.nlm.nih.gov/37450573/). This is important because ZI-DBS so far had very mixed clinical effects. The authors should provide plausible reasons for these discrepancies. Is cell-specificity, that only optogenetic interventions can achieve, necessary? Can new forms of cyclic burst DBS achieve similar specificity (Spix et al, Science 2021)? Please comment.

In a recent study, Jeon et al (Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus, 2022, Cell Reports) provided evidence on the topographically graded organization of STN afferents and McElvain et al. (Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon, 2021, Neuron) have shown similar topographical resolution for SNr efferents. Can a similar topographical organization of efferents and afferents be derived for the A13/ ZI in total?

In conclusion, this is an interesting study that can be improved taking into consideration the points mentioned above.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review):

Summary:

The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection, thus suggesting a remodeling of the A13 connectome. Whether this remodelling contributes to pro-locomotor effects of the photostimulation of the A13 region remains unknown as causality was not addressed.

Strengths:

Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients. The study also provides a description of the A13 region connectome pertaining to motor behaviors and how it changes after a dopaminergic lesion. Although there is no causal link between anatomical and behavioral data, it raises interesting questions for further studies.

Thank you for the comments.

Weaknesses:

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, some uncertainty remains regarding the phenotype of neurons underlying recovery of akinesia and improvement of bradykinesia.

The primary objective was to focus on a population of neurons that could contribute to functional recovery, with a long-term translational focus in mind. We have followed up on this by creating a rat-based DBS model of stimulating the A13 region (Bisht et al 2025). We agree that the next steps are to genetically dissect the circuits, and we have made a start on this with our recent publication (Sharma et al 2024).

Figure 4 is improved, but the results from the correlation analyses remain difficult to interpret, as they may reflect changes in various impaired brain regions independently of the A13 region. While the analysis offers a snapshot of correlated changes within the connectome, it does not identify which specific cell or axonal populations are actually increasing or decreasing. Although functional MRI connectome analyses are well-established, anatomical data seem less suitable for this purpose. How can one interpret correlated changes in anatomical inputs or outputs between two distinct regions?

We appreciate the reviewer's thoughtful comment regarding the interpretability of the correlation analyses in Figure 4. We fully acknowledge that our anatomical data cannot establish causality or identify specific cell types or axonal populations undergoing changes following unilateral nigrostriatal degeneration. However, our intent with this analysis was not to infer mechanistic pathways but rather to provide a systems-level overview of how the global organization of A13 efferents and afferents is altered following 6-OHDA lesioning. By calculating proportions of total inputs and outputs and comparing them across brain regions, we aimed to control for variability in labeling and highlight relative shifts in network organization. The correlation matrices are intended to capture coordinated changes in input/output distribution patterns, effectively reflecting how groups of regions co-vary in their input to or output from the A13 region. In our case, we used correlation analysis to identify how input and output distributions across brain regions reorganize as a network following 6-OHDA lesioning. For example, a positive correlation between inputs from Region A and Region B to the A13 suggests that across animals, when input from Region A is relatively high, input from Region B tends to be high as well, indicating that connectivity from these regions to the A13 may be co-regulated or affected similarly by the lesion. Conversely, a shift from positive to negative correlation may signal a divergence in how regions contribute to the A13 connectome after nigrostriatal degeneration (e.g., increased connectivity to Region A compared to reduced connectivity to Region B). Thus, these patterns offer new insight into the broader reorganization of the A13 connectome and may serve as systems-level signatures of altered anatomical organization, providing a foundation for future mechanistic investigations using circuit-specific tools. We have revised the text to better emphasize the correlative and descriptive nature of these analyses and to clarify that they serve as a hypothesis-generating exploration. Future studies using cell type- and/or projection-specific functional manipulations will be essential to determine the causal roles of these reorganized circuits. We believe our use of this method is justified in the context of exploring broad, lesion-induced network reorganization, and we hope this additional context helps clarify the purpose and limitations of our approach.

Figure 5 is also improved, but there is room for further enhancement. As currently presented, it is difficult to distinguish the differences between the sham and 6-OHDA groups. The first column could compare afferents, while the second column could compare efferents. Given the small sample size, it would be more appropriate to present individual data rather than the mean and standard deviation.

We have reorganized Figure 5 as suggested.

Appraisal and impact

Although the behavioral experiments are convincing, the low number of animals in the anatomical studies is insufficient to make any relevant statistical conclusions due to extremely low statistical power.

See previous comments on this.

Reviewer #2 (Recommendations for the authors):

Points that need to be addressed:

Figure S1 is supposed to illustrate the percentage of expression in all mice, but the number of mice does not match (n=3 and 3 in Figure S1 versus n=5 and 6 in Figure 1). Revise the legend or add the missing data.

We have added the additional data to this graph (Figure 2 – figure supplement 1) and have separated out 6-OHDA and sham mice for clarity.

Page 4: "There was also an increase in the number of ChR2 cells with c-fos labeling in 6-OHDA ChR2 mice compared to the 6-OHDA eYFP mice. However, there was no net increase in TH+ cells labelled with ChR2 and c-Fos suggesting a heterogeneous population of activated cells." A quantification will be necessary to advance this conclusion.

We were able to determine that there was a trend of increased c-Fos intensity within the A13 region following photostimulation. However, the variability in the data makes it premature to comment on the TH co-localization and we have deleted this statement.

Figure 3: The choice of red and green could be a problem for color-blind people.

Thank you - switched to orange and cyan instead.

Page 7, 4th paragraph: "6-OHDA mice demonstrated significantly greater descent times than sham mice (Figure 3L, p<0.01)." This is not what is shown in the Figure 3L.

We made changes in the legend and text to clarify.

Page 7, last line: PT abbreviation should be introduced in parentheses at the beginning of this section.

Removed the abbreviation.

Figure S4A: The authors should show data for the VTA or refer to the quantification of Figure S4G in the text.

Now referenced correctly in the text.

Figure S7 and S8 are not referenced in the results or methods.

References added to text.

Double-check the formatting of some references: L.-X. Li et al, 2021, L. Kim et al., 2021.

References checked and corrected.

eLife Assessment

Bonnifet et al. present data on the expression and interacting partners of the transposable element L1 in the mammalian brain. The work includes important findings addressing the potential role of L1 in aging and neurodegenerative disease. The reviewers conclude that several aspects of the study are well done and most evidence is solid, with a noted concern related to the RNA-seq analysis.

Reviewer #1 (Public review):

Summary:

In this study, Bonnifet et al. profile the presence of L1 ORF1p in the mouse and human brain and report that ORF1p is expressed in the human and mouse brain specifically in neurons at steady state and that there is an age-dependent increase in expression. This is a timely report as two recent papers have extensively documented the presence of full-length L1 transcripts in the mouse and human brain (PMID: 38773348 & PMID: 37910626). Thus, the finding that L1 ORF1p is consistently expressed in the brain is important to document and will be of value to the field.

Strengths:

Several parts of this manuscript appear to be well done and include the necessary controls. In particular, the documentation of neuron-specific expression of ORF1p in the mouse brain is an interesting finding with nice documentation. This will be very useful information for the field.

Weaknesses:

The transcriptomic data using human postmortem tissue presented in Figures 4 and 5 are not convincing. Quantification of transposon expression on short read sequencing has important limitations. Longer reads and complementary approaches are needed to study the expression of evolutionarily young L1s (see PMID: 38773348 & PMID: 37910626 for examples of the current state of the art). As presented, the human RNA data is inconclusive due to the short read length and small sample size. The value of including an inconclusive analysis in the manuscript is difficult to understand. With this data set, the authors cannot investigate age-related changes in L1 expression in human neurons.

In line with these comments, the title should be changed to better reflect the findings in the manuscript. A title that does not mention "L1 increase with aging" would be better.

Comments on Revisions:

It is notable that the expression of ORF1p in the human brain shows two strong bands in the WB. As the authors acknowledge in their discussion, some labs report only one band. The authors have performed a number of controls to address this issue, acknowledge remaining uncertainty, and discuss the discrepancy in the field.

Reviewer #2 (Public review):

Summary:

Bonnifet et al. sought to characterize the expression pattern of L1 ORF1p expression across the entire mouse brain, in young and aged animals and to corroborate their characterization with Western blotting for L1 ORF1p and L1 RNA expression data from human samples. They also queried L1 ORF1p interacting partners in the mouse brain by IP-MS.

Strengths:

A major strength of the study is the use of two approaches: a deep-learning detection method to distinguish neuronal vs. non-neuronal cells and ORF1p+ cells vs. ORF1p- cells across large-scale images encompassing multiple brain regions mapped by comparison to the Allen Brain Atlas, and confocal imaging to give higher resolution on specific brain regions. These results are also corroborated by Western blotting on six mouse brain regions. Extension of their analysis to post-mortem human samples, to the extent possible, is another strength of the paper. The identification of novel ORF1p interactors in brain is also a strength in that it provides a novel dataset for future studies.

Weaknesses:

The main weakness of the IP-MS portion of the study is that none of the interactors were individually validated or subjected to follow-up analyses. The list of interactors was compared to previously published datasets, but not to ORF1p interactors in any other mouse tissue.

Comments on revisions:

The co-staining of Orf1p with Parvalbumin (PV) presented in Supplemental Figure S5 is a welcome addition exploring the cell type-specificity of Orf1p staining, and broadly corroborates the work of Bodea et al. while revealing that Orf1p also is expressed in non-PV+ cells, consistent with L1 activity across a range of neuronal subtypes. The authors also have strengthened their findings regarding the increased intensity of ORF1p staining in aged compared to young animals, and the newly presented results are indeed more convincing. The prospect of increased neuronal L1 activity with age is exciting, and the results in this paper have provided the groundwork for ongoing discoveries in this area. While it is disappointing that no Orf1p interactors were followed up, this is understandable and the data are nonetheless valuable and will likely prove useful to future studies.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

Summary: 

In this study, Bonnifet et al. profile the presence of L1 ORF1p in the mouse and human brain and report that ORF1p is expressed in the human and mouse brain specifically in neurons at steady state and that there is an age-dependent increase in expression. This is a timely report as two recent papers have extensively documented the presence of full-length L1 transcripts in the mouse and human brain (PMID: 38773348 & PMID: 37910626). Thus, the finding that L1 ORF1p is consistently expressed in the brain is important to document and will be of value to the field. 

Strengths: 

Several parts of this manuscript appear to be well done and include the necessary controls. In particular, the documentation of neuron-specific expression of ORF1p in the mouse brain is an interesting finding with nice documentation. This will be very useful information for the field. 

We thank the reviewer for this positive comment. 

Weaknesses: 

Several parts of the manuscript appear to be more preliminary and need further experiments to validate their claims. In particular, the data suggesting expression of L1 ORF1p in the human brain and the data suggesting increased expression in the aged brain need further validation. Detailed comments: 

(1) The expression of ORF1p in the human brain shown in Fig. 1j is puzzling. Why are there two strong bands in the WB? How can the authors be sure that this signal represents ORF1p expression and not non-specific labelling? While the authors discuss that others have found double bands when examining human ORF1p, there are also several labs that report only one band. This discrepancy in the field should at least be discussed and the uncertainties with their findings should be acknowledged. 

Please see also our extensive response to this comment we made in round #1 of the revisions.

As a summary, in response to the initial review, we included several lines of additional evidence in the revised manuscript:

siRNA-mediated knockdown of ORF1p in human neurons, resulting in ≈50% signal reduction using the antibody in question (Suppl. Fig. 2C) immunoprecipitation using the human ORF1p antibody in question confirming signal specificity (Suppl. Fig. 2B) use of a second antibody in immunostainings, including a new control (Suppl. Fig. 2E) and a revised discussion acknowledging the uncertainty surrounding the lower band:

“The double band pattern in Western blots has been observed in other studies for human ORF1p outside of the brain as well as for mouse ORF1p. […] The nature of the lower band is unknown, but might be due to truncation, specific proteolysis or degradation.”

We have also now added more content to the paragraph starting from line 183 : "While there is some discrepancy in the field, the double band pattern in Western blots..."

To our understanding, this combination of independent methods using two antibodies and complementary validation strategies supports the presence of ORF1p in human brain tissue.

(2) The data showing a reduction in ORF1p expression in the aged mouse brain is an interesting observation, but the effect magnitude of effect is very limited and somewhat difficult to interpret. This finding should be supported by orthogonal methods to strengthen this conclusion. For example, by WB and by RNA-seq (to verify that the increase in protein is due to an increase in transcription). 

This would indeed be valuable but at this point, we will not be able to perform these experiments at this point (please also see revision #1 for a more detailed answer)

(3) The transcriptomic data using human postmortem tissue presented in Figure 4 and Figure 5 are not convincing. Quantification of transposon expression on short read sequencing has important limitations. Longer reads and complementary approaches are needed to study the expression of evolutionarily young L1s (see PMID: 38773348 & PMID: 37910626 for examples of the current state of the art). As presented, the human RNA data is inconclusive due to the short read length and small sample size. The value of including an inconclusive analysis in the manuscript is difficult to understand. With this data set, the authors cannot investigate age-related changes in L1 expression in human neurons. 

Please see also our extensive response to this comment we made in round #1 of the revisions.

In the revised version, we have added further statistical analyses, incorporated locus-specific mappability scores and provided an even more nuanced interpretation of our findings, as illustrated in lines 390 and 427.

We have acknowledged the limitations of short-read sequencing in this context, while referencing established methodologies (e.g., Teissandier et al., 2019) and recent benchmarking studies (e.g., Schwarz et al., 2022) that validate the use of such data under specific precautions—many of which we have implemented.

Given these considerations, and with the guidance of a co-author with specific expertise in TE bioinformatics, we believe our approach is justified and robust.

(4) In line with these comments, the title should be changed to better reflect the findings in the manuscript. A title that does not mention "L1 increase with aging" would be better. 

In line with our response to Point (3), we prefer to retain the current analyses and discussion, which we believe strike an appropriate balance between caution and added scientific value.

Reviewer #2 (Public review): 

Summary: 

Bonnifet et al. sought to characterize the expression pattern of L1 ORF1p expression across the entire mouse brain, in young and aged animals and to corroborate their characterization with Western blotting for L1 ORF1p and L1 RNA expression data from human samples. They also queried L1 ORF1p interacting partners in the mouse brain by IP-MS. 

Strengths: 

A major strength of the study is the use of two approaches: a deep-learning detection method to distinguish neuronal vs. non-neuronal cells and ORF1p+ cells vs. ORF1p- cells across large-scale images encompassing multiple brain regions mapped by comparison to the Allen Brain Atlas, and confocal imaging to give higher resolution on specific brain regions. These results are also corroborated by Western blotting on six mouse brain regions. Extension of their analysis to post-mortem human samples, to the extent possible, is another strength of the paper. The identification of novel ORF1p interactors in brain is also a strength in that it provides a novel dataset for future studies. 

We thank the reviewer for these positive comments.

Weaknesses: 

The main weakness of the IP-MS portion of the study is that none of the interactors were individually validated or subjected to follow-up analyses. The list of interactors was compared to previously published datasets, but not to ORF1p interactors in any other mouse tissue.

As we had stated in the first round of revision, the list of previously published datasets does include a mouse dataset with ORF1p interacting proteins in mouse spermatocytes (please see line 478-4479: “ORF1p interactors found in mouse spermatocytes were also present in our analysis including CNOT10, CNOT11, PRKRA and FXR2 among others (Suppl_Table4).”) -> De Luca, C., Gupta, A. & Bortvin, A. Retrotransposon LINE-1 bodies in the cytoplasm of piRNA-deficient mouse spermatocytes: Ribonucleoproteins overcoming the integrated stress response. PLoS Genet 19, e1010797 (2023)). We agree that a validation of protein interactors of ORF1p in the mouse brain would have been valuable. However, the significant overlap with previously published interactors highlights the validity of our data. As reviewer #2 points out in the comments on revisions, we hope that follow-up studies will address these points and we anticipate that this list of ORF1p protein interactors in the mouse brain will be of further use for the community.

Comments on revisions: 

The co-staining of Orf1p with Parvalbumin (PV) presented in Supplemental Figure S5 is a welcome addition exploring the cell type-specificity of Orf1p staining, and broadly corroborates the work of Bodea et al. while revealing that Orf1p also is expressed in non-PV+ cells, consistent with L1 activity across a range of neuronal subtypes. The authors also have strengthened their findings regarding the increased intensity of ORF1p staining in aged compared to young animals, and the newly presented results are indeed more convincing. The prospect of increased neuronal L1 activity with age is exciting, and the results in this paper have provided the groundwork for ongoing discoveries in this area. While it is disappointing that no Orf1p interactors were followed up, this is understandable and the data are nonetheless valuable and will likely prove useful to future studies. 

Thank you for your time and constructive comments.

Reviewer #1 (Recommendations for the authors): 

We would recommend that the human RNA-seq analysis is removed from the manuscript. The human RNA data is inconclusive due to the short read length and small sample size. The value of including an inconclusive analysis in the manuscript is difficult to understand. With this data set, the authors cannot investigate age-related changes in L1 expression in human neurons. 

Reviewer #2 (Recommendations for the authors): 

Thank you for addressing my suggestions. I have no further recommendations at this time.

eLife Assessment

This useful study advances our understanding of how organisms respond to chronic oxidative stress. Using the nematode C. elegans, the authors identified key neuronal signaling molecules and their receptors that are required for stress signaling and survival. The evidence supporting the conclusions is solid, with rigorous genetics, stress response analysis, and transcriptional profiling. This research will be of broad interest to neuroscientists and researchers working in the field of oxidative stress regulation.

Reviewer #1 (Public review):

Summary:

The researchers aimed to identify which neurotransmitter pathways are required for animals to withstand chronic oxidative stress. This work thus has important implications for disease processes that are caused/linked to oxidative stress. This work identified specific neurotransmitters and receptors that coordinate stress resilience, both prior to and during stress exposure. Further, the authors identified specific transcriptional programs coordinated by neurotransmission that may provide stress resistance.

Strengths:

The manuscript is very clearly written with a well-formulated rationale. Standard C. elegans genetic analysis and rescue experiments were performed to identify key regulators of the chronic oxidative stress response. These findings were enhanced by transcriptional profiling that identified differentially expressed genes that likely affect survival when animals are exposed to stress.

Weaknesses:

Where the gar-3 promoter drives expression was not discussed in the context of the rescue experiments in Figure 7.

Reviewer #2 (Public review):

In this paper, Biswas et al. describe the role of acetylcholine (ACh) signaling in protection against chronic oxidative stress in C. elegans. They showed that disruption of ACh signaling in either unc-17 mutants or gar-3 mutants led to sensitivity to toxicity caused by chronic paraquat (PQ) treatment. Using RNA seq, they found that approximately 70% of the genes induced by chronic PQ exposure in wild type failed to upregulate in these mutants. The overexpression of gar-3 selectively in cholinergic neurons was sufficient to promote protection against chronic PQ exposure in an ACh-dependent manner. The study points to a previously undescribed role for ACh signaling in providing organism-wide protection from chronic oxidative stress, likely through the transcriptional regulation of numerous oxidative stress-response genes. The paper is well-written, and the data are robust, though some conclusions seem preliminary and do not fully support the current data. While the study identifies the muscarinic ACh receptor gar-3 as an important regulator of the response to PQ, the specific neurons in which gar-3 functions were not unambiguously identified, and the sources of ACh that regulate GAR-3 signaling and the identities of the tissues targeted by gar-3 were not addressed, limiting the scope of the study.

Major Comments:

(1) The site of action of cholinergic signaling for protection from PQ was not adequately explored. The authors' conclusion that cholinergic motor neurons are protective is based on studies using overexpression of gar-3 and an unc-17 allele that may selectively disrupt ACh in cholinergic motor neurons (Figure 9F), but these approaches are indirect. To more directly address the site of action, the authors should conduct rescue experiments using well-defined heterologous promoters. Figure 7G shows that gar-3 expressed under a 7.5 kb promoter fragment fully rescues the defect of gar-3 mutants, but the authors did not report where this promoter fragment is expressed, nor did they conduct rescue experiments of the specific tissues where gar-3 is known to be expressed (cholinergic neurons, GABAergic neurons, pharynx, or muscles). UNC-17 rescue experiments could also be useful to address the site of action. Does expression of unc-17 selectively in cholinergic motor neurons rescue the stress sensitivity of unc-17 mutants (or restore resistance to gar-3(OE); unc-17 mutants)? These experiments may also address whether ACh acts in an autocrine or paracrine manner to activate gar-3, which would be an important mechanistic insight to this study that is currently lacking.

(2) The genetic pan-neuronal silencing experiments presented in Figure 1 motivated the subsequent experiments, but the authors did not relate these observations to ACh/gar-3 signaling. For example, the authors did not address whether silencing just the cholinergic motor neurons at the different times tested has the same effects on survival as pan-neuronal silencing.

(3) It is assumed that protection occurs through inter-tissue signaling of ACh to target tissues, where it impacts gene expression. While this is a reasonable assumption, it has not been directly shown here. It is recommended that the authors examine GFP reporter expression of a sampling of the genes identified in this study (including proteasomal genes that the authors highlight) that are regulated by unc-17 and gar-3. This would serve to independently confirm the RNAseq data and to identify target tissues that are subject to gene expression regulation by ACh, which would significantly strengthen the study.

Author response:

Reviewer #1 (Recommendations for the authors):

“The gar-3 promoter expression pattern was not discussed in the context of rescue experiments.”

We agree that the expression pattern of the gar-3 promoter used in our rescue experiments should be clarified. We will include a description of the tissues where the 7.5 kb gar-3 promoter fragment is expressed, based on both prior studies and our own expression data. We will also discuss how the gar-3 cell and tissue expression pattern relates to both our analysis of gar-3 expression in the genome edited strain we generated as well as the observed rescue effects.

Reviewer #2 (Recommendations for the authors):

(1) The site of action of cholinergic signaling was not adequately explored.

We plan to perform additional rescue experiments using heterologous promoters to drive gar-3 expression in specific tissues (e.g. cholinergic neurons, muscle). These experiments will help clarify the sufficiency of unc-17 expression in specific cell types for rescue. However, we point out that cell-specific unc-17 knockdown by RNAi using the unc-17b promoter (expression largely restricted to ventral cord ACh motor neurons) increases sensitivity to PQ in our long-term survival assays. Combined with our analysis of unc-17(e113) mutants, we believe our data offer robust support of a requirement for unc-17 expression in cholinergic motor neurons.

(2) Pan-neuronal silencing experiments were not connected to ACh/GAR-3 signaling.

We will expand our discussion to relate the pan-neuronal silencing results to our analysis of ACh signaling. We used the pan-neuronal silencing to motivate further analysis of various neurotransmitter systems. We note that our studies implicate both glutamatergic and cholinergic systems in protective responses to oxidative stress. The effects of silencing on survival during long-term PQ exposure may therefore be derived solely from cholinergic neurons, glutamatergic neurons, or a combination of both neuronal populations. We hope the reviewer will agree that distinguishing between these possibilities may be quite complicated and is not central to the main message of our paper. We therefore suggest this additional analysis lies outside the scope of this revision.

(3) Inter-tissue signaling and transcriptional regulation by ACh were assumed but not directly shown.

We will generate GFP reporters for a subset of genes (including proteasomal genes) identified in our RNA-seq analysis or assess their expression by quantitative RT-PCR to validate cholinergic regulation. These experiments will help to identify target tissues and confirm transcriptional regulation by cholinergic signaling.

We appreciate the opportunity to revise our manuscript and believe that these additions will significantly strengthen the mechanistic insights and overall impact of our study. Please let us know if further clarification is needed.

eLife Assessment

This important work by Lesser et al provides a first and comprehensive description of Drosophila wing proprioceptors at an EM resolution. By linking peripheral neurons with information on their morphology and connectivity in the central nervous system, the authors provide new hypotheses and tools to study proprioceptive motor control of the wing in the fruit fly. The evidence and techniques supporting this work are solid, and this resource will contribute to connectome-based modeling of fly behavior.

Reviewer #1 (Public review):

Summary:

Lesser et al provide a comprehensive description of Drosophila wing proprioceptive sensory neurons at the electron microscopy resolution. This "tour-de-force" provides a strong foundation for future structural and functional research aimed at understanding wing motor control in Drosophila with implications for understanding wing control across other insects.

Strengths:

(1) The authors leverage previous research that described many of the fly wing proprioceptors, and combine this knowledge with EM connectome data such that they now provide a near-complete morphological description of all wing proprioceptors.

(2) The authors cleverly leverage genetic tools and EM connectome data to tie the location of proprioceptors on the wings with axonal projections in the connectome. This enables them to both align with previous literature as well as make some novel claims.

3) In addition to providing a full description of wing proprioceptors, the authors also identified a novel population of sensors on the wing tegula that make direct connections with the B1 wing motor neurons, implicating the role of the tegula in wing movements that was previously underappreciated.

(4) Despite being the most comprehensive description so far, it is reassuring that the authors clearly state the missing elements in the discussion.

Weaknesses:

(1) The authors do their main analysis on data from the FANC connectome but provide corresponding IDs for sensory neurons in the MANC connectome. I wonder how the connectivity matrix compares across FANC and MANC if the authors perform a similar analysis to the one they have done in Figure 2. This could be a valuable addition and potentially also pick up any sexual dimorphism.

(2) The authors speculate about the presence of gap junctions based on the density of mitochondria. I'm not convinced about this, given that mitochondrial densities could reflect other things that correlate with energy demands in sub-compartments.

(3) I'm intrigued by how the tegula CO is negative for iav. I wonder if authors tried other CO labeling genes like nompc. And what does this mean for the nature of this CO. Some more discussion on this anomaly would be helpful.

(4) The authors conclude there are no proprioceptive neurons in sclerite pterale C based on Chat-Gal4 expression analysis. It would be much more rigorous if authors also tried a pan-neuronal driver like nsyb/elav or other neurotransmitter drivers (Vglut, GAD, etc) to really rule this out. (I hope I didn't miss this somewhere.)

Overall, I consider this an exceptional analysis that will be extremely valuable to the community.

Reviewer #2 (Public review):

Summary:

Lesser et al. present an atlas of Drosophila wing sensory neurons. They proofread the axons of all sensory neurons in the wing nerve of an existing electron microscopy dataset, the female adult fly nerve cord (FANC) connectome. These reconstructed sensory axons were linked with light microscopy images of full-scale morphology to identify their origin in the periphery of the wing and encoded sensory modalities. The authors described the morphology and postsynaptic targets of proprioceptive neurons as well as previously unknown sensory neurons.

Strengths:

The authors present a valuable catalogue of wing sensory neurons, including previously undescribed sensory axons in the Drosophila wing. By providing both connectivity information with linked genetic drive lines, this research facilitates future work on the wing motor-sensory network and applications relating to Drosophila flight. The findings were linked to previous research as well as their putative role in the proprioceptive and nerve cord circuitry, providing testable hypotheses for future studies.

Weaknesses:

(1) With future use as an atlas, it should be noted that the evidence is based on sensory neurons on only one side of the nerve cord. Fruit flies have stereotyped left/right hemispheres in the brain and left/right hemisegments in the nerve cord. The comparison of left and right neurons of the nervous system can give a sense of how robust the morphological and connectivity findings are. Here, the authors have not compared the left and right side sensory axons from the wing nerve, leaving potential for developmental variability across samples and left/right hemisegments.

(2) Not all links between the EM reconstructions and driver lines are convincing. To strengthen these, for all EM-LM matches in Figures 3-7, rotated views of the driver line (matching the rotated EM views) should be shown to provide a clearer comparison of the data. In particular, Figure 3G and Figure 7B are not very convincing based on the images shown. MCFO imaging of the driver lines in Figure 3G and 7B would make this position stronger if a clone that matches the EM reconstruction could be identified.

(3) Figure 7B looks like the driver line might have stochastic expression in the sensory neuron, which further reduces confidence in the result shown in Figure 7C. Is this expression pattern in the wing consistently seen? Many split-GAL4s have stochastic expressions. The evidence would be strengthened if the authors presented multiple examples (~4-5) of each driver line's expression pattern in the supplement.

(4) Certain claims in this work lack quantitative evidence. On line 128, for instance, "Overall, our comprehensive reconstruction revealed many morphological subgroups with overlapping postsynaptic partners, suggesting a high degree of integration within wing sensorimotor circuits." If a claim of subgroups having shared postsynaptic partners is being made, there should have been quantitative evidence. For example, cosine similar amongst members of each group compared to the cosine similarity of shuffled/randomised sets of axons from different groups. The heat map of cosine similarity in Figure 2B alone is not sufficient.

(5) Similarly, claims about putative electrical connections to b1 motor neurons are very speculative. The authors state that "their terminals contain very densely packed mitochondria compared to other cells", without providing a quantitative comparison to other sensory axons. There is also no quantitative comparison to the one example of another putative electrical connection from the literature. Further, it should be noted that this connection from Trimarchi and Murphey, 1997, is also stated as putative on line 167, which further weakens this evidence. Quantification would strongly strengthen this position. Identification of an example of high mitochondrial density at a confirmed electrical connection would be even better. In the related discussion section "A potential metabolic specialization for flight circuitry", it should be more clearly noted that the dense mitochondria could be unrelated to a putative electrical connection. If the authors have an alternative hypothesis about the mitochondria density, this should be stated as well.

(6) It would be appropriate to cite previous work using a similar strategy to match sensory axons to their cell bodies/dendrites at the periphery using driver lines and connectomics (see Figure 5 for example in the following paper: https://doi.org/10.7554/eLife.40247 ).

The methods section is very sparse. For the sake of replicability, all sections should be expanded upon.

Reviewer #3 (Public review):

Summary:

The authors aim to identify the peripheral end-organ origin in the fly's wing of all sensory neurons in the anterior dorsomedial nerve. They reconstruct the neurons and their downstream partners in an electron microscopy volume of a female ventral nerve cord, analyse the resulting connectome, and identify their origin with a review of the literature and imaging of genetic driver lines. While some of the neurons were already known through previous work, the authors expand on the identification and create a near-complete map of the wing mechanosensory neurons at synapse resolution.

Strengths:

The authors elegantly combine electron microscopy, neuron morphology, connectomics, and light microscopy methods to bridge the gap between fly wing sensory neuron anatomy and ventral nerve cord morphology. Further, they use EM ultrastructural observations to make predictions on the signaling modality of some of the sensory neurons and thus their function in flight.

The work is as comprehensive as state-of-the-art methods allow to create a near-complete map of the wing mechanosensory neurons. This work will be of importance to the field of fly connectomics and modelling of fly behavior, as well as a useful resource to the Drosophila research community.

Through this comprehensive mapping of neurons to the connectome, the authors create a lot of hypotheses on neuronal function, partially already confirmed with the literature and partially to be tested in the future. The authors achieved their aim of mapping the periphery of the fly's wing to axonal projections in the ventral nerve cord, beautifully laying out their results to support their mapping.

The authors identify the neurons in a previously published connectome of a male fly ventral nerve cord to enable cross-individual analysis of connections. Further, together with their companion paper, Dhawan et al. 2025, describing the haltere sensory neurons in the same EM dataset, they cover the entire mechanosensory space involved in Drosophila flight.

Weaknesses:

The connectomic data are only available upon request; the inclusion of a connectivity table of the reconstructed neurons would aid analysis reproducibility and cross-dataset comparisons.

eLife Assessment

This fundamental study identifies specific neural mechanisms through which HIF-1 signaling in ADF serotonergic neurons extends lifespan in C. elegans, revealing that downstream signaling in multiple types of neurons, as well as other neuromodulators like GABA, tyramine, and NLP-17, is required for this effect. The strength of the evidence is largely convincing, as the authors establish the necessity and causality of key neuronal components using multiple genetic tools and functional dissection in a well-validated model organism.

Reviewer #1 (Public review):

Summary:

In this study by Kitto et al., the authors set out to identify specific signaling components regulating the hypoxic response from the neurons to the periphery and which components are required for lifespan extension. Their previous work had shown that expression of a stabilized HIF-1 mutant in the nervous system extends lifespan through the serotonin receptor SER-7 and leads to the induction of fmo-2 in the intestine. In the current study, they mapped the precise neural circuits required for this response, as well as the signaling mediators. Their work reveals that neurotransmitters GABA and tyramine, and the neuropeptide NLP-17, act downstream of neuronal HIF-1 to convey a "hypoxic signal" to peripheral tissues. Through cell-type-specific expression studies, targeted knockouts, and comprehensive lifespan analysis, the authors provide robust evidence to support their conclusions. The insights gained from the study are both moving the field forward as they advance our understanding of neuro-peripheral hypoxic signaling, but they also lay the groundwork for potential therapeutic strategies aimed at the modulation of such signaling pathways.

Strengths:

(1) This study provides new evidence further delineating signaling components required for hypoxic signaling-mediated longevity, from the nervous system to the periphery. Using a rigorous approach where they express stabilized HIF-1 mutant selectively in ADF, NSM, and HSN serotonergic neurons, followed by cell-type-specific tph-1 knockouts to pinpoint ADF-dependent serotonin signaling as essential for both lifespan extension and intestinal fmo-2 induction.

This was followed by generating 11 transgenic lines that drive SER-7 expression under distinct neuron-specific promoters, to systematically tease out in which of 27 candidate neurons SER-7 functions to mediate hypoxia-induced longevity. This ultimately highlighted the RIS interneuron as the required signaling hub.

(2) As the intestine lacks direct neuronal innervation, the authors employ neuron-specific RNAi (TU3311 strain) and dense core vesicle analyses to identify that the neuropeptide NLP-17 is required to transmit the hypoxic signal from RIS to induce fmo-2 in the intestine.

(3) Overall, the paper is very well written. The experiments were carried out carefully and thoroughly, and the conclusions drawn are also well supported by the results they are showing.

Weaknesses:

Overall, I don't see many weaknesses. One point relates to their read-outs, which rely heavily on lifespan measurements and fmo-2 induction without evaluating other physiological processes that serotonin or NLP-17 might affect. For translational relevance, it would be valuable to assess or mention potential adverse effects, such as changes in reproduction, pharyngeal pumping, or proteostasis capacity (proteostasis capacity specifically in the tissue showing fmo-2 upregulation).

While lifespan assays and fmo-2 expression do provide strong evidence, incorporating additional markers of stress resistance could strengthen the link between hypoxic signaling and organismal health as well.

Reviewer #2 (Public review):

Summary:

The authors aimed to identify the specific neurons, neurotransmitters, and neuropeptides that mediate the longevity effects of the hypoxic response in C. elegans. By genetically dissecting the pathway downstream of HIF-1, they define a neural circuit involving ADF serotonergic neurons, the SER-7 receptor in the RIS interneuron, tyraminergic signaling from RIM, and neuropeptide NLP-17, ultimately linking neuronal hypoxic sensing to pro-longevity signaling in the intestine.

Strengths:

The study employs a diverse genetic toolkit, including neuron-specific transgenes, tissue-specific knockouts and rescues, RNAi knockdowns, allowing the authors to pinpoint causality, sufficiency, and necessity with high resolution. The comprehensive mapping of cell-nonautonomous signaling adds depth to our understanding of how HIF and serotonin signaling interface with aging pathways. The conclusions are supported by consistent survival assays and fmo-2 gene expression analyses.

Weaknesses:

A key limitation is the lack of clear evidence showing epistasis of so many identified molecular/neuronal components downstream of HIF-1 and serotonin. Thus, the mechanisms of how a diverse set of molecules/neurons coordinate and mediate neuronal HIF-1 effects on intestinal fmo-2 and longevity remain murky. Some rescue strategies may inadvertently cause non-physiological expression. Additionally, environmental hypoxia was not tested in parallel, so the claim on "hypoxia respone" throughout the manuscript is not justified by genetic manipulation alone, and the translational relevance of the genetic manipulations remains somewhat uncertain.

Reviewer #3 (Public review):

Summary:

This study found that ADF serotonergic neurons have a significant role in extending lifespan mediated by HIF-1, as well as serotonin receptor SER-7 in the GABAergic RIS interneurons. The author focuses on the sufficiency and necessity of components from the central nervous system and how they contribute to aging upon hypoxia.

Previous work from the lab has identified that the stabilization of HIF-1 in neurons is sufficient to extend lifespan through the serotonin receptor, SER-7, which subsequently activates fmo-2 in the intestine and leads to lifespan extension. Building on this, the author sought to determine which serotonergic neurons are involved and found that serotonin signaling in ADF neurons is required for lifespan extension mediated by HIF-1.

The author next tested which subset of neurons requires Ser-7 expression to rescue hypoxic response. They found that ser-7 expression in multiple neurons is sufficient to induce fmo-2, with the top candidate being the RIS neuron. Ablation of the RIS neuron did not extend lifespan, suggesting that ser-7 expression in the RIS neuron is required for lifespan extension, positioning it as a key component in the longevity signaling pathway.

The author also investigated neurotransmitters and found that GABA and tyramine are important components in this circuit. They showed that the tyramine receptor called tyra-3 is required for vhl-1-mediated longevity. Given that tyra-3 is expressed in oxygen- and carbon dioxide-sensing neurons, the author demonstrated that these sensing neurons work downstream of serotonin signaling. Lastly, the author screened neuropeptide/receptor binding pairs and identified NLP-17 as playing a role in hypoxia-mediated longevity.

Originality and Significance:

This research is significant in that it uncovers components that are sufficient and necessary for lifespan extension via the hypoxic response. It provides comprehensive data supporting longevity induced by HIF-1-mediated hypoxic response, in conjunction with fmo-2, a longevity gene, as demonstrated in previous work from the lab. Moreover, it provides a number of new transgenic worm tools for C. elegans and aging communities.

Data and Methodology:

(1) The experiments were thoroughly conducted, especially the generations of strains using different neuron-type promoters and crossing into mutant strains to demonstrate sufficiency and necessity.

(2) Some figure legends from the text do not match what the data show. (Figure 6E, F, G).

(3) The lifespan graph legends are confusing and could use some revamping for better clarification.

Conclusions:

This study provides insights into how hypoxic response regulates aging in a cell non-autonomous manner, outlining a potential circuit involving neurons, neurotransmitters, and neuropeptides.

eLife Assessment

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors found that models based on multimodal and dynamic embedding features of audiovisual movies predicted brain responses better than models based on unimodal or static features. The evidence supporting the claims is generally solid, with clear benchmarking against baseline models. The work will be of interest to researchers in cognitive neuroscience and AI-based brain modeling.

Reviewer #1 (Public review):

Summary:

This study compares four models - VALOR (dynamic visual-text alignment), CLIP (static visual-text alignment), AlexNet (vision-only), and WordNet (text-only) - in their ability to predict human brain responses using voxel-wise encoding modeling. The results show that VALOR not only achieves the highest accuracy in predicting neural responses but also generalizes more effectively to novel datasets. In addition, VALOR captures meaningful semantic dimensions across the cortical surface and demonstrates impressive predictive power for brain responses elicited by future events.

Strengths:

The study leverages a multimodal machine learning model to investigate how the human brain aligns visual and textual information. Overall, the manuscript is logically organized, clearly written, and easy to follow. The results well support the main conclusions of the paper.

Weaknesses:

(1) My primary concern is that the performance difference between VALOR and CLIP is not sufficiently explained. Both models are trained using contrastive learning on visual and textual inputs, yet CLIP performs significantly worse. The authors suggest that this may be due to VALOR being trained on dynamic movie data while CLIP is trained on static images. However, this explanation remains speculative. More in-depth discussion is needed on the architectural and inductive biases of the two models, and how these may contribute to their differences in modeling brain responses.

(2) The methods section lacks clarity regarding which layers of VALOR and CLIP were used to extract features for voxel-wise encoding modeling. A more detailed methodological description is necessary to ensure reproducibility and interpretability. Furthermore, discussion of the inductive biases inherent in these models-and their implications for brain alignment - is crucial.

(3) A broader question remains insufficiently addressed: what is the purpose of visual-text alignment in the human brain? One hypothesis is that it supports the formation of abstract semantic representations that rely on no specific input modality. While VALOR performs well in voxel-wise encoding, it is unclear whether this necessarily indicates the emergence of such abstract semantics. The authors are encouraged to discuss how the computational architecture of VALOR may reflect this alignment mechanism and what implications it has for understanding brain function.

(4) The current methods section does not provide enough details about the network architectures, parameter settings, or whether pretrained models were used. If so, please provide links to the pretrained models to facilitate reproducible science.

Reviewer #2 (Public review):

Summary:

Fu and colleagues have shown that VALOR, a model of multimodal and dynamic stimulus features, better predicts brain responses compared to unimodal or static models such as AlexNet, WordNet, or CLIP. The authors demonstrated the robustness of their findings by generalizing encoding results to an external dataset. They demonstrated the models' practical benefit by showing that semantic mappings were comparable to another model that required labor-intensive manual annotation. Finally, the authors showed that the model reveals predictive coding mechanisms of the brain, which held a meaningful relationship with individuals' fluid intelligence measures.

Strengths:

Recent advances in neural network models that extract visual, linguistic, and semantic features from real-world stimuli have enabled neuroscientists to build encoding models that predict brain responses from these features. Higher prediction accuracy indicates greater explained variance in neural activity, and therefore a better model of brain function. Commonly used models include AlexNet for visual features, WordNet for audio-semantic features, and CLIP for visuo-semantic features; these served as comparison models in the study. Building on this line of work, the authors developed an encoding model using VALOR, which captures the multimodal and dynamic nature of real-world stimuli. VALOR outperformed the comparison models in predicting brain responses. It also recapitulated known semantic mappings and revealed evidence of predictive processing in the brain. These findings support VALOR as a strong candidate model of brain function.

Weaknesses:

The authors argue that this modeling contributes to a better understanding of how the brain works. However, upon reading, I am less convinced about how VALOR's superior performance over other models tells us more about the brain. VALOR is a better model of the audiovisual stimulus because it processes multimodal and dynamic stimuli compared to other unimodal or static models. If the model better captures real-world stimuli, then I almost feel that it has to better capture brain responses, assuming that the brain is a system that is optimized to process multimodal and dynamic inputs from the real world. The authors could strengthen the manuscript if the significance of their encoding model findings were better explained.

In Study 3, the authors show high alignment between WordNet and VALOR feature PCs. Upon reading the method together with Figure 3, I suspect that the alignment almost has to be high, given that the authors projected VALOR features to the Huth et al.'s PC space. Could the authors conduct non-parametric permutation tests, such as shuffling the VALOR features prior to mapping onto Huth et al.'s PC space, and then calculating the Jaccard scores? I imagine that the null distribution would be positively shifted. Still, I would be convinced if the alignment is higher than this shifted null distribution for each PC. If my understanding of this is incorrect, I suggest editing the relevant Method section (line 508) because this analysis was not easy to understand.

In Study 4, the authors show that individuals whose superior parietal gyrus (SPG) exhibited high prediction distance had high fluid cognitive scores (Figure 4C). I had a hard time believing that this was a hypothesis-driven analysis. The authors motivate the analysis that "SPG and PCu have been strongly linked to fluid intelligence (line 304)". Did the authors conduct two analyses only-SPG-fluid intelligence and PCu-fluid intelligence-without relating other brain regions to other individual differences measures? Even if so, the authors should have reported the same r-value and p-value for PCu-fluid intelligence. If SPG-fluid intelligence indeed holds specificity in terms of statistical significance compared to all possible scenarios that were tested, is this rationally an expected result, and could the authors explain the specificity? Also, the authors should explain why they considered fluid intelligence to be the proxy of one's ability to anticipate upcoming scenes during movie watching. I would have understood the rationale better if the authors had at least aggregated predictive scores for all brain regions that held significance into one summary statistic and found a significant correlation with the fluid intelligence measure.

Reviewer #3 (Public review):

Summary:

In this work, the authors aim to improve neural encoding models for naturalistic video stimuli by integrating temporally aligned multimodal features derived from a deep learning model (VALOR) to predict fMRI responses during movie viewing.

Strengths:

The major strength of the study lies in its systematic comparison across unimodal and multimodal models using large-scale, high-resolution fMRI datasets. The VALOR model demonstrates improved predictive accuracy and cross-dataset generalization. The model also reveals inherent semantic dimensions of cortical organization and can be used to evaluate the integration timescale of predictive coding.

This study demonstrates the utility of modern multimodal pretrained models for improving brain encoding in naturalistic contexts. While not conceptually novel, the application is technically sound, and the data and modeling pipeline may serve as a valuable benchmark for future studies.

Weaknesses:

The overall framework of using data-driven features derived from pretrained AI models to predict neural response has been well studied and accepted by the field of neuroAI for over a decade. The demonstrated improvements in prediction accuracy, generalization, and semantic mapping are largely attributable to the richer temporal and multimodal representations provided by the VALOR model, not a novel neural modeling framework per se. As such, the work may be viewed as an incremental application of recent advances in multimodal AI to a well-established neural encoding pipeline, rather than a conceptual advance in modeling neural mechanisms.

Several key claims are overstated or lack sufficient justification:

(1) Lines 95-96: The authors claim that "cortical areas share a common space," citing references [22-24]. However, these references primarily support the notion that different modalities or representations can be aligned in a common embedding space from a modeling perspective, rather than providing direct evidence that cortical areas themselves are aligned in a shared neural representational space.

(2) The authors discuss semantic annotation as if it is still a critical component of encoding models. However, recent advances in AI-based encoding methods rely on features derived from large-scale pretrained models (e.g., CLIP, GPT), which automatically capture semantic structure without requiring explicit annotation. While the manuscript does not systematically address this transition, it is important to clarify that the use of such pretrained models is now standard in the field and should not be positioned as an innovation of the present work. Additionally, the citation of Huth et al. (2012, Neuron) to justify the use of WordNet-based annotation omits the important methodological shift in Huth et al. (2016, Nature), which moved away from manual semantic labeling altogether.

Since the 2012 dataset is used primarily to enable comparison in study 3, the emphasis should not be placed on reiterating the disadvantages of semantic annotation, which have already been addressed in prior work. Instead, the manuscript's strength lies in its direct comparison between data-driven feature representations and semantic annotation based on WordNet categories. The authors should place greater emphasis on analyzing and discussing the differences revealed by these two approaches, rather than focusing mainly on the general advantage of automated semantic mapping.

(3) The authors use subject-specific encoding models trained on the HCP dataset to predict group-level mean responses in an independent in-house dataset. While this analysis is framed as testing model generalization, it is important to clarify that it is not assessing traditional out-of-distribution (OOD) generalization, where the same subject is tested on novel stimuli, but rather evaluating which encoding model's feature space contains more stimulus-specific and cross-subject-consistent information that can transfer across datasets.

Within this setup, the finding that VALOR outperforms CLIP, AlexNet, and WordNet is somewhat expected. VALOR encodes rich spatiotemporal information from videos, making it more aligned with movie-based neural responses. CLIP and AlexNet are static image-based models and thus lack temporal context, while WordNet only provides coarse categorical labels with no stimulus-specific detail. Therefore, the results primarily reflect the advantage of temporally-aware features in capturing shared neural dynamics, rather than revealing surprising model generalization. A direct comparison to pure video-based models, such as Video Swin Transformers or other more recent video models, would help strengthen the argument.

Moreover, while WordNet-based encoding models perform reasonably well within-subject in the HCP dataset, their generalization to group-level responses in the Short Fun Movies (SFM) dataset is markedly poorer. This could indicate that these models capture a considerable amount of subject-specific variance, which fails to translate to consistent group-level activity. This observation highlights the importance of distinguishing between encoding models that capture stimulus-driven representations and those that overfit to individual heterogeneities.

eLife Assessment

This important Research Advance builds on the authors' previous work delineating the roles of the rodent perirhinal cortex and the basolateral amygdala in first- and second-order learning. The convincing results show that serial exposure of non-motivationally relevant stimuli influences how those stimuli are encoded within the perirhinal cortex and basolateral amygdala when paired with a shock. This manuscript will be interesting for researchers in cognitive and behavioral neuroscience.

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: (1) why the perirhinal cortex is necessary for mediated learning but not direct fear learning, and (2) the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Weaknesses:

As a reader, it is difficult to interpret how first-order fear could be impaired while preconditioned fear is intact; it requires a bit of "reading between the lines".

Reviewer #2 (Public review):

Summary:

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second-order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention, whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for the fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job of pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Weaknesses:

The authors have attempted to resolve the question of the roles of the PRh and BLA in SPC and SOC, which the authors have explored in previous papers. Laudably, the authors have produced substantial results indicating how these two regions function in the learning of first- and second-order cues, providing an opportunity to narrow in on possible theories for their functionality. Yet the authors have framed this experiment in terms of an attentional framework and have argued that the results support this particular framework and hypothesis - that the PRh encodes peripheral and the BLA encodes focal states of learning. This certainly seems like a viable and exciting hypothesis, yet I don't see why the results have been completely framed and interpreted this way. It seems to me that there are still some alternative interpretations that are plausible and should be included in the paper.

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous. My comments below mainly concern areas where the writing could be improved to help readers more easily grasp the logic behind the experiments.

Weaknesses:

(1) Lines 56-58: The two previous findings should be more clearly summarized. Specifically, it's unclear whether the "mediated S2-shock" association occurred during Stage 2 or Stage 3. I assume the authors mean Stage 2, but Stage 2 alone would not yet involve "fear of S2," making this expression a bit confusing.

(2) Line 61: The phrase "Pavlovian fear conditioning" is ambiguous in this context. I assume it refers to S1-shock or S2-shock conditioning. If so, it would be clearer to state this explicitly.

(3) Regarding the distinction between having or not having Stage 1 S2-S1 pairings, is "novel vs. familiar" the most accurate way to frame this? This terminology could be misleading, especially since one might wonder why S2 couldn't just be presented alone on Stage 1 if novelty is the critical factor. Would "outcome relevance" or "predictability" be more appropriate descriptors? If the authors choose to retain the "novel vs. familiar" framing, I suggest providing a clear explanation of this rationale before introducing the predictions around Line 118.

(4) Line 121: This statement should refer to S1, not S2.

(5) Line 124: This one should refer to S2, not S1.

(6) Additionally, the rationale for Experiment 4 is not introduced before the Results section. While it is understandable that Experiment 4 functions as a follow-up to Experiment 3, it would be helpful to briefly explain the reasoning behind its inclusion.

eLife Assessment

This manuscript describes the identification and characterization of 12 specific phosphomimetic mutations in the recombinant full-length human tau protein that trigger tau to form fibrils. This fundamental study will allow in vitro mechanistic investigations. The presented evidence is convincing. This manuscript will be of interest to all scientists in the amyloid formation field.

Reviewer #1 (Public review):

Summary and Strengths:

The very well-written manuscript by Lövestam et al. from the Scheres/Goedert groups entitled "Twelve phosphomimetic mutations induce the assembly of recombinant full-length human tau into paired helical filaments" demonstrates the in vitro production of the so-called paired helical filament Alzheimer's disease (AD) polymorph fold of tau amyloids through the introduction of 12 point mutations that attempt to mimic the disease-associated hyper-phosphorylation of tau. The presented work is very important because it enables disease-related scientific work, including seeded amyloid replication in cells, to be performed in vitro using recombinant-expressed tau protein.

Comments on revised version:

The manuscript is significantly improved, as also indicated by Reviewer 2, with the 100% formation of the PHF and the additional experiments to elucidate on the potential mechanism by the PTMs. This is a great work.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important impediment in the field of Alzheimer's disease (AD) and tauapathy research by showing that 12 specific phosphomimetic mutations in full-length tau allow the protein to aggregate into fibrils with the AD fold and the fold of chronic traumatic encephalopathy fibrils in vitro. The paper presents comprehensive structural and cell based seeding data indicating the improvement of their approach over previous in vitro attempts on non-full-length tau constructs. The main weaknesses of this work results from the fact that only up to 70% of the tau fibrils form the desired fibril polymorphs. In addition, some of the figures are of low quality and confusing.

Strengths:

This study provides significant progress towards a very important and timely topic in the amyloid community, namely the in vitro production of tau fibrils found in patients.

The 12 specific phosphomimetic mutations presented in this work will have an immediate impact in the field since they can be easily reproduced.

Multiple high-resolution structures support the success of the phosphomimetic mutation approach.

Additional data show the seeding efficiency of the resulting fibrils, their reduced tendency to bundle, and their ability to be labeled without affecting core structure or seeding capability.

Comments on revised version:

Generally, I am satisfied with the revisions. Specifically, the new results showing 100% formation of PHF is a significant improvement.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

Summary and Strengths:

The very well-written manuscript by Lövestam et al. from the Scheres/Goedert groups entitled "Twelve phosphomimetic mutations induce the assembly of recombinant fulllength human tau into paired helical filaments" demonstrates the in vitro production of the so-called paired helical filament Alzheimer's disease (AD) polymorph fold of tau amyloids through the introduction of 12 point mutations that attempt to mimic the disease-associated hyper-phosphorylation of tau. The presented work is very important because it enables disease-related scientific work, including seeded amyloid replication in cells, to be performed in vitro using recombinant-expressed tau protein. 

Weaknesses: 

The following points are asked to be addressed by the authors:

(i) In the discussion it would be helpful to note the findings that in AD the chemical structure tau (including phosphorylation) is what defines the polymorph fold and not the buffer/cellular environment. It would be further interesting to discuss these findings in respect to the relationship between disease and structure. The presented findings suggest that due to a cellular/organismal alteration, such as aging or Abeta aggregation, tau is specifically hyper-phosphorylated which then leads to its aggregation into the paired helical filaments that are associated with AD. 

We have added an extra sentence to the Introduction to emphasise this possibility: “Besides the cellular environment in which they assemble, different tau folds may also be determined by chemical modifications of tau itself.”

In addition, the last paragraph of the Discussion now reads: “It could be that, besides different cellular environments in which the filaments assemble, different posttranslational modification patterns are also important for the assembly of tau into protofilament folds that are specific for the other tauopathies.”

(ii) The conditions used for each assembly reaction are a bit hard to keep track of and somewhat ambiguous. In order to help the reader, I would suggest making a table to show conditions used for each type of assembly (including the diameter / throw of the orbital shaker) and the results (structural/biological) of those conditions. For example, presumably the authors did not have ThT in the samples used for cryo-EM but the methods section does not specify this. Also, the presence of trace NaCl is proposed as a possible cause for the CTE fold to appear in the 0N4R sample (page 4) but no explanation of why this particular sample would have more NaCl than the others. Furthermore, it appears that NaCl was actually used in the seeded assembly reactions that produced the PHF and not the CTE fold. This would seem to indicate the CTE structure of 0N4RPAD12 is not actually induced by NaCl (like it was for tau297-391). In order for the reader to better understand the reproducibility of the polymorphs, it would be helpful to indicate in how many different conditions and how many replicates with new protein preparations each polymorph was observed (could be included in the same table)  

We have added a new table (Table 1) with the buffer conditions, protein concentration and shaking speed and time, for all structures described in this paper. We never added ThT to assembly reactions that were used for cryo-EM.

We did not use NaCl in the seeded assembly reactions (we used sodium citrate). We don’t really know why 0N4R PAD12 tau more readily forms the CTE fold. The observation that it does so prompted us to use 0N3R for all ensuing experiments. 

(iii) It is not clear how the authors calculate the percentage of each filament type. In Figure 1 it is stated "discarded solved particles (coloured) and discarded filaments in grey" which leaves the reviewer wondering what a "discarded solved particle" is and which filaments were discarded. From the main text one guesses that the latter is probably false positives from automated picking but if so, these should not be referred to as filaments. Also, are the percentages calculated for filaments or segments? In any case, it would be more helpful in such are report to know the best estimate of the ratio of identified filament types without confusing the reader with a measure of the quality of the picking algorithm. Please clarify. Also, a clarification is asked for the significance of the varying degrees of PHF and AD monomer filaments in the various assembly conditions. It could be expected that there is significant variability from sample to sample but it would be interesting to know if there has been any attempt to reproduce the samples to measure this variability. If not, it might be worth mentioning so that the % values are taking with the appropriate sized grain of salt. Finally, the representation of the data in Figure 1 would seem to imply that the 0N3R forms less or no monofilament AD fold because no cross-section is shown for this structure, however it is very similar to (or statistically the same as) the 1:1 mix of 0N3R:0N4R.

In the revised manuscript, we have used bi-hierchical clustering of filaments, where each segment (or particle) is classified based on both 2D class assignment and to which filament it belongs (this method is based on [Porthula et al (2019), Ultramicroscopy 203, 132-138] and was further developed in [Lövestam et al (2024) Nature 7993, 119-125]. Based on the assumption that filament type does not change within a single filament type, we have observed that this gives excellent classification results, and that this approach allows classification of many, even small minority, filament types. Using this approach, we now quantify the different filament types on the number of segments extracted from filaments classified in this way. 

Moreover, we have also addressed the problem of having singlets among the PHF preparation: it turns out that waiting longer, just by transferring samples out of the shaker after one week and incubating it quiescently at 37 ºC for two more weeks, the singlets disappear and only PHFs remain. Filaments made for the fluorophore labelling in the revised Figure 3 were also done using the new protocol. In total, we have N=7 replicates with a mean of 95.3% PHFs and a standard deviation of 9.4%. The revised text in the Results section reads:

“To further increase the proportions of PHFs-to-singlet ratio, we removed the plate from the shaker after one week and incubated it quiescently at 37 ºC for two more weeks. This resulted in 100% PHFs formed (Figure 1 – figure supplement 4). When repeated seven times, on average 95.3% PHFs formed, with 25% of singlets formed in a single outlier (Figure 1 – figure supplement 5)” 

(iv) The interpretation of the NMR data on soluble tau that the mutations on the second site are suppressing in part long range dynamic interaction around the aggregationinitiation site (FIA) is sound. It is in particular interesting to find that the mutations have a similar effect as the truncation at residue 391. An additional experiment using solvent PREs to elaborate on the solvent exposed sequence-resolved electrostatic potential and the intra-molecular long range interactions would likely strengthen the interpretation significantly (Iwahara, for example, Yu et al, in JACS 2024). Figure 6D Figure supplement shows the NMR cross peak intensities between tau 151-391 and PAD12tau151-391. Overall the intensities of the PAD12 tau construct are more intense which could be interpreted with less conformational exchange between long range dynamic interactions. There are however several regions which do not show any intensity anymore when compared with the corresponding wildtype construct such as 259-262, 292-294 which should be discussed/explained. 

While long-range intramolecular interactions of tau have previously been reported through the use of spin labels (Mukrasch et al 2009 PLoS Biol 7(2): e1000034), we have been hesitant to introduce paramagnetic agents into our samples for two reasons. First, the bulky size of the spin label may affect filament formation or influence the dynamic properties of the protein. Second, covalent addition of the spin label requires mutation of the primary sequence to both remove native cysteine residues and add cysteines at the desired label location. We have previously shown that mutation of cysteine 322 to alanine leads to the formation of tau filaments with a structure that is different from the PHF (Santambrogio et al (2025) bioRxiv 2025.03.29.646137). 

Instead, we have included in the revised manuscript new NMR and cryo-EM data that provide further support for the model that a FIA-like interaction between residues ₃₉₂IVYK₃₉₅ and residues ₃₀₆VQIVYK₃₁₁ has an inhibiting effect on filament nucleation in unmodified full-length tau. A mutant of tau297-441 where residues ₃₉₂IVYK₃₉₅ have been deleted and that does not contain the four PAD12 mutations in the carboxy-terminal domain behaves similarly in the NMR experiment as the tau297-441 construct with those four PAD12 mutations. Moreover, full-length 0N3R tau with the eight PAD12 mutations in the amino-terminal fuzzy coat and with the deletion of₃₉₂IVYK₃₉₅, but without the four PAD12 mutations in the carboxy-terminal domain, assembles readily into amyloid filaments (of which we also solved a cryo-EM structure, see the revised Figure 6B). These observations provide mechanistic insights into the previously proposed paper-clip model [Jeganathan (2008), J Biol Chem 283, 32066-32076], where interactions between the fuzzy coat inhibit filament formation of unmodified full-length tau, and phosphorylation in the fuzzy coat interferes with these interactions, thus leading to filament nucleation. Of course, the identification of residues ₃₉₂IVYK₃₉₅ for this interaction also explain why truncation of tau at residue 391 leads to spontaneous assembly. We have introduced a new Figure 7 to the revised manuscript to explain this model in more detail. The corresponding new section in the Results reads:

“To investigate this further, we also tested a tau construct comprising residues tau297-441 without the phosphomimetic mutations, but with a deletion of residues (Δ392-395). Filaments formed rapidly and the cryo-EM structure showed that the ordered core consisted of the amino-terminal part of the construct spanning residues 297-318 (Figure 6B). NMR analysis (Figure 6 – figure supplement 5B) showed that the tau297441 Δ392-395 construct exhibited similar backbone rigidity properties to the tau297-441 PAD12 construct, despite peak locations and local secondary structural propensities being more similar to the wildtype tau297-441 (Figure 6 – figure supplement 5A; Figure 6 – figure supplement 6). HSQC peak intensities in the 297-319 and 392-404 regions of tau297-441 Δ392-395 (Figure 6A, expanded from Figure 6 - figure supplement 5C) were like those in the tau297-441 PAD12. These data suggest that the IVYK deletion has a similar effect as the phosphomimetics on residues 396, 400, 403 and 404 on disrupting an intra-molecular interaction between the FIA core region and the carboxy-terminal domain, which may therefore be mediated by interactions between the two IVYK motifs that are similar to those observed in the FIA (Lövestam et al, 2024).”

A new section in the Discussion now reads:

“Our NMR data provide insights into the mechanism by which phosphorylation in the fuzzy coat of tau, or truncations of tau, lead to the formation of filaments with ordered cores of residues that are themselves not phosphorylated. HSQC peak intensity differences between unmodified tau 297-441, PAD12 tau 297-441 and tau297-391 suggest that phosphorylation of the fuzzy coat, particularly near the ₃₉₂IVYK₃₉₅ motif in the carboxy-terminal domain, a7ects the conformation of the residues of tau that become ordered in the FIA (Lövestam et al., 2024). Removal of residues ₃₉₂IVYK₃₉₅ in the carboxyterminal domain of tau 297-441 led to rapid filament formation in the absence of phosphomimetics, while HSQC peak intensity di7erences for this construct indicate similar backbone rigidity compared to tau 297-441 without the deletion, but with the four PAD12 mutations in the carboxy-terminal domain. Combined, these observations support a model where the ₃₉₂IVYK₃₉₅ motif in unmodified full-length tau monomers interacts with the ₃₀₈IVYK₃₁₁ motif, thus inhibiting filament formation by preventing the formation of the nucleating species, the FIA. Phosphorylation of nearby residues 396, 400, 403 and 404, or truncation at residue 391, disrupt this interaction and lead to filament formation. This model agrees with the previously proposed hairpin-like model of tau (Jeganathan et al., 2008), although the corresponding interaction between the aminoterminal domain of tau and the core-forming region remains unknown (Figure 7).”

Due to the challenging nature of the assignment, it was not possible to assign all residues in the HSQC of the tau151-391 and the PAD12 tau151-391 samples, including residues 259-262 and 292-294 for PAD12 tau151-391. To make this clearer, we have marked residues that are not assigned with an asterisk in the revised version of Figure 6 – figure supplement 1.  

(v) Concerning the Cryo-EM data from the different hyper-phosphorylation mimics, it would seem that the authors could at least comment on the proportion of monofilament and paired-filaments even if they could not solve the structures. Nonetheless, based on their previous publications, one would also expect that they could show whether the nontwisted filaments are likely to have the same structure (by comparing the 2D classes to projections of non-twisted models). Also, it is very interesting to note that the twist could be so strongly controlled by the charge distribution on the non-structured regions (and may be also related to the work by Mezzenga on twist rate and buffer conditions). Is the result reported in Figure 2 a one-oT case or was it also reproducible?

As also indicated in the main text, the assembly conditions for the PAD12+4, PAD12-4 and PAD12+/-4 constructs were kept the same as those for the PAD12 construct. It is possible that further optimisation of the conditions could again lead to twisting filaments, but we chose not to pursue this route. With unlimited resources and time, one could assess in detail which of the PAD12 mutations are required and which ones could be omitted to form PHFs. However, this would require a lot of work and cryo-EM time. For now, we chose to prioritise reporting conditions that do work to reproducibly make PHFs in the laboratory (using the PAD12 construct) and leave the more detailed analysis of other constructs for future studies. 

Reviewer #2 (Public review): 

Summary: 

This manuscript addresses an important impediment in the field of Alzheimer's disease (AD) and tauapathy research by showing that 12 specific phosphomimetic mutations in full-length tau allow the protein to aggregate into fibrils with the AD fold and the fold of chronic traumatic encephalopathy fibrils in vitro. The paper presents comprehensive structural and cell based seeding data indicating the improvement of their approach over previous in vitro attempts on non-full-length tau constructs. The main weaknesses of this work results from the fact that only up to 70% of the tau fibrils form the desired fibril polymorphs. In addition, some of the figures are of low quality and confusing. 

As also explained in our response to reviewer #1, we have performed better quantification of filament types in the revised manuscript, and we have investigated how to get rid of the singlets. In the revised manuscript, we report that singlets disappear as time passes and that one can obtain 100% pure PHFs by quiescently incubating samples for another two weeks, after shaking for a week.

Strengths: 

This study provides significant progress towards a very important and timely topic in the amyloid community, namely the in vitro production of tau fibrils found in patients.

The 12 specific phosphomimetic mutations presented in this work will have an immediate impact in the field since they can be easily reproduced.

Multiple high-resolution structures support the success of the phosphomimetic mutation approach. Additional data show the seeding efficiency of the resulting fibrils, their reduced tendency to bundle, and their ability to be labeled without affecting core structure or seeding capability.

Weaknesses: 

Despite the success of making full-length AD tau fibrils, still ~30% of the fibrils are either not PHF, or not accounted for. A small fraction of the fibrils are single filaments and another ~20% are not accounted for. The authors mention that ~20% of these fibrils were not picked by the automated algorithm. However, it would be important to get additional clarity about these fibrils. Therefore, it would improve the impact of the paper if the authors could manually analyze passed-over particles to see if they are compatible with PHF or fall into a different class of fibrils. In addition, it would be helpful if the authors could comment on what can be done/tried to get the PHF yield closer to 90-100%

As mentioned above, in the revised manuscript we show that the singlets disappear over time and we now include a description of a method that leads to 100% PHF formation.

Reviewer #1 (Recommendations for the authors):

Minor points: 

(a) In Figure 6 the dashed purple vertical lines overlap with the black bars, rendering a grey color which is confusing because the grey bars used for the shorter construct. It is suggested to improve the colors (remove transparency on the purple?)

We thank the reviewers for their suggestions for improving the visualisation of our data. We have recoloured the tau297-391 data from grey to gold and moved the dashed lines to the back of image to remove the apparent colour changes.  

(b) Is there any support for the suggestion that "part of the second microtubule-binding repeat is ordered" being "related to this construct forming filaments with only a single protofilament"? It seemed to have come out of nowhere.

There is no further support for this statement, but we thought it would be worth hypothesizing about this observation. 

(c) Figures 1 and 4 E is better described as a "main chain trace" or "backbone trace" although the latter usually refers to only CA positions. Ribbon usually refers to something else in representations of protein structures. 

This has been changed into “main chain trace” in Figures 1 and 4. 

(d) Figure 1 Supplement 3: Panel letters in the legend do not match. 

This has been fixed.

Reviewer #2 (Recommendations for the authors): 

The introduction is a bit lengthy (e.g. 3rd paragraph of introduction) and could benefit by focusing specific question the manuscript addresses. 

We have shortened the Introduction. It now contains ~1150 words, which we hope provides a better compromise between length and sufficient background information.

Figure captions are generally not helpful in conveying a message to the reader.

Figure 1 - figure supplement 3 is quite confusing. The 4 structures in A) do not correspond to the grids in B-E. What is this figure supposed to show?

This confusion was probably the result of incorrect labelling of panels in the legend, which was also pointed out by reviewer #1. This has been fixed in the revised manuscript.

Page 11: Although I know what you mean, 'linear increase of ThT fluorescence' is not the correct term. 

We have replaced “linear” with “rapid”.

Page 15: Although line shape and peak intensity can be related you are not reporting on line shape or width but simply on peak intensity. Therefore, I wouldn't talk about the result of a 'line shape analysis'.

We have changed the wording accordingly. 

Figure 6 (and supplement 1) are confusing and too small to be readable in print. It might be sufficient to show the CSP and upload the remaining data to the BMRB. 

We have made a clearer version of the main NMR Figure 6 in the revised manuscript showing the most pertinent NMR data and have moved the previous version into the figure supplements. We designed these figures to be viewed as full page A4 panels, ideally seen in one image as they show multiple comparisons of different experiments and constructs.

As such we feel these will be best viewed on screen as part of the eLife web document. We have uploaded HSQC spectra and assignments to the BMRB (see below).

Figure 6 supplement 3 might benefit from pointing out key residues in the overlay.

We have added the labels (this is now Figure 6 supplement 4).

Data availability: Please upload the assignments to the BMRB together with key spectra (e.g. HSQCs). 

We have uploaded HSQC data along with our assignments to the BMRB, the accession codes are 52694 – tau297-441 wt; 52695 – tau297-441 PAD-12; 52696 – tau151-391 wt; 52697 – tau151-391 PAD-12; and 53230 – tau297-441 delta392-395.  These accession codes have been added to the manuscript. 

The quality of some of the figures (specifically Figure 1 - supplement 3 and Figure 6) is not suitable for publication. 

For the original submission to bioRxiv, we produced a single PDF with a manageable file size. We will liaise with the eLife staff to ensure the images used in the version of record will be suitable for publication.

eLife Assessment

This important work presents a stochastic branching process model of tumour-immune coevolution, incorporating stochastic antigenic mutation accumulation and escape within the cancer cell population. They then used this model to investigate how tumour-immune interactions influence tumour outcome and the summary statistics of sequencing data of bulk and single-cell sequencing of a tumour. The evidence is compelling and the work will be of interest to cancer-immune biology fields.

Reviewer #1 (Public review):

Summary:

The topic of tumor-immune co-evolution is an important, understudied topic with, as the authors noted, a general dearth of good models in this space. The authors have made important progress on the topic by introduced a stochastic branching process model of antigenicity / immunogenicity and measuring the proportion of simulated tumors which go extinct. The model is extensively explored and authors provide some nice theoretical results in addition to simulated results, including an analysis of increasing cancer/immune versus cyclical cancer/immune dynamics. The analysis appropriately builds upon the foundation of other work in the field of predicting site frequency spectrum, but extends the results into cancer-immune co-evolution in an intuitive computational framework.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The topic of tumor-immune co-evolution is an important, understudied topic with, as the authors  noted, a general dearth of good models in this space. The authors have made important progress on the topic by introducing a stochastic branching process model of antigenicity/immunogenicity and measuring the proportion of simulated tumors that go extinct. The model is extensively explored, and the authors provide some nice theoretical results in addition to simulated results. 

We thank the reviewer for the positive comments on our work.

Major comments 

The text in lines 183-191 is intuitively and nicely explained. However, I am not sure all of it follows from the figure panels in Figure 2. For example, the authors refer to a mutation that has a large immunogenicity, but it's not shown how many mutations, or the relative size of the mutations in Figure 2. The same comment holds true for the claim that spikes also arise for mutations with low antigenicity. 

We thank the reviewer for helping us to further specify this statement in our original submission. We now added muller plots in a new Appendix Figure (Figure A3) presenting the relative abundances of different types of effector cells in the population over time. Each effector type is colour-coded with its antigenicity and immunogenicity. To align with this Appendix Figure (Figure A3), we also updated our Figure 2 generated under the same realisation as Figure A3. We can now see clearly that the spikes in the mean values of the antigenicity and immunogenicity over the whole effector populations in new Figure 2B&2D indeed correspond to the expansion of single or several antigenic mutations recruiting the specific effector cell types. For example, in Figure 2B, we can see that the spikes of low average antigenicity and high immunogenicity (around time 11) happen at the same time when an effector type in Figure A3 with such a trait (coloured in green) arises and takes over the population. We have rewritten our Results section related (Line 192 - Line 222 in main text and Appendix A6).

Reviewer #2 (Public review): 

Summary: 

In this work, the authors developed a model of tumour-immune dynamics, incorporating stochastic antigenic mutation accumulation and escape within the cancer cell population. They then used this  model to investigate how tumour-immune interactions influence tumour outcome and summary  statistics of sequencing data. 

Strengths: 

This novel modeling framework addresses an important and timely topic. The authors consider the useful question of how bulk and single-cell sequencing may provide insights into the tumourimmune interactions and selection processes. 

We thank the reviewer for the positive comments.

Weaknesses: 

One set of conclusions presented in the paper is the presence of cyclic dynamics between effector/cancer cells, antigenicity, and immunogenicity. However, these conclusions are supported in the manuscript by two sample trajectories of stochastic simulations, and these provide mixed support for the conclusions (i.e. the phasing asynchrony described in the text does not seem to apply to Figure 2C). 

We have now developed a method to quantify the cyclic dynamics in our system (Appendix A7), where can track the directional changes phase portrait of the abundances of the cancer and effector cells. We first tested this method in a non-evolving stochastic predator-prey system, where our method can correctly capture the number of cycles in this system (Figure A7). We then use this method to quantify the number of cycles we observed between cancer and effector cells under different mutation rates (Figure A5) as well as whether they are counter-clockwise or clockwise cycles (Figure A6). Our results showed that the cyclic dynamics are more often to be observed when mutation rates are higher, and the majority of those cycles are counter-clockwise. When the mutation rate is high, we observe an increase of clockwise cycles, which have been observed in predator-prey systems and explained through coevolution. However, even under high mutation rates, counter-clockwise cycles are still the more frequent type. 

In our simulations, we observed rarely out-of-phase cycles, which was by chance present in our original Figure 2. We have now removed that statement about out-of-phase cycles and replaced by more systematic analysis of the cyclic dynamics as described above (Line 192 to 207 in the revised version). We thank the constructive comment of the reviewer, which motivated us to improve our analysis significantly. 

Similarly, the authors also find immune selection effects on the shape of the mutational burden in Figure 5 D/H using a qualitative comparison between the distributions and theoretical predictions in  the absence of immune response. However the discrepancy appears quite small in panel D, and  there are no quantitative comparisons provided to evaluate the significance. An analysis of the robustness of all the conclusions to parameter variation is missing. 

We have now added statistical analysis using Wasserstein distance between the simulated mutation burden distribution and theoretical (neutral) expectation in Figure 5 C, D, G, H as well as in Figure A11 C&D when there is no cancer-immune interaction. We can see that the measurements of the  Wasserstein distance agrees with our statement, that the higher immune effectiveness leads to larger deviation from the neutral expectation.

Lastly, the role of the Appendix results in the main messages of the paper is unclear. 

We agree with the review and have now removed the Appendix sections “Deterministic Analysis”. 

Reviewing Editor Comments: 

I find the abstract too long. For example, "Knowledge of this coevolutionary system and the selection taking place within it can help us understand tumour-immune dynamics both during tumorigenesis but also when treatments such as immunotherapies are applied." can be shortened to: "Knowledge of this coevolutionary system can help us understand tumour-immune dynamics both during tumorigenesis and during immunotherapy treatments." 

We agree and have taken the suggestion of the reviewer to shorten our abstract.

Reviewer #1 (Recommendations for the authors): 

The discussion at lines 134-140, centered around Figure A1, is an important and nicely constructed feature of the model. 

Reviewer #2 (Recommendations for the authors): 

I suggest that the authors conduct a more in-depth analysis of their conclusions on cyclic dynamics over a large set of sample paths.

Done and please see our detailed response to the reviewer 2 above.

In addition, statistical comparisons between the observed mutational burden distribution and  theoretical predictions in the absence of immune selection should be carried out to support their conclusions. In all cases, conclusions should be tested extensively for robustness/sensitivity to parameters. 

Done and please see our detailed response to the reviewer 2 above.

Here are some specific suggestions/comments: 

(1) Please provide a precise mathematical description of the model to complement Figure 1. 

We have significantly revised our “Model” section to provide a precise mathematical description of our model (Line 138 - 148). Please also see our document showing the difference between the revised version and original submission.

(2) Section on "Interactions dictate outcome of tumour progress" and Figure 3: please define 'tumour outcome' - are the heatmaps produced in Figure 3 tumor size reflecting whether or not the population has reached level K before a particular time? Also, I do not see a definition for the 'slowgrowing' tumour proportion plotted in Figure 3CF or in the accompanying text. 

We have now added the definition of “tumour outcome” in our “Model” section (line 171 to 176), where we explain our model parameters and quantities measured in the following “Results” section.

(3) Figure 5C/G: the green dotted vertical line is difficult to see. 

We have now changed the mean of the simulations to solid red lines instead of using the green dotted vertical lines previously.

(4) Appendix A1 text under (A2) should U/N be U/C? N does not appear to be defined. 

We have more removed the previous A1 section. Please see our response to reviewer 2 as well.

(5) Text under (A5): it is unclear what is meant by "SFS must be heavy tailed (that is, more heterogeneous)" -- a more precise statement regarding tail decay rate and associated consequences would be more helpful. 

We have more removed the previous A section, where the original text "...SFS must be heavy-tailed" was.

(6) Section A4 and Figure A1: can these calculations be compared to simulations? 

We have more removed the previous A section on the deterministic analysis as they are not so  relevant to our stochastic simulations indeed. Please see our response to reviewer 2 as well.

(7) Also, in general, please clarify how the results in the Appendix are used in the main text conclusions or provide insights relevant to these conclusions. If they are not, one can consider removing them.  

We have more removed the previous A section on the deterministic analysis. The remaining sections are about stochastic simulations and extended figures which support our main figures.  

(8) Figure A2: the two lines are difficult to tell apart on each panel. Please consider different styles.

We have changed one of the dotted lines to be solid. This figure is now Figure A1 in our revision.

eLife Assessment

This important study introduces a new class of spectrally tunable, dye-based calcium sensors optimized for imaging in organelles with high calcium concentrations, such as the endoplasmic reticulum and mitochondria. The experimental evidence supporting the applicability of these sensors is convincing, with thorough validation in cultured cells and neurons. The work will be of high interest to researchers studying calcium signaling dynamics in subcellular compartments.

Reviewer #1 (Public review):

Summary:

The manuscript by Moret et al. details the development and characterisation of novel ER- and mitochondria-targeted genetically encoded chemogenic Ca2+ sensors.

Strengths:

Compared to existing probes, these sensors exhibited superior responsiveness, brightness, and photostability within the red and far-red emission spectrum, enabling triple compartment Ca2+ measurements (ER, mitochondria, cytosol) and the detection of Ca2+ dynamics in axons and dendrites.

Weaknesses:

The data are robust and convincing, although the manuscript text lacks precision.

Reviewer #2 (Public review):

Summary:

Moret et al. present an engineered family of fluorescent calcium indicators based on HaloCamp, a HaloTag-based sensor system that utilizes Janelia Fluorophores (JF dyes) to report calcium dynamics. By introducing single or multiple amino acid substitutions, the authors reduce HaloCamp's calcium affinity, making these low-affinity variants well-suited for imaging calcium transients in high-calcium environments such as the endoplasmic reticulum (ER) and mitochondria. The study validates the sensors' dissociation constants (Kd), spectra, and multiplex capabilities. It demonstrates improved performance compared to existing tools when targeted to subcellular compartments in mammalian cells and cultured neurons. The sensors can be tuned across the red-to-far-red spectrum via JF585 and JF635 labeling, enabling flexible multiplexed imaging. For example, the authors show that HaloCamp can be targeted to mitochondria and used alongside other green and red sensors, allowing simultaneous imaging of calcium dynamics in the cytosol, ER, and mitochondria. Overall, they achieve their goals, and the data demonstrate that HaloCamp variants are effective for detecting ER and mitochondrial calcium changes under physiological conditions. The presented experiments support the conclusions. However, some key aspects, such as sensor kinetics and axonal validation, would benefit from further analysis.

This work is likely to have an important impact on the fields of calcium imaging and organelle physiology. The modular design of HaloCamp and its compatibility with a wide range of fluorophores offer a broad application range for cell biologists and neuroscientists.

Strengths:

(1) The authors introduce the first tunable, dye-based, low-affinity HaloTag calcium sensors for subcellular imaging, addressing a significant unmet need for ER and mitochondrial calcium detection.

(2) The ability to pair HaloCamp with JF585 and JF635 extends the spectral range, facilitating multiplexed imaging with existing calcium indicators.

(3) The sensors are validated in a range of subcellular compartments (ER, mitochondria, cytosol) in both mammalian cells and neurons.

(4) The authors successfully demonstrate simultaneous imaging of three compartments using orthogonal sensors, a technically impressive feat.

(5) Kd values are measured, and fluorescent responses are tested under physiologically relevant stimulation.

Weaknesses:

(1) The authors do not quantify the kinetics (e.g., decay tau or off-rate) of the fluorescent signals, particularly after stimulation. For example, in the ER imaging experiments in neurons, the decay of the HaloCamp fluorescence after field stimulation (20 APs @ 20 Hz) is not analyzed or compared to ER-GCaMP6-210 or R-CEPIer.

(2) It remains unclear whether the observed decay represents the sensor's off-kinetics or actual physiological calcium clearance from the ER. A comparison between sensors or an independent measurement of ER clearance rates in vitro would clarify this.

(3) The choice of 20 APs at 20 Hz is not justified. Specifically, single APs or low-frequency stimulations are not tested, leaving unclear what the detection threshold of the new sensors is.

(4) In neuron experiments, the authors report measuring ER calcium in axons based presumably on morphology, but no specific justification for selection, markers, or post hoc labeling is described.

(5) Figure 5 assumes that all three indicators (cytosolic, ER, and mitochondrial) are fast enough to report calcium dynamics in response to histamine. This assumption is not fully validated. Cross-controls (e.g., expressing GCaMP6-210 in mitochondria and HaloCamp in the ER) would strengthen confidence that the sensors are correctly reporting dynamic changes.

(6) It is not clear why Thapsigargin leads to depletion in HeLa cells and neurons in experiments shown in Figure 1E, but not in 2B upon field stimulation.

eLife Assessment

This study presents useful findings on the molecular mechanisms driving female-to-male sex reversal in the ricefield eel (Monopterus albus) during aging, which would be of interest to biologists studying sex determination. The manuscript describes an interesting mechanism potentially underlying sex differentiation in M. albus. However, the current data are incomplete and would benefit from more rigorous experimental approaches.

Reviewer #1 (Public review):

Summary:

This study investigates the molecular mechanism by which warm temperature induces female-to-male sex reversal in the ricefield eel (Monopterus albus), a protogynous hermaphroditic fish of significant aquacultural value in China. The study identifies Trpv4 - a temperature-sensitive Ca²⁺ channel - as a putative thermosensor linking environmental temperature to sex determination. The authors propose that Trpv4 causes Ca²⁺ influx, leading to activation of Stat3 (pStat3). pStat3 then transcriptionally upregulates the histone demethylase Kdm6b (aka Jmjd3), leading to increased dmrt1 gene expression and ovo-testes development. This work aims to bridge ecological cues with molecular and epigenetic regulators of sex change and has potential implications for sex control in aquaculture.

Strengths:

(1) This study proposes the first mechanistic pathway linking thermal cues to natural sex reversal in adult ricefield eel, extending the temperature-dependent sex determination paradigm beyond embryonic reptiles and saltwater fish.

(2) The findings could have applications for aquaculture, where skewed sex ratios apparently limit breeding efficiency.

Weaknesses:

(A) Scientific Concerns:

(1) There is insufficient replication and data transparency. First, the qPCR data are presented as bar graphs without individual data points, making it impossible to assess variability or replication. Please show all individual data points and clarify n (sample size) per group. Second, the Western blotting is only shown as single replicates. If repeated 2-3 times as stated, quantification and normalization (e.g., pStat3/Stat3, GAPDH loading control) are essential. The full, uncropped blots should be included in the supplementary data.

(2) The biological significance of the results is not clear. Many reported fold changes (e.g., kdm6b modulation by Stat3 inhibition, sox9a in S3A) are modest (<2-fold), raising concerns about biological relevance. Can the authors define thresholds of functional relevance or confirm phenotypic outcomes in these animals?

(3) The specificity of key antibodies is not validated. Key antibodies (Stat3, pStat3, Foxl2, Amh) were raised against mammalian proteins. Their specificity for ricefield eel proteins is unverified. Validation should include siRNA-mediated knockdown with immunoblot quantification with 3 replicates. Homemade antibodies (Sox9a, Dmrt1) also require rigorous validation.

(4) Most of the imaging data (immunofluorescence) is inconclusive. Immunofluorescence panels are small and lack monochrome channels, which severely limits interpretability. Larger, better-contrasted images (showing the merge and the monochrome of important channels) and quantification would enhance the clarity of these findings.

(B) Other comments about the science:

(1) In S3A, sox9a expression is not dose-responsive to Trpv4 modulation, weakening the causal inference.

(2) An antibody against Kdm6b (if available) should be used to confirm protein-level changes.

In sum, the interpretations are limited by the above concerns regarding data presentation and reagent specificity.

Reviewer #2 (Public review):

Summary:

This study presents valuable findings on the molecular mechanisms driving the female-to-male transformation in the ricefield eel (Monopterus albus) during aging. The authors explore the role of temperature-activated TRPV4 signaling in promoting testicular differentiation, proposing a TRPV4-Ca²⁺-pSTAT3-Kdm6b axis that facilitates this gonadal shift.

Strengths:

The manuscript describes an interesting mechanism potentially underlying sex differentiation in M. albus.

Weaknesses:

The current data are insufficient to fully support the central claims, and the study would benefit from more rigorous experimental approaches.

(1) Overstated Title and Claims:

The title "TRPV4 mediates temperature-induced sex change" overstates the evidence. No histological confirmation of gonadal transformation (e.g., formation of testicular structures) is presented. Conclusions are based solely on molecular markers such as dmrt1 and sox9a, which, although suggestive, are not definitive indicators of functional sex reversal.

(2) Temperature vs Growth Rate Confounding (Figure 1E):

The conclusion that warm temperature directly induces gonadal transformation is confounded by potential growth rate effects. The authors state that body size was "comparable" between 25{degree sign}C and 33{degree sign}C groups, but fail to provide supporting data. In ectotherms, growth is intrinsically temperature-dependent. Given the known correlation between size and sex change in M. albus, growth rate-rather than temperature per se-may underlie the observed sex ratio shifts. Controlled growth-matched comparisons or inclusion of growth rate metrics are needed.

(3) TRPV4 as a Thermosensor-Insufficient Evidence:

The characterisation of TRPV4 as a direct thermosensor lacks biophysical validation. The observed transcriptional upregulation of Trpv4 under heat (Figure 2) reflects downstream responses rather than primary sensor function. Functional thermosensors, including TRPV4, respond to heat via immediate ion channel activity-typically measurable within seconds-not mRNA expression over hours. No patch-clamp or electrophysiological data are provided to confirm TRPV4 activation thresholds in eel gonadal cells. Additionally, the Ca²⁺ imaging assay (Figure 2F) lacks essential details: the timing of GSK1016790A/RN1734 administration relative to imaging is unclear, making it difficult to distinguish direct channel activity from indirect transcriptional effects.

(4) Cellular Context of TRPV4 Activity Is Unclear:

In situ hybridisation suggests TRPV4 expression shifts from interstitial to somatic domains under heat (Figures. 2H, S2C), implying potential cell-type-specific roles. However, the study does not clarify: (i) whether TRPV4 plays the same role across these cell types, (ii) why somatic cells show stronger signal amplification, or (iii) the cellular composition of explants used in in vitro assays. Without this resolution, conclusions from pharmacological manipulation (e.g., GSK1016790A effects) cannot be definitively linked to specific cell populations.

(5) Rapid Trpv4 mRNA Elevation and Channel Function:

The authors report a dramatic increase in Trpv4 mRNA within one day of heat exposure (Figures 4D, S2B). Given that TRPV4 is a membrane channel, not a transcription factor, its rapid transcriptional sensitivity to temperature raises mechanistic questions. This finding, while intriguing, seems more correlational than functional. A clearer explanation of how TRPV4 senses temperature at the molecular level is needed.

(6) Inconclusive Evidence for the Ca²⁺ -pSTAT3-Kdm6b Axis:

Although the authors propose a TRPV4-Ca²⁺ -pSTAT3-Kdm6b-dmrt1 pathway, intermediate steps remain poorly supported. For example, western blot data (Figures 3C, 4B) do not convincingly demonstrate significant pSTAT3 elevation at 34{degree sign}C. Higher-resolution and properly quantified blots are essential. The inferred signalling cascade is based largely on temporal correlation and pharmacological inhibition, which are insufficient to establish direct regulatory relationships.

(7) Species-Specific STAT3-Kdm6b Regulation Is Unresolved:

The proposed activation of Kdm6b by pSTAT3 contrasts with findings in the red-eared slider turtle (Trachemys scripta), where pSTAT3 represses Kdm6b. This divergence in regulatory direction between the two TSD species is surprising and demands further justification. Cross-species differences in binding motifs or epigenetic context should be explored. Additional evidence, such as luciferase reporter assays (using wild-type and mutant pSTAT3 binding motifs in the Kdm6b promoter) is needed to confirm direct activation. A rescue experiment-testing whether Kdm6b overexpression can compensate for pSTAT3 inhibition-would also greatly strengthen the model.

(8) Immunofluorescence-Lack of Structural Markers:

All immunofluorescence images should include structural markers to delineate gonadal boundaries. Furthermore, image descriptions in the figure legends and main text lack detail and should be significantly expanded for clarity.

(9) Pharmacological Reagents-Mechanisms and References:

The manuscript lacks proper references and mechanistic descriptions for the pharmacological agents used (e.g., GSK1016790A, RN1734, Stattic). Established literature on their specificity and usage context should be cited to support their application and interpretation in this study.

(10) Efficiency of Experimental Interventions:

The percentage of gonads exhibiting sex reversal following pharmacological or RNAi treatments should be reported in the Results. This is critical for evaluating the strength and reproducibility of the interventions.

eLife Assessment

This important work advances our understanding of DNA methylation and its consequences for susceptibility to DNA damage. This work presents evidence that DNA methylation can accentuate the genomic damage propagated by DNA damaging agents as well as potentially being an independent source of such damage. The experimental results reported are sound. The evidence presented to support the conclusions drawn is convincing and alternative interpretations are considered. The work will be of broad interest to biochemists, cell and genome biologists.

Reviewer #1 (Public review):

Summary:

The manuscript titled "Introduction of cytosine-5 DNA methylation sensitizes cells to oxidative damage" proposes that 5mC modifications to DNA, despite being ancient and wide-spread throughout life, represent a vulnerability, making cells more susceptible to both chemical alkylation and, of more general importance, reactive oxygen species. Sarkies et al take the innovative approach of introducing enzymatic genome-wide cytosine methylation system (DNA methyltransferases, DNMTs) into E. coli, which normally lacks such a system. They provide compelling evidence that the introduction of DNMTs increases the sensitivity of E. coli to chemical alkylation damage. Surprisingly they also show DNMTs increase the sensitivity to reactive oxygen species and propose that the DNMT generated 5mC presents a target for the reactive oxygen species that is especially damaging to cells. Evidence is presented that DNMT activity directly or indirectly produces reactive oxygen species in vivo, which is an important discovery if correct, though the mechanism for this remains obscure.

I am satisfied that the points #2, #3 and #4 relating to non-addativity, transcriptional changes and ROS generation have been appropriately addressed in this revised manuscript. The most important point (previously #1) has not been addressed beyond the acknowledgement in the results section that: "Alternatively, 3mC induction by DNMT may lead to increased levels of ssDNA, particularly in alkB mutants, which could increase the risk of further DNA damage by MMS exposure and heighten sensitivity." This slightly miss-represents the original point that 5mC the main enzymatic product of DNMTs rather or in addition to 3mC is likely to lead to transient damage susceptible ssDNA, especially in an alkB deficient background. And more centrally to the main claims of this manuscript, the authors have not resolved whether methylated cytosine introduced into bacteria is deleterious in the context of genotoxic stress because of the oxidative modification to 5mC and 3mC, or because of oxidative/chemical attack to ssDNA that is transiently exposed in the repair processing of 5mC and 3mC, especially in an alkB deficient background. This is a crucial distinction because chemical vulnerability of 5mC would likely be a universal property of cytosine methylation across life, but the wide-spread exposure of ssDNA is expected to be peculiarity of introducing cytosine methylation into a system not evolved with that modification as a standard component of its genome.

These two models make different predictions about the predominant mutation types generated, in the authors system using M.SssI that targets C in a CG context - if oxidative damage to 5mC dominates then mutations are expected to be predominantly in a CG context, if ssDNA exposure effects dominate then the mutations are expected to be more widely distributed - sequencing post exposure clones could resolve this.

Strengths:

This work is based on an interesting initial premise, it is well motivated in the introduction and the manuscript is clearly written. The results themselves are compelling.

Weaknesses:

I am not currently convinced by the principal interpretations and think that other explanations based on known phenomena could account for key results. Specifically the authors have not resolved whether oxidative modification to 5mC and 3mC, or chemical attack to ssDNA that is transiently exposed in the repair processing of 5mC and 3mC is the principal source of the observed genotoxicity. The authors acknowledge this potential alternative model in their discussion of the revised manuscript.

Reviewer #2 (Public review):

5-methylcytosine (5mC) is a key epigenetic mark in DNA and plays a crucial role in regulating gene expression in many eukaryotes including humans. The DNA methyltransferases (DNMTs) that establish and maintain 5mC, are conserved in many species across eukaryotes, including animals, plants, and fungi, mainly in a CpG context. Interestingly, 5mC levels and distributions are quite variable across phylogenies with some species even appearing to have no such DNA methylation.

This interesting and well-written paper discusses continuation of some of the authors' work published several years ago. In that previous paper, the laboratory demonstrated that DNA methylation pathways coevolved with DNA repair mechanisms, specifically with the alkylation repair system. Specifically, they discovered that DNMTs can introduce alkylation damage into DNA, specifically in the form of 3-methylcytosine (3mC). (This appears to be an error in the DNMT enzymatic mechanism where the generation 3mC as opposed to its preferred product 5-methylcytosine (5mC), is caused by the flipped target cytosine binding to the active site pocket of the DNMT in an inverted orientation.) The presence of 3mC is potentially toxic and can cause replication stress, which this paper suggests may explain the loss of DNA methylation in different species. They further showed that the ALKB2 enzyme plays a crucial role in repairing this alkylation damage, further emphasizing the link between DNA methylation and DNA repair.

The co-evolution of DNMTs with DNA repair mechanisms suggest there can be distinct advantages and disadvantages of DNA methylation to different species which might depend on their environmental niche. In environments that expose species to high levels of DNA damage, high levels of 5mC in their genome may be disadvantageous. This present paper sets out to examine the sensitivity of an organism to genotoxic stresses such as alkylation and oxidation agents as the consequence of DNMT activity. Since such a study in eukaryotes would be complicated by DNA methylation controlling gene regulation, these authors cleverly utilize Escherichia coli (E.coli) and incorporate into it the DNMTs from other bacteria that methylate the cytosines of DNA in a CpG context like that observed in eukaryotes; the active sites of these enzymes are very similar to eukaryotic DNMTs and basically utilize the same catalytic mechanism (also this strain of E.coli does not specifically degrade this methylated DNA) .

The experiments in this paper more than adequately show that E. coli expression of these DNMTs (comparing to the same strain without the DNMTS) do indeed show increased sensitivity to alkylating agents and this sensitivity was even greater than expected when a DNA repair mechanism was inactivated. Moreover, they show that this E. coli expressing this DNMT is more sensitive to oxidizing agents such as H2O2 and has exacerbated sensitivity when a DNA repair glycosylase is inactivated. Both propensities suggest that DNMT activity itself may generate additional genotoxic stress. Intrigued that DNMT expression itself might induce sensitivity to oxidative stress, the experimenters used a fluorescent sensor to show that H2O2 induced reactive oxygen species (ROS) are markedly enhanced with DNMT expression. Importantly, they show that DNMT expression alone gave rise to increased ROS amounts and both H2O2 addition and DNMT expression has greater effect that the linear combination of the two separately. They also carefully checked that the increased sensitivity to H2O2 was not potentially caused by some effect on gene expression of detoxification genes by DNMT expression and activity. Finally, by using mass spectroscopy, they show that DNMT expression led to production of the 5mC oxidation derivatives 5-hydroxymethylcytosine (5hmC) and 5-formylcytosine (5fC) in DNA. 5fC is a substrate for base excision repair while 5hmC is not; more 5fC was observed. Introduction of non-bacterial enzymes that produce 5hmC and 5fC into the DNMT expressing bacteria again showed a greater sensitivity than expected. Remarkedly, in their assay with addition of H2O2, bacteria showed no growth with this dual expression of DNMT and these enzymes.

Overall, the authors conduct well thought-out and simple experiments to show that a disadvantageous consequence of DNMT expression leading to 5mC in DNA is increased sensitivity to oxidative stress as well as alkylating agents.

Again, the paper is well-written and organized. The hypotheses are well-examined by simple experiments. The results are interesting and can impact many scientific areas such as our understanding of evolutionary pressures on an organism by environment to impacting our understanding about how environment of a malignant cell in the human body may lead to cancer.

In a new revised version of the paper, the authors have adequately addressed issues put forth by other reviewers.

Reviewer #3 (Public review):

Summary:

Krwawicz et al., present evidence that expression of DNMTs in E. coli results in (1) introduction of alkylation damage that is repaired by AlkB; (2) confers hypersensitivity to alkylating agents such as MMS (and exacerbated by loss of AlkB); (3) confers hypersensitivity to oxidative stress (H2O2 exposure); (4) results in a modest increase in ROS in the absence of exogenous H2O2 exposure; and (5) results in the production of oxidation products of 5mC, namely 5hmC and 5fC, leading to cellular toxicity. The findings reported here have interesting implications for the concept that such genotoxic and potentially mutagenic consequences of DNMT expression (resulting in 5mC) could be selectively disadvantageous for certain organisms. The other aspect of this work which is important for understanding the biological endpoints of genotoxic stress is the notion that DNA damage per se somehow induces elevated levels of ROS.

Strengths:

The manuscript is well-written, and the experiments have been carefully executed providing data that support the authors' proposed model presented in Fig. 7 (Discussion, sources of DNA damage due to DNMT expression).

Weaknesses:

(1) The authors have established an informative system relying on expression of DNMTs to gauge the effects of such expression and subsequent induction of 3mC and 5mC on cell survival and sensitivity to an alkylating agent (MMS) and exogenous oxidative stress (H2O2 exposure). The authors state (p4) that Fig. 2 shows that "Cells expressing either M.SssI or M.MpeI showed increased sensitivity to MMS treatment compared to WT C2523, supporting the conclusion that the expression of DNMTs increased the levels of alkylation damage." This is a confusing statement and requires revision as Fig. 2 does ALL cells shown in Fig. 2 are expressing DNMTs and have been treated with MMS. It is the absence of AlkB and the expression of DNMTs that that causes the MMS sensitivity.

(2) It would be important to know whether the increased sensitivity (toxicity) to DNMT expression and MMS is also accompanied by substantial increases in mutagenicity. The authors should explain in the text why mutation frequencies were not also measured in these experiments.

(3) Materials and Methods. ROS production monitoring. The "Total Reactive Oxygen Species (ROS) Assay Kit" has not been adequately described. Who is the Vendor? What is the nature of the ROS probes employed in this assay? Which specific ROS correspond to "total ROS"?

(4) The demonstration (Fig. 4) that DNMT expression results in elevated ROS and its further synergistic increase when cells are also exposed to H2O2 is the basis for the authors' discussion of DNA damage-induced increases in cellular ROS. S. cerevisiae does not possess DNMTs/5mC, yet exposure to MMS also results in substantial increases in intracellular ROS (Rowe et al, (2008) Free Rad. Biol. Med. 45:1167-1177. PMC2643028). The authors should be aware of previous studies that have linked DNA damage to intracellular increases in ROS in other organisms and should comment on this in the text.

Comments for the revised manuscript:

In this revised manuscript, the authors have satisfactorily addressed the issues raised in the review of the original submission and have significantly improved these studies.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

I am not currently convinced by the principal interpretations and think that other explanations based on known phenomena could account for key results. Specifically the authors have not resolved whether oxidative modification to 5mC and 3mC, or chemical attack to ssDNA that is transiently exposed in the repair processing of 5mC and 3mC is the principal source of the observed genotoxicity.

(1) Original query which still stands: As noted in the manuscript, AlkB repairs alkylation damage by direct reversal (DNA strands are not cut). In the absence of AlkB, repair of alklylation damage/modification is likely through BER or other processes involving strand excision and resulting in single stranded DNA. It has previously been shown that 3mC modification from MMS exposure is highly specific to single stranded DNA (PMID:20663718) occurring at ~20,000 times the rate as double stranded DNA. Consequently the introduction of DNMTs is expected to introduce many methylation adducts genome-wide that will generate single stranded DNA tracts when repaired in an AlkB deficient background (but not in an AlkB WT background), which are then hyper-susceptible to attack by MMS. Such ssDNA tracts are also vulnerable to generating double strand breaks, especially when they contain DNA polymerase stalling adducts such as 3mC. The generation of ssDNA during repair is similarly expected follow the H2O2 or TET based conversion of 5mC to 5hmC or 5fC neither of which can be directly repaired and depend on single strand excision for their removal. The potential importance of ssDNA generation in the experiments has not been [adequately] considered.

We thank the reviewer for expanding on their previous comment.  We completely agree with the possibility that they raise and have added an extra paragraph in the discussion to expand on our consideration of the role of ssDNA in DNMT-induced DNA damage, which we reproduce here:

"The observation that TET overexpression sensitizes cells expressing DNMTs to oxidative stress strongly suggests that the site of DNA damage is the modified cytosine itself.  However, we do not currently have definitive evidence supporting this.  As mentioned in the results section, the presence of unrepaired 3mC may lead to increased levels of ssDNA; it is also possible that 5mC itself may increase ssDNA levels.  Loss of alkB would be expected to increase the amount of ssDNA.  Thus DNA damage surrounding modification sites, but not specifically localised to it, might be the cause of the increased sensitivity.  These two different models make different predictions.  If modified cytosines are the source of the damage, mutations arising would be predominantly located at CG dinucleotides.  Alternatively, ssDNA exposure would result in distributed mutations that would not necessarily be located at CG sites.  The highly biased spectrum of mutations that can be screened through the Rif resistance assay does not allow us to address this currently.  However, future experiments to create mutation accumulation lines could allow us to address the question systematically on a genome-wide level. "

eLife Assessment

This study presents DeepTX, a valuable methodological tool that integrates mechanistic stochastic models with single-cell RNA sequencing data to infer transcriptional burst kinetics at genome scale. The approach is broadly applicable and of interest to subfields such as systems biology, bioinformatics, and gene regulation. The evidence supporting the findings is solid, with appropriate validation on synthetic data and thoughtful discussion of limitations related to identifiability and model assumptions.

Joint Public Review:

In this work, the authors present DeepTX, a computational tool for studying transcriptional bursting using single-cell RNA sequencing (scRNA-seq) data and deep learning. The method aims to infer transcriptional burst dynamics-including key model parameters and the associated steady-state distributions-directly from noisy single-cell data. The authors apply DeepTX to datasets from DNA damage experiments, revealing distinct regulatory patterns: IdU treatment in mouse stem cells increases burst size, promoting differentiation, while 5FU alters burst frequency in human cancer cells, driving apoptosis or survival depending on dose. These findings underscore the role of burst regulation in mediating cell fate responses to DNA damage.

The main strength of this study lies in its methodological contribution. DeepTX integrates a non-Markovian mechanistic model with deep learning to approximate steady-state mRNA distributions as mixtures of negative binomial distributions, enabling genome-scale parameter inference with reduced computational cost. The authors provide a clear discussion of the framework's assumptions, including reliance on steady-state data and the inherent unidentifiability of parameter sets, and they outline how the model could be extended to other regulatory processes.

The revised manuscript addresses many of the original concerns, particularly regarding sample size requirements, distributional assumptions, and the biological interpretation of inferred parameters. However, the framework remains limited by the constraints of snapshot data and cannot yet resolve dynamic heterogeneity or causality. The manuscript would also benefit from a broader contextualisation of DeepTX within the landscape of existing tools linking mechanistic modelling and single-cell transcriptomics. Finally, the interpretation of pathway enrichment analyses still warrants clarification.

Overall, this work represents a valuable contribution to the integration of mechanistic models with high-dimensional single-cell data. It will be of interest to researchers in systems biology, bioinformatics, and computational modelling.

eLife Assessment

This study reveals that female moths use ultrasonic sounds emitted by dehydrated plants to guide their oviposition decisions. It highlights sound as an additional sensory modality in host searching, adding an important piece to the puzzle of how insects and plants interact. Through convincing experimental approaches, the authors provide insights that advance our understanding of plant-insect interactions.

Reviewer #1 (Public review):

Thank you for your thorough reply to my review and for conducting the additional experiments. All points from my previous review have been addressed.

Reviewer #2 (Public review):

This paper presents interesting and fresh approach as it investigates whether female moths utilize plant-emitted ultrasounds, particularly those associated with dehydration stress, in their egg-laying decision-making process. It provides the first empirical evidence suggesting that acoustic information may contribute to insect-plant interactions.

The revised version is significantly strengthened by the addition of supplementary data and improved explanations. The authors present robust results across multiple experiments, enhancing the credibility of their conclusions.

Female moths showed a preference for moist, fresh plants over dehydrated ones in experiments using actual plants. Additionally, when both plants were fresh but ultrasonic sounds specific to dehydrated plants were presented from one side, the moths chose the silent plant. However, in experiments without plants, contrary to the hypothesis derived from the above results, the moths preferred to oviposit near ultrasonic playback mimicking the sounds of dehydrated plants.　

These results clearly indicate that moths can perceive plant presence through sound.　The findings also highlight the need for future investigation into the multi-modal nature of moth decision-making, as acoustic cues alone may not fully explain the behavioral choices observed across different contexts.

Overall, the results are intriguing, and I think the experiments are very well designed. The authors successfully demonstrate that plant-derived acoustic signals influence oviposition behavior in female moths, thereby achieving the study's objectives. The experimental design and analysis protocols are reproducible and well suited for adaptation to other species.

Author response:

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

Reviewer #1 (Public review):

(1) The authors demonstrate that female Spodoptera littoralis moths prefer to oviposit on wellwatered tomato plants and avoid drought-stressed plants. The study then recorded the sounds produced by drought-stressed plants and found that they produce 30 ultrasonic clicks per minute. Thereafter, the authors tested the response of female S. littoralis moths to clicks with a frequency of 60 clicks per minute in an arena with and without plants and in an arena setting with two healthy plants of which one was associated with 60 clicks per minute. These experiments revealed that in the absence of a plant, the moths preferred to lay eggs on the side of the area in which the clicks could be heard, while in the presence of a plant the S. littoralis females preferred to oviposit on the plant where the clicks were not audible. In addition, the authors also tested the response of S. littoralis females in which the tympanic membrane had been pierced making the moths unable to detect the click sounds. As hypothesised, these females placed their eggs equally on both sites of the area.

Finally, the authors explored whether the female oviposition choice might be influenced by the courtship calls of S. littoralis males which emit clicks in a range similar to a drought-stressed tomato plant. However, no effect was found of the clicks from ten males on the oviposition behaviour of the female moths, indicating that the females can distinguish between the two types of clicks. Besides these different experiments, the authors also investigated the distribution of egg clusters within a longer arena without a plant, but with a sugar-water feeder. Here it was found that the egg clusters were mostly aggregated around the feeder and the speaker producing 60 clicks per minute. Lastly, video tracking was used to observe the behaviour of the area without a plant, which demonstrated

that the moths gradually spent more time at the arena side with the click sounds.

We thank the reviewers for their helpful comments. We agree with the summary, but would like to note that in the control experiment (Figure 2) we used a click rate of 30 clicks per minute—a design choice driven by the editor’s feedback. We have clarified this and, to further probe the system’s dynamics, added a second experiment employing the same click rate (30 clicks per minute) with a dehydrated plant (see details below). In both experiments, females again showed a clear tendency to oviposit nearer the speaker; these findings are described in the updated manuscript.

(2) The study addresses a very interesting question by asking whether female moths incorporate plant acoustic signals into their oviposition choice, unfortunately, I find it very difficult to judge how big the influence of the sound on the female choice really is as the manuscript does not provide any graphs showing the real numbers of eggs laid on the different plants, but instead only provides graphs with the Bayesian model fittings for each of the experiments. In addition, the numbers given in the text seem to be relatively similar with large variations e.g. Figure 1B3: 1.8 {plus minus} 1.6 vs. 1.1 {plus minus} 1.0. Furthermore, the authors do not provide access to any of the raw data or scripts of this study, which also makes it difficult to assess the potential impact of this study. Hence, I would very much like to encourage the authors to provide figures showing the measured values as boxplots including the individual data points, especially in Figure 1, and to provide access to all the raw data underlying the figures.

We acknowledge that there are researchers who favor Bayesian graphical representation versus raw data visualization. Therefore, we have added chartplots of the raw data from Figure 1 in the supplementary section. We are aware of the duplication in presentation and apologize for this redundancy.  

Regarding the variance and means we obtained in our experiment, we have analyzed all raw data using the statistical model presented, and if statistical significance was found despite a particular mean difference or variance, this is meaningful from a biological perspective. One can certainly discuss whether this difference has biological importance, but it should be remembered that in this experimental system, we are trying to isolate the acoustic signal from a complex system that includes multiple signals. Therefore, at no point we’ve suggested that this is a standalone factor, but rather proposed it as an informative and significant component. 

In addition to the experiments described above, we conducted an experiment in which we counted both eggs and clusters. The results indicate that cluster counts are a reliable proxy for reproductive investment at a given location. In this experiment, we present cluster numbers alongside egg counts (Figure 2).

Furthermore, we apologize for the technical error that prevented our uploaded data files from reaching the reviewers. We have also uploaded updated data and code.

(3) Regarding the analysis of the results, I am also not entirely convinced that each night can be taken as an independent egg-laying event, as the amount of eggs and the place were the eggs are laid by a female moth surely depends on the previous oviposition events. While I must admit that I am not a statistician, I would suggest, from a biological point of view, that each group of moths should be treated as a replicate and not each night. I would therefore also suggest to rather analyse the sum of eggs laid over the different consecutive nights than taking the eggs laid in each night as an independent data point.

We thank the reviewer for this question. This is a valid and point that we will address in three aspects: 

First, regarding our statistical approach, we used a model that takes into account the sequence of nights and examines whether there is an effect of the order of nights, i.e., we used GLMMs, with the night nested within the repetition. This is equivalent to addressing this as a repeated measure and is, to our best knowledge, the common way to treat such data. 

Second, following the reviewer's comment, we also reran the statistics of the third experiment (i.e., “sound gradient experiments”, Figure 2 and Supplementary figure 4) when only taking the first night when the female/s laid eggs to avoid the concern of dependency. This analysis revealed the same result – i.e., a significant preference for the sound stimulus. We have now updated our methods and results section to clarify this point.  

Third, an important detail that may not have been clearly specified in the methods: at the end of each night, we cleaned the arena of counted egg clusters using a cloth with ethanol, so that on the subsequent night, we would not expect there to be evidence of previous oviposition but thus would not exclude some sort of physiological or cognitive memories. We have now updated our methods section to clarify this important procedural point. 

(4) Furthermore, it did not become entirely clear to me why a click frequency of 60 clicks per minute was used for most experiments, while the plants only produce clicks at a range of 30 clicks per minute. Independent of the ecological relevance of these sound signals, it would be nice if the authors could provide a reason for using this frequency range. Besides this, I was also wondering about the argument that groups of plants might still produce clicks in the range of 60 clicks per minute and that the authors' tests might therefore still be reasonable. I would agree with this, but only in the case that a group of plants with these sounds would be tested. Offering the choice between two single plants while providing the sound from a group of plants is in my view not the most ecologically reasonable choice. It would be great if the authors could modify the argument in the discussion section accordingly and further explore the relevance of different frequencies and dBlevels.

This is an excellent point. We originally increased the click rate generate a strong signal. However, it was important for us to verify that there was ecological relevance in the stimulus we implemented in the system. For this purpose, we recorded a group of dehydrated plants at a distance of ~20cm and we measured a click rate of 20 clicks per minute (i.e., 0.33 Hz) (see Methods section). Therefore, as mentioned at the beginning of this letter, in the additional experiment described in Figure 2, we reduced the click frequency to 30 clicks per minute, and at this lower rate, the effect was maintained. Increasing plant density would probably lead to a higher rate of 30 clicks per minute. 

(5) Finally, I was wondering how transferable the findings are towards insects and Lepidopterans in general. Not all insects possess a tympanic organ and might therefore not be able to detect the plant clicks that were recorded. Moreover, I would imagine that generalist herbivorous like Spodoptera might be more inclined to use these clicks than specialists, which very much rely on certain chemical cues to find their host plants. It would be great if the authors would point more to the fact that your study only investigated a single moth species and that the results might therefore only hold true for S. littoralis and closely related species, but not necessary for other moth species such as Sphingidae or even butterflies.

Good point. Our research uses a specific model system of one moth species and one plant species in a particular plant-insect interaction where females select host plants for their offspring. As with any model-based research that attempts to draw broader conclusions, we've taken care to distinguish between our direct findings and potential wider implications. We believe our system may represent mechanisms relevant to a wider group of herbivorous insects with hearing capabilities, particularly considering that several moth families and other insect orders can detect ultrasound. However, additional research examining more moth and plant species is necessary to determine how broadly applicable these findings are. We have made these clarifications in the text.

Reviewer #2 (Public review):

(6) The results are intriguing, and I think the experiments are very well designed. However, if female moths use the sounds emitted by dehydrated plants as cues to decide where to oviposit, the hypothesis would predict that they would avoid such sounds. The discussion mentions the possibility of a multi-modal moth decision-making process to explain these contradictory results, and I also believe this is a strong possibility. However, since this remains speculative, careful consideration is needed regarding how to interpret the findings based solely on the direct results presented in the results section.  

Thank you for this insightful observation. We agree that the apparent attraction of females to dehydrated-plant sounds contradicts our initial prediction. Having observed this pattern consistently across multiple setups, we have now added a targeted choice experiment to the revised manuscript: here female moths were offered a choice between dehydrated plants broadcasting their natural ultrasonic emissions and a control. These results—detailed in the Discussion and presented in full in the Supplementary Materials (Supplementary Figure 4)—show that when only a dehydrated plant is available, moths would prefer it for oviposition, supporting our hypothesis that in the absence of a real plant, the plant’s sounds might represent a plant..

(7) Additionally, the final results describing differences in olfactory responses to drying and hydrated plants are included, but the corresponding figures are placed in the supplementary materials. Given this, I would suggest reconsidering how to best present the hypotheses and clarify the overarching message of the results. This might involve reordering the results or re-evaluating which data should appear in the main text versus the supplementary materials

Thank you for this suggestion. We have reorganized the manuscript and removed the olfactory response data from the current version to maintain a focused narrative on acoustic cues. We agree that a detailed investigation of multimodal interactions deserves a separate study, which we plan to pursue in future work. 

(8) There were also areas where more detailed explanations of the experimental methods would be beneficial.

Thank you for highlighting this point. We have expanded and clarified the Methods section to provide comprehensive detail on our experimental procedures.

Reviewer #1 (Recommendations for the authors):

(9) Line 1: Please include the name of the species you tested also in the title as your results might not hold true for all moth species.

We do not fully agree with this comment. Please see comment 5.

(10) Line 19-20: Please rephrase the sentence so that it becomes clear that the "dehydration stress" refers to the plant and not to the moths.

Thank you for the suggestion; we have clarified the text accordingly

(11) Line 31: Male moths might provide many different signals to the females, maybe better "male sound signals" or similar.

Thank you for the suggestion; we have clarified the text accordingly.

(12) Line 52-53: Maybe mention here that not all moth species have evolved these abilities.

Thank you for the suggestion; we have clarified the text accordingly.

(13) Line 77: add a space after 38.

Thank you for the suggestion; we have clarified the text accordingly.

(14) Line 88: Maybe change "secondary predators" to "natural enemies".

Thank you for the suggestion; we have clarified the text accordingly.

(15) Line 134: Why is "notably" in italics? I would suggest using normal spelling/formatting rules here.

Thank you for the suggestion; we have clarified the text accordingly.

(16) Line 140-144: If you did perform the experiment also with the more ecological relevant playback rate, why not present these findings as your main results and use the data with the higher playback frequency as additional support?

Thank you for this suggestion. We agree that the ecologically relevant playback data are important; as described in detail at the beginning of this letter and also in comment 4, however, to preserve a clear and cohesive narrative, we have maintained the original ordering of this section. Nevertheless, the various experiments conducted in Figure 1 differ in several components from Figure 2 and the work that examined sounds in plant groups in the appendices. Therefore, we find it more appropriate to use them as supporting evidence for the main findings rather than creating a comparison between different experimental systems. For this reason, we chose to keep them as a separate description in "The ecological playback findings (Lines 140–144) remain fully described in the Results and serve to reinforce the main observations without interrupting the manuscript's flow.

(17) Line 146: Please explain already here how you deafened the moths.

Thank you for the suggestion; we have clarified the text accordingly.

(18) Line 181: should it be "male moths' " ?

Thank you for the suggestion; we have clarified the text accordingly.

(19) Line 215: Why is "without a plant" in italics? I would suggest using normal spelling/formatting rules here.

Thank you for the suggestion; we have clarified the text accordingly.

(20) Line 234: I do not understand why this type of statistic was used to analyse the electroantennogram (EAG) results. Would a rather simple Student's t-test or a Wilcon rank sum test not have been sufficient? I would also like to caution you not to overinterpret the data derived from the EAG, as you combined the entire headspace into one mixture it is no longer possible to derive information on the different volatiles in the blends. The differences you observe might therefore mostly be due to the amount of emitted volatiles.

We have reorganized the manuscript and removed the olfactory response data from the current version to maintain a focused narrative on acoustic cues (See comment 7). 

(21) Line 268: It might be nice to add an additional reference here referring to the multimodal oviposition behaviour of the moths.

Thank you for the suggestion; we have clarified the text accordingly.

(22) Line 284: If possible, please add another reference here referring to the different cues used by moths during oviposition.

Thank you for the suggestion; we have clarified the text accordingly.

(23) Line 336: What do you mean by "closed together"?

Thank you for the suggestion; we have clarified the text accordingly.

(24) Line 434-436: Please see my overall comments. I do not think that you can call it ecologically relevant if the signal emitted by multiple plants is played in the context of just a single plant.

Please see comments 1 and 4.

(25) Line 496: Please change "stats" to statistics.

Thank you for the suggestion; we have clarified the text accordingly.

(26) Line 522-524: I am not sure whether simply listing their names does give full credit to the work these people did for your study. Maybe also explain how they contributed to your work.

Thank you for the suggestion; we have clarified the text accordingly.

Reviewer #2 (Recommendations for the authors):

(27) L54 20-60kHz --> 20Hz-60kHz or 20kHz - 60kHz?

OK. We have replaced it.

(28) L124 Are the results for the condition where nothing was placed and the condition where a decoy silent resistor was placed combined in the analysis? If so, were there no significant differences between the two conditions? Comparing these with a condition presenting band-limited noise in the same frequency range as the drought-stressed sounds might also have been an effective approach to further isolate the specific role of the ultrasonic emissions.

We have used both conditions due to technical constrains and pooled them tougher for analysis— statistical tests confirmed no significant differences between them—and this clarification has now been added to the Methods section including the results of the statistical test.

(29) L125 (Fig. 1A), see Exp. 1 in the Methods). -> (Fig.1B. See Exp.1 in the Methods).

Thank you for the suggestion; we have clarified the text accordingly.

(30) L132 "The opposite choice to what was seen in the initial experiment (Fig.1B)"

Thank you for the suggestion; we have clarified the text accordingly.

(31) L137-143 If you are writing about results, why not describe them with figures and statistics? The current description reads like a discussion.

These findings were not among our primary research questions; however, we believe that including them in the Results section underscores the experimental differences. In our opinion, introducing an additional figure or expanding the statistical analysis at this point would disrupt the narrative flow and risk confusing the reader.

(32) L141 "This is higher than the rate reported for a single young plant" Are you referring to the tomato plants used in the experiments? It might be helpful to include in the main text the natural click rate emitted by tomato plants, as this information is currently only mentioned in the Methods section.

See comment 4.  

(33) L191 Is the main point here to convey that the plant playback effect remained significant even when the sound presentation frequency was reduced to 30 clicks per minute? The inclusion of the feeder element, however, seems to complicate the message. To simplify the results, moving the content from lines 185-202 to the supplementary materials might be a better approach. Additionally, what is the rationale for placing the sugar solution in the arena? Is it to maintain the moths' vitality during the experiment? Clarifying this in the methods section would help provide context for this experimental detail.

In this series of experiments, we manipulated four variables—single moths, ultrasonic click rate, arena configuration (from a two-choice design to an elongated enclosure), and the response metric (total egg counts rather than cluster counts)—to evaluate moth oviposition under more ecologically realistic conditions. We demonstrate the system’s robustness and validity in a more realistic setting (by tracking individual moths, counting single eggs, etc.).  

As noted in the text, feeders were included to preserve the moths’ natural behavior and vitality. We have further clarified this in the revised manuscript.

(34) L215 Is the click presentation frequency 30 or 60 per minute? Since Figure 3 illustrates examples of moth movement from the experiment described in Figure 1, it might be more effective to present Figure 3 when discussing the results of Figure 1 or to include it in the supplementary materials for better clarity and organization.

See comments 1 and 4. As mentioned in the above 

(35) L291 Please provide a detailed explanation of the experiments and measurements for the results shown in Figure S3 (and Figure S2). If the multi-modal hypothesis discussed in the study is a key focus, it might be better to include these results in the main results section rather than in the supplementary materials.

Thank you for this suggestion. Figure S2 was removed, see comments above. We’ve added now the context to figure S3.

(36) L303 It might be helpful to include information about the relationship between the moth species used in this study and tomato plants somewhere in the text. This would provide an important context for understanding the ecological relevance of the experiments.

Thank you for the suggestion; we have clarified the text accordingly.

(37) Table 1 The significant figures in the numbers presented in the tables should be consistent.

Thank you for the suggestion; we have clarified the text accordingly.

(38) L341 The text mentions that experiments were conducted in a greenhouse, but does this mean the arena was placed inside the greenhouse? Also, the term "arena" is used - does this refer to a sealed rectangular case or something similar? For the sound presentation experiments, it seems that the arena cage was placed inside a soundproof room. If the arena is indeed a case-like structure, were there any specific measures taken to prevent sound scattering within the case, such as the choice of materials or structural modifications?

Here, “arena” refers to the plastic boxes used throughout this study. In this particular experiment, we presented plants alone—reflecting ongoing debate in the literature—and used these trials as a baseline for our subsequent sound-presentation experiments, during which we measured sound intensity as described in the Methods section. All sound-playback experiments were conducted in sound-proof rooms, and acoustic levels were measured beforehand—sound on the control side fell below our system’s detection threshold. 

(39) L373 "resister similar to the speaker" Could you explain it in more detail? I think this would depend on the type of speaker used-particularly whether it includes magnets. From an experimental perspective, presenting different sounds such as white noise from the speaker might have been a better control. Was there a specific reason for not doing so? Additionally, the study does not clearly demonstrate whether the electric and magnetic field environments on both sides of the arena were appropriately controlled. Without this information, it is difficult to evaluate whether using a resistor as a substitute was adequate.

Thank you for this comment. We have now addressed this point in the Discussion. We acknowledge that we did not account for the magnetic field, which might have differed between the speaker and the resistor. We agree that using an alternative control, such as white noise, could have been informative, and we now mention this as a limitation in the revised Methods.

(40) L435 60Hz? The representation of frequencies in the text is inconsistent, with some values expressed in Hz and others as "clicks per second." It would be better to standardize these units for clarity, such as using Hz throughout the manuscript.

We agree that this is confusing. We reviewed the text and made sure that when we addressed click per second, we meant how many clicks were produced and when we addressed Hz units it was in the context of sound frequencies.  

(41) L484 "we quantified how many times each individual crossed the center of the arena" Is this data being used in the results?

Yes. Mentioned in the text just before Figure 3. L220

eLife Assessment

IL-10 balances protective and deleterious immune functions in mice and humans, but if IL-10 also controls avian intestinal homeostasis remains unclear. Generating genetic knockouts, Meunier et al. established that a complete lack of IL-10 strengthened immunity against enteric bacteria in chickens, while also aggravating infection-inflicted inflammatory tissue damage and dysbiosis upon parasite infection, but unlike mouse models, IL-10 deficiency did not provoke spontaneous colitis in chickens. The findings presented are valuable, and the strength of evidence is convincing. The observation may have implications for the livestock industry and additional studies involving genetic knockouts may further unravel conserved and distinct avian IL-10 controls.

Reviewer #1 (Public review):

Summary:

In this study, Meunier et al. investigated the functional role of IL-10 in avian mucosal immunity. While the anti-inflammatory role of IL-10 is well established in mammals, and several confirmatory Knock-out models available in mice, IL-10's role in avian mucosal immunity is so far correlative. In this study the authors generated two different models of IL-10 ablation in Chickens. A whole body knock-out model, and an enhancer KO model leading to reduced IL10 expression. The authors first performed in vitro LPS stimulation based experiments, and then in vivo two different infection models employing C. jejuni and E. tenella, to demonstrate that complete ablation of IL10 leads to enhanced inflammation related pathology and gene expression, and enhanced pathogen clearance. At a steady-state level, however, IL-10 ablation did not lead to spontaneous colitis.

Strengths:

Overall the study is well executed and establishes an anti-inflammatory role of IL-10 in birds. While the results are expected, and not surprising, this appears to be the first report to conclusively demonstrate IL-10's anti-inflammatory role upon its genetic ablation in avian model. Provided the applicability of this information in combating pathogen infection in livestock species in sustainable industries like poultry, the study is worth publishing.

Weaknesses:

The study is primarily a confirmation of the already established anti-inflammatory role of IL-10.

Comments on revised version:

The authors have incorporated most of the points raised, and provided a reasonable argument for not considering DSS mediated colitis as an additional model.

Reviewer #2 (Public review):

Summary:

The authors were to investigate functional role of IL10 on mucosal immunity in chickens. CRISPR technology was employed to generate IL10 knock out chickens in both exon and putative enhancer regions. IL10 expressions were either abolished (knockout in exon) or reduced (enhancer knock-out). IL-10 play an important role in the composition of the caecal microbiome. Through various enteric pathogens challenge, deficient IL10 expression was associated with enhanced pathogen clearance, but with more severe lesion score and body weight loss.

Strengths:

Both in vitro and in vivo knock-out in abolished and reduced IL10 expression and broad enteric pathogens were challenged in vivo and various parameters were examined to evaluate the functional role of IL10 on mucosal immunity.

Weaknesses:

Overexpression of IL10 either in vitro or in vivo may further support the findings from this study.

Comments on revised version:

The authors' response and justifications are appropriate.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

In this study, Meunier et al. investigated the functional role of IL-10 in avian mucosal immunity. While the anti-inflammatory role of IL-10 is well established in mammals, and several confirmatory knockout models are available in mice, IL-10's role in avian mucosal immunity is so far correlative. In this study, the authors generated two different models of IL-10 ablation in Chickens. A whole body knock-out model and an enhancer KO model leading to reduced IL10 expression. The authors first performed in vitro LPS stimulation-based experiments, and then in vivo two different infection models employing C. jejuni and E. tenella, to demonstrate that complete ablation of IL10 leads to enhanced inflammation-related pathology and gene expression, and enhanced pathogen clearance. At a steady-state level, however, IL-10 ablation did not lead to spontaneous colitis. 

Strengths: 

Overall, the study is well executed and establishes an anti-inflammatory role of IL-10 in birds. While the results are expected and not surprising, this appears to be the first report to conclusively demonstrate IL-10's anti-inflammatory role upon its genetic ablation in the avian model. Provided this information is applicable in combating pathogen infection in livestock species in sustainable industries like poultry, the study will be of interest to the field. 

Weaknesses: 

The study is primarily a confirmation of the already established anti-inflammatory role of IL-10. 

We do not agree that this work is primarily confirmatory. The anti-inflammatory role of IL10 was indeed known previously from studies in mammals. The much more general insight from the current study is our demonstration of the intrinsic trade-off between inflammation and tolerance in the response to both the microbiome (which was significantly altered in the IL10 knockout birds) and mucosal pathogens. The study of Eimeria challenge in particular highlights the fact that it may be better for the host to tolerate a potential pathogen than to take on the cost of elimination.

Reviewer #2 (Public review): 

Summary: 

The authors were to investigate the functional role of IL10 on mucosal immunity in chickens. CRISPR technology was employed to generate IL10 knock-out chickens in both exon and putative enhancer regions. IL10 expressions were either abolished (knockout in exon) or reduced (enhancer knock-out). IL-10 plays an important role in the composition of the caecal microbiome. Through various enteric pathogen challenges, deficient IL10 expression was associated with enhanced pathogen clearance, but with more severe lesion scores and body weight loss. 

Strengths: 

Both in vitro and in vivo knock-out abolished and reduced IL10 expression, and broad enteric pathogens were challenged in vivo, and various parameters were examined to evaluate the functional role of IL10 on mucosal immunity. 

Weaknesses: 

Overexpression of IL-10 either in vitro or in vivo may further support the findings from this study. 

An overexpression experiment, regardless of outcome, would not necessarily support or invalidate the findings of the current study. It would address the question of whether the absolute concentration of IL10 produced alters the outcome of an infection.

Reviewer #1 (Recommendations for the authors): 

The following are the recommendations that, in my opinion, will be helpful to enhance the quality of the study. 

Major point: 

The authors at a steady state did not observe any sign of spontaneous colitis. Since IL-10 KO in mice leads to enhanced pathological score upon DSS-mediated induction of colitis, and several colitis models are well established in birds, it will be worthwhile to test the consequence of experimentally inducing colitis in this context. 

One of the novel features of this study is the observation that the microbiome is modified in the IL10KO HOM chicks, which may serve to mitigate potential spontaneous pathology; we now mention this in the discussion. We agree that it could be worthwhile in the future to look at additional challenge models. However, we would argue that the Eimeria challenge is a sufficiently adequate experimentally-induced model of colitis to demonstrate the increased inflammation that occurs in an IL10-deficient bird. This is further supported by evidence of enhanced inflammatory responses in the caeca of IL10KO HOM birds challenged with Campylobacter or Salmonella relative to WT controls. See in the revised manuscript (pages 12-13).

Minor points: 

(1) In Figure 2B, the authors should confirm whether the ROS-AV163 groups also have LPS treatment. 

The legend for Figure 2B already states that neutralizing anti-IL10 antibody was added to LPS-stimulated BMDMs: “Nitric oxide production was assessed by measuring nitrite levels using Griess assay for LPS-stimulated BMDMs […] in the absence or presence of neutralizing anti-IL10 antibody ROS-AV163”. However, for added clarity we have now modified the x-axis label for Figure 2B (“+ROS-AV163” replaced by “+LPS +anti-IL10”) and we have also made minor changes to the figure legend. See in the revised manuscript (page 33).

(2) In Figure 3F, the authors should discuss why the duodenum of KO birds has enhanced infiltration compared to WT? 

We are not sure what the reviewer is referring to here. Although not specifically mentioned in Figure 3F, there is no statistically significant difference in cellular infiltration in the duodenum of IL10KO WT and HOM birds raised in our specified pathogen-free (SPF) facility, nor in the duodenum of IL10KO WT and HOM birds raised in our conventional facility (Mann-Whitney U tests, p>0.1 in both cases); this can be seen in the sums of histopathological scores shown in Figures 3C (SPF facility) and 3E (conventional facility). Figure 3F shows that there is a statistically significant difference in cellular infiltration scores in the duodenum and proximal colon of both IL10KO WT and HOM birds based on the environment they are raised in (SPF vs conventional). We have made minor changes to the text to clarify this. See in the revised manuscript (page 7).

(3) The authors should discuss the observed differences in the C. jejuni colonization results among the two cohorts at week 1 and week 2 post-infection. 

Numbers of C. jejuni in the caeca of IL10KO HOM birds were markedly lower than for WT controls at 1-week post-infection in cohort 1, and at both time intervals post-infection in cohort 2 (Figure 4A). This reached statistical significance at 1-week post-infection in cohort 1 and at 2-weeks post-infection in cohort 2. It is evident from Figure 4A that considerable inter-animal variance existed in each group, and in the IL10KO HOM birds in particular. This is typical of C. jejuni colonisation in chickens, where bacterial population structures have been reported to be variable and unpredictable (Coward et al., Appl Environ Microbiol 2008, PMID: 18424530). Similar variation between time intervals, birds and repeated experiments has been reported when evaluating vaccines against C. jejuni colonisation (e.g. Buckley et al., Vaccine 2010, PMID: 19853682; Nothaft et al., Front Microbiol 2021, PMID: 34867850). We performed two independent studies for this reason. Taken together, we consider that our data provide convincing evidence of elevated pro-inflammatory responses upon C. jejuni infection in IL10KO HOM birds relative to WT controls that associates with reduced bacterial burden. Our data is also consistent with a published observation that a commercial broiler line with low IL10 expression had correspondingly elevated expression of CXCLi-1, CXCLi-2 and IL-1b (Humphrey et al., mBio 2014, reference 33 in our original submission). We have added text to the discussion to capture the points above.  See in the revised manuscript (page 13).

Reviewer #2 (Recommendations for the authors): 

For the animal challenging experiments, both IL10KO HOM and IL10EnKO HOM chickens were used for Eimeria challenge, but not for Salmonella and Campylobacter. Could the authors justify why? 

The Eimeria challenge produced a much higher and more reproducible level of inflammation than either of the bacterial challenge models. Within the parasite challenge cohorts, IL10KO HET and IL10EnKO HOM birds were only marginally different from WT controls (e.g. parasite replication: Figures 5A and B; lesion scores: Figures 5E and F; body weight gain: Figures 5G and H). Given the more limited response and the inter-individual variation in the bacterial challenge models, we felt that analysis of a sufficiently large cohort of the IL10KO HOM was appropriate, while additional cohorts of IL10KO HET and IL10EnKO HOM birds large enough to detect statistically significant differences could not be justified.

In the M&M, there was no mention of # of birds generated for IL10EnKO HOM, HET, etc. 

Full details of bird numbers can be found in SI Appendix Table S1 “Number of IL10KO and IL10EnKO WT, HET and HOM chicks hatched in the NARF SPF chicken facility in the first (G1) and second (G2) generations”. Table S1 is already referred to in the Results section “Generation of IL10-deficient chickens”; we have now also clearly referred to it in the “Animals” and “Generation of surrogate host chickens and establishment of the IL10KO and IL10EnKO lines under SPF conditions” sections of the Materials and Methods. In all three sections we have also added some text to clarify that the table details G1 and G2 bird numbers. See in the revised manuscript (pages 5, 15, 17).

From the results of Campylobacter challenge, the results from the cohort 1 and cohort 2 were not consistent at both 1 and 2 weeks of post-infection. There is not much discussion on this inconsistency. What is the final conclusion: significant difference in week 1 or week 2, OR none of them, OR both of them. What would happen if an additional cohort were conducted for Salmonella and Eimeria? 

As noted in response to Reviewer 1 (minor point 3), we have now added text to the discussion on the partial inconsistency between independent C. jejuni challenge studies. We do not feel that additional experiments to address this comment are required. Highly significant increases in the infiltration of lymphoplasmacytic cells and heterophils were detected in IL10KO HOM chickens relative to WT controls in the caeca, a key site of Campylobacter colonisation. This was consistently observed in two independent cohorts at both 1- and 2-weeks post-infection (SI Appendix Figures S7 and S8) and was reflected in similar patterns of expression of pro-inflammatory genes at these intervals in both cohorts (Figure 4B). As our laboratory has observed substantially less variation between repeated Salmonella challenges, a single study was performed, but with adequate power to detect statistical differences.  The effects of E. tenella infection in IL10KO WT and HOM birds were replicated (compare Figure 4 with data from day 6 in Figure 5).

eLife Assessment

The authors present a software (TEKRABber) to analyze how expression of transposable elements (TEs) and TE silencing factors KRAB zinc finger (KRAB-ZNF) genes are correlated in experimentally validated datasets. TEKRABber is used to reconstruct regulatory networks of KRAB-ZNFs and TEs during human brain evolution and in Alzheimer's disease. The direction of the work is important, with potentially significant interest from others looking for a tool for correlative gene expression analysis across individual genomes and species. However, the reviews identified biases and shortcomings in the pipeline that could lead to an unacceptable number of false positive and negative signals and thus impact the conclusions, leaving the work in its current form incomplete.

Reviewer #1 (Public review):

The authors present their new bioinformatic tool called TEKRABber, and use it to correlate expression between KRAB ZNFs and TEs across different brain tissues, and across species. While the aims of the authors are clear and there would be significant interest from other researchers in the field for a program that can do such correlative gene expression analysis across individual genomes and species, the presented approach and work display significant shortcomings. In the current state of the analysis pipeline, the biases and shortcomings mentioned below, for which I have seen no proof of that they are accounted for by the authors, are severely impacting the presented results and conclusions. It is therefore essential that the points below are addressed, involving significant changes in the TEKRABber progamm as well as the analysis pipeline, to prevent the identification of false positive and negative signals, that would severely affect the conclusions one can raise about the analysis.

My main concerns are provided below:

One important shortcoming of the biocomputational approach is that most TEs are not actually expressed, and others (Alus) are not a proxy of the activity of the TE class at all. I will explain: While specific TE classes can act as (species-specific) promoters for genes (such as LTRs) or are expressed as TE derived transcripts (LINEs, SVAs), the majority of other older TE classes do not have such behavior and are either neutral to the genome or may have some enhancer activity (as mapped in the program they refer to 'TEffectR'. A big focus is on Alus, but Alus contribute to a transcriptome in a different way too: They often become part of transcripts due to alternative splicing. As such, the presence of Alu derived transcripts is not a proxy for the expression/activity of the Alu class, but rather a result of some Alus being part of gene transcripts (see also next point). Bottom line is that the TEKRABber software/approach is heavily prone to picking up both false positives (TEs being part of transcribed loci) and false negatives (TEs not producing any transcripts at all) , which has a big implication for how reads from TEs as done in this study should be interpreted: The TE expression used to correlate the KRAB ZNF expression is simply not representing the species-specific influences of TEs where the authors are after.

With the strategy as described, a lot of TE expression is misinterpreted: TEs can be part of gene-derived transcripts due to alternative splicing (often happens for Alus) or as a result of the TE being present in an inefficiently spliced out intron (happens a lot) which leads to TE-derived reads as a result of that TE being part of that intron, rather than that TE being actively expressed. As a result, the data as analysed is not reliably indicating the expression of TEs (as the authors intend too) and should be filtered for any reads that are coming from the above scenarios: These reads have nothing to do with KRAB ZNF control, and are not representing actively expressed TEs and therefore should be removed. Given that from my lab's experience in brain (and other) tissues, the proportion of RNA sequencing reads that are actually derived from active TEs is a stark minority compared to reads derived from TEs that happen to be in any of the many transcribed loci, applying this filtering is expected to have a huge impact on the results and conclusions of this study.

Another potential problem that I don't see addressed is that due to the high level of similarity of the many hundreds of KRAB ZNF genes in primates and the reads derived from them, and the inaccurate annotations of many KZNFs in non-human genomes, the expression data derived from RNA-seq datasets cannot be simply used to plot KZNF expression values, without significant work and manual curation to safeguard proper cross species ortholog-annotation: The work of Thomas and Schneider (2011) has studied this in great detail but genome-assemblies of non-human primates tend to be highly inaccurate in appointing the right ortholog of human ZNF genes. The problem becomes even bigger when RNA-sequencing reads are analyzed: RNA-sequencing reads from a human ZNF that emerged in great apes by duplication from an older parental gene (we have a decent number of those in the human genome) may be mapped to that older parental gene in Macaque genome: So, the expression of human-specific ZNF-B, that derived from the parental ZNF-A, is likely to be compared in their DESeq to the expression of ZNF-A in Macaque RNA-seq data. In other words, without a significant amount of manual curation, the DE-seq analysis is prone to lead to false comparisons which make the stategy and KRABber software approach described highly biased and unreliable.

There is no doubt that there are differences in expression and activity of KRAB-ZNFs and TEs repspectively that may have had important evolutionary consequences. However, because all of the network analyses in this paper rely on the analyses of RNA-seq data and the processing through the TE-KRABber software with the shortcomings and potential biases that I mentioned above, I need to emphasize that the results and conclusions are likely to be significantly different if the appropriate measures are taken to get more accurate and curated TE and KRAB ZNF expression data.

Finally, there are some minor but important notes I want to share:

The association with certain variations in ZNF genes with neurological disorders such as AD, as reported in the introduction is not entirely convincing without further functional support. Such associations could be merely happen by chance, given the high number of ZNF genes in the human genome and the high chance that variations in these loci happen associate with certatin disease associated traits. So using these associations as an argument that changes in TEs and KRAB ZNF networks are important for diseases like AD should be used with much more caution.

There is a number of papers where KRAB ZNF and TE expression are analysed in parallel in human brain tissues. So the novelty of that aspect of the presented study may be limited.

Additional note after reviewing the revised version of the manuscript:

After reviewing the revised version of the manuscript, my criticism and concerns with this study are still evenly high and unchanged. To clarify, the revised version did not differ in essence from the original version; it seems that unfortunately, no efforts were taken to address the concerns raised on the original version of the manuscript, the results section as well as the discussion section are virtually unchanged.

eLife Assessment

Axon growth is essential to formation of neural connections. This manuscript presents a useful presentation of a new method for assessing the adhesion strength of axons with the use of a laser-induced shock wave. However, the strength of the evidence is incomplete as critical controls for calibration and time course are lacking.

Reviewer #1 (Public review):

Summary:

Axon growth is of course essential to formation of neural connections. Adhesion is generally needed to anchor and rectify such motion, but whether the tenacity or forces of adhesion must be optimal for maximal axon extension is unknown. Measurements and contributing factors are generally lacking and are pursued here with a laser-induced shock wave approach near the axon growth cone. The authors claim to make measurements of the pressure required to detach axon from low to high matrix density. The results seem to support the authors' conclusions, and the work -- with further support per below - is likely to impact the field of cell adhesion. In particular, there could be some utility of the methods for the adhesion and those interested in aspects of axon growth

Strengths:

A potential ability to control the pressure simply via proximity of the laser spot is convenient and perhaps responsible. The 0 to 1 scale for matrix density is a good and appropriate measure for comparing adhesion and other results. The attention to detachment speed, time, F-actin, and adhesion protein mutant provides key supporting evidence. Lastly, the final figure of traction force microscopy with matrix varied on a gel is reasonable and more physiological because neural tissue is soft (cite PMID: 16923388); an optimum in Fig.6 also perhaps aligns with axon length results in Fig.5.

Weaknesses:

The results seem incomplete and less than convincing. This is because the force calibration curve seems to be from a >10 yr old paper without any more recent checks or validating measurements. Secondly, the claimed effect of pressure on detachment of the growth cone does not consider other effects such as cavitation or temperature and certainly needs validation with additional methods that overcome such uncertainties. The authors need to check whether the laser perturbs the matrix, particularly local density. A relation between traction stresses of ~20-50 pN/um2 in Fig.6 and the adhesion pressure of 3-5 kPa of FIg.3 needs to be carefully explained; the former units equate to 0.02-0.05 kPa, and would perhaps suggest cells cannot detach themselves and move forward.

The authors need to measure axon length on gels (Fig.6) as more physiological because neural tissue is soft. The studies are also limited to a rudimentary in vitro model without clear relevance to in vivo.

Weaknesses concerning the laser method have been addressed, but alternative methods and relevance to in vivo remain lacking.

Reviewer #3 (Public review):

Summary:

Yamada et al. build on classic and more recent studies (Chen et al., 2023; Lemmon et al., 1992; Nichol et al., 2016; Zheng et al., 1994; Schense and Hubbell, 2000) to better understand the relationship between substrate adhesion and neurite outgrowth.

Strengths:

The primary strength of the manuscript lies in developing a method for investigating the role of adhesion in axon outgrowth and traction force generation using a femtosecond laser technique. The most exciting finding is that both outgrowth and traction force generation have a biphasic relationship with laminin concentration.

Weaknesses:

The primary weaknesses, as written, are a lack of discussion of prior studies that have directly measured the strength of growth cone adhesions to the substrate (Zheng et al., 1994) and traction forces (Koch et al., 2012), the inverse correlation between retrograde flow rate and outgrowth (Nichol et al., 2016), and prior studies noting a biphasic effect of substrate concentration of neurite outgrowth (Schense and Hubbell, 2000).

Overall, the claims and conclusions are well justified by the data. The main exception is that the data is more relevant to how the rate of neurite outgrowth is controlled rather than axonal guidance.

This manuscript will help foster interest in the interrelationship between neurite outgrowth, traction forces, and substrate adhesion, and the use of a novel method to study this problem.

The authors did an excellent job in addressing my original concerns in the revision.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Axon growth is of course essential to the formation of neural connections. Adhesion is generally needed to anchor and rectify such motion, but whether the tenacity or forces of adhesion must be optimal for maximal axon extension is unknown. Measurements and contributing factors are generally lacking and are pursued here with a laser-induced shock wave approach near the axon growth cone. The authors claim to make measurements of the pressure required to detach axons from low to high matrix density. The results seem to support the authors' conclusions, and the work - with further support - is likely to impact the field of cell adhesion. In particular, there could be some utility of the methods for the adhesion and those interested in aspects of axon growth.

Strengths:

A potential ability to control the pressure simply via proximity of the laser spot is convenient and perhaps reasonable. The 0 to 1 scale for matrix density is a good and appropriate measure for comparing adhesion and other results. The attention to detachment speed, time, F-actin, and adhesion protein mutant provides key supporting evidence. Lastly, the final figure of traction force microscopy with matrix varied on a gel is reasonable and more physiological because neural tissue is soft (cite PMID: 16923388); an optimum in Fig.6 also perhaps aligns with axon length results in Fig.5.

We thank you for your many suggestions to improve the presentation to explain our experimental results obtained. We carefully reconsidered problems you pointed out and revised the manuscripts as follows.

Weaknesses:

The results seem incomplete and less than convincing. This is because the force calibration curve seems to be from a >10 yr old paper without any more recent checks or validating measurements.

As the force calibration data, although we have indicated by the experimental system over 10 years ago, we have used the same system under appropriate maintenance. The system performance has been checked regularly and maintained. Therefore, the calibration data displayed is suitable even in the present. There is no problem with the calibration data.

Secondly, the claimed effect of pressure on the detachment of the growth cone does not consider other effects such as cavitation or temperature, and certainly needs validation with additional methods that overcome such uncertainties.

The authors need to check whether the laser perturbs the matrix, particularly local density. A relation between traction stresses of ~20-50 pN/um² in Fig.6 and the adhesion pressure of 3-5 kPa of FIg.3 needs to be carefully explained; the former units equate to 0.02-0.05 kPa, and would perhaps suggest cells cannot detach themselves and move forward.

We have previously reported that a single pulse from a Ti:sapphire femtosecond laser amplifier can effectively generate shockwave and stress waves with minimal thermal effects. Notably, during this process, the temperature elevation at the laser focal point is sufficiently suppressed, allowing efficient force generation without causing significant heating in the surrounding area. By applying this method, we have confirmed that cell have any damage after the force loading. Therefore, this approach enables cell detachment while minimizing thermal and cavitation-induced damage to the cell. This clarification has been incorporated into the revised results section (lines 119-120). We agree with the reviewer that the presented data was insufficient for supporting the proposed model. To this end, we have performed additional experiments and analyses, which are included in the revised version of the manuscript. To examine the impact of femtosecond laser irradiation on laminin, fluorescently labeled laminin was coated onto glass-bottom dishes, and the fluorescent intensity was analyzed before and after the impulsive force loading. The result indicates that the fluorescent intensity at the laser focal point remained unaffected by laser irradiation. This finding suggests that axon detachment results from the dissociation between L1 and laminin rather than the detachment of laminin from the substrate. These data have been incorporated into Supplementary Fig. 1 and page 5 (lines 113-120). In addition, explanation of the relationship between the adhesion pressure and the traction stress has been specified in page 8 (lines 253-258).

The authors need to measure axon length on gels (Fig.6) as more physiological because neural tissue is soft. The studies are also limited to a rudimentary in vitro model without clear relevance to in vivo.

In response to the reviewer’s request, we measured the axon length on the polyacrylamide gel with stiffness comparable to brain tissue (0.3kPa). The axon length was consistently shorter on the gel on the glass under our experimental conditions, in agreement with previous findings (Abe at al., 2021). Furthermore, a biphasic relationship between axon outgrowth and laminin concentration was observed. These results suggest that the biphasic behavior of axon outgrowth identified in this study is likely to occur in vivo. We have updated the Fig. 6 and specified the result (lines 224-225) in revised manuscript.

Reviewer #1 (Recommendations For The Authors):

The force calibration curve seems to be from a >10 yr old paper without any more recent checks or validating measurements - which are essential. Effects of cavitation and temperature must be checked, and validated with additional methods that overcome such uncertainties. The authors need to check whether the laser perturbs the matrix, particularly local density. A relation between traction stresses of ~20-50 pN/um2 in Fig.6 and the adhesion pressure of 3-5 kPa of FIg.3 needs to be carefully explained; the former units equate to 0.02-0.05 kPa, and would perhaps suggest cells cannot detach themselves and move forward. The authors need to measure axon length on gels (Fig.6) as more physiological because neural tissue is soft. The studies are also limited to a rudimentary in vitro model without clear relevance to in vivo.

Thank you this reviewer for the recommendations on our manuscript. For this, we have answered above comments. Please find our response there.

Reviewer #2 (Public Review):

Summary:

The authors measure axon outgrowth rate, laminin adhesion strength, and actin rearward flow rate. They find that the axon outgrowth rate has a biphasic dependence on adhesion strength. In interpreting the results, they suggest that the results "imply that adhesion modulation is key to the regulation of axon guidance"; however, they measure elongation rate, not guidance.

Strengths:

The measurements of adhesion strength by laser-induced shock waves are reasonable as is the measurement of actin flow rates by speckle microscopy.

Weaknesses:

They only measure the length of the axons after 3 days and have no measurements of the actual rate of growth cone movements when they are moving. They do not measure the rate of actin growth at the leading edge to know its contribution to the extension rate. This is inadequate.

These studies are unlikely to have an impact on the field because the measurement of axon growth rate at short times is missing.

We thank the reviewer for understanding novelty of our study. We agree with the reviewer’s comment. Following the comment, we performed time-lapse imaging of growth cone movements and quantified the migration rate. Consistent with the length of axons, the migration rate did not exhibit a monotonic increase with increased L1CAM-laminin binding but rather displayed biphasic behavior, where excessive L1CAM-laminin binding led to a reduction in the migration rate. Notably, the biphasic migration behavior was abolished in the L1CAM knockdown neurons. We believe these results provide further support for our proposed model. This has been incorporated into new Fig.5 and page 7 (lines 209-218) of the revised manuscript. In addition, the experimental method has been added in page 13 (lines 385-391).

Reviewer #2 (Recommendations For The Authors):

This is a very weak paper because of the lack of relevant measurements to enable correlations between actual extension rate, traction force, and rates of speckle movement.

Thank you this reviewer for the critical comment on our model. we performed time-lapse imaging of growth cone movements and quantified the migration rate. From this reviewer and reviewer #3 comments, we recognized the importance of prior studies that the measurement of adhesion strength in the growth cone, traction force, the correlation between retrograde flow and outgrowth, and biphasic dependence of substrate concentration of neurite outgrowth (Please also find our response to recommendations from reviewer #3).

Reviewer #3 (Public Review):

Summary:

Yamada et al. build on classic and more recent studies (Chen et al., 2023; Lemmon et al., 1992; Nichol et al., 2016; Zheng et al., 1994; Schense and Hubbell, 2000) to better understand the relationship between substrate adhesion and neurite outgrowth.

Strengths:

The primary strength of the manuscript lies in developing a method for investigating the role of adhesion in axon outgrowth and traction force generation using a femtosecond laser technique. The most exciting finding is that both outgrowth and traction force generation have a biphasic relationship with laminin concentration.

Weaknesses:

The primary weaknesses are a lack of discussion of prior studies that have directly measured the strength of growth cone adhesions to the substrate (Zheng et al., 1994) and traction forces (Koch et al., 2012), the inverse correlation between retrograde flow rate and outgrowth (Nichol et al., 2016), and prior studies noting a biphasic effect of substrate concentration of neurite outgrowth (Schense and Hubbell, 2000).

Overall, the claims and conclusions are well justified by the data. The main exception is that the data is more relevant to how the rate of neurite outgrowth is controlled rather than axonal guidance.

This manuscript will help foster interest in the interrelationship between neurite outgrowth, traction forces, and substrate adhesion, and the use of a novel method to study this problem.

We thank the reviewer for appropriate comments and recognition of the strength to our manuscript. Regarding to these comments, we recognized the importance of prior studies that the measurement of adhesion strength in the growth cone, traction force, the correlation between retrograde flow and outgrowth, and biphasic dependence of substrate concentration of neurite outgrowth. With respecting the prior studies, we revised the introduction (lines 38-44, 61-65) and discussion (lines 272-281) in the manuscript. The references suggested by the reviewer have been added (Ref. 17, 26, 27, 31, and 35) (see also below responses).

Reviewer #3 (Recommendations For The Authors):

Overall, I found the experiments discussed in the manuscript to be excellent. My primary suggestion is to slightly expand the introduction and discussion to put this work in context better. Additionally, the writing is unclear in places and would be helped by a careful edit.

We appreciate the reviewer’s constructive critiques and would like to thank him/her for the experimental suggestions, which we have taken into account in the revised version of the manuscript. We trust that the additional modification of the text will satisfactorily address the reviewer’s concerns.

In more detail:

The introduction is well-written but could be improved by discussing how these studies build earlier work. Through the 1980s and 90s, an important question was whether growth cone guidance occurred as the result of chemical cues that altered the activity of signaling pathways or differences in the adhesion between growth cones and substrates. While there was some clear evidence that growth cones were steered to more adhesive substrates (Hammarback and Letourneau, 1986), there were also important exceptions. For example, (Calof and Lander, 1991) examined the biophysical relationship between neuronal migration and substrate adhesion and found that laminin, which tends to support rapid migration and neurite outgrowth, tended to decrease adhesion.

Thank you for critical comments to our manuscript. We have modified the introduction to discuss our understanding of the growth cone guidance, particularly regarding the role of neurite migration and substrate adhesion into introduction (line 38-40, 42-44) in revised manuscript.

To better understand the relationship between substrate adhesion and outgrowth, Heidemann's group (Zheng et al., 1994) was, to the best of my knowledge, the first paper to directly measure the force required to detach growth cones from substrates; including laminin and L1. For DRG neurons, this was ~ 1000 - 3000 dynes (i.e., 10 to 30 nN) and they noted that traction force generation is 3 to 15 times less than the force needed to dislodge growth cones. Additionally, that manuscript goes on to suggest, "These data argue against the differential adhesion mechanism for growth cone guidance preferences in culture." With the rising development of powerful molecular genetic tools and a growing appreciation of the importance of signaling pathways in neurite outgrowth (Huber et al., 2003), the field as the whole has focused on the molecular aspects of growth cone guidance, leaving many aspects of the physical process of neurite outgrowth unanswered. The strength of this manuscript is that it develops a new method for measuring growth cone adhesion forces, which reassuringly generates similar results to classic studies. In turn, it combines this with molecular genetic analysis to determine the contribution L1-LN interaction makes to the overall adhesion strength.

We will ensure that the manuscript explicitly acknowledges the significance of Zheng et al. (1994) in shaping the field and clarifies how our study expands upon these foundational findings. Following the reviewer’s suggestion we have added Zheng et al. (1994) in reference and modified discussion (line 272-281, Ref. 17) in revised manuscript.

There are also a couple of other papers directly relevant to this work. In particular, (Koch et al., 2012) measured the traction forces generated by hippocampal neurons on polyacrylamide gels. They estimated it to be ~ 5 to 10 Pa. While the overall results are similar, in this manuscript, it is reported that the forces generated by hippocampal neurons are significantly higher, in the range of 25-75 Pa. I don't have an issue with this difference, but please look at the Koch paper and see if there is some technical reason for the different estimates of traction forces. Along these lines, please note the Young's modulus of the gels used in the experiments.

As you mentioned, the traction force measured in our experiments is more than 5 times stronger than that reported by Koch et al., While the exact reason remains unclear, difference in gel-coating may have influenced the result. In the study by Koch et al., pre-coating was performed using Cell-Tak before laminin coating. in contrast, our study used poly-lysin for pre-coating. This methodological difference may have affected the measurement of traction force. However, at least, our experiments have consistently yielded reproducible results.

(Nichol et al., 2016) nicely shows an inverse relationship between RF rate and LN density at low concentrations. While the results reported here are similar, a strength of this paper is that it extends the work to higher LN concentrations.

Thank you for pointing out the relevance of Nichol et al., 2016 to our study. We agree that their study provides important insights into the relationship between RF rate and LN density at low concentrations. The novelty our study lies not only in extending the analysis to higher LN concentrations, but also performed analysis that include adhesion strength, traction force, and migration rate in the growth cone. We have included this discussion (line 259-261, Ref. 26) in revised manuscript.

My understanding is that the biphasic effect of LN in neurite outgrowth was previously established. For example, Buetter and Pittman, 1991 note a biphasic effect of LN conc on some parameters of neurite outgrowth, such as RMS, a measure of growth cone velocity, but not others, such as total neurite length. Likewise, (Schense and Hubbell, 2000) noted a biphasic effect of RGB peptides on outgrowth. In light of this, it would seem the main contribution of this paper is the finding that traction force generation has a bi-phasic relationship with LN concentration.

Thank you for your thoughtful comment. We agree that the main contribution of this study is demonstrating that the biphasic behavior of axon migration arises from the biphasic dependence of the traction force on laminin concentration. We have included this discussion (line 272-281, Ref. 31) in the revised manuscript.

Please appreciate that I'm not asking the authors to copy-paste the text above into the manuscript. Instead, the references provide a starting point for better explaining the novel contributions here. The interaction of adhesions, traction force generation, the rate of neurite outgrowth, and biophysics of growth cone guidance is a classic problem in neuronal mechanics but is far from solved. My hope is that this manuscript might inspire more interest in this problem.

Thank you for your thoughtful feedback and for highlighting the importance of better contextualizing our novel contributions within the broader field of neuronal mechanics. We appreciate your emphasis on the classic yet unresolved nature of the interactions between adhesions, traction force generation, axon outgrowth rate, and the biophysics of growth cone guidance.

We hope these revisions help strengthen the manuscript’s impact and inspire further investigation into this important problem. We appreciate your insightful comments and the opportunity to improve our work.

The text would be improved with a careful copy edit, for example:

The last sentence of the introduction currently reads, "We suggested mechanism of the axon outgrowth which depends on the density of laminin on the substrate, revealing L1CAM-laminin binding as a mechanism for the regulation of axon outgrowth." which is challenging to understand.

We appreciate the reviewer’s comment pointing out the lack of clarity in the final sentence of the introduction. To improve readability and clarity, we have revised the sentence as follows:

“In this study, we suggested mechanism of the axon outgrowth that depends on the density of laminin on the substrate, i.e. the L1CAM-laminin binding is key to the regulation of axon outgrowth..” We believe this revised version better conveys our main finding in a more concise and comprehensible manner.

Line 224 needs to be F-actin and the next sentence is difficult to understand.

Thank you for pointing this out. We have corrected "F-action" to "F-actin" to ensure accuracy (line 256). Additionally, we have revised the following sentence to improve clarity (line 256-258).

Line 232 instead of "traction force slows", did you mean the rate of retrograde flow slows?

Thank you for pointing this out. We mean to refer to the rate of retrograde flow, not the traction force itself. We have revised the wording accordingly to avoid confusion (line 266).

Line 242, shear-stress instead of share-stress.

We have corrected the typo into "shear-stress" (line 282).

Lines 255, 267, and the abstract. The paper doesn't directly address axonal guidance. It would be more accurate to replace axonal guidance with neurite outgrowth.

Thank you for your insightful comment. We agree that the term "neurite outgrowth" more accurately reflects the scope of our study, as we do not directly examine the mechanisms of axonal guidance. Accordingly, we have revised the text in Lines 273, 275, and the abstract to replace "axonal guidance" with "neurite outgrowth" to better align with the presented data and experimental focus.

Line 362, perhaps reference (Minegishi et al., 2021) here as it provides a nice explanation of the technique.

Thank you for the helpful suggestion. We have now added a reference to Minegishi et al., 2021 (line 416, Ref.35) in revised manuscript, as it indeed provides a clear explanation of the method.

eLife Assessment

In this useful study, the authors conducted a set of computational and experimental investigations of the mechanism of cholesterol transport in the smoothened (SMO) protein. The computational component integrated multiple state-of-the-art approaches such as adaptive sampling, free energy simulations, and Markov state modeling, providing support for the proposed mechanistic model, which is also consistent with the experimental mutagenesis data. However, substantial revisions are needed for the discussion of the computational results and interpretation of the literature to provide a more balanced and accurate perspective on cholesterol-mediated SMO regulation. In the current form, therefore, the strength of evidence of the study is considered incomplete.

Reviewer #1 (Public review):

Summary:

This manuscript uses primarily simulation tools to probe the pathway of cholesterol transport with the smoothened (SMO) protein. The pathway to the protein and within SMO is clearly discovered, and interactions deemed important are tested experimentally to validate the model predictions.

Strengths:

The authors have clearly demonstrated how cholesterol might go from the membrane through SMO for the inner and outer leaflets of a symmetrical membrane model. The free energy profiles, structural conformations, and cholesterol-residue interactions are clearly described.

Weaknesses:

(1) Membrane Model:

The authors decided to use a rather simple symmetric membrane with just cholesterol, POPC, and PSM at the same concentration for the inner and outer leaflets. This is not representative of asymmetry known to exist in plasma membranes (SM only in the outer leaflet and more cholesterol in this leaflet). This may also be important to the free energy pathway into SMO. Moreover, PE and anionic lipids are present in the inner leaflet and are ignored. While I am not requesting new simulations, I would suggest that the authors should clearly state that their model does not consider lipid concentration leaflet asymmetry, which might play an important role.

(2) Statistical comparison of barriers:

The barriers for pathways 1 and 2 are compared in the text, suggesting that pathway 2 has a slightly higher barrier than pathway 1. However, are these statistically different? If so, the authors should state the p-value. If not, then the text in the manuscript should not state that one pathway is preferred over the other.

(3) Barrier of cholesterol (reasoning):

The authors on page 7 argue that there is an enthalpy barrier between the membrane and SMO due to the change in environment. However, cholesterol lies in the membrane with its hydroxyl interacting with the hydrophilic part of the membrane and the other parts in the hydrophobic part. How is the SMO surface any different? It has both characteristics and is likely balanced similarly to uptake cholesterol. Unless this can be better quantified, I would suggest that this logic be removed.

Reviewer #2 (Public review):

Summary:

In this work, the authors applied a range of computational methods to probe the translocation of cholesterol through the Smoothened receptor. They test whether cholesterol is more likely to enter the receptor straight from the outer leaflet of the membrane or via a binding pathway in the inner leaflet first. Their data reveal that both pathways are plausible but that the free energy barriers of pathway 1 are lower, suggesting this route is preferable. They also probe the pathway of cholesterol transport from the transmembrane region to the cysteine-rich domain (CRD).

Strengths:

(1) A wide range of computational techniques is used, including potential of mean force calculations, adaptive sampling, dimensionality reduction using tICA, and MSM modelling. These are all applied in a rigorous manner, and the data are very convincing. The computational work is an exemplar of a well-carried out study.

(2) The computational predictions are experimentally supported using mutagenesis, with an excellent agreement between their PMF and mRNA fold change data.

(3) The data are described clearly and coherently, with excellent use of figures. They combine their findings into a mechanism for cholesterol transport, which on the whole seems sound.

(4) The methods are described well, and many of their analysis methods have been made available via GitHub, which is an additional strength.

Weaknesses:

(1) Some of the data could be presented a little more clearly. In particular, Figure 7 needs additional annotation to be interpretable. Can the position of the cholesterol be shown on the graph so that we can see the diameter change more clearly?

(2) In Figure 3C, it doesn't look like the Met is constricting the tunnel at all. What residue is constricting the tunnel here? Can we see the Ala and Met panels from the same angle to compare the landscapes? Or does the mutation significantly change the tunnel? Why not A283 to a bulkier residue? Finally, the legend says that the figure shows that cholesterol can still pass this residue, but it doesn't really show this. Perhaps if the HOLE graph was plotted, we could see the narrowest point of the tunnel and compare it to the size of cholesterol.

(3) The PMF axis in 3b and d confused me for a bit. Looking at the Supplementary data, it's clear that, e.g., the F455I change increases the energy barrier for chol entering the receptor. But in 3d this is shown as a -ve change, i.e., favourable. This seems the wrong way around for me. Either switch the sign or make this clearer in the legend, please.

(4) The impact of G280V is put down to a decrease in flexibility, but it could also be a steric hindrance. This should be discussed.

(5) Are the reported energy barriers of the two pathways (5.8{plus minus}0.7 and 6.5{plus minus}0.8 kcal/mol) significantly and/or substantially different enough to favour one over the other? This could be discussed in the manuscript.

(6) Are the energy barriers consistent with a passive diffusion-driven process? It feels like, without a source of free energy input (e.g., ion or ATP), these barriers would be difficult to overcome. This could be discussed.

(7) Regarding the kinetics from MSM, it is stated that the values seen here are similar to MFS transporters, but this then references another MSM study. A comparison to experimental values would support this section a lot.

Reviewer #3 (Public review):

This manuscript presents a study combining molecular dynamics simulations and Hedgehog (Hh) pathway assays to investigate cholesterol translocation pathways to Smoothened (SMO), a G protein-coupled receptor central to Hedgehog signal transduction. The authors identify and characterize two putative cholesterol access routes to the transmembrane domain (TMD) of SMO and propose a model whereby cholesterol traverses through the TMD to the cysteine-rich domain (CRD), which is presented as the primary site of SMO activation.

The MD simulations and biochemical experiments are carefully executed and provide useful data. However, the manuscript is significantly weakened by a narrow and selective interpretation of the literature, overstatement of certain conclusions, and a lack of appropriate engagement with alternative models that are well-supported by published data-including data from prior work by several of the coauthors of this manuscript. In its current form, the manuscript gives a biased impression of the field and overemphasizes the role of the CRD in cholesterol-mediated SMO activation. Below, I provide specific points where revisions are needed to ensure a more accurate and comprehensive treatment of the biology.

Major Comments:

(1) Overstatement of the CRD as the Orthosteric Site of SMO Activation

The manuscript repeatedly implies or states that the CRD is the orthosteric site of SMO activation, without adequate acknowledgment of alternative models. To give just a few examples (of many in this manuscript):

a) "PTCH is proposed to modulate the Hh signal by decreasing the ability of membrane cholesterol to access SMO's extracellular cysteine-rich domain (CRD)" (p. 3).

b) "In recent years there has been a vigorous debate on the orthosteric site of SMO" (p. 3).

c) "cholesterol must travel through the SMO TMD to reach the orthosteric site in the CRD" (p. 4).

d) "we observe cholesterol moving along TM6 to the TMD-CRD interface (common pathway, Fig. 1d) to access the orthosteric binding site in the CRD" (p. 6).

While the second quote in this list at least acknowledges a debate, the surrounding text suggests that this debate has been entirely resolved in favor of the CRD model. This is misleading and not reflective of the views of other investigators in the field (see, for example, a recent comprehensive review from Zhang and Beachy, Nature Reviews Molecular and Cell Biology 2023, which makes the point that both the CRD and 7TM sites are critical for cholesterol activation of SMO as well as PTCH-mediated regulation of SMO-cholesterol interactions).

In contrast, a large body of literature supports a dual-site model in which both the CRD and the TMD are bona fide cholesterol-binding sites essential for SMO activation. Examples include:

a) Byrne et al., Nature 2016: point mutation of the CRD cholesterol binding site impairs-but does not abolish-SMO activation by cholesterol (SMO D99A, Y134F, and combination mutants - Fig 3 of the 2016 study).

b) Myers et al., Dev Cell 2013 and PNAS 2017: CRD deletion mutants retain responsiveness to PTCH regulation and cholesterol mimetics (similar Hh responsiveness of a CRD deletion mutant is also observed in Fig 4 Byrne et al, Nature 2016).

c) Deshpande et al., Nature 2019: mutation of residues in the TMD cholesterol binding site blocks SMO activation entirely, strongly implicating the TMD as a required site, in contrast to the partial effects of mutating or deleting the CRD site.

Qi et al., Nature 2019, and Deshpande et al., Nature 2019, both reported cholesterol binding at the TMD site based on high-resolution structural data. Oddly, Deshpande et al., Nature 2019, is not cited in the discussion of TMD binding on p. 3, despite being one of the first papers to describe cholesterol in the TMD site and its necessity for activation (the authors only cite it regarding activation of SMO by synthetic small molecules).

Kinnebrew et al., Sci Adv 2022 report that CRD deletion abolished PTCH regulation, which is seemingly at odds with several studies above (e.g., Byrne et al, Nature 2016; Myers et al, Dev Cell 2013); but this difference may reflect the use of an N-terminal GFP fusion to SMO in the Kinnebrew et al 2022, which could alter SMO activation properties by sterically hindering activation at the TMD site by cholesterol (but not synthetic SMO agonists like SAG); in contrast, the earlier work by Byrne et al is not subject to this caveat because it used an untagged, unmodified form of SMO.

Although overexpression of PTCH1 and SMO (wild-type or mutant) has been noted as a caveat in studies of CRD-independent SMO activation by cholesterol, this reviewer points out that several of the studies listed above include experiments with endogenous PTCH1 and low-level SMO expression, demonstrating that SMO can clearly undergo activation by cholesterol (as well as regulation by PTCH1) in a manner that does not require the CRD.

Recommendation:

The authors should revise the manuscript to provide a more balanced overview of the field and explicitly acknowledge that the CRD is not the sole activation site. Instead, a dual-site model is more consistent with available structural, mutational, and functional data. In addition, the authors should reframe their interpretation of their MD studies to reflect this broader and more accurate view of how cholesterol binds and activates SMO.

(2) Bias in Presentation of Translocation Pathways

The manuscript presents the model of cholesterol translocation through SMO to the CRD as the predominant (if not sole) mechanism of activation. Statements such as: "Cholesterol traverses SMO to ultimately reach the CRD binding site" (p. 6) suggest an exclusivity that is not supported by prior literature in the field. Indeed, the authors' own MD data presented here demonstrate more stable cholesterol binding at the TMD than at the CRD (p 17), and binding of cholesterol to the TMD site is essential for SMO activation. As such, it is appropriate to acknowledge that cholesterol may activate SMO by translocating through the TM5/6 tunnel, then binding to the TMD site, as this is a likely route of SMO activation in addition to the CRD translocation route they highlight in their discussion.

The authors describe two possible translocation pathways (Pathway 1: TM2/3 entry to TMD; Pathway 2: TM5/6 entry and direct CRD transfer), but do not sufficiently acknowledge that their own empirical data support Pathway 2 as more relevant. Indeed, because their experimental data suggest Pathway 2 is more strongly linked to SMO activation, this pathway should be weighted more heavily in the authors' discussion. In addition, Pathway 2 is linked to cholesterol binding to both the TMD and CRD sites (the former because the TMD binding site is at the terminus of the hydrophobic tunnel, the latter via the translocation pathway described in the present manuscript), so it is appropriate that Pathway 2 figure more prominently than Pathway 1 into the authors' discussion.

The authors also claim that "there is no experimental structure with cholesterol in the inner leaflet region of SMO TMD" (p 16). However, a structural study of apo-SMO from the Manglik and Cheng labs (Zhang et al., Nat Comm, 2022) identified a cholesterol molecule docked at the TM5/6 interface and also proposed a "squeezing" mechanism by which cholesterol could enter the TM5/6 pocket from the membrane. The authors do not take this SMO conformation into account in their models, nor do they discuss the possibility that conformational dynamics at the TM5/6 interface could facilitate cholesterol flipping and translocation into the hydrophobic conduit, even though both possibilities have precedent in the 2022 empirical cryoEM structural analysis.

Recommendation:

The authors should avoid oversimplification of the SMO cholesterol activation process, either by tempering these claims or broadening their discussion to better reflect the complexity and multiplicity of cholesterol access and activation routes for SMO, and consider the 2022 apo-SMO cryoEM structure in their analysis of the TM5/6 translocation pathway.

(3) Alternative Possibility: Direct Membrane Access to CRD

The possibility that the CRD extracts cholesterol directly from the membrane outer leaflet is not considered. While the crystal structures place the CRD in a stable pose above the membrane, multiple cryo-EM studies suggest that the CRD is dynamic and adopts a variety of conformations, raising the possibility that the stability of the CRD in the crystal structures is a result of crystal packing and that the CRD may be far more dynamic under more physiological conditions.

Recommendation:

The authors should explicitly acknowledge and evaluate this potential mechanism and, if feasible, assess its plausibility through MD simulations.

(4) Inconsistent Framing of Study Scope and Limitations

The discussion contains some contradictory and misleading language. For example, the authors state that "In this study we only focused on the cholesterol movement from the membrane to CRD binding site." and then several sentences later state that "We outline the entire translocation mechanism from a kinetic and thermodynamic perspective.". These statements are at odds. The former appropriately (albeit briefly) notes the limited scope of the modeling, while the latter overstates the generality of the findings.

In addition, the authors' narrow focus on the CRD site constitutes a major caveat to the entire work. It should be acknowledged much earlier in the manuscript, preferably in the introduction, rather than mentioned as an aside in the penultimate paragraph of the conclusion.

Recommendation:<br /> The authors should clarify the scope of the study and expand the discussion of its limitations. They should explicitly acknowledge that the study models one of several cholesterol access routes and that the findings do not rule out alternative pathways.

Summary:

This study has the potential to make a useful contribution to our understanding of cholesterol translocation and SMO activation. However, in its current form, the manuscript presents an overly narrow and, at times, misleading view of the literature and biological models; as such, it is not nearly as impactful as it could be. I strongly encourage the authors to revise the manuscript to include:

(1) A more balanced discussion of the CRD vs. TMD binding sites.

(2) Acknowledgment of alternative cholesterol access pathways.

(3) More comprehensive citation of prior structural and functional studies.

(4) Clarification of assumptions and scope.

Of note, the above suggestions require little to no additional MD simulations or experimental studies, but would significantly enhance the rigor and impact of the work.

eLife Assessment

This study is valuable for understanding how dysfunctional mitochondria contribute to vascular diseases by investigating the influence of Miro1 on smooth muscle cell proliferation and neointima development. The solid findings collectively indicate that Miro1 regulates mitochondrial cristae architecture and the efficiency of the respiratory chain. Nevertheless, the analysis would benefit from a more thorough assessment of the relationship between Miro1-dependent mitochondrial defects and vascular smooth muscle cell proliferation.

Reviewer #1 (Public review):

Summary:

In this paper, the authors investigate the effects of Miro1 on VSMC biology after injury. Using conditional knockout animals, they provide the important observation that Miro1 is required for neointima formation. They also confirm that Miro1 is expressed in human coronary arteries. Specifically, in conditions of coronary diseases, it is localized in both media and neointima, and, in atherosclerotic plaque, Miro1 is expressed in proliferating cells.

However, the role of Miro1 in VSMC in CV diseases is poorly studied, and the data available are limited; therefore, the authors decided to deepen this aspect. The evidence that Miro-/- VSMCs show impaired proliferation and an arrest in S phase is solid and further sustained by restoring Miro1 to control levels, normalizing proliferation. Miro1 also affects mitochondrial distribution, which is strikingly changed after Miro1 deletion. Both effects are associated with impaired energy metabolism due to the ability of Miro1 to participate in MICOS/MIB complex assembly, influencing mitochondrial cristae folding. Interestingly, the authors also show the interaction of Miro1 with NDUFA9, globally affecting super complex 2 assembly and complex I activity.

Finally, these important findings also apply to human cells and can be partially replicated using a pharmacological approach, proposing Miro1 as a target for vasoproliferative diseases.

Strengths:

The discovery of Miro1 relevance in neointima information is compelling, as well as the evidence in VSMC that MIRO1 loss impairs mitochondrial cristae formation, expanding observations previously obtained in embryonic fibroblasts.

The identification of MIRO1 interaction with NDUFA9 is novel and adds value to this paper. Similarly, the findings that VSMC proliferation requires mitochondrial ATP support the new idea that these cells do not rely mostly on glycolysis.

Weaknesses:

(1) Figure 3:

I appreciate the system used to assess mitochondrial distribution; however, I believe that time-lapse microscopy to evaluate mitochondrial movements in real time should be mandatory. The experimental timing is compatible with time-lapse imaging, and these experiments will provide a quantitative estimation of the distance travelled by mitochondria and the fraction of mitochondria that change position over time. I also suggest evaluating mitochondrial shape in control and MIRO1-/- VSMC to assess whether MIRO1 absence could impact mitochondrial morphology, altering fission/fusion machinery, since mitochondrial shape could differently influence the mobility.

(2) Figure 6:

The evidence of MIRO1 ablation on cristae remodeling is solid; however, considering that the mechanism proposed to explain the finding is the modulation of MICOS/MIB complex, as shown in Figure 6D, I suggest performing EM analysis in each condition. In my mind, Miro1 KK and Miro1 TM should lead to different cristae phenotypes according to the different impact on MICOS/MIB complex assembly. Especially, Miro1 TM should mimic Miro1 -/- condition, while Miro1 KK should drive a less severe phenotype. This would supply a good correlation between Miro1, MICOS/MIB complex formation and cristae folding.

I also suggest performing supercomplex assembly and complex I activity with each plasmid to correlate MICOS/MIB complex assembly with the respiratory chain efficiency.

(3) I noticed that none of the in vitro findings have been validated in an in vivo model. I believe this represents a significant gap that would be valuable to address. In your animal model, it should not be too complex to analyze mitochondria by electron microscopy to assess cristae morphology. Additionally, supercomplex assembly and complex I activity could be evaluated in tissue homogenates to corroborate the in vitro observations.

(4) I find the results presented in Figure S7 somewhat unclear. The authors employ a pharmacological strategy to reduce Miro1 and validate the findings previously obtained with the genetic knockout model. They report increased mitophagy and a reduction in mitochondrial mass. However, in my opinion, these changes alone could significantly impact cellular metabolism. A lower number of mitochondria would naturally result in decreased ATP production and reduced mitochondrial respiration. This, in turn, weakens the proposed direct link between Miro1 deletion and impaired metabolic function or altered electron transport chain (ETC) activity. I believe this section would benefit from additional experiments and a more in-depth discussion.

Reviewer #2 (Public review):

Summary:

This study identifies the outer‑mitochondrial GTPase MIRO1 as a central regulator of vascular smooth muscle cell (VSMC) proliferation and neointima formation after carotid injury in vivo and PDGF-stimulation ex vivo. Using smooth muscle-specific knockout male mice, complementary in vitro murine and human VSMC cell models, and analyses of mitochondrial positioning, cristae architecture, and respirometry, the authors provide solid evidence that MIRO1 couples mitochondrial motility with ATP production to meet the energetic demands of the G1/S cell cycle transition. However, a component of the metabolic analyses is suboptimal and would benefit from more robust methodologies. The work is valuable because it links mitochondrial dynamics to vascular remodelling and suggests MIRO1 as a therapeutic target for vasoproliferative diseases, although whether pharmacological targeting of MIRO1 in vivo can effectively reduce neointima after carotid injury has not been explored. This paper will be of interest to those working on VSMCs and mitochondrial biology.

Strengths:

The strength of the study lies in its comprehensive approach, assessing the role of MIRO1 in VSMC proliferation in vivo, ex vivo, and importantly in human cells. The subject provides mechanistic links between MIRO1-mediated regulation of mitochondrial mobility and optimal respiratory chain function to cell cycle progression and proliferation. Finally, the findings are potentially clinically relevant given the presence of MIRO1 in human atherosclerotic plaques and the available small molecule MIRO1.

Weaknesses:

(1) There is a consistent lack of reporting across figure legends, including group sizes, n numbers, how many independent experiments were performed, or whether the data is mean +/- SD or SEM, etc. This needs to be corrected.

(2) The in vivo carotid injury experiments are in male mice fed a high-fat diet; this should be explicitly stated in the abstract, as it's unclear if there are any sex- or diet-dependent differences. Is VSMC proliferation/neointima formation different in chow-fed mice after carotid injury?

(3) The main body of the methods section is thin, and it's unclear why the majority of the methods are in the supplemental file. The authors should consider moving these to the main article, especially in an online-only journal.

(4) Certain metabolic analyses are suboptimal, including ATP concentration and Complex I activity measurements. The measurement of ATP/ADP and ATP/AMP ratios for energy charge status (luminometer or mass spectrometry), while high-resolution respirometry (Oroboros) to determine mitochondrial complex I activity in permeabilized VSMCs would be more informative.

(5) The statement that 'mitochondrial mobility is not required for optimal ATP production' is poorly supported. XF Seahorse analysis should be performed with nocodazole and also following MIRO1 reconstitution +/- EF hands.

(6) The authors should consider moving MIRO1 small molecule data into the main figures. A lot of value would be added to the study if the authors could demonstrate that therapeutic targeting of MIRO1 could prevent neointima formation in vivo.

Reviewer #3 (Public review):

Summary:

This study addresses the role of MIRO1 in vascular smooth muscle cell proliferation, proposing a link between MIRO1 loss and altered growth due to disrupted mitochondrial dynamics and function. While the findings are potentially useful for understanding the importance of mitochondrial positioning and function in this specific cell type within health and disease contexts, the evidence presented appears incomplete, with key bioenergetic and mechanistic claims lacking adequate support.

Strengths:

(1) The study focuses on an important regulatory protein, MIRO1, and its role in vascular smooth muscle cell (VSMC) proliferation, a relatively underexplored context.

(2) It explores the link between smooth muscle cell growth, mitochondrial dynamics, and bioenergetics, which is a potentially significant area for both basic and translational biology.

(3) The use of both in vivo and in vitro systems provides a potentially useful experimental framework to interrogate MIRO1 function in this context.

Weaknesses:

(1) The central claim that MIRO1 loss impairs mitochondrial bioenergetics is not convincingly demonstrated, with only modest changes in respiratory parameters and no direct evidence of functional respiratory chain deficiency.

(2) The proposed link between MIRO1 and respiratory supercomplex assembly or function is speculative, lacking mechanistic detail and supported by incomplete or inconsistent biochemical data.

(3) Key mitochondrial assays are either insufficiently controlled or poorly interpreted, undermining the strength of the conclusions regarding oxidative phosphorylation.

(4) The study does not adequately assess mitochondrial content or biogenesis, which could confound interpretations of changes in respiratory activity.

(5) Overall, the evidence for a direct impact of MIRO1 on mitochondrial respiratory function in the experimental setting is weak, and the conclusions overreach the data.

eLife Assessment

This study reports a dynamic association/dissociation between malate dehydrogenase (MDH1) and citrate synthase (CIT1) in Saccharomyces cerevisiae under different metabolic conditions that control TCA pathway flux rate. The research question is timely, the use of the NanoBiT split-luciferase system to monitor protein-protein interactions is innovative, and the significance of the findings is valuable. However, the strength of evidence needed to support the conclusions was found to be incomplete based on a lack of critical control and mechanistic experiments.

Reviewer #1 (Public review):

Summary:

The study by the Obata group characterizes the dynamics of the canonical malate dehydrogenase-citrate synthase metabolon in yeast.

Strengths:

The study is well-written and appears to give clear demonstrations of this phenomenon.

Studies of the dynamics of metabolon formation are rare; if the authors can address the concern detailed below, then they have provided such for one of the canonical metabolons in nature.

Weaknesses:

There is a fundamental issue with the study, which is that the authors do not provide enough support or information concerning the split luciferase system that they use. Is the binding reversible or not? How the data is interpreted is massively influenced by this fact. What are the pros and cons of this method in comparison to, for example, FLIM-FRET? The authors state that the method is semi-quantitative - can they document this? All of the conclusions are based on the quality of this method. I know that it has been used by others, but at least some preliminary documentation to address these questions is required.

Reviewer #2 (Public review):

This study explores the dynamic association between malate dehydrogenase (MDH1) and citrate synthase (CIT1) in Saccharomyces cerevisiae, with the aim of linking this interaction to respiratory metabolism. Utilizing a NanoBiT split-luciferase system, the authors monitor protein-protein interactions in vivo under various metabolic conditions.

Major Concerns:

(1) NanoBiT Signal May Reflect Protein Abundance Rather Than Interaction Strength

In Figure 1C, the authors report increased MDH1-CIT1 interaction under respiratory (acetate) conditions and decreased interaction during fermentation (glucose), as indicated by NanoBiT luminescence. However, this signal appears to correlate strongly with the expression levels of MDH1 and CIT1, raising the possibility that the observed luminescence reflects protein abundance rather than specific interaction dynamics. To resolve this, NanoBiT signals should be normalized to the expression levels of both proteins to distinguish between abundance-driven and interaction-driven changes.

(2) Lack of Causal Evidence

The study presents a series of metabolic perturbation experiments (e.g., arsenite, AOA, antimycin A, malonate) and correlates changes in metabolite levels with NanoBiT signals. However, these data are correlative and do not establish a functional role for the MDH1-CIT1 interaction in metabolic regulation. To demonstrate causality, the authors should implement approaches to specifically disrupt the MDH1-CIT1 interaction. One strategy could involve using a 15-residue peptide (Pept1) derived from the Pro354-Pro366 region of CIT1, previously shown to mediate the interaction, or introducing the cit1Δ3 (Arg362Glu) mutation, which perturbs binding. Metabolic flux analysis using ^13C-labeled glucose and mitochondrial respiration assays (e.g., Seahorse) could then assess functional consequences.

(3) Absence of Protein Expression Controls Under Perturbation Conditions

In experiments involving acetate, arsenite, AOA, antimycin A, and malonate, the authors infer changes in MDH1-CIT1 association based solely on NanoBiT signals. However, no accompanying data are provided on MDH1 and CIT1 protein levels under these conditions. This omission weakens the conclusions, as altered expression rather than interaction strength could underlie the observed luminescence changes. Immunoblotting or quantitative proteomics should be used to confirm constant protein expression across conditions.

Conclusion:

Although the central question is compelling and the use of NanoBiT in live cells is a strength, the manuscript requires additional experimental rigor. Specifically, normalization of interaction signals, introduction of causative perturbations, and validation of protein expression are essential to substantiate the study's claims.

Reviewer #3 (Public review):

Summary:

Metabolons are multisubunit complexes that promote the physical association of sequential enzymes within a metabolic pathway. Such complexes are proposed to increase metabolic flux and efficiency by channeling reaction intermediates between enzymes. The TCA cycle enzymes malate dehydrogenase (MDH1) and citrate synthase (CIT1) have been linked to metabolon formation, yet the conditions under which these enzymes interact, and whether such interactions are dynamic in response to metabolic cues, remain unclear, particularly in the native cellular context. This study uses a nanoBIT protein-protein interaction assay to map the dynamic behavior of the MDH1-CIT1 interaction in response to multiple metabolic stimuli and challenges in yeast. Beyond mapping these interactions in real time, the authors also performed GC-MS metabolomics to map whole-cell metabolite alterations across experimental conditions. Finally, the authors use microscale thermophoresis to determine components that alter the MDH1-CIT1 interaction in vitro. Collectively, the authors synthesize their collected data into a model in which the MDH1-CIT1 metabolon dissociates in conditions of low respiratory flux, and is stimulated during conditions of high respiratory flux. While their data largely support these models, some key exceptions are found that suggest this model is likely oversimplified and will require further work to understand the complexities associated with MDH1-CIT1 interaction dynamics. Nonetheless, the authors put forth an interesting and timely toolkit to begin to understand the interaction kinetics and dynamics of key metabolic enzymes that should serve as a platform to begin disentangling these important yet understudied aspects of metabolic regulation.

Strengths:

(1) The authors address an important question: how do metabolon-associated protein-protein interactions change across altered metabolic conditions?

(2) The development and validation of the MDH1-CIT1 nanoBIT assay provides an important tool to allow the quantification of this protein-protein interaction in vivo. Importantly, the authors demonstrate that the assay allows kinetic and real time assessment of these protein interactions, which reveal interesting and dynamic behavior across conditions.

(3) The use of classic biochemical techniques to confirm that pH and various metabolites can alter the MDH1-CIT1 interaction in vitro is rigorous and supports the model put forth by the authors.

Weaknesses:

(1) Some of the data collected seem to be merely reported rather than synthesized and interpreted for the reader. This is particularly true for data that seem to reflect more complex trends, such as the GC-MS experiments that map metabolites across multiple experiments, or treatments that show somewhat counterintuitive results, such as the antimycin A treatment, which promotes rather than disrupts the MDH1-CIT1 interaction.

(2) Some of the assertions put forth in the manuscript are not substantiated by the data presented, and the authors are at times overly reliant on previous findings from the literature to support their claims. This is particularly notable for claims about "TCA cycle flux"; the authors do not perform flux analysis anywhere in their study and should be cautious when insinuating correlations between their observations and "flux".

(3) The manuscript presentation could be improved. For figures, at times, the axes do not have intuitive labels (example, Figure 1A), data points and details about the number of samples analyzed are missing (bar graphs and box plots), and molecular weight markers are not reported on western blots. The authors refer to the figures out of order in the text, which makes the manuscript challenging to navigate as a reader.

eLife Assessment

This useful study analyzed 335 Mycobacterium tuberculosis Complex genomes and found that MTBC has a closed pangenome with few accessory genes. The research provides solid evidence for gene presence-absence patterns which support the appending conclusions however, the main criticism regarding the dominance of genome reduction remains.

Reviewer #1 (Public review):

Summary:

In this paper, Behruznia and colleagues use long-read sequencing data for 339 strains of the Mycobacterium tuberculosis complex to study genome evolution in this clonal bacterial pathogen. They use both a "classical" pangenome approach that looks at the presence and absence of genes, and a pangenome graph based on whole genomes in order to investigate structural variants in non-coding regions. The comparison of the two approaches is informative and shows that much is missed when focusing only on genes. The two main biological results of the study are that 1) the MTBC has a small pangenome with few accessory genes, and that 2) pangenome evolution is driven by genome reduction. The second result is still questionable because it relies on a method that disregards paralogs.

Strengths:

The authors put together the so-far largest data set of long-read assemblies representing most lineages of the Mycobacterium tuberculosis context, and covering a large geographic area. They sequenced and assembled genomes for strains of M. pinnipedi, L9, and La2, for which no high-quality assemblies were available previously. State-of-the-art methods are used to analyze gene presence-absence polymorphisms (Panaroo) and to construct a pangenome graph (PanGraph). Additional analysis steps are performed to address known problems with misannotated or misassembled genes.

Weaknesses:

The main criticism regarding the dominance of genome reduction remains after two rounds of revisions. A method that systematically excludes paralogs is hardly suitable to draw conclusions about the relative importance of insertions/duplications and deletions in a clonal organism, where any insertion/duplication will result in a paralog. I understand that a re-analysis of the data might not be practical, and the authors have added a few sentences in the discussion that touch on this problem. However, the statements regarding the dominance of genome reduction remain too assertive given this basic flaw.

Here are the more detailed argument from the previous review:

In a fully clonal organism, any insertion/duplication will be an insertion/duplication of an existing sequence and thus produce a paralog. If I'm correctly understanding your methods section, paralogs are systematically excluded in the pangraph analysis. Genomic blocks are summarized at the sublineage level as follows (l.184 ): "The DNA sequences from genomic blocks present in at least one sub-lineage but completely absent in others were extracted to look for long-term evolution patterns in the pangenome." I presume this is done using blastn, as in other steps of the analysis.

So a sublineage-specific copy of IS6110 would be excluded here, because IS6110 is present somewhere in the genome in all sublineages. However, the appropriate category of comparison, at least for the discussion of genome reduction, is orthology rather than homology: is the same, orthologous copy of IS6110, at the same position in the genome, present or absent in other sublineages? The same considerations apply to potential sublineage-specific duplicates of PE, PPE, and Esx genes. These gene families play important roles in host-pathogen interactions, so I'd argue that the neglect of paralogs is not a finicky detail, but could be of broader biological relevance.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this paper, Behruznia and colleagues use long-read sequencing data for 339 strains of the Mycobacterium tuberculosis complex to study genome evolution in this clonal bacterial pathogen. They use both a "classical" pangenome approach that looks at the presence and absence of genes, and a pangenome graph based on whole genomes in order to investigate structural variants in non-coding regions. The comparison of the two approaches is informative and shows that much is missed when focussing only on genes. The two main biological results of the study are that 1) the MTBC has a small pangenome with few accessory genes, and that 2) pangenome evolution is driven by genome reduction. In the revised article, the description of the data set and the methods is much improved, and the comparison of the two pangenome approaches is more consistent. I still think, however, that the discussion of genome reduction suffers from a basic flaw, namely the failure to distinguish clearly between orthologs and homologs/paralogs.

Strengths:

The authors put together the so-far largest data set of long-read assemblies representing most lineages of the Mycobacterium tuberculosis context, and covering a large geographic area. They sequenced and assembled genomes for strains of M. pinnipedi, L9, and La2, for which no high-quality assemblies were available previously. State-of-the-art methods are used to analyze gene presence-absence polymorphisms (Panaroo) and to construct a pangenome graph (PanGraph). Additional analysis steps are performed to address known problems with misannotated or misassembled genes.

Weaknesses:

The revised manuscript has gained much clarity and consistency. One previous criticism, however, has in my opinion not been properly addressed. I think the problem boils down to not clearly distinguishing between orthologs and paralogs/homologs. As this problem affects a main conclusion - the prevalence of deletions over insertions in the MTBC - it should be addressed, if not through additional analyses, then at least in the discussion.

Insertions and deletions are now distinguished in the following way: "Accessory regions were further classified as a deletion if present in over 50% of the 192 sub-lineages or an insertion/duplication if present in less than 50% of sub-lineages." The outcome of this classification is suspicious: not a single accessory region was classified as an insertion/duplication. As a check of sanity, I'd expect at least some insertions of IS6110 to show up, which has produced lineage- or sublineage-specific insertions (Roychowdhury et al. 2015, Shitikov et al. 2019). Why, for example, wouldn't IS6110 insertions in the single L8 strain show up here?

In a fully clonal organism, any insertion/duplication will be an insertion/duplication of an existing sequence, and thus produce a paralog. If I'm correctly understanding your methods section, paralogs are systematically excluded in the pangraph analysis. Genomic blocks are summarized at the sublineage levels as follows (l.184 ): "The DNA sequences from genomic blocks present in at least one sub-lineage but completely absent in others were extracted to look for long-term evolution patterns in the pangenome." I presume this is done using blastn, as in other steps of the analysis.

So a sublineage-specific copy of IS6110 would be excluded here, because IS6110 is present somewhere in the genome in all sublineages. However, the appropriate category of comparison, at least for the discussion of genome reduction, is orthology rather than homology: is the same, orthologous copy of IS6110, at the same position in the genome, present or absent in other sublineages? The same considerations apply to potential sublineage-specific duplicates of PE, PPE, and Esx genes. These gene families play important roles in host-pathogen interactions, so I'd argue that the neglect of paralogs is not a finicky detail, but could be of broader biological relevance.

Reviewer #2 (Public review):

Summary:

The authors attempted to investigate the pangenome of MTBC by using a selection of state-of-the-art bioinformatic tools to analyse 324 complete and 11 new genomes representing all known lineages and sublineages. The aim of their work was to describe the total diversity of the MTBC and to investigate the driving evolutionary force. By using long read and hybrid approaches for genome assembly, an important attempt was made to understand why the MTBC pangenome size was reported to vary in size by previous reports. This study provides strong evidence that the MTBC pangenome is closed and that genome reduction is the main driver of this species evolution.

Strengths:

A stand-out feature of this work is the inclusion of non-coding regions as opposed to only coding regions which was a focus of previous papers and analyses which investigated the MTBC pangenome. A unique feature of this work is that it highlights sublineage-specific regions of difference (RDs) that was previously unknown. Another major strength is the utilisation of long-read whole genomes sequences, in combination with short-read sequences when available. It is known that using only short reads for genome assembly has several pitfalls. The parallel approach of utilizing both Panaroo and Pangraph for pangenomic reconstruction illuminated limitations of both tools while highlighting genomic features identified by both. This is important for any future work and perhaps alludes to the need for more MTBC-specific tools to be developed. Lastly, ample statistical support in the form of Heaps law and genome fluidity calculations for each pangenome to demonstrate that they are indeed closed.

Weaknesses:

There are no major weaknesses in the revised version of this manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

l. 27: "lineage-specific and -independent deletions": it is still not clear to me what a lineage-independent, or convergent, deletion is supposed to be. TBD1, for instance, is not lineage-specific, but it is also not convergent: it occurred once in the common ancestor of lineages 1, 2, and 3, while convergence implies multiple parallel occurrences.

We have changed this and in other places to more evolutionary terms, such as divergent (single event) and convergent (multiple events), or explain exactly what is meant where needed.

l. 118: "where relevant", what does that mean?

This was superfluous to the description and so is now removed.

l. 178ff.: It is not clear to me what issue is addressed by this correction of the pangenome graph. Also here there seems to be some confusion regarding orthologs and paralogs. A gene or IS copy can be present at one locus but absent at another, which is not a mistake of Pangraph that would require correction. It's rather the notion of "truly absent region" which is ambiguous.

We have changed the text to be more specific on the utility of this step. Since it is known that Panaroo mislabels some genes as being absent due to over splitting (see Ceres et al 2022 and our reclassification earlier in the paper), we wanted to see if the same occurred in Pangraph. We have modified the methods text to be more specific (line 181) and in the results included the percentage of total genes/regions affected by this correction.

In relation to copy number, Pangraph is not syntenic in its approach; if a region is present anywhere it is labelled as present in the genome. Pangraph will look for multiple copies of that region (e.g. an IS element) but indeed we did not look for specific syntenic changes across the genomes. This would be a great analysis and something we will consider in the future; we have indicated such in the discussion (line 454).

l. 305: "mislabelled as absent": see above, is this really 'mislabelled'?

See answer to question above

l. 372: "using the approach": something missing here.

This was superfluous to the description and so is now removed.

l. 381: the "additional analysis of paralogous blocks" (l. 381) seems to suffer from the same confusion of ortho- and paralogy described above: no new sub-lineage-specific accessory regions are found presumably because the analysis did consider any copy rather than orthologous copies.

Paralogous copies were looked for by Pangraph, and we did not find any sub-lineage where all members had additional copies compared to other sub-lineages. Indeed, single genomes could have these, and shorter timescales could see a lot of such insertions, but we looked at longer-scale (all genomes within a sub-lineage) patterns and did not find these. These limitations are already outlined in the discussion.

l. 415: see above. There is no diagnosis of a problem that would motivate a "correction". That's different from the correction of the Panaroo results, where fragmented annotations have been shown to be a problem.

Of interest, the refining of regions did re-label multiple regions as being core when Pangraph labelled it as absent from some genomes was at about the same rate as the correction to Pangraph (2% of genes/regions). This indicates there is a stringency issue with pangraph where blocks are mislabelled as absent. The underlying reason or this is not clear but the correction is evidently required in this version of Pangraph.

l. 430ff.: The issue of paralogy and that the "same" gene or region is defined in terms of homology rather than orthology should be addressed here. For me the given evidence does not support the claim that deletion is driving molecular evolution in the MTBC.

As outlined above, indeed paralogy may be driving some elements of the overall evolutionary patterns; our analysis just did not find this. Panaroo without merged paralogs did not find paralogous genes as a main differentiating factor for any sub-lineage. Pangraph also did not find multiple copies of blocks present in all genomes in a sub-lineage. As outlined above, indeed single genomes show such patterns but we did not include single genome analyses here, and outline that as a next steps in the discussion. We have also linked to a recent pangenome paper that showed duplication is present in the pangenome of Mtbc, although not related to any specific lineage (Discussion line 485).

l. 443 ff: "lineage-independent deletions (convergent evolution)": see above, I still think this terminology is unclear

This has now been made clearer to be specifically about convergent and divergent evolutionary patterns.

eLife Assessment

The authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in non-small cell lung cancer, proposing that resistance arises from signaling rewiring rather than additional mutations. While the study addresses a valuable clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation, meaning the strength of evidence is incomplete

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in NSCLC, proposing that resistance arises from signaling rewiring rather than additional mutations.

Strengths:

Using a panel of AR models - including cell lines, PDXs, CDXs, and PDXOs - they report activation of KRAS and PI3K/AKT/mTOR pathways, with elevated PI3K levels. Pharmacologic inhibition or CRISPR-Cas9 knockout of PI3K partially restores sotorasib sensitivity, and p-4EBP1 upregulation is implicated as an additional contributor, with dual mTORC1/2 inhibition more effective than mTORC1 inhibition alone.

Weaknesses:

While the study addresses an important clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation. The mechanistic findings are not entirely novel, since the role of PI3K-AKT-mTOR signaling in therapeutic resistance is already well-established in the literature. Rather than uncovering new resistance mechanisms, the study largely confirms known pathways. Several key conclusions are not supported by the data, and critical alternative explanations - such as additional mutations or increased KRAS expression - are not thoroughly investigated or ruled out. Furthermore, while the authors use CRISPR-Cas9 to knock out PI3K and 4E-BP1 in H23-AR and H358-AR cells to restore sotorasib sensitivity, they do not perform reconstitution experiments to confirm that re-expressing PI3K or 4E-BP1 reverses the sensitization. This prevents full characterization of PI3K and p-4EBP1 upregulation as contributors to resistance. The manuscript also has several errors, poor figure quality, and a lack of proper quantification. Additional experimental validation, data improvement, and text revisions are required.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids, and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics, and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT, and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the current manuscript is very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Overall, the techniques and methodology seem to be performed in agreement with standard practice, and the results support most of the conclusions made by the authors. However, there are some points that, if addressed, would increase the value and relevance of the findings and further extend the impact of this work. Some of the recommendations for changes relate to the way things are explained and presented, which need some work. Other changes might require the performance of additional experiments or reanalysis of the existing data.

Strengths:

(1) One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs, and the fact that the authors have different clones for each, made this collection especially relevant, as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

(2) Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

(3) The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells.

(4) The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

(5) The way they develop the PDX-resistant and the PDXO seems to be appropriate.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

(1) The abstract is rather long and gives details that are not usually included in one. This makes it very complicated to identify the most relevant findings of the work. The use of acronyms PDX, PDXO, and CDX without defining them makes it complicated for the non-specialist to know what the models are. Rewriting and reorganisation of the abstract would benefit the manuscript.

(2) Expression, presentation, and grammar should be reviewed in all sections of the manuscript.

(3) In the different parts of the result section where the models shown in Figure 2 are described the authors indicate "Whole-exome sequencing (WES) confirmed that XXX model retained the KRASG12C mutation with no additional KRAS mutations detected" however, it is not indicated where this data is shown and in not all the cases there is explanation to other possible modifications that might relate to mechanisms of resistance. This information should be included in the manuscript, and the WES made publicly available.

(4) The way the proteomics analysis of the TC303 and TC314 parental and resistant PDX is described in the text is confusing. The addition of an experimental layout figure would facilitate the understanding. As it is written, it is not obvious that the parental PDX were also analysed. For instance, the authors say, "The global and phosphoproteomic analyses identified over 8,000 and 4,000 gene protein products (GPPs), respectively". Is this comparing only resistant cells, or from the comparison of the parental and resistant pairs? And where are these numbers presented in the figures? Also, there is information that seems more adequate for the materials and methods sections, i.e., "Samples were analyzed using label-free nanoscale liquid chromatography coupled with tandem mass spectrometry (nanoLC-MS/MS) on a Thermo Fusion Mass Spectrometer. The resulting data were processed and quantified using the Proteome Discoverer 2.5 interface with the Mascot search engine, referencing the NCBI RefSeq protein database (Saltzman, Ruprecht). Two-component analysis is better named principal component analysis."

(5) While the presentation of the proteomics data could be done in different ways, the way the data is presented in Figure 3 does not allow the reader to get an idea of many of the findings from this experiment. Although it is indicated that a table with the data will be made available, this should be central to the way the data is presented and explained. A table (ie, Excel doc) where the raw data and all the analysis are presented should be included and referenced. Additionally, heat maps for the whole proteomes identified should be included. In the text, it is said, "Global proteomic heatmap analysis revealed unique protein profiles in TC303AR and TC314AR PDXs compared to their sensitive counterparts (Figure 3C)." However, this figure only shows the histogram of the differentially regulated cells. Inclusion of the histogram showing all the cells is necessary, and it might be informative to include the histogram comparing the two isogenic pairs, which could identify common mechanisms and differences between both sets. In Figure 3C, the protein names should be readable, or a reference to tables where the proteins are listed should be included.

(6) In Figure 3, the pathway enrichment tool and GO used should be mentioned in the text. The tables with all significant tables should also be provided. The proteomics data seems to convincingly identify mTOR as one of the pathways deregulated in resistant cells, but there is little explanation of what is considered a significant FDR value and if there are other pathways or networks that are also modified, which might not be common to both isogenic models. In MS-based Phosphoproteome could help with the identification of differentially regulated pathways, but it is not really presented in the current manuscript. Most of the analysis of phospho-proteomics comes from the RPPA analysis, which is targeted proteomics. With the way the data is presented, the authors show evidence for a role of mTOR in the acquisition of resistance, but unfortunately, they do not discuss or allow the reader to explore if other pathways might also contribute to this change.

(7) Where is the proteomics data going to be deposited, and will it be made public to comply with FAIR principles?

(8) The authors claim that the resistance shown for H23AR and H353AR cells is due to reactivation of KRAS signalling. This is done by looking to phosphorylation of ERK as a surrogate, as they claim, "KRAS inhibition is commonly assessed by evaluating the inhibition of ERK phosphorylation (p-ERK)". While this might be true in many cases, the data presented does not demonstrate that the increase in p-ERK is due to reactivation of KRAS. To make this claim, the authors should measure activation of KRAS (and possibly H- and NRAS) using GST-pull down or an image-based method.

(9) The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and H358 parental cells. Is the increase shown in resistant cells shown in parental or is it exclusive for resistant cells only (and therefore acquired)? Experiment 4B should include this control. What is clear is that there is an increase in the expression of AKT and PI3K.

(10) The main point here is whether this is acquired resistance or the sensitivity to the drug is already there, and there was no need to do an omics experiment to find this. In some cases, it seems that the single treatment with PI3K inhibitors is as effective as Sotorasib treatment, promoting the death of the parental cells. This is in line with previous data in H23 and H353 that show sensitivity to PI3K inhibition ( i.e., H358 10.1016/j.jtcvs.2005.06.051 ; 10.1016/j.jtcvs.2005.06.051H23 10.20892/j.issn.2095-3941.2018.0361). The data is clear, especially for copanlisib, but would it be the case that this treatment could be used for the treatment of NSCLC alone or directly in combination with Sotorasib and prevent resistance? The results shown in Figure 4C strongly support that a single treatment might be effective in cases that do not respond to Sotorasib. The data in figure 4D-F (please correct typo "inhibition" in labels) seem to support that PI3K treatment of parental cells is as effective as in the resistant cells.

(11) The experiments presented in Figure 7 show synergy between Sotorasib and copanlisib treatment in some of the resistant cells. But in Figure 7G, the single treatment of H23AR is as effective as the combination. Did the authors check the effect of this drug on the parental cells? As they do not include this control, it is not possible to know if this is acquired sensitivity to PI3K inhibition or if the parental cells were already sensitive (as indicated by the Figure 4 results).

Author response:

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in NSCLC, proposing that resistance arises from signaling rewiring rather than additional mutations.

Strengths:

Using a panel of AR models - including cell lines, PDXs, CDXs, and PDXOs - they report activation of KRAS and PI3K/AKT/mTOR pathways, with elevated PI3K levels. Pharmacologic inhibition or CRISPR-Cas9 knockout of PI3K partially restores sotorasib sensitivity, and p-4EBP1 upregulation is implicated as an additional contributor, with dual mTORC1/2 inhibition more effective than mTORC1 inhibition alone.

Weaknesses:

While the study addresses an important clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation. The mechanistic findings are not entirely novel, since the role of PI3K-AKT-mTOR signaling in therapeutic resistance is already well-established in the literature. Rather than uncovering new resistance mechanisms, the study largely confirms known pathways. Several key conclusions are not supported by the data, and critical alternative explanations - such as additional mutations or increased KRAS expression - are not thoroughly investigated or ruled out. Furthermore, while the authors use CRISPR-Cas9 to knock out PI3K and 4E-BP1 in H23-AR and H358-AR cells to restore sotorasib sensitivity, they do not perform reconstitution experiments to confirm that re-expressing PI3K or 4E-BP1 reverses the sensitization. This prevents full characterization of PI3K and p-4EBP1 upregulation as contributors to resistance. The manuscript also has several errors, poor figure quality, and a lack of proper quantification. Additional experimental validation, data improvement, and text revisions are required.

Acquired resistance to KRAS^G12C inhibitors such as sotorasib or adagrasib remains a significant clinical challenge. Therefore, the identification of mechanisms of acquired resistance, along with the development of alternative therapeutic strategies, including combination therapies with KRAS inhibitors, represents an urgent unmet clinical need. The emergence of secondary KRAS mutations or new mutations in other oncogenic drivers has been observed as a primary cause of acquired resistance in a fraction of patients. No identifiable mutations were detected in more than half of the tumors from patients who developed acquired resistance after treatment with sotorasib or adagrasib.

Using a discovery-based approach that integrated global proteomic and phosphoproteomic analyses in the TC303AR and TC314AR PDX models, we identified distinct protein signatures associated with KRAS reactivation, upregulation of mTORC1 signaling, and activation of the PI3K/AKT/mTOR pathway. These findings prompted further investigation into these mechanisms of resistance and evaluation of novel therapeutic combinations to overcome resistance. Notably, the combination of sotorasib with copanlisib (a PI3K inhibitor), or the combination of sotorasib with AZD8055 or sapanisertib (mTORC1/2 dual inhibitors) demonstrated strong potential for future clinical use. These regimens effectively restored sotorasib sensitivity in both in vitro and in vivo models and produced robust, synergistic antitumor effects across various acquired resistance models.

CRISPR-Cas9-mediated PI3K and 4E-BP1 knockout clones were generated in more than one resistant cell line that expressed a robust level of the knockout target, and multiple independent clones in each cell line were evaluated with and without gene disruption. Given the thorough nature of this analysis, additional reconstitution experiments were deemed unnecessary, as they would not yield further insight.

Whole exome sequencing was performed on resistant cells or PDX models to confirm retention of the KRAS^G12C mutation and to identify secondary KRAS mutations, none of which were found. We acknowledge that additional resistance mechanisms may be involved. These will be the focus of future investigations.

The revised manuscript will feature improved figure quality, complete and clarified figure legends, and corrected textual errors to enhance overall clarity and presentation.  

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids, and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics, and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT, and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the current manuscript is very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Overall, the techniques and methodology seem to be performed in agreement with standard practice, and the results support most of the conclusions made by the authors. However, there are some points that, if addressed, would increase the value and relevance of the findings and further extend the impact of this work. Some of the recommendations for changes relate to the way things are explained and presented, which need some work. Other changes might require the performance of additional experiments or reanalysis of the existing data.

Strengths:

(1) One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs, and the fact that the authors have different clones for each, made this collection especially relevant, as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

(2) Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

(3) The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells.

(4) The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

(5) The way they develop the PDX-resistant and the PDXO seems to be appropriate.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

(1) The abstract is rather long and gives details that are not usually included in one. This makes it very complicated to identify the most relevant findings of the work. The use of acronyms PDX, PDXO, and CDX without defining them makes it complicated for the non-specialist to know what the models are. Rewriting and reorganisation of the abstract would benefit the manuscript.

We will revise the abstract to ensure that the key findings and overall message are clearly communicated and easily understood by readers.

2) Expression, presentation, and grammar should be reviewed in all sections of the manuscript.

Will be done accordingly in the revised version

(3) In the different parts of the result section where the models shown in Figure 2 are described the authors indicate "Whole-exome sequencing (WES) confirmed that XXX model retained the KRASG12C mutation with no additional KRAS mutations detected" however, it is not indicated where this data is shown and in not all the cases there is explanation to other possible modifications that might relate to mechanisms of resistance. This information should be included in the manuscript, and the WES made publicly available.

WES was done for KRAS to identify secondary mutations in the KRAS as well as to verify the retention of the KRAS^G12C mutation in these AR models. WES data will be provided as supplements

(4) The way the proteomics analysis of the TC303 and TC314 parental and resistant PDX is described in the text is confusing. The addition of an experimental layout figure would facilitate the understanding. As it is written, it is not obvious that the parental PDX were also analysed. For instance, the authors say, "The global and phosphoproteomic analyses identified over 8,000 and 4,000 gene protein products (GPPs), respectively". Is this comparing only resistant cells, or from the comparison of the parental and resistant pairs? And where are these numbers presented in the figures? Also, there is information that seems more adequate for the materials and methods sections, i.e., "Samples were analyzed using label-free nanoscale liquid chromatography coupled with tandem mass spectrometry (nanoLC-MS/MS) on a Thermo Fusion Mass Spectrometer. The resulting data were processed and quantified using the Proteome Discoverer 2.5 interface with the Mascot search engine, referencing the NCBI RefSeq protein database (Saltzman, Ruprecht). Two-component analysis is better named principal component analysis."

The texts will be revised accordingly

(5) While the presentation of the proteomics data could be done in different ways, the way the data is presented in Figure 3 does not allow the reader to get an idea of many of the findings from this experiment. Although it is indicated that a table with the data will be made available, this should be central to the way the data is presented and explained. A table (ie, Excel doc) where the raw data and all the analysis are presented should be included and referenced. Additionally, heat maps for the whole proteomes identified should be included. In the text, it is said, "Global proteomic heatmap analysis revealed unique protein profiles in TC303AR and TC314AR PDXs compared to their sensitive counterparts (Figure 3C)." However, this figure only shows the histogram of the differentially regulated cells. Inclusion of the histogram showing all the cells is necessary, and it might be informative to include the histogram comparing the two isogenic pairs, which could identify common mechanisms and differences between both sets. In Figure 3C, the protein names should be readable, or a reference to tables where the proteins are listed should be included.

The raw data associated with the proteomics and global proteomics will be added as supplements.

(6) In Figure 3, the pathway enrichment tool and GO used should be mentioned in the text. The tables with all significant tables should also be provided. The proteomics data seems to convincingly identify mTOR as one of the pathways deregulated in resistant cells, but there is little explanation of what is considered a significant FDR value and if there are other pathways or networks that are also modified, which might not be common to both isogenic models. In MS-based Phosphoproteome could help with the identification of differentially regulated pathways, but it is not really presented in the current manuscript. Most of the analysis of phospho-proteomics comes from the RPPA analysis, which is targeted proteomics. With the way the data is presented, the authors show evidence for a role of mTOR in the acquisition of resistance, but unfortunately, they do not discuss or allow the reader to explore if other pathways might also contribute to this change.

The authors agree that other pathways may be involved, and this will be the subject of future studies. The raw data will be added as supplements.

(7) Where is the proteomics data going to be deposited, and will it be made public to comply with FAIR principles?

will be uploaded according to the journal guidelines

(8) The authors claim that the resistance shown for H23AR and H353AR cells is due to reactivation of KRAS signalling. This is done by looking to phosphorylation of ERK as a surrogate, as they claim, "KRAS inhibition is commonly assessed by evaluating the inhibition of ERK phosphorylation (p-ERK)". While this might be true in many cases, the data presented does not demonstrate that the increase in p-ERK is due to reactivation of KRAS. To make this claim, the authors should measure activation of KRAS (and possibly H- and NRAS) using GST-pull down or an image-based method.

We agree that KRAS activation can be assessed through various methods. In this manuscript, which primarily focuses on mechanisms of resistance, pathway analysis revealed upregulation of KRAS signaling. This finding correlated with the incomplete inhibition of p-ERK by sotorasib in resistant cells. Notably, p-ERK status is widely recognized and routinely used as a surrogate marker for KRAS pathway activation.

(9) The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and H358 parental cells. Is the increase shown in resistant cells shown in parental or is it exclusive for resistant cells only (and therefore acquired)? Experiment 4B should include this control. What is clear is that there is an increase in the expression of AKT and PI3K.

H23 and H358 cells are highly sensitive to sotorasib, as demonstrated by the cell viability assays presented in Figure 2. As shown in Figure 3—figure supplement 3, sotorasib treatment led to complete inhibition of p-ERK in these parental cell lines. In contrast, p-ERK inhibition was incomplete in the resistant H23AR and H358AR cells. Moreover, these AR cells were continuously cultured under sotorasib pressure to maintain resistance.

(10) The main point here is whether this is acquired resistance or the sensitivity to the drug is already there, and there was no need to do an omics experiment to find this. In some cases, it seems that the single treatment with PI3K inhibitors is as effective as Sotorasib treatment, promoting the death of the parental cells. This is in line with previous data in H23 and H353 that show sensitivity to PI3K inhibition ( i.e., H358 10.1016/j.jtcvs.2005.06.051 ; 10.1016/j.jtcvs.2005.06.051H23 10.20892/j.issn.2095-3941.2018.0361). The data is clear, especially for copanlisib, but would it be the case that this treatment could be used for the treatment of NSCLC alone or directly in combination with Sotorasib and prevent resistance? The results shown in Figure 4C strongly support that a single treatment might be effective in cases that do not respond to Sotorasib. The data in figure 4D-F (please correct typo "inhibition" in labels) seem to support that PI3K treatment of parental cells is as effective as in the resistant cells.

We agree. Based on our in vitro (Figure 4) and in vivo (Figure 7) data, copanlisib was able to overcome sotorasib resistance, demonstrating either synergistic or additive effects depending on the specific model. These findings support the potential of combining PI3K inhibition with KRAS^G12C inhibition as a promising strategy to address acquired resistance.

(11) The experiments presented in Figure 7 show synergy between Sotorasib and copanlisib treatment in some of the resistant cells. But in Figure 7G, the single treatment of H23AR is as effective as the combination. Did the authors check the effect of this drug on the parental cells? As they do not include this control, it is not possible to know if this is acquired sensitivity to PI3K inhibition or if the parental cells were already sensitive (as indicated by the Figure 4 results).

Both H23 and H23AR cells showed high sensitivity to copanlisib, as shown in Figure 4. Combination index analysis for the copanlisib + sotorasib treatment (Figure 7A) revealed synergistic effects on cell viability at specific concentrations. However, in the in vivo experiment (Figure 7G), we did not observe a clear synergistic effect of the combination treatment against H23AR xenografts. This may be attributed to the dose of copanlisib used, which was potentially sufficient on its own to produce a strong antitumor response, thereby masking any additional benefit from the combination.

eLife Assessment

This important work substantially advances our understanding of how accessory olfactory bulb neurons respond to social odor cues across the estrous cycle, showing that responses vary with the strain and sex of the odor source but display no consistent differences between estrous and non-estrous states. It employs a unique electrophysiology preparation that activates the vomeronasal organ pump via electric stimulation, enabling precise recordings of accessory olfactory bulb cell responses to different chemosignals in anesthetized mice. Overall, the study presents convincing findings on the stability and variability of accessory olfactory bulb response patterns, indicating that while accessory olfactory bulb detects social signals, it does not appear to interpret them based on reproductive state. This work will be of interest to those studying olfaction, social behavior, reproductive cycles, and systems neuroscience more broadly.

Reviewer #1 (Public review):

Summary:

In this detailed study, Cohen and Ben-Shaul characterized the AOB cell responses to various conspecific urine samples in female mice across the estrous cycle. The authors found that AOB cell responses vary with strains and sexes of the samples. Between estrous and non-estrous females, no clear or consistent difference in responses was found. The cell response patterns, as measured by the distance between pairs of stimuli, are largely stable. When some changes do occur, they are not consistent across strains or male status. The authors concluded that AOB detects the signals without interpreting them. Overall, this study will provide useful information for scientists in the field of olfaction.

Strengths:

The study uses electrophysiological recording to characterize the responses of AOB cells to various urines in female mice. AOB recording is not trivial as it requires activation of VNO pump. The team uses a unique preparation to activate the VNO pump with electric stimulation, allowing them to record AOB cell responses to urines in anesthetized animals. The study comprehensively described the AOB cell responses to social stimuli and how the responses vary (or not) with features of the urine source and the reproductive state of the recording females. The dataset could be a valuable resource for scientists in the field of olfaction.

Weaknesses:

The study will be significantly strengthened by understanding the "distance" of chemical composition in different urine. This could be an important future direction.

eLife Assessment

The study presents a valuable resource of proline hydroxylation proteins for molecular biology studies in oxygen-sensing and cell signaling with the characterization of Repo-man proline hydroxylation site. The evidence supporting the claim of the authors is solid, although further clarification of the overall efficiency of the HILIC analysis, the specificity/sensitivity of immonium ion analysis, as well as quantification of proline hydroxylation identifications will be helpful. The work will be of interest to researchers studying post-translational modification, oxygen sensing, and cell signaling.

Reviewer #1 (Public review):

Summary:

The manuscript by Hao Jiang et al described a systematic approach to identify proline hydroxylation proteins. The authors implemented a proteomic strategy with HILIC-chromatographic separation and reported an identification of 4993 sites from HEK293 cells (4 replicates) and 3247 sites from RCC4 sites (3 replicates) with 1412 sites overlapping between the two cell lines. From the analysis, the authors identified 225 sites and 184 sites respectively from 293 and RCC4 cells with HyPro diagnostic ion. The identifications were validated by analyzing a few synthetic peptides, with a specific focus on Repo-man (CDCA2) through comparing MS/MS spectra, retention time, and diagnostic ions. With SILAC analysis and recombinant enzyme assay, the study showed that Repo-man HyPro604 is a target of the PHD1 enzyme.

Strengths:

The study involved extensive LC-MS analysis and was carefully implemented. The identification of over 4000 confident proline hydroxylation sites would be a valuable resource for the community. The characterization of Repo-man proline hydroxylation is a novel finding.

Weaknesses:

However, as a study mainly focused on methodology, the findings from the experimental data did not convincingly demonstrate the sensitivity and specificity of the workflow for site-specific identification of proline hydroxylation in global studies.

Major concerns:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyllysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Weaknesses:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

Reviewer #3 (Public review):

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses.

Author response:

Reviewer #1 (Recommendations for the authors):

We appreciate the reviewer recognising that our study has been carefully performed and provides a valuable resource for the community. The characterization of Repo-man proline hydroxylation is also recognised as a novel finding.

With respect to Concerns raised by reviewer 1:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations.

We do not agree that the apparent concern raised here, i.e., that the method we present is not 100% specific for enriching only hydroxylated peptides, is a serious issue. We show specifically that our method indeed enriches samples for hydroxylated peptides, thereby increasing the chances of identifying proline hydroxylated peptides in a cell extract. We never claimed that it was mono-specific for enrichment of hydroxylated peptides. Further, we note that almost no chromatographic method we know of, including those commonly used to enrich for different types of post translationally-modified peptides (including phospho-peptides) is completely mono-specific for a single type of modified peptide. The reviewer comments that it could have been possible to use alternative methods to identify proline-hydroxylated peptides. This may be true, but we know of no published examples, or previous studies, where this has been demonstrated experimentally on a scale comparable to that we show here. Of course there is always more than one way to approach technical challenges and it may be that future methods will be demonstrated that achieve equivalent, or even superior, results with respect to the detection of proline hydroxylated peptides. To the best of our knowledge, however, our current study provides a robust methodology that goes well beyond any previously published analysis of proline hydroxylation.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyllysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

We feel that the reviewer’s initial comment is potentially misleading - it implies that we were proposing here that the 'HyPro immonium ion is a diagnostic ion for HyPro identification’. In contrast, this concept was already widely held in the field before we started this project. Indeed, the fact that the diagnostic HyPro immonium ion is often difficult to detect, has been used as one of the arguments by other researchers to support the view that HIF-α is the only physiologically relevant target for PHD enzymes, a controversy referenced explicitly by Reviewer 2 below. What we actually show here are novel data that help to explain why the diagnostic HyPro immonium ion is often difficult to detect, when standard approaches and technical parameters for MS analysis are used. We beleive that this observation, along with other data we present, is a useful contribution to the field that can help to resolve the previous controversies concerning the true prevalence and biological roles of PHD-catalysed proline hydroxylation on protein targets.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

We agree that this study is not quantifying or addressing the stoichiometry of proline hydroxylation across the very large number of new PHD target sites we identify. That was not claimed and was not the objective of our study. Nonetheless, we feel the comments of the referee do not adequately take into account the SILAC data we included (cf Figure 8) or the full range of experimental data presented in this study. We would further refer the reviewer also to the data presented in the companion paper by Druker et al., which we cross-referenced extensively in our study and have also made available previously on biorxiv.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

Here again we refer to the recent controversy referenced explicitly by Reviewer 2 below, concerning the view expressed by some researchers that only HIF-α is a physiological substrate for PHD enzymes in cells. We were challenged to show that any of the novel protein targets of PHDs we identified were indeed hydroxylated by PHD enzymes in vitro and that is what we demonstrated in Figure 9. This was not an experiment performed to quantify stoichiometry and indeed, it is not possible to draw any firm conclusions about efficiency or stiochiometry in vitro when using catalytic PHD subunits alone, given that we do not yet know whether PHDs may show different properties in cells, dependent on interactions with other factors and/or modifications.

Reviewer #2 (Recommendations for the authors):

We appreciate the reviewer’s comments that our manuscript presents an advanced, standardized protocol for identifying proline hydroxylation, with well designed experiments, which may help resolve confusion in the field.

With respect to Concerns raised by reviewer 2:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

We agree and plan to provide a clearly described, step by step guide to assist other researchers who wish to employ our methods for proline hydroxylation analysis in their own studies.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

We agree that our study provides valuable information germane to the recent controversy in the field and the views published by Cockman et al., to the effect that HIF-α is the only physiologically relevant target for PHDs. We will carefully review our statements when preparing a suitably revised version of record with the aim of providing a balanced and objective discussion of this issue.

Reviewer #3 (Recommendations for the authors):

We appreciate the reviewer’s comments that our study employs state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, along with their recognition that our study is, 'an advance compared to other similar approaches before.’ We also appreciate their reference to our companion study by Druker et al, in which we characterise the mechanism and biological role in regulation of mitotic progression of the hydroxylation of P604 in the target protein RepoMan (CDCA2), that is identified in this study.

With respect to the Concern raised by reviewer 3:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses..

We agree that this study, which has a focus on methodology and technical approaches for detecting sites of PHD- catalysed proline hydroxylation, cannot exhaustively validate the biological significance of all of the putative sites and targets identified. As the reviewer notes, we have performed a detailed functional characterisation of one such novel PHD-catalyed proline hydroxylation site, i.e. P604 in the protein RepoMan (CDCA2). This functional analysis is presented in the companion paper by Druker et al., which has also been reviewed by eLife and placed on biorxiv (doi: https://doi.org/10.1101/2025.05.06.652400). We hope that publication of our identification of many new putative PHD target sites will encourage other researchers to pursue characterisation of their functional reoles in different biological mechanisms and have tried here to provide some degree of guidance to focus attention on the identification of those sites for which we currently have highest confidence.

eLife Assessment

This valuable study advances our understanding of how bactofilin cytoskeletal proteins associate with cell membranes by identifying and characterizing a conserved membrane-targeting sequence. The evidence is solid, with a well-integrated combination of mutagenesis, biophysical analysis, molecular simulations, and bioinformatics supporting the mechanistic model. The work will be of particular interest to microbiologists and structural biologists studying bacterial cytoskeletons and membrane-protein interactions.

Reviewer #2 (Public review):

Summary:

The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a frequent property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

Although the phenotypic effects of an abolished BacA-PbpC interaction are mild, these data significantly advance our understanding of bactofilin membrane binding, polymerization, and function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The investigators undertook detailed characterization of a previously proposed membrane targeting sequence (MTS), a short N-terminal peptide, of the bactofilin BacA in Caulobacter crescentus. Using light microscopy, single molecule tracking, liposome binding assays, and molecular dynamics simulations, they provide data to suggest that this sequence indeed does function in membrane targeting and further conclude that membrane targeting is required for polymerization. While the membrane association data are reasonably convincing, there are no direct assays to assess polymerization and some assays used lack proper controls as detailed below. Since the MTS isn't required for bactofilin polymerization in other bacterial homologues, showing that membrane binding facilitates polymerization would be a significant advance for the field.

We agree that additional experiments were required to consolidate our results and conclusions. Please see below for a description of the new data included in the revised version of the manuscript.

Major concerns

(1) This work claims that the N-termina MTS domain of BacA is required for polymerization, but they do not provide sufficient evidence that the ∆2-8 mutant or any of the other MTS variants actually do not polymerize (or form higher order structures). Bactofilins are known to form filaments, bundles of filaments, and lattice sheets in vitro and bundles of filaments have been observed in cells. Whether puncta or diffuse labeling represents different polymerized states or filaments vs. monomers has not been established. Microscopy shows mis-localization away from the stalk, but resolution is limited. Further experiments using higher resolution microscopy and TEM of purified protein would prove that the MTS is required for polymerization.

We do not propose that the MTS is directly involved in the polymerization process and state this more clearly now in the Results and Discussion sections of the revised manuscript. To address this point, we performed transmission electron microscopy studies comparing the polymerization behavior of wild-type and mutant BacA variants. The results clearly show that the MTS-free BacA variant (∆2-8) forms polymers that are indistinguishable from those formed by the wild-type protein, when purified from an E. coli overproduction strain (new Figure 1–figure supplement 1). This finding is consistent with structural work showing that bactofilin polymerization is exclusively mediated by the conserved bactofilin domain (Deng et al, Nat Microbiol, 2019). However, at native expression levels, BacA only accumulates to ~200 molecules per cell (Kühn et al, EMBO J, 2006). Under these conditions, the MTS-mediated increase in the local concentration of BacA at the membrane surface and, potentially, steric constraints imposed by membrane curvature, may facilitate the polymerization process. This hypothesis has now been stated more clearly in the Results and Discussion sections.

For polymer-forming proteins, defined localized signals are typically interpreted as slow-moving or stationary polymeric complexes. A diffuse localization, by contrast, suggests that a protein exists in a monomeric or, at most, (small) oligomeric state in which it diffuses rapidly within the cell and is thus no longer detected as distinct foci by widefield microscopy. Our single-molecule data show that BacA variants that are no longer able to interact with the membrane (as verified by cell fractionation studies and in vitro liposome binding assays) have a high diffusion rate, similar to that measured for the non-polymerizing and non-membrane-bound F130R variant. These results demonstrate that a defect in membrane binding strongly reduces the ability of BacA to form polymeric assemblies. To support this hypothesis, we have now repeated all single-particle tracking experiments and included mVenus as a freely diffusible reference protein. Our data confirm that the mobilities of the ∆2-8 and F130R variants are similar and approach those of free mVenus, supporting the idea that the deficiency to interact with the membrane prevents the formation of extended polymeric structures (which should show much lower mobilities). To underscore the relevance of membrane binding for BacA assembly, we have now included a new experiment, in which we used the PbpC membrane anchor (PbpC_1-132-mcherry) to restore the recruitment of the ∆2-8 variant to the membrane (Figure 9 and Figure 9–figure supplement 1). The results obtained show that the ∆2-8 variant transitions from a diffuse localization to polar foci upon overproduction of PbpC_1-132-mcherry. The polymerization-impaired F130R variant, by contrast, remains evenly distributed throughout the cytoplasm under all conditions. These findings further support the idea that polymerization and membrane-association are mutually interdependent processes.

(2) Liposome binding data would be strengthened with TEM images to show BacA binding to liposomes. From this experiment, gross polymerization structures of MTS variants could also be characterized.

We do not have the possibility to perform cryo-electron microscopy studies of liposomes bound to BacA. However, the results of the cell fractionation and liposome sedimentation assays clearly support a critical role of the MTS in membrane binding.

(3) The use of the BacA F130R mutant throughout the study to probe the effect of polymerization on membrane binding is concerning as there is no evidence showing that this variant cannot polymerize. Looking through the papers the authors referenced, there was no evidence of an identical mutation in BacA that was shown to be depolymerized or any discussion in this study of how the F130R mutation might to analogous to polymerization-deficient variants in other bactofilins mentioned in these references.

Residue F130 in the C-terminal polymerization interface of BacA is conserved among bactofilin homologs, although its absolute position in the protein sequence may vary, depending on the length of the N-terminal unstructured tail. The papers cited in our manuscript show that an exchange of this conserved phenylalanine residue abolishes polymer formation. Nevertheless, we agree that it is important to verify the polymerization defect of the F130R variant in the system under study. We have now included size-exclusion chromatography data showing that BacA-F130R forms a low-molecular-weight complex, whereas the wild-type protein largely elutes in the exclusion volume, indicating the formation of large, polymeric species (new Figure 1–figure supplement 1). In addition, we performed transmission electron microscopy analyses of BacA-F130R, which verified the absence of larger oligomers (new Figure 1–figure supplement 2).

(4) Microscopy shows that a BacA variant lacking the native MTS regains the ability to form puncta, albeit mis-localized, in the cell when fused to a heterologous MTS from MreB. While this swap suggests a link between puncta formation and membrane binding the relationship between puncta and polymerization has not been established (see comment 1).

We show that a BacA variant lacking the MTS (∆2-8) regains the ability to form membrane-associated foci when fused to the MTS of MreB. By contrast, a similar variant that additionally carries the F130R exchange (preventing its polymerization) shows a diffuse cytoplasmic localization. In addition, we show that the F130R exchange leads to a loss of membrane binding and to a considerable increase in the mobility of the variants carrying the MTS of E. coli MreB. As described above, we now provide additional data demonstrating that elevated levels of the PbpC membrane anchor can reinstate polar localization for the ∆2-8 variant, whereas it fails to do so for the polymerization-deficient F130R variant (Figure 9 and Figure 9–figure supplement 1). Together, these results support the hypothesis that membrane association and polymerization act synergistically to establish localized bactofilin assemblies at the stalked cell pole.

(5) The authors provide no primary data for single molecule tracking. There is no tracking mapped onto microscopy images to show membrane localization or lack of localization in MTS deletion/ variants. A known soluble protein (e.g. unfused mVenus) and a known membrane bound protein would serve as valuable controls to interpret the data presented. It also is unclear why the authors chose to report molecular dynamics as mean squared displacement rather than mean squared displacement per unit time, and the number of localizations is not indicated. Extrapolating from the graph in figure 4 D for example, it looks like WT BacA-mVenus would have a mobility of 0.5 (0.02/0.04) micrometers squared per second which is approaching diffusive behavior. Further justification/details of their analysis method is needed. It's also not clear how one should interpret the finding that several of the double point mutants show higher displacement than deleting the entire MTS. These experiments as they stand don't account for any other cause of molecular behavior change and assume that a decrease in movement is synonymous with membrane binding.

We now provide additional information on the single-particle analysis. A new supplemental figure now shows a mapping of single-particle tracks onto the cells in which they were recorded for all proteins analyzed (Figure 2–figure supplement 1). Due to the small size of C. crescentus, it is difficult to clearly differentiate between membrane-associated and cytoplasmic protein species. However, overall, slow-diffusing particles tend to be localized to the cell periphery, supporting the idea that membrane-associated particles form larger assemblies (apart from diffusing more slowly due to their membrane association). In addition, we have included a movie that shows the single-particle diffusion dynamics of all proteins in representative cells (Figure 2-video 1). Finally, we have included a table that gives an overview of the number of cells and tracks analyzed for all proteins investigated (Supplementary file 1). Figure 2A and 4D show the mean squared displacement as a function of time, which makes it possible to assess whether the particles observed move by normal, Brownian diffusion (which is the case here). We repeated the entire single-particle tracking analysis to verify the data obtained previously and obtained very similar results. Among the different mutant proteins, only the K4E-K7E variant consistently shows a higher mobility than the MTS-free ∆2-8 variant, with MSD values similar to that of free mVenus. The underlying reason remains unclear. However, we believe that an in-depth analysis of this phenomenon is beyond the scope of this paper. We re-confirmed the integrity of the construct encoding the K4E/K7E variant by DNA sequencing and once again verified the size and stability of the fusion protein by Western blot analysis, excluding artifacts due to errors during cloning and strain construction.

We agree that the single-molecule tracking data alone are certainly not sufficient to draw firm conclusions on the relationship between membrane binding and protein mobility. However, they are consistent with the results of our other in vivo and in vitro analyses, which together indicate a clear correlation between the mobility of BacA and its ability to interact with the membrane and polymerize (processes that promote each other synergistically).

(6) The experiments that map the interaction surface between the N-terminal unstructured region of PbpC and a specific part of the BacA bactofilin domain seem distinct from the main focus of the paper and the data somewhat preliminary. While the PbpC side has been probed by orthogonal approaches (mutation with localization in cells and affinity in vitro), the BacA region side has only been suggested by the deuterium exchange experiment and needs some kind of validation.

The results of the HDX analysis per se are not preliminary and clearly show a change in the solvent accessibility of backbone amides in the C-terminal region in the bactofilin domain in the presence of the PbpC_1-13 peptide. However, we agree that additional experiments would be required to verify the binding site suggested by these data. We agree that further research is required to precisely map and verify the PbpC binding site. However, as this is not the main focus of the paper, we would like to proceed without conducting further experiments in this area.

We now provide additional data showing that elevated levels of the PbpC membrane anchor are able to recruit the MTS-free BacA variant (∆2-8) to the cytoplasmic membrane and stimulate its assembly at the stalked pole (Figure 9). These results now integrate Figure 8 more effectively into the overall theme of the paper.

Reviewer #2 (Public review):

Summary:

The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a common property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

Strengths:

These data significantly advance the understanding of the membrane binding determinants of bactofilins and thus their function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

Thank you for this positive feedback.

Weaknesses:

The results are limited to protein localization and interaction, as there is no data on phenotypic effects. Therefore, the cell biological significance remains somewhat underrepresented.

We agree that it is interesting to investigate the phenotypic effects caused by the reduced membrane binding activity of BacA variants with defects in the MTS. We have now included phenotypic analyses that shed light on the role of region C1 in the localization of PbpC and its function in stalk elongation under phosphate-limiting conditions (see below).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

To address the missing estimation of biological relevance, some additional experiments may be carried out.

For example, given that BacA localizes PbpC by direct interaction, one might expect an effect on stalk formation if BacA is unable to bind the membrane or to polymerize. The same applies to PbpC variants lacking the C1 region. As the mutant strains are available, these data are not difficult to obtain but would help to compare the effect of the deletions with previous data (e.g. Kühn et al.) even if the differences are small.

We have now analyzed the effect of the removal of region C1 on the ability of mVenus-PbpC to promote stalk elongation in C. crescentus under phosphate starvation. Interestingly, our results show that the lack of the BacA-interaction motif impairs the recruitment of the fusion protein to the stalked pole, but it does not interfere with its stimulatory effect on stalk biogenesis. Thus, the polar localization of PbpC does not appear to be critical for its function in localized peptidoglycan synthesis at the stalk base. These results are now shown in Figure 8–Figure supplement 4. The results obtained may be explained by residual transient interactions of mVenus-PbpC with proteins other than BacA at the stalked pole. Notably, PbpC has also been implicated in the attachment of the stalk-specific protein StpX to components of the outer membrane at the stalk base. The polar localization of PbpC may therefore be primarily required to ensure proper StpX localization, consistent with previous work by Hughes et al. (Mol Microbiol, 2013) showing that StpX is partially mislocalized in a strain producing an N-terminally truncated PbpC variant that no longer localizes to the stalk base.

We have also attempted to investigate the ability of the Δ2-8 and F130R variants of BacA-mVenus to promote stalk elongation under phosphate starvation. However, the levels of the WT, Δ2-8 and F130R proteins and their stabilities were dramatically different after prolonged incubation of the cells in phosphate-limited medium, so that it was not possible to draw any firm conclusions from the results obtained (not shown).

In addition, the M23-like endopeptidase LdpA is proposed to be a client protein of BacA (in C. crescentus, Billini et al. 2018, and H. neptunium or R. rubrum, Pöhl et al. 2024). In H. neptunium, it is suggested that the interaction is mediated by a cytoplasmic peptide of LmdC reminiscent of PbpC. This should at least be commented on. It would be interesting to see, if LpdA in C. crescentus is also delocalized and if so, this could identify another client protein of BacA.

We agree that it would be interesting to study the role of BacA in LdpA function. However, we have not yet succeeded in generating a stable fluorescent protein fusion to LdpA, which currently makes it impossible to study the interplay between these two proteins in vivo. The focus of the present paper is on the mode of interaction between bactofilins and the cytoplasmic membrane and on the mutual interdependence of membrane binding and bactofilin polymerization. Given that PbpC is so far the only verified interaction partner of BacA in C. crescentus, we would like to limit our analysis to this client protein.

Further comments:

L105: analyze --> analyzed

Done.

L169: Is there any reason why the MTS of E. coli MreB was doubled?

Previous work has shown that two tandem copies of the N-terminal amphiphilic helix of E. coli MreB were required to partially target a heterologous fusion partner protein (GFP) to the cytoplasmic membrane of E. coli cells (Salje et al, 2011).

Fig. S3:

a) Please decide which tag was used (mNG or mVenus) and adapt the figure or legend accordingly.<br /> b) In the legend for panel (C), please describe how the relative amounts were calculated, as the fractions arithmetically cannot add to > 100%. I guess each band was densiometrically rated and independently normalized to the whole-cell signal?

The fluorescent tag used was mNeonGreen, as indicated in the figure. We have now corrected the legend accordingly. Thank you for making us aware of the wrong labeling of the y-axis. We have now corrected the figure and describe the method used to calculate the plotted values in the legend.

Legend of Fig 1b: It is not clear to me, to which part of panel B the somewhat cryptic LY... strain names belong. I suggest putting them either next to the images, to delete them, or at least to unify the layout (compare, e.g. to Fig S7). (I would delete the LY numbers and stay with the genes/mutations throughout. This is just a suggestion).

These names indicate the strains analyzed in panel B, and we have now clarified this in the legend. It is more straightforward to label the images according to the mutations carried by the different strains. Nevertheless, we would like to keep the strain names in the legend, so that the material used for the analysis can be clearly identified.

Fig. 2a: As some of the colors are difficult to distinguish, I suggest sorting the names in the legend within the graph according to the slope of the curves (e.g. K4E K7E (?) on top and WT being at the bottom).

Thank you for this suggestion. We have now rearranged the labels as proposed.

In the legend (L924), correct typo "panel C" to "panel B".

Done.

Fig. 3: In the legend, I suggest deleting the abbreviations "S" and "P" as they do not show up in the image. In line 929, I suggest adding: average "relative" amount... or even more precisely: "average relative signal intensities obtained..."

We have removed the abbreviations and now state that the bars indicate the “average relative signal intensities” obtained for the different fractions.

Fig 4d: same suggestion as for Fig. 2a.

Done.

Fig 8: In the legend (L978), delete 1x "the"

Done.

L258 and Fig. S5: The expression "To account for biases in the coverage of bacterial species" seems somewhat unclear. I suggest rephrasing and adding information from the M+M section here (e.g. from L593, if this is meant).

We now state that this step in the analysis pipeline was performed “To avoid biases arising from the over-representation of certain bacterial species in UniProt”.

I appreciate the outline of the workflow in panel (a) of Fig. S5. It would be even more useful when some more details about the applied criteria for filtering would be provided (e.g. concerning what is meant with "detailed taxonomic information" or "filter out closely related sequences". Does the latter mean that only one bactofilin sequence per species was used? (As quite many bacteria have more than one but similar bactofilins.)

We removed sequences from species with unclear phylogeny (e.g. candidate species whose precise taxonomic position has not yet been determined). For many pathogenic species, numerous strains have been sequenced. To account for this bias, only one sequence from clusters of highly similar bactofilin sequences (>90% identity) was retained per species. This information has now been included in the diagram. It is true that many bacteria have more than one bactofilin homolog. However, the sequences of these proteins are typically quite different. For instance, the BacA and BacB from C. crescentus only share 52% identity. Therefore, our analysis does not systematically eliminate bactofilin paralogs that coexist in the same species.

L281: Although likely, I am not sure if membrane binding has ever been shown for a bactofilin from these phyla. (See also L 380.) Is there an example? Otherwise, membrane binding may not be a property of these bactofilins.

To our knowledge, the ability of bactofilins from these clades to interact with membranes has not been investigated to date. We agree that the absence of an MTS-like motif may indicate that they lack membrane binding activity, and we have now stated this possibility in the Results and Discussion.

L285: See comment above concerning the M23-like peptidase LpdA. Although not yet directly shown for C. crescentus, it seems likely that BacACc does also localize this peptidase in addition to PbpC. I suggest rephrasing, e.g. "known" --> "shown"

We now use the word “reported”.

L295 and Fig S8: PbpC is ubiquitous. Which criteria/filters have been applied to select the shown sequences?

C. crescentus PbpC is different from E. coli Pbp1C. It is characterized by distinctive, conserved N- and C-terminal tails and only found in C. crescentus and close relatives. The C. crescentus homolog of E. coli PbpC is called PbpZ (Yakhnina et al, J Bacteriol, 2013; Strobel et al, J Bacterol, 2014), whereas C. crescentus PbpC is related to E. coli PBP1A. We have now added this information to the text to avoid confusion.

L311: may replace "assembly" by "polymerization"

Done.

L320: bactofilin --> bactofilin domain?

Yes, this was supposed to read “bactofilin domain”. Thank you for spotting this issue.

L324: The HDX analysis of BacA suggests that the exchange is slowed down in the presence of the PbpC peptide, which is indicative of a physical interaction between these two molecules. To corroborate the claim that BacA polymerization is critical for interaction with the peptide (resp. PbpC), this experiment should be carried out with the polymerization defective BacA version F130R.

(Or tone this statement down, e.g. show --> suggest.)

“suggest”

L386: undergoes --> undergo

Done.

L391-400: This idea is tempting but the suggested mechanism then would be restricted to bactofilins of C. crescentus and close relatives. The bactofilin of Rhodomicrobium, for example, was shown to localize dynamically and not to stick to a positively curved membrane.

In the vast majority of species investigated so far, bactofilins were found to associate with specifically curved membrane regions and to contribute to the establishment of membrane curvature. Unfortu­nately, the sequences of the three co-polymerizing bactofilin paralogs of R. vannielii DSM 166 studied by Richter et al (2023) have not been reported and the genome sequence of this strain is not publicly available. However, in related species with three bactofilin paralogs, only one paralog shows an MTS-like N-terminal peptide and another paralog typically contains an unusual cadherin-like domain of unknown function, as also reported for R. vannielii DSM 166. Therefore, the mechanism controlling the localization dynamics of bactofilins may be complex in the Rhodomicrobium lineage. Nevertheless, at native expression levels, the major bactofilin (BacA) of R. vannielii DSM 166 was shown to localize predominantly to the hyphal tips and the (incipient) bud necks, suggesting that regions of distinct membrane curvature could also play a role in its recruitment. We do not claim that all bactofilins recognize positive membrane curvature, which is clearly not the case. It rather appears as though the curvature preference of bactofilins varies depending on their specific function.

L405-406: I agree that localization of BacA has been shown to be independent of PbpC. However, this does not generally preclude an effect on BacA localization by other "client" or interacting proteins. (See also comment above about the putative BacA interactor LpdA). I suggest either to corroborate or to change this statement from "client binding" to "PbpC binding".

Thank you for pointing out the imprecision of this statement. We now conclude that “PbpC binding” is not critical for BacA assembly and positioning.

Suppl. Fig. S11: In the legend, please correct the copy-paste mismatch (...VirB...).

Done.

L482: delete 1x "at"

Done.

L484: may be better "soluble and insoluble fractions"?

We now describe the two fractions as “soluble and membrane-containing insoluble fractions” to make clear to all readers that membrane vesicles are found in the pellet after ultracentrifugation.

L489-490: check spelling immunoglobulin – immuneglobulin

Done.

L500 and 504: º_C --> ºC

Done.

Suppl. file X (HDX data): please check the table headline, table should be included in Suppl. file 1

We have now included a headline in this file (now Supplementary file 3).

eLife Assessment

This manuscript offers valuable structural and mechanistic insights into the structure and assembly of the Type II internal ribosome entry site (IRES) from encephalomyocarditis virus (EMCV) and the translation initiation complex, revealing a direct interaction between the IRES and the 40S ribosomal subunit. While a solid cryo-EM method was used, enhancing the overall resolution or adding complementary biochemical data would further improve the clarity and impact of this study. This manuscript will attract researchers in cap-independent translation, host-pathogen interactions, and virology.

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is well-justified.

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing map-sharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

c) Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryo-EM maps, as employed here, can be implemented for other RNA structures.

Weaknesses:

(1) Maps at the resolution presented prevent unambiguous modelling of the EMCV-IRES. This, combined with the lack of any biochemical data, calls into question any inferences made at the level of individual nucleotides, such as the GNRA loop and CAAA loop (Figure 4).

(2) The EMCV IRES contains an upstream AUG at position 826, where the PIC can assemble (Pestova et al 1996; PMID 8943341). It is unclear if this start codon was mutated in this study. If it were not mutated, placement of AUG-834 over AUG-826 in the P-site is unexplained.

(3) The claims the authors make about (i) the general overall shape and binding site of the IRES, (ii) its gross interaction with the two ribosomal proteins, (iii) the P-in state of the 48S, (iv) the rearrangement of the ternary complex are all warranted. Their claims about individual nucleotides or smaller stretches of the IRES-without any supporting biochemical data-is not warranted by the data.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Weaknesses:

While this reviewer acknowledges the technical challenges inherent in determining the structure of such a highly flexible complex, the overall resolution remains insufficient to fully support the authors' conclusions, particularly given that cryo-EM is the sole experimental approach presented in the manuscript.

The study is biologically significant; however, the authors should improve the resolution or include complementary biochemical validation.

Author response:

Reviewer #1 (Public review):

Summary:

The authors have studied how a virus (EMCV) uses its RNA (Type 2 IRES) to hijack the host's protein-making machinery. They use cryo-EM to extract structural information about the recruitment of viral Type 2 IRES to ribosomal pre-IC. The authors propose a novel interaction mechanism in which the EMCV Type 2 IRES mimics 28S rRNA and interacts with ribosomal proteins and initiator tRNA (tRNAi).

Strengths:

(1) Getting structural insights about the Type 2 IRES-based initiation is novel.

(2) The study allows a good comparison of other IRES-based initiation systems.

(3) The manuscript is well-written and clearly explains the background, methods, and results.

We thank Reviewer 1 for appreciating our efforts and finding structural insights about the type 2 IRES-based initiation presented in this study as novel.

Weaknesses:

(1) The main weakness of the work is the low resolution of the structure. This limits the possibility of data interpretation at the molecular level.

However, despite the moderate resolution of the cryo-EM reconstructions, the model fits well into the density. The analysis of the EMCV IRES-48S PIC structure is thorough and includes meaningful comparisons to previously published structures (e.g., PDB IDs - 7QP6 and 7QP7). These comparisons showed that Map B1 represents a closed conformation, in contrast to Map A in the open state (Figure 2). Additionally, the proposed 28S rRNA mimicry strategy supported by structural superposition with the 80S ribosome and sequence similarity between the I domain of the IRES and the h38 region of 28S rRNA (Fig. 4) is welljustified.

We agree that the low resolution of the map has compromised the data interpretation at the molecular level, and we thank the reviewer for appreciating our findings at this resolution. Due to the compromise in resolution, we have reported findings related to stretches or regions such as loops and stems, rather than individual nucleotides and interactions.  

(2) The lack of experimental validation of the functional importance of regions like the GNRA and RAAA loops is another limitation of this study.

We agree with the lack of any additional experiments other than Cryo-EM for probing the importance of regions such as GNRA and RAAA loops in this study. However, we have cited earlier reports that demonstrate the importance of these regions for overall IRES activity. The essentiality of RAAA loop for type 2 IRES was demonstrated in earlier report López de Quinto and Martínez-Salas, 1997 (Cited in manuscript). Further, the conservation of this loop across the type 2 IRES family adds to the importance of this loop (Manuscript Figure 6B). This loop and its flanking G-C stem are similar to h38 of 28S rRNA, and it appears that RAAA loop adopts a mimicry mechanism to interact with the 40S ribosomal protein- uS19, thus highlighting its importance for interaction with 40S. Experiments destabilising the G-C stem also compromise IRES activity, as shown in the case of FMDV IRES (Fernández et al 2011). Previous studies related to the mutation of the GNRA or GCGA loop in EMCV IRES have shown a deficiency in IRES activity (Roberts and Belsham, 1997; Robertson et al 1999), suggesting the importance of these regions in the viral IRES biology, and these reports are cited in the manuscript. Not only EMCV IRES, but mutation in the GUAA (representative of GNRA) loop of FMDV IRES also showed significant reduction in IRES activity (López de Quinto and Martínez-Salas, 1997). In our study, we observe that GCGA loop interacts with tRNA_i in EMCV IRES-48S PIC, thus implicating the importance of this loop. Moreover, incubation of FMDV IRES with 40S ribosomes has shown a decrease in SHAPE reactivity in domain 3 apex (position 170- 200 nucleotides) (Lozano et al 2018), which corresponds to EMCV IRES domain I apex. Further, we will attempt to address the concern of lack of experimental validation of GNRA and RAAA loops by performing biochemical assays.

(3) Minor modifications related to data processing and biochemical studies will further validate and strengthen the findings.

a) In the cryo-EM data section, the authors should include an image showing rejected particles during 2D classification. This would help readers understand why, despite having over 22k micrographs with sufficient particle distribution and good contrast, only a smaller number of particles were used in the final reconstruction. Additionally, employing mapsharpening tools such as Ewald sphere correction, Bayesian polishing, or reference-based motion correction might further improve the quality of the maps. Targeting high-resolution structures would be particularly informative.

We thank the reviewer for the suggestions, and we would employ suggested processes that may help improve the quality of the maps further. We will include image for rejected 2D classes in the revised manuscript. We agree with the Reviewer’s query related to the substantial number of micrographs and smaller number of particles for the final reconstruction. The total number of micrographs is the summation of multiple datasets, prepared and collected at various times. Among these, around 8000 micrographs have extremely poor particle number and distribution. As a result, the number of particles per micrograph is heterogeneous in the compiled dataset. We obtained only 237054 ‘good particles’ after multiple rounds of 2D & 3D classifications, and the final reconstruction has 28439 particles (~12%). This class was obtained after masked classification for IRES and ternary complex density. Hence, only the particles that show the best density for both IRES and ternary complex are used for reconstructing this map. Another set of particles that have only a portion of IRES and tRNA but NO density for eIF2 forms another map (26792 particles, 11.3%). Thus, we obtained a total of 55231 particles (23.3%) with IRES density.  

b) The strategic modelling of different IRES domains into the density, particularly the domain into the region above the 40S head, is appreciable. However, providing the full RNA tertiary structure (RNAfold) of the EMCV IRES (nucleotides 280-905) would better explain the logic behind the model building and its molecular interpretation.

We thank the reviewer for appreciating the modelling of the domain I apex in the cryo-EM density. We tried to predict the full tertiary structure of the IRES, however, inclusion of the full-length sequence from 280-905 gave models of extremely low confidence, and few domains do not abide by the secondary structure of EMCV IRES as reported in Duke et al 1992. Hence, we used individual domains of EMCV IRES and predicted the tertiary structure independent of other IRES domains. Furthermore, 3D models of FMDV IRES domains 2, 3, and 4 (corresponding to EMCV IRES domains- H, I, and J-K) were predicted from SHAPE reactivity values and RNAComposer server (Figure 3 in Lozano et al 2018). The predicted architecture of domain 3 apex (FMDV IRES) coincides with our I domain apex model (EMCV IRES).

c)  Although the authors compare their findings with other types of IRESs (Types 1, 3, and 4), there is no experimental validation of the functional importance of regions like the GNRA and RAAA loops. Including luciferase-based assays or mutational studies of these regions for validation of structural interpretations is strongly recommended.

We have discussed the possibility of how the other IRESs, such as type 1 and type 5 (Aichi virus), might use similar strategies as EMCV IRES to assemble the 48S PIC, given the similarity in the motif sequence and position across the viral IRESs. Like EMCV IRES, the type 1 IRES (e.g. Poliovirus, Coxsackie virus) also harbours the GNRA loop, preceded by a C-rich loop at its longest domain, known for long-range RNA-RNA interactions. The segment harbouring GNRA loop is highly conserved across the type 1 family of IRESs (Kim et al 2015).The Aichi viral IRES (type 5) harbours a GNRA loop in its longest domain, which is domain J. Deletion of the GNRA loop has compromised the IRES activity; however, substitution mutations in this region either elevated the IRES activity or it remained unaltered (Yu et al 2011). We have hypothesized that these IRESs (type 1 and type 5) might use the GNRA motifs in their longest domain (domain IV in type 1, and domain J in type 5) similar to that of EMCV IRES, where GNRA is present in the longest domain (I) and preceded by a C-rich loop. Thus, GNRA can potentially mediate long-range interactions with tRNA_i as all these IRESs require eIF2-ternary complex for the formation of 48S PIC. Parallelly, like EMCV IRES, type 1 and type 5 IRESs also have similar placement of GNRA motif-containing domain before the eIF4G-binding domain (domain J-K in EMCV IRES, domain V in poliovirus, domain K in Aichi virus). Hence, we suggest the possibility of a similar strategy by these IRESs to interact with tRNA_i during the formation of 48S PIC.  

Reviewer #2 (Public review):

Summary:

The field of protein translation has long sought the structure of a Type 2 Internal Ribosome Entry Site (IRES). In this work, Das and Hussain pair cryo-EM with algorithmic RNA structure prediction to present a structure of the Type 2 IRES found in Encephalomyocarditis virus (EMCV). Using medium to low resolution cryo-EM maps, they resolve the overall shape of a critical domain of this Type 2 IRES. They use algorithmic RNA prediction to model this domain onto their maps and attempt to explain previous results using this model.

Strengths:

(1) This study reveals a previously unknown/unseen binding modality used by IRESes: a direct interaction of the IRES with the initiator tRNA.

(2) Use of an IRES-associated factor to assemble and pull down an IRES bound to the small subunit of the ribosome from cellular extracts is innovative.

(3) Algorithmic modeling of RNA structure to complement medium to low resolution cryoEM maps, as employed here, can be implemented for other RNA structures.

We thank Reviewer 2 for positive and encouraging comments on our work, appreciating our ‘innovative’ approach of using IRES-associated factor to assemble and pull down IRES-bound ribosomal complex.  

Weaknesses:

(1) Maps at the resolution presented prevent unambiguous modelling of the EMCV-IRES. This, combined with the lack of any biochemical data, calls into question any inferences made at the level of individual nucleotides, such as the GNRA loop and CAAA loop (Figure 4).

We understand the concerns raised by the reviewer related to the resolution of the EMCV IRES-48S PIC map. However, we would like to mention that we refrained from commenting on individual nucleotides or molecular interactions in the manuscript. Instead, we discuss about loops, RNA stretches or motifs that could be inferred with more confidence as shown in Manuscript Figure 4. The EMCV IRES can directly interact with the 40S ribosome using its domain H and I (Chamond et al 2014), however, the details this interaction was unknown. We observe that the CAAA loop of domain I apex interacts with 40S ribosome based on the placement of portion of domain I in the cryo-EM map. This is also reflected in the earlier reported SHAPE data (Supplementary figures 2, and 8 in Chamond et al 2014), where a decrease in reactivity is evident in the presence of 40S ribosome. In addition, incubation of EMCV IRES with rabbit reticulocyte lysate (RRL) offered protection to domain I apex regions, which included the CAAA loop (Figure 4b in Maloney and Joseph, 2024).

Furthermore, this decrease in SHAPE reactivity pattern is also evident for FMDV IRES domain 3 apex (like domain I in EMCV IRES) in the presence of 40S ribosome (Lozano et al 2018).

Thus, these studies are consistent with the placement of IRES model in the cryo-EM map.

We aim to improve the resolution of the maps for better clarity and add biochemical experiments to justify the possible interactions.

(2) The EMCV IRES contains an upstream AUG at position 826, where the PIC can assemble (Pestova et al 1996; PMID 8943341). It is unclear if this start codon was mutated in this study. If it were not mutated, placement of AUG-834 over AUG-826 in the P-site is unexplained.

We thank the reviewer for bringing up this point, as we missed mentioning this in the manuscript. The EMCV IRES does not require scanning and directly positions the AUG-834 at the P site (Pestova et al 1996). In Pestova et al 1996, the intensity of the toeprint at AUG-834 is much more intense than that of AUG-826. Further, AUG-834 lies in the Kozak context, whereas AUG-826 has a poor Kozak context. Furthermore, the synthesis of the polypeptide requires placement of AUG-834 at the P site. In our cryo-EM map, we observed that the tRNA_i is in a P_IN state, which indicates the recognition of the start codon, and we reasoned that it is more likely that AUG-834 is placed at the P site than AUG-826. We will mention this in the revised manuscript, as we had NOT mutated AUG-826.

(3) The claims the authors make about (i) the general overall shape and binding site of the IRES, (ii) its gross interaction with the two ribosomal proteins, (iii) the P-in state of the 48S, (iv) the rearrangement of the ternary complex are all warranted. Their claims about individual nucleotides or smaller stretches of the IRES-without any supporting biochemical data-is not warranted by the data.

We thank the reviewer for warranting major claims, and we wish to make further improvements to support our assessment of small stretches and individual nucleotides.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Weaknesses:

While this reviewer acknowledges the technical challenges inherent in determining the structure of such a highly flexible complex, the overall resolution remains insufficient to fully support the authors' conclusions, particularly given that cryo-EM is the sole experimental approach presented in the manuscript.

The study is biologically significant; however, the authors should improve the resolution or include complementary biochemical validation.

We thank Reviewer 3 for acknowledging the technical challenges in this study and finding our study biologically significant. We understand the concerns related to low resolution and the requirement of complementary biochemical validation for our reported observations and interpretations in the manuscript. We are attempting to improve the resolution and complement the interpretations with biochemical experiments.

eLife Assessment

This valuable investigation provides new and solid evidence for a specific cognitive deficit in cerebellar degeneration patients. The authors use three tasks that modulate complexity and violations of cognitive expectations. They show specific slowing of reaction times in the presence of violations but not with task complexity. While some alternative interpretations of the results are possible and are discussed, the work provides a new, invaluable data point in describing the cognitive contribution of cerebellar processing.

Reviewer #1 (Public review):

Summary:

The authors test the hypothesis that the contribution of the cerebellum to cognitive tasks is similar to motor tasks, and is related to the processing of prediction errors (here: violation of expectations, VE). In three experiments, they find that cerebellar patients show differences compared to controls in measures of VE, but not task complexity. The findings show that cerebellar disease results in deficits in VE processing in cognitive tasks, and makes a valuable contribution of the field. The authors were able to test a large number of patients with cerebellar disease which is known to primarily affect the cerebellum (i.e. SCA6).

Strengths:

A strength of the study is that it is hypothesis-driven and that the three experiments are very well thought out. Furthermore, a comparatively large group of patients with spinocerebellar ataxia type 6 (SCA6) was tested, a disease which affects primarily the cerebellum.

Weaknesses:

- Acquisition of brain MRI scans would have been useful to perform lesion-behaviour-mapping. But this does not limit the significance of the behavioural findings.<br /> - Exp. 1 and 2: The lack of difference in accuracy was that an unexpected finding? How meaningful are the used paradigms when accuracy was the same in cerebellar patients and controls?<br /> - Exp. 1 and 2: Cerebellar patients have motor dysfunction which impacts reaction time. Can the authors exclude that this contributed at least in part to their findings? Any correlations to SARA score (upper limb function) or oculomotor dysfunction (e.g. presence of nystagmus)?<br /> - Data on the attention probes which have been done would be of interest. Were there any differences in attention between patients and controls, any correlations with the findings?

Comments on revisions:

I am not sure if I can follow the interpretation of the authors that the cerebellum contributes to prediction errors, but not predictions; These two are tightly connected? It may rather be that in patients with slowly progressive chronic disease there is a lot of compensation? It is not so rare that in cognitive tasks cerebellar patients do not perform differently from controls, even though one would expect a difference (e.g. based on fMRI data in controls)? Another factor which likely adds is age, Patients and controls are often middle-aged and elderly, adding to variability, decreasing the chance to see group differences?

Author response:

The following is the authors’ response to the original reviews

Joint Public Review:

Summary:

In this study, Daniel et al. used three cognitive tasks to investigate behavioral signatures of cerebellar degeneration. In the first two tasks, the authors found that if an equation was incorrect, reaction times slowed significantly more for cerebellar patients than for healthy controls. In comparison, the slowing in the reaction times when the task required more operations was comparable to normal controls. In the third task, the authors show increased errors in cerebellar patients when they had to judge whether a letter string corresponded to an artificial grammar.

Strengths:

Overall, the work is methodologically sound and the manuscript well written. The data do show some evidence for specific cognitive deficits in cerebellar degeneration patients.

Thank you for the thoughtful summary and constructive feedback. We are pleased that the methodological rigor and clarity of the manuscript were appreciated, and that the data were recognized as providing meaningful evidence regarding cognitive deficits in cerebellar degeneration.

Weaknesses:

The current version has some weaknesses in the visual presentation of results. Overall, the study lacks a more precise discussion on how the patterns of deficits relate to the hypothesized cerebellar function. The reviewers and the editor agreed that the data are interesting and point to a specific cognitive deficit in cerebellar patients. However, in the discussion, we were somewhat confused about the interpretation of the result: If the cerebellum (as proposed in the introduction) is involved in forming expectations in a cognitive task, should they not show problems both in the expected (1+3 =4) and unexpected (1+3=2) conditions? Without having formed the correct expectation, how can you correctly say "yes" in the expected condition? No increase in error rate is observed - just slowing in the unexpected condition. But this increase in error rate was not observed. If the patients make up for the lack of prediction by using some other strategy, why are they only slowing in the unexpected case? If the cerebellum is NOT involved in making the prediction, but only involved in detecting the mismatch between predicted and real outcome, why would the patients not show specifically more errors in the unexpected condition?

Thank you for asking these important questions and initiating an interesting discussion. While decision errors and processing efficiency are not fully orthogonal and are likely related, they are not necessarily the same internal construct. The data from Experiments 1 and 2 suggest impaired processing efficiency rather than increased decision error. Reaction time slowing without increased error rates suggests that the CA group can form expectations but respond more slowly, possibly due to reduced processing efficiency. Thus, this analysis of our data suggests that the cerebellum is not essential for forming expectations, but it plays a critical role in processing their violations.

Relatedly, a few important questions remain open in the literature concerning the cerebellum’s role in expectation-related processes. The first is whether the cerebellum contributes to the formation of expectations or the processing of their violations. In Experiments 1 and 2, the CA group did not show impairments in the complexity manipulation. Solving these problems requires the formation of expectations during the reasoning process. Given the intact performance of the CA group, these results suggest that they are not impaired in forming expectations. However, in both Experiments 1 and 2, patients exhibited selective impairments in solving incorrect problems compared to correct problems. Since expectation formation is required in both conditions, but only incorrect problems involve a VE, we hypothesize that the cerebellum is involved in VE processes. We suggest that the CA group can form expectations in familiar tasks, but are impaired in processing unexpected compared to expected outcomes. This supports the notion that the cerebellum contributes to VE, rather than to forming expectations.

In Experiment 3, during training, the participant is learning a novel rule (grammar), forming new expectations on how strings of letters should be. Afterwards, during testing, the participant is requested to identify if a novel string is following the rule or not. We examined sensitivity to distinguish between grammatical and non‐grammatical strings of letters, thus taking into account a baseline ability to identify expected strings. Additionally, both in the low‐similarity and highsimilarity conditions, there are expectations regarding whether the strings are following the rule or not. However, in the high‐similarity condition, there is more uncertainty regarding which strings are following the grammatical rule, as demonstrated in a lower sensitivity (d prime). Given the group differences only in the low similarity condition, these results suggest the CA group is impaired only when the rules are more certain. Given these results, we suggest that forming cognitive expectations is not necessarily dependent on the cerebellum. Rather, we propose that the cerebellum is critical for processing rule-based VE (detection or processing of detected errors) under conditions of more certainty. One remaining question for future studies is whether the cerebellum contributes to detection of a mismatch between the expectation and sensory evidence, or the processing of a detected VE. 

We suggest that these key questions are relevant to both motor and non-motor domains and were not fully addressed even in the previous, well-studied motor domain. Importantly, while previous experimental manipulations17,19,40,94–96 have provided important insights regarding the cerebellar role in these processes, some may have confounded these internal constructs due to task design limitations (e.g., lack of baseline conditions). Notably, some of these previous studies did not include control conditions, such as correct trials, where there was no VE. In addition, other studies did not include a control measure (e.g., complexity effect), which limits their ability to infer the specific cerebellar role in expectation manipulation. 

Thus, the current experimental design used in three different experiments provides a valuable novel experimental perspective, allowing us to distinguish between some, but not all, of the processes involved in the formation of expectations and their violations. For instance, to our knowledge, this is the first study to demonstrate a selective impairment in rule-based VE processing in cerebellar patients across both numerical reasoning and artificial grammar tasks. If feasible, we propose that future studies should disentangle different forms of VE by operationalizing them in experimental tasks in an orthogonal manner. This will allow us to achieve a more detailed and well-defined cerebellar motor and non-motor mechanistic account.

Recommendations for the authors:

Editors comments:

The Figures are somewhat sub-standard and should be improved before the paper is made the VOR. Ensure consistent ordering of the group factor (CA, NT) and experimental factor across Figure 3,4, and 6 (panels A). Having the patient group as columns in Figure 4a and in rows in Figure 6a is very confusing.

We have standardized the layout across Figures 2, 4, and 6 so that the group factor (CA, NT) and experimental conditions are consistently ordered. In all panels, the group factor now appears as a column.

Subpanels should be numbered A,B,C... not A, B1, B2.

Subpanel labels have been updated to follow the standard A, B, C format across all figures.

Fonts should have a 100% aspect ratio - they should not be stretched (Figure 6B).

We have corrected the font aspect ratios in all figures (e.g., Figure 6B) to ensure proper proportions and readability. 

Colors should be more suitable to print - use a CYMK color scheme (i.e. avoid neon colors such as the neon green for the CA).

The color scheme across all figures has been revised to be print-friendly using CMYKcompatible, colorblind-accessible palettes. Neon green for the CA group was replaced with a more muted, distinguishable color.

Abstract: "The CA group exhibited a disproportionate cost when comparing expected problems compared to unexpected problems" - I recommend switching unexpected and expected, as the disproportional cost in on the former.

We have changed the wording of the sentence accordingly. 

Upon re-reading the details for the AGL task were not clear to us. Please do not rely on the reference (78) for the details - your paper should contain enough information to have the reader understand the experimental details. For you to appreciate the depth of our not-understanding, here a simple question: The test strings either followed the grammar in Fig 5 or they did not. If they did not, how exactly was similarity to the grammar measured? If they did, what was the difference between the “Grammatical-high” and “Grammatical-low” trials? If the string was grammatical, there should not be a notion of similarity, no? Or where these trials arbitrary split in half? 

We have clarified that 50% of the test strings followed the grammar of the training strings. We also elaborated on the calculation of chunk strength as a measure of similarity between the training and testing strings, similar to the previous papers. The differences between low and high similarity are explained in the paper. Specifically, for each test string, we calculated chunk strength by summing the frequencies of all relevant substrings (e.g., bigrams and trigrams) that appeared in the training set. The test strings whose chunk‐strength values fell above the median for grammatical items were classified as “high similarity,” while those falling below the median were classified as “low similarity.” Also, grammatical strings can be of both low and high similarity; this is precisely the beautiful aspect of this experimental manipulation, showing the importance of uncertainty. We have utilized a 2 × 2 fully orthogonal design (grammaticality × similarity).

Experimental details of the task should be added to the Method section. In the results you should only mention the experimental details that are necessary for understanding the experiments, but details such as the number of trials, etc, can be moved to the methods. 

We have now moved the experimental task details to the Method sections.

Reviewer #1 (Recommendations for the author):

Studies have been done online and not in the lab. Could that have affected the results?

We addressed this in the Methods section, referring to established protocols for online neuropsychological testing[9–12]. Our results align with similar in-lab findings in both the subtraction and AGL tasks, supporting the online approach's robustness. 

Figure 2, B1; Figure 4, B1; Figure 6B: How many patients performed worse than the (worst-performing) controls? There appears to be quite some overlap between patients and controls. In the patients who performed worse, was there any difference from the other patients (e.g. disease severity as assessed by SARA score, repeat length, data of attention probes)?

We appreciate the reviewer’s thoughtful comment. We considered conducting individual-level comparisons to identify patients who performed worse than the lowest-performing controls. However, defining "worse" based on the performance of the lowest control is only one possible criterion. Other definitions—such as a specific number (1/2/3?) of standard deviations below the control mean—are also commonly used in literature, and each may yield different conclusions. This variability highlights the lack of a standardized threshold for what constitutes “worse” or "impaired" performance at the individual level. Given this ambiguity, and in line with prior studies that focus on average group differences rather than “impairment” prevalence, we chose not to include these individual-level comparisons. We believe this approach better aligns with the goals and design of the current study. That said, we agree that examining individual variability is important and may be more appropriate in future studies with larger samples so that percentage is a more robust measure. However, given the rarity of the disease, this would also be a challenge for future studies.  

SARA ataxia scale does not include oculomotor function. In SCA6 oculomotor deficits are frequent, eg, downbeat nystagmus. Please include information on oculomotor dysfunction.

We thank the reviewer for this important observation. While it is true that the SARA scale does not explicitly assess oculomotor function, our experimental design – in all three experiments – has control conditions that help account for general processing differences, including those that could arise from oculomotor deficits. These conditions, such as the correct trials and the complexity effects, allow us to isolate effects specifically related to the violation of expectation while minimizing the influence of broader performance factors, such as eye movement abnormalities. We also note that, while some patients can experience oculomotor symptoms such as downbeat nystagmus, none of our tasks required precise visual tracking or gaze shifts. In our experimental tasks, stimuli were centrally presented, and no visual tracking or saccadic responses were required. Moreover, the response time windows and stimulus durations (>2–5 s) were sufficient to mitigate the effects of delayed visual processing due to oculomotor impairment.

Why was MoCA used and not the CCAS-Schmahmann scale to assess cognitive function?

We selected the MoCA due to its broad clinical utility, time efficiency, and ability to detect mild cognitive impairment specifically in CA[101,102].  

Were there any signs of depression in the patient group that could have affected the results?

None of the patients had a clinical diagnosis of depression or were undergoing psychiatric treatment.  

Additionally, the interaction between group and expectancy was insignificant when RT was the depended vaibale .." = variable

This has been corrected to "variable" in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

The terms 'unexpected' and 'expected' conditions are confusing. [...] Terming this 'violation of expectation' seems unnecessarily complicated to me. 

We thank the reviewer for raising this important concern. We recognize that the terms "expected" and "unexpected" can be ambiguous without clarification, and that "violation of expectation" (VE) may initially appear unnecessary. Our choice to use VE terminology is grounded in an established theoretical framework that distinguishes between mere stimulus correctness and prediction mechanisms. Specifically, VE captures the internal processing of mismatches between anticipated and observed outcomes, which we believe is central to the cerebellar function under investigation. While simpler, technical alternatives (e.g., "correct" vs. "incorrect") could describe the stimuli, we find that VE more accurately reflects the mental constructs under study and is consistent with previous literature in both motor and cognitive domains. 

Both tasks provide an error (or violation of expectation) that is non-informative and therefore unlikely to be used to update a forward model. The authors draw on motor literature to formulate a cognitive task where the presence of an error would engage the cerebellum and lead to longer reaction times in cerebellar patients. But in the motor domain, mismatch of sensory feedback and expectations would lead to an updating of the internal forward model. It seems unlikely to me in the arithmetic and alphabetic addition tasks that patients would update their internal model of addition according to an error presented at the end of each trial. If the error processed in these tasks will not lead to the updating of the internal forward model, can the authors discuss to what extent the cerebellum will be engaged similarly in these tasks, and what exactly connects cerebellar processing in these motor and cognitive tasks.

We thank the reviewer for this thoughtful and important comment. We fully agree that the current tasks do not directly probe learning-related updating of internal models. As stated in the paper, the goal of the present study was not to support or refute a specific claim regarding the cerebellum’s role in learning processes. Rather, our focus was on examining cerebellar involvement in the processing of VE. While we were inspired by models from the motor domain, our design was not intended to induce learning or adaptation per se, but to isolate the processing of unexpected outcomes. We agree that the tasks in their current form are unlikely to engage forward model updating in the same way as in sensorimotor adaptation paradigms. That said, we believe the current findings can serve as a basis for future research exploring the relationship between cerebellar prediction error processing and learning over time. As we also noted in the paper, this is a direction we propose, and actively pursuing, in ongoing research work.

The colour scheme is difficult for anyone with colour blindness or red-green visual impairment. Please adjust.

All figures have been revised to use CMYK-compatible, colorblind-safe palettes, and neon colors have been removed.

The introduction is a bit difficult to understand, because the authors draw on a number of different theories about cerebellar functioning, without clearly delineating how these relate to each other. For example: a) In the paragraph beginning with 'notably': If the cerebellum is required for sequential operations, why does it show the impairment with the rotation of the letters?

We understand the concern that if the cerebellum is involved in sequential operations, its involvement in mental letter rotation, which can be assumed as “continuous transformation,” may appear contradictory. We note that the boundary between continuous and stepwise, procedural operations is not always clear-cut and may vary depending on the participant's strategy or previous knowledge, which is not fully known to the researchers. Furthermore, to our knowledge, prior work on mental rotation has not directly investigated the impact of VE during this task. However, these are two debatable considerations. 

More importantly, a careful reading of our paper suggests that our experiments were designed to examine VE within tasks that involve sequential processing. Notably, we are not claiming that the cerebellum is involved in sequential or procedural processing per se. Rather, our findings point to a more specific role for the cerebellum in processing VE that arises during the construction of multistep procedural tasks. In fact, the results indicate that while the cerebellum may not be directly involved in the procedural process itself, it is critical when expectations are violated within such a context. This distinction is made possible in our study by the inclusion of a control condition (the complexity effect), which allows for a unique dissociation in our experimental design—one that, to our knowledge, has not been sufficiently addressed in previous studies.

Additionally, in the case of arithmetic problem solving—such as the tasks used in prior studies cited in our manuscript21—there is substantial evidence that these problems are typically solved through stepwise, procedural operations. Arithmetic reasoning, used in Experiments 1 and 2, has been robustly associated with procedural, multi-step strategies, which may be more clearly aligned with traditional views of cerebellar involvement in sequential operations. Thus, we propose that the role of the cerebellum in continuous transformations should be further examined. 

We suggest a more parsimonious theory that the cerebellum contributes to VE,  a field that was highly examined before. Yet, to reconcile ours and previous findings, we propose that the cerebellum’s contribution may not be limited to either continuous or stepwise operations per se, but rather to a domain-general process: the processing of VE. This theoretical framework can explain performance patterns across both mental rotation tasks and stepwise, procedural arithmetic.   

The authors mention generation prediction as a function of the cerebellum, processing of prediction errors (or violations of expectations), sequentially, and continuous transformations - but it is unclear whether the authors are trying to dissociate these from each other or whether ALL of these functions have informed task design.

We propose that the cerebellum’s contribution may not be limited to either continuous transformations or stepwise, procedural operations per se, but rather to a domain-general process: the processing of VE. We would like to clarify that we do not claim the cerebellum contributes to continuous transformations only, as suggested in some earlier work[21]. Rather, it could be that the cerebellum may contribute to continuous transformations, but we propose that it also supports multi-step, procedural processes. Given that framework, in the current study, across three separate experiments, we demonstrated that the cerebellum can also contribute to procedural, multi-step reasoning tasks.  

Minor Comments

Typo under paragraph beginning with 'notably' - cerebellum role should be cerebellar role.

Corrected as suggested.

When mentioning sequences as a recruiting feature for the cerebellum in the introduction, Van Overwalle's extensive work in the social domain should be referenced for completeness.

Thank you for the suggestion. We have now cited Van Overwalle’s work on cerebellar involvement in sequence processing within the social domain in the revised Introduction.

eLife Assessment

This study provides fundamental insights into eukaryotic phosphate homeostasis by demonstrating how yeast vacuoles dynamically regulate cytosolic phosphate levels. The conclusions are convincing, supported by an elegant combination of in vitro assays and in vivo measurements. This study will be of interest to cell biologists, particularly for those who are working in the field of phosphate metabolism.

Reviewer #1 (Public review):

The manuscript by Bru et al. focuses on the role of vacuoles as a phosphate buffering system for yeast cells. The authors describe here the crosstalk between the vacuole and the cytosol using a combination of in vitro analyses of vacuoles and in vivo assays. They show that the luminal polyphosphatases of the vacuole can hydrolyze polyphosphates to generate inorganic phosphate, yet they are inhibited by high concentrations. This balances the synthesis of polyphosphates against the inorganic phosphate pool. Their data further show that the Pho91 transporter provides a valve for the cytosol as it gets activated by a decline in inositol pyrophosphate levels. The authors thus demonstrate how the vacuole functions as a phosphate buffering system to maintain a constant cytosolic inorganic phosphate pool.

This is a very consistent and well-written manuscript with a number of convincing experiments, where the authors use isolated vacuoles and cellular read-out systems to demonstrate the interplay of polyphosphate synthesis, hydrolysis, and release. The beauty of this system the authors present is the clear correlation between product inhibition and the role of Pho91 as a valve to release Pi to the cytosol to replenish the cytosolic pool. I find the paper overall an excellent fit and only have a few issues, including :

(1) Figure 3: The authors use in their assays 1 mM ZnCl2 or 1mM MgCl2. Is this concentration in the range of the vacuolar luminal ion concentration? Did they also test the effect of Ca2+, as this ion is also highly concentrated in the lumen?

(2) Regarding the concentration of 30 mM K-PI, did the authors also use higher and lower concentrations? I agree that there is inhibition by 30 mM, but they cannot derive conclusions on the luminal concentration if they use just one in their assay. A titration is necessary here.

(3) What are the consequences on vacuole morphology if the cells lack Pho91?

(4) Discussion: The authors do not refer to the effect of calcium, even though I would expect that the levels of the counterion should affect the phosphate metabolism. I would appreciate it if they would extend their discussion accordingly.

(5) I would appreciate a brief discussion on how phosphate sensing and control are done in human cells. Do they use a similar lysosomal buffer system?

Reviewer #2 (Public review):

Summary:

This manuscript presents a well-conceived and concise study that significantly advances our understanding of polyphosphate (polyP) metabolism and its role in cytosolic phosphate (Pi) homeostasis in a model unicellular eukaryote. The authors provide evidence that yeast vacuoles function as dynamic regulatory buffers for Pi homeostasis, integrating polyP synthesis, storage, and hydrolysis in response to cellular metabolic demands. The work is methodologically sound and offers valuable insights into the conserved mechanisms of phosphate regulation across eukaryotes.

Strengths:

The results demonstrate that the vacuolar transporter chaperone (VTC) complex, in conjunction with luminal polyphosphatases (Ppn1/Ppn2) and the Pi exporter Pho91, establishes a finely tuned feedback system that balances cytosolic Pi levels. Under Pi-replete conditions, inositol pyrophosphates (InsPPs) promote polyP synthesis and storage while inhibiting polyP hydrolysis, leading to vacuolar Pi accumulation.

Conversely, Pi scarcity triggers InsPP depletion, activating Pho91-mediated Pi export and polyP mobilization to sustain cytosolic phosphate levels. This regulatory circuit ensures metabolic flexibility, particularly during critical processes such as glycolysis, nucleotide synthesis, and cell cycle progression, where phosphate demand fluctuates dramatically.

From my viewpoint, one of the most important findings is the demonstration that vacuoles act as a rapidly accessible Pi reservoir, capable of switching between storage (as polyP) and release (as free Pi) in response to metabolic cues. The energetic cost of polyP synthesis-driven by ATP and the vacuolar proton gradient-highlights the evolutionary importance of this buffering system. The study also draws parallels between yeast vacuoles and acidocalcisomes in other eukaryotes, such as Trypanosoma and Chlamydomonas, suggesting a conserved role for these organelles in phosphate homeostasis.

Weaknesses:

While the manuscript is highly insightful, referring to yeast vacuoles as "acidocalcisome-like" may warrant further discussion. Canonical acidocalcisomes are structurally and chemically distinct (e.g., electron-dense, in most cases spherical, and not routinely subjected to morphological changes, and enriched with specific ions), whereas yeast vacuoles have well-established roles beyond phosphate storage. A comment on this terminology could strengthen the comparative analysis and avoid potential confusion in the field.

Reviewer #3 (Public review):

Bru et al. investigated how inorganic phosphate (Pi) is buffered in cells using S. cerevisiae as a model. Pi is stored in cells in the form of polyphosphates in acidocalcisomes. In S. cerevisiae, the vacuole, which is the yeast lysosome, also fulfills the function of Pi storage organelle. Therefore, yeast is an ideal system to study Pi storage and mobilization.

They can recapitulate in their previously established system, using isolated yeast vacuoles, findings from their own and other groups. They integrate the available data and propose a working model of feedback loops to control the level of Pi on the cellular level.

This is a solid study, in which the biological significance of their findings is not entirely clear. The data analysis and statistical significance need to be improved and included, respectively. The manuscript would have benefited from rigorously testing the model, which would also have increased the impact of the study.

Author response:

Reviewer #1 (Public review): 

The manuscript by Bru et al. focuses on the role of vacuoles as a phosphate buffering system for yeast cells. The authors describe here the crosstalk between the vacuole and the cytosol using a combination of in vitro analyses of vacuoles and in vivo assays. They show that the luminal polyphosphatases of the vacuole can hydrolyse polyphosphates to generate inorganic phosphate, yet they are inhibited by high concentrations. This balances the synthesis of polyphosphates against the inorganic phosphate pool. Their data further show that the Pho91 transporter provides a valve for the cytosol as it gets activated by a decline in inositol pyrophosphate levels. The authors thus demonstrate how the vacuole functions as a phosphate buffering system to maintain a constant cytosolic inorganic phosphate pool. 

This is a very consistent and well-written manuscript with a number of convincing experiments, where the authors use isolated vacuoles and cellular read-out systems to demonstrate the interplay of polyphosphate synthesis, hydrolysis, and release. The beauty of this system the authors present is the clear correlation between product inhibition and the role of Pho91 as a valve to release Pi to the cytosol to replenish the cytosolic pool. I find the paper overall an excellent fit and only have a few issues, including: 

(1) Figure 3: The authors use in their assays 1 mM ZnCl2 or 1mM MgCl2. Is this concentration in the range of the vacuolar luminal ion concentration? Did they also test the effect of Ca2+, as this ion is also highly concentrated in the lumen? 

The concentrations inside vacuoles can reach those values. However, given that polyP is a potent chelator of divalent metal ions, what would matter are the concentrations of free Zn²⁺ or Mg²⁺ inside the organelle. These are not known. This is not critical since we use those two conditions only as a convenient tool to differentiate Ppn1 and Ppn2 activity in vitro. In our initial characterisation of Ppn2 (10.1242/jcs.201061), we had also tested Mn, Co, Ca, Ni, Cu. Only Zn and Co supported activity. Ca did not. Andreeva et al. (10.1016/j.biochi.2019.06.001) reached similar conclusions and extended our results.

(2) Regarding the concentration of 30 mM K-PI, did the authors also use higher and lower concentrations? I agree that there is inhibition by 30 mM, but they cannot derive conclusions on the luminal concentration if they use just one in their assay. A titration is necessary here. 

The concentration of 30 mM was not arbitrarily chosen. It is the luminal P_i concentration that the vacuoles could reach through when they entered a plateau of luminal Pi. We consider this as an upper limit because polyP kept increasing which luminal P_i did not. Thus, there is in principle no physiological motivation for trying higher values. But we will probably add a titration to the revised version.

(3) What are the consequences on vacuole morphology if the cells lack Pho91? 

We had not observed significant abnormalities during a screen of the genome-wide deletion collection of yeast (10.1371/journal.pone.0054160)

(4) Discussion: The authors do not refer to the effect of calcium, even though I would expect that the levels of the counterion should affect the phosphate metabolism. I would appreciate it if they would extend their discussion accordingly. 

We will pick this up in the discussion. However, the situation is much more complex because major pools of counterions (up to hundreds of mM) are constituted by vacuolar lysine, arginine, polyamines, Mg, Zn etc. Their interplay with polyP is probably complex and worth to be treated in a dedicated project.

(5) I would appreciate a brief discussion on how phosphate sensing and control are done in human cells. Do they use a similar lysosomal buffer system? 

Mammalian cells have their Pi exporter XPR1 mainly on a lysosome-like compartment (10.1016/j.celrep.2024.114316). Whether and how it functions there for Pi export from the cytosol is not entirely clear. We will address this situation in the revision.

Reviewer #2 (Public review): 

Summary: 

This manuscript presents a well-conceived and concise study that significantly advances our understanding of polyphosphate (polyP) metabolism and its role in cytosolic phosphate (Pi) homeostasis in a model unicellular eukaryote. The authors provide evidence that yeast vacuoles function as dynamic regulatory buffers for Pi homeostasis, integrating polyP synthesis, storage, and hydrolysis in response to cellular metabolic demands. The work is methodologically sound and offers valuable insights into the conserved mechanisms of phosphate regulation across eukaryotes. 

Strengths: 

The results demonstrate that the vacuolar transporter chaperone (VTC) complex, in conjunction with luminal polyphosphatases (Ppn1/Ppn2) and the Pi exporter Pho91, establishes a finely tuned feedback system that balances cytosolic Pi levels. Under Pi-replete conditions, inositol pyrophosphates (InsPPs) promote polyP synthesis and storage while inhibiting polyP hydrolysis, leading to vacuolar Pi accumulation. 

Conversely, Pi scarcity triggers InsPP depletion, activating Pho91-mediated Pi export and polyP mobilization to sustain cytosolic phosphate levels. This regulatory circuit ensures metabolic flexibility, particularly during critical processes such as glycolysis, nucleotide synthesis, and cell cycle progression, where phosphate demand fluctuates dramatically. 

From my viewpoint, one of the most important findings is the demonstration that vacuoles act as a rapidly accessible Pi reservoir, capable of switching between storage (as polyP) and release (as free Pi) in response to metabolic cues. The energetic cost of polyP synthesis-driven by ATP and the vacuolar proton gradient-highlights the evolutionary importance of this buffering system. The study also draws parallels between yeast vacuoles and acidocalcisomes in other eukaryotes, such as Trypanosoma and Chlamydomonas, suggesting a conserved role for these organelles in phosphate homeostasis. 

Weaknesses: 

While the manuscript is highly insightful, referring to yeast vacuoles as "acidocalcisome-like" may warrant further discussion. Canonical acidocalcisomes are structurally and chemically distinct (e.g., electron-dense, in most cases spherical, and not routinely subjected to morphological changes, and enriched with specific ions), whereas yeast vacuoles have well-established roles beyond phosphate storage. A comment on this terminology could strengthen the comparative analysis and avoid potential confusion in the field. 

Yeast vacuoles show all major chemical features of acidocalcisomes. They are acidified, contain high concentrations of Ca, polyP (which make them electron-dense, too), other divalent ions, such as Mg, Zn, Mn etc, and high concentrations of basic amino acids. Thus, they clearly have an acidocalcisome-like character. In addition, they have hydrolytic, lysosome-like functions and, depending on the strain background, they can be larger than acidocalcisomes described e.g. in protists. We will elaborate this point, which is obvious to us but probably not to most readers, in the revised version.

Reviewer #3 (Public review): 

Bru et al. investigated how inorganic phosphate (Pi) is buffered in cells using S. cerevisiae as a model. Pi is stored in cells in the form of polyphosphates in acidocalcisomes. In S. cerevisiae, the vacuole, which is the yeast lysosome, also fulfills the function of Pi storage organelle. Therefore, yeast is an ideal system to study Pi storage and mobilization. 

They can recapitulate in their previously established system, using isolated yeast vacuoles, findings from their own and other groups. They integrate the available data and propose a working model of feedback loops to control the level of Pi on the cellular level. 

This is a solid study, in which the biological significance of their findings is not entirely clear. The data analysis and statistical significance need to be improved and included, respectively. The manuscript would have benefited from rigorously testing the model, which would also have increased the impact of the study.

It is not clear to us what the reviewer would see as a more rigorous test of the model.

eLife Assessment

This important study suggests that adolescent mice exhibit less accuracy than adult mice in a sound discrimination task when the sound frequencies are very similar. The evidence supporting this observation is solid and suggests that it arises from cognitive control differences between adolescent and adult mice. The adolescent period is largely understudied, despite its contribution to shaping the adult brain, which makes this study interesting for a broad range of neuroscientists.

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

The weaknesses listed by this reviewer were addressed by adequate revisions.

Author response:

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

Reviewer #1 (Public review):

Summary:

Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.

Strengths:

The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.

The analysis, including the systematic comparison of task performance across the two age groups, is most interesting, and reveals differences in learning (or learning strategies?) that are compelling.

Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.

Weaknesses:

The presentation of the paper must be strengthened. Inconsistencies, missing information or confusing descriptions should be fixed.

We have carefully re-read the manuscript and reviewed it for inconsistencies. We made several corrections in the figures. For example, we removed redundant lines from violin plots and statistics, applied consistent labels, matched y- and x-limits of graphics, and adjusted labels. We also clarified descriptions of some experiment by adding explanations to the text.

The recording electrodes cover regions in the primary and secondary cortices. It is well known that these two regions process sounds quite differently (for example, one has tonotopy, the other not), and separating recordings from both regions is important to conclude anything about sound representations. The authors show that the conclusions are the same across regions for Figure 4, but is it also the case for the subsequent analysis? Comparing to the original manuscript, the authors have now done the analysis for AuDp and AUDv separately, and say that the differences are similar in both regions. The data however shows that this is not the case (Fig S7). And even if it were the case, how would it compatible with the published literature?

To address this and previous concerns about regional differences, the manuscript now includes 4 figures (4-1, 4-3, 6-2, 7-1) and 5 supplemental tables (3,4, 5, 6, 8) that explicitly compare results across brain regions.

Following the reviewer’s request for subsequent analysis, we now added a new supplemental figure (Fig. S6-2) and two new supplementary tables (Tables S5, S6). We show that similar to expert mice (supplementary Table 3, and supplementary Table 4), the firing properties of adolescent and adult novice mice differ across auditory subregions (supplementary Table 5). We also show that the different auditory subregions have different firing properties (supplementary Table 6). With respect to task engagement, we show that (similar to Fig. S4-2) the neuronal discriminability in different auditory subregions is similar in both novice and expert mice (Fig. S6-2).

Following the comment on Fig. S7-1, we made three changes to the revised manuscript. First, we now highlight that the differences firing properties between adolescent and adult neurons in AUDp and AUDv were distinct, but not significantly different within age-group comparisons. Second, we clearly state that the learning related changes in the measured parameters are different between AUDp and AUDv. Note, however, the greater changes in adult neurons after learning remains consistent between AUDp and AUDv. Third, we softened our original claim but still highlighted the stronger learning-induced plasticity in adults.

Regarding the concern that different regions should show different patterns due to their known differences (e.g. tonotopy). Of course we agree that different areas differ functionally (as shown in our own previous work and here as well). However, it is still plausible, and biologically reasonable, that developmental changes may proceed in a similar direction across different areas, even if their baseline coding properties differ.

Reviewer #2 (Public review):

Summary:

The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.

Strengths:

The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.

Weaknesses:

For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

We have carefully re-read the manuscript and reviewed it for analyses that lacked a clear rationale or conclusion. To address this, we have made several changes to clarify the reasoning and strengthen the interpretation of the results.

Reviewer #1 (Recommendations for the authors):

It would have helped if the authors had highlighted the changes they made to the manuscript compared to the original version - especially since many replies to the reviewers' comments were as vague as "...we fixed some of the wording so it adheres to the data shown", or "we refined our interpretation", without further details.

The revised version has improved substantially, and the main claims have been discussed in a more objective way. Important new analyses have been added to allow for a refined interpretation of the results. However, the presentation of the data could still be strengthened significantly (in response to comment A from last review).

We apologize for the lack of detail in some of our previous responses. Our intention was to keep the replies concise, assuming that the side-by-side version with tracked changes would make the edits sufficiently clear. However, we understand the need for greater transparency. Thus, below we provide the following five lists describing the major changes: (1) List of specific reviewer recommendations, (2) list of corrections in figures, (3) list of clarity issues, (4) list of fixed mistakes, (5) list of new figures. We hope this breakdown makes the revisions clearer and more accessible.

List of specific reviewer recommendations:

l.108 mentions a significant change in the vertical line of Fig 1F - Could this significance be indicated and quantified in the figure?

We quantified and indicated the significance of the vertical line in Fig. 1f and Fig. 1i.

Fig.1G - the thick and thin lines should be defined, as well as the grey and white dots (same values for adolescents, not for adults).

(a) We removed the thin inner lines from the violin plot. We define the bar (thick line) of the violin plot in an additional sentence in the methods section under data analysis (LL820-823). b) We adjusted the marker outlines in the adult data (Fig. 1G).

the figure axis legends should be consistent (trails in Fig D vs # trails in Fig 1F)

We adjusted the axis legend to # trials in Fig. 1D.

l.110: is d' always calculated based on the 100 last trials of a session, or is it just for Figure 1F? -etc...

d’ is always calculated based on the last 100 trials. To clarify this, we added a description in the methods section (L830).

List of corrections in the figures:

(1) We removed the internal lines from violin plots in throughout Fig. 1-7.

(2) We removed the underline of the statistics throughout Fig. 1-7.

(3) We consistently applied ‘adolescent’ and ‘adult’ figure labels and titles with lowercase letters throughout Fig. 1-7.

(4) We applied consistent labelling of ‘time (ms)’ throughout Fig. 1-7.

(5) We matched the size of dashed lines throughout Fig. 1-6.

(6) We adjusted the x-label of Fig. 1d, Fig. S-1-1 a, Fig. 3c, Fig. 3h-i, Fig, 4d to ‘# trials’.

(7) We removed the x-label of ‘Experimental Group’ from Fig. 1 to enhance consistency with other figures.

(8) We removed misaligned dots from the violin plots in Fig. 1g, Fig. 2f, Fig. 3f,g.

(9) We corrected the plot in Fig. S1-1b.

(10) We adjusted the y-limits of Fig. S1-1c to be consistent with Fig. S1-1d,e.

(11) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(12) We added the age of adolescent and adult mice to the schematic timeline in Fig. 2a.

(13) We added a label of the reinforcement delay to the schematic trial structure in Fig. 3b.

(14) We added within-group statistics to Fig. 3e and the figure legend.

(15) We adjusted the x-label of Fig. 3d to ‘# sessions’.

(16) We adjusted the x-label of Fig. 3d and Fig. S3-1b to ‘# licks’.

(17) We changed the y-label in Fig. S3-1a, and Fig. S3-2d, e to ‘lick ratio’ to avoid confusion with the lick rate (Hz) that was calculated in Fig. 4 and Fig. 6.

(18) We replaced the titles ‘CAMKII’ with ‘dTomato’ in Fig. S3-2 to correctly highlight that both the experimental and control injection were CAMKII injections.

(19) We adjusted the x-labels and y-labels of Fig, 2, Fig. S3-1, Fig, S3-2 and Fig. 3b to ‘freq. (kHz)’.

(20) We adjusted the y-label of Fig. S4-1c to ‘# neurons’.

(21) We matched the x-ticks in Fig. 4e,f.

(22) We matched the x-ticks in Fig. 6d-g.

(23) We changed the x-label in Fig. 4g, S4-2 and S6-2 to ‘duration (ms)’ to match the figure label with the manuscript.

(24) We consistently label ‘Hit’, ‘Miss’, ‘FA’ and ‘CR’ with capital letters in Fig. 4d-e.

(25) We replaced the double figure label ‘C.’ in Fig. S4-2 with ‘D.’.

(26) We adjusted the dot-size in Fig. 5 to be equal for all graphs.

(27) We added ticks to the experimental timeline in Fig. 6a.

(28) We corrected the y-label in Fig.7c. Now it correctly reflects 5 attenuations from 72-32 dB SPL.

(29) We matched the y-label of Fig. 7e-h and Fig. S7-1.

List of clarity issues:

(1) We replaced the term ‘lower response bias’ with ‘higher lick bias’ (L24) to accurately describe the more negative (lower) criterion-bias, which highlights a higher tendency to lick.

(2) We replaced the term ‘response bias’ with ‘lick bias’ to consistently describe the calculated criterion-bias (L24, L149, L164, L455, L456, L468).

(3) We clarify that the age-related differences were ‘more pronounced’ instead of simply ‘higher’ to accurately reflect not simply the increase in adolescent lick-bias, but also the decrease in adult lick-bias (L31).

(4) We clarified that adolescent sound representations are not merely ’distinct’, but ‘not fully mature’ in L83.

(5) We clarified in L180 that the impulsive responses we observed in adolescent mice could be related to being ‘less impacted by punishments’.

(6) We clarified the differences in firing properties of auditory sub-regions analyzed in Supplementary Table 3 (L287-295).

(7) We explained and clarified the reference to Fig. 3j (LL252-253).

(8) We added statistics to Fig.S4-2 to support our claim that there are no differences in the onset-latency, duration of discriminability and maximal discriminability between different sub-regions within age-groups (LL 314-315).

(9) We expanded our explanation of the results in Table 3 (LL370-379).

(10) We separated the reference to Fig. 6b and Fig. 6c to clarify their meaning (LL358-361).

(11) We clarified the differences in basic firing properties during the FRA protocol in Fig. 7 (LL409-418).

(12) We expanded our explanation of the differences of the learning related firing properties in AUDp and AUDv of Fig. S7-1 (LL426-433).

(13) We changed the term ‘plasticity profiles’ to ‘learning related plasticity’ to further clarify our limitation that L5/6 and L2/3 may exhibit distinct learning related changes (L496).

(14) We changed the term ‘sluggish’ (L481) to ‘delayed’ to more precisely explain differences between adolescent and adult tuning properties.

(15) We clarified that the running d’ was calculated in bins of 25 trials, instead of ‘the last 25 trials’ (LL845-846).

List of fixed mistakes:

(1) We corrected and matched the age to more accurately reflect the age mice were recorded (P37-42 and P77-82).

(2) We corrected the attenuation range from 72-42 to 72-32 dB SPL to correctly reflect the 5 attenuations used in the protocol.

(3) We corrected the number of channels shown in the voltage trace from 10 to 11 (Fig. S4-1a)

(4) We corrected the number of neurons recorded in novice adolescent mice in the legend of Fig. 6 from 140 to 130 (Fig. 6b).

(5) We removed redundant, or double brackets, commas, dots, and semi-colons in the figure legends.

(6) We corrected the LME statistics Table 2.

List of new figures and tables:

(1) We added a new supplementary figure to accompany Figure 6. Specifically, Fig. S6-2, shows the interaction of the three measured discriminability properties (onset delay, duration of discriminability, and maximal discriminability) in novice compared to expert mice in the easy and hard task (Go compared to No Go). The figure compares the different auditory sub-regions (similar to Fig. S4-2). We show that the discriminability properties within different groups is not significantly different among the four different sub-regions.

(2) Supplementary Table 5: We compared the firing properties in different auditory subregions in novice mice, and found (similar to expert mice) that the firing properties differ between adult and adolescent mice across the four different sub-regions.

(3) Supplementary Table 6: We compared the firing properties between different subregions, separately for adolescent and adult novice mice. Similar to expert mice, we found that different auditory subregions differ in their auditory firing properties.

Reviewer #2 (Recommendations for the authors):

The authors largely addressed my suggestions.

Comparing hit vs correct rejection trials in the population decoding analysis (L313-314): The authors acknowledge that comparing these two trial types conflates choice and stimulus decoding but I am not convinced that the changes to the manuscript text make this clear enough to the reader.

Thank you for pointing this out. We have made additional revisions to clarify this, and other issues more explicitly, as follows:

(1) We have expanded the explanation of how our population decoding analysis conflates stimulus and choice, and we acknowledge the limitations of this approach in the Abstract (L28), the Results section (L324-326, LL367-370) and the Discussion (LL516-519).

(2) We replaced the analysis of impulsivity on the head-fixed task. Instead of analyzing all it is, we focus only on ITIs following FA trials (Fig. S3-1c,d). This is more consistent with the analysis in the Educage (Fig. S2-1), where we show that adolescents exhibit increased impulsivity after FA trials. We found a similar result for ITIs following FA trials in the head-fixed task.

(3) To provide complementary insight, we now further justify our use of the Fisher separation metric alongside decoding accuracy in Figure 5, with a clearer rationale provided in LL343-345

(4) We also clarified our reasoning for focusing on 62 dB SPL in the FRA-based analysis in LL400-403.

eLife Assessment

This study presents a valuable finding on the representational structure of task encoding in the prefrontal cortex. The evidence supporting the claims of the authors is solid, representing an impressive data collection effort and best-practice fMRI analyses. However, at least including visual regions as a control and controlling for behavioral differences in the task in representation analyses would have strengthened the study. The work will be of interest to cognitive neuroscientists interested in the neural basis of cognitive control.

Reviewer #1 (Public review):

Summary:

Bhandari and colleagues present tour-de-force analyses that compare the representational geometry in the lateral prefrontal cortex and primary auditory cortex between two complex cognitive control tasks, with one having a "flat" structure where subjects are asked to form rote memory of all the stimulus-action mappings in the task and one having a "hierarchical" task structure that allows clustering of task conditions and that renders certain stimulus dimensions irrelevant for choices. They discovered that the lPFC geometry is high-dimensional in nature in that it allows above-chance separation between different dichotomies of task conditions. The separability is significantly higher for task-relevant features than task-irrelevant ones. They also found task features that are represented in an "abstract" format (e.g., audio features), i.e., the neural representation generalizes across specific task conditions that share this variable. The neural patterns in lPFC are highly relevant for behaviors as they are correlated with subjects' reaction times and choices.

Strengths:

Typically, geometry in coding patterns is reflected in single-unit firings; this manuscript demonstrates that such geometry can be recovered using fMRI BOLD signals, which is both surprising and important. The tasks are well designed and powerful in revealing the differences in neural geometry, and analyses are all done in a rigorous way. I am thus very enthusiastic about this paper and identify no major issues.

I am curious about the consequence of dimensionality collapse in lPFC. The authors propose a very interesting idea that separability is critical for cognitive control; indeed, separability is high for task-relevant information. What happens when task-relevant separation is low or task-irrelevant separation is high, and will this lead to behavioral errors? Maybe a difference score between the separability of task-relevant and task-irrelevant features is a signature of the strength of cognitive control?

Weaknesses:

The authors show a difference between flat and hierarchical tasks, but the two tasks are different in accuracy, with the flat task having more errors. Will this difference in task difficulty/errors contribute to the task differences in results reported?

Reviewer #2 (Public review):

Summary:

The authors study the influence of tasks on the representational geometry of the lPFC and auditory cortex (AC). In particular, they use two context-dependent tasks: a task with a hierarchical structure and a task with a flat structure, in which each context/stimulus maps to a specific response. Their primary finding is that the representational geometry in the lPFC, in contrast to AC, aligns with the optimal organization of the task. They conclude that the geometry of representations adapts, or is tailored, to the task in the lPFC, therefore supporting control processes.

Strengths:

(1) Dataset:<br /> The dataset is impressive and well-sampled. Having data from both tasks collected in the same subjects is a great property. If it is publicly available, it will be a significant contribution to the community.

(2) Choice of methods:<br /> The choice of analyses are largely well-suited towards the questions at hand - cross-condition generalization, RSA + regression, in combination with ANOVAs, are well-suited to characterizing task representations.

(3) I found some of their results, in particular, those presented in Figures 4 and 5, to be particularly compelling.

(4) The correlation analysis with behavior is also a nice result.

Weaknesses:

(1) Choice of ROIs:<br /> A strength of fMRI is its spatial coverage of the whole brain. In this study, however, the authors focus on only two ROIs: the lPFC and auditory cortex. Though I understand the justification for choosing lPFC from decades of research, the choice of AC as a control feels somewhat arbitrary - AC is known to have worse SNR in fMRI data, and limiting a 'control' to a single region seems arbitrary. For example, why not also include visual regions, given that the task also involves two visual features?

(2) Construction of ROIs:<br /> The choice and construction of the ROIs feel a bit arbitrary, as the lPFC region was constructed out of 10 parcels from Schaefer, while the AC was constructed from a different methodology (neurosynth). Did both parcels have the same number of voxels/vertices? It would be helpful to include a visualization of these masks as a figure.

(3) Task dimensionality:<br /> In some ways, the main findings - that representation dimensionality is tailored to the task - seem to obviously follow from the choice of two tasks, particularly from a normative modeling perspective. For example, the flat task is effectively a memorization task, and is incompressible in the sense that there are no heuristics to solve it. In contrast, the hierarchical task can have several strategies, an uncompressed (memorized) strategy, and a compressed strategy. This is analogous to other studies evaluating representations during 'rich' vs. 'lazy'/kernel learning in ANNs. However, it seems unlikely (if not impossible) to form a 'rich' representation in the flat task. Posed another way, the flat task will always necessarily have a higher dimensionality than the hierarchical task. Thus, is their hypothesis - that representational geometry is tailored to the task - actually falsifiable? I understand the authors posit alternative hypotheses, e.g., "a fully compressed global axis with no separation among individual stimulus inputs could support responding [in the flat task]" (p. 36). But is this a realistic outcome, for example, in the space of all possible computational models performing this task? I understand that directly addressing this comment is challenging (without additional data collection or modeling work), but perhaps some additional discussion around this would be helpful.

(4) Related to the above:<br /> The authors have a section on p. 27: "Local structure of lPFC representational geometry of the flat task shows high separability with no evidence for abstraction" - I understand a generalization analysis can be done in the feature space, but in practice, the fact that the flat task doubles as a memorization task implies that there are no useful abstractions, so it seems to trivially follow that there would be no abstract representations. In fact, the use of task abstractions in the stimulus space would be detrimental to task performance here. I could understand the use of this analysis as a control, but the phrasing of this section seems to indicate that this is a surprising result.

(5) Statistical inferences:<br /> Throughout the manuscript, the authors appear to conflate failure to reject the null with acceptance of the null. For example, p. 24: "However, unlike left lPFC, paired t-tests showed no reliable difference in the separability of the task-relevant features vs the orthogonal, task-irrelevant features... Therefore, the overall separability of pAC representations is not shaped by either task-relevance of task structure."

Reviewer #3 (Public review):

Summary:

In this paper, Bhandari, Keglovits, et al. explore the representational structure of task encoding in the lateral prefrontal cortex. Through an impressive fMRI data-collection effort, they compare and contrast neural representations across tasks with different high-level stimulus-response structures. They find that the lateral prefrontal cortex shows enhanced encoding of task-relevant information, but that most of these representations do not generalize across conditions (i.e., have low abstraction). This appears to be driven in part by the representation of task conditions being clustered by the higher-order task properties ('global' representations), with poor generalization across these clusters ('local' representations). Overall, this paper provides an interesting account of how task representations are encoded in the PFC.

Strengths:

(1) Impressive dataset, which may provide further opportunities for investigating prefrontal representations.

(2) Clever task design, allowing the authors to confound several features within a complex paradigm.

(3) Best-practice analysis for decoding, similarity analyses, and assessments of representational geometry.

(4) Extensive analyses to quantify the structure of PFC task representations.

Weaknesses:

(1) The paper would benefit from improved presentational clarity: more scaffolding of design and analysis decisions, clearer grounding to understand the high-level interpretations of the analyses (e.g., context, cluster, abstraction), and better visualizations of the key findings.

(2) The paper would benefit from stronger theoretical motivation for the experimental design, as well as a refined discussion on the implications of these findings for theories of cognitive control.

Author response:

We thank the reviewers and editors for their careful and constructive assessment of our manuscript. We have provided a provisional response to the eLife assessment and the reviewer’s public comments below, addressing their main concerns and outlining our planned revisions that we believe will substantially strengthen our paper.  

eLife Assessment

This study presents a valuable finding on the representational structure of task encoding in the prefrontal cortex. The evidence supporting the claims of the authors is solid, representing an impressive data collection effort and best-practice fMRI analyses. However, at least including visual regions as a control and controlling for behavioral differences in the task in representation analyses would have strengthened the study. The work will be of interest to cognitive neuroscientists interested in the neural basis of cognitive control.

We plan to address both specific methodological weaknesses mentioned in the assessment in our forthcoming revision. First, the revision will include analyses of an early visual cortex ROI as an additional control region, allowing us to test whether the primary auditory cortex findings generalize to the sensory cortex across input modalities. Preliminary results indicate that the early visual cortex ROI exhibits a similar pattern of results, with evidence for coding both task-relevant and task-irrelevant visual dimensions across both tasks, as well as the context dimension specifically in the hierarchy task. Second, we will include behavioral performance as a covariate for the relevant statistical comparison across tasks to mitigate concerns over performance-related confounds. In addition, we will include a set of control analyses that demonstrate that equating the amount of data for pattern analyses across the two tasks by subsampling from the hierarchy task, while reducing our overall power, does not appreciably alter our results. We note that our analyses of representational geometries relied only on neural data from correct trials and, in the first-level modelling of the fMRI data, already controlled for differences in trial-by-trial response times. Therefore, our analyses of decoding and representation similarity are not directly affected by differences in performance across the two tasks. Finally, we have provided clarifications regarding Reviewer 2’s questions about the size and construction of the regions of interest employed in the study, as well as about the language employed to discuss null results.  

Public Reviews:

Reviewer #1 (Public review):

Summary:

Bhandari and colleagues present tour-de-force analyses that compare the representational geometry in the lateral prefrontal cortex and primary auditory cortex between two complex cognitive control tasks, with one having a "flat" structure where subjects are asked to form rote memory of all the stimulus-action mappings in the task and one having a "hierarchical" task structure that allows clustering of task conditions and that renders certain stimulus dimensions irrelevant for choices. They discovered that the lPFC geometry is high-dimensional in nature in that it allows above-chance separation between different dichotomies of task conditions. The separability is significantly higher for task-relevant features than task-irrelevant ones. They also found task features that are represented in an "abstract" format (e.g., audio features), i.e., the neural representation generalizes across specific task conditions that share this variable. The neural patterns in lPFC are highly relevant for behaviors as they are correlated with subjects' reaction times and choices.

Strengths:

Typically, geometry in coding patterns is reflected in single-unit firings; this manuscript demonstrates that such geometry can be recovered using fMRI BOLD signals, which is both surprising and important. The tasks are well designed and powerful in revealing the differences in neural geometry, and analyses are all done in a rigorous way. I am thus very enthusiastic about this paper and identify no major issues.

I am curious about the consequence of dimensionality collapse in lPFC. The authors propose a very interesting idea that separability is critical for cognitive control; indeed, separability is high for task-relevant information. What happens when task-relevant separation is low or task-irrelevant separation is high, and will this lead to behavioral errors? Maybe a difference score between the separability of task-relevant and taskirrelevant features is a signature of the strength of cognitive control?

We appreciate the reviewers’ positive evaluation of our paper.

Weaknesses:

The authors show a difference between flat and hierarchical tasks, but the two tasks are different in accuracy, with the flat task having more errors. Will this difference in task difficulty/errors contribute to the task differences in results reported?

To address the Reviewer’s concern about the difference in behavioural performance between the two tasks influencing our results, we will take several approaches. First, we will include behavioral performance as a covariate for the relevant statistical comparison across tasks. This should ensure that any differences we observe across tasks are over and above those that can be explained by the difference in behavioral performance. Second, we will include a set of decoding analyses that control for differences in performance across the tasks. We note that all our analyses of representational geometries relied on neural data from correct trials only. In addition, the first-level modelling of the fMRI data already controlled for trial-by-trial variability in response times. Therefore, our decoding and representation similarity analyses should not directly be affected by differences in performance across the two tasks. However, one possible issue with this approach is that the larger number of errors in the flat task means that less data was available for estimating multivoxel patterns in the flat task compared to the hierarchy task, resulting in differential power to detect decoding effects across the two tasks. We note that the on average, this difference was not substantial: on average, 21.7 runs were available per participant for the flat task, while 23.8 runs per participant were available for the hierarchy task. Moreover, rerunning our analyses with the number of runs equated for each participant does not meaningfully alter the pattern of results. These additional analyses will be included in the supplement in the forthcoming revised manuscript.  

Reviewer #2 (Public review):

Summary:

The authors study the influence of tasks on the representational geometry of the lPFC and auditory cortex (AC). In particular, they use two context-dependent tasks: a task with a hierarchical structure and a task with a flat structure, in which each context/stimulus maps to a specific response. Their primary finding is that the representational geometry in the lPFC, in contrast to AC, aligns with the optimal organization of the task. They conclude that the geometry of representations adapts, or is tailored, to the task in the lPFC, therefore supporting control processes.

Strengths:

(1) Dataset:

The dataset is impressive and well-sampled. Having data from both tasks collected in the same subjects is a great property. If it is publicly available, it will be a significant contribution to the community.

(2) Choice of methods:

The choice of analyses are largely well-suited towards the questions at hand - crosscondition generalization, RSA + regression, in combination with ANOVAs, are well-suited to characterizing task representations.

(3) I found some of their results, in particular, those presented in Figures 4 and 5, to be particularly compelling.

(4) The correlation analysis with behavior is also a nice result.

We thank the reviewer for noting the strengths of the paper. We respond to the weaknesses noted below. 

Weaknesses:

(1) Choice of ROIs:

A strength of fMRI is its spatial coverage of the whole brain. In this study, however, the authors focus on only two ROIs: the lPFC and auditory cortex. Though I understand the justification for choosing lPFC from decades of research, the choice of AC as a control feels somewhat arbitrary - AC is known to have worse SNR in fMRI data, and limiting a 'control' to a single region seems arbitrary. For example, why not also include visual regions, given that the task also involves two visual features?

We agree with the reviewer that the whole-brain fMRI data certainly provide ample opportunities to explore the nature of these representations across the brain. Our focus in this paper is squarely on the principles of coding and flexibility in the lPFC. We believe that a whole-brain exploration addresses a separate question that would be out of the scope of this study. To clarify, we are not arguing that the lPFC is the only region in the brain that employs the coding principles that our study brings to light. Our contention is only that lPFC employs these principles, and it differs at least from the primary sensory cortex. The questions of whether these principles generalize beyond lPFC (quite likely) and, if so, how broadly, are distinct from the ones addressed in the manuscript. We intend to follow up with another manuscript that addresses these questions.

Nevertheless, given the focus of this paper, we agree that a second control region, which allows one to test if the primary auditory cortex findings generalize to the sensory cortex more broadly, would strengthen our claims. We will include an early visual cortex ROI in our forthcoming revision. Preliminary results indicate that the early visual cortex ROI shows a similar set of findings – with evidence for coding of task-relevant and taskirrelevant visual dimensions across both tasks, but also specifically the context dimension in the hierarchy task. These results will be detailed in the forthcoming revision

(2) Construction of ROIs:

The choice and construction of the ROIs feel a bit arbitrary, as the lPFC region was constructed out of 10 parcels from Schaefer, while the AC was constructed from a different methodology (neurosynth). Did both parcels have the same number of voxels/vertices? It would be helpful to include a visualization of these masks as a figure.

We defined the lPFC ROIs by selecting Schaefer parcels in the frontal lobe that were previously mapped onto the Control A resting state network identified by Yeo et al. (2011). This network aligns with the multiple-demand network, which has also been identified in the macaque, where it includes the lPFC regions that abut the principal sulcus. Prior results from these regions in the monkey brain provide the scientific premise for our hypotheses. The two lPFC ROIs in each hemisphere were constructed out of 5 Schaefer parcels in each hemisphere. These parcels cluster into the same functional network and tend to behave similarly in univariate analyses. Given that our hypotheses do not distinguish between the different parcels, we elected to improve power by merging them into left and right dlPFC ROIs. 

On the other hand, the same approach could not be used to identify the primary auditory cortex. As Yeo et al. noted in their paper, the 17 resting state networks they identify did not adequately parcellate somatomotor and auditory cortices into distinct networks, likely due to their proximity (see Fig 14 and related text in Yeo et al. (2011)). We therefore relied on a different approach to define the primary auditory cortex, using an association test in Neurosynth to obtain a map of regions associated with the term “primary auditory”. In the revised manuscript, we will also include a primary auditory cortex ROI, defined again using a term-based association test in Neurosynth.

Our lPFC ROIs and pAC ROIs are of similar size. In the left hemisphere, the lPFC ROI (constructed from merging Schaefer parcels 128-thru-132) has, on average, 624.55 voxels. The left pAC ROI (defined with Neurosynth) has, on average, 628 voxels. In the right hemisphere, the lPFC ROI (constructed from merging Schaefer parcels 330-thru334), has 470.8 voxels on average. The right pAC ROI has, on average, 568 voxels. A table reporting the size of our parcels and ROIs was included in the supplement. In our forthcoming revision, we will additionally include a supplementary figure visualizing the ROI masks. 

(3) Task dimensionality:

In some ways, the main findings - that representation dimensionality is tailored to the task - seem to obviously follow from the choice of two tasks, particularly from a normative modeling perspective. For example, the flat task is effectively a memorization task, and is incompressible in the sense that there are no heuristics to solve it. In contrast, the hierarchical task can have several strategies, an uncompressed (memorized) strategy, and a compressed strategy. This is analogous to other studies evaluating representations during 'rich' vs. 'lazy'/kernel learning in ANNs. However, it seems unlikely (if not impossible) to form a 'rich' representation in the flat task. Posed another way, the flat task will always necessarily have a higher dimensionality than the hierarchical task. Thus, is their hypothesis - that representational geometry is tailored to the task - actually falsifiable? I understand the authors posit alternative hypotheses, e.g., "a fully compressed global axis with no separation among individual stimulus inputs could support responding [in the flat task]" (p. 36). But is this a realistic outcome, for example, in the space of all possible computational models performing this task? I understand that directly addressing this comment is challenging (without additional data collection or modeling work), but perhaps some additional discussion around this would be helpful.

We thank the reviewer for this comment, which gives us a chance to clarify our argument.

As noted by the reviewer, whether a network takes advantage of the compressibility of a task depends on its learning regime (i.e. rich vs lazy). One way to frame our question regarding the lPFC’s coding strategy, then, is to ask whether it operates in a rich or a lazy learning regime (which would predict, respectively, task-tailored vs task-agnostic representations). The reviewer’s concern is that the two task structures we employed are differentially compressible, and therefore, it is inevitable that we observe tailored representations and therefore, our hypotheses are not falsifiable.

First, it is important to clarify the theoretical premise behind our design and how it relates logically to our hypotheses. Under a lazy learning regime, a network would encode highdimensional representations of both tasks, regardless of their compressibility. On the other hand, under a rich learning regime, representational dimensionality will likely be shaped by the tasks’ structure. If the two tasks differ in their compressibility, only in the rich learning regime would the network learn representations of different dimensionality. Therefore, observing representations with dimensionality tailored to the task structure rules out the possibility that the lPFC is operating in a lazy regime. Therefore, the hypotheses are certainly testable.

The second point of clarification is that, contrary to the reviewer’s assertion, the flat task is, in fact, compressible – the task can be solved with a categorical representation of the response categories, with no sensitivity to the different specific stimuli within each category. Indeed, it is possible to train a simple, three-layer feedforward artificial neural network to perform the flat task perfectly with only 2 units in the hidden layer, demonstrating this compressibility. While we agree with the reviewer that in the space of all possible architectures one might consider the two tasks may differ in compressibility, particularly at the local levels, as we noted above, this does not imply that our hypotheses are not testable.

Finally, as a third point of clarification, our focus in this paper is on understanding the nature of coding in the lPFC in particular. Arguments based on a normative modelling perspective properly apply to the representations learned by an agent (such as an ANN or a human) as a whole. In a minimal feedforward ANN with a single hidden layer trained in a regime which encourages compression (i.e. a rich learning regime), it would indeed be the case that the representational dimensionality in that hidden layer would be higher for less compressible tasks. However, when applied to humans, such an argument applies to the brain as a whole rather than to an individual region of the brain like the lPFC. As such, it is less straightforward to predict how a single region might represent a task without additional information about the region’s inputs, outputs and broader position in a network. Even for a highly compressible task, a particular brain region may nevertheless be sensitive to all task dimensions. Conversely, even when a task is not compressible, a particular population within the brain may be invariant to some task features. For example, the primary auditory cortex is expected to be invariant to visual task dimensions.

Therefore, how a task is represented in the lPFC in particular (as opposed to the whole brain) depends on its computational function and coding principles, which remain debated. For instance, as some accounts (such as the guided activation theory) posit, if the primary function of the lPFC is to encode ‘context’ and shape downstream processing based on context, we might only expect to see the abstract coding of the auditory context in the hierarchy task (and, perhaps, the response categories across both tasks as they encode the ’context’ for the lower-level response decision), while being invariant to lowerlevel features of the input. In our paper, we specifically contrast two accounts of lPFC coding that have emerged in the literature – one positing that the lPFC learns a representation tailored to the structure of the task, and another that the lPFC encodes a high-dimensional representation that privileges sensitivity to many task features and their non-linear mixture at the cost of generalization. Regardless of the compressibility of the tasks in question, how the lPFC encodes the two tasks is an empirical question.

In our forthcoming revision, we will clarify these points in the discussion. We will also include the results of neural network simulations alluded to above.

(4) Related to the above:

The authors have a section on p. 27: "Local structure of lPFC representational geometry of the flat task shows high separability with no evidence for abstraction" - I understand a generalization analysis can be done in the feature space, but in practice, the fact that the flat task doubles as a memorization task implies that there are no useful abstractions, so it seems to trivially follow that there would be no abstract representations. In fact, the use of task abstractions in the stimulus space would be detrimental to task performance here. I could understand the use of this analysis as a control, but the phrasing of this section seems to indicate that this is a surprising result.

As explained above, there is no need for high local separability in the flat task. The lPFC could have completely abstracted over the individual trial-types that contributed to each response category, encoding only the response categories. Indeed, as also noted above, it is possible to train a simple, three-layer feedforward artificial neural network to perform the flat task perfectly with only 2 units in the hidden layer. The two hidden layer units code for each of the two response categories. 

(5) Statistical inferences:

Throughout the manuscript, the authors appear to conflate failure to reject the null with acceptance of the null. For example, p. 24: "However, unlike left lPFC, paired t-tests showed no reliable difference in the separability of the task-relevant features vs the orthogonal, task-irrelevant features... Therefore, the overall separability of pAC representations is not shaped by either task-relevance of task structure."

We thank the reviewer for pointing these out. These sentences will be corrected in the revision. For instance, the sentence above will be modified to “Therefore, we find no evidence that the overall separability of pAC representations is shaped by either taskrelevance or task structure.”

Reviewer #3 (Public review):

Summary:

In this paper, Bhandari, Keglovits, et al. explore the representational structure of task encoding in the lateral prefrontal cortex. Through an impressive fMRI data-collection effort, they compare and contrast neural representations across tasks with different highlevel stimulus-response structures. They find that the lateral prefrontal cortex shows enhanced encoding of task-relevant information, but that most of these representations do not generalize across conditions (i.e., have low abstraction). This appears to be driven in part by the representation of task conditions being clustered by the higher-order task properties ('global' representations), with poor generalization across these clusters ('local' representations). Overall, this paper provides an interesting account of how task representations are encoded in the PFC.

Strengths:

(1) Impressive dataset, which may provide further opportunities for investigating prefrontal representations.

(2) Clever task design, allowing the authors to confound several features within a complex paradigm.

(3) Best-practice analysis for decoding, similarity analyses, and assessments of representational geometry.

(4) Extensive analyses to quantify the structure of PFC task representations.

Weaknesses:

(1) The paper would benefit from improved presentational clarity: more scaffolding of design and analysis decisions, clearer grounding to understand the high-level interpretations of the analyses (e.g., context, cluster, abstraction), and better visualizations of the key findings.

(2) The paper would benefit from stronger theoretical motivation for the experimental design, as well as a refined discussion on the implications of these findings for theories of cognitive control.

We thank the reviewer for highlighting the strengths of our paper and their feedback on the writing. We have reviewed these helpful suggestions with an eye to which we may implement in our revision to improve clarity. Our forthcoming revision will 1) provide clearer scaffolding to aid the reader in understanding our design, analyses and our interpretation of the results 2) incorporate the MDS-based visualization of the representational geometries, which is currently presented in the Supplement, as a figure panel in the main text, 3) provide a justification for the particular task structures we picked in the introduction and 4) incorporate a new paragraph in the Discussion section to highlight the implications of our findings for cognitive control.

eLife Assessment

The study introduces new tools for measuring the intracellular calcium concentration close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. This approach yields important new information about the spatial and temporal profile of calcium concentrations near the site of entry at the plasma membrane. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in calcium domains. The conclusions are solid and well supported by the data.

Reviewer #1 (Public Review):

This paper describes technically impressive measurements of calcium signals near synaptic ribbons in zebrafish bipolar cells. The data presented provides high spatial and temporal resolution information about calcium concentrations along the ribbon at various distances from the site of entry at the plasma membrane. This is important information. The experiments appear to be well-done and provide strong evidence for the main conclusions reached.

Strengths

The technical aspects of the measurements are impressive. The authors use calcium indicators bound to the ribbon and high-speed line scans to resolve changes with a spatial resolution of ~250 nm and temporal resolution of less than 10 ms. These spatial and temporal scales are much closer to those relevant for vesicle release than previous measurements. Hence the results provide a unique window onto these events.

The use of calcium indicators with very different affinities and of different intracellular calcium buffers helps provide confirmation of key results.

Reviewer #2 (Public review):

Summary:

The study introduces new tools for measuring intracellular Ca2+ concentration gradients around retinal rod bipolar cell (rbc) synaptic ribbons. This is done by comparing the Ca2+ profiles measured with mobile Ca2+ indicator dyes versus ribbon-tethered (immobile) Ca2+ indicator dyes. The Ca2+ imaging results provide a straightforward demonstration of Ca2+ gradients around the ribbon and validate their experimental strategy. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in Ca2+ domains as a function of Ca2+ buffering. In addition, the authors try to demonstrate that there is heterogeneity among synaptic ribbons within an individual rbc terminal.

Strengths:

The study introduces a new set of tools for estimating Ca2+ concentration gradients at ribbon AZs, and the experimental results are accompanied by an open-source, computational model that nicely describes Ca2+ buffering at the rbc synaptic ribbon. In addition, the dissociated retinal preparation remains a valuable approach for studying ribbon synapses. Lastly, excellent EM.

Comments on revisions:

Several concerns were raised about the kinetic analyses, and the authors have carefully acknowledged the critiques. The ideal outcome would have been a more complete kinetic readout and analyses (in particular a better readout of risetime would have improved the results). In the absence of a suitable readout of the risetime, the authors scaled back their claims and improved on the description of the falling phase of the signals. The authors have given a reasonable response under the circumstances.

In addition, the authors provided more context to their results.

I have no further concerns.

Reviewer #3 (Public review):

Summary:

In this study, the authors have developed a new Ca indicator conjugated to the peptide, which likely recognizes synaptic ribbons and have measured microdomain Ca near synaptic ribbons at retinal bipolar cells. This interesting approach allows one to measure Ca close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. Though microdomain Ca at the active zone of ribbon synapses has been measured by Hudspeth and Moser, the new study uses the peptide recognizing synaptic ribbons, potentially measuring the Ca concentration relatively proximal to the release sites.

Strengths:

The study is, in principle, technically well done, and the peptide approach is technically interesting, which allows one to image Ca near the particular protein complexes. The approach is potentially applicable to other types of imaging.

Weaknesses:

Peptides may not be entirely specific, and genetic approach tagging particular active zone proteins with fluorescent Ca indicator proteins may well be more specific. The readers should be aware of this, when interpreting the results.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

This paper describes technically-impressive measurements of calcium signals near synaptic ribbons in goldfish bipolar cells. The data presented provides high spatial and temporal resolution information about calcium concentrations along the ribbon at various distances from the site of entry at the plasma membrane. This is important information. Important gaps in the data presented mean that the evidence for the main conclusions is currently inadequate. 

Strengths 

The technical aspects of the measurements are impressive. The authors use calcium indicators bound to the ribbon and high speed line scans to resolve changes with a spatial resolution of ~250 nm and temporal resolution of less than 10 ms. These spatial and temporal scales are much closer to those relevant for vesicle release than previous measurements. 

The use of calcium indicators with very different affinities and of different intracellular calcium buffers helps provide confirmation of key results. 

Thank you very much for this positive evaluation of our work.

Weaknesses 

Multiple key points of the paper lack a statistical test or summary data from populations of cells. For example, the text states that the proximal and distal calcium kinetics in Figure 2A differ. This is not clear from the inset to Figure 2A - where the traces look like scaled versions of each other. Values for time to half-maximal peak fluorescence are given for one example cell but no statistics or summary are provided. Figure 8 shows examples from one cell with no summary data. This issue comes up in other places as well. 

Thank you for this fair and valuable feedback. Following also the suggestion by the Editor, we have now removed the rise-time kinetic fitting results from the manuscript and only retain the bi-exponential decay time constant values. Further, we explicitly detail the issues with kinetic fitting, and state that the precise quantitative conclusions should not be drawn from the differences in kinetic parameters (pages 7 and 2728). 

We have included the results of paired-t-tests to compare the amplitudes of proximal vs. distal calcium signals shown in Fig. 2A & B, Fig. 3C & D, Fig. 4C & D, Fig. 5A-D, and Fig. 8E&F. Because proximal and distal calcium signals were obtained from the same ribbons within 500-nm distances, as the Reviewer pointed out, “the traces look like scaled versions of each other”. For experiments where we make comparisons across cells or different calcium indicators, as shown in Fig. 3E & F, Fig.5E, and Fig. 8B&C, we have included the results of an unpaired t-test. We have also included the t-test statistics information in the respective figure legends in the revised version.

In Figure 8, we have shown example fluorescence traces from two different cells at the bottom of the A panel, and example traces from different ribbons of RBC a in the D, and the summary data is described in B-C and E-F, with statistics provided in the figure legends.

The rise time measurements in Figure 2 are very different for low and high affinity indicators, but no explanation is given for this difference. Similarly, the measurements of peak calcium concentration in Figure 4 are very different with the two indicators. That might suggest that the high affinity indicator is strongly saturated, which raises concerns about whether that is impacting the kinetic measurements. 

Yes, we do believe that the high-affinity indicator is partially saturated, and therefore, the measurement with the low-affinity indicator dye is a more accurate reflection of the measured Ca²⁺ signal. We now state this more explicitly in the text. Further, we note that the rise time values are no longer listed due to lack of statistical significance for such comparisons, as noted above.

Reviewer #2 (Public review): 

Summary: 

The study introduces new tools for measuring intracellular Ca2+ concentration gradients around retinal rod bipolar cell (rbc) synaptic ribbons. This is done by comparing the Ca2+ profiles measured with mobile Ca2+ indicator dyes versus ribbon-tethered (immobile) Ca2+ indicator dyes. The Ca2+ imaging results provide a straightforward demonstration of Ca2+ gradients around the ribbon and validate their experimental strategy. This experimental work is complemented by a coherent, open-source, computational model that successfully describes changes in Ca2+ domains as a function of Ca2+ buffering. In addition, the authors try to demonstrate that there is heterogeneity among synaptic ribbons within an individual rbc terminal. 

Strengths: 

The study introduces a new set of tools for estimating Ca2+ concentration gradients at ribbon AZs, and the experimental results are accompanied by an open-source, computational model that nicely describes Ca2+ buffering at the rbc synaptic ribbon. In addition, the dissociated retinal preparation remains a valuable approach for studying ribbon synapses. Lastly, excellent EM. 

Thank you very much for this positive evaluation of our work.

Comments on revisions: 

Specific minor comments: 

(1) Rewrite the final sentence of the Abstract. It is difficult to understand. 

Thank you for pointing that out. We have updated the final sentence of the Abstract.

(2) Add a definition in the Introduction (and revisit in the Discussion) that delineates between micro- and nano-domain. A practical approach would be to round up and round down. If you round up from 0.6 um, then it is microdomain which means ~ 1 um or higher. Likewise, round down from 0.3 um to nanodomain? If you are using confocal, or even STED, the resolution for Ca imaging will be in the 100 to 300 nm range. The point of your study is that your new immobile Ca2-ribbon indicator may actually be operating on a tens of nm scale: nanophysiology. The Results are clearly written in a way that acknowledges this point but maybe make such a "definition" comment in the intro/discussion in order to: 1) demonstrate the power of the new Ca2+ indicator to resolve signals at the base of the ribbon (effectively nano), and 2) (Discussion) to acknowledge that some are achieving nanoscopic resolution (50 to 100nm?) with light microscopy (as you ref'd Neef et al., 2018 Nat Comm).  

Thank you for the valuable comments. We have now provided this information in the introduction and discussion.  

(3) Suggested reference: Grabner et al. 2022 (Sci Adv, Supp video 13, and Fig S5). Here rod Cav channels are shown to be expressed on both sides the ribbon, at its base, and they are within nanometers from other AZ proteins. This agrees with the conclusions from your imaging work.  

Thank you for the valuable suggestion. We have now provided this information in the introduction and discussion.

(4) In the Discussion, add a little more context to what is known about synaptic transmission in the outer and inner retina.. First, state that the postsynaptic receptors (for example: mGluR6-OnBCs vs KARs-OffBCs, vs. AMPAR-HCs), and possibly the synaptic cleft (ground squirrel), are known to have a significant impact on signaling in the outer retina. In the inner retina, there are many more unknowns. For example, when I think of the pioneering Palmer JPhysio study, which you sight, I think of NMDAR vs AMPAR, and uncertainty in what type postsynaptic cell was patched (GC or AC....). Once you have informed the reader that the postsynapse is known to have a significant impact on signaling, then promote your experimental work that addresses presynaptic processes: "...the new tool and results allow us to explore release heterogeneity, ribbon by ribbon in dissociated preps, which we eventually plan to use at ribbon synapses within slices......to better understand how the presynapse shapes signaling......". 

Thank you for the valuable comments. We have now provided this information in the introduction and discussion.

Reviewer #3 (Public review): 

Summary: 

In this study, the authors have developed a new Ca indicator conjugated to the peptide, which likely recognizes synaptic ribbons and have measured microdomain Ca near synaptic ribbons at retinal bipolar cells. This interesting approach allows one to measure Ca close to transmitter release sites, which may be relevant for synaptic vesicle fusion and replenishment. Though microdomain Ca at the active zone of ribbon synapses has been measured by Hudspeth and Moser, the new study uses the peptide recognizing synaptic ribbons, potentially measuring the Ca concentration relatively proximal to the release sites. 

Strengths: 

The study is, in principle, technically well done, and the peptide approach is technically interesting, which allows one to image Ca near the particular protein complexes. The approach is potentially applicable to other types of imaging. 

Thank you very much for this appreciation.

Weaknesses: 

Peptides may not be entirely specific, and genetic approach tagging particular active zone proteins with fluorescent Ca indicator proteins may well be more specific. Although the authors are aware of this and the peptide approach is generally used for ribbon synapses, the authors should be aware of this, when interpreting the results. 

We acknowledge the reviewer’s point and believe the peptides and genetic approaches to measure local calcium signals have their merits, each with separate advantages and disadvantages.  

Reviewer #1 (Recommendations for the authors): 

The revisions helped with some concerns about the original paper, but some issues were not adequately addressed. I have left two primary concerns in my public review. To summarize those: 

The difference in kinetics of proximal and distal locations is emphasized and quantified in the paper, but the quantification consists of a fit to the average responses. This does not give an idea of whether the difference observed is significant or not. Without an estimate of the error across measurements the difference in kinetic quoted is not interpretable. 

Thank you for this feedback. Since the kinetics information is a minor part of the manuscript, we have followed the Editor’s advice to significantly tone down the comparison of kinetic fit parameters (completely removing the rise-time comparisons), in order to put more focus on the better-documented conclusions. We also note that we did establish statistical significance of the differences in fluorescence signal amplitudes. 

Somewhat relatedly, the difference in amplitude and kinetics of the calcium signals measured with low and high affinity indicators is quite concerning. The authors added one sentence stating that the high affinity indicator might be saturated. This is not adequate. Should we distrust the measurements using the high affinity indicator? The differences between the results using the low and high affinity indicators is in some cases large - e.g. larger than the differences cited as a key result between distal and proximal locations. This issue needs to be dealt with directly in the paper. 

Thank you for this feedback. Yes, the measurements from high-affinity indicators cannot report the Ca2+ as accurately as low-affinity indicators. However, the value of HA indicators is in their ability to detect lowamplitude signals that lower-affinity indicators may miss due to lower signal-to-noise resolution.  We added a sentence on page 12 to further stress this point.

Related to the point about statistics, it is not clear how to related the horizontal lines in Figure 8 to the actual measurements. It is critical for the evaluation of the conclusions from that figure to understand what is plotted and what the error bars are on the plotted data. 

We apologize for the earlier ambiguity in Fig. 8. In this figure, we first compare proximal (panel B) and distal (panel C) calcium signals across several RBCs, labeled RBC-a through RBC-d. Each RBC contains multiple ribbons, and for each cell, we present the average calcium signals from multiple ribbons using box plots in panels B and C. In these box plots, the horizontal lines represent the average calcium signal for each cell, while the size of the error bars reflects the variability in proximal and distal calcium signals among the ribbons within that RBC.

For example, RBC-a had five identifiable ribbons. In panels D–F, we use RBC-a to illustrate the variability in calcium signals across individual ribbons. Specifically, we distinguished proximal and distal calcium signals from five ribbons (ribbons 1–5) within RBC-a. When feasible, we acquired multiple x–t line scans at a single ribbon, shown now as individual data points, to assess variability in calcium signals recorded from the same ribbon.

The box plots in panels E and F display the average calcium signal (horizontal lines) for each ribbon, based on multiple recordings. These plots demonstrate considerable variability between ribbons of RBC-a. Importantly, the lack of or minimal error bars for repeated measurements at the same ribbon indicates that the proximal and distal calcium signals are consistent within a ribbon. These findings emphasize that the observed variability among ribbons and among cells reflects true biological heterogeneity in local calcium domains, rather than experimental noise.

eLife Assessment

This useful study presents a hierarchical computational model that integrates locomotion, navigation, and learning in Drosophila larvae. The evidence supporting the model is solid, as it qualitatively replicates empirical behavioral data, but the experimental data is incomplete. While some simplifications in neuromechanical representation and sensory-motor integration are limiting factors, the study could be of use to researchers interested in computational modeling of biological movement and adaptive behavior.

Reviewer #1 (Public review):

Summary:

The paper presents a three-layered hierarchical model for simulating Drosophila larva locomotion, navigation, and learning. The model consists of a basic locomotory layer that generates crawling and turning using a coupled oscillator framework, incorporating intermittency in movement through alternating runs and pauses. The intermediate layer enables navigation by allowing larvae to actively sense and respond to odor gradients, facilitating chemotaxis. The adaptive learning layer integrates a spiking neural network model of the Mushroom Body, simulating associative learning where larvae modify their behavior based on past experiences. The model is validated through simulations of free exploration, chemotaxis, and odor preference learning, demonstrating close agreement with empirical behavioral data. This modular framework provides a valuable advance for modeling larva behavior.

Strengths:

Every modeling paper requires certain assumptions and abstractions. The main strength of this paper lies in its modular and hierarchical approach to modeling behavior, making connections to influential theories of motor control in the brain. The authors also provide a convincing discussion of the experimental evidence supporting their layered behavioral architecture. This abstraction is valuable, offering researchers a useful conceptual framework and marking a significant step forward in the field. Connections to empirical larval movement are another major strength.

Weaknesses:

While the model represents a conceptual advance in the field, some of its assumptions and choices fall behind state-of-the-art approaches. One limitation is the paper's simplified representation of larval neuromechanics, in which the body is reduced to a two-segment structure with basic neural control. Another limitation is the absence of an explicit neuromuscular control system, which would better capture the role of segmental central pattern generators (CPGs) and neuronal circuits in regulating peristalsis and turning in Drosophila larvae. Many detailed neuromechanical models, as cited by the authors, have already been published. These abstractions overlook valuable experimental studies that detail segmental dynamics during crawling and the larval connectome.

The strength of the model could also be its weakness. The model follows a subsumption architecture, where low-level behaviors operate autonomously while higher layers modulate them. However, this approach may underestimate the complexity of real neural circuits, which likely exhibit more intricate feedback mechanisms between sensory input and motor execution.

Reviewer #2 (Public review):

Summary:

Sakagiannis et al. propose a hierarchically layer architecture to larval locomotion and foraging. They go from exploration to chemotaxis and odour preference test after associative learning.

Strengths:

A new locomotion model based on two oscillators that also incorporates peristaltic strides.

Weaknesses:

• The model is not always clearly or sufficiently explained (chemotaxis and odour test).

• Data analysis of the model movement is not very thorough.

• Comparisons with locomotion of behaving animals missing in chemotaxis and odour preference test after associative learning.

• Overall it is hard to judge the descriptive and predictive value of the model.

Reviewer #3 (Public review):

Summary:

This paper presents a framework for a multilevel agent-based model of the drosophila larva, using a simplified larval body and locomotor equations coupled to oscillators and sensory input. The model itself is built upon significant existing literature, particularly Wystrach, Lagogiannis, and Webb 2016 and Jürgensen et al. 2024. The aim is to generate an easily configurable, well-documented platform for organism-scale behavioral simulation in specific experiments. The authors demonstrate qualitative similarity between in vivo behavioral experiments to calibrated models.

Strengths:

The goal is excellent - a system to rapidly run computational experiments that align naturally with behavioral experiments would be well-suited to develop intuitions and cut through hypotheses. The authors provide quantitative descriptions that show that the best-fit parameters in their models produce results that agree with several properties of larval locomotion.

The description of model calibration in the appendix is clear and explains several aspects of the model better than the main text.

In addition, the code is well-organized using contemporary Python tooling and the documentation is nicely in progress (although it remains incomplete). However, see notes for difficulties with installation.

Weaknesses:

(1) As presented here the modeling itself is described in an unclear fashion and without a particular scientific question. The majority of the effort appears to be calibrating modest extensions of existing models and applying them to very simple experiments. This could be an effective first part of a paper on the software tool, but the paper needs to point to a scientific question or, if it is a tool paper, a gap in the current state of modeling tools needed to address scientific goals. While the manuscript has a good overview of larval behavioral papers, the discussion of modeling is more of an afterthought. However, the paper is a modeling paper and the contribution is to modeling and particularly with this work's minor adaptions of existing models, it is unclear what the principle contribution is intended to be.

(2) While the models presented do qualitatively agree with experimental data in specific situations, there is no effort to challenge the model assumptions or compare them to alternative models. Simply because the data is consistent in a small number of simple experiments does not mean that the models are correct. Moreover, given the highly empirical nature of the modeling, I wonder what results are largely the model putting out what was put in, particularly with regards to kinematic results like frequency and body length or the effect of learning simply changing the sensory gain constant. It is difficult to imagine how at this level of empirical modeling, it would appear quite difficult to integrate the type of cell-type-specific perturbation or functional observation that is common in larval experiments.

(3) The central framing of a "layered control architecture" does not have a significant impact on the work presented here and the paper would do better with less emphasis on it. Given the limited empirical models, there are only so many parameters where different components can influence one another, and as best as I can tell from the paper there is only chemotaxis and modulation of a chemotactic gain constant that are incorporated so far. However, since these are empirical functions it says little about how the layers are actually controlled by the nervous system - indeed, the larval nervous system appears to have many levels of local and long-range module of circuits at both the sensory and motor layers. It is not clear how this aspect would contribute beyond the well-appreciated concept of a relatively finite set of behavioral primitives in an insect brain, particularly for the fly larva. What would be a contradictory model and how would the authors differentiate between that and the one they currently propose? If focusing only on olfactory learning and chemotaxis, how does the current framing add to the existing understanding?

(4) The paper uses experimental data to calibrate the models, however, the experiments are not described at all in the text.

Author response:

We thank all three anonymous reviewers for their thoughtful evaluations of our manuscript and for recognizing the conceptual advance in combining agent-based behavioral simulations with systems neuroscience models. We are especially encouraged by the acknowledgement of the framework’s potential to support simulation of neural control of individual animal behavior in realistic sensory environments.

Below, we respond to each reviewer’s public comments in turn. Throughout, we have aimed to clarify our rationale for modeling choices, acknowledge limitations, and outline concrete steps for improvement in the revised manuscript.

Furthermore, the call for a better description of the model implementation as voiced by all three reviewers and additional requests from community members has prompted us to formulate a separate technically detailed description of the publicly available larvaworld software package as well as of the readily implemented models in form of a preprint paper (Sakagiannis et al., 2025, bioRxiv, DOI: https://doi.org/10.1101/2025.06.15.659765).

Reviewer #1:

We are happy to read that this reviewer considers the proposed behavioral architecture ‘a significant step forward in the field’, and that she/he recognizes the strengths of our work in the modular and hierarchical approach that provides connections to influential theories of motor control in the brain, in the experimental evidence it is based on, and in the valuable abstractions that we have chosen for the larval behavioral modeling.

The reviewer raises important points about the simplifications we have made, both conceptually and in the specific implementation of larval behaviors. Our main goal in this study is to introduce a conceptual framework that integrates agent-based modeling with systems neuroscience models in a modular fashion. To serve this purpose, we aimed for a minimal yet representative implementation at the motor layer of the architecture, calibrated to larval locomotion kinematics. This choice enables efficient simulation while allowing us to test top-down modulation and adaptive mechanisms in higher layers without the computational overhead of a full neuromechanical model. In addition to chemotaxis, we have recently used this simplified approach to model thermotaxis in larvae (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The reviewer notes the absence of explicit segmental neuromuscular control or central pattern generators (CPGs). We deliberately abstracted from these mechanisms, representing the larval body as two segments with basic kinematic control, to focus on reproducing overall locomotor patterns. This bisegmental simplification, which we illustrate in Supplemental Video “Bisegmental larva-body simplification”, retains the behavioral features relevant to our current aims. However, the modular structure of the framework means that more detailed neuromechanical models—incorporating CPG dynamics or connectome-derived circuit models—can be integrated in future work without altering the architecture as a whole.

We fully agree that real neural circuits are more complex than a strict subsumption architecture implies. In the Drosophila larva, there is clear evidence for ascending sensory feedback from the motor periphery to premotor and higher brain circuits, as well as neuromodulatory influences. These add layers of complexity beyond the predominantly descending control in our present model. At the same time, both larval and adult connectome data show that across-level descending and ascending connections are sparse compared to the dense within-layer connectivity. We see value in casting our model as a hierarchical control system precisely to make the strengths and limitations of such an abstraction explicit. The revised manuscript will include further discussion of these points.

In summary, our design choices reflect a trade-off: by limiting the biological detail in the lower layers, we gain computational efficiency and maintain a clear modular structure that can host models at different levels of abstraction. This ensures that the architecture remains both a tool for immediate behavioral simulation and a scaffold for integrating richer neural and biomechanical models as they become available.

Reviewer #2:

We thank the reviewer for recognizing the novelty of our locomotory model, particularly the implementation of peristaltic strides based on our new analyses of empirical larval tracks, and for providing constructive feedback that will help us improve the manuscript.

The reviewer highlights the need for clearer explanations of the chemotaxis and odor preference modules. We expand these sections in the revised manuscript with more explicit descriptions of model structure, parameterization, and calibration. As mentioned above, we have also prepared a separate preprint dedicated to the larvaworld Python package, which contains detailed implementation notes and hands-on tutorials that allow users to adapt or extend individual modules.

Regarding the comparison to empirical behavior in chemotaxis, our present analysis is indeed primarily qualitative. However, we would like to emphasize that the temporal profile of odor concentration at the larval head in our simulations matches that measured in Gomez-Marin et al. (Nature Comm., 2011, DOI: https://doi.org/10.1038/ncomms1455) using only one additional free parameter, while all parameters of the basic locomotory model had been fitted to a separate exploration dataset before and were kept fixed in the chemotaxis experiments. In addition to the simulation of chemotaxis in the present paper, we recently used larvaworld in a practical model application to estimate a species-specific parameter of thermotaxis from experiments across different drosophilids (Kafle et al., 2025, iScience, DOI: https://doi.org/10.1016/j.isci.2025.112809).

The preference index in our simulations was computed using the same definition as in the established experimental group assay for larval memory retention, enabling a direct quantitative comparison between simulated and empirical results. Variability in the simulated outcomes arose naturally from inter-individual differences in body length and locomotory parameters, derived from real larval measurements, as well as from the random initial orientation of each individual in the arena. These factors contributed to variation in individual tracks and ultimately produced preference index values that closely matched those observed experimentally. In the revised manuscript, we also discuss handedness, as highlighted by the reviewer, as another meaningful expression of inter-individual variability in Drosophila larvae and insects more generally.

Finally, we acknowledge the reviewer’s concern about the scalability and broader applicability of the model. While the present paper focuses on three specific behavioral paradigms (exploration, chemotaxis, odor preference), the modular structure of the architecture is designed for flexibility: modules at any layer can be exchanged for more detailed or alternative implementations, and new sensory modalities or behaviors can be integrated without redesigning the system. The larvaworld package, associated codebase, and documentation are openly available to encourage adoption and adaptation by the larval research community.

Reviewer #3:

This public review provides an excellent account of our central aim to build an easily configurable, well-documented platform for organism-scale behavioral simulation and we are happy to read that the reviewer considers this an excellent goal.

We thank the reviewer for her/his account of our well-organized code using contemporary Python tooling. We are currently further improving code readability and code documentation, and we will release a new version of the larvaworld Python package. We further agree with the reviewer’s assessment that understanding the model calibration currently requires reading of the appendix. For the revised manuscript we thus aim at improving our description of all calibration and modeling steps along the way. We will also make sure to improve the description of the experimental datasets used for calibration.

We recognize that our description of the paper’s scientific contribution could be clearer. In revision, we will sharpen the Introduction and Discussion to highlight our main contributions:

(1) Promoting a shift from isolated neural circuit modeling to integrated agent-based simulations in realistic environments.

(2) Proposing the layered behavioral architecture, adopting the subsumption paradigm for modular integration.

(3) Providing the larvaworld software as a ready-to-use, extensible modeling platform.

(4) Implementing an empirically calibrated locomotory model and demonstrating its integration with navigation and learning modules in replicated behavioral paradigms.

We agree with the reviewer that the next challenge is to integrate the empirically based behavioral simulations presented here with functional brain models capable of reproducing or predicting experimental findings at the level of cellular neurophysiology, including the effects of cell-type-specific manipulations such as gene knock-down or optogenetic activation/inhibition. However, based on our experience with systems-level modeling, we deliberately invested in behavioral simulation because functional models of the nervous system—including our own—often lack translation into simulated agent behavior. In many cases, model output is limited to one or more variables that can at best be interpreted as a behavioral bias, and most often represents an “average animal” that fails to capture inter-individual differences. By linking our spiking mushroom body model to behavioral simulations in a group of individual agents during memory retention tests (Figure 6C,D), we were able to achieve a first successful direct comparison between simulated and experimental behavior metrics—in this case, the behavioral preference index reported in Jürgensen et al. (iScience, 2024, DOI: https://doi.org/10.1016/j.isci.2023.108640).

Finally, we reiterate that the layered behavioral architecture is designed to promote a modular modeling paradigm. Our adoption of a subsumption architecture does not conflict with the concept of behavioral primitives; on the contrary, the notion that such primitives follow (semi-)autonomous motor programs and can be combined into more complex behaviors was the starting point for our implementation of the architecture in the fly larva. In our view, a genuinely contradictory paradigm for neural control of behavior would require a non-modular, strictly non-hierarchical organization of the nervous system and, by extension, of behavioral control.

eLife Assessment

NeuroSC is an accessible and interactive tool for streamlined observation of neuronal morphology, membrane contact, and synaptic connectivity across developmental stages in the nematode C. elegans. This important tool relies on solid electron microscopy datasets. This resource will be of high interest to C. elegans researchers interested in nervous system wiring and circuit function.

Reviewer #1 (Public review):

The authors have done a terrific job and addressed the questions raised in my previous review. There are only some minor requests that I have and list below.

Reviewer #2 (Public review):

Summary

The past several years has seen publication of both new (Witvliet et al., 2021) and newly analyzed (Cook et al., 2019; Moyle et al., 2021; Brittin et al., 2021) data for the C. elegans connectome. The increase in data availability for a single species allows researchers to examine variability due to both stochastic events and due to changes over development. The quantity of these data are huge. To help the community make these data more accessible, the authors present a new online tool that allows examination of 3D models for C. elegans neurons in the central neuropil across development. In addition to visualizing the overall structure of the neuronal processes and locations of synapses, the NeuroSC tool also allows users to probe into the C-PHATE visualization results, which this group previously pioneered to describe similarities in neuron adjacency (Moyle et al., 2021).

Strengths

The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings are incredibly useful, they were necessary simplifications for a 2D publication and lack details of the complex architecture seen within each EM image. Koonce et al takes advantage of their own and others segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization is intended to allow users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3-D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source. This revision includes an option where hovering over an individual neurons, synapse, or contact will pull up a statistics panel. The addition of text to the video tutorials in the revision is very useful.

Weaknesses

There are several bugs with this tool, which make it a bit clunky to use and suggest a lack of rigorous testing. There are also issues with data availability. I was disappointed that my "recommendations for the authors", which focused on the user interface, were not addressed in the response to reviewers.

Reviewer #3 (Public review):

Summary:

This work provides graphical tools for reconstructing the detailed anatomy of a nervous system from a series of sections imaged by electron microscopy. Contact between neuronal processes can direct outgrowth and is necessary for connectivity, thus function. A bioinformatic approach is used to group neurons according to shared features (e.g., contact, synapses) in a hierarchy of "relatedness" that can be interrogated at each step. In this work, Koonze et al analyze vEM data sets for the C. elegans nerve ring (NR), a dense fascicle of processes from181 neurons. In a bioinformatic approach, the clustering algorithm Diffusion Condensation (DC) groups neurons according to similar cell biological features in iterations that remove chunks of differences in feature data with each step ultimately merging all NR neurons in one cluster. DC results are displayed with C-Phate a 3D visualization tool to produce a trajectory that can be interrogated for cell identities and other features at each iterative step. In previous work by these authors, this approach was utilized to identify subgroups of neuronal processes or "strata" in the NR that can be grouped by physical contact and connectivity. Here they expand their analysis to include a series of available vEM data sets across C. elegans larval development. This approach suggests that strata initially established during embryonic development are largely preserved in the adult. Importantly, exceptions involving stage specific-specific reorganization of neuronal placement in specific strata were also detected. A case study featured in the paper demonstrates the utility of this approach for visualizing the integration of newly generated neurons into the existing NR anatomy. Visualization tools used in this work are publicly available at NeuroSCAN.

Strengths:

A web-based app, NeuroSCAN, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development.

Weaknesses:

minor revisions

Comments on Revisions:

The authors have satisfactorily addressed my critiques.

Author response:

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

Reviewer #1 (Public review)

Comment 

Koonce et al. have generated a web-based visualization tool for exploring C. elegans neuronal morphology, contact area between neurons, and synaptic connectivity data. Here, the authors integrate volumetric segmentation of neurons and visualization of contact area patterns of individual neurons generated from Diffusion Condensation and C-PHATE embedding based on previous work from adult volumetric electron microscopy (vEM) data, extended to available vEM data for earlier developmental stages, which effectively summarizes modularity within the collated C. elegans contactomes to date. Overall, NeuroSC's relative ease of use for generating visualizations, its ability to quickly toggle between developmental stages, and its integration of a concise visualization of individual neurons' contact patterns strengthen its utility.

We thank that reviewer for this positive assessment of our work.

Comment

NeuroSC provides an accessible and convenient platform. However, many of the characteristics of NeuroSC overlap with that of an existing tool for visualizing connectomics data, Neuroglancer, which is a widely-used and shared platform with data from other organisms. The authors do not make clear their motivation for generating this new tool rather than building on a system that has already collated previous connectomics data. Although the field will benefit from any tool that collates connectomics data and makes it more accessible and user-friendly, such a tool is only useful if it is kept up-to-date, and if data formatting for submitting electron microscopy data to be added to the tool is made clear. It is unclear from this manuscript whether NeuroSC will be updated with recently published and future C. elegans connectomes, or how additional datasets can be submitted to be added in the future.

We have added new language to more explicitly state the motivations for developing NeuroSC (Introduction, lines 98-111, and discussion lines 375-384). In a new discussion section, we also include comparisons of the features of NeuroSC with other existing tools, like Neuroglancer and Webknossos, (lines 393-417).

Briefly, the functional features of NeuroSC are substantially different (and do not exist) in other web-based tools for navigating EM datasets, including NeuroGlancer. This is because the intended use of NeuroSC is substantially different (and purposefully synergistic) to the intended use, and tools available, in NeuroGlancer. 

NeuroGlancer is a versatile tool designed primarily for web-based visualizations and sharing of large EM datasets. NeuroSC was not designed to enable this type of access to the primary EM data (purposefully done because these features were already available through tools like NeuroGlancer). 

Instead, the explicit goal of NeuroSC is to provide a platform specifically optimized for examining neuronal relationships across connectomic datasets. NeuroSC builds on the segmentations emerging from programs like NeuroGlancer, but the tools are tailored to explore relationships such as contact profiles in the context of neuronal morphologies and synaptic positions, and across datasets that represent different animals or different developmental stages. 

To achieve this, all datasets in NeuroSC were optimized to facilitate comparisons across different connectomes of segmented neuronal features, including: 1) alignment of the neurons that are compared upon the display of the segmentations; 2) synchronization of the 3D windows; 3) implementation of a ‘universal color code’ across datasets for each neuron and relationship for easy visual comparisons; 4) use of the specific neuronal names to label instances of the same cells across all available datasets. The use of precise neuronal names among separate data sets allows integration of these objects with other catalogued datasets, including genomic and neuronal activity profiles.

The formatting and display of the datasets used in NeuroSC was accompanied by the development of new tools including: 1) Rendering of the contact profiles of all neurons in the context of the morphology of the cell and the synapses and 2) C-PHATE diagrams to inspect multidimensional relationship hierarchies based on these contact profiles. In NeuroSC, C-PHATEs can be navigated and compared across multiple stages of development while visualizing neuronal reconstructions, allowing users to compare neuronal relationships across individual datasets.

We agree with the reviewer that these tools are most useful when integrated. With that intention in mind, we designed NeuroSC as a series of modular, open-source tools that could be integrated into other programs, including Neuroglancer. In that sense our intent was not to produce another free-standing tool, but a set of tools that, if useful, could be integrated to other existing web-based connectomic resources to enhance the user experience of navigating complex EM datasets and draw biological meaning from the relationships between the neurons. Additionally, we intentionally designed NeuroSC to enable the ability to integrate new methods of understanding neuron relationships as they arise. We have dedicated a more detailed section to the discussion (lines 369- 417) to better convey this intention and directly address the unique abilities of NeuroSC as a complementary tool to the powerful existing tools, including Neuroglancer.

Comment

The interface for visualizing contacts and synapses would be improved with better user access to the quantitative underlying data. When contact areas or synapses are added to the viewer, adding statistics on the magnitude of the contact area, the number of synapses, and the rank of these values among the neuron's top connections, would make the viewer more useful for hypothesis generation. Furthermore, synapses are currently listed individually, with names that are not very legible to the web user. Grouping them by pre- and postsynaptic neurons and linking these groups across developmental stages would also be an improvement.

[what do they even mean by linking?]

We thank the reviewer for this insightful comment and have implemented several improvements to address these suggestions. Specifically, we have added new features to enhance user access to quantitative data within the NeuroDevSCAN viewer:

Cell, Patch, and Synapse Statistics: Users can now see a statistics panel when clicking on a rendered neuron, contact patch, or a synapse. These panels provide the following information, respectively, and are highlighted in lines 303-315):

Cell Stats: Click on a cell rendering to show cell stats which displays the total volume and surface area of the selected neuron within the defined neuropil area of our datasets (see Methods). 

Contact Stats: Click on a patch rendering to show ‘contact stats’. This pop up displays quantifications of the selected contact relationship. Rank compares the summed surface area of contacts ("patches") between these two neurons relative to all other contact relationships for the primary neuron for the cell and the whole nerve ring. A rank of 1, for example, means this neuron pair shares the largest contact surface area of the examined relationship. “Total surface area” is displayed in nanometers, and is the summed surface area of all patches of this identity. Contact percentages are presented in two ways: (1) as the proportion of the primary cell's total surface area occupied by the contact in question, and (2) as the proportion of the total surface area of the nerve ring occupied by that same contact. (Showcased in figure S5). 

Synapse Stats: A click on a synapse rendering now shows ‘synapse stats’, which displays the number of synapses of the selected identity within the primary neuron, including any polyadic synapse combinations involving the primary neurons. (Showcased in figure S7).

(1) Grouping and Readability Improvements: While individual synapses are still visualized, their display has been improved for legibility. We have condensed the lengthy naming scheme to improve clarity and codified the synapse type by using superscript letters C, E, U to represent chemical, electrical and undefined synapses, respectively. This is explained and shown in figure S7, we added arrows to indicate the directionality of presumed information flow at each synapse. 

(2) Developmental Linkage: We can link objects across datasets via cellular identity, but each synapse in the dataset does not yet have an identity attributed to its spatial coordinates, preventing us from linking specific synapses across development beyond their connectivity (ie, that a given synapses connects cell X to cell Y, for instance), also addressed in R1.11.  

Together, these improvements substantially enhance the utility of the viewer for hypothesis generation by making key quantitative data readily accessible.

Comment

While the DC/C-PHATE visualizations are a useful tool for the user, it is difficult to understand when grouping or splitting of cell contact patterns is biologically significant. DC is a deterministic algorithm applied to a contactome from a single organism, and the authors do not provide quantitative metrics of distances between individual neurons or a number of DC iterations on the C-PHATE plot, nor is the selection process for the threshold for DC described in this manuscript. In the application of DC/C-PHATE to larval stage nerve ring strata organization shown by the authors, qualitative observations of C-PHATE plots colored based on adult data seem to be the only evidence shown for persistent strata during development (Figure 3) or changing architectural motifs across stages (Figure 4). Quantitation of differences in neuron position within the DC hierarchy, or differences in modularity across stages, is needed to support these conclusions. Furthermore, illustrating the quantitative differences in C-PHATE plots used to make these conclusions will provide a more instructive guide for users of NeuroSC in generating future hypotheses.

There are several ways to visualize DC outputs, and one way to quantitatively compare DC clustering events of neurons is via Sankey diagrams. To make the inclusion of these resources more clear, we have highlighted them in lines 175-178 (Supplemental Tables 3-6). ‘DC outputs for each strata across animals can also be inspected using Sankey diagrams (Supplemental Tables 3-6). These spreadsheets detail the neuron members at each iteration of DC, allowing the user to derive quantitative comparisons of clustering events.’

As the reviewer points out, DC is a deterministic algorithm that will iteratively cluster neurons based on the similarity of their contact profiles. To better explain the selection process for the threshold, the number of DC iterations and the quantitative metrics between the neurons, we have added new text in the Diffusion Condensation methods section.  Briefly:

Number of DC iterations: During diffusion Condensation (DC) we track the modularity of the resulting clusters at each iteration and select the iteration with the highest modularity to define the clusters that represent the strata  (Moyle et al., 2021), (Brugnone et al., 2019). Mathematically, modularity is calculated by comparing the actual number of edges within clusters to the expected number of such edges in a randomized network with the same degree distribution (Newman et al., 2006). A higher modularity value implies that nodes within the same cluster are more densely connected to each other than to nodes in other clusters. We now better explain this in lines 562-567.

Threshold for merging points: The threshold (epsilon) used to merge data points in each iteration is set as a small fraction of the spatial extent of the data: for each coordinate dimension (x, y, z), we compute the range (maximum minus minimum), take the maximum of these three values, and divide it by 10,000. This process is performed iteratively for each round of clustering until all data points cluster into a single point. We have updated the manuscript to clarify this threshold selection and included this information in the revised algorithm description and pseudocode. We now better explain this in lines 556-559.

Distances between neurons in DC C-PHATE: In our previous description in Box 1 algorithm 1, we had provided a general algorithm for DC for any high dimensional dataset. We have now revised the algorithm to indicate how we used DC for these EM datasets. 

Distances between neurons are determined by the pixel overlap between their segmented shapes in the EM dataset. We use these distances to build a graph with weighted edges, in which the weight of the edge represents the pixel overlap (the adjacency in the actual EM segmentation). Affinities between neurons, which are a proxy for their distance in the graph, are then computed as now revised in Box 1, Algorithm 1. This process is done iteratively as neurons cluster. To better communicate this, we have changed the text in lines 533-538.  

Comment

R1.5. While the case studies presented by the authors help to highlight the utility of the different visualizations offered by the NeuroSC platform, the authors need to be more careful with the claims they make from these correlative observations. For example, in Figure 4, the authors use C-PHATE clustering patterns to make conclusions about changes in clustering patterns of individual neurons across development based on single animal datasets. In this and many other cases presented in this study with the limited existing datasets, it is difficult to differentiate between developmental changes and individual variability between the neurite positions, contacts, and synapse differences within these data. This caveat needs to be clearly addressed.

We now better explain in the manuscript that the selected case study, of the AVF neuron outgrowth, is not one of just correlation based solely on an EM dataset. Instead, the case study represents the NeuroSC-driven exploration of a biologically significant event supported by several independent datasets, as now explained in lines 257-276.

Briefly, we agree with the reviewer that examining differences across individual EM datasets is insufficient evidence to make conclusions about developmental changes. But the strength of NeuroSC is in its ability to combine and compare multiple datasets, bolstering observations that are not possible by looking at just one dataset, and providing new insights on the way to new hypotheses. We now better explain that we are not looking at single connectomes in isolation and then deriving conclusions, but instead using NeuroSC to compare across 9 EM datasets. We better explain how the tools in NeuroSC, including C-PHATE, enabled comparisons across these multiple connectomes to identify apparent differences in neuronal relationships. We then explain that by using NeuroSC, we could examine these variations in neuronal relationships at the level of individual, cell biological differences of neuronal morphologies between the developmental datasets. This could be due, as pointed by the reviewer, to differences due to development, or just differences between individual animals. In the case of AVF, that features are absent in all early specimens, then arise and persist in all specimens after a certain time point, which lead us to hypothesize they result from a developmental event. Because the segmented objects in NeuroSC are linked to neuronal identities, we are also able to cross reference our observations from the EM datasets with information in other datasets and the literature. In the specific case of postembryonic development of AVF outgrowth, we can now tie the knowledge, from developmental lineage information and molecular profiles, that AVF is a postembryonically born neuron (Sulston et al. 1977, Sun et al 2022, Poole et al 2024, wormatlas.org) to the outgrowth dynamics of its neurites using the postembryonic EM datasets. Our findings using  NeuroSC provide a proof of concept of the utility of the resource and extended our understanding of how the outgrowth of this neuron affects the relationships between the neural circuits in the nerve ring.

Comment

R1.6. Given that recent studies have also quantified contact area between neurons across multiple connectomes (Cook et al., Current Biology, 2023; Yim et al., Nature Communications, 2024), and that the authors use a slightly different approach to quantify contact area, a direct comparison between contact area values obtained in this study with prior studies seems appropriate.

We acknowledge that there are multiple different approaches to calculate adjacencies. In the papers cited above, there are 3 different algorithms used:

(1) Brittin 2019 (python parse Track EM, boundary thresholds), used in Cook et al 2023, Moyle 2021, and this study).

(2) Witvliet 2021 (Matlab 2D masks), used in Cook et al 2023.

(3) Yim 2024 (3D masks), used in Yim et al 2024.

To briefly describe the different approaches, and the methods we chose for this paper:

Algorithm 1 (used in this study) defines adjacency based on distances between boundary points in TrakEM2 segmentations, allowing threshold tuning to accommodate differences in resolution and image quality across datasets—an important feature for consistent cross-dataset comparisons.

Algorithm 2 infers contact via morphological dilation of VAST segmentations, identifying adjacency through overlapping expanded boundaries. 

Algorithm 3 uses voxelwise contact detection with directional surface area measurements and normalization to account for dataset size differences. 

In NeuroSC, we use algorithm 1, mostly because we had tested the rigor of this method in (Moyle et al. 2021), where we have shown that results were robust across a range of thresholds. This flexibility enables tailored application across datasets of varying quality and scale, critical for NeuroSC’s mission of curating data sets across differing methodologies to allow for direct relationship comparisons. We detail the methodology for defining thresholds for each dataset in methods section lines 492-521, defined in Supplementary table 1. Another difference between our analysis and the previously cited work is that for our analysis we also chose to include all individually resolved neurons, including post-embryonic cells, without collapsing them into left/right or dorsal/ventral symmetry classes. In this way our approach retains the full cellular resolution of the nervous system. 

Comment

Neuroglancer is not mentioned at all in the manuscript, despite it being a very similar and widely accepted platform for vEM data visualization across model organisms. An explicit comparison of NeuroSC and Neuroglancer would be appropriate, given the similarity of the tools. Currently, published C. elegans data (Witvliet et al., 2021; Yim et al., 2024) use Neuroglancer-based viewers, and directly comparing NeuroSC and highlighting its strengths relative to Neuroglancer would strengthen the paper.

In the original manuscript we had not mentioned tools like Neuroglancer because we envisioned them as distinct, in intended use and output, from NeuroSC. But, as explained in R1.2 comment, in the revised version we have included a section in the Introduction lines 98-108 and in the Discussion (lines 369- 417) that compares these types of web-based tools and highlights synergies. 

Comment

Assigning shorthand names to strata, such as "shallow reflex circuit" (page 4, line 172), may oversimplify this group of neurons. Either more detailed support for shorthand names of C-PHATE modules should be included, or less speculative names for strata should be used.

We appreciate this comment and understand that the original language used in the manuscript to describe strata categorizations may run the risk of oversimplification. We have now clarified the text to communicate that: 1) Strata are labeled by numbers (Strata 1, Strata 2, Strata 3 and Strata 4), rather than functional features of the neurons forming part of the strata, and that 2) the assignment of ‘strata’ is just one level of classification available via DC/CPHATE (as explained below). 

To be sure, we have observed and published (Moyle et. al. Nature 2021) that within a given stratum, many neurons share the functional identities that we have used as summary descriptors for the strata (eg, shallow reflex circuits for Stratum 1; sensory and integrative circuits in Strata 3 and Strata 4; command interneurons in Strata 2, etc). However, those cell types are not the only members of the strata. We have adjusted the language in lines 197-204 to reflect this more clearly. “Stratum 1, which contains most neurons contributing to shallow reflex circuits that control aversive head movements in response to noxious stimuli, displayed the fewest changes among the developmental connectomes (Figure 3B–F; Supplementary Table 3). In contrast, C. elegans exhibit tractable behaviors that adapt to changing environmental conditions (Flavell et al., 2020). Strata 3 and 4 contain most neurons involved in circuits associated with such learned behaviors, including mechano- and thermo-sensation. This is reflected in Strata 3 and 4 showing the most change in neuronal relationships across postembryonic development.“

Comment

The authors state that NeuroSC can be applied to other model organisms. Since model organisms with greater neuron numbers include more individual neurons per cell class, the authors should support this by quantitatively demonstrating how DC/C-PHATE relationships correlate with shared functional roles among C. elegans neurons.

We now clarify in the manuscript that, like in other organisms, C. elegans neurons are also grouped into functional classes with shared characteristics. In the context of the cylindrical nerve ring of the animal, these neuronal classes are sometimes bilaterally symmetric (forming left-right pairs), four-fold symmetric and six-fold symmetric. We now explain in the discussion that the DC/CPHATE analyses group these neuron classes and their relationships (lines 442-451). In the specific section mentioned by the reviewer, we now also add new text to contextualize this concept and how it might relate to the possible use of these tools in organisms with larger nervous systems: ‘However, our previous work has demonstrated that DC/CPHATE clustering of C. elegans neurons consistently pulls out clusters of shared neuron classes and shared functional roles Moyle et al. (2021). Building on this foundation, we envision applying similar clustering approaches to larger connectomes, aiming to identify classes and functionally related neuronal groups in more complex nervous systems. We suggest that contact profiles, along with neuron morphologies and synaptic partners, can act as ‘fingerprints’ for individual neurons and neuron classes. These ‘fingerprints’ can be aligned across animals of the same species to create identities for neurons. Frameworks for systematic connectomics analysis in tractable model systems such as C. elegans are critical in laying a foundation for future analyses in other organisms with up to a billion-fold increase in neurons (Toga et al., 2012).’

Comment

Lack of surface smoothing in NeuroSC leads to processes sometimes appearing to have gaps, which could be remedied by smoothing with a surface mesh. 

We thank the reviewer for the suggestion, and understand the visibility of gaps in certain neuron processes can be distracting. But this was an intentional choice, with our main goal being to show the most accurate representation of the available data segmentation and avoid any rendering interpretations. In this way, we render the data with the highest fidelity we can and as close as possible to the ground truth of the EM segmentation. We have added language to describe this in the methods, lines 490-491, and in Figure legend 5b.

Comment

Toggling between time points while maintaining the same neurons and contact area in NeuroSC is a really valuable feature. The tool would be improved even more by extending this feature to synapses, specifically by allowing the user to add an entire group of synapses to the viewer at once (e.g. "all synapses between AIM and PVQ"), and to keep this synapse group invariant when toggling between developmental stages.

We thank the reviewer for this suggestion. In response we have now implemented a new feature to ‘clone’ a rendered scene across time while preserving the original elements to ease comparisons. Once the user has rendered a scene, they can use the in-viewer developmental slider to clone the renderings and assigned colors, but display the renderings of the newly selected timepoint. These renderings populate a new window tab which can be dragged to align developmental stage windows side by side. We have added a sentence to account for this in lines 315-317 and to the legend of supplemental Figure S11. 

Reviewer #2 (Public review)

Comment

The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings remain incredibly useful, they were also necessary simplifications for a 2D publication and they lack details of the complex architecture seen within each EM image. Koonce et al take advantage of segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization allows users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source.

We thank that reviewer for this positive assessment of our work.

Comment

While it's impossible to create one tool that will satisfy all potential users, I found myself wanting to have numbers associated with the data. For example, knowing the number of connections or the total surface area of contacts between individual neurons wasn't possible through the viewer, which limits the utility of taking deep analytical dives. While connectivity data is readily accessible through other interfaces such as Nemanode and WormWiring, a more thorough integration may be helpful to some users.

We thank the reviewer for this feedback and in response have now implemented displays with quantitative information in NeuroSC. Now, upon hovering over a contact patch or synapse, the user will see the quantitative data of the relationship. For contact patches, you will see the total area shared between two neurons in that dataset. On hovering over a synapse, you will see how many synapses there are in total with the same members and throughout the dataset. We agree that this improves user analyses, (see also R1.3 response).

Comment

There were several issues with the user interface that made it a bit clunky to use. For example, as I added additional neurons to the filter search box, the loading time got longer and longer. I ran an experiment uploading all of the amphid neurons, one pair at a time. Each additional neuron pair added an additional 5-10 seconds to the loading. By the time I got to the last pair, it took over a minute to load. Issues like these, some of which may be unavoidable given the size of the data, could be conveyed through better documentation. I did not find the tutorial very helpful and the supplementary movies lacked any voiceover, so it wasn't always clear what they were trying to show.

We appreciate that some of the more complex models can take a while to load. One of our core goals is to keep the high resolution of our models to most accurately represent the EM data, so we had to compromise between resolution and loading times. But to address this concern we have now added a ‘loading’ prompt that reassures the user when there is a wait. We also added, as suggested, text guidance throughout all of the supplemental videos (Supplemental Videos 1-4).

Reviewer #3 (Public review)

Comment

A web-based app, NeuroSC, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development In the opinion of this reviewer, only minor revisions are required.

We thank that reviewer for this positive assessment of our work.

Comment

Contact is defined by length, why not contact area? How are these normalized for changes in the overall dimensions of neurons during development?

To clarify our methodology: the adjacency algorithm that we use generates a 2D adjacency profile by summing the number of adjacent boundary points per EM section, which are then summed across all EM z slices.

Contact area can be derived by multiplying the adjacency length in each slice by pixel resolution and z-thickness. Prompted by the reviewer we have now also calculated and display contact surface areas, along with their ranks among all contact relationships for a given neuron. These can be inspected directly via the interface by clicking on a rendered cell or contact patch (Figure S5 and lines 308-312). We believe these additional surface area metrics enhance the interpretability and utility of the viewer.

We apply normalization at the level of the adjacency threshold to account for dataset-specific differences such as contrast, boundary definition, and age-related changes in neuropil packing density. This normalization is applied before running the adjacency algorithm. We do not normalize by individual neuron size, as the contact data are intended to reflect relational differences between neurons, rather than absolute morphological scaling. In fact, our addition of a scale-spheroid within each rendered model emphasizes the large increase in spatial scale that the nerve ring experiences during larval growth.  

Comment

Figure 1, C&D, explanation unclear for how the adjacency matrix is correlated with C-Phate schematic in D.

We thank the reviewer for the comment and have clarified this section by adding greater detail to the explanation of how an adjacency matrix is computed (lines 149-155), as well as a description now in the figure legend 1C. Additionally, we revised Figure 1C and D to simplify neuron representations/colors and to simplify the adjacency heat map gradient. We also extended the area of contact between neurons on Figure 1C to better reflect what would be considered a “contact”. Lastly, in the figure, we changed the color and placement for the z plane arrow and label from black to white, to make it more visible, to highlight the method of computing adjacency for each z slice. 

Comment

Figure 4, panels F & G, unclear why AVF is shown in panel G (L3) but not panel F (L1). Explanation (see below) should be provided earlier, i.e., AVF is not generated until the end of the L1.

We have now clarified this important point by adding labels to Figure 4 panels F and G, ‘Pre-AVF outgrowth’ and ‘Post-AVF outgrowth’ respectively. Briefly, the point is that AVF grows into the nerve ring after the L2 stage, and that is why it is absent in panel F (L1 stage, now with the label ‘Pre-AVF outgrowth’).  

Comment

Line 146 What is the justification for the statement: "By end of Larval Stage 1 (L1), neuronal differentiation has concluded...."? This statement is confusing since this sentence also states that "90% of neurons in the neuropil...have entered the nerve ring..." which would suggest that at least 10% additional NR neurons have NOT fully differentiated.

We have fixed this sentence in the text. Now the sentence reads ‘By Larval stage 1 (L1) 90% of the neurons in the neuropil (161 neurons out of the 181 neurons) have grown into the nerve ring and adopted characteristic morphologies and positions. 

Lines 171-175 What is meant by the statement that "degree of these changes mapped onto...plasticity? What are examples of "behavioral plasticity?"

We have added the following new lines of text (lines 200-204) and now additionally cite a review discussing C. elegans behaviors to clarify and give context to behavioral plasticity. ‘C. elegans exhibit tractable behaviors which can adapt due to changing environmental conditions  (Flavell et. al. Genetics 2020). Strata 3 and 4 contain most neurons belonging to circuits associated with such learned behaviors, including chemo, mechano and thermo sensation. This is seemingly reflected by strata 3 and 4 harboring the most readily recognized set of changes in neuronal relationships across postembryonic development.’  

Comment

Lines 189-190 The meaning of this sentence is unclear, "The logic in....merge events."

This sentence has been deleted and we have instead refocused our descriptions of C-PHATES comparisons by neuronal clustering trajectories and cluster members (rather than iterations).

Comment

Lines 193-208 This section reports varying levels of convergence across larval development in C-Phate maps for the interneurons AIML and PVQL. Iterations leading to convergence varied: 16 (L1), 14 (L2), 22 (L3), 20 (l4), 14 (adult). The authors suggest that these differences are biologically significant and reflect the reorganization of AIML and PVQL contact relationships especially between the L4 and adult. Are these differences in iterations significant?

We agree this could be confusing and instead of focusing on comparing the iteration at which each merging event occurs, we now focus on examining the differences in members of clusters, before and after the merge event. Cluster membership is easier to interpret than the differences in the number of DC iterations (lines 224-229).

Lines 240-241 States that AVF neurons "terminally differentiate in the embryo" which is not correct. AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage which accounts for their outgrowth into the NR during the L2 stage. 

We thank the reviewer for the correction and have edited the text to read: ‘AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage (Sulston et al. (1983); Sun and Hobert (2023); Poole et al. (2024); Hall and Altun (2008); Sulston and Horvitz (1977). AVF neurons do not grow into the nerve ring until the L2 stage, and continue to grow until the Adult stage (lines 261-266).’

Comment

Lines 289-315. A detailed and highly technical description of website architecture would seem more appropriate for the Methods section.

We agree and have moved this section to the methods as suggested (lines 663-690).

Comment

Line 307 "source data is" should be "source data are"

Thank you- we have fixed this grammatical error.

Comment

Line 324 "circuits identities" should be "circuit identity".

Thank you- we have fixed this grammatical error.

Comment

Trademark/copyright conflict with these sites? https://compumedicsneuroscan.com/about/ https://www.neuroscanai.com/

We thank the reviewer for drawing our attention to this. To avoid potential conflicts, we have proactively altered the name to NeuroSC throughout the paper.

eLife Assessment

This valuable study reports convincing evidence about associations between 35 polygenic indices (PGIs) for social, behavioral, and psychological traits, along with some non-fatal health conditions (e.g., BMI) and all-cause mortality in data from Finnish population-based surveys and a twin cohort linked with administrative registers. PGIs for education, depression, alcohol use, smoking, BMI, and self-rated health showed the strongest associations with all-cause mortality, on the order of ~10% increment in risk per PGI standard deviation. Effect sizes from twin-difference analyses tended to be slightly larger than the effect sizes from population cohorts, opposite the pattern generally observed when testing PGI associations with their target phenotypes and supporting robustness of findings to confounding by population stratification.

Reviewer #1 (Public review):

Lahtinen et al. evaluated the association between polygenic scores and mortality. This question has been intensely studied (Sakaue 2020 Nature Medicine, Jukarainen 2022 Nature Medicine, Argentieri 2025 Nature Medicine), where most studies use PRS as an instrument to attribute death to different causes. The presented study focuses on polygenic scores of non-fatal outcomes and separates the cause of death into "external" and "internal". The majority of the results are descriptive, and the data doesn't have the power to distinguish effect sizes of the interesting comparisons: (1) differences between external vs. internal (2) differences between PGI effect and measured phenotype. I have two main comments:

(1) The authors should clarify whether the p-value reported in the text will remain significant after multiple testing adjustment. Some of the large effects might be significant; for example, Figure 2C (note that the small prediction accuracy of PGI in older age groups has been extensively studied, see Jiang, Holmes, and McVean, 2021, PLoS Genetics).

(2) The authors might check if PGI+Phenotype has improved performance over Phenotype only. This is similar to Model 2 in Table 1, but slightly different.

Reviewer #2 (Public review):

Summary:

This study provides a comprehensive evaluation of the association between polygenic indices (PGIs) for 35 lifestyle and behavioral traits and all-cause mortality, using data from Finnish population- and family-based cohorts. The analysis was stratified by sex, cause of death (natural vs. external), age at death, and participants' educational attainment. Additional analyses focused on the six most predictive PGIs, examining their independent associations after mutual adjustment and adjustment for corresponding directly measured baseline risk factors.

Strengths:

Large sample size with long-term follow-up.

Use of both population- and family-based analytical approaches to evaluate associations.

Weaknesses:

It is unclear whether the PGIs used for each trait represent the most current or optimal versions based on the latest GWAS data.

If the Finnish data used in this study also contributed to the development of some of the PGIs, there is a risk of overestimating their associations with mortality due to overfitting or "double-dipping." Similar inflation of effect sizes has been observed in studies using the UK Biobank, which is widely used for PGI construction.

eLife Assessment

In this valuable study, the authors developed long-term imaging tools to simultaneously monitor the temporal and spatial dynamics of excitatory and inhibitory synapses and reported that excitatory and inhibitory synapses need to develop synergistically during synaptogenesis to maintain balance. While the analysis and quantification of the imaging data are incomplete, there is convincing evidence that the developed tools are feasible. If these tools can function stably in vivo, their applications will be much broader.

Reviewer #1 (Public review):

Summary:

By imaging the dynamics of synaptic proteins in cultured neurons, this study presents significant findings regarding the dynamics of excitatory and inhibitory synaptic proteins during development. The evidence shows that the ratios of excitatory and inhibitory synaptic proteins are stable during synapse development. This discovery advances our understanding of the complex mechanisms governing synapse formation. The strength of the evidence is robust, as it is supported by a combination of biological assays and endogenous labeling.

Strengths:

This research sheds light on the dynamics of the excitatory and inhibitory synapses during development. It is crucial to understand that while excitatory synapses and inhibitory synapses are developed independently, the ratio of their number is relatively stable during development, maintaining a stable excitatory/inhibitory ratio.

Important findings and implications in the research include:

(1) Persistent Synapse Dynamics: Excitatory and inhibitory synapses remain highly dynamic even in mature neurons (DIV12-14), challenging the dogma that synaptic structures are stable after the synaptogenesis stage.

(2) Maintained E/I Balance: Despite ongoing synapse turnover (formation/elimination) and presynaptic terminal reduction, the overall density and ratio of excitatory-to-inhibitory synapses remain relatively stable during circuit maturation (Figure 7).

(3) Developmental Shifts: While presynaptic compartments decrease over time, postsynaptic sites increase, suggesting independent regulation of pre- and postsynaptic elements within a stable E/I framework.

Weaknesses:

This study focuses on specific synaptic proteins within synapses, which may not fully represent the dynamics of other synaptic machinery; also, whether similar observations exist in vivo is still unknown. Further research is needed to explore the implications of these findings in more complex neuronal environments.

Reviewer #2 (Public review):

Summary:

The Garbett et al. identified a critical need to begin to understand the interplay between the assembly, maturation, and elimination of excitatory and inhibitory synapses. They also detail the lack of reliable tools to address this gap in knowledge. Here, the authors developed synaptic reporters expressed by lentiviruses (mClover3-Homer1c, HaloTag-Syb2, and tdTomato-Gephyrin). They combined these reporters with resonance scanning confocal imaging to measure synapses over a 15-hour period during neuron development and in mature neurons in primary hippocampal cultures. Using these reporters in the same neuron, the authors compared the ratios of postsynaptic excitatory and inhibitory specializations that co-localize with presynaptic terminals during development and in mature neurons and found that they are stable across time points. Finally, the authors developed CRISPR/Cas9 tools (TKIT) to knock-in endogenous fluorescent tags (GFP/tdTomato-Gephyrin) or epitope tags (HA-Bassoon and HA-Homer1) to begin to study synapse dynamics using endogenous proteins. I believe this paper highlights an important gap in knowledge and begins to offer methodologies to determine the dynamic coordination between excitatory and inhibitory synapses.

Strengths:

(1) The experiments are well-designed and carefully controlled.

(2) The authors carefully validated the reporter and TKIT constructs.

(3) The authors provide strong proof-of-principle for the use of the reporter constructs to track synapse formation, maintenance, and elimination over a 15-hour period.

(4) Ingenious use of technologies (reporters, TKIT, and resonance scanning confocal microscopy) to develop a platform for future studies of synapse dynamics.

(5) Strong evidence supporting that the ratio of excitatory and inhibitory synapses (those that oppose syb2) stays constant through development.

Weaknesses:

Overall, this is a well-executed study that develops tools to simultaneously image excitatory and inhibitory synapse dynamics and represents an important first step to address the fundamental question regarding the coordination between these two types of synapses.

Minor weaknesses of the manuscript include:

(1) The lack of a characterization of endogenous Homer1-positive excitatory synapses using TKIT.

(2) Discussion about other approaches to study excitatory and inhibitory synapses using endogenous proteins (e.g., intrabodies - FingR or nanobodies) should be included.

(3) The activity state of a neuron and/or a synapse might alter the dynamic properties (formation, maintenance, and/or elimination). A discussion on whether the overexpression of Homer1 and/or gephyrin might alter synapse/neuron activity would provide greater interpretability of the results. A discussion of the potential limitations and benefits of the reporter and TKIT approaches would be beneficial.

(4) A description and interpretation of the computational approach to calculate particle tracking would be helpful. I found that particle tracking figures, while elegant, are difficult to interpret.

Reviewer #3 (Public review):

In the present study, the authors describe the development of new tools and imaging strategies to assess the concomitant development of excitatory and inhibitory synapses in dissociated neuron cultures. To this end, they generate fluorescently tagged constructs of excitatory and inhibitory synapse marker proteins using either conventional overexpression or CRISPR-based strategies. They then image these marker proteins over a timespan of 15 hours to assess synaptic dynamics at different developmental timepoints. Based on their data, they conclude that excitatory and inhibitory synapse development occur in concert to maintain a functional balance despite individual synapse turnover.

Overall, this study addresses an interesting question, i.e., the interplay between the development of excitatory and inhibitory synapses, which has important implications, particularly for neurodevelopmental disorders in which the balance of excitation and inhibition is disrupted. The experiments are technically solid and well-executed, and the individual images are highly compelling.

However, a number of aspects remain to be addressed in order for the study to support the claims made by the authors. First, the novelty aspect of the development of the fluorescently tagged synaptic proteins is unclear, since reporters of this nature are in routine use in many labs. Second, the analysis of the acquired images often seems incomplete, with only example images but no quantification shown, or the distinction between spatial and temporal dynamics appearing unclear. Third, given this incomplete analysis, the interpretations of the authors are not always convincingly supported by the data presented. In conclusion, substantial improvements are required to render the main messages of the study clear and compelling.

eLife Assessment

This paper presents valuable findings on the processing of sound mixtures in the auditory cortex of ferrets, a species widely used for studies of auditory processing. Using the convenient and relatively high-resolution method of functional ultrasound imaging, the authors provide convincing evidence that background noise invariance emerges across the auditory cortical processing hierarchy. They also draw informative comparisons with previously published fMRI data obtained in humans. This work will be of interest to researchers studying the auditory cortex and the neural mechanisms underlying auditory scene analysis and hearing in noise.

Reviewer #1 (Public review):

This is a very interesting paper addressing the hierarchical nature of the mammalian auditory system. The authors use an unconventional technique to assess brain responses -- functional ultrasound imaging (fUSI). This measures blood volume in cortex at a relatively high spatial resolution. They present dynamic and stationary sounds in isolation and together, and show that the effect of the stationary sounds (relative to the dynamic sounds) on blood volume measurements decreases as one ascends the auditory hierarchy. Since the dynamic/stationary nature of sounds is related to their perception as foreground/background sounds, this suggests that neurons in higher levels of the cortex may be increasingly invariant to background sounds.

The study is interesting, well conducted and well written. In the revised manuscript, the authors have addressed all the points I raised in my review.

Reviewer #2 (Public review):

Summary:

Noise invariance is an essential computation in sensory systems for stable perception across a wide range of contexts. In this paper, Landemard et al. perform functional ultrasound imaging across primary, secondary and tertiary auditory cortex in ferrets to uncover the mesoscale organization of background invariance in auditory cortex. Consistent with previous work, they find that background invariance increases throughout the cortical hierarchy. Importantly, they find that background invariance is largely explained by progressive changes in spectro-temporal tuning across cortical stations which are biased towards foreground sound features. To test if these results are broadly relevant, they then re-analyze human fMRI data and find that spectro-temporal tuning fails to explain background invariance in human auditory cortex.

Strengths:

(1) Novelty of approach: Though the authors have published on this technique previously, functional ultrasound imaging offers unprecedented temporal and spatial resolution in a species where large-scale calcium imaging is not possible and electrophysiological mapping would take weeks or months. Combining mesoscale imaging with a clever stimulus paradigm, they address a fundamental question in sensory coding.

(2) Quantification and execution: the results are generally clear and well supported by statistical quantification.

(3) Elegance of modeling: The spectrotemporal model presented here is explained clearly and most importantly, provides a compelling framework for understanding differences in background invariance across cortical areas.

Comments on revised version:

The authors have addressed all of my previous concerns and their publicly shared data is easy to view, this is a nice contribution to the field.

Reviewer #3 (Public review):

This paper investigates invariance to natural background noise in the auditory cortex of ferrets and humans. The authors first replicate, in ferrets, a finding from human neuroimaging showing that invariance to background noise increases along the cortical hierarchy (i.e. from primary to non-primary auditory cortex). Next, the authors ask whether this pattern of invariance could be explained by differences in tuning to low-level acoustic features across primary and non-primary regions. The authors conclude that this tuning can explain the spatial organization of background invariance in ferrets, but not in humans. The conclusions of the paper are well supported by the data.

The paper is very straightforwardly written, with a generally clear presentation including well-designed and visually appealing figures. Not only does this paper provide an important replication in a non-human animal model commonly used in auditory neuroscience, but also it extends the original findings in three ways. First, the authors reveal a more fine-grained gradient of background invariance by showing that background invariance increases across primary, secondary and tertiary cortical regions. Second, the authors address a potential mechanism that might underlie this pattern of invariance by considering whether differences in tuning to frequency and spectrotemporal modulations across regions could account for the observed pattern of invariance. The spectrotemporal modulation encoding model used here is a well-established approach in auditory neuroscience and seems appropriate for exploring potential mechanisms underlying invariance in auditory cortex, particularly in ferrets. Third, the authors provide a more complete picture of invariance by additionally analyzing foreground invariance, a complementary measure not explored in the original study.

Comments on author revisions:

The authors have thoroughly addressed the concerns raised in my initial review.

Author response:

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

Reviewer #1(Public review):

(1) Changes in blood volume due to brain activity are indirectly related to neuronal responses. The exact relationship is not clear, however, we do know two things for certain: (a) each measurable unit of blood volume change depends on the response of hundreds or thousands of neurons, and (b) the time course of the volume changes are slow compared to the potential time course of the underlying neuronal responses. Both of these mean that important variability in neuronal responses will be averaged out when measuring blood changes. For example, if two neighbouring neurons have opposite responses to a given stimulus, this will produce opposite changes in blood volume, which will cancel each other out in the blood volume measurement due to (a). This is important in the present study because blood volume changes are implicitly being used as a measure of coding in the underlying neuronal population. The authors need to acknowledge that this is a coarse measure of neuronal responses and that important aspects of neuronal responses may be missing from the blood volume measure.

The reviewer is correct: we do not measure neuronal firing but use blood volume as a proxy for bulk local neuronal activity, which does not capture the richness of single neuron responses. This is why the paper focuses on large-scale spatial representations as well as cross-species comparison. For this latter purpose, fMRI responses are on par with our fUSI data, with both neuroimaging techniques showing the same weakness. We have now added this point to the discussion: 

“Second, we used blood volume as a proxy for local neuronal activity. Thus, our signal ignores any heterogeneity that might exist at the level of local neuronal populations. However, our main findings are related to the large-scale organization of cortical responses and how they relate to those of humans. For this purpose, the functional spatial resolution of our signal, driven by the spatial resolution of neurovascular coupling, should be adapted. In addition, using hemodynamic signals provides a much better comparison with human fMRI data, where the same limitations are present.”

(2) More importantly for the present study, however, the effect of (b) is that any rapid changes in the response of a single neuron will be cancelled out by temporal averaging. Imagine a neuron whose response is transient, consisting of rapid excitation followed by rapid inhibition. Temporal averaging of these two responses will tend to cancel out both of them. As a result, blood volume measurements will tend to smooth out any fast, dynamic responses in the underlying neuronal population. In the present study, this temporal averaging is likely to be particularly important because the authors are comparing responses to dynamic (nonstationary) stimuli with responses to more constant stimuli. To a first approximation, neuronal responses to dynamic stimuli are themselves dynamic, and responses to constant stimuli are themselves constant. Therefore, the averaging will mean that the responses to dynamic stimuli are suppressed relative to the real responses in the underlying neurons, whereas the responses to constant stimuli are more veridical. On top of this, temporal following rates tend to decrease as one ascends the auditory hierarchy, meaning that the comparison between dynamic and stationary responses will be differently affected in different brain areas. As a result, the dynamic/stationary balance is expected to change as you ascend the hierarchy, and I would expect this to directly affect the results observed in this study.

It is not trivial to extrapolate from what we know about temporal following in the cortex to know exactly what the expected effect would be on the authors' results. As a first-pass control, I would strongly suggest incorporating into the authors' filterbank model a range of realistic temporal following rates (decreasing at higher levels), and spatially and temporally average these responses to get modelled cerebral blood flow measurements. I would want to know whether this model showed similar effects as in Figure 2. From my guess about what this model would show, I think it would not predict the effects shown by the authors in Figure 2. Nevertheless, this is an important issue to address and to provide control for.

We understand the reviewer’s concern about potential differences in response dynamics in stationary vs non-stationary sounds. It seems that the reviewer is concerned that responses to foregrounds may be suppressed in non-primary fields because foregrounds are not stationary, and non-primary regions could struggle to track and respond to these sounds. Nevertheless, we observed the contrary, with non-primary regions overrepresenting non-stationary (dynamic) sounds, over stationary ones. For this reason, we are inclined to think that this explanation cannot falsify our findings. 

We understand the comment that temporal following rates might differ across regions in the auditory hierarchy and agree. In fact, we do show that tuning to temporal rates differs across regions and partly explains the differences in background invariance we observe. In this regard, we think the reviewer’s suggestion is already implemented by our spectrotemporal model, which incorporates the full range of realistic temporal following rates (up to 128 Hz). The temporal averaging is done as we take the output of the model (which varies continuously through time) and average it in the same window as we used for fUSI data. When we fit this model to the ferret data, we find that voxels in non-primary regions, especially VP (tertiary auditory cortex), tend to be more tuned to low temporal rates (Figure 2F, G), and that background invariance is stronger in voxels tuned to low rates. This is, however, not true in humans, suggesting that background invariance in humans relies on different computational mechanisms. We have added a sentence to clarify this: “The model included a range of realistic temporal rates and this axis was the most informative to discriminate foregrounds from backgrounds.”

(3) I do not agree with the equivalence that the authors draw between the statistical stationarity of sounds and their classification as foreground or background sounds. It is true that, in a common foreground/background situation - speech against a background of white noise - the foreground is non-stationary and the background is stationary. However, it is easy to come up with examples where this relationship is reversed. For example, a continuous pure tone is perfectly stationary, but will be perceived as a foreground sound if played loudly. Background music may be very non-stationary but still easily ignored as a background sound when listening to overlaid speech. Ultimately, the foreground/background distinction is a perceptual one that is not exclusively determined by physical characteristics of the sounds, and certainly not by a simple measure of stationarity. I understand that the use of foreground/background in the present study increases the likely reach of the paper, but I don't think it is appropriate to use this subjective/imprecise terminology in the results section of the paper.

We appreciate the reviewer’s comment that the classification of our sounds into foregrounds and backgrounds is not verified by any perceptual experiments. We use those terms to be consistent with the literature (McWalter and McDermott, 2018; McWalter and McDermott, 2019), including the paper we derived this definition from (Kell et al., 2019). These terms are widely used in studies where no perceptual or behavioral experiments are included, and even when animals are anesthetized. We have clarified and justified this choice in the beginning of the Results section:

“We used three types of stimuli: foregrounds, backgrounds, and combinations of those. We use those terms to refer to sounds differing in their stationarity, under the assumption that stationary sounds carry less information than non-stationary sounds, and are thus typically ignored.”

We have also added a paragraph in the discussion to emphasize the limits of this definition:

“First, this study defined foregrounds and backgrounds solely based on their acoustic stationarity, rather than perceptual judgments. This choice allowed us to isolate the contribution of acoustic factors in a simplified setting. Within this controlled framework, we show that acoustic features of foreground and background sounds drive their separation in the brain and the hierarchical extraction of foreground sound features.”

(4) Related to the above, I think further caveats need to be acknowledged in the study. We do not know what sounds are perceived as foreground or background sounds by ferrets, or indeed whether they make this distinction reliably to the degree that humans do. Furthermore, the individual sounds used here have not been tested for their foreground/background-ness. Thus, the analysis relies on two logical jumps - first, that the stationarity of these sounds predicts their foreground/background perception in humans, and second, that this perceptual distinction is similar in ferrets and humans. I don't think it is known to what degree these jumps are justified. These issues do not directly affect the results, but I think it is essential to address these issues in the Discussion, because they are potentially major caveats to our understanding of the work.

We agree with the reviewer that the foreground-background distinction might be different in ferrets. In anticipation of that issue, we had enriched the sound set with more ecologically relevant sounds, such as ferret and other animal vocalizations. Nevertheless, we have emphasized this limitation in addition to the limitation of our definition of foregrounds and backgrounds in the discussion: 

“In addition, most of the sounds included in our study likely have more relevance for humans compared to ferrets (see table \ref{tbl1}). Despite including ferret vocalizations and environmental sounds that are more ecologically relevant for ferrets, it is not clear whether ferrets would behaviorally categorize foregrounds and backgrounds as humans do. Examining how ferrets naturally orient or respond to foreground and background sounds under more ecologically valid conditions, potentially with free exploration or spontaneous listening paradigms, could help address this issue.”

Reviewer #2(Public review);

(1) Interpretation of the cerebral blood volume signal: While the results are compelling, more caution should be exercised by the authors in framing their results, given that they are measuring an indirect measure of neural activity, this is the difference between stating "CBV in area MEG was less background invariant than in higher areas" vs. saying "MEG was less background invariant than other areas". Beyond framing, the basic properties of the CBV signal should be better explored:

a) Cortical vasculature is highly structured (e.g. Kirst et al.( 2020) Cell). One potential explanation for the results is simply differences in vasculature and blood flow between primary and secondary areas of auditory cortex, even if fUS is sensitive to changes in blood flow, changes in capillary beds, etc (Mace et al., 2011) Nat. Methods.. This concern could be addressed by either analyzing spontaneous fluctuations in the CBV signal during silent periods or computing a signal-to-noise ratio of voxels across areas across all sound types. This is especially important given the complex 3D geometry of gyri and sulci in the ferret brain.

We agree with the reviewers that there could be differences in vasculature across subregions of the auditory cortex and note that this point would also be valid for the published human fMRI data. Nevertheless, even if small differences in vasculature were present, it is unlikely that they would affect our analyses and results, which are designed to be independent of local vascular density. First, we normalize the signal in each voxel using the silent periods, so that the absolute strength of the raw signal, or baseline blood volume in each voxel, is factored in our analysis. Second, we only focus on reliably responsive voxels in each region and do see comparable sound-evoked responses in all regions (Figure S2). Third, our analysis mostly relies on voxel-based correlation across sounds, which is independent of the mean and variance of the voxel responses. Differences in noise, measured through test-retest reliability, can affect values of correlation, which is why we used a noise-correction procedure. After this procedure, invariance does not depend on test-retest, and differences across regions are still seen when matching for test-retest (new  Figure S7). Thus, we believe that differences in vascular architecture across regions are unlikely to affect our results. We added this point in the Methods section when discussing the noise-correction:

“After this correction, the differences we observed between brain regions were present regardless of voxels' test-retest reliability, or noise level (Figure S7). Thus, potential differences in vasculature across regions are unlikely to affect our results.”

b) Figure 1 leaves the reader uncertain what exactly is being encoded by the CBV signal, as temporal responses to different stimuli look very similar in the examples shown. One possibility is that the CBV is an acoustic change signal. In that case, sounds that are farther apart in acoustic space from previous sounds would elicit larger responses, which is straightforward to test. Another possibility is that the fUS signal reflects time-varying features in the acoustic signal (e.g. the low-frequency envelope). This could be addressed by cross-correlating the stimulus envelope with fUS waveform. The third possibility, which the authors argue, is that the magnitude of the fUS signal encodes the stimulus ID. A better understanding of the justification for only looking at the fUS magnitude in a short time window (2-4.8 s re: stimulus onset) would increase my confidence in the results.

We thank the reviewer for raising that point as it highlights that the layout of Figure 1 is misleading. While Figure 1B shows an example snippet of our sound streams, Figure 1D shows the average timecourse of CBV time-locked to a change in sound (foreground or background, isolated or in a mixture). This is the average across all voxels and sounds, aiming at illustrating the dynamics for the three broad categories. In Figure 1E however, we show the cross-validated cross-correlation of CBV across sounds (and different time lags). To obtain this, we compute for each voxel the response to each sound at each time lag, thus obtaining two vectors (size: number of sounds) per lag, one per repeat. Then, we correlate all these vectors across the two repeats, obtaining one cross-correlation matrix per voxel. We finally average these matrices across all voxels. The presence of red squares with high correlations demonstrates that the signal encodes sound identity, since CBV is more similar across two repeats of the same sound (e.g., in the foreground only matrix, 0-5 s vs 0-5 s), than two different sounds (0-5 s vs. 7-12 s). We modified the figure layout as well as the legend to improve clarity.

(2) Interpretation of the human data: The authors acknowledge in the discussion that there are several differences between fMRI and fUS. The results would be more compelling if they performed a control analysis where they downsampled the Ferret fUS data spatially and temporally to match the resolution of fMRI and demonstrated that their ferret results hold with lower spatiotemporal resolution.

We agree with the reviewer that the use of different techniques might come in the way of cross-species comparison. We already control for the temporal aspect by using the average of stimulus-evoked activity across time (note that due to scanner noise, sounds are presented cut into small pieces in the fMRI experiments). Regarding the spatial aspect, there are several things to consider. First, both species have brains of very different sizes, a factor that is conveniently compensated for by the higher spatial resolution of fUSI compared to fMRI (0.1 vs 2 mm). Downsampling to fMRI resolution would lead to having one voxel per region per slice, which is not feasible. We also summarize results with one value per region, which is a form of downsampling that is fairer across species. Furthermore, we believe that we already established in a previous study (Landemard et al, 2021 eLife) that fUSI and fMRI data are comparable signals. We indeed could predict human fMRI responses to most sounds from ferret fUSI responses to the same identical sounds. We clarified these points in the discussion:

“In addition, fMRI has a worse spatial resolution than fUSI (here, 2 vs. 0.1 mm voxels). However, this difference in resolution compensates for the difference in brain size between humans and ferrets. In our previous work, we showed that a large fraction of cortical responses to natural sounds could be predicted from one species to the other using these methods (Landemard et al., 2021).”

Reviewer #3 (Public review):

As mentioned above, interpretation of the invariance analyses using predictions from the spectrotemporal modulation encoding model hinges on the model's ability to accurately predict neural responses. Although Figure S5 suggests the encoding model was generally able to predict voxel responses accurately, the authors note in the introduction that, in human auditory cortex, this kind of tuning can explain responses in primary areas but not in non-primary areas (Norman-Haignere & McDermott, PLOS Biol. 2018). Indeed, the prediction accuracy histograms in Figure  S5C suggest a slight difference in the model's ability to predict responses in primary versus non-primary voxels. Additional analyses should be done to a) determine whether the prediction accuracies are meaningfully different across regions and b) examine whether controlling for prediction accuracy across regions (i.e., subselecting voxels across regions with matched prediction accuracy) affects the outcomes of the invariance analyses.

The reviewer is correct: the spectrotemporal model tends to perform less well in human non-primary cortex. We believe this does not contradict our results but goes in the same direction: while there is a gradient in invariance in both ferrets and humans, this gradient is predicted by the spectrotemporal model in ferrets, but not in humans (possibly indeed because predictions are less good in human non-primary auditory cortex). Regardless of the mechanism, this result points to a difference across species. In ferrets, we found a significantly better prediction accuracy in VP (p=0.001, permutation test) and no differences between MEG and dPEG (p=0.89). In humans, prediction accuracy was slightly higher in primary compared to non-primary auditory cortex, but this effect was not significant (p=0.076). In both species, when matching prediction accuracy between regions, the gradients in invariance were preserved. We have added these analyses to the manuscript (Figure S5).

A related concern is the procedure used to train the encoding model. From the methods, it appears that the model may have been fit using responses to both isolated and mixture sounds. If so, this raises questions about the interpretability of the invariance analyses. In particular, fitting the model to all stimuli, including mixtures, may inflate the apparent ability of the model to "explain" invariance, since it is effectively trained on the phenomenon it is later evaluated on. Put another way, if a voxel exhibits invariance, and the model is trained to predict the voxel's responses to all types of stimuli (both isolated sounds and mixtures), then the model must also show invariance to the extent it can accurately predict voxel responses, making the result somewhat circular. A more informative approach would be to train the encoding model only on responses to isolated sounds (or even better, a completely independent set of sounds), as this would help clarify whether any observed invariance is emergent from the model (i.e., truly a result of low-level tuning to spectrotemporal features) or simply reflects what it was trained to reproduce.

We thank the reviewer for this suggestion. We have run an additional prediction using only the sounds presented in isolation, which replicates our main results (new Figure S6). We have added this control to the manuscript:

“Results were similar if the model was fit solely on isolated sounds, excluding mixtures from the training set (Figure S6).”

Finally, the interpretation of the foreground invariance results remains somewhat unclear. In ferrets (Figure 2I), the authors report relatively little foreground invariance, whereas in humans (Figure 5G), most participants appear to show relatively high levels of foreground invariance in primary auditory cortex (around 0.6 or greater). However, the paper does not explicitly address these apparent crossspecies differences. Moreover, the findings in ferrets seem at odds with other recent work in ferrets (Hamersky et al. 2025 J. Neurosci.), which shows that background sounds tend to dominate responses to mixtures, suggesting a prevalence of foreground invariance at the neuronal level. Although this comparison comes with the caveat that the methods differ substantially from those used in the current study, given the contrast with the findings of this paper, further discussion would nonetheless be valuable to help contextualize the current findings and clarify how they relate to prior work.

We thank the reviewer for this point. While we found a trend for higher background invariance than foreground invariance in ferret primary auditory cortex, this difference was not significant and many voxels exhibit similar levels of background and foreground invariance (for example in Figure 2D, G). Thus, we do not think our results are inconsistent with Hamersky et al., 2025, though we agree the bias towards background sounds is not as strong in our data. This might indeed reflect differences in methodology, both in the signal that is measured (blood volume vs spikes), and the sound presentation paradigm. Our timescales are much slower and likely reflect responses post-adaptation, which might not be as true for Hamersky et al. We have added this point to the discussion, as well as a comment on the difference between ferrets and humans in foreground invariance in primary auditory cortex:

“In ferrets, primary auditory cortex has been found to over-represent backgrounds in mixtures compared to foregrounds (Hamersky et al., 2025). In contrast, we found a slight, non-significant bias towards foregrounds in primary regions. This difference could be driven by a difference in timescales, as we looked at slower timescales in which adaptation might be more present, reducing the strength of background encoding. In humans, we found a much smaller gap between background and foreground invariance in primary auditory cortex, which was not predicted by the spectrotemporal model. Additional, more closely controlled experiments would be needed to confirm and understand this species difference.”

Reviewer #1 (Recommendations for the authors):

(1) In the introduction, explain the relationship between background/foreground and stationarity/non-stationarity, and thus why stationary/nonstationary stimuli could be used to probe differences in background/foreground processing.

We have added a sentence at the beginning of the results section to justify our choice (see public review).