Author response:

We thank the reviewers for their primarily positive comments and the critiques about where the manuscript could be improved. We agree with the vast majority of points raised. In our revised submission, we will:

• Clarify some of the wording such as “unified mechanism” so that our intended meaning is clear to all readers

• Completely change figure 2, as we accept the critique that an X-Y plot is not the logical way to present this concept

• Amend the legends of figures 1 and 3 so that the disease pathways we are attempting to illustrate are clear for all readers

• Expand on the genetic interactions between humans and TB and cite the manuscripts suggested

• Add further discussion on multiple disease endotypes, and the immunological events that may lead to these distinct end points, along with how this may inform treatment stratification approaches

• Extend the discussion about trained immunity

• Make specific changes to address each of the reviewers’ points in the recommendations to authors

• In the minority of cases where we feel a change is not necessary, we will justify this in our response to reviews

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

Summary: 

Kang et al. provide the first experimental insights from holographic stimulation of auditory cortex. Using stimulation of functionally-defined ensembles, they test whether overactivation of a specific subpopulation biases simultaneous and subsequent sensory-evoked network activations. 

Strengths: 

The investigators use a novel technique to investigate the sensory response properties in functionally defined cell assemblies in auditory cortex. These data provide the first evidence of how acutely perturbing specific frequency-tuned neurons impacts the tuning across a broader population. Their revised manuscript appropriately tempers any claims about specific plasticity mechanisms involved. 

Weaknesses: 

Although the single cell analyses in this manuscript are comprehensive, questions about how holographic stimulation impacts population coding are left to future manuscripts, or perhaps re-analyses of this unique dataset. 

Reviewer #2 (Public review): 

The goal of HiJee Kang et al. in this study is to explore the interaction between assemblies of neurons with similar pure-tone selectivity in mouse auditory cortex. Using holographic optogenetic stimulation in a small subset of target cells selective for a given pure tone (PTsel), while optically monitoring calcium activity in surrounding non-target cells, they discovered a subtle rebalancing process: co-tuned neurons that are not optogenetically stimulated tend to reduce their activity. The cortical network reacts as if an increased response to PTsel in some tuned assemblies is immediately offset by a reduction in activity in the rest of the PTseltuned assemblies, leaving the overall response to PTsel unchanged. The authors show that this rebalancing process affects only the responses of neurons to PTsel, not to other pure tones. They also show that assemblies of neurons that are not selective for PTsel don't participate in the rebalancing process. They conclude that assemblies of neurons with similar pure-tone selectivity must interact in some way to organize this rebalancing process, and they suggest that mechanisms based on homeostatic signaling may play a role. 

The authors have successfully controlled for potential artefacts resulting from their optogenetic stimulation. This study is therefore pioneering in the field of the auditory cortex (AC), as it is the first to use single-cell optogenetic stimulation to explore the functional organization of AC circuits in vivo. The conclusions of this paper are very interesting. They raise new questions about the mechanisms that could underlie such a rebalancing process. 

(1) This study uses an all-optical approach to excite a restricted group of neurons chosen for their functional characteristics (their frequency tuning), and simultaneously record from the entire network observable in the FOV. As stated by the authors, this approach is applied for the first time to the auditory cortex, which is a tour de force. However, such approach is complex and requires precise controls to be convincing. The authors provide important controls to demonstrate the precise ability of their optogenetic methods. In particular, holographic patterns used to excite 5 cells simultaneously may be associated with out-of-focus laser hot spots. Cells located outside of the FOV could be activated, therefore engaging other cells than the targeted ones in the stimulation. This would be problematic in this study as their tuning may be unrelated to the tuning of the targeted cells. To control for such effect, the authors have decoupled the imaging and the excitation planes, and checked for the absence of out-of-focus unwanted excitation (Suppl Fig1). 

(2) In the auditory cortex, assemblies of cells with similar pure-tone selectivity are linked together not only by their ability to respond to the same sound, but also by other factors. This study clearly shows that such assemblies are structured in a way that maintains a stable global response through a rebalancing process. If a group of cells within an assembly increases its response, the rest of the assembly must be inhibited to maintain the total response. 

One surprising result is the clear boundary between assemblies: a rebalancing process occurring in one assembly does not affect the response in another assembly comprising cells tuned to a different frequency. However, this is slightly challenged by the data shown in Figure 3. 

Figure 3B-left, for example, shows that, compared to controls, non-target 16 kHzpreferring neurons only decrease their response to a 16 kHz pure tone when the cells targeted by the opto stimulation also prefer 16 kHz, but not when the targeted cells prefer 54 kHz. However, the inverse is not entirely true. Again compared to controls, Figure 3B (right) shows that non-target 54 kHz-preferring neurons decrease their response to a 54 kHz pure tone when the targeted cells also prefer 54 kHz; however, they also tend to be inhibited when the targeted cells prefer 16 kHz. 

The authors suggest this may be due to the partial activation of 54 kHz-preferring cells by 16 kHz tones and propose examining the response of highly selective neurons. The results are shown in Figure 3F. It would have been more logical to show the same results as in Figure 3B, but with the left part restricted to highly 16 kHz-selective cells and the right part to highly 54 kHz-selective cells. However, the authors chose to pool all responses to 16 kHz and 54 kHz tones in every triplet of conditions (control, opto stimulation on 16 kHz-preferring cells and opto stimulation on 54 kHz-preferring cells), which blurs the result of the analysis. 

We thank reviewers for highlighting the strengths of our work and providing valuable feedback. We further developed our manuscript mainly from Reviewer 2’s point on the overall effect explained as the main result. One of the main reasons why we chose to pool all tone preferring cells instead of highly selective cells was to ensure that the observed effect not necessarily driven by only a small group of neurons but rather that the effect was driven at the population level, especially at a subject level for Figure 3B. While Figure 3F represents how highly selective cells to each frequency play a major role in the effect, we now have added additional results with only highly selective neurons as Supplementary Figure 3. The left panel shows restricting the population to highly selective neurons to 16 kHz and the right panel restricting the population to highly selective neurons to 54 kHz at cell population level to emphasize the result (Supplementary Figure 3). 

We appreciate an additional raised point by Reviewer 1 regarding the stimulation effect on population coding. Our primary focus in this manuscript was to establish single cell level effects of holographic stimulation, and we believe that population coding analyses would benefit from a more cell-type-specific approach. We plan to pursue such analyses in follow-up studies where cell types can be better identified and linked to network dynamics. 

Reviewer #1 (Recommendations for the authors): 

The authors have appropriately addressed my concerns. 

As this dataset will be of general interest, it would be helpful to include a doi/link to their data repository in the data availability section. 

Updating the data repository to the institution server is currently in progress. We will provide the correct doi or link as soon as it becomes available. In the meantime, we will ensure to share them with anyone who contacts to us directly. 

Reviewer #2 (Recommendations for the authors): 

Many references to Figures have not been updated between the two versions of the manuscript. See lines 107, 128, 297, 321 and 346. 

We are sorry for the confusion with mislabelled figures. We now have updated all the figure numbers accordingly.

In the paragraph beginning on line 266, there is no explicit reference to Figure 3C. 

We now added Figure 3C reference in the main text (line 290). 

If the new analysis includes 15 FOV for stim on 54 kHz-preferring cells, as indicated in the rebuttal, the corresponding numbers should be corrected in lines 152 and 180. 

We now updated the number of FOVs accordingly. 

The added model is not explained well enough. How are the calcium traces simulated? It is difficult to ascertain whether the result shown in Figure 3C is merely a trivial consequence of the hypothesis that suppression is applied to co-tuned neurons or to all neurons. 

We are sorry for the lack of important details in the explanation of the model. We simulated time-varying sound-evoked calcium transient especially by applying different decay time constant (faster decay for co-tuned neurons and slower decay for non co-tuned neurons) to closely match the real data. More detailed explanation on this is now included in the manuscript (lines 644 – 650). Since our data do not currently allow us to identify specific cell types, we focused on modelling the stronger suppression observed in co-tuned neurons, especially by adapting the stimulation effect of target cells from the real data. In this revision, we now added data showing that ‘Randomly selected cells’ from the two groups (co-tuned or non co-tuned cell groups) did not exhibit any stimulation effect (added column in Figure 3D) to further indicate that suppression specific to co-tuned neurons is the key factor underlying the observed effects in the real data. We hope to build on this work in future studies to identify cell-type-specific effects and their computational roles. 

Although the rebuttal clearly states that experiments are carried out on awake animals, this information is still missing from the manuscript. 

We now stated ‘Fully awake animals’ in the experimental procedures.

Author response:

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

Reviewer #1 (Public Review)

The weaknesses are in the clarity and resolution of the data that forms the basis of the model. In addition to whole embryo morphology that is used as evidence for convergent extension (CE) defects, two forms of data are presented, co-expression and IP, as well as a strong reliance on IF of exogenously expressed proteins. Thus, it is critical that both forms of evidence be very strong and clear, and this is where there are deficiencies; 1) For vast majority of experiments general morphology and LWR was used as evidence of effects on convergent extension movements rather than Keller explants or actual cell movements in the embryo. 2) The study would benefit from high or super resolution microscopy, since in many cases the differences in protein localization are not very pronounced. 3) The IP and Western analysis data often show subtle differences, and not apparent in some cases. 4) It is not clear how many biological repeats were performed or how and whether statistical analyses were performed. 

(1) To more objectively assess the convergent extension phenotypes, we developed a Fiji macro to automatically quantify the LWR in various injected Xenopus embryos, as detailed in the Methods section. We acknowledge that a limitation in the current manuscript is how to link our mechanistic model at the molecular level with the actual cellular behavior during convergent extension, and we plan to perform cell biological studies in the future to elucidate the link;

(2) We have repeated some of the imaging experiments in DMZ explants using a Zeiss LSM 900 confocal equipped with Airyscan2 detector that can increase the resolution to ~100 nm. The new data are in Suppl. Fig. 4, 9, 11, 16;

(3) We have repeated all IP and western blots at least three times and provided quantification and statistical analyses;

(4) We have added the information on biological repeats and statistical analyses in all figures and figure legends.

Reviewer #2 (Public Review):

The protein localization experiments in animal cap assays are for the most part convincing, but with the caveat that the authors assume that the proteins are acting within the same cell. As Fzd and Vangl2 are thought to localize to opposite cell ends in many contexts, can the authors be sure that the effects they observe are not due to trans interactions? 

In our previous publication, we provided evidence that Vangl is necessary and sufficient to recruit Dvl to the plasma membrane within the same cell (Figure 3 in 10.1093/hmg/ddx095). In a more recent publication ( 10.1038/s41467-025-57658-0 ), we further elucidated a mechanism through which Dvl oligomerization switches its binding from Vangl to Fz, and determined that Dvl binding to Vangl and Fz are differentially mediated by its PDZ and DEP domain, respectively. In the current manuscript, we also performed co-IP experiment under various conditions to demonstrate binding between Dvl and Vangl. We feel that these evidences together provide a strong argument for our model where Vangl2 acts within the same cell to sequester Dvl from Fz.

In regards to the Dvl patches induced by Wnt11 (Fig. 3 and Suppl. Fig. 9), we performed separate injection of EGFP- and mSc-tagged Dvl into adjacent blastomeres, and demonstrated that the Wnt11-induced patches arise from symmetrical accumulation of Dvl at contact of two neighboring cells (Suppl. Fig. 9a-c’). This scenario is different from epithelial PCP where Fz/Dvl and Vangl/Pk are asymmetrically accumulated at the contact between two adjacent cells.

The authors propose a model whereby Vangl2 acts as an adaptor between Dvl and Ror, to first prevent ectopic activation of signaling, and then to relay Dvl to Fzd upon Wnt stimulation. This is based on the observation that Ror2 can be co-IPed with Vangl2 but not Dvl; and secondly that the distribution of Ror2 in membrane patches after Wnt11 stimulation is broader than that of Fzd7/Dvl, while Vangl2 localizes to the edges of these patches. The data for both these points is not wholly convincing. The co-IP of Ror2 and Vangl2 is very weak, and the input of Dvl into the same experiment is very low, so any direct interaction could have been missed. Secondly, the broader distribution of Ror2 in membrane patches is very subtle, and further analysis would be needed to firm up this conclusion. 

(1) We repeated the co-IP experiment with Myc-tagged Vangl or Dvl. Using the same anti-Myc antibody and experimental condition (including the expression level of Vangl, Dvl and Ror2), we still found that Ror2 could be pulled down by Vangl but not Dvl (Suppl. Fig. 15b). Whereas this data confirms our previous conclusion, we acknowledge that a negative data does not fully exclude the possibility for direct biding between Ror and Dvl.

(2) We re-analyzed the signal intensity of Dvl and Ror in Wnt11-induced patches. By quantifying the intensity ratio between Ror and Dvl along the patches, we found an increase over two folds at the border of the patches (Fig. 7j, bottom panel). We interpret this data to suggest that Ror is accumulated to a higher level than Dvl at the patch borders.     

A final caveat to these experiments is that in the animal cap assays, loss of function and gain of function both cause convergence and extension defects, so any genetic interactions need to be treated with caution i.e. two injected factors enhancing a phenotype does not imply they act in the same direction in a pathway, in particular as there are both cis/trans and positive/negative feedbacks between the PCP proteins. 

We agree with the reviewer that a difficulty in studying PCP/ non-canonical signaling is that both loss and gain of function of any its components can cause convergence and extension defects. Genetic interactions, especially synergistic interactions, should be interpreted with caution. But we do want to point out that, in a number of case, we were also able to demonstrate epistasis. For instance, we found that Dvl2 over-expression induced CE defects can be rescued by Pk over-expression (Fig. 1e and f), whereas Vangl/ Pk co-injection induced severe CE defects can be reciprocally rescued by Dvl2 over-expression (Fig. 1g). Likewise, we showed that Fz2/ Dvl2 co-injection induced CE defects can be rescued by wild-type Vangl2 but not Vangl2 RH mutant (Suppl. Fig. 6b), and Ror2 can rescue Vangl2 overexpression induced CE defect (Suppl. Fig. 14). Collectively, these functional interaction data consistently demonstrate an antagonism between Dvl/ Fz/ Ror2 and Vangl2/ Pk, which is correlated with our imaging and biochemical studies.

As you can see from the reviews, the referees generally agree that your paper is a potentially valuable contribution to the field. Your observations are important because of the novel model based on the inhibitory feedback regulation between planar cell polarity (PCP) protein complexes. However, the reviewers also stated that the model is only partly supported by data because of insufficient clarity and missing controls in several experiments supporting the proposed model. The paper would be significantly improved if your conclusions are backed up by additional experimentation. Specifically, the referees wanted to see the reproducibility of the results shown in Figures 3, 4, 8, S3, S7, S12. 

We hope that you are able to revise the paper along the lines suggested by the referees to increase the impact of your study on the current understanding of PCP signaling mechanisms. 

We thank the reviewers for careful reading of our manuscript and for their constructive critiques and suggestions. We have repeated the animal cap studies in original Figures 3, 4, 8 and S3 with DMZ explants, and the new data are in Supplementary Fig. 9, 11, 16 and 4, respectively. We also repeated the biochemical studies in original Figure S 7and 12, and the new data are in Supplementary Fig. 8 and 15.

Reviewer #1 (Recommendations For The Authors):

Major points:(1) The author conducted an analysis of the subcellular localization of PCP core proteins, including Vangl2, Pk, Fz, and Dvl, within animal cap explants (ectodermal explants). To validate the model proposing that 'non-canonical Wnt induces Dvl to transition from Vangl to Fz, while PK inhibits this transition, and they function synergistically with Vangl to suppress Dvl during Convergent Extension (CE),' it is crucial to assess the subcellular localization of PCP core proteins in dorsal marginal zone (DMZ) cells, which are known to undergo CE. Notably, the overexpression of Wnt11 alone, as employed by the author, does not induce animal cap elongation. Therefore, the use of animal cap explants may not be sufficient to substantiate the model during Convergent Extension (CE). Indeed, previous knowledge indicates that Vangl2 and Pk localize to the anterior region in DMZ explants. However, the results presented in this manuscript appear to differ from this established understanding. Consequently, to provide more robust support for the proposed model, it is advisable to replicate the key experiments (Figures 3, 4, 8, and Figure S3) using DMZ explants. 

We repeated the experiments in Figure 3, 4, 8 and Figure S3 with DMZ explant and the new data are in new Supplementary Fig. 9, 11, 16 and 4, respectively.In regards to “previous knowledge indicates that Vangl2 and Pk localize to the anterior region in DMZ explants”, we are aware Vangl/ Pk localization to the anterior cell cortex in neural epithelium from the studies by the Sokol and Wallingford labs, but are not aware of similar reports in DMZ explants. When we examined the localization of small amount of injected EGFP-mPk2 (0.1 ng mRNA) in DMZ explants, we saw a somewhat uniform distribution on the plasma membrane (Suppl. Fig. 4). In addition, in a related recent publication, we examined endogenous XVangl2 protein localization in activin induced animal cap explants that do undergo CE. What we observed was that whereas low level injected Dvl2 and Fz form clusters on the plasma member, endogenous XVangl2 remains uniformly distributed on the plasma membrane (Suppl. Fig. 3S-Z in 10.1038/s41467-025-57658-0 ). These observations may suggest potential differences of PCP protein localization during neural vs. mesodermal convergence and extension.

(2) The author suggests that 'Vangl2 and Pk together synergistically disrupt Fz7-Dvl2 patches.' As shown in Figure 4 (panels J' to I'), it is evident that the co-expression of Pk and Vangl2 increases Fz7 endocytosis. Nevertheless, a significant amount of Fz7 still co-localizes with Dvl2. To strengthen the author's hypothesis, additional clear assay is required such as Fluorescence resonance energy transfer (FRET) assay. 

We appreciate this valuable advice. Since none of the tagged Fz/ Dvl/ Vangl proteins we had were suitable for FRET, we made proteins tagged with mClover and mRuby2, which were reported as optimized FRET pairs. But in our hands mRuby2 seems to require very long time (~2 days) to mature and become detectable at room temperature, and is not suitable for our Xenopus experiments. We are in the process of establishing a luciferase based NanoBiT system to detect Fz-Dvl and Dvl-Vangl interactions in live cells and cell lysates, and will use it in future studies to investigate their interaction dynamics.

For the current manuscript, we reason that a substantial reduction of Fz7-Dvl2 clusters with Vangl2/ Pk co-injection would still support our idea that Vangl2 and Pk act synergistically to sequester Dvl from Fz to prevent their clustering in response to non-canonical Wnt ligands.

(3) The IP data is less clear and evident. A couple of examples are: a) Fig 2g where the authors report that the Vangl2 R177H variant reduced Vangl2 interaction with Pk and recruitment of Pk to the plasma membrane, but it appears that the variant interacts slightly better than WT Vangl2 with Pk. In Fig. S7a, the authors state that Pk overexpression can indeed significantly reduce Wnt11-induced dissociation of EGFP-Vangl2 and Flag-Dvl2 in the DMZ. However, there is a minimal impact when compared to the Wnt11 absent control. Based on the results presented in Fig S12a the authors indicate that Wnt11 reduces the association between Vangl2 and Dvl2, which can be discerned, but loss of Ror2 does not change this in any obvious way - but the authors indicate it does. In S12b, the authors have suggested that Ror and Dvl do not form a direct binding interaction. However, the interpretation of Figure S12b is not entirely convincing due to several issues. Notably, the expression levels of each protein appear inconsistent, the bands are not sufficiently clear, and there is the detection of three different tag proteins on a single blot. To strengthen the validity of these findings, it is advisable to repeat this experiment with improved quality. 

We repeated all the co-IP and western blot analyses pointed out by the reviewer, and performed quantification and statistical analyses.

Fig 2g had a mistake in the labeling and is replaced with new Figure 2g;

Fig. S7a is replaced by new data in Supplementary Figure 8a and b;

Fig. S12a and 12b are replaced by new data in Supplementary Figure 15a, a’ and b, respectively. In 15a and a’, we noticed a consistent decrease of Dvl2-Vangl2 co-IP in Xror2 morphant. The reason for this is not yet clear and will need further study in the future.

Minor points: (1) In all the whole embryo injection assays examining morphology, no Western analysis is performed to show roughly equivalent and appropriate levels of the various proteins are being expressed. Differences will affect the data. 

Although we did not do western analyses to examine the protein levels in various functional interaction assays, we did examine how co-expression of Vangl2, mPk2 or Dvl2 may impact each other’s protein levels in Supplementary Fig. 2, which did not reveal any significant change when co-injected in different combination.

(2) The author's prior publication (Bimodal regulation of Dishevelled function by Vangl2 during morphogenesis, Hum Mol Genet. 2017) presented clear evidence of Vangl2 overexpression inducing Dvl2 membrane localization. However, Figure S4 in the current manuscript did not provide clear evidence of membrane localization. To strengthen the hypothesis that Vangl2-RH mutant also induces Dvl2 membrane localization, further comprehensive imaging analysis is needed. 

We re-analyzed the imaging data and replaced old Figure S4 with a new Supplementary Fig. 5.

(3) In Supplementary Figure 9, the authors propose that the overexpression of Vangl2/Pk induces Fz7 endocytosis, as indicated by its co-localization with FM4-64. However, it raises a question: how does the Fz7-GFP protein internalize into the cells without endocytosis, as seen in Figures S9a-c'? To enhance readers' understanding, a discussion addressing this point should be included. 

We think that this might be a technical issue. As detailed in the Method section, we only incubated the embryos transiently with FM4-64 for 30 minutes, and the embryos were subsequently washed and dissected in 0.1X MMR without the dye. Therefore, only the Fz7-GFP protein endocytosed during the 30 minute-incubation would be labeled by FM-64, whereas that endocytosed before or after the incubation would not. Alternatively, the very few Fz7-GFP puncta occasionally observed in the absence of Vangl2/Pk overexpression could be vesicles trafficking to the plasma membrane.

(4) Statistical analyses are absent for several results, including those in Figure 2f, Figure S4d, and Figure S7b. 

We repeated these experiments and included statistical analyses. The new data are in Figure 2f, Supplementary Fig. 5d and Supplementary Fig. 8b.

(5) This manuscript lacks any results regarding Ck1. Therefore, it is advisable to consider removing the discussion or mention of CK1. 

We agree, and tune down the discussion on CK1 and removed CK1 from our model in Fig. 9.

Reviewer #2 (Recommendations For The Authors):

(1) In all the convergence and extension assays, the authors should report n numbers (i.e. number of animals), what statistical test is used, and what the error bars show. Ideally dot-plots would be used instead of bar charts as they give a better insight into the data distribution. It might be useful to give a section on the statistical analyses used in the M&M, including e.g. any power calculations carried out, as now required by many journals. 

We have follow the advice to use dot-plots for all the quantification analyses in the manuscript. We include in the figure legends the statistical test used and what the error bars show. The number of embryos analyzed were included in each panel in the figures. We also provided more details in the Methods section on how the LWR quantification was carried out.

(2) I think Figure 2g is wrongly labelled? FLAG bands are in all three lanes in the western blot, but not labelled as such in the schematic. 

We corrected the schematic labeling in Figure 2g, and thank the reviewer for catching this mistake.

(3) In Figure S7, the authors show that co-IP of Dvl and Vangl2 is reduced by Wnt11 and the effects of Wnt are blocked by Pk. Does Pk have any effect in the absence of Wnt? 

We examined the effect of Pk over-expression on Dvl2-Vangl2 co-IP as advised, and did not see a significant impact in the absence of Wnt11 co-injection. The data is included in the new Supplementary Figure 8a. We interpret the data to suggest that “at least under the condition of our co-IP experiment, Pk may not directly impact the steady-state binding between Vangl and Dvl”.

(4) In Figure 3, the authors show (as published previously) that Wnt11 induces patches of Dvl at the plasma membrane. It would be useful to see Dvl in the absence of Wnt and Vangl2/Dvl in the absence of Wnt. 

Dvl is widely known as a cytoplasmic protein and its localization has been published by many labs over the past 20-30 years. In our recent publication (10.1038/s41467-025-57658-0 ), we also re-examined Dvl localization when injected at various dosages. So we did not feel it was necessary to show its localization in the absence of Wnt11 again, but included a reference to our prior publication. In regards to Vangl/Dvl distribution in the absence of Wnt11, the readers can see Suppl. Fig. 5b as an example, in addition to our previous publications referenced in the manuscript.

(5) In the review figures, the difference in Fz7-GFP patch formation in d' and e' (vs e.g. a') is not very clear. Could the images be improved or (better) quantified in some way? 

We assume that “review figures” refer to Figure 3 or 4? If so, we felt that Fz7-GFP patch formation was clear in Fig. 3d’, e’ or Fig. 4d’, e’. Nevertheless, we repeated these experiments in DMZ explants as advised by Reviewer 1, and additional examples of Fz7-EGFP patch formation can be seen in the new Suppl. Fig. 9d-f’ and Suppl. Fig. 11d-f’.

(6) In Figure 6d, I'm concerned that the loss of flag-Dvl2 might occur via dephosphorylation in the IP reaction. Also the M&M don't include methodological details about buffers and whether phosphatase inhibitors were used. A compelling control would be anti-FLAG pulldown showing retention of phosphorylation. Also Figure 6f shows a reduced ratio of fast-to-slow migrating bands of Dvl with Vangl2/Pk - unless I have misunderstood, is this ratio the wrong way round? 

We added co-IP buffer and protease inhibitor information in Methods.

We agree that the concern about dephosphorylation during IP reaction is valid, and that direct pull down of Dvl to show the phosphorylated form is a compelling control. We therefore note that in Suppl. Fig. 8a and 15b, direct pull down of Flag-Dvl or Myc-Dvl (with anti-Flag or anti-Myc) did show the slower migrating, phosphorylated form. Additional examples in which Vangl only co-IP the faster migrating unphosphorylated Dvl include Suppl. Fig. 15a, and in a related paper we published recently (Fig. 3R and R’ in 10.1038/s41467-025-57658-0 ).

Finally, we did wrongly label Figure 6f in the last submission, and the ratio should have been “slow/fast”. We have made the correction, and appreaicte the reviewer for the meticulousness in perusing our manuscript.

(7) In Figure 7, what does Ror2 look like in the absence of Wnt11? 

We included new Figure 7a-c to show that without Wnt11 co-injection, Ror2 is uniformly distributed on the plasma membrane.

(8) Also in Figure 7, Ror2 patches are said to be slightly wider than Dvl2 patches "reminiscent of Vangl2" - I wouldn't describe them as being similar. Vangl2 shows a distinct dip in the center of the Dvl patches, Ror2 does not show a dip, and is only (at best) in a slightly wider patch, and I would want to see further examples to be convinced that the localization domain is reproducibly wider. The merge of many samples in 7d may actually be making the distribution harder to see and if the Xror2 and Dvl2 intensities were normalized I'm not sure how different the curves would appear. (i.e. the Xror2 curve looks like a flattened version of the Dvl2 curve). 

We have added an additional panel in the new Figure 7j to compare the intensity ratio of Ror/ Dvl2 along the patches, and this analysis reveals an over two folds increase of the ratio at the border region. This quantification may make a more convincing argument that at the patch border region, Dvl is diminished whereas Ror2 accumulate with Vangl2. 

(9) In Figure S12a, the authors suggest Wnt11 induced dissociation of Dvl from Vangl2 (by co-IP), and this is reduced after Ror2 MO. This would be more convincing with replicates and quantitation. 

We have repeated this experiment with Vangl2 pull down and added quantification. The data is in the new Suppl. Fig. 15a.

(10) In Figure S12b, the authors suggest Ror2 can co-IP Vangl2 but not Dvl. This is not very convincing, as the Dvl input band is very weak, and the Vangl2 co-IP band is very weak. 

We repeated the co-IP experiment with Myc-tagged Vangl or Dvl. Using the same anti-Myc antibody and experimental condition (including the expression level of Vangl, Dvl and Ror2), we still found that Ror2 could be pulled down by Vangl but not Dvl (Suppl. Fig. 15b).

(11) "Prickle" spelled "Prickel" in the abstract (and abbreviated to "PK" not "Pk" at one place in the abstract and several places in text) 

We have corrected these typos.

(12) Quite a lot of interesting observations are in supplemental figures. Normally it might be expected that extra data supporting a conclusion would be in supplemental, but here some of the supplemental data feels like it is more than simply additional evidence. For instance supplemental Figures 2 and 3 feel more than just supplemental (and Supplemental Figure 3 if merged with Figure 2 would make it easier for the reader). Moreover, for example, the description of the results in Figure 2 is punctuated by references to supplemental Figures 4 and 5 that contain key data to support the conclusions, which means the reader has to flick backwards and forwards from place to place in the manuscript to follow the argument. It is of course up to the authors, but in some cases putting supplemental data back into the main figures (for which there is no size or number limit) would increase clarity. 

These are excellent points; in the resubmitted manuscript we have a total of 24 data figures, and we used 8 as main figures since we felt that they provide the most relevant and conclusive evidence to our model. We will consult the copy editors at eLife on how to arrange the rest as main vs. supporting figures when requesting publication as version of record.

Author response:

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

Joint Public Review:

Summary:

This study investigates plasticity effects in brain function and structure from training in navigation and verbal memory.

The authors used a longitudinal design with a total of 75 participants across two sites. Participants were randomised to one of three conditions: verbal memory training, navigation training, or a video control condition. The results show behavioural effects in relevant tasks following the training interventions. The central claim of the paper is that network-based measures of task-based activation are affected by the training interventions, but structural brain metrics (T2w-derived volume and diffusion-weighted imaging microstructure) are not impacted by any of the training protocols tested.

Strengths:

(1) This is a well-designed study which uses two training conditions, an active control, and randomisation, as appropriate. It is also notable that the authors combined data acquisition across two sites to reach the needed sample size and accounted for it in their statistical analyses quite thoroughly. In addition, I commend the authors on using pre-registration of the analysis to enhance the reproducibility of their work.

(2) Some analyses in the paper are exhaustive and compelling in showcasing the presence of longitudinal behavioural effects, functional activation changes, and lack of hippocampal volume changes. The breadth of analysis on hippocampal volume (including hippocampal subfields) is convincing in supporting the claim regarding a lack of volumetric effect in the hippocampus.

Weaknesses:

(1) The rationale for the study and its relationship with previous literature is not fully clear from the paper. In particular, there is a very large literature that has already explored the longitudinal effects of different types of training on functional and structural neuroimaging. However, this literature is barely acknowledged in the Introduction, which focuses on cross-sectional studies. Studies like the one by Draganski et al. 2004 are cited but not discussed, and are clumped together with cross-sectional studies, which is confusing. As a reader, it is difficult to understand whether the study was meant to be confirmatory based on previous literature, or whether it fills a specific gap in the literature on longitudinal neuroimaging effects of training interventions.

We thank the reviewer for these comments and feedback. 

We want to clarify that through our pre-registered analysis plan, our approach was confirmatory, rather than exploratory (or rather than post-hoc justified.) This confirmatory approach allowed us to critically evaluate the theoretically novel and important hypotheses which tested what no other study like our longitudinal/intervention study proposed or performed previously. We have now clarified this in the introduction. 

“This allowed us to address the following novel theoretical questions: 1) what neural changes, if any, result from an intensive within-participant intervention that improves memory or navigation skills in healthy young adults 2) if such changes occur, what is the degree of neural overlap between the acquisition of these cognitive skills.”

“We pre-registered three novel and specific hypotheses, which are described in more detail here (https://osf.io/etxvj) ”

We have also attempted to better separate cross-section and longitudinal studies. Due to space limitations, we have focused on interventional studies that involved gray matter changes that could relevance to either navigation, episodic memory, or the hypothesized time frame we chose for the training. We also note that some of these relevant studies are discussed in more depth in the discussion.

“Successful cognitive interventions suggest that targeted within-participant cognitive training, even for as little as 1-2 weeks, can result in improvements to specific cognitive functions, including changes in focal gray matter [4,23-27]; but see[28].”

We have also added some additional citations to relevant cognitive intervention work, although we agree that this is an extensive literature, only a subset of which we are able to capture here:

“In some instances, interventions may even generalize to areas not explicitly trained but closely related to the training (termed “near transfer”)[29-33].”

(2.1) The main claim regarding the lack of changes in brain structure seems only partially supported by the analyses provided. The limited whole-brain evidence from structural neuroimaging makes it difficult to confirm whether there is indeed no effect of training. Beyond hippocampal analyses, many whole-brain analyses of both volumetric and diffusion-weighted imaging metrics are only based on coarse ROIs (for example, 34 cortical parcellations for grey matter analyses).

Although vertex-wise analyses in FreeSurfer are reported, it is unclear what metrics were examined (cortical thickness? area? volume?). 

We appreciate the reviewer’s thoughtful feedback. We apologize for the lack of clarity in the original manuscript regarding the type of metric used in the vertex-wise analysis. We confirm that these analyses were based on cortical volume, not thickness or area. To clarify this, we have explicitly stated in the revised Methods that the vertex-wise analyses were conducted on cortical volume using FreeSurfer’s mri_glmfit.

In addition, in response to the concern regarding the coarse nature of the ROI-based analyses, we have re-analyzed the volumetric data using the more fine-grained Destrieux atlas, which contains 148 cortical ROIs (74 per hemisphere), instead of the original, coarser 34-region atlas. These more detailed analyses still revealed no significant volume changes from pre- to post-training in any of the three groups. We believe this provides stronger support for the lack of training-induced volumetric changes outside the medial temporal lobe.

Relevant revisions have been made to the Results and Methods sections. Below is the updated content added to the manuscript:

In Results:

“We also analyzed gray matter volume changes outside of the medial temporal lobe using FreeSurfer (see Methods) to determine if any cortical or other relevant brain areas might have been affected by the training. We applied a vertex-wise analysis of cortical volume, again finding no significant differences across the entire cortex (see Methods). This finding was further validated using the Destrieux atlas, which includes 74 cortical parcellations per hemisphere (148 ROIs in total). Paired-sample t-tests revealed that none of the ROIs exhibited significant volume changes from pre- to post-test in any of the three groups (all ps > 0.542, FDR-corrected). These findings suggest that training did not result in any measurable cortical volumetric changes.”

In Methods:

“Whole-brain structural analyses were conducted using FreeSurfer (version 7.4.1; https://surfer.nmr.mgh.harvard.edu). T1-weighted anatomical images were processed using the longitudinal processing pipeline. Vertex-wise analyses of cortical volume were performed using FreeSurfer’s general linear modeling tool, mri_glmfit. Group-level comparisons were corrected for multiple comparisons using mri_glmfit-sim, which implements cluster-wise correction based on Monte Carlo simulations. A vertex-wise threshold of Z > 3.0 (corresponding to p < 0.001, two-sided) was applied to detect both positive and negative effects. Clusters were retained if they survived a cluster-wise corrected p < 0.05.

In addition to vertex-wise analysis, cortical parcellation was performed using the Destrieux atlas (aparc.a2009s), which includes 74 cortical regions per hemisphere, yielding 148 ROIs in total. To account for variability in brain size, each ROI volume was normalized by estimated intracranial volume (ICV) and scaled by a factor of 100. Longitudinal comparisons were conducted using paired-sample t-tests. To correct for multiple comparisons, we applied FDR correction (q < 0.05).”

(2.2) Diffusion-weighted imaging seems to focus on whole-tract atlas ROIs, which can be less accurate/sensitive than tractography-defined ROIs or voxel-wise approaches.

We appreciate the reviewer’s important point regarding diffusion-weighted imaging (DWI) analysis. We focused primarily on atlas-defined tract-level ROIs derived from a standard white matter tract atlas as we did not feel that we had the resolution for more fine-grained analyses with our sequences. While this approach has the advantage of robust anatomical correspondence and improved interpretability, we agree that it may be less sensitive than tractography-defined or voxel-wise methods for detecting more subtle, localized training-related changes. Because of limitations in our DWI sequence, which was optimized to be shorter and identical between different scanners, we are not able to provide more fine-grained analysis of the DWI data.

(3) Quality control of images is only mentioned for FA images in subject space. Given that most analyses are based on atlas ROIs, visual checks following registration are fundamental and should be described in further detail.

Thank you for your thoughtful comment. We agree that visual quality control is critical when using atlas-based ROI analyses. In our study, we implemented comprehensive quality control procedures across all structural and functional imaging analyses.

For hippocampal segmentation using ASHS, we performed manual visual inspections of each participant's subfield segmentation to verify the accuracy of the automated outputs. This is now clearly described in the revised Methods section:

“Each participant's subfield segmentations were manually inspected to ensure the accuracy and reliability of the segmentation protocol.”

For FreeSurfer-based hippocampal and cortical segmentation, we also conducted detailed visual inspections and manual edits following the standard FreeSurfer longitudinal pipeline. We have added the following description to the Methods section to clarify this process:

“Visual quality control was conducted by three trained raters who systematically inspected skull stripping, surface reconstruction, and segmentation accuracy at both the within-subject template and individual timepoints. Manual edits were primarily applied to the within-subject template to correct segmentation errors—particularly in challenging regions such as the hippocampus—since corrections to the template automatically propagate to all timepoints. Raters followed standardized FreeSurfer longitudinal editing guidelines to ensure consistent and reproducible corrections across subjects. Discrepancies were resolved via consensus discussion. This quality control approach enhanced the accuracy and consistency of segmentation across longitudinal scans, thereby improving the reliability of morphometric analyses and atlas-based ROI extractions.”

For functional MRI preprocessing, all registration steps—including transformations from individual functional runs to MNI space—were visually checked for each participant to ensure accurate alignment with the Schaefer atlas. We have clarified this point in the revised Methods section with the following statement:

“Prior to ROI extraction, all registration steps—from individual functional space to MNI space—were visually inspected for each participant to confirm accurate alignment between the functional images and the atlas parcellation.”

These additions now more clearly reflect the robust quality control procedures that were employed throughout our pipeline to ensure the validity of atlas-based analyses.

Recommendations for the authors:

(1) As a reader, I would have appreciated a short section in the methods regarding the preregistration and power analysis. Currently, it is not too straightforward to understand which analyses were included in the preregistration, and at what point in the project the pre-registration was written. Finding all the relevant information from OSF is feasible, but it would be more accessible if a summary of the information were available inside the text.

We thank the reviewer for this valuable suggestion. We agree that providing a concise summary within the manuscript's methods section will significantly improve accessibility for readers. 

The full preregistration is now explicitly referenced in the Methods:

“Preregistration and Power Analysis

This study was preregistered on the Open Science Framework (OSF; https://osf.io/etxvj). The preregistration was completed on October 30, 2023, after approximately 80% of data collection had been completed, but prior to any analysis of the primary outcome variables. The preregistration outlines the study hypotheses, design, target sample size, and planned behavioral and neuroimaging analyses, including longitudinal ROI comparisons and statistical correction procedures.

A priori power analysis was conducted using G*Power 3.1 to estimate the required sample size for detecting a Group × Time interaction in a mixed-design ANOVA. Assuming a small-to-medium effect size (f = 0.35), we determined that 24 participants per group would provide 80% power to detect a significant effect at α = 0.05. To allow for potential attrition and data exclusion (e.g., due to excessive motion or incomplete datasets), we targeted recruitment of 30 participants per group across two study sites.

All primary hypotheses, analytic plans, and inference criteria are documented in the preregistration. Exploratory analyses are clearly delineated in both the preregistration and the present manuscript.”

(2) The relevance of the study for "disease" is mentioned in the Abstract but is absent in the Introduction. This may be worth removing?

Thank you for pointing this out. We agree that the reference to "disease" in the Abstract was not well-supported in the Introduction. To maintain consistency and avoid overstatement, we have removed the mention of "disease" from the Abstract in the revised manuscript.

In Abstract:

“Training cognitive skills, such as remembering a list of words or navigating a new city, has important implications for everyday life.”

Author response:

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

Reviewer #1(Public Review):

The correlation between rebound excitation and song structure (e.g., harmonic stack duration) may depend on outliers, such as birds with harmonic stacks >150ms.

If in wild zebra finch, or even if in domesticated zebra finch including our birds and the birds from the other labs that we evaluated, the distribution of durations of longest harmonic stacks has a long tail, it is not apparent that birds with long duration harmonic stacks are properly considered as outliers. Examining the distribution of motif durations (a less derived statistic) in 33 birds (Fig. 2C) does not support the idea that birds with longer duration songs are outliers. Thus, we view the reviewer question as addressing whether there are different mechanisms operating in birds with long harmonic stacks than for other birds. Unfortunately, the numbers of long-duration harmonic stack birds are too small to give confidence in any statistical analysis of that group. Thus, we limited our re-analysis to the data excluding birds with harmonic stacks >150ms (which is arbitrary), examining how these birds influence our conclusions. We conclude that the influence of the excluded birds on the overall result is modest. The updated results are presented in Supplemental Figure 6, and the Results section has been revised to state:

“We found that while some of the p values increased above 0.05 (p = 0.058 for rebound area vs. longest harmonic stack and p = 0.082 for sag ratio and longest harmonic stack), it remained significant for firing frequency and longest stack (Pearson’s R, p = 0.0017) and for sag ratio and motif duration (p = 0.024). However, when sag ratio was compared against the duration of the motif excluding the longest harmonic stack, there was no relationship (p = 0.85).”

There is a disconnect between the physiological measurements and the HH model presented.

We acknowledge that addressing this limitation would involve additional experimental and modeling assumptions. Rather than overextending our interpretations, we have clarified the limitations of the current study in the Discussion:

“While this HH model provides a plausible framework for linking intrinsic properties to sequence propagation, it does not fully account for the observed relationship between IPs and song structure. A principal limitation constraining the current model is the absence of information for the same neurons combining characterization of both IPs and network activity during singing (or song playback), when HVC_X express activity related to song features. Addressing this gap would requires additional and challenging experiments and is beyond the scope of this study.”

Although disynaptic inhibition between HVC_X neurons and between HVC_RA and HVC_X neurons is well established, I am not aware of any data indicating direct synaptic connections between HVC_X neurons.

This is an important theoretical point about the reliance of the intervaldetecting network model on HVC_X neurons and about how the model would change if many of the HVC_X were swapped for HVC_RA neurons. Connections between HVC_RA neurons to HVC_X neurons are established, whereas there is relative paucity of evidence for HVC_X to HVC_X connectivity. This is based on work from Prather and Mooney, 2005 (among others) which performed paired sharp electrode recordings to characterized connections in HVC. This work found very few HVC_X - HVC_X connections. However, if connected HVC_X neurons are physically more distant from each other than are connected HVC_RA – HVC_X neurons, they would more likely be missed in blind paired recordings. Using different approaches, recent results from the Roberts lab (Trusel et al.,eLife,  2025) supports the existence of robust HVC_X  - HVC_X  connections.

Reviewer #2(Public Review):

The interpretation of p-values is rigid, and near-significant results (e.g., p = 0.06) are dismissed without discussion.

We revised the text to reflect a more nuanced and consistent interpretation of p-values and updated the reporting to include exact values. For example, the Results section now states:

"Nonetheless, the longest syllable duration was not significantly correlated with the average sag ratio for each bird (Pearson’s R: R² = 0.12, p = 0.065, Supplemental Fig. 2, top left panel), though it is trending toward significance (see Discussion)”

The conclusion that harmonic stacks influence intrinsic properties lacks necessary controls.

We have attempted to further clarify that harmonic stacks were used as a representative feature of temporal song structure rather than a unique determinant of intrinsic properties. The Discussion now states:

“Although harmonic stacks provide a useful test case for studying temporal integration, our findings suggest that IPs are broadly linked to song duration and structure, rather than specific syllable types. This is also consistent with prior results that found all HVC_X ion currents that were modeled were influenced by song learning[31].”

The relationship between rebound area and experimentally tutored birds was not fully explored.

We expanded the analysis to include rebound area in instrumentally tutored birds, which has now been incorporated into Figure 4C. These additional analyses also robustly support our hypotheses. The Results section has been updated to state:

“We then evaluated the IPs of HVC_X in the birds from the two groups. HVC_X neurons from birds who sang unmodified songs (N = 5 birds, 31 neurons), which had shorter harmonic stacks and shorter overall duration, had lower sag ratios (Mann-Whitney: p = 0.025), firing frequency (Mann-Whitney, p = 0.0051) and rebound area (Mann-Whitney: p = 0.0003)”

Reviewer #3 (Public Review):

Limited data supports the claim that intrinsic properties influence temporal integration windows.

While we agree that further data could strengthen this claim, we show that this can happen in principle (Figure 5) but believe that the appropriate experiment to test this requires further experiments in-vivo. We emphasize in the Discussion:

“Our findings suggest that post-inhibitory rebound excitation in HVC_X could expand temporal integration. Ultimately, experiments combining in vitro with in vivo recordings can directly quantify this effect. We hope our results motivate such experiments.”

Technical Corrections

(1) Fixed typographical errors (e.g., Line 177: corrected "r2 = 4" to "r2 = 0.4").

(2) Revised figure legends for clarity (e.g., Figure 4E now includes tutoring design details).

(3) Updated methods to specify how motifs were defined and measured.

Revised Figures

Figure 4: Updated to include analysis of rebound area in instrumentally tutored birds, reflecting the relationship between experimental tutoring and intrinsic properties.

Supplemental Figure 6: Correlation analysis excluding outliers

Author response:

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

Reviewer #1 (Public Review):

Summary:

This is a manuscript describing outbreaks of Pseudomonas aeruginosa ST 621 in a facility in the US using genomic data. The authors identified and analysed 254 P. aeruginosa ST 621 isolates collected from a facility from 2011 to 2020. The authors described the relatedness of the isolates across different locations, specimen types (sources), and sampling years. Two concurrently emerged subclones were identified from the 254 isolates. The authors predicted that the most recent common ancestor for the isolates can be dated back to approximately 1999 after the opening of the main building of the facility in 1996. Then the authors grouped the 254 isolates into two categories: 1) patient-to-patient; or 2) environment-to-patient using SNP thresholds and known epidemiological links. Finally, the authors described the changes in resistance gene profiles, virulence genes, cell wall biogenesis, and signaling pathway genes of the isolates over the sampling years.

Strengths:

The major strength of this study is the utilisation of genomic data to comprehensively describe the characteristics of a long-term Pseudomonas aeruginosa ST 621 outbreak in a facility. This fills the data gap of a clone that could be clinically important but easily missed from microbiology data alone.

Weaknesses:

The work would further benefit from a more detailed discussion on the limitations due to the lack of data on patient clinical information, ward movement, and swabs collected from healthcare workers to verify the transmission of Pseudomonas aeruginosa ST 621, including potential healthcare worker to patient transmission, patient-to-patient transmission, patient-to-environment transmission, and environment-to-patient transmission. For instance, the definition given in the manuscript for patient-to-patient transmission could not rule out the possibility of the existence of a shared contaminated environment. Equally, as patients were not routinely swabbed, unobserved carriers of Pseudomonas aeruginosa ST 621 could not be identified and the possibility of misclassifying the environment-to-patient transmissions could not be ruled out. Moreover, reporting of changes in rates of resistance to imipenem and cefepime could be improved by showing the exact p-values (perhaps with three decimal places) rather than dichotomising the value at 0.05. By doing so, readers could interpret the strength of the evidence of changes.

Impact of the work:

First, the work adds to the growing evidence implicating sinks as long-term reservoirs for important MDR pathogens, with direct infection control implications. Moreover, the work could potentially motivate investments in generating and integrating genomic data into routine surveillance. The comprehensive descriptions of the Pseudomonas aeruginosa ST 621 clones outbreak is a great example to demonstrate how genomic data can provide additional information about long-term outbreaks that otherwise could not be detected using microbiology data alone. Moreover, identifying the changes in resistance genes and virulence genes over time would not be possible without genomic data. Finally, this work provided additional evidence for the existence of long-term persistence of Pseudomonas aeruginosa ST 621 clones, which likely occur in other similar settings.

We thank the reviewer for their thorough evaluation of our work, and for the suggested improvements. A main goal of this study was to show that integrating routine wgs in the clinic was a game changer for infection control efforts. We appreciate this aspect was highlighted as a strength by this reviewer. While some of the weaknesses identified are inherent to the data (or lack thereof) available for this study, we have revised the manuscript to include a detailed discussion on limitations (sampling, thresholds of genetic relatedness, definition and categories etc.) that could influence the genomic inferences. We also provided exact p-values for the changes in rates of resistance, as requested. Finally, we have positively answered all the specific recommendations suggested by the reviewer and modified the manuscript accordingly.

Reviewer #2 (Public Review):

Summary:

The authors present a report of a large Pseudomonas aeruginosa hospital outbreak affecting more than 80 patients with first sampling dates in 2011 that stretched over more than 10 years and was only identified through genomic surveillance in 2020. The outbreak strain was assigned to the sequence type 621, an ST that has been associated with carpabapenem resistance across the globe. Ongoing transmission coincided with both increasing resistance without acquisition of carbapenemase genes as well as the convergence of mutations towards a host-adapted lifestyle.

Strengths:

The convincing genomic analyses indicate spread throughout the hospital since the beginning of the century and provide important benchmark findings for future comparison.

The sampling was based on all organisms sent to the Multidrug-resistant Organism Repository and Surveillance Network across the U.S. Military Health System.

Using sequencing data from patient and environmental samples for phylogenetic and transmission analyses as well as determining recurring mutations in outbreak isolates allows for insights into the evolution of potentially harmful pathogens with the ultimate aim of reducing their spread in hospitals.

Weaknesses:

The epidemiological information was limited and the sampling methodology was inconsistent, thus complicating the inference of exact transmission routes. Epidemiological data relevant to this analysis include information on the reason for sampling, patient admission and discharge data, and underlying frequency of sampling and sampling results in relation to patient turnover.

We thank the reviewer for their thoughtful feedback on our manuscript and for highlighting the quality of the genomic analyses. We agree that the lack of patient epi data (e.g. date of admission and discharge) and the inconsistent sampling through the years are limitations of this study. We have revised the manuscript to acknowledge these limitations and discuss how not having this data complicates the inference of exact transmission routes. Finally, we have positively answered all the specific recommendations suggested by the reviewer and modified the manuscript accordingly.

Reviewer #3 (Public Review):

Summary:

This paper by Stribling and colleagues sheds light on a decade-long P. aeruginosa outbreak of the high-risk lineage ST-621 in a US Military hospital. The origins of the outbreak date back to the late 90s and it was mainly caused by two distinct subclones SC1 and SC2. The data of this outbreak showed the emergence of antibiotic resistance to cephalosporin, carbapenems, and colistin over time highlighting the emerging risk of extensively resistant infections due to P. aeruginosa and the need for ongoing surveillance.

Strengths:

This study overall is well constructed and clearly written. Since detailed information on floor plans of the building and transfers between facilities was available, the authors were able to show that these two subclones emerged in two separate buildings of the hospital. The authors support their conclusions with prospective environmental sampling in 2021 and 2022 and link the role of persistent environmental contamination to sustaining nosocomial transmission. Information on resistance genes in repeat isolates for the same patients allowed the authors to detect the emergence of resistance within patients. The conclusions have broader implications for infection control at other facilities. In particular, the paper highlights the value of real-time surveillance and environmental sampling in slowing nosocomial transmission of P. aeruginosa.

Weaknesses:

My major concern is that the authors used fixed thresholds and definitions to classify the origin of an infection. As such, they were not able to give uncertainty measures around transmission routes nor quantify the relative contribution of persistent environmental contamination vs patient-to-patient transmission. The latter would allow the authors to quantify the impact of certain interventions. In addition, these results represent a specific US military facility and the transmission patterns might be specific to that facility. The study also lacked any data on antibiotic use that could have been used to relate to and discuss the temporal trends of antimicrobial resistance.

We thank the reviewer for their evaluation of our work and for highlighting the broad implications of our findings regarding the application of real-time surveillance to suppress nosocomial transmission. We agree with the reviewer that fixed thresholds and definitions are imperfect to classify the origin of an infection. The design of this study (e.g. inconsistent sampling through time) was not conducive to provide a comprehensive/quantitative measurement of transmission routes. Thus, we decided to apply conservative thresholds of genetic relatedness and strict conditions (e.g. time between isolate collection, shared hospital location etc.) to favor specificity as our goal was simply to establish that cases of environmentto-patient transmission did happen. In the absence of a truth set, we have not performed sensitivity analysis, but we are conducting a follow-up study to compare inferences from MCMC models to our original fixed-thresholds predictions. This limitation is now discussed in the revised manuscript. Finally, we have positively answered all the specific recommendations suggested by the reviewer and modified the manuscript accordingly including the addition of Figure S3.

Reviewer #1 (Recommendations For The Authors):

The definitions used on lines 391-396 are necessarily somewhat arbitrary, but it would be helpful to have a little bit more justification for the choices made, particularly for the definition of environmental involving the "3x the number of years they were separated". It seems a little hard to square this with the more relaxed 10 SNP cutoff for a patient-to-patient designation. Are there reasons for thinking SNP differences associated with environmental transmission should be smaller than for patient-to-patient, or is the aim here just to set the bar higher for assuming an environmental source? Because these definitions are quite arbitrary, there could also be some value in exploring the sensitivity of the results to these assumptions.

Thank you. We agree with the reviewers that SNP thresholds, albeit necessarily, are arbitrary and that more discussion/justification was needed to put the genomic inferences in context. We have revised the manuscript to indicate that: 1/ the 10 SNP cutoff for a patient-to-patient designation was set to account for the known evolution rate of P. aeruginosa (inferred by BEAST at 2.987E-7 subs/site/year in this study and similar to previous estimates PMID: 24039595) and the observed within host variability (now displayed in revised Fig. 1E). We note that this SNP distance was not sufficient and that an epi link (patients on the same ward at the same time) needed to be established. 2/ the environment-to-patient definition was indeed set to be most conservative (nearly identical isolates in two patients from the same ward with no known temporal overlap for > 365 days). This was indeed done to favor high specificity as this inference relied solely on clinical isolates (i.e. the identical environmental strain in the patientenvironment-patient chain was not sampled). For these clinical isolates to have acquired no/very little mutation in that much time, no/low replication is expected and, although unsampled, we propose this most likely happened on hospital surfaces.

While the term "core genome" should be familiar to most readers, "shell genome" and "cloud genome" are less widely known, and an explanation of what these terms mean here would be helpful.

Thank you. We have revised the manuscript to define the core, shell, and cloud genomes as genes sets found in ≥ 99%, ≥ 95% and ≥ 15% of isolates, respectively.

In the first paragraph of the discussion, it could be added that in many cases for clinically important Gram negatives short read sequencing alone will fail to detect transmission events as outbreaks can be driven by plasmid spread with only very limited clonal spread (see, for example, https://www.nature.com/articles/s41564-021-00879-y )

Thank you. We agree this is an important/emerging aspect of surveillance. However, the goal of this discussion point was to explain why such a large outbreak was missed prior to implementing WGS (short read) surveillance. We feel that discussing “plasmid outbreaks” (which is not at play here, and relatively rare in P. aeruginosa compared to the Enterobacteriaceae) and the need for long read will distract from the narrative. 

line 599 What does "Mock" mean here? Would it be more accurate to say it is a simplified floor plan?

Thank you. “Mock” was changed to “simplified”

IPAC abbreviation is only used once - spelling it out in full would increase readability.

Revised manuscript was edited as suggested.

MHS is only used twice.

Revised manuscript was edited to spell out Military Health System

Line 364: full stop missing.

Revised manuscript was edited as suggested.

Line 401: Bayesian rather than bayesian.

Revised manuscript was edited as suggested.

Reviewer #2 (Recommendations For The Authors):

Thank you for giving me the opportunity to review this interesting manuscript.

The conclusions of this paper are mostly well supported by the data presented, but epidemiological information was limited and the sampling methodology was inconsistent, thus complicating inference of exact transmission routes.

Major issues:

What was the baseline frequency of clinical and/or screening samples of Pseudomonas aeruginosa at the hospital? Neither Figure 1D nor Table S1 allows for differentiating between clinical and screening samples. Most isolates were cultured from clinical materials, and there is no information about the patients' length of stay and their respective sampling dates. Is there any possibility of finding out whether the samples were collected for clinical or screening purposes? Would it be possible to include the patients' admission data to determine whether the strains were imported into the hospital or related to a previous stay, e.g. among known carriers? Also, the issue of sampling dates vs. patient stay on the ward should be addressed, as there may be an overlap in patients' stay on the ward but no overlap in terms of sampling dates or even missing samples (missing links).

We have revised the manuscript to address this important point: i) 16 isolates were from surveillance swabs and are labelled “Surveillance” in Table S1. The remaining 237 were clinical isolates; ii) unfortunately, because the sampling was done under a public health surveillance framework, we do not have access to historical patient data (admission/discharge date, wards, rooms, etc.) and we can not calculate length of stay or better identify patient overlap. These limitations are now acknowledged in the discussion of the revised manuscript.

In order to evaluate the extent of the outbreak, more epidemiological data would be useful What is the size of the hospital, what is the average patient turnover, and what is the average length of stay in ICU and non-ICU? Is there any specialization besides the military label?

We have revised the manuscript to indicate that facility A is 425-bed medical center and is the only Level 1 trauma center in the Military Health System. Unfortunately, the data to calculate length of stay, throughout the years, in ICU and non-ICU, was not available to us. This limitation is now also acknowledged in the discussion.

Perhaps the authors could attempwt to discuss the extent to which large outbreaks like these may be considered as part of unavoidable evolutionary processes within the hospital microbiome as opposed to accumulation and transmission of potentially harmful genes/clones, and differentiate between the putative community spread without any epidemiological links on the one hand, and hospital outbreaks that could be targeted by local infection prevention activities on the other hand.,

We respectfully disagree with the suggestion that this large outbreak “may be considered as part of unavoidable evolutionary processes within the hospital microbiome” and should be opposed to “transmission of potentially harmful genes/clones”. As a matter of fact, our data showed that infection control staff at Facility A responded with multiple interventions, including closing sinks, replacing tubing, and using foaming detergents. This resulted in slowing the spread of the ST621 outbreak with just 3 cases identified in 2022, 0 cases in 2023 and 1 case in 2024. This is now discussed in the revised manuscript.

Page 5, lines 88-92 lines 101-104. It seems as if the outbreak was identified only by the means of genomic surveillance. This raises questions as to the rationale for sampling and sequencing, especially prior to 2020. Considering 11 cases per year between 2011 and 2016, one could assume such an outbreak would have been noticed without sequencing data.

The MRSN was created in 2010, in response to the outbreak of MDR Acinetobacter baumannii in US military personnel returning from Iraq and Afghanistan. Between 2011 and 2017, the MRSN collected MDR isolates (mandate for all MDR ESKAPE but compliance varied between years and facilities) from across the Military Health System and, for select isolates (e.g. high-risk isolates carrying ESBLs or carbapenemases) performed molecular typing by PFGE. In 2017 the MRSN started to perform whole genome sequencing of its entire repository. In 2020, a routine prospective sequencing service was started and first detected the ST621 outbreak. A retrospective analysis of historical isolate genomes (2011-2019) identified additional cases. The first paragraph of the discussion lists possible factors to explain why the ST621 escaped detection by traditional approaches. We believe 11 cases per year is not a strong signal when stratified by month, wards, or both, especially for a clone lacking a carbapenemase and without a remarkable antibiotic susceptibility profile. 

Did the infection control personnel suspect transmission? If yes, was the sampling and submission of samples to the MRSN adapted based on the epidemiologic findings?

The ST621 outbreak was unsuspected before the initial genomic detection in 2020. Until that point, MDR isolates only (Magiorakos et al PMID: 21793988) were collected but compliance was variable through time. Quickly thereafter (starting in 2021), complete sampling of all clinical P. aeruginosa (MDR or not) from Facility A was started. The manuscript was revised to clarify those details of the sampling strategy.

Is there any information about how many environmental sites were sampled without evidence of ST621 / screening samples were cultured without evidence of Pseudomonas aeruginosa?

For patient isolates, only 16 isolates were from surveillance swabs. The remaining 237 were clinical isolates. No denominator data was available to calculate P. aeruginosa and ST-621 positivity rate in surveillance swabs throughout the time period. For environmental isolates, a total of 159 swabs were taken from 55 distinct locations in 8 wards/units including the ER. This data is now included in the revised manuscript. However, a complete analysis of these swabs (positivity rate for ESKAPE pathogens, P. aeruginosa, per ward/floor/room, per swab type (sink drain, bed rail etc.) etc.) is beyond the scope of this study and is being performed as a follow up investigation.

Page 5 lines 89 and 39 Figure S1B. Please describe how the allelic distance for the cluster threshold was selected.

As indicated in the legend of Figure S1B, no thresholds were applied. All ST621 isolates ever sequenced by the MRSN were included. All except 3 isolates shared between 023 cgMLST allelic differences. The remaining 3 were distant by 88-89 allelic differences. The text was revised to clarify this point.

Page 5 lines 99-100. Could the authors please provide some distribution measures (e.g. IQR).

Done as requested. The revised manuscript now reads “…of just 38 single nucleotide polymorphisms (SNPs), and an IQR of 19 (Fig. 1A, Table S1).”

Page 5 line 102. Could the authors please provide some distribution measures (e.g. IQR).

Please see above. A chart was created and is now included as Fig. S2.

Page 6 line 107 and page 34 figure 1c. In the text it is stated that isolates were collected in 27 wards, the figure 1C depicts 26 wards and n/a.

Thank you for spotting this inconsistency. This has been fixed in the revised manuscript.

Page 6 lines 117-118. Samples collected in the emergency room would imply samples collected on admission, already addressed previously. Did the authors investigate a potential import into the hospital from community reservoirs or were all these isolates collected among patients who had been previously admitted to the hospital and/or tested positive for the outbreak strain?

We agree that samples collected in the ER imply samples collected on admission. Of the 29 ER isolates only 9 (31%) were primary isolates (first detection in a new patient) which suggests a majority were from returning patients at Facility A. Because the sampling was done under a public health surveillance framework, we do not have access to historical patient data (admission/discharge date, wards, rooms, etc.) to investigate/confirm that these 9 patients had previous visits at Facility A. This point is now discussed in the revised manuscript.

Page 6 line 128. This could also represent increased selective pressure. However, according to Table S1, the 28 isolates collected in 2011 (the number does not match with Figure 1D) were from many different wards, thus indicating earlier spread throughout the hospital.

Yes, we agree. Please note that table S1 lists all isolates for 2011 whereas Figure 1D focuses on primary (first isolate from each patients) only.  

Page 7 line 133. Both Figure 2 and the discussion section, page 13 line 296 suggest the year 2005 instead of 2004?

Thank you for catching this typographical error. This was corrected to 2004 in the revised manuscript.

Figure 1E. The figure should also depict intra-patient diversity for comparison.

Thank you for this great suggestion. We have revised Figure 1E accordingly.

Page 7, lines 146-147 Could the authors attempt explaining the upper part of the bimodal peaks?

This is an all-vs-all SNP analysis for all inter-patient isolates. For each isolates all distances to other isolates are reported, not only the smallest. The upper peaks represent comparisons to isolates from a different outbreak subclone (SC1 vs SC2).

Page 7, line 150 This is a very small number considering the extent of the outbreak and suggests a large number of missing links. Or does this rather imply continuous import and evolution over time that does not necessarily represent transmission within the hospital?

We believe all cases were due to transmission happening within the hospital. Based on conservative thresholds (genetic relatedness and epi link, or lack thereof) the precise origin from another patient (n=10) or a contaminated surface (n=12) can be inferred. For the remaining 60 patients, with the available sampling, the conditions we chose are not met and we simply do not conclude whether a direct patient-to-patient or an environmental origin was more likely.

Page 8 line 155. What does the temporal overlap refer to - sampling date versus patient's stay on the ward? Please specify.

The temporal overlap was investigated from sampling dates, as dates of patient admission/discharged were not available.

Page 8, line 157: What does primary/serial isolate mean - first and follow-up samples of ST621 per patient?

Yes. Primary isolate is used to designate the first isolate from a patient. Serial isolates designate follow-up samples of ST621.

Page 8 line 165: Table S3 and Figure 3 only refer to environmental samples from three wards. Ward 20 rooms 2 and 18 as well as ward 1 rooms 1 and 6 were hotspots - is there any information on the specific infection control/disinfection measures? Addressed in discussion page 12, lines 273-275, but no information on what was actually done.

The manuscript was revised to indicate the precise disinfection measures that were taken. A follow-up study is ongoing to assess long-term efficacy and monitor possible retrograde growth from previously contaminated sinks.

Page 8 line 175: Evaluation of change in resistance fraction over time - There may have been a selection bias with an inconsistent number of strains sequenced per year.

Yes, incomplete sampling and possible selection bias are now listed with other limitations of this study in the discussion of the revised manuscript.

Page 9 line 183: The referral to Table S1 is unclear, I could not find the number and the specific isolates selected for long-read sequencing.

Thank you. This has been added to the revised Table S1.

Page 10 lines 217-225 and Figure 4C: Perhaps it is possible to better align what is written in the text and the caption of the figure. The caption does not clarify that only one patient develops colistin resistance (what was the reason to include the other patients?).

Thank you. We have revised the text and the caption of the figure to clarify that only isolates from one patient developed colistin resistance. The isolates from the other patients on Fig. 4C are shown to provide context and accurately map the emergence of the PhoQE77fs mutation.  

Page 10, lines 228-229 and Table S5: How is it possible to identify those 64 genes in Table S5?

We have revised Table S5 to facilitate the identification of the 64 genes with ≥ 2 independently acquired mutations (excluding SYN). Specifically, we have added column E labeled “Counts independent mutations per locus (excluding SYN)”. A total of 205 rows (in this table each row is a variant) have a value ≥ 2 and these represent 64 genes (upon deduplication of locus tags).  

Page 13, lines 280-281: Where is the information on chronic infection presented? Serial cultures would not necessarily mean chronic infection.

Authors response: Yes, we agree this was not the appropriate characterization and this was revised to ‘long-term’ infections.

Page 14 line 306: Emergence of colistin resistance in a single patient, correct?

Yes. This was further clarified in the text.

Page 14 lines 315-320: This should go to the results section. In particular disinfection, closing, and replacing of tubing should be mentioned in the results section in reference to the results presented in Table S3.

Thank you. We have considered this suggestion and have decided to leave this discussion as the closing paragraph of this publication. A follow-up study is ongoing to assess long-term efficacy of these interventions on the ST-621 bur also other outbreak clones at Facility A.

Methods

Page 15 lines 330-333: Perhaps it is possible to avoid redundancy.

Thank you. We have revised the text accordingly.

Page 15 lines 341: Information on which isolates were subjected to long-read sequencing is missing.

Thank you. This has been added to the revised Table S1.

Page 16 line 345: Was there a particular reason why Newbler was chosen?

No. At the time Newbler was the default assembler built in the MRSN bacterial genome analysis pipeline and QC processes.

Page 16, line 357-358: What was the rationale for selecting this isolate as reference genome?

This isolate was chosen because it was collected early in the outbreak and phylogenetic analysis revealed it had low root to tip divergence.

Page 16 line 361: Why 310 isolates, if only 253 were assigned to the outbreak clone and only a subset of those were collected in facility A?

This was a typographical error that has corrected (it now reads “…set of 253 isolates.”) in the revised manuscript.  

Page 17 lines 387-395: What is the reason that intra-patient diversity was not included in the set of criteria for SNP distances?

The observed within host variability (now displayed in revised Fig. 1E) was taken into consideration when setting SNP thresholds for categorizing patient-to-patient transmission or environment-to-patient event. This is now clarified in the revised manuscript.

Page 17 line 392: How was the threshold of <=10 SNPs determined?

The 10 SNP cutoff to infer a patient-to-patient transmission event was set to account for the known evolution rate of P. aeruginosa (inferred by BEAST at 2.987E-7 subs/site/year in this study, and similar to previous estimates PMID: 24039595) and the observed within host variability (now displayed in revised Fig. 1E). We note that this SNP distance was not sufficient and that an epi link (patients on the same ward within the same month) needed to be established.

Page 17 line 395 and Figure 2: What was the assumed average mutation rate per genome per year?

Thank you. The mean substitution rate inferred by BEAST was 2.987E-7 similar to estimate from previous studies on P. aeruginosa outbreaks (e.g. PMID: 24039595).

Reviewer #3 (Recommendations For The Authors):

Please find (line-by-line comments) on each section of the manuscript below:

Introduction

Line 86: I am wondering why the authors state ">28 facilities" instead of the exact number of facilities from which these lineages were recovered.

Thank you. Manuscript was revised to provide the exact number of facilities. It now reads “…recovered from 37 and 28 facilities, respectively.”

Methods

It's not clear to me which criteria were used for collecting these isolates (both prospective and retrospective). I understand that some of the data are described in more detail in Lebreton et al but I did not find the specific criteria for the collection of the isolates and I imagine that these might differ if different facilities. Would it be possible to comment on that and add a short paragraph in the Methods section?

Thank you. This lack of clarity was also raised by other reviewers, and we have revised the manuscript to indicate that: 1/MDR isolates only (Magiorakos et al PMID: 21793988) were collected from 2011-2020 with the same criteria for all facilities although compliance was variable through time and between facilities; and 2/ starting in 2021 all P. aeruginosa isolates, irrespective of their susceptibility profile, were collected from Facility A

The data comes from a US Military hospital. Is this related to the US Veterans Affairs Healthcare system? Is there more detailed information about the demographics of the patient population?

Facility A is part of the Military Health System (MHS) which provides care for active service members and their families. This is distinct from the US Veterans Affairs Healthcare system. Only limited patient data was accessible to us as this study was done as part of our public health surveillance activities. Patient age (avg. 57.2 +/- 21.0) and gender (ratio male/female 1.7) are provided in the revised manuscript. 

Line 384ff: The origin of infection was inferred based on the SNP threshold and epidemiological links. However, recombination events can complicate the interpretation of SNP data. Have the authors attempted to account for this?

Thank you. We agree that recombination events can complicate the interpretation of SNP data. We used Gubbins v2.3.1 to filter out recombination from the core SNP alignment, as indicated in the revised manuscript.

The authors' definition of environment-to-patient transmission seems conservative (nearly identical strain and no known temporal overlap for > 365 days). Have the authors changed the threshold, performed sensitivity analyses, and tested how this would affect their results?

Indeed, acknowledging that fixed thresholds have limitations in their ability to accurately predict the origin of infections, we took a conservative approach to favor specificity as our goal was simply to establish that cases of environment-to-patient transmission did happen. In the absence of a truth set, we have not performed sensitivity analysis, but we are conducting a follow-up study to compare inferences from MCMC models to our original predictions. This limitation is now discussed in the revised manuscript.

The authors don't seem to incorporate the role of healthcare workers in the transmission process. Could they comment on this? I am assuming that environment-to-patient transmission could either be directly from the environment to the patient or via a healthcare worker. I think it's fine to make simplifying assumptions here but it would be great if this was explicitly described.

Thank you for this suggestion. We have not sampled the hands of healthcare workers in this study. As a result, the reviewer is correct to say that we made the simplifying assumption that healthcare workers would be possible intermediates in either environment-topatient or patient-to-patient transmissions, as previously described by others (PMID: 8452949). This limitation is now discussed in the revised manuscript.

Page 5, line 100: What does "all vs all" mean? Based on the supplement, I assume it's the pairwise distance and then averaged across all of those. It would improve the readability of the manuscript if the authors could briefly define this term and then maybe refer to Table S1.

Thank you. We have created Fig.S2 and revised the manuscript to state that ST-621 isolates from facility A belonged to the same outbreak clone with a distance (averaged all vs all pairwise comparison) of just 38 single nucleotide polymorphisms (SNPs), and an IQR of 19 (Fig. S2, Table S1).

Figure 1D: It would be interesting to see additional figures in the supplement on the percentage of sequenced isolates per year and whether it varies across the different sources/sites. Is there any information on which isolates were chosen for sequencing?

Lack of clarity in the sampling/sequencing scheme was raised by multiple reviewers and we have provided a thorough response to earlier comments. We also have revised the material and methods section accordingly. Finally, we have created Fig. S3 to show the percentage of sequenced isolates per year across different sources/sites, as suggested by the reviewer. No noticeable patterns were observed. 

It seems like only a subset of all clinical isolates were sequenced. Would it be possible that SC2 was present already earlier but not picked up until a certain date?

Although all isolates received by the MRSN were sequenced, compliance varied through time so it is true that not all clinical isolates were sequenced between 2011-2019. As such, we fully agree with this hypothesis and discuss this possibility as BEAST analysis placed the origin of SC2 in 2004 while the first detection of an SC2 isolate was in December 2012. This limitation is now discussed in the revised manuscript.

Could the authors elaborate on whether the isolates resulted from single-colony picks? Is it possible that the different absence of a subclone is due to the fact that they picked only a colony?

Yes, the isolates resulted from single-colony picks except when the presence of different colony morphologies was noted. In the latter, representative isolates for each colony morphologies were processed. We have revised the methods to make that clear.

Figure 2: It is difficult to see which nodes belong to which patient due to the small font size. I wonder if it was possible to color the nodes for each patient, to make it more readable.

We tried coloring the nodes but with > 60 distinct patients/colors we decided it did not improve clarity. We have revised figure 2 to increase the font size.  

Page 7-8, lines 154-155: Did the authors check whether there were isolates of the same strain (that were found in the environment) present in other patients elsewhere in the ward?

Yes. In rare cases, we observed virtually genetically identical isolates from two patients collected in different wards. Because we only have access to clinical isolate data (collected from patient X in ward Y) and do not have access to patient data (admission/discharge date, wards, rooms, etc.), we do not know but cannot exclude that patients overlap in a room prior to the sampling of their P. aeruginosa isolates. We designed our fixed thresholds to be conservative. As a result, in this analysis, these cases are labelled as “undetermined”.  

Page 8: Do the authors have any information on antibiotic use during this timeframe? From the discussion, it seems like there is no patient-level prescription data. Is there any data on overall trends? How were trends in antibiotic use correlated with trends in antibiotic resistance?

Unfortunately, patient-level prescription data (or any other data not linked to the bacterial specimens) was not accessible to us as this study was done as part of our public health surveillance activities.

To infer the origin of infection, the authors used a static method with fixed thresholds and definitions. This study does not provide any uncertainty with their estimates. Maybe the authors could add a sentence in the discussion section that MCMC methods to infer transmission trees incorporating WGS could provide these estimates. These methods have not been applied to PA a lot but two examples where MCMC methods have been used without WGS (though the definition of environmental contamination may differ between these studies and this study).

https://doi.org/10.1186/s13756-022-01095-x

https://doi.org/10.1371/journal.pcbi.1006697

Thank you for this great suggestion. We have revised the manuscript to include a discussion on the limitations of fixed thresholds to infer transmission chains/origins, and to discuss existing alternatives including MCMC methods. 

Line 322-323: This sentence is a bit vague since not all of these HAI are due to P. aeruginosa. I would suggest citing a number that is specific to PA.

Thank you. While our paper shows a particular example of protracted P. aeruginosa outbreak, the roll-out of routine WGS surveillance in the clinic will help prevent hospital-associated drug-resistant infections for more than this species. We believe that broadening the scope in the last sentence of the manuscript is important and we decline to revise as suggested.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This report addresses a compelling topic. However, I have significant concerns, which necessitate a reassessment of the report's overall value.

Anatomical Specificity and Stimulation Site:

While the authors clarify that the ventral MGB (MGv) was the intended stimulation target, the electrode track (Fig. 1A) and viral spread (Fig. 2E) suggest possible involvement of the dorsal MGB (MGd) and broader area. Given that MGv-AI and MGd-AC pathways have distinct-and sometimes opposing-effects on plasticity, the reported LTP values (with unusually small standard deviations) raise concerns about the specificity of the findings. Additional anatomical verification would help resolve this issue.

We thank the reviewer for highlighting the importance of anatomical specificity in MGv targeting. In the revised manuscript, we have taken several steps to address these issues:

(1) Higher-magnification histology has been added to Figure 1A, clearly identifying the electrode tip localized within the MGv.

(2) Figure 2E has been replaced with a new image showing viral expression largely confined to MGB, with minimal spread to surrounding structures.

(3) In the Discussion, we explicitly acknowledge that although targeting was guided by stereotaxic coordinates and histological confirmation, some viral spread throughout the MGB occurred. We also discuss the possibility that both MGv-A1 and MGd-AC pathways may contribute to the recorded responses, which could influence the observed plasticity, as previously suggested by the reviewer.

These additions and acknowledgments are now incorporated to ensure the reader can interpret the data with full consideration of anatomical targeting limitations.

Results section:

“Higher-magnification histology confirmed accurate MGv targeting (Figure 1A, lower-middle panel)’”

Discussion section:

“Although our experiment targeting the MGv was guided by stereotaxic coordinates and verified post hoc, we acknowledge potential contributions from non-lemniscal medial geniculate nucleus dorsal (MGd) projections. Anatomical and physiological evidence indicates that MGv-AC projections provide rapid, frequency‑specific, tonotopically organized excitation, whereas MGd pathways target higher‑order auditory cortex with broader tuning, less precise tonotopy, longer response latencies, and greater context‑dependence, features that can differentially shape cortical sensory integration and plasticity (Lee and Sherman, 2010; Smith et al., 2012; Ohga et al., 2018; Lee, 2015; Hu, 2003). While the co-recruitment of lemniscal and non-lemniscal inputs may enhance the generality of our CCK-dependent mechanism, the differing response characteristics of these pathways suggest subtle differences in their relative engagement in the observed plasticity. Future pathway-specific manipulations will help clarify their respective contributions”

Lee, C.C., and Sherman, S.M. (2010). Topography and physiology of ascending streams in the auditory tectothalamic pathway. Proceedings of the National Academy of Sciences 107, 372-377. doi:10.1073/pnas.0907873107.

Smith, P.H., Uhlrich, D.J., Manning, K.A., and Banks, M.I. (2012). Thalamocortical projections to rat auditory cortex from the ventral and dorsal divisions of the medial geniculate nucleus. Journal of Comparative Neurology 520, 34-51.

Ohga, S., Tsukano, H., Horie, M., Terashima, H., Nishio, N., Kubota, Y., Takahashi, K., Hishida, R., Takebayashi, H., and Shibuki, K. (2018). Direct Relay Pathways from Lemniscal Auditory Thalamus to Secondary Auditory Field in Mice. Cerebral Cortex 28, 4424-4439. 10.1093/cercor/bhy234.

Lee, C.C. (2015). Exploring functions for the non-lemniscal auditory thalamus. Frontiers in Neural Circuits 9, 69.

Hu, B. (2003). Functional organization of lemniscal and nonlemniscal auditory thalamus. Experimental Brain Research 153, 543-549. 10.1007/s00221-003-1611-5.

Figure legend section:

“Post-hoc histology at higher magnification (lower-middle) shows the electrode tip confined within the MGv. White lines delineate the MGv/MGd border based on cytoarchitectonic landmarks.”

Statistical Rigor and Data Variability:

The remarkably low standard deviations in LTP measurements are unexpected based on established variability in thalamocortical plasticity. The authors' response confirms these values are accurate, but further justification, such as methodological controls or replication-would bolster confidence in these results. Additionally, the comparison of in vivo vs. in vitro LTP variability requires more substantive support.

We appreciate the reviewer's concern regarding the unusually small variability. We would like to clarify that the error bars in our figures represent Standard Error of the Mean (SEM) rather than Standard Deviations (SD). As SEM is derived from the SD while incorporating sample size, it is inherently smaller than SD, which may have led to the impression of unrealistically low variability. This has now been explicitly clarified in the figure legends and Methods.

To illustrate the raw variability, we have added Supplementary Figure S1E showing unaveraged fEPSP slopes compare to SEM, corresponding to Figure S1C. This addition ensures transparency and allows readers to directly assess the quality and consistency of our recordings.

Regarding the comparison between in vivo and in vitro LTP variability:

We agree that clarifying the basis of our in vivo vs. in vitro variability comparison is important. For example, in Chen et al., 2019, using identical LTP induction protocols (Fig. J), the SED of in vitro slice measurements (Fig. K) was substantially larger than that of in vivo recordings (Fig. L).

This difference likely reflects:

(1) In vitro: neighboring data points within a single experiment are highly correlated; variability across experiments is large due to heterogeneous sensitivity to LTP induction (10–200% increasement).

(2) In vivo: lower correlation between neighboring data points, but each is averaged from 12 recordings over 2 min, reducing cross-trial variability; sensitivity to LTP induction is less variable across experiments (5–60% changes).

We hope that these clarifications and additional data address the reviewer’s concerns regarding statistical rigor and data variability.

Methods section:

“The slopes of the evoked fEPSPs were calculated and normalized using a customized MATLAB script, and the group data were plotted as mean ± Standard Error of the Mean (SEM).”

“All data are presented as mean ± SEM. Error bars and shaded areas represent SEM. Here, n represents the number of stimulation-recording sites or and N represents the number of animals in each experiment. At each time point, fEPSPs were averaged across 12 consecutive trials (2 min) to reduce within-experiment fluctuation. Normalized time courses were then used for repeated-measures analyses.”

Figure legend section:

“Data are mean ± SEM; error bars indicate SEM.”

“(E) Unaveraged fEPSP slopes are shown for each time point, with individual data points corresponding to all sites included in Fig. 1C; mean ± SEM overlays are shown in black. Note that all individual data points are displayed in this figure, whereas in Figure S1C, only the averaged values are shown.”

Viral Targeting and Specificity:

The manuscript does not clearly address whether cortical neurons were inadvertently infected by AAV9. Given the potential for off-target effects, explicit confirmation (e.g., microphotograph of stimulation site) would strengthen the study's conclusions.

We appreciate the request for quantitative confirmation of off-target cortical infection. We clarify that our histological verification was conducted by systematic sampling rather than exhaustive quantification. Under the same sampling procedure, we did not detect tdTomato-positive cortical somata after AAV9‑Syn‑ChrimsonR‑tdTomato injections into the MGB, whereas we observed rare EYFP-positive cortical somata after AAV9‑EF1a‑DIO‑ChETA‑EYFP (median < 1 cell per 0.4 × 0.4 mm² section, Supplementary Figure S1E). Although these observations do not constitute a formal statistical estimate, they were consistent across sampled sections and are in line with the low-level trans-synaptic transfer reported for AAV9. We have discussed their potential implications for data interpretation in the Discussion.

We hope these clarifications and the newly presented histological evidence address the reviewer’s concerns and further strengthen the rigor of our study.

Discussion section:

“Another potential limitation of our study is the trans-synaptic transfer property of AAV9 (Figure S1F). To mitigate this risk, we carefully control the injection volume, rate, and viral expression time, while also verifying expression post-hoc. Systematic sampling histological analysis detected no tdTomato-positive cortical somata in the ACx (Figure 2E lower panel), whereas rare EYFP-positive cortical somata were observed after AAV9-EF1a-DIO-ChETA-EYFP injections (median < 1 cell in 0.4 × 0.4 mm2 section, Figure S1F, corresponds to Figure 2A upper-middle panel). These construct‑dependent observations align with occasional low‑level trans‑synaptic transfer reported for AAV9 (Zingg et al., 2017) and indicate that off‑target cortical infection was negligible for ChrimsonR and exceedingly rare for ChETA under our experimental conditions.”

Zingg, B., Chou, X.L., Zhang, Z.G., Mesik, L., Liang, F., Tao, H.W., and Zhang, L.I. (2017). AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors. Neuron 93, 33-47. 10.1016/j.neuron.2016.11.045.

Figure legend:

“Representative histological images demonstrating low-level transsynaptic spread following AAV9-EF1a-DIO-ChETA-EYFP injection into the MGv. Rare EYFP-positive cortical neurons were observed (median < 1 cell per 0.4 × 0.4 mm² section). Scale bar: 100 µm.”

Integration of Prior Literature:

The discussion of existing work is adequate but could be more comprehensive. A deeper engagement with contrasting findings would provide better context for the study's contributions.

We appreciate the reviewer’s suggestion to engage more deeply with contrasting findings. In the revised Introduction and Discussion, we have:

(1) Refocused the historical context toward adult auditory thalamocortical plasticity and explicitly contrasted it with visual and somatosensory cortices, while adult ACx exhibits weaker and more gated NMDAR dependence.

(2) Positioned CCK–CCKBR signaling as a permissive/gating mechanism that can complement or partially compensate for postsynaptic NMDAR signaling, potentially reconciling variability across cortical areas and life stages.

(3) Clarified the potential differential contributions of lemniscal (MGv) and non‑lemniscal (MGd) streams to plasticity expression and variability, acknowledging pathway-specific response properties.

These additions are now integrated in the Introduction (paragraphs 2–3) and Discussion (sections “CCK Dependence of Thalamocortical Neuroplasticity in the ACx” and “Developmental and Age‑Dependent CCK‑Mediated Plasticity”), providing a more comprehensive and balanced context for our findings.

Introduction section:

“However, converging evidence shows that thalamocortical inputs retain a capacity for experience-dependent modification in adulthood. Sensory enrichment or deprivation can gate or reinstate thalamocortical plasticity. In the adult ACx, pairing sounds with neuromodulatory drive can reshape cortical representations. In vivo high-frequency stimulation (HFS) of dorsal lateral geniculate nucleus (LGN) or medial geniculate body (MGB) induces LTP in sensory cortices and has been linked to perceptual learning beyond the critical period. Notably, auditory thalamocortical plasticity appears less dependent on NMDA receptors compared to other cortical regions. The mechanisms underlying thalamocortical plasticity in the mature brain remain poorly understood.

Cholecystokinin (CCK) and its receptor CCK-B receptor (CCKBR) are well positioned to influence thalamocortical transmission: Cck mRNA is abundant in MGB neurons and CCKBR is enriched in layer IV of ACx, the principal thalamorecipient layer.”

Discussion section:

“These findings suggest a potential involvement of CCK in thalamocortical plasticity. Our data extend this framework by identifying CCK–CCKBR signaling as a permissive modulator of adult thalamocortical LTP.”

“We propose that CCKBR activation may trigger intracellular calcium release and AMPAR recruitment in parallel to, or partially compensating for,independently of postsynaptic NMDAR signaling, while the complementarity of CCKBR and NMDARs may contribute to robust thalamocortical plasticity. This complementary arrangement may reconcile differences across developmental stages and cortical areas, and highlights neuropeptidergic signaling as a lever to re-enable adult thalamocortical plasticity.

Notably, exogenous CCK alone failed to induce LTP in the absence of accompanying stimulation (Figure S2A and S2B), emphasizing that CCK function as a modulator rather than a direct initiator of LTP. Activation of the thalamocortical pathway is also essential for LTP induction. Although our experiment targeting the MGv was guided by stereotaxic coordinates and verified post hoc, we acknowledge potential contributions from non-lemniscal medial geniculate nucleus dorsal (MGd) projections. Anatomical and physiological evidence indicates that MGv-AC projections provide rapid, frequency‑specific, tonotopically organized excitation, whereas MGd pathways target higher‑order auditory cortex with broader tuning, less precise tonotopy, longer response latencies, and greater context‑dependence, features that can differentially shape cortical sensory integration and plasticity. While the co-recruitment of lemniscal and non-lemniscal inputs may enhance the generality of our CCK-dependent mechanism, the differing response characteristics of these pathways suggest subtle differences in their relative engagement in the observed plasticity. Future pathway-specific manipulations will help clarify their respective contributions. Another potential limitation of our study is the trans-synaptic transfer property of AAV9 (Figure S1F). To mitigate this, we carefully controlled the injection volume, rate, and viral expression time, and conducted post-hoc histological analyses to minimize off-target effects, thereby reducing the likelihood of trans-synaptic transfer confounding the interpretation of our findings.”

Therapeutic Implications:

The authors' discussion of therapeutic potential is now appropriately cautious and well-reasoned.

Conclusion:

While the study presents intriguing findings, the concerns outlined above must be addressed to fully establish the validity and impact of the results. I appreciate the authors' efforts thus far and hope they can provide additional data or clarification to resolve these issues. With these revisions, the manuscript could make a valuable contribution to the field.

Reviewer #2 (Public review):

Summary:

This work used multiple approaches to show that CCK is critical for long-term potentiation (LTP) in the auditory thalamocortical pathway. They also showed that the CCK mediation of LTP is age-dependent and supports frequency discrimination. This work is important because is opens up a new avenue of investigation of the roles of neuropeptides in sensory plasticity.

Strengths:

The main strength is the multiple approaches used to comprehensively examine the role of CCK in auditory thalamocortical LTP. Thus, the authors do provide a compelling set of data that CCK mediates thalamocortical LTP in an age-dependent manner.

Weaknesses:

There are some details that should be addressed, primarily regarding potential baseline differences in comparison groups. The behavioral assessment is relatively limited, but may be fleshed out in future work.

We appreciate the reviewer’s suggestion regarding potential baseline differences. In our study, all groups underwent harmonized procedures, including identical exposure, timing, and acquisition parameters. Group allocation and data collection were performed under standardized conditions. For electrophysiology, baseline fEPSP measures and stimulation intensities were calibrated per site using consistent input-output procedures, with analyses based on normalized slopes relative to each site’s own baseline. For behavior, animals from the same litter served as both experimental and control groups, matched for handling conditions; startle/PPI data were acquired using identical hardware and timing settings. While no additional post hoc re-processing was performed, we have clarified these controls in the Methods to enhance transparency.

We agree that the behavioral assessment is intentionally focused and does not encompass broader auditory perceptual functions (e.g., temporal processing). We now explicitly state this limitation and propose future studies to examine temporal acuity and cell-type-specific manipulations. These experiments will clarify how CCK-dependent thalamocortical plasticity generalizes to other perceptual domains.

Reviewer #3 (Public review):

Summary:

Cholecystokinin (CCK) is highly expressed in auditory thalamocortical (MGB) neurons and CCK has been found to shape cortical plasticity dynamics. In order to understand how CCK shapes synaptic plasticity in the auditory thalamocortical pathway, they assessed the role of CCK signaling across multiple mechanisms of LTP induction with the auditory thalamocortical (MGB - layer IV Auditory Cortex) circuit in mice. In these physiology experiments that leverage multiple mechanisms of LTP induction and a rigorous manipulation of CCK and CCK-dependent signaling, they establish an essential role of auditory thalamocortical LTP on the co-release of CCK from auditory thalamic neurons. By carefully assessing the development of this plasticity over time and CCK expression, they go on to identify a window of time that CCK is produced throughout early and middle adulthood in auditory thalamocortical neurons to establish a window for plasticity from 3 weeks to 1.5 years in mice, with limited LTP occurring outside of this window. The authors go on to show that CCK signaling and its effect on LTP in the auditory cortex is also capable of modifying frequency discrimination accuracy in an auditory PPI task. In evaluating the impact of CCK on modulating PPI task performance, it also seems that in mice <1.5 years old CCK-dependent effects on cortical plasticity is almost saturated. While exogenous CCK can modestly improve discrimination of only very similar tones, exogenous focal delivery of CCK in older mice can significantly improve learning in a PPI task to bring their discrimination ability in line with those from young adult mice.

Strengths:

(1) The clarity of the results, along with the rigor multi-angled approach, provide significant support for the claim that CCK is essential for auditory thalamocortical synaptic LTP. This approach uses a combination of electrical, acoustic, and optogenetic pathway stimulation alongside conditional expression approaches, germline knockout, viral RNA downregulation and pharmacological blockade. Through the combination of these experimental configures the authors demonstrate that high-frequency stimulation-induced LTP is reliant on co-release of CCK from glutamatergic MGB terminals projecting to the auditory cortex.

(2) The careful analysis of the CCK, CCKB receptor, and LTP expression is also a strength that puts the finding into the context of mechanistic causes and potential therapies for age-dependent sensory/auditory processing changes. Similarly, not only do these data identify a fundamental biological mechanism, but they also provide support for the idea that exogenous asynchronous stimulation of the CCKBR is capable of restoring an age-dependent loss in plasticity.

(3) Although experiments to simultaneously relate LTP and behavioral change or identify a causal relationship between LTP and frequency discrimination are not made, there is still convincing evidence that CCK signaling in the auditory cortex (known to determine synaptic LTP) is important for auditory processing/frequency discrimination. These experiments are key for establishing the relevance of this mechanism.

Weaknesses:

(1) Given the magnitude of the evoked responses, one expects that pyramidal neurons in layer IV are primarily those that undergo CCK-dependent plasticity, but the degree to which PV-interneurons and pyramidal neurons participate in this process differently is unclear.

We agree with the reviewer that the relative contributions of pyramidal neurons and PV-interneurons to CCK-dependent thalamocortical plasticity remain to be determined. Our recordings primarily reflected excitatory postsynaptic activity from layer IV pyramidal neurons, given the fEPSP metrics used. As PV-interneurons are essential in shaping cortical inhibition and temporal precision, they may also be modulated by CCK release from thalamocortical inputs. We have explicitly acknowledged this limitation in the Discussion section of the manuscript and propose that future studies should employ cell-type-specific recording or manipulation approaches to dissect the respective roles of inhibitory and excitatory neuronal populations in CCK-dependent thalamocortical plasticity. We appreciate the reviewer’s suggestion and believe this is a valuable direction for ongoing research.

(2) While these data support an important role for CCK in synaptic LTP in the auditory thalamocortical pathway, perhaps temporal processing of acoustic stimuli is as or more important than frequency discrimination. Given the enhanced responsivity of the system, it is unclear whether this mechanism would improve or reduce the fidelity of temporal processing in this circuit. Understanding this dynamic may also require consideration of cell type as raised in weakness #1.

We acknowledge that the current study primarily examined frequency discrimination and did not directly assess temporal processing. Enhanced network responsivity could have variable effects on temporal precision, depending on the balance between excitation and inhibition. PV-interneurons, in particular, are known to support temporal fidelity in auditory processing (Nocon et al., 2023; Cai et al., 2018). We discussion that future work should investigate how CCK modulation influences temporal coding at both the circuit and single-cell level, and whether such changes align with or diverge from the mechanisms underlying frequency discrimination improvements.

(3) In Figure 1, an example of increased spontaneous and evoked firing activity of single neurons after HFS is provided. Yet it is surprising that the group data are analyzed only for the fEPSP. It seems that single neuron data would also be useful at this point to provide insight into how CCK and HFS affect temporal processing and spontaneous activity/excitability, especially given the example in 1F.

We appreciate the reviewer’s suggestion. While we recorded single-unit activity during HFS protocols, long-term stability over >1.5 hours was less consistent compared to fEPSP measurements, leading to higher variability in spike-based metrics. We therefore used fEPSPs as our primary quantitative measure for robustness. We agree, however, that single-neuron data could yield valuable complementary insights. In future experiments combining stable single-unit recording with synaptic measurements will be conducted to better link cellular excitability and network plasticity.

(4) The circuitry that determines PPI requires multiple brain areas, including the auditory cortex. Given the complicated dynamics of this process, it may be helpful to consider what, if anything, is known specifically about how layer IV synaptic plasticity in the auditory cortex may shape this behavior.

We agree that PPI involves multiple cortical and subcortical nodes. In our paradigm, layer IV neurons receive segregated MGv inputs, high-frequency activation of thalamocortical projections induces robust synaptic plasticity in layer IV. The potentiation at these synapses could amplify the cortical representation of weak prepulses, facilitating their detection and enhancing PPI performance. This interpretation is consistent with prior work showing that local CCK infusion combined with auditory stimuli can augment cortical responses (Li et al., 2014). We have expanded the Discussion to highlight that in aged animals, where baseline PPI performance is often reduced due to degraded auditory inputs (Ouagazzal et al., 2006; Young et al., 2010), restoring thalamocortical plasticity via CCK may partially compensate for sensory gating deficits. We further note that the exact contribution of layer IV to PPI circuitry warrants future investigation using pathway-specific perturbations.

Comments on revisions:

The manuscript is much improved and many of the issues or questions have been addressed. Ideally, evidence for the degree of transsynaptic spread for AAV9-Syn-ChrimsonR-tdTomato would also be provided in some form since in the authors' response in sounds like some was observed, as expected.

We thank the reviewer for this important point and for the opportunity to clarify. As requested, we have carefully examined the possibility of transsynaptic spread in our experiments:

We clarify that our histological verification was conducted by systematic sampling rather than exhaustive quantification. Under the same sampling procedure, we did not detect tdTomato-positive cortical somata after AAV9‑Syn‑ChrimsonR‑tdTomato injections into the MGB, whereas we observed rare EYFP-positive cortical somata after AAV9‑EF1a‑DIO‑ChETA‑EYFP (median < 1 cell per 0.4 × 0.4 mm² section, see Figure 2A and Figure S1F), consistent with occasional low-level transsynaptic spread reported in the literature.

We have updated the Discussion sections to clearly report these findings, and to emphasize the potential for vector- and construct-dependent variability in transsynaptic spread. We also explicitly acknowledge this technical limitation and discuss its implications for data interpretation.

We hope these clarifications and additions address the reviewer’s concern regarding viral specificity and transsynaptic spread.

Discussion section:

“Another potential limitation of our study is the trans-synaptic transfer property of AAV9 (Figure S1F). To mitigate this risk, we carefully control the injection volume, rate, and viral expression time, while also verifying expression post-hoc. Systematic sampling histological analysis detected no tdTomato-positive cortical somata in the ACx (Figure 2E lower panel), whereas rare EYFP-positive cortical somata were observed after AAV9-EF1a-DIO-ChETA-EYFP injections (median < 1 cell in 0.4 × 0.4 mm2 section, Figure S1F, corresponds to Figure 2A upper-middle panel). These construct‑dependent observations align with occasional low‑level trans‑synaptic transfer reported for AAV9 (Zingg et al., 2017) and indicate that off‑target cortical infection was negligible for ChrimsonR and exceedingly rare for ChETA under our experimental conditions.”

Zingg, B., Chou, X.L., Zhang, Z.G., Mesik, L., Liang, F., Tao, H.W., and Zhang, L.I. (2017). AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors. Neuron 93, 33-47. 10.1016/j.neuron.2016.11.045.

Figure legend:

" Representative histological images demonstrating low-level transsynaptic spread following AAV9-EF1a-DIO-ChETA-EYFP injection into the MGv. Rare EYFP-positive cortical neurons were observed (median < 1 cell per 0.4 × 0.4 mm² section). Scale bar: 100 µm."

Reviewer #1 (Recommendations for the authors):

Thank you for your efforts in revising the manuscript. While progress has been made, I have a few remaining concerns that I hope you can address to further strengthen the study.

Focus of the Introduction:

Auditory thalamocortical plasticity is known to be NMDA-dependent, albeit with weaker dependence during early development. Given that this work examines thalamocortical LTP in young adult and aged mice, I recommend refining the Introduction to place greater emphasis on auditory thalamocortical plasticity in the adult brain. The current discussion of somatosensory plasticity during early development, while interesting, seems less directly relevant to the present study. A sharper focus on the auditory system would better frame your research questions.

We thank the reviewer for this constructive suggestion. We have revised the Introduction to emphasize adult auditory thalamocortical plasticity and to streamline content less directly related to our study. Specifically:

(1) We now foreground evidence that thalamocortical inputs retain experience-dependent plasticity beyond the critical period in adult ACx, including neuromodulatory pairing, HFS-induced LTP, and experience-dependent reinstatement.

(2) We explicitly note that adult auditory thalamocortical plasticity is more weakly NMDAR-dependent than in other cortices, thereby motivating our focus on CCK–CCKBR signaling as a permissive mechanism for adult LTP.

(3) We have condensed the discussion of somatosensory plasticity during early development to a brief background and shifted the focus to adult auditory mechanisms and knowledge gaps that directly frame our research questions.

These changes appear in the revised Introduction (paragraphs 2–3), which now provide a sharper rationale for investigating CCK‑dependent thalamocortical LTP in young adult and aged mice.

Introduction section:

“However, converging evidence shows that thalamocortical inputs retain a capacity for experience-dependent modification in adulthood. Sensory enrichment or deprivation can gate or reinstate thalamocortical plasticity. In the adult ACx, pairing sounds with neuromodulatory drive can reshape cortical representations. In vivo high-frequency stimulation (HFS) of dorsal lateral geniculate nucleus (LGN) or medial geniculate body (MGB) induces LTP in sensory cortices and has been linked to perceptual learning beyond the critical period. Notably, auditory thalamocortical plasticity appears less dependent on NMDA receptors compared to other cortical regions. The mechanisms underlying thalamocortical plasticity in the mature brain remain poorly understood.

Cholecystokinin (CCK) and its receptor CCK-B receptor (CCKBR) are well positioned to influence thalamocortical transmission: Cck mRNA is abundant in MGB neurons and CCKBR is enriched in layer IV of ACx, the principal thalamorecipient layer.”

Anatomical Specificity of MGv Targeting:

The mouse MGv is a small and deep structure, and precise targeting is critical given the functional differences between MGv and MGd pathways. In the current figures:

Fig. 1A suggests the electrode track may have approached the MGd.

Fig. 2E indicates some viral spread beyond the MGB.

Since MGv-AI and MGd-AC pathways exhibit distinct (and sometimes opposing) effects on plasticity, I encourage you to provide additional clarification or verification of the stimulated/infected regions. This would greatly enhance the interpretability of your LTP data.

Please see above.

Data Variability and Transparency:

The reported thalamocortical LTP values exhibit remarkably small standard deviations, which is somewhat unexpected given typical experimental variability in such measurements. To address this concern, it would be helpful to include example raw traces of the recorded LTP (e.g., in a supplementary figure). This would allow readers to better evaluate the data quality and consistency.

Please see above.

Reviewer #2 (Recommendations for the authors):

Overall, the authors did an excellent job of responding to our critiques, both in their direct responses and in the modified text. The modified text is also more readable than before. Two issues that the authors should consider addressing;

(1) Unless I missed it, there is no commentary stated about the impact of using aged C57 mice, which lose their hearing, such that the effects seen in the older mice could be related to hearing loss rather than aging alone. Some discussion of this point should be made.

We thank the reviewer for raising this important point. C57BL/6 mice are known to develop age-related hearing loss, which could potentially affect PPI performance in older animals. We note that in our internal screening we observed markedly reduced startle amplitudes and frequent negative PPI values in many mice >20 months, indicating severe auditory impairment. To minimize this confound a priori, we excluded mice older than 20 months and restricted the aged cohort to 17–19 months, which consistently exhibited robust startle responses and reliable PPI. While some degree of presbycusis may still be present in this age range in C57BL/6 mice, the improvement of PPI following CCK administration combined with acoustic exposure indicates that the auditory pathways remained sufficiently functional to support sensorimotor gating. In fact, the presence of partial hearing loss in these aged mice may have allowed us to better detect the beneficial effects of CCK, further highlighting its therapeutic potential for age-related deficits. The greater improvement in PPI observed in older mice —as compared to younger mice, whose PPI in control group is already high—likely reflect the combined effects of age-related hearing loss and CCK deficiency, with CCK-induced restoration of thalamocortical plasticity being the primary focus of our study. We have now added a discussion of this point in the revised manuscript.

Discussion section:

“In aged mice, PPI deficits are commonly observed due to impaired auditory processing. Notably, C57BL/6 mice exhibit age-related hearing loss (Johnson et al., 1997). Both age-associated changes in auditory function and CCK deficiency contribute to impaired sensory gating. The presence of partial hearing loss in aged mice may have facilitated the detection of CCK’s beneficial effects, further highlighting its therapeutic potential for age-related deficits. Our results suggest that enhanced thalamocortical plasticity mediated by CCK might partially compensate for these deficits by amplifying residual auditory signals in aged mice.”

Johnson, K.R., Erway, L.C., Cook, S.A., Willott, J.F., and Zheng, Q.Y. (1997). A major gene affecting age-related hearing loss in C57BL/6J mice. Hearing Research 114, 83-92. https://doi.org/10.1016/S0378-5955(97)00155-X.

(2) Minor point - I do not agree with the use of the term "ventral to bregma" to describe where the craniotomies were placed (e.g., line 599). The direction being described is more typically referred to as "lateral." If the authors prefer to use the term "ventral," perhaps additional clarification can be added.

We thank the reviewer for pointing out this issue and apologize for any confusion. We agree that “ventral to bregma” is not the standard terminology and have revised the Methods section to use “below the temporal ridge”. We have also clarified that the craniotomy for accessing the auditory cortex was performed on the lateral aspect of the skull in rodents, just below the temporal ridge. We hope this revision resolves the ambiguity.

Method section:

“A craniotomy was performed over the temporal bone, as the auditory cortex is located on the lateral surface of the brain (coordinates: 1.5 to 3.0 mm below the temporal ridge and 2.0 to 4.0 mm posterior to bregma for mice; 2.5 to 6.5 mm below the temporal ridge and 3.0 to 5.0 mm posterior to bregma for rats) to access the auditory cortex.”

“Six-week after CCK-sensor virus injection, a craniotomy was performed to access the auditory cortex at the temporal bone (1.5 to 3.0 mm below the temporal ridge and 2.0 to 4.0 mm posterior to bregma), and the dura mater was opened.”

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The temporal regulation of neuronal specification and its molecular mechanisms are important problems in developmental neurobiology. This study focuses on Kenyon cells (KCs), which form the mushroom body in Drosophila melanogaster, in order to address this issue. Building on previous findings, the authors examine the role of the transcription factor Eip93F in the development of late-born KCs. The authors revealed that Eip93F controls the activity of flies at night through the expression of the calcium channel Ca-α1T. Thus, the study clarifies the molecular machinery that controls temporal neuronal specification and animal behavior.

Strengths:

The convincing results are based on state-of-the-art molecular genetics, imaging, and behavioral analysis.

Weaknesses:

Temporal mechanisms of neuronal specification are found in many nervous systems. However, the relationship between the temporal mechanisms identified in this study and those in other systems remains unclear.

We will expand the Discussion section to highlight the temporal mechanisms between different nervous systems.

Reviewer #2 (Public review):

Summary:

Understanding the mechanisms of neural specification is a central question in neurobiology. In Drosophila, the mushroom body (MB), which is the associative learning region in the brain, consists of three major cell types: γ, α'/β', and α/β kenyon cells. These classes can be further subdivided into seven subtypes, together comprising

~2000 KCs per hemi-brain. Remarkably, all of these neurons are derived from just four neuroblasts in each hemisphere. Therefore, a lot of endeavors are put into understanding how the neuron is specified in the fly MB.

Over the past decade, studies have revealed that MB neuroblasts employ a temporal patterning mechanism, producing distinct neuronal types at different developmental stages. Temporal identity is conveyed through transcription factor expression in KCs. High levels of Chinmo, a BTB-zinc finger transcription factor, promote γ-cell fate (Zhu et al., Cell, 2006). Reduced Chinmo levels trigger expression of mamo, a zinc finger transcription factor that specifies α'/β' identity (Liu et al., eLife, 2019). However, the specification of α/β neurons remains poorly understood. Some evidence suggests that microRNAs regulate the transition from α'/β' to α/β fate (Wu et al., Dev Cell, 2012; Kucherenko et al., EMBO J, 2012). One hypothesis even proposes that α/β represents a "default" state of MB neurons, which could explain the difficulty in identifying dedicated regulators.

The study by Chung et al. challenges this hypothesis. By leveraging previously published RNA-seq datasets (Shih et al., G3, 2019), they systematically screened BAC transgenic lines to selectively label MB subtypes. Using these tools, they analyzed the consequences of manipulating E93 expression and found that E93 is required for α/β specification. Furthermore, loss of E93 impairs MB-dependent behaviors, highlighting its functional importance.

Strengths:

The authors conducted a thorough analysis of E93 manipulation phenotypes using LexA tools generated from the Janelia Farm and Bloomington collections. They demonstrated that E93 knockdown reduces expression of Ca-α1T, a calcium channel gene identified as an α/β marker. Supporting this conclusion, one LexA line driven by a DNA fragment near EcR (R44E04) showed consistent results. Conversely, overexpression of E93 in γ and α'/β' Kenyon cells led to downregulation of their respective subtype markers.

Another notable strength is the authors' effort to dissect the genetic epistasis between E93 and previously known regulators. Through MARCM and reporter analyses, they showed that Chinmo and Mamo suppress E93, while E93 itself suppresses Mamo. This work establishes a compelling molecular model for the regulatory network underlying MB cell-type specification.

Weaknesses:

The interpretation of E93's role in neuronal specification requires caution. Typically, two criteria are used to establish whether a gene directs neuronal identity:

(1) gene manipulation shifts the neuronal transcriptome from one subtype to another, and

(2) gene manipulation alters axonal projection patterns.

The results presented here only partially satisfy the first criterion. Although markers are affected, it remains possible that the reporter lines and subtype markers used are direct transcriptional targets of E93 in α/β neurons, rather than reflecting broader fate changes. Future studies using single-cell transcriptomics would provide a more comprehensive assessment of neuronal identity following E93 perturbation.

We do plan to conduct multi-omics experiments to provide a more comprehensive assessment of neuronal identity upon loss-of-function of E93. However, omics results will be summarized in a new manuscript, but not for the revised manuscript.

With respect to the second criterion, the evidence is also incomplete. While reporter patterns were altered, the overall morphology of the α/β lobes appeared largely intact after E93 knockdown. Overexpression of E93 in γ neurons produced a small subset of cells with α/β-like projections, but this effect warrants deeper characterization before firm conclusions can be drawn. While the results might be an intrinsic nature of KC types in flies, the interpretation of the reader of the data should be more careful, and the authors should also mention this in their main text.

We will describe and interpret this part of results in the main text in a more careful manner.

Author response:

Reviewer 1:

We appreciate the reviewer’s positive assessment and in revision will expand the Discussion to clarify some of the mechanistic insights of this work, as well as to include expanded treatment of related studies in other model systems.

Reviewer 2:

We are grateful for the reviewer’s thorough and supportive comments. We will carefully revise assertions and conclusions for objectivity. Additional analysis of the Zelda experiments will be performed and experimental data tables will be updated to report these results. For the point about providing “insight into models explaining why H3K27me3 is absent prior to NC14,” we have recently submitted a related preprint that addresses this issue directly (Degen, Gonzaga-Saavedra, and Blythe, bioRxiv 2025). In summary, we find evidence that a maternal PcG imprint is indeed maintained through cleavage divisions, albeit through lower-order methylation states (maximally H3K27me2). We chose not to include these additional results in this manuscript to maintain the focus of this study on ZGA. Our revision of this manuscript will include a section in the Discussion that synthesizes the conclusions of the two studies.

Reviewer 3:

We thank the reviewer for recognizing the strength of our data and conclusions, and we agree that our results help settle conflicting claims in the field. We will emphasize Zelda’s context-dependent effects more clearly in the revised manuscript.

References:

Degen EA, Gonzaga-Saavedra N, Blythe SA. Lower-order methylation states underlie the maintenance and re-establishment of Polycomb modifications in Drosophila embryogenesis. bioRxiv [Preprint]. 2025 Jul 29:2025.07.25.666882. doi: 10.1101/2025.07.25.666882. PMID: 40766521; PMCID: PMC12324246.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Taber et al report the biochemical characterization of 7 mutations in PHD2 that induce erythrocytosis. Their goal is to provide a mechanism for how these mutations cause the disease. PHD2 hydroxylates HIF1a in the presence of oxygen at two distinct proline residues (P564 and P402) in the "oxygen degradation domain" (ODD). This leads to the ubiquitylation of HIF1a by the VHL E3 ligase and its subsequent degradation. Multiple mutations have been reported in the EGLN1 gene (coding for PHD2), which are associated with pseudohypoxic diseases that include erythrocytosis. Furthermore, 3 mutations in PHD2 also cause pheochromocytoma and paraganglioma (PPGL), a neuroendocrine tumour. These mutations likely cause elevated levels of HIF1a, but their mechanisms are unclear. Here, the authors analyze mutations from 152 case reports and map them on the crystal structure. They then focus on 7 mutations, which they clone in a plasmid and transfect into PHD2-KO to monitor HIF1a transcriptional activity via a luciferase assay. All mutants show impaired activation. Some mutants also impaired stability in pulse chase turnover assays (except A228S, P317R, and F366L). In vitro purified PHD2 mutants display a minor loss in thermal stability and some propensity to aggregate. Using MST technology, they show that P317R is strongly impaired in binding to HIF1a and HIF2a, whereas other mutants are only slightly affected. Using NMR, they show that the PHD2 P317R mutation greatly reduces hydroxylation of P402 (HIF1a NODD), as well as P562 (HIF1a CODD), but to a lesser extent. Finally, BLI shows that the P317R mutation reduces affinity for CODD by 3-fold, but not NODD.  

Strengths: 

(1) Simple, easy-to-follow manuscript. Generally well-written. 

(2) Disease-relevant mutations are studied in PHD2 that provide insights into its mechanism of action. 

(3) Good, well-researched background section. 

Weaknesses: 

(1) Poor use of existing structural data on the complexes of PHD2 with HIF1a peptides and various metals and substrates. A quick survey of the impact of these mutations (as well as analysis by Chowdhury et al, 2016) on the structure and interactions between PHD2 peptides of HIF1a shows that the P317R mutation interferes with peptide binding. By contrast, F366L will affect the hydrophobic core, and A228S is on the surface, and it's not obvious how it would interfere with the stability of the protein. 

Thank you for the comment.  We have further analyzed the mutations on the available PHD2 crystal structures in complex with HIFα to discern how these substitution mutations may impact PHD2 structure and function.  This analysis has been added into the discussion.

(2) To determine aggregation and monodispersity of the PHD2 mutants using size-exclusion chromatography (SEC), equal quantities of the protein must be loaded on the column. This is not what was done. As an aside, the colors used for the SEC are very similar and nearly indistinguishable. 

Agreed. We have performed an additional experiment as suggested by the reviewer to further assess aggregation and hydrodynamic size.  The colors used in the graph were changed for clearer differentiation between samples.

(3) The interpretation of some mutants remains incomplete. For A228S, what is the explanation for its reduced activity? It is not substantially less stable than WT and does not seem to affect peptide hydroxylation. 

We agree with the reviewer that the causal mechanism for some of the tested disease-causing mutants remain unclear.  The negative findings also raise the notion, perhaps considered controversial, that there may be other substrates of PHD2 that are impacted by certain mutations, which contribute to disease pathogenesis.  A brief paragraph discussing this has been included in the discussion.

(4) The interpretation of the NMR prolyl hydroxylation is tainted by the high concentrations used here. First of all, there is a likely a typo in the method section; the final concentration of ODD is likely 0.18 mM, and not 0.18 uM (PNAS paper by the same group in 2024 reports using a final concentration of 230 uM). Here, I will assume the concentration is 180 uM. Flashman et al (JBC 2008) showed that the affinity of the NODD site (P402; around 10 uM) for PHD2 is 10-fold weaker than CODD (P564, around 1 uM). This likely explains the much faster kinetics of hydroxylation towards the latter. Now, using the MST data, let's say the P317R mutation reduces the affinity by 40-fold; the affinity becomes 400 uM for NODD (above the protein concentration) and 40 uM for CODD (below the protein concentration). Thus, CODD would still be hydroxylated by the P317R mutant, but not NODD. 

The HIF1α concentration was indeed an oversight, which will be corrected to 0.18 mM.  The study by Flashman et al.[1] showing PHD2 having a lower affinity to the NODD than CODD likely contributes to the differential hydroxylation rates via PHD2 WT.  We showed here via MST that PHD2 P317R had K[d] of 320 ± 20 uM for HIF1αCODD, which should have led to a severe enzymatic defect, even at the high concentrations used for NMR (180 uM).  However, we observed only a subtle reduction in hydroxylation efficiency in comparison to PHD2 WT.  Thus, we performed another binding method using BLI that showed a mild binding defect on CODD by PHD2 P317R, consistent with NMR data.  The perplexing result is the WT-like binding to the NODD by PHD2 P317R, which appears inconsistent with the severe defect in NODD hydroxylation via PHD2 P317R as measured via NMR.  These results suggest that there are supporting residues within the PHD2/NODD interface that help maintain binding to NODD but compromise the efficiency of NODD hydroxylation upon PHD2 P317R mutation. 

(5) The discrepancy between the MST and BLI results does not make sense, especially regarding the P317R mutant. Based on the crystal structures of PHD2 in complex with the ODD peptides, the P317R mutation should have a major impact on the affinity, which is what is reported by MST. This suggests that the MST is more likely to be valid than BLI, and the latter is subject to some kind of artefact. Furthermore, the BLI results are inconsistent with previous results showing that PHD2 has a 10-fold lower affinity for NODD compared to CODD. 

The reviewer’s structural prediction that P317R mutation should cause a major binding defect, while agreeable with our MST data, is incongruent with our NMR and the data from Chowdhury et al.[2] that showed efficient hydroxylation of CODD via PHD2 P317R.  Moreover, we have attempted to model NODD and CODD on apo PHD2 P317R structure and found that the mutation had no major impact on CODD while the mutated residue could clash with NODD, causing a shifting of peptide positioning on the protein.  However, these modeling predictions, like any in silico projections, would need experimental validation.  As mentioned in our preceding response, we also performed BLI, which showed that PHD2 P317R had a minor binding defect for CODD, consistent with the NMR results and findings by Chowdhury et al[2].  NODD binding was also measured with BLI as purified NODD peptides were not amenable for soluble-based MST assay, which showed similar K[d]’s for PHD2 WT and P317R.  Considering the absence of NODD hydroxylation via PHD2 P317R as measured by NMR and modeling on apo PHD2 P317R, we posit that P317R causes deviation of NODD from its original orientation that may not affect binding due to the other interactions from the surrounding elements but unfortunately disallows NODD from turnover.  Further study would be required to validate such notion, which we feel is beyond the scope of this manuscript.  

(6) Overall, the study provides some insights into mutants inducing erythrocytosis, but the impact is limited. Most insights are provided on the P317R mutant, but this mutant had already been characterized by Chowdhury et al (2016). Some mutants affect the stability of the protein in cells, but then no mechanism is provided for A228S or F366L, which have stabilities similar to WT, yet have impaired HIF1a activation. 

We thank the reviewer for raising these and other limitations.  We have expanded on the shortcomings of the present study but would like to underscore that the current work using the recently described NMR assay along with other biophysical analyses suggests a previously under-appreciated role of NODD hydroxylation in the normal oxygen-sensing pathway.  

Reviewer #2 (Public review): 

Summary: 

Mutations in the prolyl hydroxylase, PHD2, cause erythrocytosis and, in some cases, can result in tumorigenesis. Taber and colleagues test the structural and functional consequences of seven patientderived missense mutations in PHD2 using cell-based reporter and stability assays, and multiple biophysical assays, and find that most mutations are destabilizing. Interestingly, they discover a PHD2 mutant that can hydroxylate the C-terminal ODD, but not the N-terminal ODD, which suggests the importance of N-terminal ODD for biology. A major strength of the manuscript is the multidisciplinary approach used by the authors to characterize the functional and structural consequences of the mutations. However, the manuscript had several major weaknesses, such as an incomplete description of how the NMR was performed, a justification for using neighboring residues as a surrogate for looking at prolyl hydroxylation directly, or a reference to the clinical case studies describing the phenotypes of patient mutations. Additionally, the experimental descriptions for several experiments are missing descriptions of controls or validation, which limits their strength in supporting the claims of the authors. 

Strengths: 

(1) This manuscript is well-written and clear. 

(2) The authors use multiple assays to look at the effects of several disease-associated mutations, which support the claims. 

(3) The identification of P317R as a mutant that loses activity specifically against NODD, which could be a useful tool for further studies in cells. 

Weaknesses: 

Major: 

(1) The source data for the patient mutations (Figure 1) in PHD2 is not referenced, and it's not clear where this data came from or if it's publicly available. There is no section describing this in the methods. 

Clinical and patient information on disease-causing PHD2 mutants was compiled from various case reports and summarized in an excel sheet found in the Supplementary Information.  The case reports are cited in this excel file.  A reference to the supplementary data has been added to the Figure 1 legend and in the introduction.

(2) The NMR hydroxylation assay. 

A. The description of these experiments is really confusing. The authors have published a recent paper describing a method using 13C-NMR to directly detect proly-hydroxylation over time, and they refer to this manuscript multiple times as the method used for the studies under review. However, it appears the current study is using 15N-HSQC-based experiments to track the CSP of neighboring residues to the target prolines, so not the target prolines themselves. The authors should make this clear in the text, especially on page 9, 5th line, where they describe proline cross-peaks and refer to the 15N-HSQC data in Figure 5B. 

As the reviewer mentioned, the assay that we developed directly measures the target proline residues.  This assay is ideal when mutations near the prolines are studied, such as A403, Y565 (He et al[3]).  In this previous work, we observed that the shifting of the target proline cross-peaks due to change in electronegativity on the pyrrolidine ring of proline in turn impacted the neighboring residues[3], which meant that the neighboring residues can be used as reporter residues for certain purposes.  In this study, we focused on investigating the mutations on PHD2 while leaving the sequence of the HIF-1α unchanged by using solely 15N-HSQC-based experiments without the need for double-labeled samples.  Nonetheless, we thank the reviewer for pointing out the confusion in the text and we have corrected and clarified our description of this assay.

B. The authors are using neighboring residues as reporters for proline hydroxylation, without validating this approach. How well do CSPs of A403 and I566 track with proline hydroxylation? Have the authors confirmed this using their 13C-NMR data or mass spec? 

For previous studies, we performed intercalated 15N-HSQC and 13C-CON experiments for the kinetic measurements of wild-type HIF-1α and mutants.  We observed that the shifting pattern of A403 and I566 in the 15N-HSQC spectra aligned well with the ones of P402 and P564, respectively, in the 13C-CON spectra.  Representative data has been added to Supplemental Data.

C. Peak intensities. In some cases, the peak intensities of the end point residue look weaker than the peak intensities of the starting residue (5B, PHD2 WT I566, 6 ct lines vs. 4 ct lines). Is this because of sample dilution (i.e., should happen globally)? Can the authors comment on this? 

This is an astute observation by the reviewer.  We checked and confirmed that for all kinetic datasets, the peak intensities of the end point residue are always slightly lower than the ones of the starting.  This includes the cases for PHD2 A228S and P317R in 5B, although not as obvious as the one of PHD2 WT.  We agree with the reviewer that the sample dilution is a factor as a total volume of 16 microliters of reaction components was added to the solution to trigger the reaction after the first spectrum was acquired.  It is also likely that rate of prolyl hydroxylation becomes extremely slow with only a low amount of substrate available in the system.  Therefore, the reaction would not be 100% complete which was detected by the sensitive NMR experimentation.

(3) Data validating the CRISPR KO HEK293A cells is missing. 

We thank the reviewer for noting this oversight.  Western blots validating PHD2 KO in HEK293A cells have been added to the Supplementary Data file.

(4) The interpretation of the SEC data for the PHD2 mutants is a little problematic. Subtle alterations in the elution profiles may hint at different hydrodynamic radii, but as the samples were not loaded at equal concentrations or volumes, these data seem more anecdotal, rather than definitive. Repeating this multiple times, using matched samples, followed by comparison with standards loaded under identical buffer conditions, would significantly strengthen the conclusions one could make from the data. 

Agreed.  We have performed an additional experiment as suggested with equal volume and concentration of each PHD2 construct loaded onto the SEC column for better assessment of aggregation.  Notably, our conclusion remained unchanged.

Minor: 

(1) Justification for picking the seven residues is not clearly articulated. The authors say they picked 7 mutants with "distinct residue changes", but no further rationale is provided. 

Additional justification for the selection of the mutants has been added to the ‘Mutations across the PHD2 enzyme induce erythrocytosis’ section.  Briefly, some mutants were chosen based on their frequency in the clinical data and their presence in potential mutational hot spots.  Various mutations were noted at W334 and R371, while F366L was identified in multiple individuals.  Additionally, 9 cases of PHD2-driven disease were reported to be caused from mutations located between residues 200 to 210 while 13 cases were reported between residues 369-379, so G206C and R371H were chosen to represent potential hot spots.  To examine a potential genotype-phenotype relationship, two of the mutants responsible for neuroendocrine tumor development, A228S and H374R, were also selected.  Finally, mutations located close or on catalytic core residues (P317R, R371H, and H374R) were chosen to test for suspected defects.   

(2) A major finding of the paper is that a disease-associated mutation, P317R, can differentially affect HIF1 prolyhydroxylation, however, additional follow-up studies have not been performed to test this in cells or to validate the mutant in another method. Is it the position of the proline within the catalytic core, or the identity of the mutation that accounts for the selectivity? 

This is the very question that we are currently addressing but as a part of a follow-up study.  Indeed, one thought is that the preferential defect observed could be the result of the loss of proline, an exceptionally rigid amino acid that makes contact with the backbone twice, or the addition of a specific amino acid, namely arginine, a flexible amino acid with an added charge at this site.  Although beyond the scope of this manuscript, we will investigate whether such and other characteristics in this region of PHD2/HIF1α interface contribute to the differential hydroxylation. 

Reviewer #3 (Public review): 

Summary: 

This is an interesting and clinically relevant in vitro study by Taber et al., exploring how mutations in PHD2 contribute to erythrocytosis and/or neuroendocrine tumors. PHD2 regulates HIFα degradation through prolyl-hydroxylation, a key step in the cellular oxygen-sensing pathway. 

Using a time-resolved NMR-based assay, the authors systematically analyze seven patient-derived PHD2 mutants and demonstrate that all exhibit structural and/or catalytic defects. Strikingly, the P317R variant retains normal activity toward the C-terminal proline but fails to hydroxylate the N-terminal site. This provides the first direct evidence that N-terminal prolyl-hydroxylation is not dispensable, as previously thought. 

The findings offer valuable mechanistic insight into PHD2-driven effects and refine our understanding of HIF regulation in hypoxia-related diseases. 

Strengths: 

The manuscript has several notable strengths. By applying a novel time-resolved NMR approach, the authors directly assess hydroxylation at both HIF1α ODD sites, offering a clear functional readout. This method allows them to identify the P317R variant as uniquely defective in NODD hydroxylation, despite retaining normal activity toward CODD, thereby challenging the long-held view that the N-terminal proline is biologically dispensable. The work significantly advances our understanding of PHD2 function and its role in oxygen sensing, and might help in the future interpretation and clinical management of associated erythrocytosis. 

Weaknesses: 

(1) There is a lack of in vivo/ex vivo validation. This is actually required to confirm whether the observed defects in hydroxylation-especially the selective NODD impairment in P317R-are sufficient to drive disease phenotypes such as erythrocytosis.

We thank the reviewer for this comment, and while we agree with this statement, the objective of this study per se was to elucidate the structural and/or functional defect caused by the various diseaseassociated mutations on PHD2.  The subsequent study would be to validate whether the identified defects, in particular the selective NODD impairment, would lead to erythrocytosis in vivo.  However, we feel that such study would be beyond the scope of this manuscript.

(2) The reliance on HRE-luciferase reporter assays may not reliably reflect the PHD2 function and highlights a limitation in the assessment of downstream hypoxic signaling. 

Agreed.  All experimental assays and systems have limitations.  The HRE-luciferase assay used in the present manuscript also has limitations such as the continuous expression of exogenous PHD2 mutants driven via CMV promoter.  Thus, we performed several additional biophysical methodologies to interrogate the disease-causing PHD2 mutants.  The limitations of the luciferase assay have been expanded in the revised manuscript. 

(3) The study clearly documents the selective defect of the P317R mutant, but the structural basis for this selectivity is not addressed through high-resolution structural analysis (e.g., cryo-EM). 

We thank the reviewer for the comment.  While solving the structure of PHD2 P317R in complex with HIFα substrate is beyond the scope for this study, a structure of PHD2 P317R in complex with a clinically used inhibitor has been solved (PDB:5LAT).  In analyzing this structure and that of PHD2 WT in complex with NODD, Chowdhury et al[2] stated that P317 makes hydrophobic contacts with LXXLAP motif on HIFα and R317 is predicted to interact differently with this motif.  While this analysis does not directly elucidate the reason for the preferential NODD defect, it supports the possibility that P317R substitution may be more detrimental for enzymatic activity on NODD than CODD.  We have discussed this notion in the revised manuscript. 

(4) Given the proposed central role of HIF2α in erythrocytosis, direct assessment of HIF2α hydroxylation by the mutants would have strengthened the conclusions. 

We thank the reviewer for this comment, but we feel that such study would be beyond the scope of the present study.  We observed that the PHD2 binding patterns to HIF1α and HIF2α were similar, and we have previously assigned >95% of the amino acids in HIF1α ODD for NMR study[3]. Thus, we first focused on the elucidation of possible defects on disease-associated PHD2 mutants using HIF1α as the substrate with the supposition that an identified deregulation on HIF1α could be extended to HIF2α paralog.  However, we agree with the reviewer that future studies should examine the impact of PHD2 mutants directly on HIF2α.  

References:

(1) Flashman, E. et al. Kinetic rationale for selectivity toward N- and C-terminal oxygen-dependent degradation domain substrates mediated by a loop region of hypoxia-inducible factor prolyl hydroxylases. J Biol Chem 283, 3808-3815 (2008).

(2) Chowdhury, R. et al. Structural basis for oxygen degradation domain selectivity of the HIF prolyl hydroxylases. Nat Commun 7, 12673 (2016).

(3) He, W., Gasmi-Seabrook, G.M.C., Ikura, M., Lee, J.E. & Ohh, M. Time-resolved NMR detection of prolyl-hydroxylation in intrinsically disordered region of HIF-1alpha. Proc Natl Acad Sci U S A 121, e2408104121 (2024).

Reviewer #1 (Recommendations for the authors): 

(1) To increase the impact and significance of this work, I would recommend determining the mechanism by which A228S and F366L impair PHD2. Are these mutations affecting interactions with proteins other than HIF1a? Furthermore, does the F366L mutation affect the hydroxylation rate? This should be measured. The authors should also perform a more in-depth structural analysis of these mutations and perhaps use AlphaFold to identify how these sites may be involved in other interactions. 

We thank the reviewer for the recommendations.  A paragraph discussing the quandary of A228S and F366L has been added to the discussion as well as an in-depth structural analysis of each selected mutant.  While AlphaFold is excellent at predicting protein structures overall, its capability to predict the effect of single point mutation, such as those in this study, is limited.  Therefore, it was not utilized for this paper.

(2) For the aggregation assay, I recommended injecting the same quantity of protein on the SEC. If the aggregation-prone mutants' yields were too low, then reduced amounts of the other mutants should be injected. 

Agreed.  An additional experiment was performed in which similar concentrations of each mutant protein was loaded onto the SEC column and chromatograms was normalized according to the molecular concentration.  Results from this experiment have been added to replace the previously performed aggregation assay.  Notably, the data from the revised experiment did not change the outcome or conclusion of the study.

(3) For the NMR kinetics data, the authors should discuss the impact of affinities and concentrations on the reaction rate and incorporate this analysis framework to interpret their data. 

Done.  As discussed in depth in response to Public Reviewer 1’s fourth comment, we observed only a subtle reduction in hydroxylation efficiency of HIF1aCODD by PHD2 P317R in comparison to PHD2 WT.  Upon performing BLI, we found PHD2 P317R displays only a mild binding defect on the CODD and NODD.  The WT-like binding to the NODD by PHD2 P317R appears to be inconsistent with the severe defect in NODD hydroxylation via PHD2 P317R as measured via NMR.   These results suggest that there are supporting residues within the PHD2/NODD interface that help maintain binding to NODD but compromise the efficiency of NODD hydroxylation upon PHD2 P317R mutation.

Reviewer #2 (Recommendations for the authors): 

It is unclear where the source data came from describing the patient mutations, or if it is publicly available. Several minor issues were noted with several of the figures or methods: 

(1) Figure 2C. It is not clear what data are being compared for significance. The lines don't seem to clearly distinguish this. 

Done.  The significance lines have been adjusted in the figure to better convey which data are being compared.

(2) Please incorporate the calculated biophysical constants (KD, TM, etc, average +/- std dev) from the tables into the figures or figure legends that show the data from which they are calculated.  

Done.  References to the corresponding tables have been added to the appropriate figure legends.

(3) Figure 3C, the data for F366L do not appear normalized in the same way as the other constructs. 

CD melt values for F366L were normalized in the same way as other constructs but due to noisier data acquired between 25-37°C, the top value of the sigmoidal curve is slightly higher than the other constructs (F366L: 1.066, WT: 1.007, A228S: 1.000, P317R: 1.015, R371H: 1.005). 

(4) For Figure 1B, it would be helpful to highlight the mutants characterized in the current study with a different color/symbol to help show the number of cases. 

Done.  Dots representing the selected mutants have been highlighted in red in Figure 1B.

(5) A description of the isotopic labeling of PHD2 is missing from the methods.

Due to the nature of the NMR assay, no isotopic labeling was required for PHD2.

Reviewer #3 (Recommendations for the authors): 

(1) To further strengthen the manuscript, the authors could consider exploring the relevance of their in vitro findings in a more physiological context. 

We thank the reviewer for the suggestion, and we will certainly consider furthering our investigation in a more physiological context for future studies.

(2) If technically feasible, integrating direct analyses of HIF2α regulation by the PHD2 mutants would better reflect the clinical phenotype, given the known importance of HIF2α in erythrocytosis. 

We agree that HIF2α is important in the context of erythrocytosis, but through MST we observed no difference in binding pattern between HIF1 and HIF2 and the selected PHD2 mutants.  As we had previously assigned >95% of residues for HIF1α ODD for NMR assay, we analyzed HIF1 with the supposition that any defects observed would likely apply to HIF2.  However, we agree that future studies on the impact of PHD2 mutants directly on HIF2 would be beneficial to supplement our understanding of pseudohypoxic disease.

(3) Additionally, although perhaps more suitable for future work or discussion, structural modeling or highresolution structural studies of the P317R variant could offer valuable insight into the observed NODD selectivity defect. 

We thank the reviewer for the suggestion. While solving the structure of PHD2 P317R in complex with NODD is beyond the scope of this manuscript, a crystal structure of PHD2 P317R in complex with an inhibitor has been solved and insights from this structure have been added to the discussion. 

(4) Finally, a brief clarification or discussion of the limitations of the luciferase reporter assay-especially in the context of aggregation-prone mutants-would help readers better interpret the functional data. 

We thank the reviewer for the suggestion.  The limitations of the luciferase reporter assay in regard to its inability to detect defects with aggregation-prone mutants have been elaborated on in the discussion.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public Review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models.

Strengths and weaknesses

The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

We sincerely thank Reviewer #2 for the insightful and constructive feedback, as well as for the kind recognition of the scientific quality of our work: “The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.” We sincerely thank Reviewer #2 for their thoughtful and constructive comments during both rounds of review, which have significantly improved the quality of our manuscript. In response, we have incorporated new results from additional experiments into the figures (Figures 6M and 6N) and made comprehensive revisions throughout the text, figures, and supplementary materials. Following the reviewer’s valuable suggestions, we also revised the Discussion section. In the “Recommendations for the authors” sections, we have provided detailed point-by-point responses to each comment, which were instrumental in guiding our revisions. We believe these updates have substantially strengthened the manuscript and fully addressed all reviewer concerns.

Reviewer #2 (Recommendations for the authors): 

Although the authors have addressed each recommendation from the reviewer, further revision of the manuscript are still necessary, as outlined below.

Add these additional comments in the text to further enhance the comprehension and clarity of the data.

(1) If the authors kept the tumors of various sizes in Figure 7I, it would be important to assess the protein and/or mRNA level of ERCC1 to further support their mechanism.

Question (1): Please add the figures of new experiments (treatment diagram, curves for tumor volume and qRT-PCR data) to Figure 6.

We thank the reviewers for their constructive suggestions. In response to the reviewers’ comments, we have added the treatment diagram and qPCR results to Figure 6. In this experiment, we shortened the treatment duration to seven days to assess early molecular responses to therapy rather than downstream effects. As expected, such short-term treatment did not result in significant differences in tumor growth among groups. The new results are now presented in Figure 6, panels M and N. The corresponding results and figure legends will also be included in the revised version of the manuscript

(2) Figure 2G: please explain why two bands remain for sgPRMT1.

Question (2): In the answer, the authors stated, "Upon knockdown of the major isoforms by CRISPR/Cas9, expression of this minor isoform may have increased as part of a compensatory feedback mechanism, rendering it detectable by immunoblotting." Please put the statement into the discussion section.

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the Discussion section to provide greater clarity and context for our findings.

(3) (Previously point 5) What is the link with ERCC1 splicing because reduced overall ERCC1 expression is clear?

Question (5): Please add the explanation you provide of links between ERCC1 splicing and PRMTi into the discussion section.

"Furthermore, as shown in Figure 4G, we observed a reduction in the total ERCC1 mRNA reads following PRMTi treatment. This decrease may be attributed, at least in part, to the instability of the alternatively spliced ERCC1 transcripts, which could be more prone to degradation. In combination with the transcriptional downregulation of ERCC1 induced by PRMT inhibition, these alternative splicing events may lead to a further reduction in functional ERCC1 protein levels. This dual impact on ERCC1 expression, through both decreased transcription and the generation of unstable or nonfunctional isoforms, likely contributes to the enhanced cellular sensitivity to PARP inhibitors observed in our study."

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the Discussion section to provide greater clarity and context for our findings.

(4) (Previously 6) Figure 7J: From the graph, it seems like Olaparib+G715 and G715+G025 have a similar effect on tumor volume (two curves overlap). Please discuss.

Question (6): In the answer, the authors stated, "Our in vitro and in vivo findings, together with previously published data, consistently demonstrate that GSK715 is more potent than both GSK025 and Olaparib. Notably, treatment with GSK715 alone led to significantly greater inhibition of tumor growth compared to either GSK025 or Olaparib administered individually. This higher potency of GSK715 also explains the comparable levels of tumor suppression observed in the combination groups, including GSK715 plus Olaparib and GSK715 plus GSK025. These results suggest that GSK715 is likely the primary driver of efficacy in the two drug combination settings." Please put the statement in the corresponding result section for Figure 6J.

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the result section for Figure 6J to provide greater clarity and context for our findings.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

In this manuscript entitled "Molecular dynamics of the matrisome across sea anemone life history", Bergheim and colleagues report the prediction, using an established sequence analysis pipeline, of the "matrisome" - that is, the compendium of genes encoding constituents of the extracellular matrix - of the starlet sea anemone Nematostella vectensis. Re-analysis of an existing scRNA-Seq dataset allowed the authors to identify the cell types expressing matrisome components and different developmental stages. Last, the authors apply time-resolved proteomics to provide experimental evidence of the presence of the extracellular matrix proteins at three different stages of the life cycle of the sea anemone (larva, primary polyp, adult) and show that different subsets of matrisome components are present in the ECM at different life stages with, for example, basement membrane components accompanying the transition from larva to primary polyp and elastic fiber components and matricellular proteins accompanying the transition from primary polyp to the adult stage. 

Strengths: 

The ECM is a structure that has evolved to support the emergence of multicellularity and different transitions that have accompanied the complexification of multicellular organisms. Understanding the molecular makeup of structures that are conserved throughout evolution is thus of paramount importance. 

The in-silico predicted matrisome of the sea anemone has the potential to become an essential resource for the scientific community to support big data annotation efforts and understand better the evolution of the matrisome and of ECM proteins, an important endeavor to better understand structure/function relationships. This study is also an excellent example of how integrating datasets generated using different -omic modalities can shed light on various aspects of ECM metabolism, from identifying the cell types of origins of matrisome components using scRNA-Seq to studying ECM dynamics using proteomics. 

We greatly appreciate the positive feedback regarding the design of our study and the evolutionary significance of our findings.

Weaknesses: 

My concerns pertain to the three following areas of the manuscript: 

(1) In-silico definition of the anemone matrisome using sequence analysis: 

a) While a similar computational pipeline has been applied to predict the matrisome of several model organisms, the authors fail to provide a comprehensive definition of the anemone matrisome: In the text, the authors state the anemone matrisome is composed of "551 proteins, constituting approximately 3% of its proteome (see page 6, line 14), but Figure 1 lists 829 entries as part of the "curated" matrisome, Supplementary Table S1 lists the same 829 entries and the authors state that "Here, we identified 829 ECM proteins that comprise the matrisome of the sea anemone Nematostella vectensis" (see page 17, line 10). Is the sea anemone matrisome composed of 551 or 829 genes? If we refer to the text, the additional 278 entries should not be considered as part of the matrisome, but what is confusing is that some are listed as glycoproteins and the "new_manual_annotation" proposed by the authors and that refer to the protein domains found in these additional proteins suggest that in fact, some could or should be classified as matrisome proteins. For example, shouldn't the two lectins encoded by NV2.3951 and NV2.3157 be classified as matrisome-affiliated proteins? Based on what has been done for other model organisms, receptors have typically been excluded from the "matrisome" but included as part of the "adhesome" for consistency with previously published matrisome; the reviewer is left wondering whether the components classified as "Other" / "Receptor" should not be excluded from the matrisome and moved to a separate "adhesome" list. 

In addition to receptors, the authors identify nearly 70 glycoproteins classified as "Other". Here, does other mean "non-matrisome" or "another matrisome division" that is not core or associated? If the latter, could the authors try to propose a unifying term for these proteins? Unfortunately, since the authors do not provide the reasons for excluding these entries from the bona fide matrisome (list of excluding domains present, localization data), the reader is left wondering how to treat these entries. 

Overall, the study would gain in strength if the authors could be more definitive and, if needed, even propose novel additional matrisome annotations to include the components for now listed as "Other" (as was done, for example, for the Drosophila or C. elegans matrisomes). 

The reviewer is correct to point out the confusing terminology used throughout our manuscript, where both the total of 829 proteins constituting the curated list of ECM domain proteins and the actual matrisome (excluding "others") were referred to as "matrisomes". In general, we followed the example set by Naba & Hynes in their 2012 paper (Mol Cell Proteomics. 2012 Apr;11(4):M111.014647. doi: 10.1074/mcp.M111.014647), where they define the "matrisome" as encompassing all components of the extracellular matrix ("core matrisome") and those associated with it ("matrisome-associated" proteins). This corresponds to our group of 551 proteins, comprising both core matrisome and matrisomeassociated proteins. The Naba & Hynes paper also contains the inclusive and exclusive domain lists for the matrisome that we applied for our dataset. In the revised manuscript, we have now labelled the group of 829 proteins as "curated ECM domain proteins/genes", which includes all proteins positively selected for containing a bona fide ECM domain. After excluding non-matrisomal proteins such as receptors, we arrive at the 551 proteins that constitute the "Nematostella matrisome". We have maintained this terminology throughout the revised manuscript and have revised Figures 1B and 4B accordingly.

Regarding the category of "other" proteins, which by definition are not part of the matrisome although containing ECM domains, we have taken the reviewer's advice and classified these in more detail. We categorized all receptors as "adhesome" (202 proteins).  The remaining group of “other” secreted ECM domain proteins were then further subcategorized. Those exhibiting significant matches in the ToxProt database were subclassified as "putative venoms" (15 proteins). This group also includes the two lectins (NV2.3951 and NV2.3157), which had been originally shifted to the “other” category due to their classification as venoms. We categorized as “adhesive proteins” (28 proteins) factors such as coadhesins that due to their domain architecture resemble bioadhesive proteins described in proteomic studies of other invertebrate species, such as corals or sponges (see also https://doi.org/10.1016/j.jprot.2022.104506). Further sub-categories are stress/injury response proteins (9 proteins) and ion channels (6 proteins). The remaining 17 proteins were categorized as “uncharacterized ECM domain proteins”. These include highly diverse proteins possessing either single ECM domains or novel domain combinations. We decided to retain those in our dataset as candidates for future functional characterization.

b) It is surprising that the authors are not providing the full currently accepted protein names to the entries listed in Supplementary Table S1 and have used instead "new_manual_annotation" that resembles formal protein names. This liberty is misleading. In fact, the "new_manual_annotation" seems biased toward describing the reason the proteins were positively screened for through sequence analysis, but many are misleading because there is, in fact, more known about them, including evidence that they are not ECM proteins. The authors should at least provide the current protein names in addition to their "new_manual_annotations". 

c) To truly serve as a resource, the Table should provide links to each gene entry in the Stowers Institute for Medical Research genome database used and some sort of versioning (this could be added to columns A, B, or D). Such enhancements would facilitate the assessment of the rigor of the list beyond the manual QC of just a few entries. 

d) Since UniProt is the reference protein knowledge database, providing the UniProt IDs associated with the predicted matrisome entries would also be helpful, giving easy access to information on protein domains, protein structures, orthology information, etc. 

e) In conclusion, at present, the study only provides a preliminary draft that should be more rigorously curated and enriched with more comprehensive and authoritative annotations if the authors aspire the list to become the reference anemone matrisome and serve the community. 

Table S1 has been updated to include links to the respective Stowers Institute IDs (first two columns), as well as SwissProt IDs and current descriptions from both the Stowers Institute (SI) and Swissprot.

In our manual annotations, we prioritized these over automated ones due to the considerable effort invested in examining each sequence individually. The cnidaria-specific minicollagens and NOWA proteins might serve as an example. According to the SI descriptions, the minicollagens are annotated as “keratin-associated protein, predicted or hypothetical protein, collagen-like protein and pericardin”. We classified these as minicollagens on the basis of overall domain architecture and of signature domains and sequence motifs, such as minicollagen cysteine-rich domains (CRDs) and polyproline stretches (doi: 10.1016/j.tig.2008.07.001). NOWA is a CTLD/CRD-containing protein that is part of nematocyst tubules (doi:10.1016/j.isci.2023.106291). The first two NOWA isoforms, according to Si descriptions, were annotated as aggrecan and brevican core proteins, which is very misleading. We therefore feel that our manual annotations better serve the cnidarian research community in classifying these proteins.

Automated annotations of ECM proteins often rely on similarities between individual domains, neglecting overall domain composition. For example, Swissprot descriptions annotate 31 TSP1 domain-containing proteins in our list as "Hemicentin-1", but closer inspection reveals that only one sequence (NV2.24790) qualifies as Hemicentin-1 due to its characteristic vWFA, Ig-like, TSP1, G2 nidogen, and EGF-like domain architecture. Regarding novel protein annotations, NV2.650 might serve as an example. While SI descriptions annotate this protein as "epidermal growth factor" based on the presence of several EGF-like domains, our analysis reveals two integrin alpha N-terminal domains that classify this sequence as integrin-related. We have therefore assigned a description (Secreted integrin-N-related protein) that references this defining domain and avoids misclassification within the EGF family.

In cases where the automated annotation (including those in Genbank) matched our own findings, we adopted the existing description, as seen with netrin-1 (NV2.7734). We acknowledge that our manual annotations are not flawless and will be refined by future research. Nonetheless, we offer them as an approximation to a more accurate definition of the identified protein list.

(2) Proteomic analysis of the composition of the mesoglea during the sea anemone life cycle: 

a) The product of 287 of the 829 genes proposed to encode matrisome components was detected by proteomics. What about the other ~550 matrisome genes? When and where are they expressed? The wording employed by the authors (see line 11, page 13) implies that only these 287 components are "validated" matrisome components. Is that to say that the other ~550 predicted genes do not encode components of the ECM? This should be discussed. 

Obviously, our wording was not sufficiently accurate here. In the revised Fig. 1B we indicated that 210 of the 551 matrisome (core and associated) proteins were confirmed by mass spectrometry. In total, 287 proteins were identified by mass spectrometry, meaning that 77 of those are non-matrisomal proteins belonging to the “adhesome” (47) and “other” (30) groups. The fact that the remaining 542 proteins of the matrisome predicted by our in silico analysis could not be identified has two major reasons: (1) Our study was focussed on the molecular dynamics of the mesoglea. Therefore, only mesogleas were isolated for the mass spectrometry analysis and nematocysts were mostly excluded by extensive washing steps. As nematocysts contribute significantly to the predicted matrisome, this group of proteins is underrepresented in the mass spectrometry analysis. (2) A significant fraction of the predicted ECM proteins constitutes soluble factors and transmembrane receptors. These might not be necessarily part of the mesoglea isolates. In addition, the isolation and solubilization method we applied might have technical limitations. Although we used harsh conditions for solubilizing the mesoglea samples (90°C and high DTT concentrations), we cannot exclude that we missed proteins which resisted solubilization and thus trypsinization. We confirmed that all genes predicted by the in silico analysis have transcriptomic profiles as demonstrated in supplementary table S4. We have clarified these points in the revised results part (p.6) and also revised the statement in line 16, page 13.

b) Can the authors comment on how they have treated zero TMT values or proteins for which a TMT ratio could not be calculated because unique to one life stage, for example? 

We did not include these proteins in the analysis of the respective statistical comparison. This involved only very few proteins (about 10).  

c) Could the authors provide a plot showing the distribution of protein abundances for each matrisome category in the main figure 4? In mammals, the bulk of the ECM is composed of collagens, followed by fibrillar ECM glycoproteins, the other matrisome components being more minor. Is a similar distribution observed in the sea anemone mesoglea? 

We have included such a plot showing protein abundances across life stages and protein categories (Fig. 4A). Collagens and basement membrane proteoglycans (perlecan) are the most abundant protein categories in the core matrisome while secreted factors dominate in the matrisome-associated group.

d) Prior proteomic studies on the ECM of vertebrate organisms have shown the importance of allowing certain post-translational modifications during database search to ensure maximizing peptide-to-spectrum matching. Such PTMs include the hydroxylation of lysines and prolines that are collagen-specific PTMs. Multiple reports have shown that omitting these PTMs while analyzing LC-MS/MS data would lead to underestimating the abundance of collagens and the misidentification of certain collagens. The authors may want to reanalyze their dataset and include these PTMs as part of their search criteria to ensure capturing all collagen-derived peptides. 

Thank you for this suggestion. We have re-analyzed our dataset including lysine and proline hydroxylation as PTM. While we obtained in total 70 more proteins using this approach, this additional group did not contain any large collagen or minicollagen we had not detected before. We only obtained two additional collagen-like proteins with very short triple helical domains (V2t013973001.1, NV2t024002001.1), one being a fragment. We don’t feel this justifies implementing a re-analysis of the proteome in our study.

e) The authors should ensure that reviewers are provided with access to the private PRIDE repository so the data deposited can also be evaluated. They should also ensure that sufficient meta-data is provided using the SRDF format to allow the re-use of their LCMS/MS datasets. 

We apologize for not providing the reviewer access in our initial submission and have asked the editorial office to forward the PRIDE repository link to all reviewers immediately after receiving the reviews. We did upload a metadata.csv file with the proteomics dataset. This file contains an annotation of all TMT labels to the samples and conditions and replicates used in the manuscript. It contains similar information as an SRDF format file. In addition, the search output files on protein and psm level have been provided. So, from our point of view, we provided all necessary information to reproduce the analysis.

(3) Supplementary tables: 

The supplementary tables are very difficult to navigate. They would become more accessible to readers and non-specialists if they were accompanied by brief legends or "README" tabs and if the headers were more detailed (see, for example, Table S2, what does "ctrl.ratio_Larvae_rep2" exactly refer to? Or Table S6 whose column headers using extensive abbreviations are quite obscure). Similarly, what do columns K to BX in Supplementary Table S1 correspond to? Without more substantial explanations, readers have no way of assessing these data points. 

We have revised the tables and removed any redundant data columns. We also included detailed explanations of the used abbreviations, both in the headers and in a separate README file. Some of the information was apparently lost during the conversion to pdf files. We will therefore upload the original .xls files when submitting the revised manuscript.

Reviewer #2 (Public review): 

This work set out to identify all extracellular matrix proteins and associated factors present within the starlet sea anemone Nematostella vectensis at different life stages. Combining existing genomic and transcriptomic datasets, alongside new mass spectometry data, the authors provide a comprehensive description of the Nematostella matrisome. In addition, immunohistochemistry and electron microscopy were used to image whole mount and decellularized mesoglea from all life stages. This served to validate the de-cellularization methods used for proteomic analyses, but also resulted in a very nice description of mesoglea structure at different life stages. A previously published developmental cell type atlas was used to identify the cell type specificity of the matrisome, indicating that the core matrisome is predominantly expressed in the gastrodermis, as well as cnidocytes. The analyses performed were rigorous and the results were clear, supporting the conclusions made by the authors. 

Thank you. We greatly appreciate the positive assessment of our study.

Reviewer #3 (Public review): 

Summary: 

This manuscript by Bergheim et al investigates the molecular and developmental dynamics of the matrisome, a set of gene products that comprise the extracellular matrix, in the sea anemone Nematostella vectensis using transcriptomic and proteomic approaches. Previous work has examined the matrisome of the hydra, a medusozoan, but this is the first study to characterize the matrisome in an anthozoan. The major finding of this work is a description of the components of the matrisome in Nematostella, which turns out to be more complex than that previously observed in hydra. The authors also describe the remodeling of the extracellular matrix that occurs in the transition from larva to primary polyp, and from primary polyp to adult. The authors interpret these data to support previously proposed (Steinmetz et al. 2017) homology between the cnidarian endoderm with the bilaterian mesoderm. 

Strengths: 

The data described in this work are robust, combining both transcriptome and proteomic interrogation of key stages in the life history of Nematostella, and are of value to the community. 

Thank you for your positive assessment of our dataset. 

Weaknesses: 

The authors offer numerous evolutionary interpretations of their results that I believe are unfounded. The main problem with extending these results, together with previous results from hydra, into an evolutionary synthesis that aims to reconstruct the matrisome of the ancestral cnidarian is that we are considering data from only two species. I agree with the authors' depiction of hydra as "derived" relative to other medusozoans and see it as potentially misleading to consider the hydra matrisome as an exemplar for the medusozoan matrisome. Given the organismal and morphological diversity of the phylum, a more thorough comparative study that compares matrisome components across a selection of anthozoan and medusozoan species using formal comparative methods to examine hypotheses is required. 

Specifically, I question the author's interpretation of the evolutionary events depicted in this statement: 

"The observation that in Hydra both germ layers contribute to the synthesis of core matrisome proteins (Epp et al. 1986; Zhang et al. 2007) might be related to a secondary loss of the anthozoan-specific mesenteries, which represent extensions of the mesoglea into the body cavity sandwiched by two endodermal layers." 

Anthozoans and medusozoans are evolutionary sisters. Therefore, the secondary loss of "anthozoan-like mesenteries" in hydrozoans is at least as likely as the gain of this character state in anthozoans. By extension, there is no reason to prefer the hypothesis that the state observed in Nematostella, where gastroderm is responsible for the synthesis of the core matrisome components, is the ancestral state of the phylum. Moreover, the fossil evidence provided in support of this hypothesis (Ou et al. 2022) is not relevant here because the material described in that work is of a crown group anthozoan, which diversified well after the origin of Anthozoa. The phylogenetic structure of Cnidaria has been extensively studied using phylogenomic approaches and is generally well supported (Kayal et al. 2018; DeBiasse et al. 2024). Based on these analyses, anthozoans are not on a "basal" branch, as the authors suggest. The structure of cnidarian phylogeny bifurcates with Anthozoa forming one clade and Medusozoa forming the other. From the data reported by Bergheim and coworkers, it is not possible to infer the evolutionary events that gave rise to the different matrisome states observed in Nematostella (an anthozoan) and hydra (a medusozoan). Furthermore, I take the observation in Fig 5 that anthozoan matrisomes generally exhibit a higher complexity than other cnidarian species to be more supportive of a lineage-specific expansion of matrisome components in the Anthozoa, rather than those components being representative of an ancestral state for Cnidaria. Whatever the implication, I take strong issue with the statement that "the acquisition of complex life cycles in medusozoa, that are distinguished by the pelagic medusa stage, led to a secondary reduction in the matrisome repertoire." There is no causal link in any of the data or analyses reported by Bergheim and co-workers to support this statement and, as stated above, while we are dealing with limited data, insufficient to address this question, it seems more likely to me that the matrisome expanded in anthozoans, contrasting with the authors' conclusions. While the discussion raises many interesting evolutionary hypotheses related to the origin of the cnidarian matrisome, which is of vital interest if we are to understand the origin of the bilaterian matrisome, a more thorough comparative analysis, inclusive of a much greater cnidarian species diversity, is required if we are to evaluate these hypotheses. 

DeBiasse MB, Buckenmeyer A, Macrander J, Babonis LS, Bentlage B, Cartwright P, Prada C, Reitzel AM, Stampar SN, Collins A, et al. 2024. A Cnidarian Phylogenomic Tree Fitted With Hundreds of 18S Leaves. Bulletin of the Society of Systematic Biologists [Internet] 3. Available from: https://ssbbulletin.org/index.php/bssb/article/view/9267

Epp L, Smid I, Tardent P. 1986. Synthesis of the mesoglea by ectoderm and endoderm in reassembled hydra. J Morphol [Internet] 189:271-279. Available from: https://pubmed.ncbi.nlm.nih.gov/29954165/ 

Kayal E, Bentlage B, Sabrina Pankey M, Ohdera AH, Medina M, Plachetzki DC, Collins AG, Ryan JF. 2018. Phylogenomics provides a robust topology of the major cnidarian lineages and insights on the origins of key organismal traits. BMC Evol Biol [Internet] 18:1-18. Available from: https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-018-1142-0

Ou Q, Shu D, Zhang Z, Han J, Van Iten H, Cheng M, Sun J, Yao X, Wang R, Mayer G. 2022. Dawn of complex animal food webs: A new predatory anthozoan (Cnidaria) from Cambrian. The Innovation 3:100195 

Steinmetz PRH, Aman A, Kraus JEM, Technau U. 2017. Gut-like ectodermal tissue in a sea anemone challenges germ layer homology. Nature Ecology & Evolution 2017 1:10 [Internet] 1:1535-1542. Available from: https://www.nature.com/articles/s41559-017-0285-5

Zhang X, Boot-Handford RP, Huxley-Jones J, Forse LN, Mould AP, Robertson DL, Li L, Athiyal M, Sarras MP. 2007. The collagens of hydra provide insight into the evolution of metazoan extracellular matrices. J Biol Chem [Internet] 282:6792-6802. Available from: https://pubmed.ncbi.nlm.nih.gov/17204477/ 

We agree with the reviewer that only the analysis of several additional anthozoan and medusozoan representatives will yield a valid basis for a reconstruction of the ancestral cnidarian matrisome and allow statements about ancestral or novel features within the phylum. We have therefore revised our statements in the discussion part of the manuscript by implementing the cited literature and also findings from medusozoan genome analysis (e.g. Gold et al., 2018) demonstrating that changes in gene content are as common in the anthozoans as in medusozoans, which questioned the previously stated “basal” state of Nematostella or of anthozoans in general.

Reviewer #1 (Recommendations for the authors): 

(1) In Figure 2A, an "o" is missing in the labeling of the "developing cnidcytes" population. 

Thank you, we have corrected the typo.

(2) It would be helpful to have the different life stages indicated as headers of the heat maps presented in Figure 4. 

We have included symbolic representations for the different life stages on top of the heat maps in addition to the respective labels at the bottom.

Reviewer #2 (Recommendations for the authors): 

Important changes: 

(1) Figure 2B The x-axis tissue names should be changed to something more easily readable/understandable - some are clear, but others are not. Perhaps abbreviations could be expanded in the legend. 

We have expanded the legend in Fig. 2B to render it more easily readable. We have also rotated the maps in A to have them aligned with the ones in Fig.3B.

(2) Figure 3B This figure would be improved by the inclusion of cluster names, to understand better the mapping. 

We have added relevant cluster names to Fig. 3B and as stated above aligned the orientation of the maps in Fig. 2B and Fig. 3B.

(3) Figure 3C As with 2B, I find the y-axis cnidocyte cell state names to be unclear at times. Perhaps abbreviations could be expanded in the legend. 

All abbreviations were expanded in Fig.3C axis labels.

(4) Many of the supplementary tables are not well exported or easily readable as is (gene names are truncated, headers truncated, etc), which means that they may not be easily usable by researchers in the field interested in following up on this work in other contexts. Indeed, to be more usable, please consider sharing these supplementary data as .csv files, for example, instead of as .pdfs. 

We are sorry for this inconvenience, which was obviously caused by the conversion to pdf files. We will upload the original csv files when submitting the revised manuscript.

Smaller nitpicky comments: 

(5) Page 2 line 4 & page 3 line 7: Please consider a term other than "pre-bilaterian". The drawing/ordering of a phylogeny of extant species is not meaningful in terms of more or less ancestral. e.g. if the tips are flipped in the drawing of the tree, can we say that bilaterians are pre-cnidarians? What does that mean? 

We have used that term on the basis that cnidarians existed before the appearance of bilaterians according to the fossil record and molecular phylogenies (McFadden et al., 2021; Adoutte et al., 2000;Cavalier-Smith et al., 1996; Collins, 1998; Kim et al., 1999; Medina et al., 2001; Wainright et al., 1993). To acknowledge remaining uncertainties in the timing of origin of animals, we will use the term “early-diverging metazoans” instead, which is widely accepted in the cnidarian community. 

(6) Page 3 line 9 I was confused by the use of "gastrula-shaped body" to describe cnidarians, which are on the whole very morphologically diverse and don't all resemble gastrulae (that can also be quite diverse). 

This term is sometimes used to refer to the diploblastic cnidarian body plan (outer ectoderm, inner endoderm) with a mouth that corresponds to the blastopore. To avoid misunderstandings, we changed it in the revised manuscript to “Cnidarians, the sister group to bilaterians, are characterized by a simple body plan with a central body cavity and a mouth opening surrounded by tentacles.”

Reviewer #3 (Recommendations for the authors): 

(1) In general, I felt there was a lot of discussion about protein structure and diversity that is difficult to follow without a figure. I think some of the information in Supplementary Figures S5, S9, and S11 should be in the main figures. 

Following the reviewer’s suggestion, we have integrated Fig. S5 (collagens) into the main Fig. 2 and Fig. S9 (polydoms) into Fig. 4. As metalloproteases are not extensively discussed in the manuscript (and also due to the large size of the figure) we have kept Fig. S11 as a supplementary figure.

(2) Page 3, Line 7: The use of the term "pre-bilaterian" is inappropriate. Cnidarians and bilaterians are evolutionary sisters. Therefore, each lineage derives from the same split and is the same age. The cnidarian lineage is not older than the bilaterian lineage. 

Following a similar request by reviewer 2 we have replaced this term by “early diverging metazoans”.

(3) Page 5, Line 10. How were in silico matrisomes from early-branching metazoan species predicted? 

We applied the same bioinformatic pipeline as for the Nematostella matrisome. We clarified this in the respective methods part.

(4) Page 16, Line 8: This should be Thus. 

Obviously, the wording of this sentence was ambiguous. We changed it to ”In contrast, the adult mesoglea is significantly enriched in elastic fiber components, such as fibrillins and fibulin. This compositional shift likely adds to the visco-elastic properties (Gosline 1971a, b) of the growing body column (Fig. 4B,D, supplementary table S7).”

Author response:

We thank the editors and reviewers for their encouraging comments and constructive feedback. We will revise the text to enhance clarity as suggested. New experiments are planned to address questions raised regarding the time course of responses to the hit compounds. We also intend to examine additional endogenous readouts of the integrated stress response, including effects on translation. The effects of lead compound 20 will be examined in a wider range of cells, including primary cells.

Author response:

We are going to modify the text following Reviewer’s comments and perform embryo direct labelling experiments to experimentally address the contraction of the two “belts” proposed in our model. We feel that this aspect is feasible in a reasonable time and important for the model proposed. We appreciate the relevance of using this framework to identify molecular drivers of the regionalized tissue behaviours uncovered and how these might be altered in mutant models, but feel that these aspects demand efforts beyond the the reasonable revision periods.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The manuscript by Senn and colleagues presents a comprehensive study on the developing synthetic gene circuits targeting mutant RAS-expressing cells. This study aims to exploit these RAS-targeting circuits as cancer cell classifiers, enabling the selective expression of an output protein in correlation with RAS activity. The system is based on the bacterial two-component system NarX/NarL. A RAS-binding domain, the RBDCRD domain of the RAS effector protein CRAF, is fused to the histidine kinase domain, which carries an inactivating amino acid exchange either in its ATP-binding site (N509A) or in its phosphorylation site (H399Q). Dimerization or nanocluster formation of RAS-GTP reconstitutes an active histidine kinase sensor dimer that phosphorylates the response regulator NarL. The phosphorylated DNA-binding protein NarL, fused to the transcription activator domain VP48, binds its responsive element and induces the expression of the output protein. In comparison to mutated RAS, the effect of the RAS activator SOS-1 and the RAS inhibitor NF1 on the sensing ability as well as the tunability of the RAS sensor were examined. A RAS targeting circuit with an AND gate was designed by expressing the RAS sensor proteins under the control of defined MAPK response elements, resulting in a large increase in the dynamic range between mutant and wild-type RAS. Finally, the RAS targeting circuits were evaluated in detail in a set of twelve cancer cell lines expressing endogenous levels of mutant or wild-type RAS or oncogenes affecting RAS signaling upstream or downstream. 

Strengths: 

This proof-of-concept study convincingly demonstrates the potential of synthetic gene circuits to target oncogenic RAS in tumor cell lines and to function, at least in part, as an RAS mutant cell classifier. 

Weaknesses: 

The use of an appropriate "therapeutic gene" might revert the oncogenic properties of RAS mutant cell lines. However, a therapeutic strategy based on this four-plasmid-based system might be difficult to implement in RAS-driven solid cancers. 

Thank you for the insightful comments. We agree that the delivery of a four-plasmid system represents a major challenge for translating RAS-targeting circuits into therapeutic applications. Reducing the number of plasmids –ideally consolidating all components onto a single vector– will be critical for clinical implementation.

Viral delivery is generally the most efficient strategy for DNA-based therapies, but viral vectors have limited packaging capacities, which differ by virus type[1]. The RAS_sensor_F.L.T. circuit under the EF1α promoter requires ~7.7 kb for the sensing components alone, excluding the output gene. This exceeds the packaging limit of adeno-associated virus (AAV) and is at the upper boundary for lentiviral vectors but could potentially be accommodated by larger vectors such as γ-retroviruses, poxviruses, or herpesviruses¹. Co-transduction with dual AAVs [2] or ongoing engineering to expand packaging capacity [3] may also offer future solutions. An additional route to reduce construct size could be alternative splicing, especially given redundancy between the two NarX fusion proteins[4]. 

An advantage of our current architecture is that synthetic response elements replace constitutive promoters, reducing construct size. For example, the MAPK-driven PY2_NarX&NarL circuits range between 4.9 and 5.2 kb depending on the transactivation domain, bringing them within AAV packaging limits for the sensor module[5], though co-delivery of the output gene would still be necessary. For lentiviruses, this is within the packaging capacity of 8 kb¹ and would allow for inclusion of ~3 kb output genes.

Still, assembling multiple modules onto a single vector introduces new challenges, including possible crosstalk or interference between neighboring promoters [6]. For example, placing the output gene too close to MAPK response elements may trigger unwanted MAPKdependent expression, potentially bypassing the intended AND-gate logic. Moreover, expressing three genes under separate response elements may shift expression ratios and reduce circuit functionality. Nonetheless, the absence of constitutive promoters and the RAS-dependence of MAPK response elements could provide partial robustness, since even unintended activation would still reflect RAS signaling to some extent. Further, our data (Fig. 1d) show that some deviation in component levels can be tolerated, provided all parts are sufficiently expressed. Nonetheless, assembling the circuit on a single vector will require careful design and rigorous validation to ensure optimal performance. 

While addressing this is beyond the scope of the current study, we agree that future efforts should focus on vector consolidation and delivery strategies. We now include a paragraph discussing these challenges in the revised manuscript.

Reviewer #2 (Public review): 

The manuscript describes an interesting approach towards designing genetic circuits to sense different RAS mutants in the context of cancer therapeutics. The authors created sensors for mutant RAS and incorporated feed-forward control that leverages endogenous RAS/MAPK signaling pathways in order to dramatically increase the circuits' dynamic range. The modularity of the system is explored through the individual screening of several RAS binding domains, transmembrane domains, and MAPK response elements, and the author further extensively screened different combinations of circuit components. This is an impressive synthetic biology demonstration that took it all the way to cancer cell lines. However, given the sole demonstrated output in the form of fluorescent proteins, the authors' claims related to therapeutic implications require additional empirical evidence or, otherwise, expository revision. 

Thank you very much for the thoughtful evaluation, precise critique, and constructive suggestions.

As correctly noted, our study initially focused on developing and optimizing input sensors and processing units for synthetic gene circuits targeting mutated RAS. To address the concern regarding therapeutic relevance, we have now incorporated functional validation using a clinically relevant output protein: herpes simplex virus thymidine kinase (HSV-TK), which converts ganciclovir into a cytotoxic compound. We replaced the mCerulean reporter with HSV-TK and tested the resulting RAS-targeting circuits in both RAS-mutant and wild-type cancer cell lines. The results, now presented in a new chapter (Figure 8 and Supplementary Fig. 14), demonstrate robust killing of RAS-mutant cells and support the potential therapeutic utility of these circuits.

Major comments: 

"These therapies are limited to cancers with KRASG12C mutations" is technically accurate. However, in this fast-moving field, there are examples such as MRTX1133 which holds the promise to target the very G12D mutation that is the focus of this paper. There are broader efforts too. It would help the readers better appreciate the background if the authors could update the intro to reflect the most recent landscape of RAS-targeting drugs. 

Thank you for this helpful suggestion. We have updated the introduction to reflect the rapidly evolving landscape of RAS-targeting therapies, including the development of inhibitors for nonG12C mutations such as KRASG12D (e.g., MRTX1133). Given the pace and breadth of these advances, we also refer readers to a recent comprehensive review that provides an in-depth overview of current RAS-targeting strategies.

Only KRASG12D was used as a model in the design and optimization work of the genetic circuits. Other mutations should be quite experimentally feasible and comparisons of the circuits' performances across different KRAS mutations would allow for stronger claims on the circuits' generalizability. Particularly, the cancer cell line used for circuit validation harbored a KRASG13D mutation. While the data presented do indeed support the circuit's "generalizability," the model systems would not have been consistent in the current set of data presented. 

To further support the generalizability of our RAS sensor, we titrated plasmid doses for a panel of oncogenic RAS variants, including multiple KRAS mutants as well as HRAS<sup>G12D</sup and NRAS<sup>G12D</sup. Across all tested variants, we observed concentration-dependent activation of the RAS sensor. At 1.67 ng/well, the sensor output for all oncogenic RAS variants was at least as high as that for KRAS^G12D, suggesting that the behavior observed in our initial design and optimization is representative of a broader set of RAS mutations.

We also noted that high overexpression of wildtype HRAS and NRAS can lead to substantial activation of the sensor, exceeding that observed with wildtype KRAS. This underscores the importance of considering all RAS isoforms when assessing circuit specificity and avoiding potential off-target activation in healthy cells.

In Figure 2a, the text claims that "inactivation of endogenous RAS with NF1 resulted in a lower YFP/RBDCRD-NarX expression," but Figure 2a does not show a statistically significant reduction in expression of SYFP (measured by "membrane-to-total signal ratio [RU]). 

Thank you for pointing this out. We repeated the experiment to reassess the effect of NF1 on RBDCRD-NarX-SYFP2 expression and were able to confirm statistical significance. Accordingly, we have replaced Figure 2a with updated data. To facilitate better visual comparison across conditions, we also standardized the y-axis range across all relevant flow cytometry plots.

The therapeutic index of the authors' systems would be better characterized by a functional payload, other than florescent proteins, that for example induce cell death, immune responses, etc. 

Thank you for this insightful comment. We agree that fluorescent reporters are limited to approximating expression levels, and that a functional output protein is more appropriate for assessing therapeutic potential. To address this, we replaced mCerulean with the therapeutic suicide-gene, HSV-TK, and tested the circuits in RAS-mutant and wild-type cancer cell lines. These experiments demonstrate that our circuits can express functional proteins and induce cell death in two RAS-mutant cell lines while showing low toxicity in a RAS wild type cell line (new chapter including Fig. 8 and Supplementary Fig.14). 

Comparing confluence of cells transfected with the RAS-targeting circuits to cells transfected with non-toxic GFP-output negative control or the constitutively expressed EF1αHSV-TK positive control allowed us to estimate the killing-strength of the circuits in each cell line. In RAS-mutant HCT-116 the confluence curves were similar to the positive control, indicating effective killing (Fig. 8b). At lower DNA dose in HCT-116, or in SW620 with lower transfection efficiency, the killing of transfected RAS-driven cancer cells was less pronounced, falling approximately midway between the controls (Fig. 8g&j). In the RAS wild type cell line, Igrov-1, the RAS circuits showed continued growth similar to the non-toxic negative control (Fig. 8d), suggesting low toxicity. 

While this may indicate low circuit activation in Igrov-1, an alternative explanation for the low toxicity could also be insufficient transfection efficiency. Testing in SW620 –which had similar transfection efficiency as Igrov-1 (Supplementary Fig. 14a)– showed that this moderate transfection efficiency was sufficient for RAS-circuit-dependent killing (Fig. 8d & 8g), supporting the notion of low activation in Igrov-1 and selective cytotoxicity in RAS-driven cancer cells.

Nonetheless, it is important to note that comparisons between the cell lines need to be interpreted cautiously because of inter-cell line differences in transfection, growth, and HSV-TK/ganciclovir (GCV)-sensitivity (Supplementary Fig. 14) and further validation will be essential. 

A conclusive assessment will require more efficient delivery strategies, such as viral vectors (as discussed above). Efficient delivery would allow to investigate selectivity in a more realistic setting with patient-derived RAS-mutant cancer and healthy cells as well as testing in an vivo model. While beyond the scope of the current study, we view it as a critical direction for future work and have therefore added a paragraph about this to our discussion.

Regarding data presented in "Mechanism of action" (Figure 2), the observations are interesting and consistent across different fluorescent reporters. However, with regard to interpretations of the underlying molecular mechanisms, it is not clear whether the different output levels in 2b, 2c, and 2d are due to the pathway as described by the authors or simply from varied expression levels of RBDCRD-NarX itself (2a) that is nonlinearly amplified by the rest of the circuit. From a practical standpoint, this caveat is not critical with respect to the signal-to-noise ratios in later parts of the paper. From a mechanistic interpretation standpoint, claims made forth in this section are not clearly substantiated. Some additional controls would be nice. For example, if the authors express NarXs that constitutively dimerize on the membrane, what would the RasG12Dresponsiveness look like? Does RasG12D alter the input-output curve of NarL-RE? How would Figure 4f compare to a NaxR constitutively dimerized control that only relies on transcriptional amplification of the Ras-dependent promoters? 

This is a great point. We agree that the observed differences in output levels (Fig. 2) could arise from non-linear amplification due to increased expression of RBDCRD-NarX, rather than RAS binding or dimerization alone. To further investigate this possibility, we performed titrations of KRAS^G12D in combination with the functional RAS sensor and a series of constitutively active and inactive control constructs (Supplementary Fig. 4).

Inactive controls lacking NarX dimerization showed only a modest increase in output expression, similar to direct mCerulean expression under the EF1α promoter. Transfection of the output plasmid alone, with NarL, or with NarL and non-RAS-binding RBD^R89L CRD^C168S -NarX, resulted in minimal RAS-dependent increases (Supplementary Fig. 4a). Importantly, after normalization using the EF1α-driven mCherry transfection control, these effects were fully or even slightly over-compensated (Supplementary Fig. 4b), showing that we don’t include the effect of EF1α-dependent increased leakiness in the data presented throughout the manuscript, but also that –due to the normalization– we potentially underestimate the dynamic range of the RAS-targeting circuits.

In contrast, constitutively dimerizing NarX controls (both membrane-bound and cytosolic dimerized via the FKBP–FRB system) exhibited a more pronounced RAS-dependent increase in output –even after normalization– confirming the presence of non-linear amplification (up to 3–4fold). However, this effect was still lower than that achieved with the functional RAS-binding sensor (8-fold at 1.67 ng/well KRAS^G12D; 14-fold at 5–15 ng/well), indicating that the increase in expression of the sensor parts is not the full explanation of the effect we see. Instead, RAS binding and dimerization further amplify the response and are necessary for full activation (Supplementary Fig. 4b).

We also addressed the reviewer’s suggestion by testing the MAPK response elements used in Fig. 4f with constitutively dimerizing NarX. These controls generally showed lower fold changes between KRAS^G12D; and KRAS^WT than the corresponding RAS-binding circuits  (Supplementary Fig. 7), with one exception: the combination of SRE_NarX and PY2_NarL-VP48. 

Together, these data show that non-linear amplification via increased expression and dimerization contributes to output activation. However, RAS binding and induced dimerization of the NarX sensor are required for full functionality and enhanced signal strength. This underscores that integrating the MAPK response elements with the binding-based RAS sensor into RAS-targeting circuits generally improves the distinction between cells with KRAS^G12D;  and KRAS^WT and that it was the combination that allowed to reach maximal fold changes.

It's also possible that these Ras could affect protein production at the post-transcriptional or even post-translational levels, which were not adequately considered. 

Thank you for this comment. We now mention in the manuscript the potential mechanisms by which (over-)activated RAS or MAPK signaling can increase protein synthesis. We cite relevant reports of the mechanisms we found, including upregulation of translational initiation and machinery[10]  and ribosomal biogenesis[11].

The text claims that "in contrast to what we saw in HEK293 overexpressing RAS (Figure 5d), the "AND-gate" RAS-targeting circuits do not generate higher output than the EF1a-driven, bindingtriggered RAS sensor in HCT-116. Instead, the improved dynamic range results from decreased leakiness in HCT- 116k.o." Comparing the experiment from Figure 5d, which looks at activation in KRASG12D and KRASWT, to the experiments in Figure 6b-d, which looks at activation in HCT-116WT and HCT-116KO is misleading. In Fig 5d., cells are transfected with KRASG12D and KRASWT to emulate high levels of mutant RAS and high levels of wild-type RAS. In Figures 6b-d, HCT-116WT has endogenous levels of mutant RAS, while the KCT-116KO is a knock-out cell line, and does not have mutant or WT RAS. Therefore, the improved dynamic range or "decreased leakiness in HCT-116KO" in comparison to Figure 5d. is more comparable to the NF1 condition from Figure 2, which deactivates endogenous RAS. While this may not be feasible, the most accurate comparison would have been an HCT-116KO line with KRASWT stably integrated. 

Thank you for this input. We understand that comparing the results from HEK293 cells transfected with KRAS^G12D;  or KRAS^WT (Fig. 5d) to those from HCT-116^WT    and HCT-116^k.o. cells (Fig. 6b–d) may be misleading if interpreted as a direct comparison of RAS signaling levels. Our intent was not to compare HEK293 with KRAS^WT directly to HCT-116^k.o.., but rather to contrast the behavior of the EF1α-driven RAS sensor and the MAPK-responsive RAS-targeting circuits within each cell line context.

Specifically, we observed that in HEK293 cells expressing KRAS^G12D, the MAPK-based RAS-targeting circuits produced higher output than the EF1α-expressed RAS sensor. In contrast, in HCT-116^WT cells, the EF1α-expressed RAS sensor resulted in higher output levels than the RAS-targeting circuits. Despite this, the MAPK-driven circuits showed an improved dynamic range compared to the EF1α-expressed RAS sensor in HCT-116, due to the reduced background expression in the HCT-116^k.o.. cells. We have revised the manuscript text to clarify this distinction.

We agree that an HCT-116^k.o cell line with stable integration of KRAS^WT would provide a more direct comparison. Nonetheless, HCT-116^k.o.. cells still express endogenous NRAS and HRAS, both of which are capable of activating the RAS sensor (as shown in Fig. 1g). Therefore, we believe that HCT-116^k.o. cells are more comparable to HEK293 with KRAS^WT than to the NF1 condition in Fig. 2, in which all endogenous RAS isoforms are inactivated.

We couldn't locate the citation or discussion of Figure 4d in the text. Conversely, based on the text description, Figure 6g would contain exciting results. But we couldn't find Figure 6g anywhere ... unless it was a typo and the authors meant Figure 6f, in which case the cool results in Figure S8 could use more elaboration in the main text. 

Thank you for this helpful observation. The figure references were indeed incorrect due to a typo. The results discussed in the text refer to Figure 6f (not 6g), which is now Figure 7a in the revised version. To further highlight these findings, we have added a new Figure 7b that better illustrates how different MAPK response elements enabled us to identify, for each RAS-mutant cell line, a RAS-targeting circuit that showed stronger activation than in all RAS wild-type lines. We have also expanded the corresponding section in the main text to elaborate on these results and their significance.

Reviewer #3 (Public review): 

Summary: 

Mutations that result in consistent RAS activation constitute a major driver of cancer. Therefore, RAS is a favorable target for cancer therapy. However, since normal RAS activity is essential for the function of normal cells, a mechanism that differentiates aberrant RAS activity from normal one is required to avoid severe adverse effects. To this end, the authors designed and optimized a synthetic gene circuit that is induced by active RAS-GTP. The circuit components, such as RAS-GTP sensors, dimerization domains, and linkers. To enhance the circuit selectivity and dynamic range, the authors designed a synthetic promoter comprised of MAPK-responsive elements to regulate the expression of the RAS sensors, thus generating a feed-forward loop regulating the circuit components. Circuit outputs with respect to circuit design modification were characterized in standard model cell lines using basal RAS activity, active RAS mutants, and RAS inactivation. 

This approach is interesting. The design is novel and could be implemented for other RASmediated applications. The data support the claims, and while this circuit may require further optimization for clinical application, it is an interesting proof of concept for targeting aberrant RAS activity. 

Strengths: 

Novel circuit design, through optimization and characterization of the circuit components, solid data. 

Weaknesses: 

This manuscript could significantly benefit from testing the circuit performance in more realistic cell lines, such as patient-derived cells driven by RAS mutations, as well as in corresponding non-cancer cell lines with normal RAS activity. Furthermore, testing with therapeutic output proteins in vitro, and especially in vivo, would significantly strengthen the findings and claims. 

Thank you very much for the thoughtful and supportive comments. We fully agree with the reviewer’s suggestions for improving the translational potential of the RAS-targeting circuits.

As a first step toward therapeutic relevance, we replaced the fluorescent reporter with HSV-TK, a clinically validated suicide gene, and demonstrated killing in RAS-mutant cancer cell lines. This is described above and in the new section of the manuscript (Figure 8).

We also agree that testing in patient-derived cancer cells and especially healthy cells with wild-type RAS activity will be essential. However, testing in primary or patient-derived cells presents delivery challenges: transient transfection of our current four-plasmid system is unlikely to achieve sufficient expression. As discussed in our response to Reviewer #1, development of a more efficient delivery strategy –such as viral vector-based delivery– is a necessary next step.

Once a delivery system is established, identifying relevant off-target tissues throughout the body with high physiological RAS signaling will be key to assessing selectivity. While comparative data on RAS activation across healthy tissues are scarce[12,13], recent atlases of transcription factor activity[14,15] provide insights to identify off-target cells with high activation of RAS-dependent transcription factors and may even approximate RAS activity across healthy tissue. Alternatively, our single-input sensors for RAS and MAPK pathway activity could be used in vivo to identify off-target cells based on endogenous activity.

Once relevant target and off-target cells have been identified, patient-derived cancer and healthy cells can help select and adapt cancer-specific RAS-targeting circuits and nominate therapeutic candidates for further safety and efficacy assessment[6,8].

Reviewer #1 (Recommendations for the authors): 

For the most part, the data in this study are very convincing and very well presented. The cartoons make it easier to understand the complex experimental setups. 

(1) Did the authors use wild-type Sos-1 or a constitutively active membrane-bound catalytic domain in their studies? How is SOS-1 activated when in case Sos-1 wild-type was used? 

Thank you for this feedback. We used the constitutively active catalytic domain of Sos-1 (AA5641049; PDB ID 2II0). 

(2) Figure 1f: In case of KRAS-G12D, it looks like the output expression does not really correlate with the RAS-GTP level. Can the authors give an explanation? 

Thank you for this interesting question. We believe the observed discrepancy arises primarily from differences in the sensitivity and readout dynamics of the two assays. The RAS-GTP pulldown ELISA appears insufficiently sensitive to detect small changes in RAS-GTP levels at lower KRAS^G12D plasmid doses (0.19, 0.56, or 1.67 ng). Only at 5 ng and 15 ng do we observe clear increases in RAS-GTP signal (25% and 700%, respectively). In contrast, the RAS sensor shows strong activation already in the 0.56–5 ng range but begins to saturate at higher doses (see Figure 1f and Figure 1e).

Beyond the differing technical sensitivities of the ELISA (plate reader) and flow cytometry, an important conceptual distinction may further explain this behavior: the RAS sensor likely integrates RAS signaling over time. Once NarX binds RAS-GTP and dimerizes, it activates NarL, triggering mCerulean expression. If the rate of mCerulean production exceeds its degradation, signal accumulates throughout the assay duration. Thus, the flow cytometry readout reflects time-integrated signaling, allowing small differences in RAS-GTP to be amplified into measurable differences in output—especially at low input levels. This may explain why flow cytometry detects circuit activation earlier and more steeply than the pulldown assay, which provides a snapshot of RAS-GTP abundance at a single time point and saturates less readily at high input levels.

Together, these factors likely explain the observed differences in signal dynamics: the RAS sensor exhibits steep activation followed by saturation at high plasmid doses (flow cytometry), while the ELISA shows limited sensitivity at low doses but a broader linear range at higher doses.

(3) Figure 2b: It appears that even in the case of KRAS-G12D and Sos-1, only a few cells are positive. Does this result depend on low cell density, low transfection efficiency, or a wide range of the expression level? As a control, nuclear staining could be shown. 

Thank you for this question. In the experiment shown in Figure 2b, our goal was to assess the membrane localization of the RBD^CRD-NarX-SYFP2 construct, which serves as a proxy for RAS-bound sensor. To enable accurate computational segmentation and separation of membrane signal from adjacent cells, we intentionally reseeded cells at low density in glassbottom plates for confocal imaging.

The observed variability in signal likely reflects a combination of transient transfection and heterogeneous expression levels. While the overall transfection efficiency was approximately 70%, expression varied between individual cells. To account for this, we analyzed the membrane-to-total signal ratio per cell, which internally normalizes the membrane signal to the total cellular expression of SYFP2 and controls for differences in transfection efficiency.

In response to the reviewer’s suggestion, we have updated the figure to include nuclear staining to aid interpretation. We would like to emphasize, however, that the images are intended to illustrate subcellular localization per cell, not expression frequency or intensity across the population.

Minor points 

(1) Figure 1b: "The third plasmid expresses NarL, .." should be changed to "The third plasmid expresses NarL-VP48, .." 

Done

(2) Figure 1c, right part: The orange arrow should be labeled NarX-H399Q (not N509A). 

Done

(3) Supplementary Table 6 and 7: [cells/wells] - should probably be [cells 10*3/well]. 

Thank you for these points, we updated the manuscript accordingly

Reviewer #2 (Recommendations for the authors): 

Minor comments: 

(1) N509A seems mislabeled in Figure 1b. 

(2) It would help the readers if the authors could elaborate a bit on what is known about the RBD and CRD mutations used here. 

Thank you for the input, we added a paragraph in the paper to expand on the effect of these commonly used mutations.

(3) The KRASWT&Sos1 condition is not explained within the text for Figure 1f, which is the first figure with the KRASWT&Sos1 condition, but rather later on for Figure 2a. Adding a description of this condition to the discussion of Figure 1f would add clarity to this figure. 

Thank you, we corrected this.

(4) Citing AlphaFold2 structural predictions as having "revealed that longer linkers between the sensor's RBDCRD and NarX-derived domains could bring the NarX domains into closer proximity" is probably an overstatement. AlphaFold2 generally has low confidence in the placement of long flexible linkers, and the longer linkers in the illustration could facilitate NarX and NarL being even farther apart than they are in the original design. 

Thank you for this input. We agree that AlphaFold2 predictions generally have low confidence in the placement of long, flexible linkers, and we did not intend to imply that the structural models were predictive of actual linker conformations. Rather, the models were used heuristically to generate the hypothesis that longer linkers might facilitate better positioning of the NarX domains for dimerization.

As described in the Methods, we manually rotated the flexible linker regions to explore plausible conformations. These exploratory models showed that with a short (1x GGGGS) linker, it was more challenging to bring the NarX domains into close proximity, whereas longer linkers allowed greater positional flexibility. This modeling exercise provided a structural rationale for experimentally testing longer linkers. We have revised the manuscript text to clarify that the structural predictions were used to motivate linker design –not to validate or predict structural outcomes.

(5) Figure 3b shows that the fold change (KRASG12D/KRASWT) is higher at shorter linker lengths and lower at longer linker lengths, and that the output expression of mCerulean is lower at shorter linker lengths and higher at longer linker lengths. Having a bar plot with the output expression mCerulean levels comparing KRASG12D and KRASWT next to each other would be a significantly more informative representation of this data. In particular, the readers might be interested in understanding the effect of linker length on off-target activation from the sensor, which is not clear from this figure. 

Thank you for the suggestion. We adapted Figure 3b to better present this. 

(6) While it is implied that the sentence "Among the tested binding domains, the Ras association domain (RA) of the natural RAS effector Rassf5, the RAS association domain 2 (RA2) of the phospholipase C epsilon (PLCe)33, and the synthetic RAS binder K5534 showed a slightly higher or similar dynamic range." is comparing these RAS binding domains to RBDCRD, for clarity it should be noted what the point of reference is for this "slightly higher or similar dynamic range." 

(7) Claims are made throughout the text that require supporting data, and thus require a reference to a figure, but there are a few instances where the reference is several sentences after the discussion of data and findings begins. For example, the discussion of Figure 3c begins with the claim "Among the tested binding domains, the Ras association domain (RA) of the natural RAS effector Rassf5, the RAS association domain 2 (RA2) of the phospholipase C epsilon (PLCe)33, and the synthetic RAS binder K5534 showed a slightly higher or similar dynamic range," but there is no reference to the data or figure being discussed until the end of the discussion of Figure 3c. This formatting is also present in Figure 3d and Figure 6f. 

Thank you for mentioning these imprecisions and inconsistencies, we addressed them in the manuscript. 

(8) In Figures 5d and 5e, the formatting of underscores and dashes is occasionally inconsistent within the text. (ex. "PY2_NarX_FLT or PY2_NarL-FLT" on page 13.). 

Thank you for this precise observation. The formatting differences were intentional and reflect distinct design principles. Specifically:

An underscore (e.g., PY2_NarX_FLT) denotes that two separate proteins are expressed –here, PY2-driven RBDCRD-NarX and EF1α-driven NarL-F.L.T.

A dash (e.g., PY2_NarL-F.L.T.) indicates a fusion protein –i.e., PY2-driven NarL-F.L.T. combined with EF1α-driven RBDCRD-NarX.

This notation is used to distinguish expression sources and fusion constructs while avoiding redundancy with the base circuit (EF1α_NarX + EF1α_NarL-VP48). We hope the included schematic diagrams in each relevant figure helps the reader interpret these combinations.

(9) The text claims that "loss-of-function mutations in RBDCRD decreased activation. However, the dynamic range was only 3-fold" and attributes this claim to Figure 6a. For a claim about specific fold-change activation, one would expect a corresponding figure with quantitative measurements of this fluorescence to be referenced. 

Thank you for this remark. We made a supplementary figure (Supplementary Fig. 11) to show the quantitative measurement of the 3-fold dynamic range between HCT-116^WT and HCT-116^k.o. when using the EF1a-expressed RAS sensor with NarL-VP48.

(10) The claim of this Figure 2d is that the effect of RAS-GTP levels on mCerulean output is amplified in comparison to Figures 2a, 2b, and 3c, representing expression, RAS binding, and dimerization respectively. While visually this might be true from the figure, the readers might be confused by the lack of significance between the control and the NF1 condition, alongside the variation between the triplicates. Could this experiment be repeated to gain clearer data and to support their claim more effectively? 

Thank you for this important observation. To address the concern regarding variability and statistical significance in Figure 2d, we repeated the experiment using 24-well plates to increase the number of cells analyzed per condition. This improved the consistency of the data and allowed us to reduce variability across replicates. As a result, we now observe a statistically significant difference between the control and the NF1 condition. The updated results are shown in the revised Figure 2.

(11) The readers might be less familiar with the concept of "composability" than "modularity" and it would be good to explain it if the authors did intend to use the former. 

Thank you for this comment. We changed it to modularity to avoid confusion. 

References

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(2) Mcclements, M. E. & Maclaren, R. E. Adeno-Associated Virus (AAV) Dual Vector Strategies for Gene Therapy Encoding Large Transgenes. YALE JOURNAL OF BIOLOGY AND MEDICINE vol. 90 (2017).

(3) Wagner, H. J., Weber, W. & Fussenegger, M. Synthetic Biology: Emerging Concepts to Design and Advance Adeno-Associated Viral Vectors for Gene Therapy. Advanced Science vol. 8 Preprint at https://doi.org/10.1002/advs.202004018 (2021).

(4) Doshi, J., Willis, K., Madurga, A., Stelzer, C. & Benenson, Y. Multiple Alternative Promoters and Alternative Splicing Enable Universal Transcription-Based Logic Computation in Mammalian Cells. Cell Rep 33, 108437 (2020).

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Author response:

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

Reviewer 1:

While BAP1 mutant UM cell lines were included for some of the experiments, it seems the in-vivo data mentioned in the response to the reviewers comment is missing? The authors stated that "MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor." But the CDX model data shown in Figure 4 is from 92.1 cells. If this data is available, then the manuscript would benefit from its addition.

We thank the reviewer for bringing this to our attention. As the reviewer mentioned, we show 92-1 CDX model in our manuscript. Additionally, strong tumor growth inhibition in MP-46  CDX model treated with our BAF ATPase inhibitor can be found in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

Reviewer 3:<br /> Supplementary Figure 2C<br /> Is the T910M mutation in the parental MP41 cells heterozygous? If so, the authors should indicate this in the figure legend. If this is a homozygous mutation, the authors should explain how the inhibitors suppress SMARCA4 activity in cells that have a LOF mutation.

We thank the reviewer for bringing this to our attention. We updated the figure legend accordingly to reflect the genotype of the mutations highlighted in the table.

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

Reviewer #1 (Public Review):

Summary:

The presented study by Centore and colleagues investigates the inhibition of BAF chromatin remodeling complexes. The study is well-written, and includes comprehensive datasets, including compound screens, gene expression analysis, epigenetics, as well as animal studies. This is an important piece of work for the uveal melanoma research field, and sheds light on a new inhibitor class, as well as a mechanism that might be exploited to target this deadly cancer for which no good treatment options exist.

Strengths:

This is a comprehensive and well-written study.

Weaknesses:

There are minimal weaknesses.

We thank the reviewer for the positive comments.

Reviewer #2 (Public Review):

Summary:

The authors generate an optimized small molecule inhibitor of SMARCA2/4 and test it in a panel of cell lines. All uveal melanoma (UM) cell lines in the panel are growth-inhibited by the inhibitor making the focus of the paper. This inhibition is correlated with the loss of promoter occupancy of key melanocyte transcription factors e.g. SOX10. SOX10 overexpression and a point mutation in SMARCA4 can rescue growth inhibition exerted by the SMARCA2/4 inhibitor. Treatment of a UM xenograft model results in growth inhibition and regression which correlates with reduced expression of SOX10 but not discernible toxicity in the mice. Collectively the data suggest a novel treatment of uveal melanoma.

Strengths:

There are many strengths of the study including the strong challenge of the on-target effect, the assays used, and the mechanistic data. The results are compelling as are the effects of the inhibitor. The in vivo data is dose-dependent and doses are low enough to be meaningful and associated with evidence of target engagement.

Weaknesses:

The authors introduce the field stating that SMARCA4 inhibitors are more effective in SMARCA2 deficient cancers and the converse. Since the desirable outcome of cancer therapy would be synthetic lethality it is not clear why a dual inhibitor is desirable. Wouldn't this be associated with more side effects? It is not known how the inhibitor developed here impacts normal cells, in particular T cells which are essential for any durable response to cancer therapies in patients. Another weakness is that the UM cell lines used do not molecularly resemble metastatic UM. These UM most frequently have mutations in the BAP1 tumor suppressor gene. It is not clear if the described SMARCA2/4 inhibitor is efficacious in BAP1 mutant UM cell lines in vitro or BAP1 mutant patient-derived xenografts in vivo.

We thank the reviewer for their insightful and constructive comments. As we demonstrate in Fig. 1d, uveal melanoma cells are selectively and deeply sensitive to BAF ATPase inhibition, and provides a therapeutic window. This is confirmed in Fig. 4a-c, as we demonstrated robust tumor growth inhibition, achieved at a dose well-tolerated in xenograft study. FHD-286, a dual BRM/BRG1 inhibitor similar to FHT-1015 with optimized physical properties, has been evaluated in a Phase I trial in patients with metastatic uveal melanoma (NCT04879017) and manuscript describing results of this clinical trial is currently in preparation.

As the reviewer mentioned, BAP1 loss is a signature of metastatic uveal melanoma. MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Reviewer #3 (Public Review):

Summary:

This manuscript reports the discovery of new compounds that selectively inhibit SMARCA4/SMARCA2 ATPase activity that work through a different mode as previously developed SMARCA4/SMARCA2 inhibitors. They also demonstrate the anti-tumor effects of the compounds on uveal melanoma cell proliferation and tumor growth. The findings indicate that the drugs exert their effects by altering chromatin accessibility at binding sites for lineage-specific transcription factors within gene enhancer regions. In uveal melanoma, altered expression of the transcription factor, SOX10, and SOX10 target gene underlies the anti-proliferative effects of the compounds. This study is significant because the discovery of new SMARCA4/SMARCA2 inhibitory compounds that can abrogate uveal melanoma tumorigenicity has therapeutic value. In addition, the findings provide evidence for the therapeutic use of these compounds in other transcription factor-dependent cancers.

Strengths:

The strengths of this manuscript include biochemical evidence that the new compounds are selective for SMARCA4/SMARCA2 over other ATPases and that the mode of action is distinct from a previously developed compound, BRM014, which binds the RecA lobe of SMARCA2. There is also strong evidence that FHT1015 suppresses uveal melanoma proliferation by inducing apoptosis. The in vivo suppression of tumor growth without toxicity validates the potential therapeutic utility of one of the new drugs. The conclusion that FHT1015 primarily inhibits SMARCA4 activity and thereby suppresses chromatin accessibility at lineage-specific enhancers is substantiated by ATAC-seq and ChIP-seq studies.

Weaknesses:

The weaknesses include a lack of more precise information on which SMARCA4/SMARCA2 residues the drugs bind. Although the I1173M/I1143M mutations are evidence that the critical residues for binding reside outside the RecA lobe, this site is conserved in CHD4, which is not affected by the compounds. Hence, this site may be necessary but not sufficient for drug binding or specifying selectivity. A more precise evaluation of the region specifying the effect of the new compounds would strengthen the evidence that they work through a novel mode and that they are selective. Another concern is that the mechanisms by which FHT1015 promotes apoptosis rather than simply cell cycle arrest are not clear. Does SOX10 or another lineage-specific transcription factor underlie the apoptotic effects of the compounds?

We thank the reviewer for the valuable comments.

We believe that our dual ATPase inhibitor is selective and additional insights into binding specificity and selectivity for earlier stage compounds of this series were recently published in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

The reviewer also poses a great question regarding the mechanism of apoptosis. The mechanism of apoptosis is extremely complex, but we observed a decrease in pro-survival BCL-2 protein expression in response to FHT-1015, in the experiment corresponding to Supplementary Fig. 5e. In the experiment described in Fig. 3k, we also monitored caspase 3/7 activity over time, and SOX10 overexpression rescued 92-1 cells from FHT-1015 induced apoptosis. This suggests the role of SOX10 as an important mediator of response to BAF ATPase inhibition, including apoptosis induced by FHT-1015.

Additional Reviews:

The referees would like to draw the authors' attention to the following issues that would best benefit from additional revision. 

The clinical relevance of the study would be strengthened by the use of uveal melanoma cell lines with BAP1 mutations that better represent metastatic uveal melanoma. The use of patient-derived xenografts would also be pertinent and would be a useful addition. Similarly, attention to the effects of the inhibitor on non-cancerous proliferative cells such as blood/T/immune cells would also strengthen the manuscript. As the study reports the administration of one of the inhibitors in mice for the xenograft experiments, it would be important to assess any potential effects on blood cell counts and better discuss the eventual toxicity or lack of toxicity and how it was assessed. 

The authors should better explain how SOX10 over expression can rescue viability in the presence of the inhibitor. Similarly given the critical roles of BRG1, SOX10, and MITF in cutaneous melanoma some specific discussion on the sensitivity of cutaneous melanoma cells to the inhibitor should be considered, and potential differences with uveal melanoma highlighted. 

Aside from these issues, the authors are urged to consider the other points mentioned below. 

Reviewer #1 (Recommendations For The Authors): 

Figure 1d, as well as the text in the manuscript referring to this figure, would benefit from indicating specific cell lines used for UM. The same for the sentence in line 153. 

We thank the reviewer for bringing this to our attention. We have added the cell line names and updated the manuscript accordingly.

For any of the studies conducted, is there any link with the genetics of UM? E.g. BAP1 wildtype/BAP1 mutant? 

As addressed above in the public review section, MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Row 191 - How were peaks classified as enhancer-occupied? 

We used annotatePeaks function of HOMER package to annotate genomic locations, as well as H3K27ac ChIP-seq to annotate peaks as enhancer-occupied. We thank the reviewer to pointing it out and have updated the manuscript accordingly to include this information.

Row 259, the two cell lines should be named, also in Figure 3i. 

We have added the cell line names and updated the manuscript accordingly.

Reviewer #2 (Recommendations For The Authors): 

As a proof of concept, this study is truly excellent and the authors should be commended. However, it is desirable that new knowledge in cancer is translated to the clinic. To this end there are a few things needed to strengthen the study. 

I am rephrasing my statements from the public review to say that I would recommend testing the inhibitor in T cells (side effects) and BAP1 mutant cell lines (for clinical relevance). 

As addressed in the public review section, MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Regarding concerns for any potential side effect on T cells, we observed an increase in both CD4 and CD8 T-cell populations in the peripheral blood and the spleen, when naïve, non-tumor bearing CD-1 mice were dosed with SMARCA2/4 dual ATPase inhibitor FHD-286 once daily for 14 days. FHD-286 is a compound similar to FHT-1015 described in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/). In addition, FHD-286 has been tested in tumor bearing syngeneic models. When B16F10 tumor bearing C57BL/6 were dosed with FHD-286 for 10 days, we observed an increase in CD69+ activated CD8 T-cell infiltration in the tumor microenvironment (doi:10.1136/jitc-2022-SITC2022.0888).

Reviewer #3 (Recommendations For The Authors): 

(1) Determine drug binding by crystal structure or generate additional SMARCA4 or SMARCA2 mutations in the region near I1173/I1143 that are not conserved in CHD4 and test them in an ATPase assay for effects on drug inhibition. For example, Q1166 in SMARCA4 and Q1136 in SMARCA4 could be changed to Alanine as in CHD4. Would this abrogate drug inhibition? 

We believe that our dual ATPase inhibitor is selective and additional insights into binding specificity and selectivity for earlier stage compounds of this series were recently published in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

(2) The finding that SOX10 can rescue the antiproliferative effects of FHT1015 suggests that SMARCA4 is primarily needed for SOX10 expression. However, the co-occupancy of SMARCA4 and SOX10 at enhancers suggests that they cooperate to promote chromatin accessibility. It is unclear how over-expression of SOX10 can promote chromatin accessibility in drug-inhibited cells since SOX10 does not have chromatin remodeling activity. ATAC-seq in cells over-expressing SOX10 and treated with the drug could identify SOX10-dependent targets that do not require SMARCA4 activity and clarify the mechanism. It would also be informative to determine if SOX10 over-expression abrogates the effects of FHT1015 on both cell cycle and apoptosis, helping to resolve whether it is a partial or complete rescue of proliferation. 

We agree that running ATAC-seq in cells overexpressing SOX10 would clarify this mechanism. However, shifts in corporate strategy deprioritized any further experiments for this project. One potential mechanism that SOX10 overexpression can partially rescue BAF inhibition phenotype is through overexpressed SOX10 localizing to open chromatin regions (mostly promoters) across the genome. We know from our ATAC-seq data (Fig. 2) that BAF inhibition leads to loss of chromatin accessibility at SOX10 enhancer sites, while promoter regions are only partially affected. Therefore, we think that overexpression of SOX10 would allow upregulation of its target genes via binding to the promoter regions. In this model, the enhancer-driven SOX10 target genes are likely to remain silenced.  

(3) Although the in vivo studies indicate that the drugs are well-tolerated, additional in vitro studies to determine the effects of the drug on the proliferation/survival of non-cancerous cells would further validate their therapeutic utility.

Author Response: The reviewer raises a critical question. FHD-286, a dual BRM/BRG1 inhibitor similar to FHT-1015 with optimized physical properties, has been evaluated in a Phase I trial in patients with metastatic uveal melanoma (NCT04879017), and it was well tolerated at continuous daily dose of up to 7.5 mg QD and at intermittent dose of up to 17.5 mg QD.  Manuscript describing results of this clinical trial is currently in preparation.

Author response:

Reviewer #1 (Public review):

It appears obvious that with no or a little fitness penalty, it becomes beneficial to have MHC-coding genes specific to each pathogen. A more thorough study that takes into account a realistic (most probably non-linear in gene number) fitness penalty, various numbers of pathogens that could grossly exceed the self-consistent fitness limit on the number of MHC genes, etc, could be more informative.

The reviewer seems to be referring to the cost of excessively high presentation breadth.  Such a cost is irrelevant to the inferior fitness of a polymorphic population with heterozygote advantage compared to a monomorphic population with merely doubled gene copy number.  It is relevant to the possibility of a fitness valley separating these two states, but this issue is addressed explicitly in the manuscript.

An addition or removal of one of the pathogens is reported to affect "the maximum condition", a key ecological characteristic of the model, by an enormous factor 10^43, naturally breaking down all the estimates and conclusions made in [RS]. This observation is not substantiated by any formulas, recipes for how to compute this number numerically, or other details, and is presented just as a self-standing number in the text.

It is encouraging that the reviewer agrees that this observation, if correct, would cast doubt on the conclusions of Siljestam and Rueffler.  I would add that it is not the enormity of this factor per se that invalidates those conclusions, but the fact that the automatic compensatory adjustment of c_max conceals the true effects of removing a pathogen, which are quite large.

I am not sure why the reviewer doubts that this observation is correct.  The factor of 2.7∙10⁴³ was determined in a straightforward manner in the course of simulating the symmetric Gaussian model of Siljestam and Rueffler with the specified parameter values.  A simple way to determine this number is to have the simulation code print the value to which c_max  is set, or would be set, by the procedure of Siljestam and Rueffler for different parameter values.  In another section of this response I will describe how to do this with the simulation code written and used by Siljestam and Rueffler; doing so confirms the value that I obtained with my own code.  Furthermore, I will now give a theoretical derivation of this factor.

As specified by Siljestam and Rueffler, the positions of the m pathogens in (m-1)-dimensional antigenic space correspond to the vertices of a regular simplex centered at the origin, with distance between vertices equal to 1.  The squared distance from the origin to each of the m vertices of such a simplex is (m-1)/2m (https://polytope.miraheze.org/wiki/Simplex).  Thus, the sum of the m squared distances is (m-1)/2.  For the (0, 0) homozygote, condition is multiplied by a factor of exp(-(vr)²/2) for each pathogen, where r is the distance from the origin.  It follows that, with v=20, all the pathogens together decrease condition by a factor of exp(20²∙(m-1)/4) = exp(100∙(m-1)).  Thus, increasing or decreasing m by 1 changes this value by a factor of exp(100) = 2.7∙10⁴³.

This begs the conclusion that the branching remains robust to changes in c_max that span 4 decades as well.

That shows only that the results are not extremely sensitive to c_max or K.  They are, nonetheless, exquisitely sensitive to m and v.  This difference in sensitivities is the reason that a relatively small change to m leads to such a large compensatory change in c_max a change large enough to have a major effect on the results.

As I wrote above, there is no explanation behind this number, so I can only guess that such a number is created by the removal or addition of a pathogen that is very far away from the other pathogens. Very far in this context means being separated in the x-space by a much greater distance than 1/\nu, the width of the pathogens' gaussians. Once again, I am not totally sure if this was the case, but if it were, some basic notions of how models are set up were broken. It appears very strange that nothing is said in the manuscript about the spatial distribution of the pathogens, which is crucial to their effects on the condition c.

I did not explicitly describe the distribution of pathogens in antigenic space because it is exactly the same as in Siljestam and Rueffler, Fig. 4: the vertices of a regular simplex, centered at the origin, with unity edge length.

The number in question (2.7∙10⁴³) pertains to the Gaussian model with v=20.  As specified by Siljestam and Rueffler, each pathogen lies at a distance of 1 from every other pathogen, so the distance of any pathogen from the others is indeed much greater than 1/v.  This condition holds, however, for most of the parameter space explored by Siljestam and Rueffler (their Fig. 4), and for all of the parameter space that seemingly supports their conclusions.  Thus, if this condition indicates that “basic notions of how models are set up were broken”, they must have been broken by Siljestam and Rueffler.

Overall, I strongly suspect that an unfortunately poor setup of the model reported in the manuscript has led to the conclusions that dispute the much better-substantiated claims made in [SD].

The reviewer seems to be suggesting that my simulations are somehow flawed and my conclusions unreliable.  I will therefore describe how my conclusions about sensitivity to parameter values can be verified using the simulation code provided by Siljestam and Rueffler themselves, with only small, easily understood modifications.  I will consider adding this description as a supplement when I revise the manuscript.

The starting point is the Matlab file MHC_sim_Dryad.m, available at https://doi.org/10.5061/dryad.69p8cz98j.  First, we can add a line that prints the value of the variable logcmax, which represents the natural logarithm of cmax determined and used by the code.  Below line 116 (‘prework’), add the line ‘logcmax’ (with no semicolon).

Now, at the Matlab prompt, execute MHC_sim_Dryad(false, 8, 20, 1) to run the simulation for the Gaussian model with m=8, v=20, and K=1.  The output will indicate that logcmax=700, in accord with the theoretical factor exp(100*(m-1)) derived above.  The allelic diversity, n_e, will rise to a steady state-level of about 140, as in the red curve of my Fig. 2.

Now lower m to 7, i.e,  run MHC_sim_Dryad(false, 7, 20, 1).  The output will indicate that logcmax=600.  This confirms that lowering m by 1 causes the code to lower the value of c_max by a factor exp(100)=2.7∙10⁴³, which must also be the factor by which the condition of the most fit homozygote would increase without this adjustment.

With the change of m to 7 and the compensatory change in c_max, steady-state allelic diversity remains high.  But what if m changes but c_max remains the same, as it would in reality?

To find out, we can fix the value of c_max to the value used with m=8 by adding the following line below the line previously added: ‘logcmax = 700’.  With this additional modification in place, executing MHC_sim_Dryad(false, 7, 20, 1) confirms that without a compensatory change to c_max, lowering m from 8 to 7 mostly eliminates allelic diversity, in accord with the corresponding curve in my Fig. 2.  Similarly, raising m from 8 to 9, or changing v from 20 to 19.5 or 20.5 (executing MHC_sim_Dryad(false, 8, 19.5, 1) or MHC_sim_Dryad(false, 8, 20.5, 1)), largely eliminates diversity, confirming the other results in my Fig. 2.  Results for the bitstring model can also be confirmed, though this requires additional changes to the code.

Thus, the extreme sensitivity of the results of Siljestam and Rueffler to parameter values can be verified with the code that they used for their simulations, indicating that my conclusions are not consequences of my having done a “poor setup of the model”.

Response to Reviewer #2 (Public review):

(1) The statement that the model outcome of Siljestam and Rueffler is very sensitive to parameter values is, in this form, not correct. The sensitivity is only visible once a strong assumption by Siljestam and Rueffler is removed. This assumption is questionable, and it is well explained in the manuscript by J. Cherry why it should not be used. This may be seen as a subtle difference, but I think it is important to pin done the exact nature of the problem (see, for example, the abstract, where this is presented in a misleading way).

I appreciate the distinction, and the importance of clearly specifying the nature of the problem.  However, Siljestam and Rueffler do not invoke the implausible assumption that changes to the number of pathogens or their virulence will be accompanied by compensatory changes to c_max.  Rather, they describe the adjustment of c_max (Appendix 7) as a “helpful” standardization that applies “without loss of generality”.  Indeed, my low-diversity results could be obtained, despite such adjustment, by combining the small change to m or v with a very large change to K (e.g., a factor of 2.7∙10⁴³).  In this sense there is no loss of generality, but the automatic adjustment of c_max obscures the extreme sensitivity of the results to m and v.

(2) The title of the study is very catchy, but it needs to be explained better in the text.

I had hoped that the final paragraph of the Discussion would make the basis for the title clear.  I will consider whether this can be clarified in a revision.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Comments on revised version: 

I have reviewed the revised manuscript and read the rebuttal. The authors have carefully addressed my concerns. There is however one point that needs further work: 

This follows up on my major point #1 in my initial review. I had I asked the authors to demonstrate that FOLFIRI + AZD are less toxic to untransformed colorectal cells than colorectal cancer cell lines.  It is good to see that the authors took my advice and show effects of the drug treatments on the untransformed colorectal cell line CCD841. It seems to be less sensitive to AZD and FOLFIRI in the figure in the rebuttal. What surprises me is that I cannot find these new figures anywhere in the revised manuscript. Also, the data seem preliminary, because I do not see any standard errors in the graphs, and I cannot find a description of the time of drug incubation. I ask the authors to make sure that the CCD841 data are reproducible, and make sure they incorporate the data in the revised manuscript. 

We thank the reviewer for this insightful comment. In the initial revised version of the manuscript, we did not include results from the untransformed colorectal cell line CCD841, as those experiments had only been performed once and were considered preliminary. However, we fully agree with the reviewer on the importance of including these data.

To address this, we have repeated the experiments in CCD841 cells to ensure reproducibility. We now report the results from three independent experiments testing the combination of AZD2858 and FOLFIRI on healthy epithelial colon cells. These results are shown in Supplementary Figure S7, where blue matrices represent cell viability and black matrices reflect the level of synergy between AZD2858 and FOLFIRI.

Our results confirm that, individually, each drug has little to no effect on healthy cells, and no consistent synergistic interaction was observed, except in Experiment 1, which could not be reproduced. Importantly, the drug concentrations used were identical to those applied in the cancer cell experiments, allowing for direct comparison between normal and malignant cell responses.

Reviewer #2:

Comments on latest version: 

Morano et al. have revised their manuscript in response to the points raised by reviewer #3 as follows.

(1) Fig. 2E: Correcting the previously erroneous labelling of this Fig. makes it match the textual description. 

(2) Figs 3A and B: The revised textual description of the flow cytometry BrdU incorporation is now precise. 

(3) Fig. 3E: Removing the suspect WB images is a pragmatic decision that does not significantly affect the overall conclusions of the paper. 

(4) Fig. 3D: Despite its puzzling appearance this data is now described accurately in the text as "DSBs remained elevated after the combined treatment" rather than "increased after the combined treatment. A more convincing increase in the presumed damaged DNA band is evident in Fig. 4D when AZD2858 is combined with a much lower concentration of SN38 (1.5nM) which could mean that the concentration used in Fig. 3D (300nM) induced maximal damage that could not be further enhanced. 

We thank the reviewer for their thoughtful comments and constructive feedback, which have helped us improve the clarity and rigor of the manuscript.

Reviewer #3:

Comments on latest version: 

The authors have addressed most of the concerns that I raised in the first round of revision and I have no further questions. I appreciate the authors's efforts in carrying out an preliminary in vivo work, although as the authors pointed out the compound seems to be not effective in vivo. Future work is desired to address this to clarify the significance of the work. 

We thank the reviewer for acknowledging our efforts in addressing the previous concerns. We also appreciate the recognition of our preliminary in vivo work. While these results suggest limited in vivo efficacy of the compound at this stage, we agree that additional studies will be necessary to fully evaluate its therapeutic relevance. We consider this an important next step and are committed to pursuing it in future work.

Author response:

General Statements

In this paper we demonstrate that the lipid packing of the plasma membrane has a huge impact on the stability of caveolae. By using interdisciplinary techniques, we show that the widely used dynamin inhibitor Dyngo-4a adsorbs and inserts to lipid bilayers leading to a decreased lipid packing and hence reduced caveolae dynamics and internalization even in cells lacking dynamin. We have added experiments that validates that Dyngo-4a treatment does not result in fragmentation or disassembly of the caveolae.  A FRAP assay of cytosolic caveolae has been employed to address questions concerning scission. Moreover, as suggested by the reviewers, we have also included new simulation data that show and expand on the fact that Dyngo-4a positions in the lipid leaflet similar to cholesterol and preferentially associates with cholesterol clusters, affecting the spatial distribution of cholesterol in the membrane. We believe that these added data have greatly improved the paper and strengthened our conclusions that the lipid packing is a critical determinant in the balance between internalization and stable plasma membrane association of membrane vesicles.

As requested, we have expanded the introduction to provide more detailed information about previous findings in the field. Changes and addition to the text has been highlighted in red for easier tracking.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

The authors use Dyngo-4a, a known Dynami inhibitor to test its influence on caveolar assembly and surface mobility. They investigate, whether it incorporates into membranes with Quartz-Crystal Microbalance, they investigate how it is organized in membranes using simulations. Finally, they use lipid-packing sensitive dyes to investigate lipid packing in the presence of Dyngo-4a, membrane stiffness using AFM and membrane undulation using fluorescence microscopy. They also use a measure they call "caveola duration time" to claim that something happens to caveolae after Dyngo-4a addition and using this parameter, they do indeed see an increase in it in response to Dyngo-4a, which is reduced back to the baseline after addition of cholesterol.

Overall, the authors claim: 1) Dyngo-4a inserts into the membrane and this 2) results in "a dramatic dynamin-independent inhibition of caveola scission". 3) Dyngo-4a was inserted and positioned at the level of cholesterol in the bilayer and 4) Dyngo-4a-treatment resulted in decreased lipid packing in the outer leaflet of the plasma membrane 5) but Dyngo-4a did not affect caveola morphology, caveolae-associated proteins, or the overall membrane stiffness 6) acute addition of cholesterol counteracts the block in caveola scission caused by Dyngo-4a.

Overall, in this reviewers opinion, claims 1, 3, 4, 5 are well-supported by the presented data from electron and live cell microscopy, QCM-D and AFM.

However, there is no convincing assay for caveolar endocytosis presented besides the "caveola duration" which although unclearly described seems to be the time it takes in imaging until a caveolae is not picked up by the tracking software anymore in TIRF microscopy.

Since the main claim of the paper is a mechanism of caveolar endocytosis being blocked by Dyngo-4a, a true caveolar internalization assays is required to make this claim. This means either the intracellular detection of not surface connected caveolar cargo or the quantification of caveolar movement from TIRF into epifluorescence detection in the fluorescence microscope. Otherwise, the authors could remove the claim and just claim that caveolar mobility is influenced.

We thank the reviewer for the nice constructive comments, and we very much appreciate the positive critique. We have now included a FRAP experiment of endocytic Cav1-GFP supporting the effect on internalization. In addition, we are currently preforming CTxB HRP experiments to quantify the number of caveolae at PM using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Reviewer #1 (Significance):

A number of small molecule inhibitors for the GTPase dynamics exist, that are commonly used tools in the investigation of endocytosis. This goes as far that the use of some of these inhibitors alone is considered in some publications as sufficient to declare a process to be dynamin-dependent. However, this is not correct, as there are considerable off-target effects, including the inhibition of caveolar internalization by a dynamin-independent mechanism. This is important, as for example the influence of dynamin small molecule inhibitors on chemotherapy resistance is currently investigated (see for example Tremblay et al., Nature Communications, 2020).

The investigation of the true effect of small molecules discovered as and used as specific inhibitors and their offside effects is extremely important and this reviewer applauds the effort. It is important that inhibitors are not used alone, but other means of targeting a mechanism are exploited as well in functional studies. The audience here thus is besides membrane biophysicists interested in the immediate effect of the small molecule Dyngo-4a also cell biologists and everyone using dynamic inhibitors to investigate cellular function.

Reviewer #2 (Evidence, reproducibility and clarity):

This manuscript uses the small molecule dynamin inhibitors dynasore and dyngo to show that in dynamin triple knockout cells that these inhibitors impact lipid packing and organization in the plasma membrane. Data showing that dyngo affects caveolin dynamics using tirf microscopy is also shown and is interpreted to reflect inhibition of caveolae scission from the membrane.

This data showing that dyngo and dynasore target membrane order is quite compelling and argues that the effects of these inhibitors is not dynamin specific and that inhibition of endocytosis by these small molecule inhibitors is dynamin-independent. The in vitro and in vivo data they provide is convincing.

Similarly, the data showing that dynasore and dyngo affect caveolin dynamics and clathrin endocytosis (transferrin) is quite convincing and argues that altered lipid packing is impacting membrane dynamics at the plasma membrane.

What is less convincing is the conclusion that dyngo is preventing caveolae scission from the membrane. Study of caveolae endocytosis is based on a TIRF assay that has inherent limitations:

- Caveolae are defined as bright cav1-positive spots in diffraction limited TIRF and their disappearance presumed to be endocytic events. Cav1 spots are presumed to be caveolae but the authors do not consider that they may be flat non-caveolar oligomers. The diffraction limited TIRF approach interprets the large structures as caveolae but evidence to that effect is lacking.

This is a valid comment and to address this we have now included data showing colocalization of cavin1 and EHD2 to the Cav1-GFP spots. We can however not determine if they are flat or invaginated. We do have extensive experience imaging caveolae using TIRF microscopy and carefully chose cells that display low expression of fluorescently labelled caveolin to avoid non-caveolar structures.

- The analysis (and the diagram presented in figure 4) considers that caveolae can either diffuse laterally in the membrane or internalize and does not consider that caveolae can flatten and possibly fragment in the membrane. Is it not possible that loss of Cav1 spots is a fragmentation event and not necessarily a scission event?

This is a good question, yet, fragmentation and disassembly would result in shorter track durations and this is not what is observed in data. We have now also included data showing that cavin1 is persistently associated with the Cav1 spots identified as caveolae during Dyngo-4a treatment indicating that these are caveolae. Furthermore, IF stainings showing colocalization of Cav1GFP with cavin1 or EHD2 after Dyngo-4a treatment have also been added. We have now also expanded on the different interpretations of the data in the results section.

- The analysis is based on overexpression of Cav1-GFP that may alter the stoichiometry between Cav1 and cavin1 such that while caveolae may be expressed, larger non-caveolar structures may accumulate.

Yes, this is correct, we have specifically imaged cell expressing low levels of Cav1-GFP to avoid accumulated non-caveolar structures that can be spotted in cells with high expression.

- Cav1 has been shown to be internalized via the CLIC pathway (Chaudary et al, 2014) and if dyngo is impacting clathrin then maybe it is also impacting CLIC endocytosis and thereby Cav1 endocytosis via this pathway?

Dyngo-4a has been shown to not affect CLIC endocytosis (McCluskey et al., 2013) and in our data we do not see internalization following Dyngo-4a treatment.

- The longer Cav1 TIRF track time and shorter displacement with dyngo is consistent with inhibition of caveolae scission. However, as the authors discuss, could not reduced membrane undulations due to dyngo's impact on membrane order be responsible for the longer tracks? Alternatively, perhaps the altered lipid packing is corralling Cav1 movement and reducing non-caveolar Cav1 endocytosis, resulting in shorter tracks of longer duration? The proposed interaction of dyngo with cholesterol could prevent scission but also stabilize large (flat?) Cav1 oligomers in the membrane, perhaps reducing Cav1 oligomer fragmentation.

We completely agree that membrane undulations contribute to instability of the TIRF-field and therefore disruption of cav1-GFP tracks as we discuss in the results section and have been described in previous work (Larsson et al., 2023). Yet, we have also shown that internalization of caveolae results in shorter tracks (Hubert et al., 2020; Larsson et al., 2023; Mohan et al., 2015). Furthermore, the tracked Cav1-GFP spots are persistently positive for cavin1 both with and without Dyngo-4a treatment showing that the majority do not disassemble become internalized by other pathways. Additionally, the added IF stainings after 30 min Dyngo-4a treatment also show that the Cav1-GFP spots remain positive for cavin1 and EHD2 just as ctrl-treated cells.

My point here is not to discredit the data but only to suggest that the TIRF approach used is an indirect measure of caveolae scission from the membrane that requires substantiation using other approaches.

We appreciate these comments and have tried to address these by adding new data and discussions on the interpretation of the tracking data in the results section.

Dyngo is certainly generally affecting lipid packing via cholesterol and thereby affecting Cav1 dynamics in the plasma membrane. The claim of caveolae scission should be qualified and alternative possibilities considered and discussed. If the authors persist in arguing that dyngo is affecting caveolae scission then the effect should be substantiated by accumulation of caveolae by quantitative EM and high spatial and temporal resolution imaging of Cav1 and cavin1 to define the endocytic events. As the latter represents a new, and potentially very challenging, line of experimentation, I would suggest that it is beyond the scope of the current study. As indicated above the additional experiments are not necessary and qualification of the claims would be sufficient.

We have now included a FRAP experiment of endocytic Cav1-GFP supporting the effect on internalization. We are also currently preforming CTxB HRP experiments to quantify the number of caveolae at the PM using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Other points

Figure 1C - Cav1 positive spots cannot be interpreted to be caveolae from diffraction limited confocal images. Same comment applies to Fig 4G - caveola? duration.

We completely agree with this and that the claims should be qualified. We have added IF stainings showing that the Cav1-GFP structures are also positive for cavin1. We have now clarified that we cannot distinguish between flat or different curved states of caveolae using this methodology. We have also changed the labelling of Fig. 4G.

Figure 4C - it is not clear why this EM data is not quantified - for both the number of caveolae and clathrin coated pits - as this would help clarify the interpretation of the effect reported.

We are currently preforming CTxB HRP experiments to quantify the number of caveolae using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Figure 4D - the AFM experiments should perhaps be repeated as the non-significant effect of dyngo on the Young's modulus may be a result of insufficient n values.

We would like to clarify that to ensure the robustness of our AFM measurements, we performed the experiments with sufficient biological and technical replicates. Specifically, each data point shown in Figure 4D represents a Young’s modulus value averaged from approximately sixty force-distance curves per cell. For each condition, we collected force-distance maps on eight to nine individual cells, obtained from two separate petri dishes per day. We repeated this process on two independent days. In total, we analysed thirty-one cells for the DMSO control and thirty-three cells for the Dyngo-4a treatment. We performed the “student’s t-test with Welch’s correction” to access the statistical significance between the two conditions, as described in the main text. We believe that the sample size and statistical approach are sufficient to support the conclusions presented. Furthermore, we also analysed cell stiffness by calculating the slope of the linear portion of the force-distance curves. This analysis also did not reveal any statistically significant differences between the conditions (data not shown), further supporting our conclusion that Dyngo-4a treatment does not significantly alter the Young’s modulus under our experimental setup (or conditions).

Reviewer #2 (Significance):

This data showing that dyngo and dynasore target membrane order is quite compelling and argues that the effects of these inhibitors is not dynamin specific and that inhibition of endocytosis by these small molecule inhibitors is dynamin-independent. The in vitro and in vivo data they provide is convincing.

Similarly, the data showing that dynasore and dyngo affect caveolin dynamics and clathrin endocytosis (transferrin) is quite convincing and argues that altered lipid packing is impacting membrane dynamics at the plasma membrane.

What is less convincing is the conclusion is that dyngo is preventing caveolae scission from the membrane.

Reviewer #3 (Evidence, reproducibility and clarity):

Larsson et al present experimental and computational data on the role of Dyngo4a (a compound that was developed to inhibit dynamin) on the dynamics of caveolae. The manuscript mostly documents effects of Dyngo on caveolae, with one experiment to suggest a mechanism for this result. This one rather unconvincing result forms the focus of the manuscript contributing to a disconnect between the data and the presentation. Additionally, there are concerns with data interpretation. The writing could also benefit from revision to address grammar mistakes, strengthen referencing, and increase precision. Overall, the manuscript requires substantial revisions before being considered for publication. The central claim, in particular, needs stronger evidence to support the proposed mechanism.

We thank the reviewer for the thorough review and for experimental suggestions that we believe has strengthened our data further.

Significant issues (in approximate order of importance):

(1) The data supporting the central mechanistic explanation appears limited. There is no evidence that Dyngo remains in one leaflet

The simulations show that the energy barrier for moving in between bilayers is very high. Furthermore, simulations of C-Laurdan has shown that it does not readily flip in between membrane leaflets (Barucha-Kraszewska et al., 2013) supporting that it reports on the outer lipid leaflet when added to cells. We have however now changed this and state that Dyngo-4a decreased the lipid order in the plasma membrane.

- the GP of the PM is very low compared to previous measurements,

The absolute GP-values will vary between setups depending on what filters are used so they are not comparable between laboratories. What is of importance is that we found a significant change in the relative GP-values in cells treated with Dyngo-4a and control cells. It is this change that we report. We have not performed any GP-measurements on this cell type earlier so it is unclear what previous measurements reviewer #3 are referring to.

- effects on other membranes are not explored,

The order of the intracellular membranes is as expected lower than that of the plasma membrane. Differentiating different intracellular membranes of interest like endocytotic vesicles from other intracellular membranes would be very difficult but, more importantly, our study is focused on what is happening in the plasma membrane where caveolae reside and would be of minor interest for plasma membrane dynamics.

- dynamin-directed effects of Dyngo are not considered,

In the discussion section we discuss the difficulties with disentangling dynamin-direct and indirect effects.

(2) The QCM-D measurements and claims require explanation as several aspects remains unclear. In Fig S2, the 'softness' (what does this mean?) changes by 4-fold with DMSO alone (what does this mean?), then fractionally more with Dyngo. Then fractionally more again when Dyngo is removed (why?). Then it remains somewhat higher when both Dyngo and DMSO are removed, which is somehow interpreted as Dyngo remaining in the bilayer, but not DMSO.

We understand the confusion of the reviewer and hope our explanations provide clarity. QCM-D measurements are based on an oscillating quartz crystal sensor. Specifically, alterations in oscillation frequency (ΔF) and the rate of energy dissipation from the sensor surface (ΔD) are what is measured. Allowing the measurement of: 1) materials adsorbing to the sensor surface, 2) changes in the viscoelastic properties of a solution in contact with the sensor surface, 3) changes in the material adsorbed to the sensor surface upone exposure to different solutions. The ratio of ΔD/-ΔF reports the mechanical softness or rigidity of an adsorbed material, in this case the SLB.

A “buffer shift” is the term used when there is not an adsorption to the sensor surface, but rather an effect from altering the solution above the sensor surface. One reason is because different solutions can have different densities (e.g., a DMSO-buffer mixture vs buffer alone), which impacts the oscillations of the sensor. It was observed that the DMSO-buffer mixture alone gave a large buffer shift in comparison to the adsorption of the Dyngo-4a into the SLB, thereby muddling the data interpretation. Thus, in Fig. S2 the system was first equilibrated with the DMSO-buffer mixture prior to addition of the Dyngo-4a solution to allow for clearer visualization of the two events. In QCMD to assess if something has made a permeant change to the system you change back to the solutions used before the addition, thus first we washed with a DMSO-Buffer mixture followed by buffer alone. Control experiments were carried out in which no Dyngo-4a was added (also shown in Fig. S2). The control shows the same “buffer shift” from the DMSO-buffer mixture occurs in both systems and that upon returning to a buffer only condition there is no permanent change to the system caused from exposure to the DMSO. In contrast, once the system that received Dyngo-4a is changes back to a buffer only system we see that mass has been added to the system (ΔF) with little change to the dissipation (ΔD), thereby resulting in a lower ratio of ΔD/-ΔF, which is to say that the SLB after the adsorption of Dyngo-4a was more rigid that the SLB without Dyngo-4a.

These interpretations are difficult to grasp, as the authors seem to be implying simple amphiphilic partitioning into the membrane, which should all be removable by efficient washing.

Amphiphilic partitioning is not fully reversible by “efficient washing” it depends on partitioning coefficients.

I do not doubt that this compound interacts with membranes, but the quantifications appear ambiguous. A bilayer with 16 mol% (or worse, 30% if all in one leaflet) Dyngo is very unlikely (to remain a bilayer). Even if such a bilayer was conceivable, the authors are claiming an ADDITION of Dyngo that would INCREASE the area of one leaflet by 30%, which needs explanation as it appears unlikely.

We understand that in our attempt provide numbers in the results section for the amount of binding observed in QCM-D, this can easily be interpreted as this is what is observed to insert into the PM. However, as discussed in the discussion, we also see aggregations of Dyngo-4a that associate with the membrane in the simulations which likely could contribute to the binding observed in QCM-D prior to washing. The precise amount of membrane inserted Dyngo-4a is difficult to measure as we discuss in the text. In order to make this clearer, we have now moved all these details to the discussion section where we elaborate on this. Furthermore, since Dyngo-4a, like cholesterol, is intercalating in between the head groups of the lipids the area would not increase in direct proportion to the mol%.

Also, there are no replicates shown, so unclear how reproducible these effects are?

For clarity, only single experiments are shown. However, multiple experiments were performed and the range in measured values for 3 technical repeats can be observed in the standard deviations found in the main text (e.g., 6 ± 2 mol%).

(3) The simulations are insufficiently described and difficult to interpret. How big are these systems? Why do the figures show the aqueous system with lateral boundaries?

There are no explicit boundaries used in the simulations, periodic boundary conditions are applied in all three dimensions. The lateral boundaries observed in the figures correspond to the simulation box edges and are a visual artifact of 2D projections with QuickSurf representation. No artificial wall or constraints were introduced laterally. Additional technical details, including the system size and periodic boundary conditions have now been added to the methods section.

It seems quite important that multiple Dyngo molecules aggregate rather than partition into membranes - is this likely to occur in experiment?

Yes, this is important and with the additional simulation experiments suggested by Reviewer #3 it has been clarified that they contribute a great deal to the change in lipid packing of lipid bilayers containing cholesterol.  However, it is hard to test aggregation is the cellular system, but we believe that this happens and contribute to the effect on membranes. We have now emphasized the effect of the aggregates in the text.

PMF simulations are strongly suggesting that Dyngo does not spontaneously cross membranes, which is inconsistent with its drug-like amphiphilicity (cLogP~2.5 is optimally suited for membrane permeation) and known effects on intracellular proteins. This suggests an artefact in these PMFs.

As stated in the submitted version of the manuscript, logP was used to validate the topology and the observed value was in a very good agreement with cLogP. Moreover, this validation complemented the standard procedure of CHARMM-GUI ligand modelling, that provided a reasonable penalty score (around 20) for the Dyngo-4a topology. POPC and cholesterol molecules are standard in the force field and validated by numerous studies. The parameters used for the membrane simulations and AWH in particular are very common for this type of studies. Thus, we do not see what may cause any artifacts in the free energy profile construction. In fact, amphiphilicity of the molecule may be one of the key reasons that Dyngo-4a molecule remains at the aqueous interface of the membrane and does not cross the membrane spontaneously. Also, we believe that the energy barrier of 40-60 kJ/mol is not prohibitively high and Dyngo-4a molecules may still overcome the barrier eventually, though we expect majority to reside in the upper leaflet.

The authors should experimentally measure the permeation of Dyngo through bilayers (or lack thereof), to more robustly support their finding that Dyngo does not cross membranes spontaneously.

We thank the reviewer for the suggestion, however this if very technically challenging and would require establishment of precise systems which is beyond the scope of this manuscript.

(4) Why not measure effect of Dyngo on lipid packing directly and more broadly in model membranes?

With the added modelling experiments supporting the previous simulations and the calculated GP values from the C-Laurdan experiments on cellular plasma membrane, we do not find it necessary to include more model membranes experiments than the already existing ones on lipid monolayers and supported lipid bilayers.

(5) Statistics should not be done on individual cells (n>26), but rather on independent experiment (N=3?)

We have performed the statistics on live cell particle tracking according to previous literature on similar systems (Boucrot et al., 2011; Larsson et al., 2023; Shvets et al., 2015; Stoeber et al., 2012).

(6) Fig 1G is important but rather unclear. Firstly, these kymographs are an odd way to show that the caveolae are not moving. More importantly, caveolae in normal cells have been shown to be quite stable and immobile (eg doi: 10.1074/jbc.M117.791400), yet here they are claimed to be very mobile.

Although this might be an odd and unconventional way to depict dynamic processes, we believe that this is a very illustrative way to show track stability over time in bulk rather than just a kymograph over a few structures in a cell. Furthermore, we are not claiming that caveolae are very mobile but rather the opposite very stable in agreement with previous work (Boucrot et al., 2011; Larsson et al., 2023; Mohan et al., 2015). We have now edited the text to make this even clearer.

Also, if Dyngo prevents caveolae scission, there should be more of them at the membrane - why no quantification like Fig 1C to show accumulation of caveolae upon Dyngo treatment? Or directly counting caveolae via EM, as in Fig 4C?

We are currently preforming CTxB HRP experiments using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long. However, Dynasore has previously been shown, by EM, to increase the number of caveolae at the PM (Moren et al., 2012; Sinha et al., 2011).

(7) The writing can be made more precise and referencing could be strengthened.

The introduction was written in a short format, and we have now extended this and made it more precise.

Some examples:

(a) 'scissoned' is not a word in English,

Thanks, we have now changed this.

(b) what is meant by "Cav1 assembly is driven by high chol content"? There are many types of caveolin assemblies.

We agree that this can be made more precise and have now clarified this in the introduction.

(c) "This generates a unique membrane domain with distinct lipid packing and a very high curvature." Unclear what 'this' refers to and there is no reference here, so what is the evidence for either of these claims? Caveolin-8S oligomers are not curved. Perhaps 'this' is caveolae, but they are relatively large and also not very highly curved and I am unaware of measurements of lipid packing therein.

Caveolae are around 50 nm which in biology is a very high curvature of a membrane. It has been extensively proven that caveolae have a distinct lipid composition highly enriched in cholesterol and sphingolipids, which thereby also will generate a unique lipid packing as compared to the surrounding membrane. Yet, the reviewer is correct that lipid packing has not been measured in a caveola for obvious technical challenges. Thus, we have now changed the text to “special lipid composition”.

The sentence following that one again makes a specific, but unreferenced, claim.

(d) intro claims that lipid packing is critical for fission, but it is unclear quite what is meant by this claim. The references do not help, as they are often about the basic biophysics of lipids, rather than how packing affects fission.

We have now edited the text.  

(e) intro strongly implies that caveolae remain membrane attached because of stalled scission. How strong is the evidence for this? The fact that EHD2 is at the neck is not definitive,

We used the term stalled scission to describe that all omega shaped membrane invaginations do not scission in the same automatic way as clathrin coated vesicles. We have now changed this in the text. Caveolae are shown to be released (undergo scission) and be detected as internal caveolae if the protein EHD2 is removed. Hence this must be interpreted as if EHD2 stalls scission. The evidence includes data compiled over the last 12 years from others and us which include for example: 1) Caveolae with EHD2 have a longer duration time (Larsson et al., 2023; Mohan et al., 2015; Moren et al., 2012; Stoeber et al., 2012), Knock down of EHD2 results in more internalized caveolae as measured by CTxB HRP using EM (Moren et al., 2012) and shorter duration time at the PM (Hubert et al., 2020; Larsson et al., 2023; Mohan et al., 2015; Stoeber et al., 2012). 2) EHD2 overexpression results in less internalized caveolae as measured by CTxB HRP using EM (Stoeber et al., 2012). Furthermore, 3) overexpression or acute addition of purified EHD2 via microinjection counteracts lipid induced scission of caveolae and hence result in caveolae stabilization at the PM (Hubert et al., 2020). It is very hard to see that the release and internalization of caveolae could result from anything else than that these have undergone scission. EHD2 has been found around the rim of caveolae (Matthaeus et al., 2022) and overexpression of EHD2 oligomerizing mutants have been shown to expand the caveola neck (Hoernke et al., 2017; Larsson et al., 2023).

(f) unclear what is meant by 'lipid packing frustration' and how Dyngo supposedly induces it.

Lipid packing frustration refers to what is usually referred to as lipid packing defect, but since lipid membranes are describe as a fluid system it should not have defects whereby, we believe that lipid packing frustration is more accurate. However, we have now changed the text and use “decreased lipid packing” or “decreased lipid order” more thoroughly to describe the effect on the plasma membrane.

(8) IF of Cav1 is insufficient to claim puncta as caveolae. Co-stained puncta of caveolin with cavin are much stronger evidence. Same issue for Cav1-GFP puncta.

We agree and have now provided IF showing cavin1 and EHD2 colocalization to Cav1GFP in non and Dyngo-4a-treated cells.

(9) Fig 3E claims that "preferred position of Dyngo-4a was closer to the head groups" but the minimum looks to be in similar place as Fig 3B without cholesterol. Response:

We appreciate the reviewer’s observation. The PMF minima in the POPC and POPC:Chol membranes are indeed close in absolute position (~1.1–1.2 nm from the bilayer center). However, as clarified in the revised text, the presence of cholesterol leads to a slight shift of Dyngo-4a closer to the headgroup region and broadens the positional distribution. This is also evident from the added density profiles (Fig. S3A) and is now described more precisely in the manuscript.

Critically, these results do not support the notion that Dyngo affects lipid packing sufficiently, which is not measured in the simulations (though could be).

We thank the reviewer for the excellent suggestion. In response, we have now included a detailed analysis of Dyngo-4a’s effect on lipid packing in the simulations. As described in the revised manuscript, we measured deuterium order parameters, area per lipid (APL), and lipid–Dyngo–cholesterol spatial distributions (Figs. 3-H, S3C-E). The results demonstrate that Dyngo-4a decreases lipid order in POPC:Chol membranes. Both single molecules and clusters reduce the order parameter by up to 0.04 units, particularly in the upper leaflet, where Dyngo-4a reside.The reduction is most pronounced in the midchain region of the sn1 tail and around the double bond of the sn2 tail. These effects were accompanied by increased APL in POPC:Chol membranes and by colocalization of Dyngo-4a near cholesterol-rich regions. Together, these data confirm that Dyngo-4a perturbs membrane organization and lipid packing in a composition-dependent manner. We believe these additions directly address the concern and demonstrate that the simulations indeed support the conclusion that Dyngo-4a modulates lipid packing.

Finally, the simulation data do not show "that Dyngo-4a is competing with cholesterol"; it is unclear what 'competition' means in this context, but regardless, the data only shows that Dyngo sits at a similar location as cholesterol.

We agree with the reviewer that “competition” was an imprecise term. We have rephrased the relevant sections to clarify that Dyngo-4a and cholesterol localize to overlapping regions and exhibit spatial coordination. As now stated in the manuscript, cholesterol appears to partially displace Dyngo-4a from its preferred depth seen in pure POPC, broadens its membrane distribution, and alters lipid packing. According to the order parameters there is an interplay between chol and Dyngo-4a and the heatmaps show that the distribution of chol in the membrane gets less uniform in the presence of Dyngo-4a. These interactions suggest that Dyngo-4a perturbs cholesterol-rich domains.

As new analysis routines were added to the study, we have now also added the details on those to the Methods section of the text.

(10) AFM measures the stiffness of the cell (as correctly explained in Results section) not "overall stiffness of the PM" as stated in the Discussion.

We thank the reviewer for pointing this out, we have now altered this in the discussion section.

(11) Fig2A: what was the starting lipid surface pressure? How does Dyngo insertion depend on initial lipid packing?

The starting pressure lipid pressure was 20 mN m<sup>-1</sup which we now have incorporated in the figure legend. We performed several such experiments with a starting pressure ranging from 20-23 mN m^-1 showing consistent results which we described in the materials and methods section. Given that we also performed QCMD analysis and simulations on bilayers showing that Dyngo-4a adsorbed and inserted respectively, we have not performed a titration of starting pressures resulting in a MIP of Dygo-4a.

(12) Fig 4B is a strange approach to measure membrane motion. Why not RMSD or some other displacement based method? As its shown, it implies that the area of the cell changes.

The method that we used to quantify the area of the cell which is attached (or close to) the glass and thereby is visible in TIRF microscopy. This is area indeed changes over time which has been frequently observed and used to describe and quantify the mobility, lamellipodia and filopodia formation among other things. We agree that RMSD can also be used to analyze the data before and after treatments and we have now included RMSD­­­­ analysis in the manuscript.

Reviewer #3 (Significance):

The title, abstract, and introduction of the manuscript are largely framed around lipid packing, but most of the data investigate other unexpected effects of treating cells with Dyngo4a. The only measurement for lipid packing (or any other membrane properties) is Fig 4E-F. Therefore, this paper is effectively an investigation of an artefact of a common reagent, which itself could be a valuable contribution. However, the mechanism to explain its effect requires stronger evidence, and its broad biological significance needs further exploration.

Overall, the impact of documenting the effects of Dyngo4a on membranes appears modest but may be valuable to the membrane trafficking community.

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Author response:

Reviewer #1 (Public review):

We greatly appreciate Reviewer #1’s accurate public review of our study on the kinesin motor using the DNA origami nanospring (NS). With respect to the strengths, we fully agree with Reviewer #1’s comments. Regarding the weakness, we would like to respond as follows.

It is true that, unlike optical tweezers, our method does not provide real-time data display. Optical tweezers enable real-time observation and manipulation of kinesin molecules at arbitrary time points. Achieving real-time observation and manipulation is indeed an important challenge for the future development of the NS technique. On the other hand, Iwaki et al. (our co-corresponding author) has already investigated dynamic properties of motor proteins under load, such as step size and force–velocity relationship of myosin VI using NS. We are now preparing high spatiotemporal resolution microscopy experiments on the KIF1A system to measure its step size and force–velocity relationship, which inherently require such resolution.

Reviewer #2 (Public review):

We would like to thank Reviewer #2 for providing a highly accurate assessment of the strengths of our experiments. Regarding the weaknesses, we would like to respond as follows.

First, Iwaki et al. (our co-corresponding author) have already succeeded in observing the stepping motion of myosin VI using the nanospring (NS) in their previous work. We are also currently preparing high spatiotemporal resolution microscopy experiments to observe the stepping motion of KIF1A in our system. Second, while it is true that the NS does not follow Hooke’s law, it is possible to design and construct NSs with an appropriate dynamic range by tuning the spring constant to match the forces exerted by protein molecules. Finally, we agree that our first observation of the stall plateau in KIF1A using the NS is a meaningful achievement. However, with respect to the suggestion that “increasing validity requires also studying kinesin-1,” we have a somewhat different perspective. The validity of the NS method has already been thoroughly examined in the previous work on myosin VI by Iwaki et al., where results were compared with those obtained using optical tweezers. Moreover, the focus of this manuscript is on KAND caused by KIF1A mutations. From this perspective, although we appreciate the suggestion, we consider it important to keep the present study focused on KIF1A and its implications for KAND.

Author response:

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

Reviewer #1 (Public review):  

(1) To distinguish autophagosomes from autolysosomes, the authors used vps16 RNAi cells, which are supposed to be fusion deficient. However, the extent to which fusion is actually inhibited by knockdown of Vps16A is not shown. The co-localization rate of Atg8 and Lamp1 should be shown (as in Figure 8). Then, after identifying pre-fusion autophagosomes and lysosomes, the localization of each should be analyzed.

Thank you for this insightful comment. We analyzed the colocalization of 3xmCherry-Atg8a and GFP-Lamp1, which label autophagic structures and lysosomes, respectively, in Vps16A RNAi fat body cells. As expected, Vps16A silencing markedly reduced the overlap between these two signals, indicating a strong block in autophagosome–lysosome fusion. Moreover, both 3xmCherry-Atg8a and GFP-Lamp1 became more perinuclearly localized compared to the control (luciferase RNAi) cells.

It is also possible that autophagosomes and lysosomes are tethered by factors other than HOPS (even if they are not fused). If this is the case, autophagosomal trafficking would be affected by the movement of lysosomes.  

Thank you for raising this possibility. While we cannot fully exclude that autophagosomes might be indirectly transported via tethering to lysosomes, we consider this unlikely. We believe that in Drosophila fat cells, autophagosomes and lysosomes rapidly fuse once in close proximity. Therefore, even if alternative tethering mechanisms exist, they are unlikely to permit prolonged joint trafficking without fusion.

(2) The authors analyze autolysosomes in Figures 6 and 7. This is based on the assumption that autophagosome-lysosome fusion takes place in cells without vps16A RNAi. However, even in the presence of Vps16A, both pre-fusion autophagosomes and autolysosomes should exist. This is also true in Figure 8H, where the fusion of autophagosomes and lysosomes is partially suppressed in knockdown cells of dynein, dynactin, Rab7, and Epg5. If the effect of fusion is to be examined, it is reasonable to distinguish between autophagosomes and autolysosomes and analyze only autolysosomes.  

Thank you for this careful observation. The 3xmCherry-Atg8a reporter is well suited to identify both autophagosomes and autolysosomes, as the mCherry fluorophore is resistant to degradation in the acidic environment of autolysosomes. Notably, mCherry-Atg8a–positive autolysosomes appear larger and brighter than pre-fusion autophagosomes, which are typically smaller and dimmer, especially under fusion-deficient conditions (e.g., Figure 4). Therefore, we use these morphological differences as a proxy to distinguish between the two.

To improve structural assignment, we incorporated endogenous Lamp1 staining (Figure 10) and a Lamp1-GFP reporter (Figure 10—figure supplement 1). Vesicles positive for mCherryAtg8a but negative for Lamp1 are considered pre-fusion autophagosomes. Structures double-positive for mCherry-Atg8a and Lamp1 represent autolysosomes, while Lamp1positive, Atg8a-negative vesicles correspond to non-autophagic lysosomes. To clarify these interpretations, we revised the Results section and explained these reporters in more detail.

(3) In this study, only vps16a RNAi cells were used to inhibit autophagosome-lysosome fusion. However, since HOPS has many roles besides autophagosome-lysosome fusion, it would be better to confirm the conclusion by knockdown of other factors (e.g., Stx17 RNAi).  

Thank you for this valuable suggestion. We initially considered using Syntaxin17 RNAi; however, our recent findings indicate that loss of Syx17 results in a HOPS-dependent tethering lock between autophagosomes and lysosomes (DOI: 10.1126/sciadv.adu9605). In this case, tethered vesicles would likely move together, confounding the interpretation of autophagosome-specific trafficking.

Therefore, we turned to other SNAREs such as Vamp7 and Snap29. One Snap29 RNAi was located on the appropriate chromosome needed for our genetic experiments. We generated a transgenic fly line expressing both Snap29 RNAi and the mCherry-Atg8a reporter under a fat body-specific R4 promoter. When we tested our key trafficking hits in this background, we observed similar autophagosome localization phenotypes as in Vps16A RNAi cells. These results, now included in the revised manuscript (see Figure 6), confirm that the observed transport phenotypes are not specific to Vps16A or HOPS complex loss.

(4) Figure 8: Rab7 and Epg5 are also known to be directly involved in autophagosomelysosome tethering/fusion. Even if the fusion rate is reduced in the absence of Rab7 and Epg5, it may not be the result of defective autophagosome movement, but may simply indicate that these molecules are required for fusion itself. How do the authors distinguish between the two possibilities?

Thank you for this important point. While Rab7 and Epg5 indeed participate in autophagosome–lysosome tethering and fusion, our data suggest they also contribute to autophagosome movement. This is evident from the distinct phenotypes observed upon Rab7 or Epg5 RNAi compared to Vps16A or SNARE RNAi. Depletion of Vps16A, Syx17, Vamp7, or Snap29 (factors involved specifically in fusion) results in perinuclear accumulation of autophagosomes. In contrast, Rab7 or Epg5 RNAi leads to a dispersed autophagosome pattern throughout the cytoplasm.

These differences suggest that Rab7 and Epg5 play additional roles in positioning autophagosomes. Supporting this, our co-immunoprecipitation experiments show that Epg5 interacts with dynein motors. Therefore, we propose that Rab7 and Epg5 influence both autophagosome fusion and their microtubule-based transport.

Reviewer #2 (Public review):  

One limitation of the study is the genetic background that serves as the basis for the screening. In addition to preventing autophagosome-lysosome fusion, disruption of Vps16A has been shown to inhibit endosomal maturation and block the trafficking of components to the lysosome from both the endosome and Golgi apparatus. Additional effects previously reported by the authors include increased autophagosome production and reduced mTOR signaling. Thus Vps16A-depleted cells have a number of endosome, lysosome, and autophagosome-related defects, with unknown downstream consequences. Additionally, the cause and significance of the perinuclear localization of autophagosomes in this background is unclear. Thus, interpretations of the observed reversal of this phenotype are difficult, and have the caveat that they may apply only to this condition, rather than to normal autophagosomes. Additional experiments to observe autophagosome movement or positioning in a more normal environment would improve the manuscript.  

Thank you for highlighting this limitation. We have tried to conduct time-lapse imaging of live fat body cells expressing 3xmCherry-Atg8a and GFP-Lamp1 to visualize the movement and fusion events of pre-fusion autophagosomes (3xmCherry-Atg8a positive and GFP-Lamp1 negative) and lysosomes (GFP-Lamp1 positive). Despite different experimental setups and durations of starvation, no vesicle movement was observed at all, so live imaging of larval Drosophila fat tissue will require time-consuming optimizations of in vitro culture conditions. Consistent with this, we did not find any literature data where organelle motility in fat body cells was successfully observed. Nuclear positioning in fat body cells was investigated in detail in an excellent study, however the authors were able to observe only very little movement of the nuclei by live imaging (Zheng et al. Nat Cell Biol. 2020 Mar;22(3):297-309. doi: 10.1038/s41556-020-0470-7), further highlighting the technical difficulties of live or timelapse imaging in this tissue type.

Specific comments  

(1) Several genes have been described that when depleted lead to perinuclear accumulation of Atg8-labeled vesicles. There seems to be a correlation of this phenotype with genes required for autophagosome-lysosome fusion; however, some genes required for lysosomal fusion such as Rab2 and Arl8 apparently did not affect autophagosome positioning as reported here. Thus, it is unclear whether the perinuclear positioning of autophagosomes is truly a general response to disruption of autophagosome-lysosome fusion, or may reflect additional aspects of Vps16A/HOPS function. A few things here would help. One would be an analysis of Atg8a vesicle localization in response to the depletion of a larger set of fusionrelated genes. Another would be to repeat some of the key findings of this study (effects of specific dynein, dynactin, rabs, effectors) on Atg8a localization when Syx17 is depleted, rather than Vps16A. This should generate a more autophagosome-specific fusion defect.  

Thank you for this insightful suggestion. We recently discovered that Syx17 depletion induces a HOPS-dependent tethering lock between autophagosomes and lysosomes (DOI: 10.1126/sciadv.adu9605), making it unsuitable for modeling autophagosome-specific fusion defects. In contrast, Vamp7 and Snap29 knockdowns do not appear to cause such tethering lock. We were able to generate a suitable Drosophila line using a Snap29 RNAi transgene located on a compatible chromosome. Upon testing key hits from our screen in this background, we found that autophagosomes redistributed similarly, supporting our conclusions. These new results have been included in the revised manuscript (see Figure 6)

Third, it would greatly strengthen the findings to monitor pre-fusion autophagosome localization without disrupting fusion. Such vesicles could be identified as Atg8a-positive Lamp-negative structures. The effects of dynein and rab depletion on the tracking of these structures in a post-induction time course would serve as an important validation of the authors' findings.  

Thank you for this helpful suggestion. As described above, we attempted time-lapse imaging of 3xmCherry-Atg8a and GFP-Lamp1-expressing fat body cells under various conditions to identify motile pre-fusion autophagosomes. However, we did not observe any vesicle movement, regardless of the starvation duration or experimental setup. As this likely reflects technical limitations of ex vivo fat body imaging, we were unable to achieve live tracking of autophagosome dynamics without introducing perturbations. This limitation is now discussed in the revised manuscript.

(2) The authors nicely show that depletion of Shot leads to relocalization of Atg8a to ectopic foci in Vps16A-depleted cells; they should confirm that this is a mislocalized ncMTOC by colabeling Atg8a with an MTOC component such as MSP300. The effect of Shot depletion on Atg8a localization should also be analyzed in the absence of Vps16A depletion.  

Thank you for this positive comment. We co-labeled Atg8a with the minus-end microtubule marker Khc-nod-LacZ in both shot single knockdown and shot; vps16A double knockdown cells. Ectopic Khc-nod-LacZ-positive MTOC foci were clearly visible in both conditions, and Atg8a-positive autophagosomes accumulated around these structures. These findings confirm that Shot depletion induces ectopic MTOC formation, which correlates with autophagosome relocalization. The new data have been incorporated into the revised manuscript (see Figure 1O-S).

(3) The authors report that depletion of Dynein subunits, either alone (Figure 6) or codepleted with Vps16A (Figure 2), leads to redistribution of mCherry-Atg8a punctae to the "cell periphery". However, only cell clones that contact an edge of the fat body tissue are shown in these figures. Furthermore, in these cells, mCherry-Atg8a punctae appear to localize only to contact-free regions of these cells, and not to internal regions of clones that share a border with adjacent cells. Thus, these vesicles would seem to be redistributed to the periphery of the fat body itself, not to the periphery of individual cells. Microtubules emanating from the perinuclear ncMTOC have been described as having a radial organization, and thus it is unclear that this redistribution of mCherry-Atg8a punctae to the fat body edge would reflect a kinesin-dependent process as suggested by the authors.  

Thank you for this detailed observation. We frequently observe autophagosomes accumulating in contact-free peripheral regions of dynein-depleted cells, resulting in an asymmetric distribution. While previous studies describe a radial microtubule organization in fat body cells, none of them directly label MT plus ends, the direction of kinesin-based transport.

To further explore this, we overexpressed a HA-tagged kinesin, Klp98A-3xHA, in both control and Vps16A RNAi backgrounds. Immunolabeling revealed that Klp98A localizes to the contact-free peripheral regions in both conditions, consistent with the distribution of autophagosomes in dynein knockdown cells. This supports our interpretation that kinesindependent transport drives autophagosome redistribution in the absence of dynein, and that fat body cells exhibit subtle asymmetries in MT polarity that influence this transport. These new results have been included in the revised manuscript (see Figure 3G, H).

(4) To validate whether the mCherry-Atg8a structures in Vps16A-depleted cells were of autophagic origin, the authors depleted Atg8a and observed a loss of mCherry- Atg8a signal from the mosaic cells (Figure S1D, J). A more rigorous experiment would be to deplete other Atg genes (not Atg8a) and examine whether these structures persist.  

Thank you for the suggestion to further validate our reporter. We depleted Atg1, a key kinase required for phagophore initiation, in the Vps16A RNAi background. This completely abolished the punctate mCherry-Atg8a distribution in knockdown cells (see Figure 1—figure supplement 1E, K), confirming that the labeled structures are indeed of autophagic origin.

(5) The authors found that only a subset of dynein, dynactin, rab, and rab effector depletions affected mCherry-Atg8a localization, leading to their suggestion that the most important factors involved in autophagosome motility have been identified here. However, this conclusion has the caveat that depletion efficiency was not examined in this study, and thus any conclusions about negative results should be more conservative.  

Thank you for this constructive feedback. We agree that negative results must be interpreted conservatively due to potential differences in knockdown efficiency. We have revised our conclusions accordingly, clarifying that the factors identified are key for autophagosome motility, while acknowledging the possibility of false negatives.

Reviewer #3 (Public review):  

Major concerns:

(1) The localization of EPG5 should be determined. The authors showed that EPG5 colocalizes with endogenous Rab7. Rab7 labels late endosomes and lysosomes. Previous studies in mammalian cells have shown that EPG5 is targeted to late endosomes/lysosomes by interacting with Rab7. EPG5 promotes the fusion of autophagosomes with late endosomes/lysosomes by directly recognizing LC3 on autophagosomes and also by facilitating the assembly of the SNARE complex for fusion. In Figure 5I, the EPG5/Rab7colocalized vesicles are large and they are likely to be lysosomes/autolysosomes.

Thank you for suggesting to improve our Epg5 localization data. We performed triple immunostaining for Atg8a, Lamp1-3xmCherry, and Epg5-9xHA in S2R+ cells. In addition to triple-positive structures—likely representing autolysosomes—we observed Atg8a and Epg59xHA double-positive vesicles that lacked Lamp1-3xmCherry signal, which likely correspond to pre-fusion autophagosomes. Based on these results, we propose that in addition to arriving via the endocytic route, Epg5 may also reach lysosomes through autophagosomes. These findings have been included in the revised manuscript (see Figure 5K).

(2) The experiments were performed in Vps16A RNAi KD cells. Vps16A knockdown blocks fusion of vesicles derived from the endolysosomal compartments such as fusion between lysosomes. The pleiotropic effect of Vps16A RNAi may complicate the interpretation. The authors need to verify their findings in Stx17 KO cells, as it has a relatively specific effect on the fusion of autophagosomes with late endosomes/lysosomes.  

Thank you for this valuable suggestion. We initially considered Syntaxin17 for validation; however, we recently found that loss of Syx17 leads to a HOPS-dependent tethering lock between autophagosomes and lysosomes, which would confound interpretation, as autophagosomes remain tethered to lysosomes (DOI: 10.1126/sciadv.adu9605). Therefore, Syntaxin17 loss is not suitable for our purpose. Among the remaining fusion SNAREs, one RNAi line targeting Snap29 was available on a compatible chromosome for generating Drosophila lines equivalent to those used in the screen. We established this Snap29 RNAicontaining tester line and crossed it with our top hits. We observed that autophagosome motility was comparable to that in the Vps16A RNAi background, further supporting our conclusions. These results have been incorporated into the revised manuscript (see Figure 6)

(3) Quantification should be performed in many places such as in Figure S4D for the number of FYVE-GFP labeled endosomes and in Figures S4H and S4I for the number and size of lysosomes.  

Thank you for pointing this out. We performed the suggested quantifications and statistical analyses for FYVE-GFP labeled endosomes, as well as for the number and size of lysosomes. The updated data are now presented in the revised Figure 5—figure supplement 1.

(4) In this study, the transport of autophagosomes is investigated in fly fat cells. In fat cells, a large number of large lipid droplets accumulate and the endomembrane systems are distinct from that in other cell types. The knowledge gained from this study may not apply to other cell types. This needs to be discussed.

Thank you for raising this important point. We agree that our findings may not be fully generalizable to all cell types. Given that the organization of the microtubule network depends on both cell function and developmental stage, it is plausible that the molecular machinery described here operates differently elsewhere. We now mention this limitation in the Discussion.

Minor concerns:  

(5) Data in some panels are of low quality. For example, the mCherry-Atg8a signal in Figure 5C is hard to see; the input bands of Dhc64c in Figure 5L are smeared.  

Thank you for pointing this out. We repeated the experiment shown in Figure 5C and replaced the panel with a clearer image. The smeared Dhc64C input bands in Figure 5L result from the unusually large size of this protein, which affects its electrophoretic migration. We mentioned this point in the corresponding figure legend.

(6) In this study, both 3xmCherry-Atg8a and mCherry-Atg8a were used. Different reporters make it difficult to compare the results presented in different figures.  

Thank you for this comment. Both 3xmCherry-Atg8a and mCherry-Atg8a are well-established reporters that behave similarly as autophagic markers. Nevertheless, to avoid confusion, we ensured that each figure uses only one type of reporter consistently, which is now clearly indicated in the revised manuscript.

(7) The small autophagosomes presented in Figures such as in Figure 1D and 1E are not clear. Enlarged images should be presented.  

Thank you for your suggestion. We repeated these experiments and replaced the relevant panels with higher-quality images, including enlarged insets to better visualize small autophagosomes. These updated figures are now included in the revised manuscript.

(8) The authors showed that Epg5-9xHA coprecipitates with the endogenous dynein motor Dhc64C. Is Rab7 required for the interaction?  

Thank you for this insightful question. We tested this by co-transfecting S2R+ cells with Epg5-9xHA and different forms of Rab7: wild-type, GTP-locked (constitutively active), and GDP-locked (dominant-negative). Our results indicate that the strength of Epg5-Dhc interaction does not change in the presence of either GTP-locked or GDP-locked Rab7. However, we believe that Epg5 and dynein are recruited to the vesicle membranes via Rab7 in vivo, so we did not include these results in the revised manuscript.

(9) The perinuclear lysosome localization in Epg5 KD cells has no indication that Epg5 is an autophagosome-specific adaptor.

Thank you for this important comment. Accordingly, we have toned down our statements about Epg5 functions throughout the revised manuscript.

Reviewer #1 (Recommendations for the authors):  

(1) Figure 6: What do "autolysosome maturation" and "small autolysosomes" mean? Do different numbers of lysosomes fuse to a single autophagosome?

Thank you for highlighting this point. We concluded that the formation of smaller autolysosomes—compared to controls—is likely due to a defect in autolysosome maturation, as is often the case. We had not explicitly considered whether a different number of lysosomes fuse with each autophagosome during this process. We clarified this issue in the revised manuscript.

(2) Figure 5A shows that the localization of endogenous Atg8 requires Epg5, but the data is not as clear as for mCherry-Atg8 (Figure 4B). Why the difference?  

Thank you for this question. The difference arises because the mCherry-Atg8a reporter strongly labels autolysosomes, as the mCherry fluorophore remains stable in acidic compartments. As a result, mCherry-Atg8a labels both autophagosomes and autolysosomes, but the strong autolysosomal signal originating from the surrounding GFP negative, nonRNAi cells can make accumulated autophagosomes appear fainter in fusion-defective cells (as in Figure 4). In contrast, endogenous Atg8a is degraded in lysosomes, and therefore labels only autophagosomes. This means that the appearance of these two experiments can be slightly different, but since in both cases autophagosomes no longer accumulate in the perinuclear region of Vps16A,Epg5 double RNAi cells we can conclude that Epg5 is required for autophagosome positioning. We explained this difference of the two methods in the revised manuscript where it first appears (Figure 1B and Figure 1—figure supplement 1A).

(3) Blue letters on the black micrographs are hard to see. Some of the other letters are also small and hard to read.  

Thank you for this suggestion. We improved the visibility and readability of the labels in the revised figures.

Author response:

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

Reviewer #1 (Public Review):

In this manuscript, the authors employ a combined proteomic and genetic approach to identify the glycoprotein QC factor malectin as an important protein involved in promoting coronavirus infection. Using proteomic approaches, they show that the non-structural protein NSP2 and malectin interact in the absence of viral infection, but not in the presence of viral infection. However, both NSP2 and malectin engage the OST complex during viral infection, with malectin also showing reduced interactions with other glycoprotein QC proteins. Malectin KD reduce replication of coronaviruses, including SARS-COV2. Collectively, these results identify Malectin as a glycoprotein QC protein involved in regulating coronavirus replication that could potentially be targeted to mitigate coronavirus replication.

Overall, the experiments described appear well performed and the interpretations generally reflect the results. Moreover, this work identifies Malectin as an important pro-viral protein whose activity could potentially be therapeutically targeted for the broad treatment of coronavirus infection. However, there are some weaknesses in the work that, if addressed, would improve the impact of the manuscript.

Notably, the mechanism by which malectin regulates viral replication is not well described. It is clear from the work that malectin is a pro-viral protein in the work presented, but the mechanistic basis of this activity is not pursued. Some potential mechanisms are proposed in the discussion, but the manuscript would be strengthened if additional insight was included. For example, does the UPR activated to higher levels in infected cells depleted of malectin? Do glycosylation patterns of viral (or non-viral) proteins change in malectindepleted cells? Additional insight into this specific question would significantly improve the manuscript.

We concur with the reviewer that the mechanism by which Malectin regulates viral replication is an important point to elucidate further. Our proteomics data were able to offer additional insight into the questions posed here. We examined the upregulation of protein markers of the UPR and other stress response pathways in cells depleted of MLEC (Fig. S15D). We find that the UPR pathways are moderately but insignificantly upregulated, while the Heat Shock Factor 1 (HSF1) pathway is moderately and significantly upregulated. The fold change increase of these marker proteins are relatively small, so while upregulation of this pathway may contribute to the suppression of CoV replication, it may not fully explain the phenotype.

In addition, to address the second question, we compared the glycosylation patterns of endogenous proteins in MLEC-KD cells (Fig. S15E-G). We found that there is a small increase in abundance of glycopeptides associated with LAMP2, SERPHINH1, RDX, RPL3/5, CADM4, and ITGB1, however these fold changes are small and tested to be insignificant. These results indicate there is relatively little modification of endogenous glycoproteins upon MLEC-depletion. These findings support a more direct role for MLEC in regulating viral replication.

We added the following section to the manuscript text to discuss these results:

“In uninfected cells, MLEC KD leads to relatively little proteome-wide changes, with MLEC being the only protein significantly downregulated and no other proteins significantly upregulated, supporting the specificity of MLEC KD in MHV suppression (Fig.  S15C). To determine whether MLEC KD alters general host proteostasis, we further examined the levels of protein markers of stress pathways based on previous gene pathway definitions(Davies et al., 2023; Grandjean et al., 2019; Shoulders et al., 2013) (Fig. S15D). We find that there are modest but significant increases in protein levels associated with the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response (UPR) pathways are largely unmodified. 

We also probed the effect of MLEC KD on endogenous protein glycosylation. We find that there is only a small increase in abundance of glycopeptides, including those associated with the ribosome (Rpl3, Rpl5), a cytoskeletal protein (Rdx), the integrin Itgb1, and the ER-resident chaperone Serphinh1 (Fig. S15E-G).”

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). In addition, there are only minor increases in endogenous glycopeptide levels (Fig. S15E-G). Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

Further, the evidence for increased interactions between OST and malectin during viral infection is fairly weak, despite being a major talking point throughout the manuscript. The reduced interactions between malectin and other glycoproteostasis QC factors is evident, but the increased interactions with OST are not well supported. I'd recommend backing off on this point throughout the text, instead, continuing to highlight the reduced interactions.

We agree that the fold change increase of OST interactions with malectin are small compared to the fold change decrease of other glycoproteostasis factors We have modified the text to less emphasize this point and instead highlight the reduced interactions:

“Further, MHV infection retains the association of MLEC with the OST complex while titrating off other interactors, potentially leading to more efficient glycoprotein biogenesis.”

I was also curious as to why non-structural proteins, nsp2 and nsp4, showed robust interactions with host proteins localized to both the ER and mitochondria? Do these proteins localize to different organelles or do these interactions reflect some other type of dysregulation? It would be useful to provide a bit of speculation on this point.

We also find these ER and mitochondrial protein interactions curious, which we initially reported on (Davies, Almasy et al. 2020 ACS Infectious Diseases). In this prior report, we found that when expressed in HEK293T cells, SARS-CoV-2 nsp2 and nsp4 have partial localization to mitochondrial-associated ER membranes (MAMs), as determined by subcellular fractionation. Given that malectin has also been shown to have MAMs localization (Carreras-Sureda, et al. 2019 Nature Cell Biology), we have added additional text in the Discussion to speculate on this point:

“Additionally, MLEC has also been shown to localize to ER-mitochondria contact sites (MAMs)(Carreras-Sureda et al., 2019), which regulate mitochondrial bioenergetics. We have previously shown that SARS-CoV-2 nsp2 and nsp4 can partially localize to MAMs(Davies et al., 2020), so these viral proteins may also dysregulate MLEC and MAMs activity to promote infection.”

Again, the overall identification of malectin as a pro-viral protein involved in the replication of multiple different coronaviruses is interesting and important, but additional insights into the mechanism of this activity would strengthen the overall impact of this work.

Thank you for this endorsement. We hope the additional analyses and discussion points in the revised manuscript further homed in on a direct mechanistic function for MLEC in modulating viral replication.

Reviewer #2 (Public Review):

Summary:

A strong case is presented to establish that the endoplasmic reticulum carbohydrate binding protein malectin is an important factor for coronavirus propagation. Malectin was identified as a coronavirus nsp2 protein interactor using quantitative proteomics and its importance in the viral life cycle was supported by using a functional genetic screen and viral assays. Malectin binds diglucosylated proteins, an early glycoform thought to transiently exist on nascent chains shortly after translation and translocation; yet a role for malectin has previously been proposed in later quality control decisions and degradation targeting. These two observations have been difficult to reconcile temporally. In agreement with results from the Locher lab, the malectininteractome shown here includes a number of subunits of the oligosaccharyltransferase complex (OST). These results place malectin in close proximity to both the co-translational (STT3A or OST-A) and post-translational (STT3B or OST-B) complexes. It follows that malectin knockdown was associated with coronavirus Spike protein hypoglycosylation.

Strengths:

Strengths include using multiple viruses to identify interactors of nsp2 and quantitative proteomics along with multiple viral assays to monitor the viral life cycle.

Weaknesses:

Malectin knockdown was shown to be associated with Spike protein hypoglycosylation. This was further supported by malectin interactions with the OSTs. However, no specific role of malectin in glycosylation was discussed or proposed.

We have emphasized our hypotheses on this point in the discussion and added a summary figure to highlight the specific role of malectin.

Given the likelihood that malectin plays a role in the glycosylation of heavily glycosylated proteins like Spike, it is unfortunate that only 5 glycosites on Spike were identified using the MS deamidation assay when Spike has a large number of glycans (~22 sites). The mass spec data set would also include endogenous proteins. Were any heavily glycosylated endogenous proteins hypoglycosylated in the MS analysis in Fig 5D?

Thank you for this suggestion. We compared the glycosylation patterns of endogenous proteins in MLEC-KD cells (Fig. S15E-G). We found that there is a small increase in abundance of glycopeptides associated with LAMP2, SERPHINH1, RDX, RPL3/5, CADM4, and ITGB1, however these fold changes are small and tested insignificant. These results indicate there is relatively little modification of endogenous glycoproteins upon MLEC-depletion. We added the following sections:

“We also probed the effect of MLEC KD on endogenous protein glycosylation. We find that there is only a small increase in abundance of glycopeptides, including those associated with the ribosome (Rpl3, Rpl5), a cytoskeletal protein (Rdx), the integrin Itgb1, and the ER-resident chaperone Serphinh1 (Fig. S15E-G).”

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). In addition, there are only minor increases in endogenous glycopeptide levels (Fig. S15E-G). Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

The inclusion of the nsp4 interactome and its partial characterization is a distraction from the storyline that focuses on malectin and nsp2.

We believe the nsp4 comparative interactome and functional genomics data offers a rich resource for further functional investigation by others, if made public. While we found the malectin and nsp2 storyline the most compelling to pursue, we believe the inclusion of the nsp4 data strengthens the overall approach, in agreement with Reviewer #3’s comments.

Reviewer #3 (Public Review):

Summary:

In this study, Davies and Plate set out to discover conserved host interactors of coronavirus non-structural proteins (Nsp). They used 293T cells to ectopically express flag-tagged Nsp2 and Nsp4 from five human and mouse coronaviruses, including SARS-CoV-1 and 2, and analyzed their interaction with host proteins by affinity purification mass-spectrometry (AP-MS). To confirm whether such interactors play a role in coronavirus infection, the authors measured the effects of individual knockdowns on replication of murine hepatitis virus (MHV) in mouse Delayed Brain Tumor cells. Using this approach, they identified a previously undescribed interactor of Nsp2, Malectin (Mlec), which is involved in glycoprotein processing and shows a potent pro-viral function in both MHV and SARS-CoV-2. Although the authors were unable to confirm this interaction in MHVinfected cells, they show that infection remodels many other Mlec interactions, recruiting it to the ER complex that catalyzes protein glycosylation (OST). Mlec knockdown reduced viral RNA and protein levels during MHV infection, although such effects were not limited to specific viral proteins. However, knockdown reduced the levels of five viral glycopeptides that map to Spike protein, suggesting it may be affected by Mlec.

Strengths:

This is an elegant study that uses a state-of-the-art quantitative proteomic approach to identify host proteins that play critical roles in viral infection. Instead of focusing on a single protein from a single virus, it compares the interactomes of two viral proteins from five related viruses, generating a high confidence dataset. The functional follow-ups using multiple live and reporter viruses, including MHV and CoV2 variants, convincingly depict a pro-viral role for Mlec, a protein not previously implicated in coronavirus biology.

Weaknesses:

Although a commonly used approach, AP-MS of ectopically expressed viral proteins may not accurately capture infection-related interactions. The authors observed Mlec-Nsp2 interactions in transfected 293T cells (1C) but were unable to reproduce those in mouse cells infected with MHV (3C). EIF4E2/GIGYF2, two bonafide interactors of CoV2 Nsp2 from previous studies, are listed as depleted compared to negative controls (S1D). Most other CoV2 Nsp2 interactors are also depleted by the same analysis (S1D). Previously reported MERS Nsp2 interactors, including ASCC1 and TCF25, are also not detected (S1D). Furthermore, although GIGYF2 was not identified as an interactor of MHV Nsp2/4 in human cells (S1D), its knockdown in mouse cells reduced MHV titers about 1000 fold (S4). The authors should attempt to explain these discrepancies.

We acknowledge these limitations in AP-MS from ectopically expressed viral proteins and have addressed these discrepancies with further elaboration in the text:

“A limitation of our study is the initial overexpression of individual proteins for AP-MS, in which we find some variation between our data with other AP-MS studies. We sought to overcome these variations by focusing on conserved interactors and testing interactions in a live infection context.”

“We also found GIGYF2-KD strongly suppressed MHV infection, despite GIGYF2 not interacting with MHV nsp2 (Fig. S1D), highlighting the importance of proteostasis factors in infection regardless of direct PPIs.”

More importantly, the authors were unable to establish a direct link between Mlec and the biogenesis of any viral or host proteins, by mass-spectrometry or otherwise. Although it is clear that Mlec promotes coronavirus infection, the mechanism remains unclear. Its knockdown does not affect the proteome composition of uninfected cells (S15B), suggesting it is not required for proteome maintenance under normal conditions. The only viral glycopeptides detected during MHV infection originated from Spike (5D), although other viral proteins are also known to be glycosylated. Cells depleted for Mlec produce ~4-fold less Spike protein (4E) but no more than 2-fold less glycosylated spike peptides (5D), compounding the interpretation of Mlec effects on viral protein biogenesis. Furthermore, Spike is not essential for the pro-viral role of Mlec, given that Mlec knockdown reduces replication of SARS-CoV-2 replicons that express all viral proteins except for Spike (6A/B).

Thank you, these are all important points. We have acknowledged these compounding factors in the Discussion:

“Concurrently, knockdown of MLEC leads to impediment of nsp production and aberrant glycosylation of other viral proteins like Spike, though it should be noted that the decrease in Spike glycopeptides is compounded by the overall decrease in Spike protein. Given that MLEC is pro-viral in a SARS-CoV-2 replicon model lacking Spike (Fig. 6), MLEC can promote CoV replication independent of Spike production.”

Any of the observed effects on viral protein levels could be secondary to multiple other processes.Interventions that delay infection for any reason could lead to an imbalance of viral protein levels because Spike and other structural proteins are produced at a much higher rate than non-structural proteins due to the higher abundance of their cognate subgenomic RNAs. Similarly, the observation that Mlec depletion attenuates MHV-mediated changes to the host proteome (S15C/D) can also be attributed to indirect effects on viral replication, regardless of glycoprotein processing. In the discussion, the authors acknowledge that Mlec may indirectly affect infection through modulation of replication complex formation or ER stress, but do not offer any supporting evidence. Interestingly, plant homologs of Mlec are implicated in innate immunity, favoring a more global role for Mlec in mammalian coronavirus infections.

We examined the upregulation of protein markers of the UPR and other stress response pathways in cells depleted of MLEC (Fig. S15D). We find that the UPR pathways are moderately but insignificantly upregulated, while the Heat Shock Factor 1 (HSF1) pathway is moderately and significantly upregulated. The fold change increase of these marker proteins are relatively small, so while upregulation of this pathway may contribute to the suppression of CoV replication, it may not fully explain the phenotype. Please all see similar points brought up by reviewer 1 (comment 1). We added the following section to the manuscript text to discuss these results:

“In uninfected cells, MLEC KD leads to relatively little proteome-wide changes, with MLEC being the only protein significantly downregulated and no other proteins significantly upregulated, supporting the specificity of MLEC KD in MHV suppression (Fig.  S15C). To determine whether MLEC KD alters general host proteostasis, we further examined the levels of protein markers of stress pathways based on previous gene pathway definitions(Davies et al., 2023; Grandjean et al., 2019; Shoulders et al., 2013) (Fig. S15D). We find that there are modest but significant increases in protein levels associated with the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response (UPR) pathways are largely unmodified. 

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). […] Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

Finally, the observation that both Nsp2 (3C) and Mlec (3E/F) are recruited to the OST complex during MHV infection neither support nor refute any of these alternate hypotheses, given that Mlec is known to interact with OST in uninfected cells and that Nsp2 may interact with OST as part of the full length unprocessed Orf1a, as it co-translationally translocates into the ER. Therefore, the main claims about the role of Mlec in coronavirus protein biogenesis are only partially supported.

We have acknowledged this point in the Discussion. 

“We find that nsp2 interacts with several OST complex members, including DDOST, STT3A, and RPN1, though whether this is as part of the uncleaved Orf1a polyprotein during co-translational ER translocation or as an individual protein is unclear.”

Reviewer #2 (Recommendations For The Authors):

What is the proof that MLEC is a type I membrane protein? If it is strictly sequence analysis, this conclusion should be tapered in the text.

Our response: We have added appropriate evidence on the biochemical characterization of MLEC topology from Galli et al., 2011, and cryo-EM structural characterization by Ramírez et al., 2019.

“As it was surprising that nsp2, a non-glycosylated, cytoplasmic protein, would interact with MLEC, an integral ER membrane protein with a short two amino acid cytoplasmic tail(Galli et al., 2011; Ramírez et al., 2019), we assessed a broader genetic interaction between nsp2 and MLEC.”

Validation of some of the nsp2 and malectin interactome components by pulldowns should be included.

Our response: The interactions between nsp2 and Ddost, Stt3A, and Rpn1 passed a stringent confidence filter in our AP-MS experiment (Fig. 3C) based on several replication. For this reason, we do not believe additional validation by Western blotting will offer much useful information.

NGI-1 inhibition of glycosylation looks to be very weak in Fig. 5B and Fig. S14B.

Our response: It is important to note that the NGI-1 inhibition assay used a suboptimal NGI-1 concentration to prevent full suppression of MHV infection, which we have found previously. We have added this justification in the Methods section and associated figure legend (Fig. S14A).

“The 5 uM NGI-1 dosage was chosen as it resulted in partial inhibition of glycosylation while not completely blocking MHV infection.”

“This dosage and timing were chosen to partially inhibit the OST complex without fully ablating viral infection, as NGI-1 has been shown previously to be a potent positive-sense RNA virus inhibitor(Puschnik et al., 2017)  (Fig. S14)”

Summary model figure at the end would help to communicate the conclusions.

Our response: Thank you for this suggestion. We agree and have added a summary model figure at the end as suggested.

Author response:

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

Reviewer #1 (Public Review):

Given that there are different mutations identified at different CDK12 sites as illustrated in Figure 1B it would be nice to know which ones have been functionally classified as pathogenic and for which ones that the pathogenicity has not been determined. This would be especially interesting to perform in light of the differences in the LOH scores and WES data presented - specifically, are the pathogenic mutations vs the mutations for which true pathogenicity is unknown more likely to display LOH or TD?

Alterations were classified as pathogenic when resulting in frameshift, nonsense, or cause an aminoacid change likely to alter function (according to ANNOVAR).  Four patients were called CDK12^BAL but were negative for TDP signatures. Three of these had CDK12 mutations downstream of the kinase domain, which may be less likely to ablate protein activity. Most functionally validated pathogenic mutations include disruption of the kinase domain (PMID: 25712099). We added a sentence to the Results section (under “Identification of genomic characteristics that associate with CDK12 loss in prostate cancer”) to highlight this caveat on pathogenic mutation calls.

For the cell inhibition studies with the CDK12/13 inhibitor, more details characterizing the specificity of this molecule to these targets would be useful. Additionally, could the authors perform short-term depletion studies with a PROTAC to the target or short shRNA or non-selected pool CRISPR deletion studies of CDK12 in these same cell lines to complement their pharmacological studies with genetic depletion studies? Also perhaps performing these same inhibitor studies in CDK12/13 deleted cells to test the specificity of the molecule would be useful.

We are not aware of a CDK12-specific PROTAC, and generate such as reagent is beyond the scope of the present study. Regarding the specificity of the CDK12/13 inhibitor molecules, additional information on the specificity and in vivo dose selection were added to the Results section (under “CDK13 is synthetic lethal in cells with biallelic CDK12 loss”). Cells with CDK12-KO did not tolerate CDK13-KO, so we were unable to generate double knockouts to test for CDK12/13 inhibitor non-specific effects. 

Additionally, expanding these studies to additional prostate cancer cell lines or organdies models would strengthen the conclusions being made. More information should be provided about the dose and schedule chosen and the rationale for choosing those doses and schedules for the in vivo studies proposed should be presented and discussed. Was there evidence for maximal evidence of inhibition of the target CDK12/13 at the dose tested given the very modest tumor growth inhibition noted in these studies.

With respect to additional acute CDK12 loss models, our Tet-inducible shCDK12 models show only minor growth slowdown and do not appear to phenocopy the strong arrest or apoptosis seen with CDK12 KO or inhibition, respectively. Future work is ongoing to generate CDK12-degron regulated cell lines. We added a new immunoblot panel showing that acute CRISPR/sgRNA targeting of CDK12 does indeed lead to BRCA2 and ATM protein decrease (Fig. S4g), providing some orthogonal genomic targeting evidence of the acute HR gene effect.  We are continuing efforts to collect and generate additional CDK12^BAL cell models, in both 2D and 3D culture systems, but none are presently available. We added a 3D culture drug dose curve with LuCaP189.4 exposed to THZ531 (Fig. S7m), which confirms heightened sensitivity vs two CDK12-intact lines. 

Regarding assessments of CDK12 targets; as we are not aware of any unique CDK12 substrates, it is fair to ask but difficult to measure precise CDK12 inhibition by the compounds in tumors. We dosed mice using the same protocol as detailed in the original report testing SR4835 in mice (PMID: 31668947). We performed immunoblots on lysates from 3 and 28 day treated PDX tumors and did not see any consistent decreases in pRBP1(Ser2) or ATM or increases in γH2A.X (data not shown). However, we did see increases in APA usage and downregulation of DNA repair transcripts with three day treatment (Fig. 6k-l), as would be expected from on target acute effects.

Reviewer #2 (Public review)

One caveat that continues to be unclear as presented, is the uncoupling of cell cycle/essentiality of CDK12/13 from HR-directed mechanisms. Is this purely a cell cycle arrest phenotype acutely with associated down-regulation of many genes?

In regard to untangling the effects of cell arrest on HR gene expression, this is a difficult question given that many HR genes, including BRCA2, are S/G2 linked. We attempted to account for those effects in the acute CDK12 inhibition experiment by including a palbociclib (CDK4/6i) control, which caused cell arrest and decreased BRCA1/2 RNA expression with no apparent 5/3’ transcript imbalance determined by qPCR (Fig. 4e,g). Though overall BRCA1 and BRCA2 mRNA levels are lower in the stable 22Rv1-CDK12-KO2 and KO5 lines, they do not show selective 3’ loss (Fig. 5c), suggesting the downregulation in these lines is mostly due to their slower growth (Fig. S4k) and not intronic polyA usage.

While the RAD51 loading ssRNA experiments are informative, the Tet-inducible knockdown of BRCA2 and CDK12 is confusing as presented in Figure 5, shBRCA2 + and -dox are clearly shown. However, were the CDK12_K02 and K05 also knocked down using inducible shRNA or a stable knockout? The importance of this statement is the difference between acute and chronic deletion of CDK12. Previously, the authors showed that acute knockdown of CDK12 led to an HR phenotype, but here it is unclear whether CDK12K02/05 are acute knockdowns of CDK12 or have been chronically adapted after single cell cloning from CRISPR-knockout. 

As a clarification, the 22Rv1-CDK12-KO2 and 22Rv1-CDK12-KO5 are stable CRISPR knockout clonal lines that were expanded from single cells. We added a new figure to include more validation of these lines (Fig. S5). We tried multiple times to reproduce the HRd phenotype and PARPi sensitivity with siRNA and inducible shRNA lines but were unable to see clear sensitivity differences, despite seeing the expected shifts with shBRCA2 controls (data not shown). It is possible the degree of knockdown (~80%), timing (8 days), or specific cell lines used in our experiments were not sufficient to expose the acute phenotype by this method.

However, we were able to see acute HR gene decreases by inhibitor treatment (Fig. 4) or acute CRISPR (Fig. S4g).

Given the multitude of lines, including some single-cell clones with growth inhibitory phenotypes and ex-vivo derived xenografts, the variability of effects with SR4835, ATM, ATR, and WEE1 inhibitors in different models can be confusing to follow. Overall, the authors suggest that the cell lines differ in therapeutic susceptibility as they may have alternate and diverse susceptibilities. It may be possible that the team could present this more succinctly and move extraneous data to the supplement.  

We appreciate the complexity of the data and attempted to use multiple models to report consistency and variability. We are not able to ascertain what data would be extraneous, and elected to present data we view as relevant in the main figures while moving supporting data in the supplement.

The in-vitro data suggests that SR4835 causes growth inhibition acutely in parental lines such as 22RV1. However, in vivo, tumor attenuation appears to be observed in both CDK12 intact and deficient xenografts, LuCAP136 and LuCaP 189.4 (albeit the latter is only nominally significant). Is there an effect of PARPi inhibition specifically in either model? What about the 22RV1-K02/05? Do these engraft? Given the role of CDK12/13 in RNAP II, these data might suggest that the window of susceptibility in CDK12 (mutant) tumors may not be that different from CDK12 intact tumors (or intact tissue) when using dual CDK12/13 inhibitors but rather represent more general canonical essential functions of CDK12 and CDK13 in transcription. From a therapeutic development strategy, the authors may want to comment in the discussion on the ability to target CDK13 specifically.

Though the response of the CDK12^BAL models to some compounds is variable, we believe those mixed results are important and future studies may be able to better explain why some show shifts in sensitivity while others do not. We hope future studies with additional models will help determine which sensitivities are more consistently true, and perhaps provide explanations for differences between models.

Regarding SR4835, we find, and others have reported, a toxic (i.e. apoptotic) effect for in vitro treatment with dual CDK12/13 inhibitors (Fig. 4f, S4e,f); in fact, that may be why previous studies have used short timepoints in cell culture assays with these dual inhibitors. In mice, SR4835 was tolerated well but only LuCaP 189.4 showed statistically significant decreases in tumor volume and weight (Fig. 6j). We did not test PARPi responses in the PDX models, nor did we attempt engrafting the 22Rv1-CDK12-KO cell lines, but both would be worthwhile experiments in the future. Beyond CDK12^BAL tumors, we agree that CDK12/13 inhibitors could be effective in cancer therapies more generally (e.g. triggering acute HRd, loss of RNAP2 phosphorylation). We added a line to the discussion section about ongoing efforts to combine PARPi and CDK12/13i, which we expect to be synergistic in CDK12-intact tumors due to the acute loss phenotype. We certainly agree that development of a specific CDK13 inhibitor would be the ideal therapeutic option for CDK12^BAL tumors. However, CDK12 and CDK13 are 43% conserved at the protein level (PMID: 26748711), with 92% conservation in the active site (PMID: 30319007), and there are no available pharmacologic inhibitors that discriminate between CDK12 and CDK13.

Reviewer #3 (Public review):

It is generally assumed that CDK12 alterations are inactivating, but it is noteworthy that homozygous deletions are comparatively uncommon (Figure 1a). Instead many tumors show missense mutations on either one or both alleles, and many of these mutations are outside of the kinase domain (Figure 1b). It remains possible that the CDK12 alterations that occur in some tumors may retain residual CDK12 function, or may confer some other neomorphic function, and therefore may not be accurately modeled by CDK12 knockout or knockdown in vitro. This would also reconcile the observation that knockout of CDK12 is cell-essential while the human genetic data suggest that CDK12 functions as a tumor suppressor gene.

Thank you for the feedback. It is a keen observation that homozygous deletions of CDK12 are not typical, though many mutations are upstream frameshifts that are expected to lead to loss of functional protein and mRNA via nonsense mediated decay. LuCaP189.4, our only natural mutant model, has two upstream frameshifts leading to complete protein loss (Fig 5b, S4h-i). We also added a caveat previously mentioned (in response to Reviewer 1) that mutations downstream of the kinase domain may be less likely to be fully pathogenic. For upstream missense mutations, the possibility of neuromorphic function remains an intriguing possibility that cannot be ruled out and would not be captured in our current models. Hopefully additional models can be developed, both natural and engineered, to help dissect that question in future studies.  

It is not entirely clear whether CDK12 altered tumors may require a co-occurring mutation to prevent loss of fitness, either in vitro or in vivo (e.g. perhaps one or more of the alterations that occur as a result of the TDP may mitigate against the essentiality of CDK12 loss).

We concur. Another caveat with the CRISPR models, beyond reliance on upstream frameshift mutations, is the simultaneous loss of alleles. In human tumors, there may be a period of single copy loss before the second hit that may provide a window for adaptation. It is possible that sequential loss is far easier for a cell to tolerate than acute bi-allelic inactivation. We agree that the question of what (if any) cooperating genetic alterations are required to tolerate CDK12 loss is an important one that we plant to further explore in future work.

Recommendations for Authors:

Reviewer #1 (Recommendations for Authors):

The authors have thoroughly addressed all issues of data availability, reagents, in vivo protocols, and animal approvals associated with the studies presented in this manuscript. Specific comments and experimental suggestions that in my opinion would strengthen the conclusions of this interesting and compelling manuscript are included above

Reviewer #2 (Recommendations for the authors):

The authors were thorough in their studies. As a general note, switching between the cell lines is often overwhelming in interpreting the data given cell-to-cell variability in response. If possible, consolidating the text/conclusions in results would improve the readability of the manuscript.

The variety of cell lines and models is perhaps expansive at times, but we hope the inclusion of these different models helps support the conclusions. 

Is it possible to knockout CDK12 acutely using a degron-based approach, instead of utilizing an inhibitor that targets both CDK12/13?

There is a HeLa cell line made with analog-sensitive CDK12 (Bartkowiak, Yan, and Greenleaf 2016) but we were unaware of any such prostate lines at the time of this work. We are attempting to develop engineered prostate lines with specific CDK12 degradation but do not yet have them available.

How do the authors address a lower BRCA1/2 level in for example 22RV1-K05, does this cell line have increased sensitivity to PARPi over its parental 22RV1 line? Could this be added to Figure 5h/i?

The lower BRCA2 levels in 22Rv1CDK12-KO5 is likely due to the slower growth rate (Fig. S4k), as BRCA2 expression is S/G2 linked. While the mRNA level of BRCA2 overall is lower in the KO5 line, we do not observe the 5’/3’ transcript imbalance (Fig. 5c). The 22Rv1-CDK12-KO lines did not show increased sensitivity to carboplatin, while inducible shBRCA2 did (Fig. S7a), so we do not believe this lower BRCA2 confers functional HRd. We did test the KO lines with olaparib (Fig. S7d) and saw a modest increased sensitivity compared to parental 22Rv1, but not to the extent measured in the BRCA1 mutant line UWB1.289.

What is the clonality of the LuCAP 189.4 lines upon derivation? Is biallelic CDK12 loss seen in all cells?

We do not know the exact clonality of the LuCAP 189.4 PDX or CL model, but we do see highly uniform CDK12 protein loss in these cells (quantified by IHC staining, data not shown).

The authors state that 22RV1-K02/05 has an increased growth arrest to CDK13 inhibition. However, in Figure 6h, it appears the viability is not significantly different compared to the parental 22RV1 line. Similar aspects noted in 189.4-vec/CDK12?

We found that 22Rv1 KO2/KO5 have growth arrest with sgCDK13 and cell death with CDK12/13 inhibitor. We did notice that SR4835 did not show the differential effects we anticipated (Fig. 6h), as was seen with THZ531 (Fig. 6i). SR4835 is a non-covalent inhibitor, while THZ531 is a covalent binder, so there are some functional differences between these compounds that might explain the lack of differential effects in the isogenic lines in a 4 day in vitro assay. We included the SR4835 in vitro data because it was used for the in vivo experiment. THZ531 is not suited for animal use.

Could the authors comment on SR4835 response in vivo as a function of tumor growth rate?

The in vivo SR4835 treated LuCaP189.4 did show signs of reduced proliferation with decreased Cell Cycle and DNA Replication in the RNA-seq signatures, but a more detailed investigation into cell cycle arrest vs apoptotic response has yet to be fully explored. We plan to conduct additional PDX experiments if we can obtain a selective CDK13 inhibitor. 

Do the authors explore TDPs in their isogenic cell lines?

We performed low coverage WGS on the 22Rv1 KO clones and added that to the paper (Fig. S5c). We did not see any obvious signs of TDP. We suspect the phenotype takes longer to accumulate and is not apparent within the ~20 passages our clones underwent in culture. This would be consistent with the tumor analysis (Fig. 2b) showing increase in TDs from primary to metastatic tumors, suggesting TDs accumulate over time.

A future study may allow for screening synthetic lethals in the context of CDK12 loss in the presence or absence of SR4835 inhibition.

We are actively pursuing experiments to identify new synthetic lethal targets by CRISPR and drug screens in CDK12 loss models and hope to report those in a future study.

Reviewer #3 (Recommendations for the authors):

As discussed above, the authors may wish to adjust their terminology to "CDK12-altered" rather than "CDK12 lost" or "CDK12-inactivated" to leave open the possibility that some tumors may retain residual CDK12 function or adopt neomorphic functions.

Thank you for the additional comments and feedback. The possibility of neomorphic CDK12 allele function is important. As our models were all complete protein loss mutations, we decided to retain “biallelic loss” as our preferred nomenclature, but the note is well taken.

The plots in Figures 1f-h are interesting and suggest that certain cancer genes (especially oncogenes) are recurrently gained in CDK12-altered tumors. It may be interesting to look at this on the individual level rather than the cohort level to see whether any groups of oncogenes tend to be gained together in an individual patient - this could inform whether certain combinations of cancer drivers cooperate with CDK12 alteration to drive oncogenesis.

Thank you for the idea of looking at the patient-level for TDP-enriched oncogenes. A preliminary assessment did not identify recurrent co-gained oncogenes. We will continue these analyses as additional patient tumors with CDK12 alterations are identified. 

The finding that AR gene or enhancer are recurrently gained with TDP is interesting and I am curious whether the authors have thoughts on whether these alterations can also be seen in the 1-2% of CDK12altered primary prostate cancers that are treatment naïve, and where AR pathway alterations are not as frequently seen.

We did not focus on CDK12 altered primary prostate cancers, but we did check if there is AR amplification enrichment in the 6 CDK12^BAL cases of the TCGA-PRAD dataset and did not identify enrichment. However, with such small numbers we would hesitate to draw any hard conclusions. 

It could be interesting to more comprehensively characterize some of the CDK12 KO-adapted lines in Figure 5 (e.g. by WES or WGS) to determine whether they exhibit the TDP and/or whether they have acquired any secondary mutations that allow them to adapt to CDK12 loss.

We are planning to do further genomics characterization of the CDK12-KO lines and will hopefully include that in a future study. Genomic analyses of the 22Rv1 clones (see copy number plots in Fig. S5c) did not identify a TDP. We plan to repeat the genomic assessments over additional cell passages and we have planned additional experiments designed to understand why some cells tolerate CDK12 loss and others do not.

Author response:

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

Reviewer #1 (Public review):

Hurtado et al. show that Sox9 is essential for retinal integrity, and its null mutation causes the loss of the outer nuclear layer (ONL). The authors then show that this absence of the ONL is due to apoptosis of photoreceptors and a reduction in the numbers of other retinal cell types such as ganglion cells, amacrine cells, and horizontal cells. They also describe that Müller Glia undergoes reactive gliosis by upregulating the Glial Fibrillary Acidic Protein. The authors then show that Sox9+ progenitors proliferate and differentiate to generate the corneal cells through Sox9 lineage-tracing experiments. They validate Sox9 expression and characterize its dynamics in limbal stem cells using an existing single-cell RNA sequencing dataset. Finally, the authors argue that Sox9 deletion causes progenitor cells to lose their clonogenic capacity by comparing the sizes of control and Sox9-null clones. Overall, Hurtado et al. underline the importance of Sox9 function in retinal and corneal cells.

Strengths:

The authors have characterized a myriad of striking phenotypes due to Sox9 deletion in the retina and limbal stem cells which will serve as a basis for future studies.

Weaknesses:

Hurtado et al. investigate the importance of Sox9 in the retina and limbal stem cells. However, the overall experimental narrative appears dispersed.

(1) The authors begin by characterizing the phenotype of Sox9 deletion in the retina and show that the absence of the ON layer is due to photoreceptor apoptosis and a reduction in other retinal cell types. The authors also note that Müller glia undergoes gliosis in the Sox9 deletion condition. These striking observations are never investigated further, and instead, the authors switch to lineage-tracing experiments in the limbus that seem disconnected from the first three figures of the paper. Another example of this disconnect is the comparison of Sox9 high and Sox9 low populations using an existing scRNA-seq dataset and the subsequent GO term analysis, which does not directly tie in with the lineage-tracing data of the succeeding Sox9∆/∆ experiments.

We thank the reviewer for their thoughtful observations. We would like to clarify the rationale behind the structure of our study and how the different parts are conceptually connected.

Our central aim was to investigate the role of Sox9 in the adult eye. Given that Sox9 has been extensively studied during embryonic development, we specifically chose to use an inducible conditional knockout strategy (CAG-CreERTM) in order to assess its function postnatally, in the adult eye. This approach revealed a severe retinal phenotype, whereas the cornea showed no overt phenotype. A major strength of our experimental design is that it allowed us to examine the role of Sox9 specifically in the adult eye, avoiding confounding effects from embryonic development. Nevertheless, this approach entails an inherent limitation: the mosaic nature of the CAG-CreERTM system leads to substantial variability in both the extent and distribution of Sox9 inactivation among individual animals. We invested considerable effort over extended periods to obtain reliable and biologically meaningful data despite this variability. We did not proceed further because this mosaicism poses a significant limitation when attempting to dissect downstream mechanisms in a consistent and reproducible manner, making it extremely challenging to perform in-depth mechanistic studies.

Regarding the cornea, given the absence of a clear phenotype upon Sox9 deletion, we expanded our investigation by adding lineage-tracing and transcriptomic analyses to better understand Sox9’s potential role in adult limbal epithelial stem cells. These additional experiments provided valuable insight into Sox9 function in the adult cornea, even in the absence of gross morphological changes. Thus, while the retinal and corneal data stem from different experimental approaches, they are unified by a shared goal: understanding the celltype-specific and tissue-specific functions of Sox9 in the adult eye.

To ensure that other readers do not perceive this apparent disconnect, and overstate our conclusions, we have modified the manuscript.  In the Introduction section, we have included the main findings from studies conducted to date on the role of Sox9 in the cornea and retina, and we have removed the corresponding section from the Discussion. We believe it is now clear that our study focuses on the role of Sox9 in the adult eye, in contrast to previous studies, which focused on the developing eye.

In the Discussion section, we have added a new paragraph at the beginning and end that explicitly addresses the relationship between the retinal and limbal findings, illustrating how a single transcription factor can play distinct roles in different tissues within the same organ.

Regarding the reviewer’s comment that the scRNA-seq analyses appear disconnected from the lineage-tracing data, we respectfully disagree. These analyses provide independent transcriptional confirmation that Sox9 is a marker of limbal stem cells, reinforcing the conclusions drawn from our in vivo experiments. These approaches are complementary and they converge on the same biological insight: Sox9 marks a population with stem-like properties in the adult limbus. Nevertheless, we acknowledge the reviewer’s concern and have moderated the tone of our statements in the revised version of the manuscript to better reflect the supporting nature of the scRNA-seq data, without overstating its functional implications.

(2) A major concern is that a single Sox9∆/∆ limbal clone has a sufficiently large size, comparable to wild-type clones, as seen in Figure 6D. This singular result is contrary to their conclusion, which states that Sox9-deficient stem cells minimally contribute to the maintenance of the cornea.

We thank the reviewer for this important observation.

Ligand-independent activity of Cre-ER fusion proteins has been repeatedly reported in various mouse models (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009). This basal recombinase activity is thought to arise from inappropriate nuclear translocation or proteolysis of the Cre-ER fusion protein, leading to low-level recombination even in the absence of tamoxifen. Consistent with this, prior studies using the same CAGG-CreERTM; R26R-LacZ system for clonal analysis in the cornea have observed sparse reporter expression before tamoxifen administration (Dorà et al., 2015).

In line with these findings, we also detected minimal background LacZ staining in Sox9Δ/ΔLacZ corneas (mean surface area: 0.85%; n = 8 eyes). This low-level staining likely reflects recombination events in transient amplifying or more differentiated cells, which are not expected to generate long-lived clones. However, in the rare instance of a large clone, as shown in Figure 6D, we believe that a spontaneous recombination event may have occurred in a bona fide limbal stem cell, giving rise to a sustained contribution. To rigorously address this potential artefact and assess the true contribution of Sox9-deficient stem cells, we conducted a comparative analysis of 8 control (Sox9Δ/+-LacZ) and 5 mutant (Sox9Δ/ΔLacZ) corneas. This analysis revealed a highly significant 8-fold reduction in the LacZpositive surface area in mutant samples (Sox9Δ/+-LacZ: 6.65 ± 1.77%; Sox9Δ/Δ-LacZ: 0.85 ± 0.85%; paired t-test, p = 0.00017; Figs. 6E and F; Table S12).

We chose to include the image of the large clone in the main figure precisely because it does not align with our working hypothesis. We believe that showing such exceptions transparently is scientifically important and may be valuable for other researchers using similar inducible systems. Nonetheless, based on previous literature, the number of samples analyzed, and the statistically significant reduction in clonal contribution, we maintain that the observed phenotype reflects a true biological effect of Sox9 loss, supporting our conclusion that Sox9-deficient stem cells contribute minimally to corneal maintenance. To make that point clearer, we have introduced the following sentence in lines 462-464 of the revised version of the manuscript.

“A possible explanation for this clone may be that spontaneous ligand-independent activity of Cre-ER fusion may have occurred in a bona fide limbal stem cell, as previously reported (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009, Dorà et al., 2015).”

Reviewer #2(Public revciew):

Sox9 is a transcription factor crucial for development and tissue homeostasis, and its expression continues in various adult eye cell types, including retinal pigmented epithelium cells, Müller glial cells, and limbal and corneal basal epithelia. To investigate its functional roles in the adult eye, this study employed inducible mouse mutagenesis. Adult-specific Sox9 depletion led to severe retinal degeneration, including the loss of Müller glial cells and photoreceptors. Further, lineage tracing revealed that Sox9 is expressed in a basal limbal stem cell population that supports stem cell maintenance and homeostasis. Mosaic analysis confirmed that Sox9 is essential for the differentiation of limbal stem cells. Overall, the study highlights that Sox9 is critical for both retinal integrity and the differentiation of limbal stem cells in the adult mouse eye.

Strengths:

In general, inducible genetic approaches in the adult mouse nervous system are rare and difficult to carry out. Here, the authors employ tamoxifen-inducible mouse mutagenesis to uncover the functional roles of Sox9 in the adult mouse eye.

Careful analysis suggests that two degeneration phenotypes (mild and severe) are detected in the adult mouse eye upon tamoxifen-dependent Sox9 depletion. Phenotype severity nicely correlates with the efficiency of Cre-mediated Sox9 depletion.

Molecular marker analysis provides strong evidence of Mueller cell loss and photoreceptor degeneration.

A clever genetic tracing strategy uncovers a critical role for Sox9 in limbal stem cell differentiation.

Weaknesses:

(1) The Introduction can be improved by explaining clearly what was previously known about Sox9 in the eye. A lot of this info is mentioned in a single, 3-page long paragraph in the Discussion. However, the current study's significance and novelty would become clearer if the authors articulated in more detail in the Introduction what was already known about Sox9 in retina cell types (in vitro and in vivo).

We appreciate this insightful comment. Following the reviewer`s suggestion, we have reorganized the manuscript to provide a clearer scientific context in the Introduction. Specifically, we have moved the relevant background information on Sox9 in different retinal cell types—previously included in a single, extended paragraph in the Discussion—into the Introduction. This helps to better frame our study within the context of existing knowledge.

Additionally, we have emphasized more explicitly that our work does not focus on embryonic development, as most previous studies on Sox9 have done, but instead investigates its role in the adult mouse retina and limbus/cornea. We believe this represents an important and novel aspect of our study, as the mechanisms of retinal maintenance and limbal stem cell differentiation in the adult have been less extensively studied.

(2) Because a ubiquitous tamoxifen-inducible CreER line is employed, non-cell autonomous mechanisms possibly contribute to the observed retina degeneration. There is precedence for this in the literature. For example, RPE-specific ablation of Otx2 results in photoreceptor degeneration (PMID: 23761884). Have the authors considered the possibility of non-cell autonomous effects upon ubiquitous Sox9 deletion?

Given the similar phenotypes between animals lacking Otx2 and Sox9 in specific cell types of the eye, the authors are encouraged to evaluate Otx2 expression in the tamoxifen-induced Sox9 adult retina.

We appreciate the insightful comment of the reviewer regarding the potential contribution of non-cell autonomous mechanisms to the retinal degeneration observed upon ubiquitous Sox9 deletion. We agree that this is an important consideration, particularly in the context of findings showing that RPE-specific deletion of Otx2 results in secondary photoreceptor degeneration.

However, we would like to emphasize that RPE-specific deletion of Sox9 does not lead to photoreceptor loss or retinal degeneration, as previously shown (Masuda et al., 2014; Goto et al., 2018; Cohen-Tayar et al., 2018) [PMID: 24634209; PMID: 29609731; PMID: 29986868]. In addition, it was shown that Sox9 deletion in the RPE caused downregulation of visual cycle genes but did not compromise photoreceptor integrity or survival. Interestingly, Otx2 expression was found to be upregulated in the absence of Sox9, further supporting the view that Sox9 is not a simple upstream regulator of Otx2 in the adult RPE (Matsuda, 2014). These findings suggest that RPE dysfunction alone cannot account for the severe retinal phenotype we observe in our model.

In our study, we observed that photoreceptor degeneration correlates strongly with the depletion of Sox9 Müller glial cells. Given the well-established supportive and neuroprotective roles of Müller glia, we interpret the retinal degeneration in our model to be primarily a consequence of Müller cell dysfunction (confirmed by the loss of Müller glia markers, such as SOX8 and S100). This interpretation is further supported by previous studies showing that selective ablation of Müller glia can lead to photoreceptor degeneration through cell-autonomous mechanisms (Shen et al., 2012) [PMID: 23136411].

Nevertheless, we agree that this possibility deserves further investigation, and we have acknowledged it in the following paragraph that has been added to the Discussion section (lines 511-523 of the revised ms):

“An important consideration in our model is the potential contribution of non-cell autonomous mechanisms to photoreceptor degeneration. Sox9 is expressed in both MG and RPE cells, and both cell types are known to support photoreceptor viability (Poché et al., 2008; Masuda et al., 2014). Notably, Sox9 and Otx2 cooperate to regulate visual cycle gene expression in the RPE (Masuda et al., 2014), and loss of Otx2 specifically in the adult RPE leads to secondary photoreceptor degeneration through non-cell autonomous mechanisms (Housset et al., 2013). However, RPE-specific deletion of Sox9 does not induce retinal degeneration and in fact results in Otx2 upregulation (Masuda et al., 2014; Goto et al., 2018; Cohen-Tayar et al., 2018), suggesting that Sox9 is not an upstream regulator of Otx2 in this context. Further investigation into the molecular and cellular interactions between MG, RPE, and photoreceptors may help to clarify the indirect pathways contributing to degeneration in the absence of Sox9.”

Consistent with the above, a new citation has been included:

Housset M, Samuel A, Ettaiche M, Bemelmans A, Béby F, Billon N, Lamonerie T. 2013. Loss of Otx2 in the adult retina disrupts retinal pigment epithelium function, causing photoreceptor degeneration. J Neurosci 33:9890–904. doi:10.1523/JNEUROSCI.1099-13.2013.

(3) The most parsimonious explanation for the dual role of Sox9 in retinal cell types and limbal stem cells is that the cell context is different. For example, Sox9 may cooperate with TF1 in photoreceptors, TF2, in Mueller cells, and TF3 in limbal stem cells, and such cell typespecific cooperation may result in different outcomes (retinal integrity, stem cell differentiation). The authors are encouraged to add a paragraph to the discussion and share their thoughts on the dual role of Sox9.

We thank the reviewer for this thoughtful and constructive suggestion. In , we have added a paragraph at the end of the Discussion addressing the potential dual role of Sox9 in the cornea and retina. In this new section, we discuss how Sox9 might exert distinct functions depending on the cellular context, possibly through interactions with different transcriptional partners in specific cell types. This may help explain the contrasting roles of Sox9 in maintaining retinal integrity versus regulating stem cell differentiation in the limbal epithelium.

(4) One more molecular marker for Mueller glial cells would strengthen the conclusion that these cells are lost upon Sox9 deletion.

We thank the reviewer for this constructive suggestion. To reinforce our conclusion that most Müller glial cells are lost following Sox9 deletion, we analysed the expression of S100, a well-established cytoplasmic marker of Müller glia. As S100 is primarily localized to the innermost Müller cell processes and not restricted to cell bodies, direct cell counting was not feasible. Instead, we quantified the S100+ signal intensity across defined retinal surface areas. This analysis revealed a statistically significant reduction in S100 signal in Sox9^Δ/Δ retinas compared to controls. These new data, included in the revised Figure 1 (panels F and G), support and extend our previous observations using SOX8, further confirming the loss of Müller glial cells in Sox9-deficient retinas.

We have also modified the manuscript based on this new evidences as follows:

In the Results section, lines 168-177 of the revised ms, we have added the following paragraph: “To independently validate the loss of MG cells in Sox9-deficient retinas, we examined the expression of S100, a cytoplasmic marker that labels the processes of adult Müller cells. In control retinas, strong S100 immunoreactivity was observed across the inner retina, outlining the typical radial projections of Müller glia (Fig. 1F). In contrast, Sox9Δ/Δ retinas with an extreme phenotype exhibited a marked reduction in S100 signal (Fig. 1G). Given the diffuse cytoplasmic localization of S100, we quantified its expression by measuring the fluorescence signal within a defined surface area of the retina. This analysis revealed a statistically significant reduction in S100 signal intensity in mutant samples (including both mild and extreme phenotypes) compared to controls (Fig. 1G; Table S4), further supporting the loss of MG cells upon Sox9 deletion.”

In Methods, line 684 of the revised ms, the anti-S100 antibody reference and its working dilution have been added.

(5) Using opsins as markers, the authors conclude that the photoreceptors are lost upon Sox9 deletion. However, an alternate possibility is that the photoreceptors are still present and that Sox9 is required for the transcription of opsin genes. In that case, Sox9 (like Otx2) may act as a terminal selector in photoreceptor cells. This point is particularly important because vertebrate terminal selectors (e.g., Nurr1, Otx2, Brn3a) initially affect neuron type identity and eventually lead to cell loss.

We perfectly understand the reviewer’s point. However, we believe that the possibility that Sox9 regulates opsin gene expression without affecting photoreceptor survival is very unlikely in our model. The primary evidence comes from the histological analysis shown in Figure 1B, where hematoxylin and eosin staining clearly demonstrates the complete loss of the ONL in Sox9^Δ/Δ retinas exhibiting the extreme phenotype. Similarly, DAPI counterstain also evidences the lack of the ONL in many of our immunofluorescence images of these samples.  This morphological disappearance of the ONL strongly supports the conclusion that photoreceptor cells are not merely transcriptionally silent but are physically absent.

Furthermore, TUNEL assays in two retinas with a mild phenotype revealed extensive apoptosis within the ONL, suggesting a progressive degeneration process rather than a selective transcriptional effect. While we acknowledge that transcriptional regulation of opsin genes by Sox9 cannot be entirely ruled out, the observed phenotype is more consistent with a structural loss of photoreceptors rather than a change in their molecular identity alone. Therefore, our data support the interpretation that Sox9 is required for photoreceptor survival, likely through non-cell autonomous mechanisms related to Müller glia dysfunction, rather than acting as a terminal selector within photoreceptor cells themselves.

(6) Quantification is needed for the TUNEL and GFAP analysis in Figure 3.

We have quantified the GFAP immunofluorescence signal across defined surface areas of the retina and found a statistically significant increase in GFAP expression in Sox9^Δ/Δ mutants compared to controls (Mann-Whitney U test, P = 0.0240; n = 4 controls, 10 mutants). These quantification data are now included in the revised Figure 3.

Regarding the TUNEL assay, although extensive apoptosis was clearly observed in two Sox9<^Δ/Δ retinas with a mild phenotype (as shown in Figure 3A), this pattern was not consistent across the full study mouse cohort. Out of 15 mutant samples analyzed (5 of them previously analyzed and 10 additional ones that have been newly analyzed), only two exhibited this pronounced apoptotic pattern. However, in the remaining 13 mutants, we did observe a small but statistically significant increase in the number of TUNEL+ cells compared to controls (zero-inflated Poisson test, P = 0.028, n = 5 controls, 13 mutants). These results are now included in Figure 3 and in Tables S7 and S8.

This pattern likely reflects the transient nature of apoptosis in the degenerative process, which may occur rapidly and thus be difficult to capture consistently at a single time point. Nevertheless, the quantification supports our conclusion that Sox9 loss is associated with increased photoreceptor cell death.

Based on the above, we have included the following paragraphs in the Results section of the manuscript:

In lines 224-252 of the revised ms, the final version of the paragraph is as follows: “Since photoreceptors are absent in severely affected Sox9-mutant retinas, we conducted TUNEL assays to study the role of cell death in the process of retinal degeneration. In control samples (n=5), almost no TUNEL signal was observed in the retina. In contrast, Sox9^Δ/Δ mice (n=15) showed numerous TUNEL+ cells, mainly located in the persisting ONL, indicating that photoreceptor cells were dying (Fig. 3A). Although extensive TUNEL staining in the ONL was clearly observed in two Sox9^Δ/Δ retinas with mild phenotypes, this pattern was not consistently present across the full cohort. In the remaining 13 mutant retinas, we observed a modest but noticeable increase in the number of apoptotic cells compared to controls (Fig. 3B; Table S7). Despite a high frequency of zero counts (particularly among controls), the difference between groups reached statistical significance when analyzed using a zeroinflated Poisson model (P = 0.028; n = 5 controls, 13 mutants). These findings suggest that photoreceptor apoptosis following Sox9 deletion may occur acutely and within a narrow temporal window, making it challenging to capture the full degenerative process at a single time point”.

Lines 263-269 of the revised ms: “To support these observations quantitatively, we measured GFAP fluorescence intensity across defined retinal surface areas in control and Sox9^Δ/Δ mice (Fig. 3D; Table S8). This analysis revealed a statistically significant increase in GFAP signal in mutant retinas compared to controls (Mann-Whitney U test, P = 0.0240; n = 4 controls, 10 mutants). These results are consistent with a progressive gliotic  following Sox9 deletion and provide further evidence that MG cells become reactive in the absence of Sox9”.

Similarly, the section “Estimation of the percentage of tamoxifen-induced, Cre-mediated recombination” has been expanded as follows:

Lines 660-665 of the revised ms: “In parallel, to quantify GFAP expression as a measure of MG reactivity, we analyzed GFAP immunofluorescence intensity across defined retinal surface areas. Given the cytoplasmic distribution of GFAP within glial processes, direct cell counting was not feasible. Instead, fluorescence intensity was measured using ImageJ, within full-thickness retinal regions in 20x microphotographs of a retinal sections stained for GAFP. The total GFAP signal was normalized to the measured area for each section”.

(7) Line 269-320: The authors examined available scRNA-Seq data on adult retina. This data provides evidence for Sox9 expression in distinct cell types. However, the dataset does not inform about the functional role of Sox9 because Sox9 mutant cells were not analyzed with RNA-Seq. Hence, all the data that claim that this experiment provides insights into possible Sox9 functional roles must be removed. This includes panels F, G, and H in Figure 5. In general, this section of the paper (Lines 269-320) needs a major revision. Similarly, lines 442-454 in the Discussion should be removed.

We thank the reviewer for this important observation. We agree that the scRNA-Seq dataset used in this section does not include Sox9 mutant cells and therefore does not allow us to assess the consequences of Sox9 loss-of-function. However, we believe that this analysis still provides valuable complementary information. Specifically, it confirms that Sox9 is expressed in a distinct population of limbal stem cells, and that its expression dynamically changes along differentiation trajectories. Although we do not infer causality or phenotypic consequences, the ability to observe how gene expression programs shift as Sox9 is downregulated offers insights into potential transcriptional programs associated with Sox9 activity.

We have carefully revised Lines 269–320 to remove any overinterpretations, and eliminated the corresponding lines in the Discussion (Lines 442–454). However, we have retained Panels G, and H in Figure 5 with updated text that reflect the descriptive nature of these findings, specifically to illustrate that the Sox9-positive cell signature is consistent with a stem cell genetic program, and that when Sox9 is downregulated some gene pathways involved in stem cell differentiation are upregulated.

Reviewer #1 (Recommendations for the authors):

Major points

(1) Figure 1C shows the proportions of Sox9+cells that express Sox8 in control, mild and extreme phenotypes. However, as no quantitative classification of mild and extreme phenotypes is reported along with Figure 1A, the large standard deviation for Sox9∆/∆ mild retina might be due to a misclassification of the sample. Therefore, the authors must ascribe each sample to "mild" or "extreme" based on a quantitative metric.

We appreciate the reviewer’s suggestion to clarify the classification criteria used to distinguish “mild” and “extreme” phenotypes in Sox9^Δ/Δ retinas. As noted, our classification was based on a qualitative, phenotypic assessment of retinal morphology in hematoxylin/eosin-stained sections. Specifically, retinas were classified as “extreme” when the outer nuclear layer (ONL) was completely absent, and as “mild” when the ONL was present, although often reduced in thickness. This classification reflects the observable structural depletion of the ONL and aligns well with the extent of Sox9 loss in Müller glial cells, as shown in Figure 1. We acknowledge that some variability exists within the “mild” group, likely due to differences in recombination efficiency and the mosaic nature of tamoxifen-induced deletion.

The phenotypic classification of each individual sample is explicitly provided in Supplementary Table S1. We have also added a statement in the Results section clarifying that this classification was based on qualitative histological criteria rather than a numerical threshold.

Lines 104-113 of the revised ms: “We categorized Sox9^Δ/Δ retinas into “mild” and “extreme” phenotypes in order to facilitate interpretation of our data. Clasification was based on a qualitative assessment of ONL integrity in histological sections. Specifically, samples were classified as “extreme” when the ONL was completely depleted, and as “mild” when the ONL persisted, albeit variably reduced in thickness. This phenotypic classification reflects observable structural differences rather than a fixed quantitative threshold. Some variability exists within the “mild” group, likely due to differences in recombination efficiency and the mosaic nature of tamoxifen-induced Cre-mediated Sox9 deletion”

(2) The authors infer Sox9 high and Sox9 low groups of limbal stem cells using an existing scRNA-seq dataset. However, an immunohistology-based validation of this difference is missing. Given that limbal stem cells express Sox9, the authors must examine the heterogeneity in Sox9 levels within the Sox8+ population to demonstrate their claim: "...Sox9 expression decreases as transiently amplifying progenitors undergo progressive differentiation from limbal to peripheral corneal cells." in Line 292. Ideally, this must be further validated using differentiation markers corresponding to CB and ILB populations that show lower Sox9 expression according to the pseudotime graph.

To validate the Sox9 expression results obtained with scRNA-seq, we performed double immunofluorescence for Sox9 and P63, the latter expressed by the basal cells of the limbal epithelium, but not by transient amplifying cells covering the corneal surface (Pellegrini et al., 2001, https://www.pnas.org/doi/abs/10.1073/ pnas.061032098). These results can be observed in the new panel 5F. Accordingly we have included a new paragraph in lines 369-396 of the revised version of the ms:

“To validate these results, we decided to closely examine Sox9 expression in the limbus using immunofluorescence. Previous analyses revealed that the outer limbus is approximately 100 μm wide, while the inner limbus is wider, around 240 μm (Altshuler 2021). We observed that in the region corresponding to the OLB, most cells showed strong Sox9 expression. In the area corresponding to the ILB, this immunoreactivity appeared weaker in the basal layer (corresponding to the ILB proper), and no expression was detected in the suprabasal layers (flattened cells; Fig 5F left). Double immunofluorescence for SOX9 and P63, which is expressed in basal cells of the limbal epithelium, but not by transient amplifying cells covering the corneal surface (Pellegrini et al., 2001) revealed that Sox9 expression was restricted to P63-positive cells (Fig 5F right). These observations confirm that Sox9 is expressed in a basal cell population within both the OLB and ILB, and that its expression decreases in differentiated transient amplifying cells. ”

We also have deleted  “This expression pattern is consistent with our immunofluorescence observations" from line 356 of the revised ms.

(3) The authors' claim of "...Sox9-null cells cannot survive or proliferate as well as their wildtype neighbors, and are hence outcompeted over time, leading to an essentially wild-type cornea" does not seem very convincing in the light of Fig.6D and S3B where Sox9 deletion can still allow for a large LacZ+ clone. Their claim of wild-type cornea due to out-competing neighbors must be validated by increasing the number of Sox9-null progenitors, which can be tested by administering tamoxifen for a significantly longer duration, leading to a majority Sox9 deficient progenitor population, and then examining limbal and corneal defects.

As previously discussed, we observed only one instance of a large LacZ+ clone across 8 Sox9^Δ/Δ-LacZ eyes. Based on prior reports of ligand-independent Cre activity (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009; Dorà et al., 2015), we believe this rare event likely resulted from spontaneous recombination in a bona fide limbal stem cell, independent of tamoxifen administration. For this reason, we do not expect that increasing the dose or duration of tamoxifen would eliminate such rare events. Furthermore, due to the mosaic and highly variable recombination efficiency of the CAGG-CreERTM system in the adult eye (see McMahon et al., 2008), attempting to increase the TX dosage would likely lead to systemic toxicity or lethality, without guaranteeing full inactivation of the gene in the limbus. Thus, this system is not well-suited for generating a fully Sox9-deficient limbal epithelium. To overcome this limitation, we crossed our mice with the R26R-LacZ reporter line to track the clonal behavior of Sox9-deficient cells. In control animals (Sox9Δ/+-LacZ), LacZ+ stripes originating from limbal stem cells are readily observed. In contrast, in Sox9Δ/Δ-LacZ mutants, these clones are either absent or drastically reduced. This suggests that Sox9-null cells have a severely impaired ability to form and sustain clones. To rigorously quantify this effect, we compared 8 control and 5 mutant corneas, revealing a highly significant 8-fold reduction in LacZ-positive area in the mutants (6.65 ± 1.77% vs. 0.85 ± 0.85%; p = 0.00017; Fig. 6F; Table S12; Supp. Fig. X???), supporting our claim that Sox9null cells cannot survive or proliferate as well as their wild-type neighbors, and are hence outcompeted over time, leading to an essentially wild-type cornea.

Minor points

(1) Quantification for Figure 2C and 2D is missing.

We have now included quantification of BRN3A+ retinal ganglion cells (Figure 2E) across control and Sox9^Δ/Δ retinas. Cell counts were performed on matched retinal sections, and the difference between groups was found to be statistically significant through Mann–Whitney U test (Table S5).

Regarding PAX6/AP2a, we quantified inner retinal neurons by analyzing AP2α+ amacrine cells and PAX6+/AP2α- horizontal cells as distinct subpopulations, rather than simply comparing total PAX6 or AP2α immunoreactivity. This approach allowed us to better resolve specific neuronal subtype changes. Both populations showed a statistically significant reduction in Sox9-deficient retinas relative to controls. The quantification for these analyses has now been incorporated into the revised Figure 2F and G (Table S6).

Consequently with the above, the following paragraph of the Results section (line 210 of the revised ms:

“We also studied the status of other retinal cell types. The transcription factor BRN3A was used to identify ganglion cells (Nadal-Nicolás et al., 2009), which were shown to decrease in number in the mutant retinas, compared to control ones (Fig. 2C). Similarly, double immunodetection of the transcription factors PAX6 and AP2A was used to identify both amacrine and horizontal cells, as previously described (Marquardt et al., 2001; Barnstable et al., 1985; Edqvist and Hallböök, 2004), showing a similar reduction in both cell types in degenerated retinas (Fig. 2D).”

Has been modified as follows:

“We also studied the status of other retinal cell types. The transcription factor BRN3A was used to identify ganglion cells (Nadal-Nicolás et al., 2009), which were shown to decrease in number in the mutant retinas, compared to control ones (Figs. 2C and 2D and Table S5; n = 5 controls, n = 12 mutants; Mann-Whitney U test, P = 3 × 10^-4). Similarly, double immunodetection of the transcription factors PAX6 and AP2A was used to identify both amacrine and horizontal cells (Fig. 2E), as previously described (Marquardt et al., 2001; Barnstable et al., 1985; Edqvist and Hallböök, 2004), showing a similar reduction in both cell types in degenerated retinas (Figs. 2F and 2G and Table S6; AP2α+ amacrine cells: n = 3 controls, n = 8 mutants;  2-sample T-tests P = 0.029; PAX6+/AP2α− horizontal cells: n = 3 controls, n = 8 mutants; Mann-Whitney U test P = 0.021). These findings indicate that the loss of Sox9 in the adult retina ultimately leads to the degeneration of multiple inner retinal neuronal populations, beyond the previously described effects on photoreceptors and Müller glia.

(2) Figure 4G & H: The authors must mention that the dashed lines enclose the limbal area.

Done

(3) The authors infer from an existing scRNA-seq dataset that OLB cells have high Sox9 expression as compared to ILB and corneal populations. However, Figures 4A and B do not indicate the anatomical positions of these cell types. The authors must label these for the reader's reference as they state that "[Sox9] expression pattern is consistent with our immunofluorescence observations" in Line 280.

As previously indicated, we have generated a new panel 5F and a corresponding paragraph to illustrate Sox9 expression pattern in the limbus. Accordingly, we have removed the sentence from line 280.

(4) Quantification for Figures 6A and 6B is missing.

We have quantified the number of Sox9 and P63 positive cells in the limbus between mutant and control corneas and found no difference in the number of positive cells. We have included these data in new panel 6C and Table S11.

Reviewer #2 (Recommendations for the authors):

Line 24: "synapsis" should be "synapses".

Done

(1) Consider starting a new paragraph after line 30.

Done

(2) Lines 42-48: make clear that this paragraph provides information only for HUMAN SOX9.

We now distinguish which studies were conducted in humans and which in mice.

(3) Line 55: explain to the non-expert reader what the "visual cycle" is.

Done (lines 64-65 of the revised ms)

(4) Line 66: consider "inactivate" instead of "suppress".

We substituted “suppress” with “inactivate”

(5) Line 90-92: ONLY PCR for the cGMP will provide formal evidence that this is not present in the mouse line.

We agree with the reviewer that PCR genotyping is the most straightforward method to exclude the presence of the Pde6^brd1 allele. Although retinal degeneration was never observed in untreated or control animals in our study, we have now removed the term “formal possibility” from the text to better reflect this limitation.

We have modified the following paragraph (page 116 in the revised version of the manuscript): “Retinal degeneration was never observed in mice that had not been tamoxifen-treated, nor any other controls, eliminating the formal possibility that the retinal degeneration allele of photoreceptor cGMP phosphodiesterase 6b (Pde6brd1) was present in our mice (Bowes et al., 1990).”

As follows: “Retinal degeneration was never observed in mice that had not been tamoxifentreated, nor any other control groups, making the presence of the retinal degeneration allele of photoreceptor cGMP phosphodiesterase 6b (Pde6^brd1) unlikely in our mice (Bowes et al., 1990). However, we acknowledge that definitive exclusion of this possibility would require PCR-based genotyping.”

(6) Line 160-166: This paragraph needs a conclusion.

We agree with the reviewer and have added the following sentence at the end of the paragraph:

“These findings indicate that the loss of Sox9 in the adult retina ultimately leads to the degeneration of multiple inner retinal neuronal populations, beyond the previously described effects on photoreceptors and Müller glia”

(7) Line: 240-265: This paragraph ends without a conclusion.

We have include the following conclusion:

“Thus, Sox9 is expressed in a basal limbal stem cell population with the ability to form two types of long-lived cell clones involved in stem cell maintenance and homeostasis.”

(8) In Results, it needs to be specified when exactly in adulthood the tamoxifen treatment started. This information is only provided in the Methods.

We have specified the age of the mice at the onset of tamoxifen treatment (two months)  and included it in the schemes of Figs 1A, 4C, 4H, 6E.

(9) Line 250: Because live imaging is not conducted, the word "dynamics" is not suitable.

We substituted “dynamics” with “contribution”

(10) Panel C in Figure 6 is nice and helpful. Consider adding a similar panel in Figure 1.

Done.

(11) Line 420: is this the human Sox9 enhancer?

Yes. It is a human enhancer. We have indicated it in the text.

(12) Line 459: typo "detected tissue".

Corrected

(13) Line 448 and 468: citations are needed.

Line 448 is deleted in the revised version of the ms.

(14) 479: typo "clones clones'.

Corrected.

Author response:

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

Reviewer #1 (Public review):

This work computationally characterized the threat-reward learning behavior of mice in a  recent study (Akiti et al.), which had prominent individual differences. The authors  constructed a Bayes-adaptive Markov decision process model and fitted the behavioral data  by the model. The model assumed (i) hazard function starting from a prior (with free mean  and SD parameters) and updated in a Bayesian manner through experience (actually no real  threat or reward was given in the experiment), (ii) risk-sensitive evaluation of future  outcomes (calculating lower 𝛼 quantile of outcomes with free 𝛼 parameter), and (iii) heuristic  exploration bonus. The authors found that (i) brave animals had more widespread hazard  priors than timid animals and thereby quickly learned that there was in fact little real threat,  (ii) brave animals may also be less risk-aversive than timid animals in future outcome  evaluation, and (iii) the exploration bonus could explain the observed behavioral features,  including the transition of behavior from the peak to steady-state frequency of bout. Overall,  this work is a novel interesting analysis of threat-reward learning, and provides useful  insights for future experimental and theoretical work. However, there are several issues that I  think need to be addressed.

Strengths:

(1) This work provides a normative Bayesian account for individual differences in  braveness/timidity in reward-threat learning behavior, which complements the analysis by  Akiti et al. based on model-free threat reinforcement learning.

(2) Specifically, the individual differences were characterized by (i) the difference in the  variance of hazard prior and potentially also (ii) the difference in the risk-sensitivity in the  evaluation of future returns.

Weakness:

(1) Theoretically the effect of prior is diluted over experience whereas the effect of biased  (risk-aversive) evaluation persists, but these two effects could not be teased apart in the  fitting analysis of the current data.

(2) It is currently unclear how (whether) the proposed model corresponds to neurobiological ( rather than behavioral) findings, different from the analysis by Akiti et al.

We thank reviewer #1 for their useful feedback which we’ve used to improve the discussion,  formatting and clarity of the paper, and for highlighting important questions for future  extensions of our work.

Major points:

(1) Line 219

It was assumed that the exploration bonus was replenished at a steady rate when the animal  was at the nest. An alternative way would be assuming that the exploration bonus slowly  degraded over time or experience, and if doing so, there appears to be a possibility that the  transition of the bout rate from peak to steady-state could be at least partially explained by  such a decrease in the exploration bonus.

Section 2.2.3 explains the mechanism of the exploration bonus which motivates approach.  We think that the mechanism suggested by the reviewer is, in essence, what is happening in  the model. The exploration pool is indeed depleted over time or bouts of experience at the  object. In the peak confident phase for brave animals and the peak cautious phase for timid  animals, the rate of depletion exceeds the rate of regeneration, since the agent spends only  a single turn at the nest between bouts. In the steady-state phase, the exploration pool has  depleted so much previously that the agent must wait multiple turns at the nest for the pool  to regenerate to a sufficiently high value to justify approaching the object again.

We have updated section 2.2.3 to explain that agents spend one turn at the nest during peak  phase but multiple turns during steady-state phase. Hopefully, this makes our mechanism  clear:

“In simulations, when 𝐺(𝑡) is high, the agent has a high motivation to explore the object,  spending only a single turn in the nest state between bouts. In other words, the depletion  from 𝐺0 substantially influences the time point at which approach makes a transition from  peak to steady-state; the steady-state time then depends on the dynamics of depletion  (when at the object) and replenishment (when at the nest). In particular, in the steady-state  phases, the agent must wait multiple turns at the nest for 𝐺(𝑡)  to regenerate so that  informational reward once again exceeds the potential cost of hazard.“

(2) Line 237- (Section 2.2.6, 2.2.7, Figures 7, 9)

I was confused by the descriptions about nCVaR. I looked at the cited original literature  Gagne & Dayan 2022, and understood that nCVaR is a risk-sensitive version of expected  future returns (equation 4) with parameter α (α-bar) (ranging from 0 to 1) representing risk  preference. Line 269-271 and Section 4.2 of the present manuscript described (in my  understanding) that α was a parameter of the model. Then, isn't it more natural to report  estimated values of α, rather than nCVaR, for individual animals in Section 2.2.6, 2.2.7,  Figures 7, 9 (even though nCVaR monotonically depends on α)? In Figures 7 and 9, nCVaR  appears to be upper-bounded to 1. The upper limit of α is 1 by definition, but I have no idea why nCVaR was also bounded by 1. So I would like to ask the authors to add more detailed  explanations on nCVaR. Currently, CVaR is explained in Lines 237-243, but actually, there is  no explanation about nCVaR rather than its formal name 'nested conditional value at risk' in  Line 237.

Thank you for pointing out this error. We have corrected the paper to use nCVaR to refer to  the objective and nCVaR's α, or sometimes just α, to refer to the risk sensitivity parameter  and thus the degree of risk sensitivity.

(3) Line 333 (and Abstract)

Given that animals' behaviors could be equally well fitted by the model having both nCVaR ( free α) and hazard prior and the alternative model having only hazard prior (with α = 1), may  it be difficult to confidently claim that brave (/timid) animals had risk-neutral (/risk-aversive)  preference in addition to widespread (/low-variance) hazard prior? Then, it might be good to  somewhat weaken the corresponding expression in the Abstract (e.g., add 'potentially also'  to the result for risk sensitivity) or mention the inseparability of risk sensitivity and prior belief  pessimism (e.g., "... although risk sensitivity and prior belief pessimism could not be teased  apart").

Thank you for this suggestion, we have duly weakened the wording in the Abstract to say  “potentially more risk neutral”:

“Some animals begin with cautious exploration, and quickly transition to confident approach  to maximize exploration for reward; we classify them as potentially more risk neutral, and  enjoying a flexible hazard prior. By contrast, other animals only ever approach in a cautious  manner and display a form of  self-censoring; they are characterized by potential risk  aversion and high and inflexible hazard priors.”

Reviewer #2 (Public Review):

Shen and Dayan build a Bayes adaptive Markov decision process model with three key  components: an adaptive hazard function capturing potential predation, an intrinsic reward  function providing the urge to explore, and a conditional value at risk (CvaR, closely related  to probability distortion explanations of risk traits). The model itself is very interesting and  has many strengths including considering different sources of risk preference in generating  behavior under uncertainty. I think this model will be useful to consider for those studying  approach/avoid behaviors in dynamic contexts.

The authors argue that the model explains behavior in a very simple and unconstrained  behavioral task in which animals are shown novel objects and retreat from them in various  manners (different body postures and patterns of motor chunks/syllables). The model itself  does capture lots of the key mouse behavioral variability (at least on average on a  mouse-by-mouse basis) which is interesting and potentially useful. However, the variables in  the model - and the internal states it implies the mice have during the behavior - are  relatively unconstrained given the wide range of explanations one can offer for the mouse  behavior in the original study (Akiti et al). This reviewer commends the authors on an original  and innovative expansion of existing models of animal behaviour, but recommends that the  authors  revise their study to reflect the obvious  challenges . I would also recommend a  reduction in claiming that this exercise gives a normative-like or at least quantitative account  of mental disorders.

We thank reviewer #2 for highlighting some of the strengths of our paper as well as pointing  out important limitations of Akiti et al’s original study which we’ve inherited as well as some  limitations of our own method. We address their concerns below.

We have added a paragraph to the discussion discussing the limitations of the state  representation we adopted from Akiti’s study.

(Reviewer #1 had the same concern, see above) “Motivated by tail-behind versus  tail-exposed in Akiti et al. (2022), we model approach using a dichotomy between cautious  and confident approach states [...]”

We have reduced the suggestion that our model provides an account of mental disorders in  the abstract.

Before:

“On the other hand, “timid” animals, characterized by risk aversion and high and inflexible  hazard priors, display self-censoring that leads to the sort of asymptotic maladaptive  behavior that is often associated with psychiatric illnesses such as anxiety and depression.”

After:

“By contrast, other animals only ever approach in a cautious manner and display a form of  self-censoring; they are characterized by potential risk aversion and high and inflexible  hazard priors. “

My main comment is that this paper is a very nice model creation that can characterize the  heterogeneity rodent behavior in a very simple approach/avoid context (Akiti et al; when a  novel object is placed in an arena) that itself can be interpreted in a multitude of ways. The  use of terms like "exploration", "brave", etc in this context is tricky because the task does not  allow the original authors (Akiti et al) to quantify these "internal states" or "traits" with the  appropriate level of quantitative detail to say whether this model is correct or not in capturing  the internal states that result in the rodent behavior. That said, the original behavioral setup  is so simple that one could imagine capturing the behavioral variability in multiple ways ( potentially without evoking complex computations that the original authors never showed  the mouse brain performs). I would recommend reframing the paper as a new model that  proposes a set of internal states that could give rise to the behavioral heterogeneity  observed in Akiti et al, but nonetheless is at this time only a hypothesis. Furthermore, an  explanation of what would be really required to test this would be appreciated to make the  point clearer.

We thought very hard about using terms that might be considered to be anthropomorphic  such as ‘timid’ and ‘brave’. We are, of course, aware, of the concerns articulated by  investigators such as LeDoux about this. However, we think that, provided that we are clear  on the first appearance (using ‘scare’ quotes) that we are using them as indeed labels for  latent characteristics that capture correlations in various aspects of behaviour, they are more  helpful than harmful in making our descriptions understandable.

Reviewer #3 (Public Review):

Summary:

The manuscript presents computational modelling of the behaviour of mice during  encounters with novel and familiar objects, originally reported by Akiti et al. (Neuron 110, 2022)          . Mice typically perform short bouts of approach followed by a retreat to a safe  distance, presumably to balance exploration to discover possible rewards with the potential  risk of predation. However, there is considerable heterogeneity in this exploratory behaviour,  both across time as an individual subject becomes more confident in approaching the object,  and across subjects; with some mice rapidly becoming confident to closely explore the  object, while other timid mice never become fully confident that the object is safe. The  current work aims to explain both the dynamics of adaptation of individual animals over time,  and the quantitative and qualitative differences in behaviour between subjects, by modelling  their behaviour as arising from model-based planning in a Bayes adaptive Markov Decision  Process (BAMDP) framework, in which the subjects maintain and update probabilistic  estimates of the uncertain hazard presented by the object, and rationally balance the  potential reward from exploring the object with the potential risk of predation it presents.

In order to fit these complex models to the behaviour the authors necessarily make  substantial simplifying assumptions, including coarse-graining the exploratory behaviour into  phases quantified by a set of summary statistics related to the approach bouts of the animal.  Inter-individual variation between subjects is modelled both by differences in their prior  beliefs about the possible hazard presented by the object and by differences in their risk  preference, modelled using a conditional value at risk (CVaR) objective, which focuses the  subject's evaluation on different quantiles of the expected distribution of outcomes.  Interestingly these two conceptually different possible sources of inter-subject variation in  brave vs timid exploratory behaviour turn out not to be dissociable in the current dataset as  they can largely compensate for each other in their effects on the measured behaviour.  Nonetheless, the modelling captures a wide range of quantitative and qualitative differences  between subjects in the dynamics of how they explore the object, essentially through  differences in how subject's beliefs about the potential risk and reward presented by the  object evolve over the course of exploration, and are combined to drive behaviour.

Exploration in the face of risk is a ubiquitous feature of the decision-making problem faced  by organisms, with strong clinical relevance, yet remains poorly understood and  under-studied, making this work a timely and welcome addition to the literature.

Strengths:

(1) Individual differences in exploratory behaviour are an interesting, important, and  under-studied topic.

(2) Application of cutting-edge modelling methods to a rich behavioural dataset, successfully  accounting for diverse qualitative and qualitative features of the data in a normative  framework.

(3) Thoughtful discussion of the results in the context of prior literature.

Limitations:

(1) The model-fitting approach used of coarse-graining the behaviour into phases and fitting  to their summary statistics may not be applicable to exploratory behaviours in more complex  environments where coarse-graining is less straightforward.

(2) Some aspects of the work could be more usefully clarified within the manuscript.

We thank reviewer #3 for their positive feedback and helping us to improve the clarity of our  paper. We have added discussion they thought was missing.

Reviewer #1 (Recommendations for the authors):

(1) Line 25-28

This part of the Abstract might give an impression that timidity (but not braveness) is  potentially associated with psychiatric illness and even that timidity is thus inferior to  braveness. However, even though extreme timidity might indeed be associated with anxiety  or depression, extreme braveness could also be associated with other psychiatric or  behavioral problems. Moreover, as a population, the existence of both timid and brave  individuals could be advantageous, and it could be a reason why both types of individuals  evolutionarily survived in the case of wild animals (although Akiti et al. used mice, which may  have no or very limited genetic varieties, and so things may be different). So I would like to  encourage the authors to elaborate on the expression of this part of the Abstract and/or  enrich the related discussion in the Discussion.

This is an important point. We note on line 38 that excessive novelty seeking (potentially  caused by excessive braveness) could also be maladaptive.

Additionally, we have added a paragraph to the discussion discussing heterogeneity in risk  sensitivity within a population.

“Our data show that there is substantial variation in the degrees of risk sensitivity across the  mice.  Previous works have reported substantial interpopulation and intrapopulation  differences in risk-sensitivity in humans which depend on gender, age, socioeconomic  status, personality characteristics, wealth and culture (Rieger et al., 2015; Frey et al., 2017).  Despite the normative appeal of 𝛼 = 1, it is possible that a population may benefit from  including individuals with $\alpha$ different from 1.0 or highly negative priors. For example,  more cautious individuals could learn from merely observing the risky behavior of less  cautious individuals. Furthermore, we have only considered risk-sensitivity under epistemic  uncertainty in our work. Risk averse individuals, for instance with 𝛼 < 1 may be more  successful than risk-neutral agents in environments where there are unexpected dangers ( unknown unknowns). Risk-aversion is thus a temperament of ecological and evolutionary  significance (Réale et al., 2007).”

(2) Line 149

Section 2.2 consists of eight subsections. I think this organization may not be very  appealing, because there are a bit too many subsections, and their relations are not  immediately clear to readers. So I would like to encourage the authors to make an  elaboration. For example, since 2.2.1 - 2.2.5 describes a summary of model construction  and model fitting whereas 2.2.6-2.2.8 shows the results, it could be good to divide these into  separate sections (2.2.1 - 2.2.5 and 2.3.1 - 2.3.3).

Thank you for pointing this out. We’ve renumbered the sections as you’ve suggested.

(3) Line 347-8

Theoretically, the effect of prior is diluted over experience whereas the effect of biased  (risk-aversive) evaluation persists, as the authors mentioned in Lines 393-394. Then isn't it  possible to consider environments/conditions in which the two effects can be separated?

We appreciate this suggestion. Indeed, our original thought in modeling this experiment was  that this would be exactly the case here - with epistemic uncertainty reducing as the object  became more familiar. However, proving to an animal that a single environment is  completely stationary/fixed is hard - reflected in our conclusion here that the exploration  bonus pool replenishes. Thus, we argued in the discussion that a series of environments  would be necessary to separate risk sensitivity from priors.

(4) Line 407

It would be nice to add a brief phrase explaining how (in what sense) this model's  assumption was consistent with the reported behavior. Also, should the assumption of  having two discrete approach states (cautious and confident) itself be regarded as a  limitation of the model? If the tail-behind and tail-exposure approaches were not merely  operationally categorized but were indicated to be two qualitatively distinct behaviors in the  experiment by Akiti et al., it is reasonable to model them as two discrete states, but  otherwise, the assumption of two discrete states would need to be mentioned as a  simplification/limitation.

We have now removed line 407, and now have an additional  paragraph in the discussion  discussing the limitations of the tail-behind and tail-exposure state representation: “Motivated by tail-behind versus tail-exposed in Akiti et al. (2022), we model approach using  a dichotomy between cautious and confident approach states. This is likely a crude  approximation to the continuous and multifaceted nature of animal approach behavior. For  example, during approach animals likely adjust their levels of vigilance continuously (or  discretely; Lloyd and Dayan (2018)) to  monitor threat, and choose different velocities for  movement, and different attentional strategies for inspecting the novel object. We hope  future works will model these additional behavioral complexities, perhaps with additional  internal states, and corroborate these states with neurobiological data.”

(5) Line 418

The authors contrasted their model-based analyses with the model-free analyses of Akiti et  al. Another aspect of differences between the authors' model and the model of Akiti et al. is  whether it is normative or mechanistic: while how the model of Akiti et al. can be biologically  implemented appears to be clear (TS dopamine represents threat TD error, and TS  dopamine-dependent cortico-striatal plasticity implements TD error-based update of  model-free threat prediction), biological implementation of the authors' model seems more  elusive. Given this, it might be a fruitful direction to explore how these two models can be  integrated in the future.

We enthusiastically agree that it would be most interesting in the future to explore the  integration of the two models - and, in the discussion ( Lines 537-548, 454-461) , point to  some first steps that might be fruitful along these lines. There are two separate  considerations here: one is that our account is mostly computational and algorithmic,  whereas Akiti’s model is mostly algorithmic and implementational; the second is, as noted by  the reviewer, that our account is model-based, whereas Akiti’s model is model-free (in the  sense of reinforcement learning; RL). These are related - thanks in no small part to the work  from the group including Akiti, we know a lot more about the implementation of model-free  than model-based RL. However, our model-based account does reach additional features of  behavior not captured in Akiti et al.’s model such as bout duration, frequency, and approach  type. Thus, the temptation of unification.

(6) Line 426

Related to the previous point, it would be nice to more specifically describe what variable TS  dopamine can represent in the authors' model if possible.

In the discussion  (Lines 454-461) , we speculate that  TS dopamine could still respond to the  physical salience of the novel object and affect choices by determining the potential cost of  the encountered threat or the prior on the hazard function. For example, perhaps ablating TS  dopamine reduces the hazard priors which leads to faster transition from cautious to  confident approach and longer bout durations, consistent with the optogenetics behavioral  data reported in Akiti et al.

Reviewer #2 (Recommendations for the authors):

My guess is simpler versions of the model would not fit the data well. But this does not mean  for example that the mice have probability distortions (CvaR) or that even probabilistic  reasoning and the internal models necessary to support them are acting in the behavioral  context studied by Akiti. So related to the above, I would ask what other models would fit and  would not fit the data? And what does this mean?

These are good points. Our model provides an approximately normative account of the  animals’ behavior  in terms of what it achieves relative to a utility function. In practice, the  animals could deploy a precompiled model-free policy (which does not rely on probabilistic  computations) that is exactly equivalent to our model-based policy. With the current  experiment, we cannot conclude whether or not the animals are performing the prospective  calculations in an online manner. Of course, the extent to which animals or humans are  performing probabilistic computations online and have internal models are on-going  questions of study.

Model comparison is difficult because currently we do not know of any other risk-sensitive  exploration models. We cannot directly compare to the model in Akiti et al. since our model  explains additional features of behavior: bout duration, frequency, and approach type.  Indeed, our model is as simple as it can be in the sense with the exception of nCVaR,  removing any of the other parameters makes it difficult to fit some animals in our dataset. In the future, our model could be used to fit other datasets of risk-sensitive exploration and,  ideally,  be compared to other models.

Explaining why animals avoid the novel object in what the offers call benign environment is a  very tricky issue. In Akiti et al, the readers are not yet convinced that the mice know that this  environment is benign. Being placed in an arena with a novel object presents mice with a  great uncertainty and we do not know whether they treat this as benign. Therefore, the  alternative explanations in this study need to be carefully discussed in lieu of the limitations  of the initial study.

It is certainly true that it is unclear if the arena is  completely  benign to the animals. However,  the amount of time the animal spends in the center of the arena decreases significantly from  habituation to novelty days. This suggests that the animals avoid the novel object largely  because of the object itself, rather than the potential danger associated with the arena.  Furthermore, the animals are not reported as exhibiting more extreme behaviours such as  freezing. In any case, our account is relative in the sense that we are comparing the time the  animal spends at the object versus elsewhere in the environment, driven by the relative  novelty and relative risk of the environment versus the object. Trying to get more absolute  measures of these quantities would require a richer experimental set-up, for instance with  different degree of habituation or experience of the occurrence of (other) novel objects, in  general.

We added a short note to the discussion to explain this:

“Fourth, we modeled the relative amount of time the animal spends at the object versus  elsewhere in the environment which depends on the differential risk in the two states.  However, it is likely the animals avoid the novel object largely because of the object itself,  rather than the potential danger associated with the arena since they spend much less time  at the center of the arena during novelty than habituation days.”

Figure 2 - how confident are the authors that each mouse differs from y=1? Related to this,  the behavior in Akiti is very noisy and changes across time. I am not sure if the authors fully  describe at what levels their model captures the behavior vs not in a detailed enough  fashion.

We have performed a random permutation test on the minute-to-minute data. We have  updated Figure 2 so that brave animals that pass the Benjamini–Hochberg procedure y>1 at  level q=0.05 are represented with solid green dots and animals that don’t pass are  represented with hollow dots. 8 out of 11 brave animals passed Benjamini–Hochberg.

Reviewer #3 (Recommendations for the authors):

(1) I could not find information in the preprint about code availability. Please consider making  the code public to help others apply these modelling methods.

We have released code and included the url in the paper in the Methods section.

(2) Though the manuscript was generally clearly written, there were a number of places  where some additional information or clarification would be useful:

a) Please define and explain the terms 'tail-behind' and 'tail-exposed' (used to describe  approach bout types) when first used.

We have added definitions when we first mention these terms:

“[...] 'tail-behind' (bouts where the animal's nose was closer to the object than the tail for the  entire bout) and 'tail-exposed' (bouts where the animal's tail is closer to the object than the  nose at some point during the bout), associated respectively with cautious risk-assessment  and engagement”

b) At lines 57-58 when contrasting the 'model-free' account of Akiti et al with the 'model-based' account of the current work, it would be worth clarifying that these terms are  being used in the RL sense rather than e.g. a model-based analysis of the data.  

We have updated the relevant lines to say “model-free/based reinforcement learning”.

c) Line 61, the phrase 'the significant long-run approach of timid animals despite having  reached the "avoid" state' is unclear as the 'avoid' state has not been defined.

We updated the terminology to “avoidance behavior” to be consistent with Akiti et al.  Avoidance refers to the animal routinely avoiding the object and therefore being unable to  learn whether it is safe.

d) It was not completely clear to me how the coarse-graining of the behaviour was  implemented. Specifically, how were animals assigned to the brave, intermediate, or timid  group, and how were the parameters of the resulting behavioural phases fit?

Sorry that this was not clear. Section 2.1 explains how the minute-to-minute behavioral data  was coarse-grained and how animal groups were assigned. We have added further  explanation of Figure 2 to the main text:

“Fig 2 summarizes our categorization of the animals into the three groups: brave,  intermediate, and timid based on the phases identified in the animal's exploratory  trajectories. Timid animals spend no time in confident approach and are plotted in orange at  the origin of Fig 2. Brave animals differ from intermediate animals in that their approach time  during the first ten minutes of the confident phase is greater than the last ten minutes ( steady-state phase). Brave animals are plotted in green above and intermediate animals  are plotted in black below the y=1 line in Fig 2.”

We also added extra information to outline the goal, and methodology of coarse-graining and  animal grouping:

“We sought to capture  these qualitative differences (cautious versus confident) as well as  aspects of the quantitative changes in bout durations and frequencies as the animal learns  about their environment. To make this readily possible, we abstracted the data in two ways:

averaging  bout statistics over time, and clustering the animals into three groups with  operationally distinct behaviors.”

e) What purpose does the 'retreat' state serve in the BAMDP model (as opposed to  transitioning directly from 'object' to 'nest' states), and why do subjects not pass through it  following 'detect' states?

Thank you for pointing this out. We have updated Figure 3 to note that the two “detected  states” also point to the “retreat” state. The reviewer is correct that there could be alternative  versions of the state diagram, and the ‘retreat’ state could indeed have been eliminated.  However, we thought that it was helpful to structure the animal’s progress through state  space.

f) Why was the hazard function parameterised via the mean and SD at each time step rather  than with a parametric form of the mean and SD as a function of time?

Since the agent can only spend 2, 3, or 4 turns at the object states, we didn’t see a need to  parameterize the mean and SD as a function of time. Doing so is a good solution to scaling  up the hazard function to more time-steps.

(3) There were also a couple of points that could potentially be usefully touched on in the  discussion:

a) What, if any, is the relationship between the CVaR objective and distributional RL? They  seem potentially related due to both focussing on quantiles of the outcome distribution.

We have added a paragraph to the discussion discussing the connection between  distributional RL and CVaR:

“CVaR is known to come in different flavors in the case of temporally-extended behavior.  Gagne and Dayan (2021) introduces two alternative time-consistent formulations of CVaR:  nested CVaR (nCVaR) and precommitted CVaR (pCVaR). nCVaR and pCVaR both enjoy  Bellman equations which make it possible to compute approximately optimal policies without  directly computing whole distributions of the outcomes. We use nCVaR in this study for its  computational efficiency. There is, of course, great current interest in distributional  reinforcement learning (Bellemare et al., 2023b) which does acquire such whole  distributions, not the least because of prominent observations linking non-linearities in the  response functions of dopamine neurons to methods for learning distributions of outcomes ( Dabney et al., 2020; Masset et al., 2023; Sousa et al., 2023). One functional motivation for  considering entire outcome distributions is the possibility of using them to determine  risk-sensitive policies (Gagne and Dayan, 2021).

While it is possible to compute CVaR directly from return distributions, Gagne and Dayan  (2021) showed that this can lead to temporally inconsistent policies where the agent  deviates from its original plans (the authors called this the fixed CVaR or fCVaR measure).

Rather further removed from our model-based methods is work from Antonov and Dayan  (2023), who consider a model-free exploration strategy which exploits full return distributions  to compute the value of perfect information which is used as a heuristic for trying actions  with uncertain consequences. Future works can examine risk-sensitive versions of Antonov  and Dayan (2023)'s computationally efficient model-free algorithm as one solution to the  burdensome computations in our model-based method.”

b) Why normatively might subjects have non-neutral risk preference as captured by the  CvaR?

We also added a paragraph to the discussion discussing the advantage of heterogeneity in  risk sensitivity within a population:

(Reviewer #1 had the same question, see above) “Our data show that there is substantial  variation in the degrees of risk sensitivity across the mice.  Previous works have reported  substantial interpopulation and intrapopulation differences in risk-sensitivity in humans which  depend on gender, age, socioeconomic status, personality characteristics, wealth and culture [...]”

c) Relevance of the current modelling work to clinical conditions characterised by  dysregulation of risk assesment (e.g. anxiety or PTSD).

We’ve added a paragraph to the discussion:

“Inter-individual differences in risk sensitivity are also of critical importance in psychiatry,  reflected in a panoply of anxiety disorders (Butler and Mathews, 1983; Giorgetta et al., 2012;  Maner et al., 2007; Charpentier et al., 2017), along with worry and rumination (Gagne and  Dayan, 2022). Understanding the spectrum of   extreme priors and extreme values of 𝛼  could have therapeutic implications, adding significance to the search for tasks that can  more cleanly separate them.”

d) Is it surprising to see differences in risk preference (nCVaR) between the familiar object  and novel object condition, given that risk preference might be conceptualised as a trait  rather than a state variable?

Thank you for raising this point. You are right that we expected risk sensitivity (nCVaR alpha)  to be the same between FONC and UONC animals on average. It is difficult to know if alpha  is higher for FONC than UONC animals due to the non-identifiability between alpha and  hazard priors. We have added this discussion to the paper:

“This is surprising if we interpret 𝛼 as a trait that is stable through time. Unfortunately, due to  the non-identifiability between 𝛼 and hazard priors, we cannot verify whether 𝛼 is actually  higher for FONC animals than UONC animals.”

Author response:

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

Response to Reviewer #3:

We thank reviewer 3 for spending their valuable time on commenting on our revised paper.

We would like to reiterate the central conclusion of this work, which appears to have been missed by Reviewer 3. Using a BFP-expressing lineage tracer hPSC line for tracking LMX1A+ midbrain-patterned neural progenitors and their differentiated progeny, we discovered a loss of the LMX1A lineage during pluripotent stem cell differentiation into astrocytes, despite BFP+ neural progenitors were the dominant population at the onset of astrocyte induction.

Hence, the take-home message of this study is, as summarized in the abstract, ‘ the lineage composition of iPSC-derived astrocytes may not accurately recapitulate the founder progenitor population’ and that one should not take for granted that in vitro/stem cell-derived astrocytes are the descendants of the dominant starting neural progenitors (which is a general assumption in PSC publications as described in the paper and our response to reviewers).

Please find below our point-by-point response to reviewer comments. We have re-ordered the points according to their relative importance to our main conclusions.

‘ the lineage composition of iPSC-derived astrocytes may not accurately recapitulate the founder progenitor population’ and that one should not take for granted that in vitro/stem cell derived astrocytes are the descendants of the dominant starting neural progenitors (which is a general assumption in PSC publications as described in the paper and our response to reviewers).

Please find below our point-by-point response to their comments. We have re-ordered the points according to their relative importance to our main conclusions.

…. They used lineage tracing with a LMX1A-Cre/AAVS1-BFP iPSCs line, where the initial expression of LMX1A and Cre allows the long-lasting expression of BFP, yielding BFP+ and BFP- populations, that were sorted when in the astrocytic progenitor expansion. BFP+ showed significantly higher number of cells positive to NFIA and SOX9 than BFP- cells …

This is a misunderstanding by reviewer 3. As indicated in the first sentence of the second section, BFP- populations used for functional and transcriptomic analysis was not sorted BFP^- cells, but those derived from unsorted, BFP⁺ enriched populations. Our scRNAseq analysis indicated that they were transcriptomically aligned to human midbrain astrocytes. This finding is consistent with the fact that they are derived from midbrain-patterned neural progenitors, presumably minority LMX1A- progenitors.

Reviewer 3’s comments indicate that they misunderstood the primary aims of our study as a mere functional and transcriptomic comparison of the two astrocyte populations.

(9) BFP+ cells did not show higher levels of transcripts for LMX1A nor FOXA2. This fact jeopardizes the claim that these cells are still patterned. In the same line, there are not significant differences with cortical astrocytes, indicating a wider repertoire of the initially patterned cells, that seems to lose the midbrain phenotype. Furthermore, common DGE shared by BFP- and BFP+ cells when compared to non-patterned cells indicate that after culture, the pre-pattern in BFP+ cells is somehow lost, and coincides with the progression of BFP- cells.

The reviewer seems to assume that astrocytes derived from LMX1A+ ventral midbrain progenitors must retain LMX1A expression. We do not take this view and do not claim this in this study. Moreover, we have discussed in the paper that due to a lack of transcriptomic studies of in vivo track regional progenitors (such as LMX1A), it remains unknown whether and to what extent patterning gene expression is maintained in astrocytes of different brain regions.

Our findings on the lack of LMX1A and FOXA2 in BFP+ astrocytes are supported by several published single-cell transcriptomic studies of human midbrain astrocytes (La Manno et al. 2016; Agarwal et al. 2020; Kamath et al. 2022). We have a paragraph of discussion on this topic in both the original and updated versions of the paper with the relevant publications cited.

Other points raised by reviewer 3

(1) It is very intriguing that GFAP is not expressed in late BFP- nor in BFP+ cultures, when authors designated them as mature astrocytes.

We did not designate our cells as ‘mature’ astrocytes but ‘astrocytes’ based on their global gene expression with the human fetal and adult brain astrocytes as references.

Moreover, ‘mature’ only appeared once in the paper indicating that our cells lie in between the fetal and adult astrocytes in maturity.

(2) In Fig. 2D, authors need to change the designation "% of positive nuclei".

To be corrected in the version of record.

(3) In Fig. 2E, the text describes a decrease caused by 2APB on the rise elicited by ATP, but the graph shows an increase with ATP+2APB. However, in Fig. 2F, the peak amplitude for BFP+ cells is higher in ATP than in ATP+2APD, which is mentioned in the text, but this is inconsistent with the graph in 2E.

To be corrected in the version of record.

(4) The description of Results in the single-cell section is confusing, particularly in the sorted CD49 and unsorted cultures. Where do these cells come from? Are they BFP-, BFP+, unsorted for BFP, or non-patterned? Which are the "all three astrocyte populations"? A more complete description of the "iPSC-derived neurons" is required in this section to allow the reader to understand the type and maturation stage of neurons, and if they are patterned or not.

As previously reported in the reference cited, CD49 is a novel human astrocyte marker. This is independent of BFP expression. For all three astrocyte populations studied here (BFP+, BFP-, and non-patterned astrocytes), we included both CD49f+ sorted and unsorted samples to account for selection bias caused by FACS. iPSC-derived neurons were included in the sequencing study to provide a reference for cell-type annotation. They were generated following a GABAergic neuron differentiation protocol. However, their maturation stages and/or regional characteristics are not relevant to astrocytes.

(5) A puzzling fact is that both BFP- and BFP- cells have similar levels of LMX1A, as shown in Fig. S6F. How do authors explain this observation?

This figure panel shows that LMX1A, LMX1B and FOXA2 are essentially NOT expressed in these astrocytes.

(6) In Fig. 3B, the non-patterned cells cluster away from the BFP+ and BFP-; on the other hand, early and late BFP- are close and the same is true for early and late BFP+. A possible interpretation of these results is that patterned astrocytes have different paths for differentiation, compared to non-patterned cells. If that can be implied from these data, authors should discuss the alternative ways for astrocytes to differentiate.

Both BFP+ and BFP- astrocyte are from ventral midbrain patterned neural progenitors, while non-patterned neural progenitors are more akin to that of forebrain. Figure 3B is expected and confirms the patterning effect.

(7) Fig. 3D shows that cluster 9 is the only one with detectable and coincident expression of both S100B and GFAP expression. Please discuss why these widely-accepted astrocyte transcripts are not found in the other astrocytes clusters. Also, Sox9 is expressed in neurons, astrocyte precursors and astrocytes. Why is that?

S100B and GFAP are classic astrocyte markers in certain states. We are not relying only on two markers but the genome-wide expression profile as the criteria for astrocytes. As shown in the unbiased reference mapping to multiple human brain astrocyte scRNA-seq datasets, all our astrocyte clusters were mapped with high confidence to human astrocytes.

SOX9 is an important regulator for astrogenesis, so its expression is expected in precursors (doi.org/10.1016/j.neuron.2012.01.024). In addition, recent studies have uncovered that SOX9 expression is also reported in foetal striatal projection neurons and early postnatal cortical neurons, where SOX9 regulates neuronal synaptogenesis and morphogenesis (dois:10.1016/j.fmre.2024.02.019; 10.1016/j.neuron.2018.10.008). Therefore, the expression of SOX9 in multiple cell types was expected. Instead of using a few selected markers for cell-type annotation, we employed a genomic approach relying on an unbiased reference mapping approach and a combination of various markers to ascertain our annotation results.

(8) Line 337, Why authors selected a log2 change of 0.25? Typically, 1 or a higher number is used to ensure at least a 2-fold increase, or a 50% decrease. A volcano plot generated by the comparison of BFP+ with BFP- cells would be appropriate. The validation of differences by immunocytochemistry, between BFP+ and BFP-, is inconclusive. The staining is blur in the images presented in Fig. S8C. Quantification of the positive cells, without significant background signal, in both populations is required.

We used a lenient threshold owing to the following considerations: 1) High FC does not necessarily mean biological relevance, as gene expression does not necessarily translate to protein expression. Therefore, a smaller FC value could also be biologically meaningful. 2) Balance between noise and biological differences. Any threshold was chosen arbitrarily. 3) We are identifying a trend rather than pinpointing a specific set of

The quality was unfortunately reduced due to restrictions on file size upon submission. A high resolution Fig. S8C is available.

(10) For the GO analyses, How did authors select 1153 genes? The previous section mentioned 287 genes unique for BFP+ cells. The Results section should include a rationale for performing a wider search for the enriched processes.

GO enrichment using unique DEGS may not capture the wider landscape of the transcriptomic characteristics of BFP⁺ astrocytes. The 287 unique genes were only differentially expressed in BFP⁺ astrocytes. However, apart from these 287 genes, other genes among the 1187 DEGs were differentially expressed in BFP⁺ astrocytes and in one other population.

(11) For Fig. 4C and 4D, both p values and the number of genes should be indicated in the graph. I would advise to select the 10 or 15 most significant categories, these panels are very difficult to read. Whereas the listed processes for BFP+ have a relation to Parkinson disease, the ones detected for BFP- cells are related to extracellular matrix and tissue development. Does it mean that BFP+ cells have impaired formation of this matrix, or defective tissue development? This is in contradiction of enhanced calcium responses of BFP+ cells compared to BFP- cells.

Information on all DEGs, including p values and numbers, is provided in Supplementary data 1-5.

BFP+ astrocytes do have enrichment for GO terms related to extracellular matrix and tissue development, although not as obvious as BFP- astrocytes. Previous work have shown that both in vitro and in vivo derived astrocytes are functionally heterogeneous, containing functionally distinct subtypes exhibiting different GO enrichment profiles (doi: 10.1016/j.ygeno.2021.01.008; 10.1038/s41598-024-74732-7).

(12) Both the comparison between midbrain and cortical astrocytes in Fig. S8A, and the volcano plot in S8B do not show consistent changes. For example, RCAN2 in Fig. S8A has the same intensity for cortical and midbrain cells, but is marked as an enriched gene in midbrain in the p vs log2FC graph in Fig. S8B.

These are integrated analyses of published human datasets. S8A and S8B show the same data in different formats. The differences are better shown in the volcano plot/easier detected by the human eye.

These are integrated analysis of published human datasets. S8A and S8B are the same data shown in different format. Differences are better shown in volcano plot /easier detected by the human eye. RCAN2 had a higher average expression in the midbrain than in the telencephalon, albeit small, and the difference was statistically significant (as shown in the volcano plot).

The following is the authors’ response to the original reviews

Reviewer 1:

In vitro nature of this work being the fundamental weakness of this paper

We disagree with this statement. As explained in the provisional response, the aim of this study was to test the validity of a general concept applied in pluripotent stem cell research that pluripotent stem cell-derived astrocytes faithfully represent the lineage heterogeneity of their ancestral neural progenitors and hence preserve the regionality of such progenitors. Our genetic lineage study is justified for addressing this in vitro-driven question. However, we have highlighted the rationale where appropriate in the revised paper.

If regional identity is not maintained, so what? Don't we already know that this can happen? The authors acknowledge that this is known in the discussion.

Importance of regional identity: Growing evidence demonstrates the functional heterogeneity of brain astrocytes in health and disease. Therefore, for in vitro disease modeling, it is believed that one should use astrocytes represent the anatomy of disease pathology; for example, midbrain astrocytes for studying dopamine neurodegeneration and Parkinson’s disease. Understanding the dynamics of stem cell-derived astrocytes and identifying astrocyte subtypes is important for their biomedical applications.

Regional identity change/Discussion: It seems that the reviewer misunderstood the context in which the ‘identity change’ was discussed. The literature referred to (in the Discussion) concerns shifts in regional gene expression in bulk-cultured cells. In the days of pre-single-cell analysis/lineage tracking, one cannot distinguish whether this was due to a change in the transcriptomic landscape in progenies of the same lineage or alterations in lineage heterogeneity, but to interpret at face value as regional identity was not maintained. In the revised paper, we have made an effort to indicate that ‘regional identity’ is used broadly to refer to lineage relationships and/or traits rather than static gene expressioin.

validation of the markers/additional work

The scNAseq analysis performed in this study compared the profiles of astrocytes derived from LMX1A+ and LMX1A- ventral midbrain-patterned neural progenitors. Since it is not possible to perform genetic lineage tracking in humans and an analogous mouse lineage tracer line is not available, in vivo validation of these markers with respect to their lineage relationship is not currently feasible. However, we took advantage of abundant single-cell human astrocyte transcriptomic datasets and validated our genes in silico. We also validated the differential expression of selected markers in late BFP+ and BFP- astrocytes using immunocytochemistry, where reliable antibodies are available. The results of the additional analyses are presented in Figure S8 and Supplemental Data 5.

Knowledge gaps concerning astrocyte development

Reviewer 1 pointed out a number of knowledge gaps concerning astrocyte development, such as the transcriptomic landscape trajectories of midbrain floor plate cells as they progress towards astrocytes. Indeed, the limited knowledge on regional astrocyte molecule heterogeneity restricts the objective validation of in vitro-derived astrocyte subtypes and the development of novel approaches for their generation in vitro. We agree with the need for in-depth in vivo studies using model organisms, although these are beyond the scope of the current work.

Reviewer 2:

(1) The authors argue that the depletion of BFP seen in the unsorted population immediately after the onset of astrogenic induction is due to the growth advantage of the derivatives of the residual LMX1A- population. However, no objective data supporting this idea is provided, and one could also hypothesize that the residual LMX1A- cells could affect the overall LMX1A expression in the culture through negative paracrine regulation.

We acknowledge the lack of evidence-based explanation for the depletion of BFP+ cells in mixed cultures. We were unable to perform additional experiments because of resource limitations. The design of the LMX1A-Cre/AAVS1-BFP lineage tracer line determines that BFP is expressed irreversibly in LMX1A-expressing cells or their derivatives regardless of their LMX1A expression status. Therefore, the potential negative paracrine regulation of LMX1A by residual LMX1A- cells should not affect cells that have already turned on BFP. We have highlighted the working principles of the LMX1A tracer line in the revised manuscript.

(2) Furthermore, on line 124 it is stated that: "Interestingly, the sorted BFP+ cells exhibited similar population growth rate to that of unsorted cultures...". In the face of the suggested growth disadvantage of those cells, this statement needs clarification.

To avoid confusion, we have removed the statement.

(3) Regarding the fidelity of the model system, it is not clear to me how the TagBFP expression was detected in the BFP+ population supposedly in d87 and d136 pooled astrocytes (Fig S6C) while no LMX1A expression was observed in the same cells (Fig S6F).

The TagBFP tracer is expressed in the progenies of LMX1A+ cells, regardless of their LMX1A expression status. We have gone through the MS text to ensure that this information has been provided.

(4) The generated single-cell RNASeq dataset is extremely valuable. However, given the number of conditions included in this study (i.e. early vs late astrocytes, BFP+ vs BFP-, sorted vs unsorted, plus non-patterned and neuronal samples) the resulting analysis lacks detail. For instance, from a developmental perspective and to better grasp the functional significance of astrocytic heterogeneity, it would be interesting to map the identified clusters to early vs late populations and to the BFP status.

We performed additional bioinformatics analysis, which provided independent support for the relative developmental maturity suggested by functional assays. The additional data are now provided in the revised Figure 3B, C, E.

Moreover, although comprehensive, Figure S7 is complex to understand given that citations rather than the reference populations are depicted.

The information provided in the revised Figure S7.

(5) Do the authors have any consideration regarding the morphology of the astrocytes obtained in this study? None of the late astrocyte images depict a prototypical stellate morphology, which is reported in many other studies involving the generation of iPSC-derived astrocytes and which is associated with the maturity status of the cell.

The morphology of our astrocytes was not unique to the present study. Many factors may influence the morphology of astrocytes, such as the culture media and supplements used, and maturity status. Based on the functional assays and limited GFAP expression, our astrocytes were relatively immature.

Author response:

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Weaknesses:

(1) The mechanism underlying spontaneous di-decamer formation in the absence of glutamine is insufficiently explored and lacks quantitative biophysical validation.

(2) Claims of decamer-only behavior in mutants rely solely on negative-stain EM and are not supported by orthogonal solution-based methods.

We thank the reviewer for the summary and noting of the strengths. We agree that the evolutionary divergence of metabolic feedback in GS homologs is a fruitful avenue for future studies. With regard to the weaknesses, the di-decamer in the absence of glutamine only forms under high (higher than physiological) concentrations of enzyme. Our primary evidence for the mutant behavior was the lack of crosslinking (Figure 1E), with supplementary support from the negative stain. In the revised version we will soften the language to say “reduced” rather than “did not support” filament formation.

Reviewer #2 (Public review):

The authors set out to resolve the high-resolution structure of a glutamine synthetase (GS) decamer using cryo-EM, investigate glutamine binding at the decamer interface, and validate structural observations through biochemical assays of ATP hydrolysis linked to enzyme activity. Their work sits at the intersection of structural and functional biology, aiming to bridge atomic-level details with biological mechanisms - a goal with clear relevance to researchers studying enzyme catalysis and metabolic regulation.

Strengths and weaknesses of methods and results:

A key strength of the study lies in its use of cryo-EM, a technique well-suited for resolving large, dynamic macromolecular complexes like the GS decamer. The reported resolutions (down to 2.15 Å) initially suggest the potential for detailed structural insights, such as side-chain interactions and ligand density. However, several methodological limitations significantly undermine the reliability of the results:

(1) Cryo-EM data processing: The absence of critical details about B-factor sharpening - a standard step to enhance map interpretability - is a major concern. For high-resolution maps (<3 Å), sharpening is typically applied to resolve side-chain features, yet the submitted maps (e.g., those in Figures 1D, 2D, and supplementary figures) appear unprocessed, with density quality inconsistent with the claimed resolutions. This makes it difficult to evaluate whether observed features (e.g., glutamine binding) are genuine or artifacts of unsharpened data.

(2) Modeling and density consistency: The structural models, particularly for glutamine binding at the decamer interface, do not align with the reported resolution. The maps shown in Figure 2D and Supplementary Figure S7 lack sufficient density to confidently place glutamine or even surrounding residues, conflicting with claims of 2.15 Å resolution. Additionally, fitting a non-symmetric ligand (glutamine) into a symmetry-refined map requires justification, as symmetry constraints may distort ligand placement.

(3) Biochemical assay controls: While the enzyme activity assays aim to link structure to function, they lack essential controls (e.g., blank reactions without GS or substrates, substrate omission tests) to confirm that ATP hydrolysis is GS-dependent. The use of TCEP, a reducing agent, is also not paired with experiments to rule out unintended effects on the PK/LDH system, further limiting confidence in activity measurements.

Achievement of aims and support for conclusions:

The study falls short of convincingly achieving its goals. The claimed high-resolution structural details (e.g., side-chain densities, ligand binding) are not supported by the provided maps, which lack sharpening and show inconsistencies in density quality. Similarly, the biochemical data do not robustly validate the structural claims due to missing controls. As a result, the evidence is insufficient to confirm glutamine binding at the decamer interface or the functional relevance of the observed structural features.

Likely impact and utility:

If these methodological gaps are addressed, the work could make a meaningful contribution to the field. A well-resolved GS decamer structure would advance understanding of enzyme assembly and ligand recognition, while validated biochemical assays would strengthen the link between structure and function. Improved data processing and clearer reporting of validation steps would also make the structural data more reliable for the community, providing a resource for future studies on GS or related enzymes.

We disagree with the reviewer’s overall assessment.

With regard to sharpening and resolution: we examined sharpened maps and in a revised version will present additional supplementary figures showing these maps side by side. We note that the resolutions reported are global and that the most interesting features are, of course, in the periphery and subject to conformational and compositional heterogeneity. We will include supplementary figures of core side chain densities that are more like what are expected by the reviewer in the revision. 

With regard to modeling: the apo filament and turnover filament datasets were handled nearly identically. The additional density is therefore likely not artefactual to the symmetry operator - however, the lower resolution in this region noted by the reviewer is worthy of further exploration. The maps are public and we think this is the most plausible interpretation of the density, which we based primarily on the biochemical data and will include more speculation in the version.

With regard to the biochemical controls: we point the reviewer to Figure S1, which shows that omission of ammonia or glutamate in the wild-type (tagless) system removes any coupling of the reactions. We will perform the additional controls to publication quality in the revised version along with the TCEP control. We note that the reducing agent is present across all experiments, ruling out an effect on any specific result. The inclusion of TCEP is also very standard in other published uses of the Coupled ATPase assay (e.g. PMID: 31778111 and PMID: 32483380 by our first author)

Additional context:

Cryo-EM has transformed structural biology by enabling high-resolution analysis of large complexes, but its success hinges on rigorous data processing and validation steps that are critical to ensuring reproducibility. The challenges highlighted here are not unique to this study; they reflect broader issues in the field where incomplete reporting of methods can obscure the reliability of results. By addressing these points, the authors would not only strengthen their current work but also set a positive example for transparent and rigorous structural biology research.

All the data is public and the reviewer or anyone is free to reinterpret the maps and models - and we encourage that rather than just an interpretation of our static figures. In addition, we will upload the raw micrograph data for the apo filament and turnover filament datasets to EMPIAR prior to submitting the revision.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

(1) In Figure 2D and Supplementary Figure 7, the extra density observed between the two decamers does not appear to have the defining features of a glutamine. A less defined density may be expected given the nature of the complex, but even though mutagenesis assays were performed to support this assignment, none of these results constitutes direct and conclusive evidence for glutamine binding at this site. I would thus suggest showing the density maps at multiple contour thresholds to allow readers to also better evaluate the various small molecules under turnover conditions that cannot be well fitted based on this density map, helping to provide a more balanced interpretation of the results.

(2) On the same point regarding the density for the enzyme under turnover conditions, more details should be provided about the symmetry expansion and classification performed, and also show the approximate ratio of reconstructions that include this density. Did you try symmetry expansion followed by focused classification, especially on the interface region?

(3) The interface between the two decamers of the model needs to be double-checked and reassigned, especially for the residues surrounding the fitted glutamine. For example, the side chain of the Lys residue shown in the attached figure is most likely modeled incorrectly.

We thank the reviewer for the feedback. As noted above, we will include supplemental figures that show maps at multiple thresholds and sharpening schemes. We noted in the manuscript and above that our interpretation here is based on integrating biochemical evidence alongside the density and will make that even more clear in the revised manuscript. The filaments +/- the putative glutamine density were processed nearly identically, but we will attempt various schemes of focused classification/symmetry expansion in the revision as well. However, we point out that there is extensive averaging there that makes modeling a bit trickier than expected given the global resolution.

Author response:

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only). 

The reviewer is correct that in this version of the tool, we focused on SNPs with effect on gene expression, as the majority of the SNPs identified by GWASs are non-coding SNPs. In the future improvement, we should also include coding SNPs that change the amino acid sequence of genes. We will discuss this point more in the revised manuscript.

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead. 

This is a simple scheme to incorporate eQTL data from multiple tissues, assuming that the tissue that gives the strongest association is most relevant, or mainly mediates the effect from the SNP to the phenotype. This is a reasonable approach given that the tissues of origin for most of the phenotypes are unknown. In the future improvement, we should incorporate eQTL data from the appropriate tissue(s) if that is known.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants. 

This is due to one of the limitations of the current algorithm: no direction of association is predicted explicitly. It could be that increasing the expression of a gene reduced the risk of one disease but increase the risk of another. Currently we have to analyze the details of the SNPs to infer direction once overlapping genes are found. This needs improvement in the future.  

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role. 

We plan to incorporate reviewer's suggestion in the revised manuscript.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional? 

We will consider a different presentation in the revised manuscript.

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals? 

We will add these information in the revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

Strengths:

The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

Weaknesses:

(1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

Missing tissues or sparse eQTL certainly can produce false negatives as the signals linking the two phenotypes are simply not captured in the data. It is less likely to produce false positives as long as the statistical test is well controlled.   

(2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this? 

When there are multiple SNPs in LD with multiple genes nearby, it is generally difficult to map the causal SNP and the causal gene it affected, and thus there will be spurious gene-trait links. When we calculate the global similarity based on the gene-trait association profiles,  we tried to control this by simulating with random GWASs that have the same power as the real GWAS and preserve the LD structure, as the spurious links will also be present in the simulated data (but may appear in different loci) that are used to calibrate the statistical significance. 

(3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these. 

We assign SNPs to genes based on their strongest eQTL association from the available data. Improvement can be made if the relevant tissues for a trait are known (see response to Reviewer 1 above).

(4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative. 

We agree with the reviewer that the reported trait links are speculative, and they should be treated as hypotheses generated from the computational analyses. To truly validate some of these proposed relationships, deeper functional analyses and experimental tests are needed.

(5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness. 

We agree more control on LD and power-matched simulations will be important for testing the robustness of the predictions.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this review, the author covered several aspects of the inflammation response, mainly focusing on the mechanisms controlling leukocyte extravasation and inflammation resolution.

Strengths:

This review is based on an impressive number of sources, trying to comprehensively present a very broad and complex topic.

Weaknesses:

(1) This reviewer feels that, despite the title, this review is quite broad and not centred on the role of the extracellular matrix.

(2) The review will benefit from a stronger focus on the specific roles of matrix components and dynamics, with more informative subheadings.

(3) The macrophage phenotype section doesn't seem well integrated with the rest of the review (and is not linked to the ECM).

(4) Table 1 is difficult to follow. It could be reformatted to facilitate reading and understanding

(5) Figure 2 appears very complex and broad.

(6) Spelling and grammar should be thoroughly checked to improve the readability.

This review focuses on the whole extravasation journey of leukocyte and highlights involvement of extracellular matrix (ECM) in multiple phases of the process. ECM may exert their roles either as a collective structure or as individual components. In the revision, for those functions involving specific matrix components, we will emphasize the matrix components and incorporate this information to subheadings as suggested. The parts of macrophage phenotype (Section 10-11) are included for its pivotal roles on deciding the tissue fate following inflammation (ie. to resolve / to regenerate damages incurred or to sustain inflammation), which is an important aspect of this review. ECM could modify macrophage phenotypes either directly (section 10) or indirectly via modulations of tissue stiffness or other cell types like fibroblasts (section 9). However, as pointed out by other reviewers as well, we acknowledge that Section 11 does not integrate well enough to the rest of the review. We plan to reorganize this part and to emphasize its link to ECM during the revision for better integration. We will reformat Table 1 for easier comprehension. We will consider restructuring Figure 2, which outlines various events influencing tissue decision of resolution/inflammation, perhaps by breaking up into two separate figures, to better focus the message. We will also check the language to improve readability.

Reviewer #2 (Public review):

Summary:

The manuscript is a timely and comprehensive review of how the extracellular matrix (ECM), particularly the vascular basement membrane, regulates leukocyte extravasation, migration, and downstream immune function. It integrates molecular, mechanical, and spatial aspects of ECM biology in the context of inflammation, drawing from recent advances. The framing of ECM as an active instructor of immune cell fate is a conceptual strength.

Strengths:

(1) Comprehensive synthesis of ECM functions across leukocyte extravasation and post-transmigration activity.

(2) Incorporation of recent high-impact findings alongside classical literature.

(3) Conceptually novel framing of ECM as an active regulator of immune function.

(4) Effective integration of molecular, mechanical, and spatial perspectives.

Weaknesses:

(1) Insufficient narrative linkage between the vascular phase (Sections 2-6) and the in-tissue phase (Sections 7-10).

(2) Underrepresentation of lymphocyte biology despite mention in early sections.

(3) The MIKA macrophage identity framework is only loosely tied to ECM mechanisms.

(4) Limited discussion of translational implications and therapeutic strategies.

(5) Overly dense figure insets and underdeveloped links between ECM carryover and downstream immune phenotypes.

(6) Acronyms and some mechanistic details may limit accessibility for a broader readership.

We will add a transition paragraph between Section 6 and Section 7 to provide a narrative that the extravasation processes affect downstream leukocyte functions. While lymphocytes follow a similar extravasation principle, their in-tissue activities differ from innate leukocytes. We will thus include discussion of lymphocyte-ECM crosstalk to Section 8 and/or 9 in the revision. We will restructure Section 11 and Figure 3 to better integrate to the rest of the review: In the current manuscript, we merely describe the capability of the MIKA framework to describe identity of any tissue macrophages and thus the framework could serve as a roadmap to facilitate identity normalization of pathological macrophages. We plan, in the revision, by employing the MIKA framework, to discuss and demonstrate linkage between macrophage identities and expression/production of modulators to functional ECM effectors described in Section 8-9. Regarding the comment of limited discussion of translational implications / therapeutic strategies, we will try to enrich this aspect throughout the manuscript where appropriate, in addition to the existing ones (eg. line 293-297; 388-391; 460-463; 512-517) We will also revise figure structure in general to avoid too dense information and to improve clarity. We will consider to provide a glossary explaining specialized terms to expand readership accessibility.

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the interaction between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently, with a few exceptions, such as a study focusing on the central nervous system (J Inflamm 21, 53 (2024) doi.org/10.1186/s12950-024-00426-6) and another on transmigration hotspots (J Cell Sci (2025) 138 (11): jcs263862 doi.org/10.1242/jcs.263862). This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author dedicates two sections to discussing the relevant barriers, namely, endothelial cell-cell junctions and the basement membrane. The following three paragraphs address how leukocytes interact with and transmigrate through endothelial junctions, the mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The subsequent question of whether the extravasation process affects leukocyte differentiation and properties is original and thought-provoking, having received limited consideration thus far. The consequences of the interaction between leukocytes and the extracellular matrix, particularly regarding efferocytosis, macrophage polarization, and the outcome of inflammation, are explored in the subsequent three chapters. The review concludes by examining tissue-specific states of macrophage identity.

Weaknesses:

Firstly, the first ten sections provide a comprehensive overview of the topic, presenting logical and well-formulated arguments that are easily accessible to a general audience. In stark contrast, the final section (Chapter 11) fails to connect coherently with the preceding review and is nearly incomprehensible without prior knowledge of the author's recent publication in Cell. Mol. Life Sci. CMLS 772 82, 14 (2024). This chapter requires significantly more background information for the general reader, including an introduction to the Macrophage Identity Kinetics Archive (MIKA), which is not even introduced in this review, its basis (meta-analysis of published scRNA-seq data), its significance (identification of major populations), and the reasons behind the revision of the proposed macrophage states and their further development. Secondly, while the attempt to integrate a vast amount of information into fewer figures is commendable, it results in figures that resemble a complex puzzle. The author may consider increasing the number of figures and providing additional, larger "zoom-in" panels, particularly for the topics of clot formation at transmigration hotspots and the interaction between ECM/ECM fragments and integrins. Specifically, the color coding (purple for leukocyte α6-integrins, blue for interacting laminins, also blue for EC α6 integrins, and red for interacting 5-1-1 laminins) is confusing, and the structures are small and difficult to recognize.

We agree with and appreciate the specific and helpful suggestions by the reviewer. During the revision, we will provide the requested background description of MIKA to enhance accessibility of general readership. As pointed out by other reviewers, since this part (Section 11) is less well-integrated to the rest of the review, we will restructure this part by linking tissue macrophage identities under MIKA framework to modulation of functional ECM effectors described in previous sections (Section 8-9). We acknowledge the current figure organization might be overly information-dense and will consider breaking down the contents to multiple figures. The size and color-coding issues will also be addressed.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work aims to elucidate the molecular mechanisms affected in hypoxic conditions, causing reduced cortical interneuron migration. They use human assembloids as a migratory assay of subpallial interneurons into cortical organoids and show substantially reduced migration upon 24 hours of hypoxia. Bulk and scRNA-seq show adrenomedullin (ADM) up-regulation, as well as its receptor RAMP2, confirmed atthe protein level. Adding ADM to the culture medium after hypoxic conditions rescues the migration deficits, even though the subtype of interneurons affected is not examined. However, the authors demonstrate very clearly that ineffective ADM does not rescue the phenotype, and blocking RAMP2 also interferes with the rescue. The authors are also applauded for using 4 different cell lines and using human fetal cortex slices as an independent method to explore the DLXi1/2GFP-labelled iPSC-derived interneuron migration in this substrate with and without ADM addition (after confirming that also in this system ADM is up-regulated). Finally, the authors demonstrate PKA-CREB signalling mediating the effect of ADM addition, which also leads to up-regulation of GABAreceptors. Taken together, this is a very carefully done study on an important subject - how hypoxia affects cortical interneuron migration. In my view, the study is of great interest.

Strengths:

The strengths of the study are the novelty and the thorough work using several culture methods and 4 independent lines.

Weaknesses:

The main weakness is that other genes regulated upon hypoxia are not confirmed, such that readers will not know until which fold change/stats cut-off data are reliable.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM.

Strengths:

The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC lines including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript.

Weaknesses:

I have a few comments and suggestions for the authors. See below.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their  conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids

recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors should examine if all cortical interneurons are affected by ADM or only subtypes (Parvalbumin/Somatostatin).

We thank the reviewer for raising this important question. In our study, we utilized the Dlx1/2b::eGFP reporter to broadly label cortical interneurons; however, this system does not distinguish specific interneuron subtypes. To address this, in the revised version of the manuscript we will use the single-cell RNA sequencing data and immunostainings to provide this information. Based on previous analyses from Birey et al (Cell Stem Cell, 2022), we expect interneurons within assembloids to express mostly calbindin (CALB2) and somatostatin (SST) at this in vitro stage of development; parvalbumin subtype appears later based on data from Birey et al (Nature, 2017) and more recently from Varela et al, (bioRxiv, 2025).

In parallel, we will analyze available scRNA-seq data from developing human primary brain tissue a similar age as the one used in the manuscript, and check whether these subtypes of interneurons are similar to the ones within assembloids.

(2) The authors should test more candidates from their bulk RNA-seq data with different fold changes for regulation after hypoxia, to allow the reader to judge at which cut-off the DEGs may be reproducible. This would make this database much more valuable for the field of hypoxia research.

We appreciate the reviewers’ thoughtful suggestion. In addition to the bulk RNA-seq analysis, we did validate several upregulated hypoxia-responsive genes with varying fold changes by qPCR; these include PDK1, PFKP, VEGFA (Figure S1). 

We go agree that in-depth investigation of specific cut-offs would be interesting, however, this could be the focus of a different manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

We thank the reviewer this important comment about inflammation. Indeed, hypoxia has been shown to activate the inflammatory response pathways. In various studies, it was found that HIF-1a can interact with NF-κB signaling, leading to the upregulation of pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α (Rius et al., Cell, 2008; Hagberg et al., Nat Rev Neurol, 2015).

In our transcriptomics data (Figure 2D), and to the reviewers’ point, we identified enrichment of inflammatory signaling response following the hypoxic exposure. Since hSO at the time of analyses do contain astrocytes, we think these glia contribute to the observed pro-inflammatory changes. Based on these results and because ADM is known to have strong anti-inflammatory properties, the effects of ADM on hypoxic astrocytes should be investigated in future studies focused on hypoxia-induced inflammation. In the revision, we will address this comment in the discussion section and cite the appropriate papers.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

Based on our scRNA-seq data in hSOs showing significant upregulation of ADM expression in astrocytes and progenitors, we speculate that the primary mechanism is likely to involve paracrine interactions. However, we cannot exclude autocrine mechanisms with the included experiments. Dissecting these interactions in a cell-type specific manner could be an important focus for future ADM-related studies.

To address the question about the possible different mechanisms in ventral versus dorsal areas, in the revision we will plot and include in the figures the data about the cell-type expression of ADM and its receptors in hCOs.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

We thank the reviewer for this comment and the observation. Although we did not include a traditional positive control in these ELISA assays, several lines of evidence indicate that the measurements are reliable. First, the standard curves behaved as expected, and all sample values fell within the assay’s dynamic range. Second, technical replicates showed low variability, and the observed changes across experimental conditions (e.g., hypoxia vs. control) were consistent with the expected biological responses based on previous literature. We agree that including western blot validation would strengthen the findings, and we will note this for our future studies focused on CREB and ADM.

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

We appreciate the reviewers’ insightful question. Currently, not much is known about the molecular pathways and downstream cellular events triggered by ADM binding to RAMP2 in inhibitory neurons, and in general in brain cells. The data from our study brings the first information about the cell-type specific expression of ADM in baseline and hypoxic conditions and is one of the key novelties of our study.

While the signaling landscape of ADM in interneurons is largely unexplored, several studies in other (non-brain) cell types have demonstrated that ADM binding to RAMP2 can activate downstream cascades such as the cAMP/PKA/CREB pathway, PI3K/AKT, and ERK/MAPK, all of which are also known to be critical regulators of neuronal development and survival. These previously published data along with our CREB-targeted findings in hypoxic interneurons, suggest ADM–RAMP2 signaling could influence multiple aspects of interneuron biology, but these remain to be evaluated in future studies.

We agree with the reviewer that CREB has a wide range of transcriptional targets. We decided to focus on GABA as a target of CREB for two main reasons, including: (i) GABA signaling has been previously shown to play an important role in the migration of cortical interneurons, and (ii) a previous study by Birey et al. (Cell Stem Cell, 2022) demonstrated that CREB pathway activity is essential for regulating interneuron migration in assembloid models of Timothy Syndrom, thus further providing evidence that dysregulation of CREB activity disrupts migration dynamics.

While our study provides a first step toward uncovering the mechanisms of interneuron migration protection by ADM, we fully acknowledge that future work will be needed to delineate the full spectrum of ADM–RAMP2 downstream signaling events in inhibitory neurons and other brain cells.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

We appreciate this question from the reviewer; however, this was not something that we focused on in this manuscript due to the already large amount of data included. A separate study focusing on neurogenesis defects and the molecular mechanisms of injury for that specific developmental process would be an important next step.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

We thank the Reviewer for the suggestion of detailing the functional impact of reduced inhibitory neuron migration. We will revise the manuscript by incorporating a paragraph about this in the Discussion section.

Reviewer #3 (Recommendations for the authors):

Most of the evidence presented is convincing in supporting the conclusions, and I have only minor suggestions for improvement:

(1) The bulk RNA-seq was performed in hSOs only, which may not fully capture the phenotypes of migrating or migrated interneurons. It would be valuable, if feasible, to sort migrated cells from hSO-hCO assembloids and specifically examine their molecular mediators.

We thank the reviewer for this suggestion. While it is likely that the cellular environment will have some influence on a subset of the molecular changes, based on all the data from the manuscript and our specific target, the RNA-sequencing on hCOs was sufficient to capture essential changes like ADM upregulation. The in-depth exploration on differential responses of migrated versus non-migrated interneurons to hypoxia could be the focus of a different project.

(2) In Figure 3, it is striking that cell-type heterogeneity dominates over hypoxia vs. control conditions. A joint embedding of hSO and hCO cells could provide further insight into molecular differences between migrated and non-migrated interneurons.

We thank the reviewer for this observation and opportunity to clarify. Since we manually separated the assembloids before the analyses, we processed these samples separately. That is why they separate like this. In the revision, we will add data about ADM expression and its receptors’ expression in the hCOs.

(3) It would be helpful to expand the discussion on how closely the migration observed in hSO-hCO assembloids reflects in vivo conditions, and what environmental aspects are absent from this model. This would better frame the interpretation and translational relevance of the findings.

We thank the Reviewer for bringing up this important point. Although the assembloid model offers the unique advantage of allowing the direct investigation of migration patterns of hypoxic interneurons, we fully agree it does not fully recapitulate the in vivo environment. While there are multiple aspects that cannot be recapitulated in vitro at this time (e.g. cellular complexity, vasculature, immune response, etc), we are encouraged by the validation of our main findings in ex vivo developing human brain tissue, which strongly supports the validity of our findings for in vivo conditions.

We will expand our discussion to include more details and the need to validate these findings using in vivo models, while also acknowledging that different species (e.g. rodents versus non-human primates versus humans) might have different responses to hypoxia.

(4) The authors suggest that hypoxia is also associated with delayed interneuron maturation, yet the bulk RNA-seq data primarily reveal stress and hypoxia-related genes. A more detailed discussion of why genes linked to interneuron maturation and function were not strongly affected would clarify this point.

We thank the Reviewer for the opportunity to clarify.

The RNAseq data was performed during the acute stages of hypoxia/reoxygenation and we think a maturation phenotype might be difficult to capture at this point and would require analysis at later in vitro assembloid maturation stages.

Our speculation about a possible maturation defect is based on data from previous studies from developmental biology that showed failure of interneurons to reach their final cortical location within a specified developmental window will impair their integration within the neuronal network, and thus lead to maturation defects and possible elimination by apoptosis.

Since preterm infants suffer from countless hypoxic events over multiple months, we suggest these repetitive events are likely to induce cumulative delays in migration, inability of interneurons to reach their target in time, followed by abnormal integration within the excitatory network, and eventual elimination of some of these interneurons through apoptosis. However, the direct demonstration of this effect following a hypoxic insult would require prolonged in vivo experiments in rodents to follow the migration, network integration and apoptosis of interneurons; to our knowledge this experimental design is not technically feasible at this time.

(5) Relatedly, while the focus on interneuron migration is well justified, acknowledging how hypoxia might also impact other aspects of cortical development (e.g., progenitor proliferation, neuronal maturation, or circuit integration) would place the findings in a broader developmental framework and strengthen their relevance.

We appreciate the Reviewer’s suggestion to discuss the role of hypoxia on other processes during cortical development. In the revised manuscript, we will include citations about the effects of hypoxia on interneuron proliferation, maturation and circuit integration as available, and also expand to other cell types known to be affected.

(6) Very minor: in Figure S3C and D, it was not stated what the colors mean (grey: control, yellow: hypoxia)

Thank you for pointing out this error and we will correct it in our revision.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review):

The only aspect that would benefit from further clarification is a more detailed discussion of aging-associated ECM changes in the context of prior literature. 

Thank you. Please refer to the new section (Lines 604-617)

Reviewer #3 (Public review):

(1) It would be useful to explain why GATA4 was chosen over HIF1a, which was the most differentially expressed. 

Thank you. Please refer to Lines 530-537.  

“Of note, Hypoxia-Inducible Factor 1α (HIF1 α) was the most differentially expressed gene predicted to regulate chondrocyte aging. The connection between HIF1 α and aging has been previously reported.[32] Furthermore, additional studies have investigated HIF1 in association with OA and assessed its use as a therapeutic target.[33,34] Therefore, we decided to focus on GATA4, which was less studied in chondrocytes but highly associated with cellular senescence, an aging hallmark. However, our selection did not dampen the importance of HIF1α and other molecules listed in Figure 1D in chondrocyte aging. They can be further studied in the future using the same strategy employed in the current work.”

(2) In Figure 5, it would be useful to demonstrate the non-surgical or naive limbs to help contextualize OARSI scores and knee hyperalgesia changes. 

In the current study, we focused on the DMM control and DMM Gata4 virus groups so we did not include a sham control group. We recognized this was a limitation of this study.  

(3) While there appear to be GATA4 small-molecule inhibitors in various stages of development that could be used to assess the effects in age-related OA, those experiments are out of scope for the current study.  

We agree with this comment that the results are still preliminary, which was the reason that we put it in the supplementary materials. However, we felt like the result is informative, which will support the potential of GATA4 as a therapeutic target and inspire the development of more specific inhibitors. Therefore, we would still keep the results in the current study.

Author response:

Reviewer #1 (Public review):

(1) The strength of the relationship between the different transcriptional parameters and the mean expression output is displayed visually in Figures 5 and 7, but is not formally quantified. Given that the tau_off times seem more correlated to mean activity for some enhancers (e.g., rho) than others (e.g., sna SE), the quantification might be useful.

We re-plot Figure 5 and Figure 7 to present the correlation between the studied burst parameters. As the reviewer suggested, after quantifying the correlation we can better study the correlation between the cells averaged tau-off and the cell-averaged fluorescence signal in some of the selected enhancers. As a result of these findings we decide to change our message and instead of claiming that the burst statistics are homogeneous over the embryo domain, to claim that these statistics have weak but significant correlations with the cell-averaged mean gene fluorescence.  

(2) There are some mechanistic details that are not discussed in depth. For example, the authors observe that the accumulation and degradation of the MS2 signal have similar slopes. However, given that the accumulation represents the transcription of MS2 loops, while the degradation represents diffusion of nascent transcripts away from the site of transcription, there is no mechanistic expectation for this. The degradation of signal seems likely to be a property of the mRNA itself, which shouldn't vary between cells or enhancer reporters, but the accumulation rate may be cell- or enhancer-specific. Similarly, the activity time depends both on the time of transcription onset and the time of transcription cessation. These two processes may be controlled by different transcription factor properties or levels and may be interesting to disentangle.

The accumulation slope represents the rate of nascent transcript production, which depends on transcription initiation frequency and RNA polymerase elongation rate. While transcription initiation rates can vary between enhancers, our results show that the loading rates are relatively comparable across different enhancer sequences (Figure 5D). Instead, the primary difference observed was in activity time and burst frequency, consistent with previous findings that enhancers predominantly modulate burst frequency (Fukaya et al., 2016). The degradation slope represents the diffusion of completed transcripts away from the transcription site, which should be an intrinsic property of the mRNA molecule and therefore independent of the regulatory sequences driving transcription.

(3) There are previous analyses of the eve stripe dynamics, which the authors cite, but do not compare the results of their work to the previous work in depth.

The goal of this manuscript is to compare transcriptional bursting properties across different enhancers, rather than to provide an in-depth analysis of eve stripe dynamics specifically. We analyzed four transgenic constructs with different enhancers alongside an endogenous eve construct, focusing on comparative bursting parameters rather than detailed eve expression patterns. Additionally, the previously published eve stripe dynamics data came from BAC constructs, whereas our data comes from the endogenous eve locus. This methodological difference makes direct comparison of stripe dynamics less straightforward and less relevant to our central research question about enhancer-driven bursting variability.

Reviewer #2 (Public review):

(1) The manuscript does not clearly delineate how this analysis extends beyond the prior landmark study (citation #40: Fukaya et al., 2016). While the current manuscript offers new modeling and statistics, more explicit clarification of what is novel in terms of biological conclusions and methodological advancement would help position the work.

The prior study (Fukaya et al., 2016) characterized transcriptional bursting qualitatively, focusing on average burst properties per nucleus without systematic mathematical modeling or statistical analysis of burst-to-burst variability. While they demonstrated that enhancer strength correlates with burst frequency, no quantitative framework was developed to dissect the molecular mechanisms underlying these differences or to connect burst dynamics to spatial gene expression patterns.

(1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.

(2) While the methods are explained in detail in the Supplementary Information, the manuscript would benefit from including a diagrammatic model and explicitly clarifying whether the model is descriptive or predictive in scope.

We plan to prepare the diagrammatic model in the formal response. 

(3) The interpretation that fluorescence decay reflects RNA degradation could be confounded by polymerase runoff or transcript diffusion from the transcription site. These potential limitations are not thoroughly discussed. (Write few lines in the discussion)

This concern, related to the interpretation of the predictive model will be addressed in a future work. The decay in the fluorescence signal can be biologically related to the transcription termination, polymerase detachment, and diffusion. A key limitation of the approach is that the model is phenomenological and does not these capture processes that can be addressed with a more mechanistic model.

(4) The so-called loading rate is used as an empirical parameter in fitting fluorescence traces, but is not convincingly linked to distinct biological processes. The manuscript would benefit from a more precise definition or reframing of this term.

We modify the language of our definition of loading rate as follows: “Loading rate is defined as the rate of increase of fluorescence signal following promoter activation. This quantity is a proxy measurement for the rate of RNA Polymerase II transcription initiation.” The full transcription process has multiple mechanisms including chromatin dynamics, 3D enhancer-promoter interactions, transcription factor binding, mRNA polymerase pausing, and interactions between developmental promoter motifs and associated proteins. We did not have access to specific measurements of these mechanisms and therefore cannot provide a solid biological meaning of the model behind the inference algorithm. However, the fact that we have reproducible results in biological replicas can support the robustness of our method at predicting the promoter state in the studied datasets. In the formal response we will compare the performance of our method with other available ones.

Reviewer #3 (Public review):

(1)The algorithm is not benchmarked against previously used algorithms in the field to infer ON and OFF times, for example, those based on Hidden Markov models. A comparison would help strengthen the support for this algorithm (if it really works well) or show at which point one must be careful when interpreting this data.

We are implementing a benchmarking protocol to compare our results with the proposed and already published models. We expect to present this comparison in the formal response.

(2) More broadly, the novelty of the findings and how those fit within the knowledge of the field is not super clear. A better account of previous findings that have already quantified ON, OFF times and so on, and how the current findings fit within those, would help better appreciate the significance of the work.

To have a better clarity of the new findings we modified the title from “Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development” to “Temporal Duration of Gene Activity is the main Regulator of Spatial Expression Patterns in Early Drosophila Embryos”.

In short, (1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.

Author response:

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

Reviewer #1 (Public review):  

Summary:  

The authors state the study's goal clearly: "The goal of our study was to understand to what extent animal individuality is influenced by situational changes in the environment, i.e., how much of an animal's individuality remains after one or more environmental features change." They use visually guided behavioral features to examine the extent of correlation over time and in a variety of contexts. They develop new behavioral instrumentation and software to measure behavior in Buridan's paradigm (and variations thereof), the Y-maze, and a flight simulator. Using these assays, they examine the correlations between conditions for a panel of locomotion parameters. They propose that inter-assay correlations will determine the persistence of locomotion individuality.

Strengths:  

The OED defines individuality as "the sum of the attributes which distinguish a person or thing from others of the same kind," a definition mirrored by other dictionaries and the scientific literature on the topic. The concept of behavioral individuality can be characterized as: (1) a large set of behavioral attributes, (2) with inter-individual variability, that are (3) stable over time. A previous study examined walking parameters in Buridan's paradigm, finding that several parameters were variable between individuals, and that these showed stability over separate days and up to 4 weeks (DOI: 10.1126/science.aaw718). The present study replicates some of those findings and extends the experiments from temporal stability to examining correlation of locomotion features between different contexts.  

The major strength of the study is using a range of different behavioral assays to examine the correlations of several different behavior parameters. It shows clearly that the inter-individual variability of some parameters is at least partially preserved between some contexts, and not preserved between others. The development of high-throughput behavior assays and sharing the information on how to make the assays is a commendable contribution.

Weaknesses:  

The definition of individuality considers a comprehensive or large set of attributes, but the authors consider only a handful. In Supplemental Fig. S8, the authors show a large correlation matrix of many behavioral parameters, but these are illegible and are only mentioned briefly in Results. Why were five or so parameters selected from the full set? How were these selected? Do the correlation trends hold true across all parameters? For assays in which only a subset of parameters can be directly compared, were all of these included in the analysis, or only a subset?  

The correlation analysis is used to establish stability between assays. For temporal re-testing, "stability" is certainly the appropriate word, but between contexts it implies that there could be 'instability'. Rather, instead of the 'instability' of a single brain process, a different behavior in a different context could arise from engaging largely (or entirely?) distinct context-dependent internal processes, and have nothing to do with process stability per se. For inter-context similarities, perhaps a better word would be "consistency".  

The parameters are considered one-by-one, not in aggregate. This focuses on the stability/consistency of the variability of a single parameter at a time, rather than holistic individuality. It would appear that an appropriate measure of individuality stability (or individuality consistency) that accounts for the high-dimensional nature of individuality would somehow summarize correlations across all parameters. Why was a multivariate approach (e.g. multiple regression/correlation) not used? Treating the data with a multivariate or averaged approach would allow the authors to directly address 'individuality stability', along with the analyses of single-parameter variability stability.

The correlation coefficients are sometimes quite low, though highly significant, and are deemed to indicate stability. For example, in Figure 4C top left, the % of time walked at 23{degree sign}C and 32{degree sign}C are correlated by 0.263, which corresponds to an R2 of 0.069 i.e. just 7% of the 32{degree sign}C variance is predictable by the 23{degree sign}C variance. Is it fair to say that 7% determination indicates parameter stability? Another example: "Vector strength was the most correlated attention parameter... correlations ranged... to -0.197," which implies that 96% (1 - R2) of Y-maze variance is not predicted by Buridan variance. At what level does an r value not represent stability?

The authors describe a dissociation between inter-group differences and inter-individual variation stability, i.e. sometimes large mean differences between contexts, but significant correlation between individual test and retest data. Given that correlation is sensitive to slope, this might be expected to underestimate the variability stability (or consistency). Is there a way to adjust for the group differences before examining correlation? For example, would it be possible to transform the values to in-group ranks prior to correlation analysis?

What is gained by classifying the five parameters into exploration, attention, and anxiety? To what extent have these classifications been validated, both in general, and with regard to these specific parameters? Is increased walking speed at higher temperature necessarily due to increased 'explorative' nature, or could it be attributed to increased metabolism, dehydration stress, or a heat-pain response? To what extent are these categories subjective?

The legends are quite brief and do not link to descriptions of specific experiments. For example, Figure 4a depicts a graphical overview of the procedure, but I could not find a detailed description of this experiment's protocol.

Using the current single-correlation analysis approach, the aims would benefit from re-wording to appropriately address single-parameter variability stability/consistency (as distinct from holistic individuality). Alternatively, the analysis could be adjusted to address the multivariate nature of individuality, so that the claims and the analysis are in concordance with each other.

The study presents a bounty of new technology to study visually guided behaviors. The Github link to the software was not available. To verify successful transfer or open-hardware and open-software, a report would demonstrate transfer by collaboration with one or more other laboratories, which the present manuscript does not appear to do. Nevertheless, making the technology available to readers is commendable.

The study discusses a number of interesting, stimulating ideas about interindividual variability and presents intriguing data that speaks to those ideas, albeit with the issues outlined above.

While the current work does not present any mechanistic analysis of interindividual variability, the implementation of high-throughput assays sets up the field to more systematically investigate fly visual behaviors, their variability, and their underlying mechanisms.  

Comments on revisions:  

I want to express my appreciation for the authors' responsiveness to the reviewer feedback. They appear to have addressed my previous concerns through various modifications including GLM analysis, however, some areas still require clarification for the benefit of an audience that includes geneticists.  

(1) GLM Analysis Explanation (Figure 9)  

While the authors state that their new GLM results support their original conclusions, the explanation of these results in the text is insufficient. Specifically:

The interpretation of coefficients and their statistical significance needs more detailed explanation. The audience includes geneticists and other nonstatistical people, so the GLM should be explained in terms of the criteria or quantities used to assess how well the results conform with the hypothesis, and to what extent they diverge.

The criteria used to judge how well the GLM results support their hypothesis are not clearly stated.

The relationship between the GLM findings and their original correlationbased conclusions needs better integration and connection, leading the reader through your reasoning.

We thank the reviewer for highlighting this important point. We have revised the Results section in the reviseed manuscript to include a more detailed explanation of the GLM analysis. Specifically, we now clarify the interpretation of the model coefficients, including the direction and statistical significance, in relation to the hypothesized effects. We also outline the criteria we used to assess how well the GLM supports our original correlation-based conclusions—namely, whether the sign and significance of the coefficients align with the expected relationships derived from our prior analysis. Finally, we explicitly describe how the GLM results confirm or extend the patterns observed in the correlation-based analysis, to guide readers through our reasoning and the integration of both approaches.

(2) Documentation of Changes  

One struggle with the revised manuscript is that no "tracked changes" version was included, so it is hard to know exactly what was done. Without access to the previous version of the manuscript, it is difficult to fully assess the extent of revisions made. The authors should provide a more comprehensive summary of the specific changes implemented, particularly regarding:

We thank the reviewer for bringing this to our attention. We were equally confused to learn that the tracked-changes version was not visible, despite having submitted one to eLife as part of our revision. 

Upon contacting the editorial office, they confirmed that we did submit a trackedchanges version, but clarified that it did not contain embedded figures (as they were added manually to the clean version).  The editorial response said in detail: “Regarding the tracked-changes file: it appears the version with markup lacked figures, while the figure-complete PDF had markup removed, which likely caused the confusion mentioned by the reviewers.” We hope this answer from eLife clarifies the reviewers’ concern.

(2)  Statistical Method Selection  

The authors mention using "ridge regression to mitigate collinearity among predictors" but do not adequately justify this choice over other approaches. They should explain:

Why ridge regression was selected as the optimal method  

How the regularization parameter (λ) was determined  

How this choice affects the interpretation of environmental parameters' influence on individuality

We appreciate the reviewer’s thoughtful question regarding our choice of statistical method. In response, we have expanded the Methods section in the revised manuscript to provide a more detailed justification for the use of a GLM, including ridge regression. Specifically, we explain that ridge regression was selected to address collinearity and to control for overfitting.

We now also describe how the regularization parameter (λ) was selected: we used 5-fold cross-validation over a log-spaced grid (10^⁻⁶ - 10<sup>⁶</sup) to identify the optimal value that minimized the mean squared error (MSE).

Finally, we clarify in both the Methods and Results sections how this modeling choice affects the interpretation of our findings. 

Reviewer #2 (Public review):  

Summary:  

The authors repeatedly measured the behavior of individual flies across several environmental situations in custom-made behavioral phenotyping rigs.

Strengths:  

The study uses several different behavioral phenotyping devices to quantify individual behavior in a number of different situations and over time. It seems to be a very impressive amount of data. The authors also make all their behavioral phenotyping rig design and tracking software available, which I think is great, and I'm sure other folks will be interested in using and adapting to their own needs.

Weaknesses/Limitations:  

I think an important limitation is that while the authors measured the flies under different environmental scenarios (i.e. with different lighting, temperature) they didn't really alter the "context" of the environment. At least within behavioral ecology, context would refer to the potential functionality of the expressed behaviors so for example, an anti-predator context, or a mating context, or foraging. Here, the authors seem to really just be measuring aspects of locomotion under benign (relatively low risk perception) contexts. This is not a flaw of the study, but rather a limitation to how strongly the authors can really say that this demonstrates that individuality is generalized across many different contexts. It's quite possible that rank-order of locomotor (or other) behaviors may shift when the flies are in a mating or risky context.  

I think the authors are missing an opportunity to use much more robust statistical methods It appears as though the authors used pearson correlations across time/situations to estimate individual variation; however far more sophisticated and elegant methods exist. The problem is that pearson correlation coefficients can be anti-conservative and additionally, the authors have thus had to perform many many tests to correlate behaviors across the different trials/scenarios. I don't see any evidence that the authors are controlling for multiple testing which I think would also help. Alternatively, though, the paper would be a lot stronger, and my guess is, much more streamlined if the authors employ hierarchical mixed models to analyse these data, which are the standard analytical tools in the study of individual behavioral variation. In this way, the authors could partition the behavioral variance into its among- and within-individual components and quantify repeatability of different behaviors across trials/scenarios simultaneously. This would remove the need to estimate 3 different correlations for day 1 & day 2, day 1 & 3, day 2 & 3 (or stripe 0 & stripe 1, etc) and instead just report a single repeatability for e.g. the time spent walking among the different strip patterns (eg. figure 3). Additionally, the authors could then use multivariate models where the response variables are all the behaviors combined and the authors could estimate the among-individual covariance in these behaviors. I see that the authors state they include generalized linear mixed models in their updated MS, but I struggled a bit to understand exactly how these models were fit? What exactly was the response? what exactly were the predictors (I just don't understand what Line404 means "a GLM was trained using the environmental parameters as predictors (0 when the parameter was not changed, 1 if it was) and the resulting individual rank differences as the response"). So were different models run for each scenario? for different behaviors? Across scenarios? What exactly? I just harp on this because I'm actually really interested in these data and think that updating these methods can really help clarify the results and make the main messages much clearer!

I appreciate that the authors now included their sample sizes in the main body of text (as opposed to the supplement) but I think that it would still help if the authors included a brief overview of their design at the start of the methods. It is still unclear to me how many rigs each individual fly was run through? Were the same individuals measured in multiple different rigs/scenarios? Or just one?

I really think a variance partitioning modeling framework could certainly improve their statistical inference and likely highlight some other cool patterns as these methods could better estimate stability and covariance in individual intercepts (and potentially slopes) across time and situation. I also genuinely think that this will improve the impact and reach of this paper as they'll be using methods that are standard in the study of individual behavioral variation

Reviewer #3 (Public review):  

This manuscript is a continuation of past work by the last author where they looked at stochasticity in developmental processes leading to inter-individual behavioural differences. In that work, the focus was on a specific behaviour under specific conditions while probing the neural basis of the variability. In this work, the authors set out to describe in detail how stable individuality of animal behaviours is in the context of various external and internal influences. They identify a few behaviours to monitor (read outs of attention, exploration, and 'anxiety'); some external stimuli (temperature, contrast, nature of visual cues, and spatial environment); and two internal states (walking and flying).

They then use high-throughput behavioural arenas - most of which they have built and made plans available for others to replicate - to quantify and compare combinations of these behaviours, stimuli, and internal states. This detailed analysis reveals that:

(1) Many individualistic behaviours remain stable over the course of many days.  

(2) That some of these (walking speed) remain stable over changing visual cues. Others (walking speed and centrophobicity) remain stable at different temperatures.

(3) All the behaviours they tested fail to remain stable over spatially varying environment (arena shape).

(4) and only angular velocity (a read out of attention) remains stable across varying internal states (walking and flying)

Thus, the authors conclude that there is a hierarchy in the influence of external stimuli and internal states on the stability of individual behaviours.

The manuscript is a technical feat with the authors having built many new high-throughput assays. The number of animals are large and many variables have been tested - different types of behavioural paradigms, flying vs walking, varying visual stimuli, different temperature among others.  

Comments on revisions:'  

The authors have addressed my previous concerns.  

We thank the reviewer for the positive feedback and are glad our revisions have satisfactorily addressed the previous concerns. We appreciate the thoughtful input that helped us improve the clarity and rigor of the manuscript.

Reviewer #1 (Recommendations for the authors):  

Comment on Revised Manuscript  

Recommendations for Improvement  

(1) Expand the Results section for Figure 9 with a more detailed interpretation of the GLM coefficients and their biological significance

(2) Provide explicit criteria (or at least explain in detail) for how the GLM results confirm or undermine their original hypothesis about environmental context hierarchy

While the claims are interesting, the additional statistical analysis appears promising. However, clearer explanation of these new results would strengthen the paper and ensure that readers from diverse backgrounds can fully understand how the evidence supports the authors' conclusions about individuality across environmental contexts. 

We thank the reviewer for these constructive suggestions. In response to these suggestions, we have expanded both the Methods and Results sections to provide a more detailed explanation of the GLM coefficients, including their interpretation and how they relate to our original correlation-based findings.

We now clarify how the direction, magnitude, and statistical significance of specific coefficients reflect the influence of different environmental factors on the persistence of individual behavioral traits. To make this accessible to readers from diverse backgrounds, we explicitly outline the criteria we used to evaluate whether the GLM results support our hypothesis about the hierarchical influence of environmental context, namely, whether the structure and strength of effects align with the patterns predicted from our prior correlation analysis.

These additions improve clarity and help readers understand how the new statistical results reinforce our conclusions about the context-dependence of behavioral individuality.

Reviewer #2 (Recommendations for the authors):  

Thanks for the revision of the paper! I updated my review to try and provide a little more guidance by what I mean about updating your analyses. I really think this is a super cool data set and I genuinely wish this were MY dataset so that way I could really dig into it to partition the variance. These variance partitioning methods are standard in my particular subfield (study of individual behavioral variation in ecology and evolution) and so I think employing them is 1) going to offer a MUCH more elegant and holistic view of the behavioral variation (e.g. you can report a single repeatability estimate for each behavior rather than 3 different correlations) and 2) improve the impact and readership for your paper as now you'll be using methods that a whole community of researchers are very familiar with. It's just a suggestion, but I hope you consider it!

We sincerely thank the reviewer for the insightful and encouraging feedback and for introducing us to this modeling approach. In response to this suggestion, we have incorporated a hierarchical linear mixed-effects model into our analysis (now presented in Figure 10), accompanied by a new supplementary table (Table T3). We also updated the Methods, Results, and Discussion sections to describe the rationale, implementation, and implications of the mixed-model analysis.

We agree with the reviewer that this approach provides a more elegant way to quantify behavioral variation and individual consistency across contexts. In particular, the ability to estimate repeatability directly aligns well with the core questions of our study. It facilitates improved communication of our findings to ecology, evolution, and behavior researchers. We greatly appreciate the suggestion; it has significantly strengthened both the analytical framework and the interpretability of the manuscript.

Author response:

Reviewer #1 (Public review):

Summary:

In this manuscript, Mahajan et. al. introduce two innovative macroscopic measures-intrachromosomal gene correlation length (𝓁∗) and transition energy barrier-to investigate chromatin structural dynamics associated with aging and age-related syndromes such as Hutchinson-Gilford Progeria Syndrome (HGPS) and Werner Syndrome (WRN). The authors propose a compelling systems-level approach that complements traditional biomarker-driven analyses, offering a more holistic and quantitative framework to assess genome-wide dysregulation. The concept of 𝓁∗ as a spatial correlation metric to capture chromatin disorganization is novel and well-motivated. The use of autocorrelation on distance-binned gene expression adds depth to the interpretation of chromatin state shifts. The energy landscape framework for gene state transitions is an elegant abstraction, with the notion of "irreversibility" providing a thermodynamic interpretation of transcriptional dysregulation. The application to multiple datasets (Fleischer, Line-1) and pathological states adds robustness to the analysis. The consistency of chromosome 6 (and to some extent chromosomes 16 and X) emerging as hotspots aligns well with known histone cluster localization and disease-relevant pathways. The manuscript does an excellent job of integrating transcriptomic trends with known epigenetic hallmarks of aging, and the proposed metrics can be used in place of traditional techniques like PCA in capturing structural transcriptome features. However, a direct correlation with ATACseq/HiC data with the present analysis will be more informative.

(1) In the manuscript, the authors mention "While it may be intuitive to assume that highly expressed genes originate from euchromatin, this cannot be conclusively stated as a complete representation of euchromatin genes, nor can LAT be definitively linked to heterochromatin". What percentage of LAT can be linked to heterochromatin? What is the distribution of LAT and HAT in the euchromatin?

Thank you for this insightful question. In the revision we will add chromatin state annotations using ChromHMM to identify overlap between HAT/LAT and corresponding chromatin state. This should provide the specific percentages and distributions you requested.

We would like to take this opportunity to clarify that based on the plots Fig S1, and differential gene expressions, HAT is most likely a subset of euchromatin and LAT may contain both euchromatin and heterochromatin. The HAT/LAT cutoff occurs around the knee point in the log-log plot (Figure S1), where the linear portion indicates scale-invariant behavior with similar relative changes across expression ranks. The non-linear portion represents departure from power-law scaling, where low-expression genes exhibit sharper decline than expected. This suggests potential biological mechanisms such as chromatin silencing, detection limits, or technical artifacts related to sequencing depth.

We will provide detailed chromatin state analysis in the revision. For reference, HAT gene lists per chromosome are available in our GitHub repository at: https://github.com/altoslabs/papers-2025-rnaseq-chrom-aging/tree/main/data/Preprocessed_dat a under /<dataset>/chromosome_{}/data_hi.

(2) In Figure 2, the authors observe "that the signal from the HAT class is the stronger between two and the signal from the LAT class, being mostly uniform, can be constituted as background noise." Is this biologically relevant? Are low-abundance transcripts constitutively expressed? The authors should discuss this in the Results section.

We apologize for the confusion arising from the usage of the term “background noise”. We agree that the distinction between high-abundance transcripts (HATs) and low-abundance transcripts (LATs) deserves more explicit discussion in the Results.

Our intention is to say that HAT has a higher signal-to-noise ratio (SNR) compared to LAT. This is coming from the power law graph of FigS1.  Our intention is to state that the HAT class provides a strong, robust signal, consistent across chromosomes and the LAT class exhibits lower SNR and a more uniform background-like distribution in the context of the problem we are solving and not rather a generic biological statement. The experiment result that led to this statement is presented in FigS3. This does not imply that low-abundance transcripts lack biological relevance, but rather that they contribute less to the spatial organization patterns we measure.

(3) The authors make a very interesting observation from Figure 3: that ASO-treated LINE-1 appears to be more effective in restoring HGPS cell lines closer to wild-type compared to WRN.. This can be explained by the difference in the basal activity of L1 elements in the HGPS vs WRN cell types. The authors should comment on this.

We thank the reviewer for this incisive biological observation. While the differential effectiveness of ASO-treated LINE-1 in HGPS versus WRN cell lines is indeed an interesting phenomenon that may relate to basal L1 activity differences, this biological mechanism falls outside the scope of our current study.

Our paper focuses on demonstrating that the 𝓁∗ metric can sensitively detect chromatin structural changes that have been independently validated. We utilize the Della Valle et al. (2022) dataset specifically because it provides experimentally confirmed chromatin structural differences (Progeroid vs wild-type vs ASO-treated Progeriod), allowing us to validate that 𝓁∗ correlates with these established changes.

For detailed discussion of the biological mechanisms underlying differential LINE-1 ASO effectiveness between progeroid syndromes, we would direct readers to Della Valle et al. (2022) and related LINE-1 biology literature. Our contribution lies in demonstrating that 𝓁∗ can capture these chromatin organizational changes with enhanced sensitivity compared to traditional expression-based approaches. We are reluctant, without further experimentation, to venture into over-interpreting these results from a biology perspective.  

(4) The authors report that "from the results on Fleischer dataset is the magnitude of the difference in similarity distance is more pronounced in 𝓁∗ than in gene expression." Does this mean that the alterations in gene distance and chromatin organization do not result in gene expression change during aging?

Thank you for this important clarification request. This observation, illustrated in Figure 3, highlights two key points: (1) 𝓁∗ shows similar trends to PCA analysis, and (2) 𝓁∗ demonstrates higher sensitivity than traditional gene expression analysis.

This enhanced sensitivity enables better discrimination between aging states, particularly in the Fleischer dataset representing natural aging where changes are more gradual. The higher sensitivity stems from 𝓁∗'s ability to capture transcriptional spatial organization through spatial autocorrelation, which can detect subtle organizational changes that may precede or accompany expression changes rather than replacing them.

We will clarify in the revision that chromatin organizational changes and gene expression changes are complementary rather than mutually exclusive phenomena during aging.

(5) "In Fleischer dataset, as evident in Figure 4a, although changes in the heterochromatin are not identical for all chromosomes shown by the different degrees of variation of 𝓁∗ in each age group." The authors should present a comprehensive map of each chromosome change in gene distance to better explain the above statement.

Thank you for the feedback. If we understand your comment correctly, we need to provide a chromosome-wise distribution for Fig3c. We will update the paper and the supplementary.

(6) While trends in 𝓁∗ are discussed at both global and chromosome-specific levels, stronger statistical testing (e.g., permutation tests, bootstrapping) would lend greater confidence, especially when differences between age groups or treatment states are modest.

Thank you for the helpful suggestion. In the revision, we will incorporate permutation-based significance testing by shuffling the gene annotation and count table to generate a null distribution for our 𝓁∗ calculation. This will allow us to more rigorously assess whether the observed differences across age groups or treatment states deviate from chance expectations and thereby lend greater statistical confidence to our findings.

(7) While the transition energy barrier is an insightful conceptual addition, further clarification on the mathematical formulation and its physical assumptions (e.g., energy normalization, symmetry conditions) would improve interpretability. Also, in between Figures 7 and 8, the authors first compare the energy barrier of Chromosome 1 and then for all other chromosomes.

What is the rationale for only analyzing chromosome 1? How many HAT or LAT are present there?

Regarding chromosome 1 focus: we initially presented chromosome 1 as a representative example, but we will include energy landscape analysis for all chromosomes in the supplementary materials

We use the same HATs that were extracted during 𝓁∗ for the energy landscape as well. The HAT details are present in the github repo, the link provided in response to 1st feedback.

The normalization of the energy barrier ensures comparability across chromosomes of different sizes and across samples with different absolute expression scales. Specifically, we normalize with respect to the total area under the two-dimensional energy landscape while using the thermal energy (k_B T) as a scaling factor to place transition energy barriers on the scale of thermal fluctuations. This is formally expressed as in Eq. (1). 

The physical consequences of symmetry in the energy landscape are discussed in lines 472-491 of the manuscript, where we also introduce the concept of irreversibility. In brief, the chromatin energy landscape (Figure 8) is constructed by quantifying the energy contributions of genes that are upregulated (lower triangular matrix) and downregulated (upper triangular matrix) between two states. If the integrated energy contributions of upregulated and downregulated genes are equal, the landscape is symmetric, representing a thermodynamically reversible process, for example, nucleosome repositioning between euchromatic and heterochromatic regions without net gain or loss of nucleosomes. However, in cases where epigenetic modifications alter nucleosome density (e.g., disease states that reduce nucleosome numbers), the integrated energies are unequal, reflecting an irreversible energy cost. In this case, restoring chromatin requires additional energy input (e.g., to replace “missing” nucleosomes), which manifests as asymmetry in the landscape.

Reviewer #2 (Public review):

The authors report that intra-chromosomal gene correlation length (spatial correlations in gene expressions along the chromosome) serves as a proxy of chromatin structure and hence gene expression. They further explore changes in these metrics with aging. These are interesting and important findings. However, there are fundamental problems at this time.

(1) The basic method lacks validation. There is no validation of the method by approaches that directly measure chromatin structure, for example ATAC-seq, ChIP-seq, or CUT n RUN.

We appreciate the reviewer’s point that direct measurements such as ATAC-seq and ChIP-seq remain the gold standard for characterizing chromatin structure. Our method is designed to complement, not replace, these approaches by leveraging RNA-seq data to detect large-scale transcriptional patterns that correlate with chromatin dynamics.

We agree that integrating datasets with paired RNA-seq and chromatin accessibility assays would strengthen the manuscript and plan to include one such dataset in the revision.

Based on this feedback, we will also take the opportunity during revision to clarify and soften certain statements. Specifically, we will reposition ℓ∗ as a sensitive, computational proxy for detecting transcriptional signatures that are suggestive of chromatin structural changes. In other words, ℓ∗ provides an indirect window into chromatin dynamics through transcriptional spatial organization, allowing detection of patterns that may precede or accompany structural changes. Direct assays such as ATAC-seq or ChIP-seq remain essential for confirming the underlying physical modifications. To make this scope clear, we will revise the title to: “Macroscopic RNA-seq Analysis to Detect Transcriptional Patterns Associated with Chromatin State Changes,” and adjust the main text.  

We would like to take this opportunity to clarify why our initial version focused on the Della Valle and Fleischer datasets rather than including new paired datasets with direct chromatin measurements. The primary objective of our paper is to introduce two macroscopic RNA-seq–based measures, ℓ∗ and the energy landscape, that are designed to detect transcriptional signatures suggestive of chromatin structural changes in the context of aging and age-related diseases. These measures explicitly model transcriptional spatial organization and provide a sensitive, scalable way to analyze RNA-seq data in domains where direct chromatin assays may not be readily available.

The datasets we used (Della Valle et al., Fleischer et al.) have been rigorously validated and independently demonstrated differences in chromatin structure between conditions. Our goal was to show that ℓ∗ and the energy landscape align with and extend these established findings, offering a more sensitive measure of transcriptional spatial organization. Specifically, in the Della Valle dataset, chromatin structural differences between progeroid and healthy donors — and their partial rescue by LINE-1 ASO treatment — were experimentally confirmed, providing a strong foundation for testing whether our metrics reflect these known changes. Similarly, the Fleischer dataset captures natural, in vivo aging, which has also been linked to chromatin alterations in prior studies.

Thus, our approach builds on this well-established biological context rather than attempting to re-demonstrate these chromatin differences from scratch. Finally, we emphasize that our current focus is aging and age-related diseases. While the framework could potentially be applied to other chromatin modification contexts, we have not tested it outside this domain and do not claim general applicability at this stage.

(2) There is no validation by interventions that directly probe chromatin structure, such as HDAC inhibitors. The authors employ datasets with knockdown of LINE-1 for validation. However, this is not a specific chromatin intervention.

We request the reviewer to refer to our response to (1) as it includes the rationale behind the selection of LINE-1 and Fleischer dataset. We would also like to state that while the focus of Della Valle et al. was LINE-1 treated ASO to show rescue of progeroid samples, it also contains data for non-treated as well as healthy samples. Importantly, untreated progeroid samples show distinctly different chromatin structure compared to healthy samples, with substantial differences detectable by both PCA and our 𝓁∗ metric.

Our 𝓁∗ method provides additional interpretability by capturing transcriptional spatial organization, resulting in shorter correlation lengths for healthy patients and longer lengths for progeroid patients.

But as mentioned in our response to (1) we will try to add an additional dataset with paired rna-seq and one of ATAC, ChIP-seq or CUT n RUN in the revision

(3) There is no statistical analysis, e.g., in Figures 4 and 5.

We have provided statistical analysis for Fig 4 (lines 237-241). We will do a similar analysis for Fig. 5. 

(4) The authors state, "in Figure 4a changes in the heterochromatin are not identical for all chromosomes shown...." I do not see the data for individual chromosomes.

The data for individual chromosomes is available in supplementary Fig. S11 – references at line 425. We will make this cross-reference clearer in the main text and consider whether some of this chromosome-specific information should be elevated to the main figures for better accessibility.

(5) In comparisons of WT vs HGPS NT or HGPS SCR (Figure S6), is this a fair comparison? The WT and HGPS are presumably from different human donors, so they have genetic and epigenetic differences unrelated to HGPS.

Figure S6 demonstrates that 𝓁∗ analysis identifies chromosome 6 as most affected, consistent with differential gene expression patterns.

Regarding donor differences in WT vs HGPS comparisons, we defer to the experimental design of Della Valle et al., which follows standard practices in progeroid research. Our review of the literature indicates that progeroid studies typically use either parent/child samples or different donor comparisons (as individuals cannot simultaneously represent both WT and HGPS states).

Importantly, the LINE-1 ASO treatment comparisons use the same cell lines, eliminating donor variability concerns. This experimental design allows us to validate that 𝓁∗ can detect rescue effects within genetically identical samples, supporting the method's sensitivity to chromatin structural changes  

Reviewing Editor Comments:

You'll note that both reviewers were very thoughtful in their comments, and in principle are supportive and excited by the work. However, their evaluation of the strength of evidence diverged substantially. I'm inclined to suggest that finding a way to support the novel method with an alternative approach would greatly improve the impact of this work. I encourage you to consider a revision that provides such data, in the context of technology currently available to the field.

We sincerely thank the editor for their thoughtful and encouraging assessment of our work. We are grateful for their recognition of the novelty of our macroscopic measures (ℓ∗ and the transition energy barrier) and their potential to provide a systems-level understanding of chromatin structural dynamics in aging and age-related syndromes. In response to the editor’s suggestion for direct validation with chromatin accessibility data, we plan to integrate an additional dataset containing paired RNA-seq and ATAC-seq or related measurements in our revision. This will help strengthen the link between our RNA-seq–based metrics and direct chromatin assays. We have also clarified and softened the manuscript text to ensure it is clear that ℓ∗ serves as a complementary, computational proxy, not a replacement, for direct experimental approaches. Very specifically, to make this scope clear, we will revise the title to: “Macroscopic RNA-seq Analysis to Detect Transcriptional Patterns Associated with Chromatin State Changes,” and adjust the main text. We thank the editor for the feedback. We have provided additional details in response to specific comments made by the reviewers.

Author response:

We thank the reviewers for their valuable feedback. We will prepare a revision of the manuscript based on these suggestions and comments. We are sure these revisions will improve the paper.

The only major point we wish to clarify is that this is the first and only manuscript describing the toolbox; it is not a version update. Although it shares a similar name with its 2015 MATLAB predecessor (Nili et al., PLoS Comput Biol), rsatoolbox was designed from scratch. Also, they have no code or structural overlap beyond implementing some similar methods.

Developed publicly since 2019, rsatoolbox reflects a decade of research in RSA methodology across multiple labs and incorporates new dissimilarity metrics, RDM comparators, inferential procedures, and visualization methods. Importantly, although we cite several papers describing methods implemented in the toolbox, this is the first manuscript to present the toolbox as a whole, its design principles, and the unified analytical framework it offers.

We are sorry about the forgotten placeholder and the links not working. The links work for us in the pdf at least and we will certainly fix the placeholder as soon as possible.

Author response:

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

Reviewer #1 (Public review):

This is an interesting and timely computational study using molecular dynamics simulation as well as quantum mechanical calculation to address why tyrosine (Y), as part of an intrinsically disordered protein (IDP) sequence, has been observed experimentally to be stronger than phenylalanine (F) as a promoter for biomolecular phase separation. Notably, the authors identified the aqueous nature of the condensate environment and the corresponding dielectric and hydrogen bonding effects as a key to understanding the experimentally observed difference. This principle is illustrated by the difference in computed transfer free energy of Y- and F-containing pentapeptides into a solvent with various degrees of polarity. The elucidation offered by this work is important. The computation appears to be carefully executed, the results are valuable, and the discussion is generally insightful. However, there is room for improvement in some parts of the presentation in terms of accuracy and clarity, including, e.g., the logic of the narrative should be clarified with additional information (and possibly additional computation), and the current effort should be better placed in the context of prior relevant theoretical and experimental works on cation-π interactions in biomolecules and dielectric properties of biomolecular condensates. Accordingly, this manuscript should be revised to address the following, with added discussion as well as inclusion of references mentioned below.

We are grateful for the referee’s assessment of our work and insightful suggestions, which we address point by point below.

(1) Page 2, line 61: "Coarse-grained simulation models have failed to account for the greater propensity of arginine to promote phase separation in Ddx4 variants with Arg to Lys mutations (Das et al., 2020)". As it stands, this statement is not accurate, because the cited reference to Das et al. showed that although some coarse-grained models, namely the HPS model of Dignon et al., 2018 PLoS Comput did not capture the Arg to Lys trend, the KH model described in the same Dignon et al. paper was demonstrated by Das et al. (2020) to be capable of mimicking the greater propensity of Arg to promote phase separation than Lys. Accordingly, a possible minimal change that would correct the inaccuracy of this statement in the manuscript would be to add the word "Some" in front of "coarse-grained simulation models ...", i.e., it should read "Some coarse-grained simulation models have failed ...". In fact, a subsequent work [Wessén et al., J Phys Chem B 126: 9222-9245 (2022)] that applied the Mpipi interaction parameters (Joseph et al., 2021, already cited in the manuscript) showed that Mpipi is capable of capturing the rank ordering of phase separation propensity of Ddx4 variants, including a charge scrambled variant as well as both the Arg to Lys and the Phe to Ala variants (see Figure 11a of the above-cited Wessén et al. 2022 reference). The authors may wish to qualify their statements in the introduction to take note of these prior results. For example, they may consider adding a note immediately after the next sentence in the manuscript "However, by replacing the hydrophobicity scales ... (Das et al., 2020)" to refer to these subsequent findings in 2021-2022.

We agree with the referee that the wording used in the original version was inaccurate. We did not want to expand too much on the previous results on Lys/Arg, to avoid overwhelming our readers with background information that was not directly relevant to the aromatic residues Phe and Tyr. We have now introduced some of the missing details in the hope that this will provide a more accurate account of what has been achieved with different versions of coarse-grained models. In the revised version, we say the following:

Das and co-workers attempted to explain arginine’s greater propensity to phase separate in Ddx4 variants using coarse-grained simulations with two different energy functions (Das et al., 2020). The model was first parametrized using a hydrophobicity scale, aimed to capture the “stickiness” of different amino acids (Dignon et al., 2018), but this did not recapitulate the correct rank order in the stability of the simulated condensates (Das et al., 2020). By replacing the hydrophobicity scale with interaction energies from amino acid contact matrices —derived from a statistical analysis of the PDB (Dignon et al., 2018; Miyazawa and Jernigan, 1996; Kim and Hummer, 2008)— they recovered the correct trends (Das et al., 2020). A key to the greater propensity for LLPS in the case of Arg may derive from the pseudo-aromaticity of this residue, which results in a greater stabilization relative to the more purely cationic character of Lys (Gobbi and Frenking, 1993; Wang et al., 2018; Hong et al., 2022).

(2) Page 8, lines 285-290 (as well as the preceding discussion under the same subheading & Figure 4): "These findings suggest that ... is not primarily driven by differences in protein-protein interaction patterns ..." The authors' logic in terms of physical explanation is somewhat problematic here. In this regard, "Protein-protein interaction patterns" appear to be a straw man, so to speak. Indeed, who (reference?) has argued that the difference in the capability of Y and F in promoting phase separation should be reflected in the pairwise amino acid interaction pattern in a condensate that contains either only Y (and G, S) and only F (and G, S) but not both Y and F? Also, this paragraph in the manuscript seems to suggest that the authors' observation of similar contact patterns in the GSY and GSF condensates is "counterintuitive" given the difference in Y-Y and F-F potentials of mean force (Joseph et al., 2021); but there is nothing particularly counterintuitive about that. The two sets of observations are not mutually exclusive. For instance, consider two different homopolymers, one with a significantly stronger monomer-monomer attraction than the other. The condensates for the two different homopolymers will have essentially the same contact pattern but very different stabilities (different critical temperatures), and there is nothing surprising about it. In other words, phase separation propensity is not "driven" by contact pattern in general, it's driven by interaction (free) energy. The relevant issue here is total interaction energy or the critical point of the phase separation. If it is computationally feasible, the authors should attempt to determine the critical temperatures for the GSY condensate versus the GSF condensate to verify that the GSY condensate has a higher critical temperature than the GSF condensate. That would be the most relevant piece of information for the question at hand.

We are grateful for this very insightful comment by the referee. We have followed this suggestion to address whether, despite similar interaction patterns in GSY and GSF condensates, their stabilities are different. As in our previous work (De Sancho, 2022), we have run replica exchange MD simulations for both condensates and derived their phase diagrams. Our results, shown in the new Figure 5 and supplementary Figs. S6-S7, clearly indicate that the GSY condensate has a lower saturation density than the GSF condensate. This result is consistent with the trends observed in experiments on mutants of the low-complexity domain of hnRNPA1, where the relative amounts of F and Y determine the saturation concentration (Bremer et al., 2022).

(3) Page 9, lines 315-316: "...Our ε [relative permittivity] values ... are surprisingly close to that derived from experiment on Ddx4 condensates (45{plus minus}13) (Nott et al., 2015)".  For accuracy, it should be noted here that the relative permittivity provided in the supplementary information of Nott et al. was not a direct experimental measurement but based on a fit using Flory-Huggins (FH), but FH is not the most appropriate theory for a polymer with long-spatial-range Coulomb interactions. To this reviewer's knowledge, no direct measurement of relative permittivity in biomolecular condensates has been made to date. Explicit-water simulation suggests that the relative permittivity of Ddx4 condensate with protein volume fraction ≈ 0.4 can have a relative permittivity ≈ 35-50 (Das et al., PNAS 2020, Fig.7A), which happens to agree with the ε = 45{plus minus}13 estimate. This information should be useful to include in the authors' manuscript.

We thank the referee for this useful comment. We are aware that the estimate we mentioned is not direct. We have now clarified this point and added the additional estimate from Das et al. In the new version of the manuscript, we say:

Our 𝜀 values for the condensates (39 ± 5 for GSY and 47 ± 3 for GSF) are surprisingly close to that derived from experiments on Ddx condensates using Flory-Huggins theory (45±13) (Nott et al., 2015) and from atomistic simulations of Ddx4 (∼35−50 at a volume fraction of 𝜙 = 0.4) (Das et al., 2020).

(4) As for the dielectric environment within biomolecular condensates, coarse-grained simulation has suggested that whereas condensates formed by essentially electric neutral polymers (as in the authors' model systems) have relative permittivities intermediate between that of bulk water and that of pure protein (ε=2-4, or at most 15), condensates formed by highly charged polymers can have relative permittivity higher than that of bulk water [Wessén et al., J Phys Chem B 125:4337-4358 (2021), Fig.14 of this reference]. In view of the role of aromatic residues (mainly Y and F) in the phase separation of IDPs such as A1-LCD and LAF-1 that contain positively and negatively charged residues (Martin et al., 2020; Schuster et al., 2020, already cited in the manuscript), it should be useful to address briefly how the relationship between the relative phase-separation promotion strength of Y vs F and dielectric environment of the condensate may or may not be change with higher relative permittivities.

We thank the referee for their comment regarding highly charged polymers. However, we have chosen not to address these systems in our manuscript, as they are significantly different from the GSY/GSF peptide condensates under investigation. In polyelectrolyte systems, condensate formation is primarily driven by electrostatic interactions and counterion release, while we highlight the role of transfer free energies. At high dielectric constants (and dielectrics even higher than that of water), the strength of electrostatic interactions will be greatly reduced. In our approach to estimate differences between Y and F, the transfer free energy should plateau at a value of ΔΔG=0 in water. At greater values of ε>80, it becomes difficult to predict whether additional effects might become relevant. As this lies beyond the scope of our current study, we prefer not to speculate further.

(5) The authors applied the dipole moment fluctuation formula (Eq.2 in the manuscript) to calculate relative permittivity in their model condensates. Does this formula apply only to an isotropic environment? The authors' model condensates were obtained from a "slab" approach (page 4 and thus the simulation box has a rectangular geometry. Did the authors apply Equation 2 to the entire simulation box or only to the central part of the box with the condensate (see, e.g., Figure 3C in the manuscript). If the latter is the case, is it necessary to use a different dipole moment formula that distinguishes between the "parallel" and "perpendicular" components of the dipole moment (see, e.g., Equation 16 in the above-cited Wessén et al. 2021 paper). A brief added comment will be useful.

We have calculated the relative permittivity from dense phases only. These dense phases were sliced from the slab geometry and then re-equilibrated. Long simulations were then run to converge the calculation of the dielectric constant. We have clarified this in the Methods section of the paper. We say:

For the calculation of the dielectric constant of condensates, we used the simulations of isolated dense phases mentioned above.

(6) Concerning the general role of Y and F in the phase separation of biomolecules containing positively charged Arg and Lys residues, the relative strength of cation-π interactions (cation-Y vs cation-F) should be addressed (in view of the generality implied by the title of the manuscript), or at least discussed briefly in the authors' manuscript if a detailed study is beyond the scope of their current effort. It has long been known that in the biomolecular context, cation-Y is slightly stronger than cation-F, whereas cation-tryptophan (W) is significantly stronger than either cation-Y and cation-F [Wu & McMahon, JACS 130:12554-12555 (2008)]. Experimental data from a study of EWS (Ewing sarcoma) transactivation domains indicated that Y is a slightly stronger promoter than F for transcription, whereas W is significantly stronger than either Y or F [Song et al., PLoS Comput Biol 9:e1003239 (2013)]. In view of the subsequent general recognition that "transcription factors activate genes through the phase-separation capacity of their activation domain" [Boija et al., Cell 175:1842-1855.e16 (2018)] which is applicable to EWS in particular [Johnson et al., JACS 146:8071-8085 (2024)], the experimental data in Song et al. 2013 (see Figure 3A of this reference) suggests that cation-Y interactions are stronger than cation-F interactions in promoting phase separation, thus generalizing the authors' observations (which focus primarily on Y-Y, Y-F and F-F interactions) to most situations in which cation-Y and cation-F interactions are relevant to biomolecular condensation.

We thank our referee for this insightful comment. While we restrict our analysis to aromatic pairs in this work, the observed crossover will certainly affect other pairs where tyrosine or phenylalanine are involved. We now comment on this point in the discussions section of the revised manuscript. This topic will be explored in detail in a follow-up manuscript we are currently completing. We say:

We note that, although we have not included in our analysis positively charged residues that form cation-π interactions with aromatics, the observed crossover will also be relevant to Arg/Lys contacts with Phe and Tyr. Following the rationale of our findings, within condensates, cation-Tyr interactions are expected to promote phase separation more strongly than cation-Phe pairs.

(7) Page 9: The observation of weaker effective F-F (and a few other nonpolar-nonpolar) interactions in a largely aqueous environment (as in an IDP condensate) than in a nonpolar environment (as in the core of a folded protein) is intimately related to (and expected from) the long-recognized distinction between "bulk" and "pair" as well as size dependence of hydrophobic effects that have been addressed in the context of protein folding [Wood & Thompson, PNAS 87:8921-8927 (1990); Shimizu & Chan, JACS 123:2083-2084 (2001); Proteins 49:560-566 (2002)]. It will be useful to add a brief pointer in the current manuscript to this body of relevant resources in protein science.

We thank the referee for bringing this body of work to our attention. In the revised version of our work, we briefly mention how it relates to our results. We also note that the suggested references have pointed to another of the limitations of our study, that of chain connectivity, addressed in the work by Shimizu and Chan. While we were well aware of these limitations, we had not mentioned them in our manuscript. Concerning the distinction between pair and bulk hydrophobicities, we include the following in the concluding lines of our work:

The observed context dependence has deep roots in the concepts of “pair” and “bulk” hydrophobicity (Wood and Thompson, 1990; Shimizu and Chan, 2002). While pair hydrophobicity is connected to dimerisation equilibria (i.e. the second step in Figure 2B), bulk hydrophobicity is related to transfer processes (the first step). Our work stresses the importance of considering both the pair contribution that dominates at high solvation, and the transfer free energy contribution, which overwhelms the interaction strength at low dielectrics.

Reviewer #2 (Public review):

Summary:

In this preprint, De Sancho and López use alchemical molecular dynamics simulations and quantum mechanical calculations to elucidate the origin of the observed preference of Tyr over Phe in phase separation. The paper is well written, and the simulations conducted are rigorous and provide good insight into the origin of the differences between the two aromatic amino acids considered.

We thank the referee for his/her positive assessment of our work. Below, we address all the questions raised one by one.

Strengths:

The study addresses a fundamental discrepancy in the field of phase separation where the predicted ranking of aromatic amino acids observed experimentally is different from their anticipated rankings when considering contact statistics of folded proteins. While the hypothesis that the difference in the microenvironment of the condensed phase and hydrophobic core of folded proteins underlies the different observations, this study provides a quantification of this effect. Further, the demonstration of the crossover between Phe and Tyr as a function of the dielectric is interesting and provides further support for the hypothesis that the differing microenvironments within the condensed phase and the core of folded proteins is the origin of the difference between contact statistics and experimental observations in phase separation literature. The simulations performed in this work systematically investigate several possible explanations and therefore provide depth to the paper.

Weaknesses:

While the study is quite comprehensive and the paper well written, there are a few instances that would benefit from additional details. In the methods section, it is unclear as to whether the GGXGG peptides upon which the alchemical transforms are conducted are positioned restrained within the condensed/dilute phase or not. If they are not, how would the position of the peptides within the condensate alter the calculated free energies reported? 

The peptides are not restrained in our simulations and can therefore diffuse out of the condensate given sufficient time. Although the GGXGG peptide can, given sufficient time, leave the peptide condensate, we did not observe any escape event in the trajectories we used to generate starting points for switching. Hence, the peptide environment captured in our calculations reflects, on average, the protein-protein and protein-solvent interactions inside the model condensate. We believe this is the right way of performing the calculation of transfer free energy differences into the condensate. We have clarified this point when we describe the equilibrium simulation results in the revised manuscript. We say:

Also, the peptide that experiences the transformation, which is not restrained, must remain buried within the condensate for all the snapshots that we use as initial frames, to avoid averaging the work in the dilute and dense phases.

On the referee’s second point of whether there would be differences if the peptide visited the dilute phase, the answer is that, indeed, we would. We expect that the behaviour of the peptide would approach ΔΔG=0, considering the low protein concentration in the dilute phase. For mixed trajectories with sampling in both dilute and dense phases, our expectation would be a bimodal distribution in the free energy estimates from switching (see e.g. Fig. 8 in DOI:10.1021/acs.jpcb.0c10263). Because we are exclusively interested in the transfer free energies into the condensate, we do not pursue such calculations in this work.

It would also be interesting to see what the variation in the transfer of free energy is across multiple independent replicates of the transform to assess the convergence of the simulations. 

Upon submission of our manuscript, we were confident that the results we had obtained would pass the test of statistical significance. We had, after all, done many more simulations than those reported, plus the comparable values of ΔΔG_Transfer for both GSY and GSF pointed in the right direction. However, we acknowledge that the more thorough test of running replicates recommended by the referee is important, considering the slow diffusion within the Tyr peptide condensates due to its stickiness. Also, the non-equilibrium switching method had not been tested before for dense phases like the ones considered here.

We have hence followed our referee's suggestion and done three different replicates, 1 μs each, of the equilibrium runs starting from independent slab configurations, for both the GSY and GSF condensates (see the new supporting figures Fig. S1, S2 and S5). We now report the errors from the three replicates as the standard error of the mean (bootstrapping errors remain for the rest of the solvents). Our results are entirely consistent with the values reported originally, confirming the validity of our estimates.

Additionally, since the authors use a slab for the calculation of these free energies, are the transfer free energies from the dilute phase to the interface significantly different from those calculated from the dilute phase to the interior of the condensate? 

We thank the referee for this valuable comment, as it has pointed us in the direction of a rapidly increasing body of work on condensate interfaces, for example, as mediators of aggregation, that we may consider for future study with the same methodology. However, as discussed above, we have not considered this possibility in our work, as we decided to focus on the condensate environment, rather than its interface.

The authors mention that the contact statistics of Phe and Tyr do not show significant difference and thereby conclude that the more favorable transfer of Tyr primarily originates from the dielectric of the condensate. However, the calculation of contacts neglects the differences in the strength of interactions involving Phe vs. Tyr. Though the authors consider the calculation of energy contact formation later in the manuscript, the scope of these interactions are quite limited (Phe-Phe, Tyr-Tyr, Tyr-Amide, Phe-Amide) which is not sufficient to make a universal conclusion regarding the underlying driving forces. A more appropriate statement would be that in the context of the minimal peptide investigated the driving force seems to be the difference in dielectric. However, it is worth mentioning that the authors do a good job of mentioning some of these caveats in the discussion section.

We thank the referee for this important comment. Indeed, the similar contact statistics and interaction patterns that we reported originally do not necessarily imply identical interaction energies. In other words, similar statistics and patterns can still result in different stabilities for the Phe and Tyr condensates if the energetics are different. Hence, we cannot conclude that the GSF and GSY condensate environments are equivalent.

To address this point, we have run new simulations for the revised version of our paper, using the temperature-replica exchange method, as before. From the new datasets, we derive the phase diagrams for both the GSF and GSY condensates (see the new Fig. 5). We find that the tyrosine-containing condensate is more stable than that of phenylalanine, as can be inferred from the lower saturation density in the low-density branch of the phase diagram. In consequence, despite the similar contact statistics, the energetics differ, making the saturation density of the GSY slightly lower than that of GSF. This result is consistent with experimental data by Bremer et al (Nat. Chem. 2022). 

Reviewer #3 (Public review):

Summary:

In this study, the authors address the paradox of how tyrosine can act as a stronger sticker for phase separation than phenylalanine, despite phenylalanine being higher on the hydrophobicity scale and exhibiting more prominent pairwise contact statistics in folded protein structures compared to tyrosine.

We are grateful for the referee’s favourable opinion on the paper. Below, we address all of the issues raised.

Strengths:

This is a fascinating problem for the protein science community with special relevance for the biophysical condensate community. Using atomistic simulations of simple model peptides and condensates as well as quantum calculations, the authors provide an explanation that relies on the dielectric constant of the medium and the hydration level that either tyrosine or phenylalanine can achieve in highly hydrophobic vs. hydrophilic media. The authors find that as the dielectric constant decreases, phenylalanine becomes a stronger sticker than tyrosine. The conclusions of the paper seem to be solid, it is well-written and it also recognises the limitations of the study. Overall, the paper represents an important contribution to the field.

Weaknesses:

How can the authors ensure that a condensate of GSY or GSF peptides is a representative environment of a protein condensate? First, the composition in terms of amino acids is highly limited, second the effect of peptide/protein length compared to real protein sequences is also an issue, and third, the water concentration within these condensates is really low as compared to real experimental condensates. Hence, how can we rely on the extracted conclusions from these condensates to be representative for real protein sequences with a much more complex composition and structural behaviour?

We agree with the main weakness identified by the referee. In fact, all these limitations had already been stated in our original submission. Our ternary peptide condensates are just a minimal model system that bears reasonable analogies with condensates, but definitely is not identical to true LCR condensates. The analogies between peptide and protein condensates are, however, worth restating: 

(1) The limited composition of the peptide condensates is inspired by LCR sequences (see Fig. 4 in Martin & Mittag, 2018).

(2) The equilibrium phase diagram, showing a UCST, is consistent with that of LCRs from Ddx4 or hnRNPA1.

(3) The dynamical behaviour is intermediate between liquid and solid (De Sancho, 2022). 

(4) The contact patterns are comparable to those observed for FUS and LAF1 (Zheng et al, 2020).

The third issue pointed out by the referee requires particular attention. Indeed, the water content in the model condensates is low (~200 mg/mL for GSY) relative to the experiment (e.g. ~600 mg/mL for FUS and LAF-1 from simulations). Considering that both interaction patterns and solvation contribute to the favorability of Tyr relative to Phe, we speculate that a greater degree of solvation in the true protein condensates will further reinforce the trends we observe.

In any case, in the revised version of the manuscript, we have made an effort to insist on the limitations of our results, some of which we plan to address in future work.

Reviewer #3 (Recommendations for the authors):

(1) The fact that protein density is so high within GSY or GSF peptide condensates may significantly alter the conclusions of the paper. Can the authors show that for condensates in which the protein density is ~0.2-0.3 g/cm3, the same conclusions hold? Could the authors use a different peptide sequence that establishes a more realistic protein concentration/density inside the condensate?

Unfortunately, recent work with a variety of peptide sequences suggests that finding peptides in the density range proposed by the referee may be very challenging. For example, Pettit and his co-workers have extensively studied the behaviour of GGXGG peptides. In a recent work, using the CHARMM36m force field and TIP3P water, they report densities of ~1.2-1.3 g/mL for capped pentapeptide condensates (Workman et al, Biophys. J. 2024; DOI: 10.1016/j.bpj.2024.05.009). Brown and Potoyan have recently run simulations of zwitterionic GXG tripeptides with the Amber99sb-ILDNQ force field and TIP3P water, starting with a homogenous distribution in cubic simulation boxes (Biophys. J. 2024, DOI: 10.1016/j.bpj.2023.12.027). In a box with an initial concentration of 0.25 g/mL, upon phase separation, the peptide ends up occupying what would seem to be ~1/3 of the box, although we could not find exact numbers. This would imply densities of ~0.75 g/mL in the dense phase, with the additional problem of many charges. Finally, Joseph and her co-workers have recently simulated a set of hexapeptide condensates with varied compositions using a combination of atomistic and coarse-grained simulations. For the atomistic simulations, the Amber03ws force field and TIP4P water were used (see BioRxiv reference 10.1101/2025.03.04.641530). They have found values of the protein density in the dense phase ranging between 0.8 and 1.2 g/mL.  The consistency in the range of densities reported in these studies suggests that short peptides, at least up to 7-residues long, tend to form quite dense condensates, akin to those investigated in our work. While the examples mentioned do not comprehensively span the full range of peptide lengths, sequences, and force fields, they nonetheless support the general behaviour we observe. A systematic exploration of all these variables would require an extensive search in parameter space, which we believe falls outside the scope of the present study.

(2) Do the conclusions hold for phase-separating systems that mostly rely on electrostatic interactions to undergo LLPS, like protein-RNA complex coacervates? In other words, could the authors try the same calculations for a binary mixture composed of polyR-polyE, or polyK-polyE?

This is an excellent idea that we may attempt in future work, but the remit of the current work is aromatic amino acids Phe and Tyr only. Hence, we do not include calculations or discussion on polyR-polyE systems in our revised manuscript.

(3) One of the major approximations made by the authors is the length of the peptides within the condensates, which is not realistic, or their density. Specifically, could they double or triple the length of these peptides while maintaining their composition so it can be quantified the impact of sequence length in the transfer of free energies?

We thank the referee for this comment and agree with the main point, which was stated as a limitation in our original submission. The suggested calculations anticipate research that we are planning but will not include in the current work. One of the advantages of our model systems is that the small size of the peptides allows for small simulation boxes and relatively rapid sampling. Longer peptide sequences would require conformational sampling beyond our current capabilities, if done systematically. An example of these limitations is the amount of data that we had to discard from the new simulations we report, which amounts to up to 200 ns of our replica exchange runs in smaller simulation boxes (i.e. >19 μs in total for the 48 replicas of the two condensates!). As stated in the answer to point 1, we have found in the literature work on peptides in the range of 1-7 residues with consistent densities. Additionally, a recent report using alchemical transformations using equilibrium techniques with tetrapeptide condensates, pointing to the role of transfer free energy as driving force for condensate formation, further supports the observations from our work.

Minor issues:

(1) The caption of Figure 3B is not clear. It can only be understood what is depicted there once you read the main text a couple of times. I encourage the authors to clarify the caption.

We have rewritten the caption for greater clarity. Now it reads as follows:

Time evolution of the density profiles calculated across the longest dimension of the simulation box (L) in the coexistence simulations. In blue we show the density of all the peptides, and in dark red that of the F/Y residue in the GGXGG peptide.

(2) Why was the RDF from Figure 5A cut at such a short distance? Can the authors expand the figure to clearly show that it has converged?

In the updated Figure 5 (now Fig. 6), we have extended the g(r) up to r=1.75 nm so that it clearly plateaus at a value of 1.

Author response:

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

Reviewer #1 (Public review):

Thank you very much for your recognition of our work and for pointing out the shortcomings. We have made revisions one by one and provided corresponding explanations regarding the issues you raised.

Weaknesses:

One of the main EEG results is based on the weighted phase lag index (wPLI) between oscillations in the alpha and theta bands. In my opinion, this is problematic, as wPLI measures the locking of oscillations at the same frequency. It quantifies how reliably the phase difference stays the same over time. If these oscillations have different frequencies, the phase difference cannot remain consistent. Even worse, modeling data show that even very small fluctuations in frequency between signals make wPLI artificially small (Cohen, 2015).

thank you for raising the question regarding the application of wPLI between the alpha and theta bands, which indeed deserves further explanation. In our study, we referred to some relevant previous literatures and adopted their approach of using wPLI to measure cross-frequency coupling strength, as this index itself can reflect the stability of phase differences. We have also considered the point you mentioned that the phase differences of oscillations with different frequencies are difficult to remain consistent. However, in this study, the presentation times of the two memory items are the same, which is fair to both from this perspective. Moreover, the study observed that the wPLI values of these two items alternately dominate over time, and this changing pattern is consistent with the regularity of behavioral data. It seems hard to explain this as a mere coincidence. 

The corresponding discussion has been added to the revised part of the paper：“the present study referenced previous research by using the wPLI index as a measure of cross-frequency coupling strength31,64-66 (this index quantifies the stability of phase differences), yet the phases of different oscillations inherently change over time. However, this is fair to the two memory items in the present study, as their presentation times were balanced. The study found that the wPLI values of the two items alternately dominated over time, consistent with the pattern of behavioral data, which is hardly explicable by coincidence”

Another result from the electrophysiology data shows that the attentional capture effect is positively correlated with the mean amplitude of alpha power. In the presented scatter plot, it seems that this result is driven by one outlier. Unfortunately, Pearson correlation is very sensitive to outliers, and the entire analysis can be driven by an extreme case. I extracted data from the plot and obtained a Pearson correlation of 0.4, similar to what the authors report. However, the Spearman correlation, which is robust against outliers, was only 0.13 (p = 0.57), indicating a non-significant relationship.

you mentioned that the correlation between the attentional capture effect and the mean amplitude of alpha power in the electrophysiological data might be influenced by an outlier, and you also compared the results of Pearson and Spearman correlation coefficients, which we fully agree with.

It is true that the small sample size of the current study makes the results vulnerable to interference from extreme data. Regarding this point, I have already explained it in the limitations section of the discussion in the revised manuscript:“the sample size of the current study is small, which may render the results vulnerable to interference from extreme cases”

The behavioral data are interesting, but in my opinion, they closely replicate Peters and colleagues (2020) using a different paradigm. In that study, participants memorized four spatial positions that formed the endpoints of two objects, and one object was cued. Similarly, reaction times fluctuated at theta frequency, and there was an anti-phase relationship between the two objects. The main novelty of the present study is that this bias can be transferred to an unrelated task. While the current study extends Peters and colleagues' findings to a different task context, the lack of a thorough, direct comparison with Peters et al. limits the clarity of the novel insights provided.

thank you very much for your attention to the behavioral data and its relevance to the study by Peters et al. (2020). We have noticed that there are similarities in some results between the two studies, which also indicates the stability of the relevant phenomena from one aspect.

However, we would also like to further explain the differences between this study and the study by Peters et al. In the study by Peters et al., participants memorized four spatial positions that formed the endpoints of two objects (one of which was cued), and their results showed that after the two objects disappeared, attention fluctuated at the theta rhythm between their original positions with an inverse correlation. In contrast, the present study explores the manner of memory maintenance indirectly by leveraging the guiding effect of working memory on attention, effectively avoiding the influence of spatial positions.

The study by Peters et al. directly examined differences in probe positions, clearly demonstrating that attention undergoes rhythmic changes at the two spatial locations and persists after the objects vanish, but it hardly clarifies the rhythmicity of working memory performance. Whereas the present study directly investigates such performance using the attention-capture effect of working memory, revealing that when maintaining multiple memory items, their attention-capturing capabilities alternate in dominance, i.e., multiple working memory items alternately become priority templates in a rhythmic manner. This is also some new attempts in the research perspective and method of this study.

The corresponding discussion has been added to the revised part of the paper

“Similar to the present study, Peters et al. had participants memorize four spatial positions forming the endpoints of two objects (one cued), and their results showed that after the two objects disappeared, attention fluctuated at the theta rhythm between their original positions with an inverse correlation; in contrast, the present study explores the manner of memory maintenance indirectly by leveraging the guiding effect of working memory on attention, effectively avoiding the influence of spatial positions—while Peters et al.’s study, which directly examined differences in probe positions, clearly demonstrates that attention undergoes rhythmic changes at the two spatial locations and persists after the objects vanish, it hardly clarifies the rhythmicity of working memory performance, whereas the present study directly investigates such performance using the attention-capture effect of working memory, revealing that when maintaining multiple memory items, their attention-capturing capabilities alternate in dominance, i.e., multiple working memory items alternately become priority templates in a rhythmic manner.”

Reviewer #2 (Public review):

The information provided in the current version of the manuscript is not sufficient to assess the scientific significance of the study.

thank you very much for pointing out the multiple issues in our manuscript. Due to several revisions of this work, including experimental adjustments, there have been some inconsistencies in details. We appreciate you identifying them one by one.  We have made corresponding revisions based on your comments:

(1) In many cases, the details of the experiments or behavioral tasks described in the main text are not consistent with those provided in the Materials and Methods section. Below, I list only a few of these discrepancies as examples:

a) For Experiment 1, the Methods section states that the detection stimulus was presented for 2000 ms (lines 494 and 498), but Figure 1 in the main text indicates a duration of 1500 ms.

we greatly appreciate you catching this inconsistency. We have made unified revisions by referring to the final implemented experimental procedures.  Corresponding revisions have been made in the paper：

b) For Experiment 2, not only is the range of SOAs mentioned in the Methods section inconsistent with that shown in the main text and the corresponding figure, but the task design also differs between sections.

Thank you for bringing this discrepancy to our attention. We have made unified revisions by referring to the final implemented experimental procedures. The correct SOAs are 233：33：867 ms.

Corresponding revisions have been made in the paper：

c) For Experiment 3, the main text indicates that EEG recordings were conducted, but in the Methods section, the EEG recording appears to have been part of Experiment 2 (lines 538-540).

we’re grateful for you noticing this mix-up. In fact, only Experiment 3 is an EEG experiment, and we have made corresponding corrections in the "Methods" section. Corresponding revisions have been made in the paper: “The remaining components after this process were then projected back into the channel space. We extracted data from -500 ms to 2000 ms relative to cue stimulus presentation in Experiment 3.”  

(2) The results described in the text often do not match what is shown in the corresponding figure. For example:

a) In lines 171-178, the SOAs at which a significant difference was found between the two conditions do not appear to match those shown in Figure 2A.

Many thanks for spotting this error. The previous results missed one SOA time, namely 33 ms, leading to a 33 ms difference in time. We have corrected it in the revised manuscript.

Corresponding revisions have been made in the paper：“Specifically, the capture effect of cued items was significantly greater than that of uncued items at SOAs of 267ms (t(24) = 2.72, p = 0.03, Cohen's d = 1.11), 667ms (t(24) = 2.37, p = 0.03, Cohen's d= 0.97) and 833ms (t(24) = 3.53, p = 0.002, Cohen's d = 1.44), while the capture effect of uncued items was significantly greater than that of cued items at SOAs of 333ms (t(24) = 2.97, p = 0.007, Cohen's d = 1.21), 367ms (t(24) = 2.14, p = 0.04, Cohen's d = 0.87), 433ms (t(24 )= 2.49, p = 0.02, Cohen's d = 1.02), 467ms (t(24)=2.37, p = 0.03, Cohen's d = 0.97) and 567ms (t(24)=2.72, p = 0.02, Cohen's d = 1.11). ”

(b) In Figure 4, the figure legend (lines 225-228) does not correspond to the content shown in the figure.

we appreciate you pointing out this oversight. When adjusting the color scheme during the revision of the manuscript, we neglected to revise the legend, which has now been corrected in the revised manuscript.

Corresponding revisions have been made in the paper：“Figure 4. The red line represents the average across all participants of the Fourier transforms of the differences in capture effects between left and right memory items at the individual level. The gray area represents values below the group average of medians derived from 1000 permutations, with each permutation involving Fourier transforms for each participant. *: p < 0.05.”

(c) In Figure 9, not sufficient information is provided within the figure or in the text, making it difficult to understand. Consequently, the results described in the text cannot be clearly linked to the figure.

Thank you for drawing our attention to this issue. We have revised Figure 9 and its legend in the revised manuscript to make them clearer and easier to understand.

Corresponding revisions have been made in the paper

(3) Insufficient information is provided regarding the data analysis procedures, particularly the permutation tests used for the data presented in Figures 2B, 4, and 10. The results shown in these figures are critical for the main conclusions drawn in the manuscript.

we’re thankful for you highlighting this gap. In the revised manuscript, we have provided a more detailed explanation in the "Methods" section, especially regarding the content related to frequency analysis, to make the expression clearer.

Corresponding revisions have been made in the paper：“As shown in Figure 8, the alpha power (8-14 Hz) induced by cued and uncued items alternated in dominance during the memory retention phase. To quantify this rhythmic alternation, we conducted a spectral analysis following these steps: First, we computed the power difference between cued and uncued items within the 8-14 Hz range during the retention phase. These differences were then downsampled to 100 Hz using a 10 ms window for averaging, generating a one-dimensional time series spanning the 0-2000 ms retention period. This time series was subsequently subjected to amplitude spectrum analysis across frequencies from 1 Hz to 50 Hz using Fourier transformation.

To assess the statistical significance of the observed spectral features, we employed a permutation test. Specifically, we randomly shuffled the temporal order of the time series of power differences between cued and uncued items—thereby preserving the amplitude distribution of the data while eliminating temporal correlations in the original sequence—and repeated the Fourier transform and spectral analysis for each shuffled time series. This permutation process was replicated 1000 times to generate a null distribution of spectral power values. A frequency component in the original data was considered statistically significant if its power ranked within the top 5% of the corresponding null distribution (p < 0.05).

We applied the same analytical pipeline to investigate differences in the weighted phase-lag index (wPLI) between the contralateral regions of the two items and the prefrontal cortex during the retention phase. Specifically, wPLI differences (i.e., the difference between the two conditions) were computed, downsampled to 100 Hz using a 10 ms window for averaging to generate a time series spanning 0-2000 ms, and then subjected to amplitude spectrum analysis (1-50 Hz) using Fourier transformation. Significance was assessed via the identical permutation test procedure described above (randomly shuffling the temporal order of the difference time series).”

Author response:

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

Reviewer #1 (Public review):

“Can the authors offer a hypothesis as to how decreased coactivity promotes increased movement velocity.” 

In our revision we have added an additional metric measuring how spatial coactivity changes during movement onset, the spatial correlation index, which replicates a previous finding that co-activity among proximal neurons is statistically greater surrounding movement onset. We did not find, as outlined in the revision, that mGluR5 manipulations significantly altered this relationship. Our data therefore shows, consistent with that shown previously, that ensembles of dSPNs that are co-active during movement onset, in particular ambulatory movement, are more likely to contain neurons that are closer together and the neurons are highly active. In contrast, rest ensembles contain neurons that are less active but have more highly correlated activity, across all pairwise distances. Additionally, mGluR5 inhibition, genetic or pharmacological, promotes the activation of rest ensembles but does not affect the properties of movement ensembles. Previous studies (e.g. Klaus A. et al., 2017) have shown that neurons in rest ensembles are, in general, unlikely to also be members of movement ensembles, We therefore hypothesize that corticostriatal synapses onto SPNs of rest ensembles are more likely, during spontaneous behavior, to have reduced synaptic weight due to mGluR5 signaling, potentially due to eCB mediated inhibition of neurotransmitter release. Therefore, when we inhibit mGluR5 at these synapses, we increase synaptic weight and increase the probability of activation of this coordinated rest ensemble, which suppresses movement. If, on the other hand, the synapses that govern activation of neurons in movement ensembles have a higher weight, they may be unaffected by mGluR5 inhibition. 

The use of the Jaccard similarity index in this study is not intuitive and not fully explained by the methods or the diagram in Figure 1. 

We have added more detail to the paper to explain the methodology of the jaccard similarity measure. The advantage of this method is that is specifically captures cells that are jointly active, as opposed to jointly inactive and is therefore useful for capturing co-activity in our sparsely active Ca²⁺ imaging data. 

The analysis of a possible 2-AG role in the mGlu5 mediated processes is incomplete. 

We agree that, as an experiment to outline which endocannabinoids are involved in modulating synaptic strength through mGluR5, this experiment alone is not sufficient.

However, our main focus in this paper is how manipulations of mGluR5 affect the spatiotemporal dynamics of dSPNs and we chose not to focus on specific mechanisms of endocannabinoid signaling, though these would certainly be interesting to investigate further in vivo.

It would seem to be a simple experiment to examine effects of the mGlu5 NAM in the dSPN mGlu5 cKO mice. If effects of the two manipulations occluded one another this would certainly support the hypothesis that the drug effects are mediated by receptors expressed in dSPNs. A similar argument can be made for examining effects of the JNJ PAM in the cKO mice. 

We agree that this experiment would be valuable and extend our findings presented in the paper, however, it has practically been outside the scope of the current work. 

Reviewer #2 (Public review):

Pharmacological and genetic manipulations of mGluR5 do not differentially/preferentially modulate the activity of proximal vs distal dSPNs, therefore, it could also be interpreted that mGluR5 is blanketly boosting/suppressing all dSPN activity as opposed to differential proximal/distal spatial relationships. 

As in the response to reviewer 1 above, we have added additional clarification to the text explaining that our manipulations do not differentially affect the co-activity of proximal vs distal dSPNs, this is also quantified throughout the text using the spatial coordination index. However, we disagree that “it could also be interpreted that mGluR5 is blanketly boosting/suppressing all dSPN activity” as we do not observe statistically significant changes in the event rate following either pharmacological or genetic manipulations of mGluR5. Rather, we consistently observe statistically significant changes in co-activity among neurons, the extent to which activity of active neurons during either rest or movement are correlated with each other. This is the central finding of our manuscript, inhibiting or potentiating mGluR5 signaling alters behavior, not by blanket suppression or enhancement of the activity as measured using the event rate, of dSPNs, but by affecting their ensemble dynamic properties.  Co-activity during rest versus ambulatory movement is statistically greater in both proximal and distal cells and inhibiting mGluR5 increases this co-activity and decreases movement. 

For these analyses of prox vs distal and all others, please include the detail of how many proximal vs distal cells were involved and per subject. 

We have added a supplemental table that details the number of cells included per subject in all analyses

Ln. 151-152: Please provide data concerning how volumes of infectivity differ between injecting AAV vs. coating the lens? If these numbers are very different, this could impact the number of Jaccard pairings and bias results. 

While viral injection may lead to a larger volume of expression, with this one photon imaging method only those cells within ~200 microns of the edge of the lens will be able to be resolved, therefore practically, if there is an additional volume of infected tissue outside of the field of view of the lens, it would not affect the results as these neurons will not be resolved by the endoscope camera. Accordingly, the average number of cells detected per session is very similar following each approach (mean # of cells per session with coating 90.93 ± 23.69 cells, with viral injection 90.03 ± 29.29 cells)

Is mGluR5 affecting dSPN activity in other measures beyond co-activity and rate? Does the amplitude of events change?

We have added supplemental data for figures 2, 3, and 5 demonstrating that manipulations of mGluR5 do not affect the amplitude or length of Ca²⁺ events included in the analysis. 

What is the model of mGluR5 signaling in a resting state vs. movement? What other behaviors are occurring when the mouse is in a low velocity "resting state" (0-0.5 cm/s). If this includes other forms of movement (i.e. rearing, grooming) then the animal really isn't in a resting state. This is not mentioned in the open field behavior section of the methods and should be described (Ln. 486) in addition to greater explanation of what behavior measures were obtained from the video tracking software (only locomotion?)

It would be very interesting to determine if during “rest,” when the animals is not engaged in ambulatory behavior, it may be engaged in some fine motor behavior. However, the resolution of the cameras used to measure locomotor activity in this dataset does not allow us to do this. 

There is large variability in co-activity in proximal dSPNs when animals are "resting" (2j). Could this be explained by different behavior states within your definition of "rest"?

We agree that if the animal is engaging in fine motor behavior that we cannot resolve with our behavior setup, this could produce some variability in coactivity. However, as shown previously (e.g. Klaus A. et al., 2017), ensembles active when the animal is not moving (our definition of “resting”), regardless of additional fine motor behaviors the animal may be engaged in when not moving, are substantially different that those ensembles that are active when the animal is moving. We therefore expect that this may limit, although potentially not eliminate, variability due to different behavioral states we may have grouped into our “resting” category. Unfortunately, as mentioned above, we are not able resolve variations in fine motor output in this behavioral data. 

Have you performed IHC, ISH or another measure to validate D1 cell specific cKO?

The mGluR5^loxP/loxP mice used in this study were characterized previously by our lab (Xu et al., 2009), we used the same mice here with a different, but also published and characterized Cre-driver line, Drd1a-Cre Ey262 (Gerfen et al., 2013).

Why are the "Mean Norm Co-activity" values in 5e so high in this experiment relative to figures 2-4?  

In experiments where we treated the same animal with vehicle and a drug (i.e., experiments in Figure 2 and 3), we normalized the values for each animal in the drug treatment group to the distal bin of that animal following vehicle treatment. This allowed us to more clearly resolve the changes within each animal due to drug treatment. As comparisons in the data in figure 5 d–f are between different animals (rather than different treatments of the same animal) we could not perform this normalization procedure.  

Reviewer #3 (Public review):

Some D1 Cre lines have expression in the cortex. Which specific Cre line was used in this study? 

We used, Drd1a-Cre Ey262. This is included in methods. 

The text says JNJ treatment .... increased locomotor speed (Figure 3b) and increased the duration but not frequency of movement bouts (Figure 3c, d). However, the statistics of the figure legends say: however the change in mean velocity (3b) is not significant (p=0.060, U=3, Mann-Whitney U test), nor is the mean bout length during vehicle and JNJ (p=0.060, U=3, Mann-Whitney U test) (3d) Comparison of mean number of bouts of each animal during vehicle and JNJ (p=0.403, U=8, Mann-Whitney U test). 

This has been corrected to indicate only the change in time spend at rest is statistically significant.

This effect was most pronounced during periods of rest (Figure 3i, j). The decrease was only in rest? Are the colors in Figure 3J inverted? Therefore, JNJ treatment had effects that were qualitatively the inverse to the effects of fenobam on locomotion and dSPN activity. 

We have corrected the text to state that, overall, and during periods of rest but not movement, JNJ had effects that were qualitatively the opposite of fenobam.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This work addresses an important question in the field of Drosophila aggression and mating. Prior social isolation is known to increase aggression in males, manifesting as increased lunging, which is suppressed by group housing (GH). However, it is also known that single housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., develop a modified aggression assay to address this issue by recording aggression in Drosophila males for 2 hours, with a virgin female immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons in promoting high frequency lunging, similar to earlier studies, whereas Or47b neurons promote low frequency but higher intensity tussling. Optogenetic activation revealed that three pairs of pC1SS2 neurons increase tussling. Cell-type-specific DsxM manipulations combined with morphological analysis of pC1SS2 neurons and side-by-side tussling quantification link the developmental role of DsxM to the functional output of these aggression-promoting cells. In contrast, although optogenetic activation of P1a neurons in the dark did not increase tussling, thermogenetic activation under visible light drove aggressive tussling. Using a further modified aggression assay, GH males exhibit increased tussling and maintain territorial control, which could contribute to a mating advantage over SH males, although direct measures of reproductive success are still needed.

Strengths:

Through a series of clever neurogenetic and behavioral approaches, the authors implicate specific subsets of ORNs and pC1 neurons in promoting distinct forms of aggressive behavior, particularly tussling. They have devised a refined territorial control paradigm, which appears more robust than earlier assays using a food cup (Chen et al., 2002). This new setup is relatively clutter-free and could be amenable to future automation using computer vision approaches. The updated Figure 5, which combines cell-type-specific developmental manipulation of pC1SS2 neurons with behavioral output, provides a link between developmental mechanisms and functional aggression circuits. The manuscript is generally well written, and the claims are largely supported by the data.

Thank you for the precise summary of the manuscript and acknowledgment of the novelty and significance of the study.

Weakness:

Although most concerns have been addressed, the manuscript still lacks a rigorous, objective method for quantifying lunging and tussling. Because scoring appears to have been done manually and a single lunge in a 30 fps video spans only 2-3 frames, the 0.2 s cutoff seems arbitrary, and there are no objective criteria distinguishing reciprocal lunging from tussling. Despite this, the study offers valuable insights into the neural and behavioral mechanisms of Drosophila aggression.

Thank you for this comment. The duration of each lunge was measured by analyzing the videos frame by frame—from the frame before the initiation of the lunge to the frame after its completion—resulting in an average span of 3–5 frames. Given a frame rate of 30 fps, this corresponds to approximately 0.1–0.17 seconds. We acknowledge that there are certain limitations for manually quantifying the two types of aggressive behaviors, which has now been stated in the newly added “Limitations of the Study” section in the revised manuscript.

Reviewer #2 (Public review):

Summary:

Gao et al. investigated the change of aggression strategies by the social experience and its biological significance by using Drosophila. Two modes of inter-male aggression in Drosophila are known: lunging, high-frequency but weak mode, and tussling, low-frequency but more vigorous mode. Previous studies have mainly focused on the lunging. In this paper, the authors developed a new behavioral experiment system for observing tussling behavior and found that tussling is enhanced by group rearing, while lunging is suppressed. They then searched for neurons involved in the generation of tussling. Although olfactory receptors named Or67d and Or65a have previously been reported to function in the control of lunging, the authors found that these neurons do not function in the execution of tussling and another olfactory receptor, Or47b, is required for tussling, as shown by the inhibition of neuronal activity and the gene knockdown experiments. Further optogenetic experiments identified a small number of central neurons pC1[SS2] that induce the tussling specifically. These neurons express doublesex (dsx), a sex-determination factor, and knockdown of dsx strongly suppresses the induction of tussling. In order to further explore the ecological significance of the aggression mode change in group-rearing, a new behavioral experiment was performed to examine the territorial control and the mating competition. And finally, the authors found that differences in the social experience (group vs. solitary rearing) and the associated change in aggression strategy are important in these biologically significant competitions. These results add a new perspective to the study of aggression behavior in Drosophila. Furthermore, this study proposes an interesting general model in which the social experience modified behavioral changes play a role in reproductive success.

Strengths:

A behavioral experiment system that allows stable observation of tussling, which could not be easily analyzed due to its low-frequency, would be very useful. The experimental setup itself is relatively simple, just the addition of a female to the platform, so it should be applicable to future research. The finding about the relationship between the social experience and the aggression mode change is quite novel. Although the intensity of aggression changes with the social experience was already reported in several papers (Liu et al., 2011 etc), the fact that the behavioral mode itself changes significantly has rarely been addressed, and is extremely interesting. The identification of sensory and central neurons required for the tussling makes appropriate use of the genetic tools and the results are clear. A major strength of this study in neurobiology is the finding that another group of neurons (Or47b-expressing olfactory neurons and pC1[SS2] neurons), distinct from the group of neurons previously thought to be involved in low-intensity aggression (i.e. lunging), function in the tussling behavior. Furthermore, the results showing that the regulation of aggression by pC1[SS2] neurons is based on the function of the dsx gene will bring a new perspective to the field. Further investigation of the detailed circuit analysis is expected to elucidate the neural substrate of the conflict between the two aggression modes. The experimental systems examining the territory control and the reproductive competition in Fig. 6 are novel and have advantages in exploring their biological significance. It is important to note that in addition to showing the effects of age and social experience on territorial and mating behaviors, the authors experimentally demonstrated that altered fighting strategy has effects with respect to these behaviors.

Thank you for your precise summary of our study and being very positive on the novelty and significance of the study.

Reviewer #3 (Public review):

In this revised manuscript, Gao et al. presented a series of well-controlled behavioral data showing that tussling, a form of high-intensity fighting among male fruit flies (Drosophila melanogaster) is enhanced specifically among socially experienced and relatively old males. Moreover, results of behavioral assays led authors to suggest that increased tussling among socially experienced males may increase mating success. They also concluded that tussling is controlled by a class of olfactory sensory neurons and sexually dimorphic central neurons that are distinct from pathways known to control lunges, a common male-type attack behavior.

A major strength of this work is that it is the first attempt to characterize behavioral function and neural circuit associated with Drosophila tussling. Many animal species use both low-intensity and high-intensity tactics to resolve conflicts. High-intensity tactics are mostly reserved for escalated fights, which are relatively rare. Because of this, tussling in the flies, like high-intensity fights in other animal species, have not been systematically investigated. Previous studies on fly aggressive behavior have often used socially isolated, relatively young flies within a short observation duration. Their discovery that 1) older (14-days old) flies tend to tussle more often than younger (2 to 7-days-old) flies, 2) group-reared flies tend to tussle more often than socially isolated flies, and 3) flies tend to tussle at later stage (mostly ~15 minutes after the onset of fighting), are the result of their creativity to look outside of conventional experimental settings. These new findings are key for quantitatively characterizing this interesting yet under-studied behavior.

Newly presented data have made several conclusions convincing. Detailed descriptions of methods to quantify behaviors help understand the basis of their claims by improving transparency. However, I remain concerned about authors' persistent attempt to link the high intensity aggression to reproductive success. The authors' effort to "tone down" the link between the two phenomena remains insufficient. There are purely correlational. I reiterate this issue because the overall value of the manuscript would not change with or without this claim.

Thank you for acknowledging the novelty and significance of the study. Regarding the relationship you mentioned between high-intensity aggression and reproductive success, we further toned down the statement between them throughout the manuscript in the revised manuscript. We also modified the title to “Social Experience Shapes Fighting Strategies in Drosophila”. In addition, we now added a ‘Limitations of the Study’ section to clearly state the correlation between tussling and reproductive success.

Reviewer #1 (Recommendations for the authors):

If possible, mention the EM-connectome data showing the minimal interneuronal path from Or47b ORNs to pC1SS2 neurons (even if derived from the female connectome), which can strengthen the model of parallel sensory-central pathways.

Thank you for this comment. According to data from the EM connectome, connecting Or47b ORNs to pC1d neurons requires at least two intermediate neurons. An example minimal pathway is: ORN_VA1v (L) → AL-AST1 (L) → PLP245 (L) → pC1d (R). We have added this point in the Discussion section of the revised manuscript.

I'm not convinced that labeling lunges as "gentle" combat behavior works, either in the abstract or elsewhere. While lunging is indeed a lower-intensity form of aggression compared to tussling, applying anthropomorphic descriptors risks misleading readers.

Thank you for this comment. We now use “low-intensity” instead of “gentle” to describe lunging.

In Materials & Methods, please cross-check all figure-panel references after the recent re-numbering (e.g. "Figure 5A6A" etc.).

Thank you for this comment. We have thoroughly verified the figure panel references in the Materials & Methods section.

Ensure that Table S1 is clearly cited in the main text where you first describe fly genotypes.

Thank you for this comment. We have now cited Table S1 in the main text.

There are multiple grammatical errors and typos throughout the manuscript. Please correct them. Some examples are below, but this is not an exhaustive list:

Line 98-102 requires rephrasing as the results are already published and not being observed by the authors.

Thank you for this comment. We have revised the manuscript to “we occasionally observed the high-intensity boxing and tussling behavior in male flies as previously reported (Chen et al., 2002; Nilsen et al., 2004), which….”

line 116- lower not 'lowed'.

Corrected.

line 942 & 945- knock-down males not 'knocking down males'.

Corrected. Thank you very much for these comments.

Reviewer #2 (Recommendations for the authors):

The authors have almost completely answered the major comments I have noted on the ver.1 manuscript: (1) They clearly show changes in fighting strategy in the territory control behavior experiment in Fig. 6-figure supplements. (2) A detailed description of how aggressive behavior is measured. Thus, I am convinced by this revision.

Thank you for these comments that make the manuscript a better version.

Furthermore, in Fig. 5, which examined the relationship of pC1[SS2] characteristics with the function of dsx, is a novel data and very interesting. I look forward to further developments.

Thank you. We will continue to explore this part in our future study.

However, one point still concerns me.

Line 192: Although the authors describe it as "usage-dependent," the trans-Tango technique is essentially a postsynaptic cell-labeling technique. It is possible that the labeling intensity in postsynaptic cells increases from the change in expression levels of the Or47b gene due to GH. However, there is no difference in the expression level of the Or47b gene labeled by GFP between SH and GH. Therefore, we cannot conclude that the expression of the Or47b gene is increased by rearing conditions.

The original paper on trans-TANGO (Talay et al., 2017) does not discuss the usage-dependency. A review of trans-synaptic labeling techniques (Ni, Front Neural Circuits. 2021) discusses that the increase in trans-TANGO signaling with aging may be related to synaptic strength, but there is no experimental evidence for this. In my opinion, the results in Figure 3-figure supplement 2 only weakly suggest that the increase in trans-TANGO signaling may be explained by an increase in synaptic strength due to group rearing.

We appreciate the reviewer’s insightful comment regarding the interpretation of the trans-Tango signal. Indeed, the original trans-Tango study (Talay et al., 2017) does not claim that the method is usage-dependent. The observed increase in trans-Tango labeling with age, as reported in their supplemental figures, may reflect accumulation over time, potentially influenced by synaptic maturation or increased component expression. To avoid overstating our results, we have revised the relevant statement in the manuscript to remove the term "usage-dependent" and now describe the change in trans-Tango signal more cautiously.  

Reviewer #3 (Recommendations for the authors):

Below are the cases where their professed attempts to "tone down the statement" appear ignored:

Lines 27-29:

"Our findings... suggest how social experience shapes fighting strategies to optimize reproductive success".

We have now revised the manuscript to “Our findings… suggest that social experience may shape fighting strategies to optimize reproductive success.”

Lines 85-86:

"... discover that this infrequent yet intense form of combat is... crucial for territory dominance and mating competition".

We have now revised the manuscript to “…discover that this infrequent yet intense form of combat is enhanced by social enrichment, while the low-intensity lunging is suppressed by social enrichment.” 

Lines 335-339:

"Here, we found that... GH males tend to... increase the high-intensity tussling, which enhances their territorial and mating competition."

We have removed “which enhances their territorial and mating competition” in the revised manuscript.

Lines 343-344:

"... presenting a paradox between social experience, aggression and reproductive success. Our result resolved this paradox..."

We have now revised the manuscript to “...Our results provide an explanation for this paradox…”

Lines 355-358:

"Interestingly, we found that the mating advantage gained through social enrichment can even offset the mating disadvantage associated with aging, further supporting the vital role of shifting fighting strategies in experienced, aged males."

We have removed “further supporting the vital role of shifting fighting strategies in experienced, aged males” in the revised manuscript.

Lines 361-362:

"These results separate the function of the two fighting forms and rectify out understanding of how social experiences regulate aggression and reproductive success."

We have removed this sentence in the revised manuscript.

Some may say that a speculative statement is harmless, but I think it indeed is harmful unless it is clearly indicated as a speculation. It is regrettable that authors remain reluctant to change their claim without providing any new supporting evidence. All three reviewers raised the same concern in the first round of review.

We apologize for not making the speculative nature of the statement clearer in the previous version. In the revised manuscript, we have now explicitly rephrased sentences to only suggest a correlation but not a causal link between tussling and reproductive success.

I have no choice but to keep my evaluation of the manuscript as "Incomplete" unless the authors thoroughly eliminate any attempt to link these two. This must go beyond changing a few words in the lines listed above.

Thank you for this comment. In addition to the lines listed above, we carefully checked all statements regarding the correlation between fighting strategies and reproductive success throughout the full text. Furthermore, we have also added a “Limitations of the Study” section to address the shortcomings of this study in the revised manuscript.

I do not have the same level of concern over the interpretation of Fig. 6A-C, because this is directly linked to aggressive interactions. Even if the socially isolated males do not engage in tussling, it is not a leap to assume that a different fighting tactic of socially experienced males can give them an advantage in defending a territory. To me, this is a sufficient ethological link with the observed behavioral change.

Thank you for this insightful comment.

The following are relatively minor, although important, concerns.

I beg to differ over the authors' definition of "tussling". Supplemental movies S1 and S2 appear to include "tussling" bouts in which 2 flies lunging at each other in rapid succession, and supplemental movie S3 appears to include bouts of "holding", in which one fly holds the opponent's wings and shakes vigorously. These cases suggest that the definition of "tussling" as opposed to "lunging" has a subjective element. However, I would not delve on this matter further because it is impossible to be completely objective over behavioral classification, even by using a computational method. An important point is that the definition is applied consistently within the publication. I have no reason to doubt that this was not the case.

Thank you for this comment. Since the analysis of tussling behavior was conducted manually, it is challenging to achieve complete objectivity. However, we made every effort to apply consistent criteria throughout the analysis. We have added a “Limitations of the Study” section in the revised manuscript to clearly state this caveat. We appreciate your understanding.

Authors now state that "all tester flies were loaded by cold anesthesia" (lines 432-433). I would like to draw attention to the well-known fact that anesthesia, whether by ice or by CO2, are long known to affect fly's subsequent behaviors (for aggression, see Trannoy S. et al., Learn. Mem. 2015. 22: 64-68). It will be prudent to acknowledge the possibility that this handling method could have contributed to unusually high levels of spontaneous tussling, which has not been reported elsewhere before.

Thank you for this comment. The increased tussling behavior observed in our study is unlikely due to cold anesthesia, as noted by Trannoy S. et al. (2015), cold anesthesia profoundly reduces locomotion and general aggressiveness in flies. We acknowledge that the use of cold anesthesia in behavioral experiments may have potential effects on aggression. To minimize this influence, we allowed the flies to recover and adapt for at least 30 minutes before behavioral recording. Moreover, both control and experimental groups were treated in exactly the same manner to ensure consistency.

It is intriguing that pC1SS2 neurons are dsx+ but fru-. Authors convincingly demonstrated that these neurons are clearly distinct from the P1a neurons, a well-characterized hub for male social behaviors. It is possible that pC1SS2 neurons overlap with previously characterized dsx+ neurons that are important for male aggressions (measured by lunges), such as in Koganezawa et al., Curr. Biol. 2016 and Chiu et al., Cell 2020, a point authors could have explicitly raised.

Thank you for this comment. We have added this point into the Discussion section of the revised manuscript, as follows: “That tussling-promoting… aggression (Koganezawa et al., 2016). Moreover, the anatomical features of pC1^SS2 neurons are highly similar to the male-specific aggression-promoting (MAP) neurons identified by another previous study (Chiu et al., 2021).

I acknowledge the authors' courage to initiate an investigation to a less characterized, high intensity fighting behavior. Tussling requires the simultaneous engagement of two flies. Even if there are confusion over the distinction between lunges and tussling, authors' conclusion that socially experienced flies and socially isolated flies employ distinct fighting strategy is convincing. The concern I raised above is about the interpretation of the data, not about the quality of data.

Thank you for your constructive comments to make this manuscript better.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary: 

This study provides compelling evidence suggesting that ghrelin, a molecule released in the surroundings of the major adult brain neurogenic niche (V-SVZ) by blood vessels with high blood flow, controls the migration of newborn interneurons towards the olfactory bulbs. 

Strengths:

This study is a tour de force as it provides a solid set of data obtained by time-lapse recordings in vivo. The data demonstrate that the migration and guidance of newborn neurons rely on factors released by selective types of blood vessels. 

Weaknesses:

Some intermediate conclusions are weak and may be reinforced by additional experiments. 

We thank the reviewer for the thoughtful evaluation and constructive comments outlined in the “Recommendations for The Authors”. In response, we have incorporated additional data, revised relevant figures, and clarified explanations in the revised manuscript.

Reviewer #2 (Public review)

Summary: 

The authors establish a close spatial relationship between RMS neurons and blood vessels. They demonstrated that high blood flow was correlated with migratory speed. In vitro, they demonstrate that Ghrelin functions as a motogen that increases migratory speed through augmentation of actin cup formation. The authors proceed to demonstrate through the knockdown of the Ghrelin receptor that fewer RMS neurons reach the OB.

They show the opposite is true when the animal is fasted. 

Strengths: 

Compelling evidence of close association of RMS neurons with blood vessels (tissue clearing 3D), preferentially arterioles. Good use of 2-photon imaging to demonstrate migratory speed and its correlation with blood flow. In vitro analysis of Ghrelin administration to cultured RMS neurons, actin visualization, Ghsr1KD, is solid and compelling. 

We sincerely thank the reviewer for the encouraging comments and helpful suggestions. As noted, our original manuscript lacked sufficient in vivo evidence connecting blood flow with ghrelin signaling. To address this, we have added new data and revised the explanations throughout the manuscript as described below.

Weaknesses: 

(1) Novelty of findings attenuated due to prior work, especially Li et al., Experimental Neurology 2014. Here, the authors demonstrated that Ghrelin enhances migration in adultborn neurons in the SVZ and RMS. 

We agree with the reviewer that the idea that ghrelin enhances migration of new neurons is not entirely novel. The study by Li et al. (2014) provided critical insights that guided our investigation into ghrelin as a blood-derived factor promoting neuronal migration. However, our study expands on this by demonstrating that ghrelin directly stimulates migration via GHSR1a in cultured new neurons, and we further identified the cellular and cytoskeletal mechanisms involved. Specifically, we showed that ghrelin enhances somal translocation by activating actin dynamics at the rear of the cell soma. We have revised the Results and Discussion sections accordingly to emphasize these novel aspects as follows:

“A previous study demonstrated that the migration of V-SVZ-derived new neurons was attenuated in ghrelin knockout mice (Li et al., 2014). In our study, we found that the migration of cultured new neurons was enhanced by the application of ghrelin to the culture medium, and this effect was abolished by Ghsr1a knockdown (KD). These findings suggest that ghrelin directly stimulates neuronal migration through its receptor, GHSR1a, on new neurons. A previous study showed that GHSR1a is expressed in various regions of the brain (Zigman et al., 2006). In our experiments, new neuron-specific KD of Ghsr1a indicated that ghrelin signaling acts in a cell-autonomous manner to regulate neuronal migration.” (Discussion, page 13, lines 10–18)

“Furthermore, we identified the cellular and cytoskeletal mechanisms underlying this effect on migration. The results indicate that ghrelin enhances somal translocation during migration by activating actin cytoskeletal dynamics at the rear of the neuronal soma.” (Discussion, page 13, lines 24–26)

(2) The evidence for blood delivery of Ghrelin is not very convincing. Fluorescently-labeled Ghrelin appears to be found throughout the brain parenchyma, irrespective of the distance from vessels. It is also not clear from the data whether there is a link between increased blood flow and Ghrelin delivery. 

We agree that the correlation between blood flow and ghrelin transcytosis is not very convincing in our study. As the reviewer pointed out, Figure 3A gives the impression that fluorescent-labeled ghrelin is uniformly distributed throughout the brain parenchyma. However, high-magnification images newly added in Figure 3 show that some, but not all, vessels have particularly strong fluorescent signals in the parenchymal area adjacent to the abluminal side of vascular endothelial cells, visualized by CD31 immunostaining (Feng et al., 2004) (Figure 3A′, A′′). To quantify these observations, we defined two regions: Area I (perivascular area), within 10 μm of the abluminal surface of CD31-positive endothelium; and Area II (distant area), located 10–20 μm away (Figure 3E). Of note, Area I corresponds to the perivascular region where new neurons are frequently observed (Figure 1).

Importantly, we found strong ghrelin signals in vascular endothelial cells of endomucin-negative high-flow vessels (Figure 3C, D). This suggests that transcytosis of blood-derived ghrelin may occur more frequently in high-flow vessels due to increased endocytosis at the endothelium. To test this, we quantified signal gradients in the extra-vessel regions as fold changes (Area I / Area II), as illustrated in Figure 3E. The proportion of vessel segments with >1.5-fold increases was significantly higher in endomucin-negative vessels than in endomucin-positive ones (Figure 3F). Furthermore, vessels with >2-fold increases were observed exclusively in the endomucinnegative group (6.48% ± 1.18%). 

These data suggest that, in high-flow vessels, blood-derived ghrelin accumulates more in the immediate perivascular region than in areas further away. This supports the possibility that elevated blood flow delivers a larger amount of ghrelin to the vascular endothelium, enhancing its transcytosis into adjacent brain parenchyma. This mechanism may underlie the preferential migration of new neurons along perivascular regions with high blood flow, as shown in Figure 1.  We have incorporated this new data in Figure 3 and corresponding explanations into the Results, Figure legend and Methods

(3) The in vivo link between Ghsr1KD and migratory speed is not established. Given the strong work to open the study on blood flow and migratory speed and the in vitro evidence that migratory speed is augmented by Ghrelin, the paper would be much stronger with direct measurement of migration speed upon Ghsr1KD. Indeed, blood flow should also be measured in this experiment since it would address concerns in 2. If blood flow and ghrelin delivery are linked, one would expect that Ghsr1KD neurons would not exhibit increased migratory speed when associated with slow or fast blood flow vessels. 

In Figure 3, we showed that ghrelin transcytosis occurs preferentially in high-flow vessels, suggesting a role for ghrelin in mediating the effects of blood flow on neuronal migration. However, whether this dependence is solely attributable to ghrelin signaling remains unclear. 

To address this, we tested whether Ghsr1a-KD modifies the impact of reduced blood  flow on neuronal migration by combining Ghsr1a-KD with bilateral common carotid artery stenosis (BCAS), a chronic cerebral hypoperfusion model (Figure S9A). We found that BCAS decreased the percentage of Ghsr1a-KD new neurons reaching the OB, similar to the effect seen in control neurons (Figure S9B, see also Figure 2A–C). This suggests that blood flow influences neuronal migration even under Ghsr1a-KD conditions. 

Furthermore, we analyzed the distribution of Ghsr1a-KD neurons with respect to vessel flow characteristics. Even under Ghsr1a-KD, a higher proportion of new neurons were located in the area of endomucin-negative (high-flow) vessels compared with endomucin-positive (low-flow) vessels (Figure S9C), indicating that Ghsr1a-KD does not abolish the preferential association of migrating neurons with high flow vessels. These findings suggest that although ghrelin signaling contributes to blood flow-dependent migration, it is not the sole factor. Other blood-derived signals may also mediate this effect. We have included these new data in Figure S9 and updated the corresponding sections in the Results

Reviewer #1 (Recommendations for the authors) :

Major 

Page 6, Line 13. Please provide in the result section some explanation about how photothrombic clot is induced.  

We added the following explanation to the Results section to clarify the method used to induce photothrombotic clot formation.

“For clot formation, a restricted area of selected vessels was irradiated by a two-photon laser immediately after intravenous injection of rose bengal.” (Results, Page 7, lines 27–28)

Page 6, Line 18. The authors use the marmoset as an additional experimental model. Here, V-SVZ-derived newborn neurons migrate in other brain regions as compared to rodents. Please provide a clear rationale for moving from rodents to "common marmosets" as an experiment model. And why use marmosets only for this set of experiments? 

We clarified the rationale for using common marmosets in addition to mice as follows:

“Because blood vessel-guided neuronal migration in the adult brain is a conserved phenomenon across species (Kishimoto et al., 2011; Akter et al., 2021; Shvedov et al., 2024), we hypothesized that blood flow may also influence neuronal migration in other brain regions of primates. The neocortex, which supports higher-order brain functions and has undergone evolutionary expansion in primates, was selected as a target region. In common marmosets, but not in mice, V-SVZ-derived new neurons migrate toward the neocortex and ventral striatum (Akter et al., 2021) (Supplemental Movies S4 and S5).” (Results, Page 6, lines 19–25)

Figure 2B. The experimental setup is possibly problematic as the lentiviral tracing measurement does not take into consideration the rate of neurogenesis or newborn neuron survival. Can authors assess the rate of proliferation and survival in the VSVZ/RMS upon BCAS to decipher whether the reduced number of cells observed in the OB only results from migration changes? (comparable remark stands for Figure 5) 

To evaluate whether the reduction in the number of new neurons observed in the OB after BCAS (Figure 2B, C) is due solely to impaired migration, we assessed cell proliferation and survival in the V-SVZ and RMS. Specifically, we quantified the density of Ki67+ proliferating cells and cleaved caspase-3+ apoptotic cells in the sham and BCAS groups. BCAS significantly decreased cell proliferation and increased cell death in both the V-SVZ and RMS (Figure S4), suggesting that reduced neurogenesis and/or survival may contribute to the decreased neuronal distribution in the OB. 

Although we cannot exclude the possibility that changes in cell proliferation or survival contributed to this effect, our photothrombotic clot formation experiments are better suited to directly examine how acute reduction in blood flow affects neuronal migration. These experiments allowed us to measure the migration speed of new neurons shortly after inducing localized blood flow inhibition. We found that clot formation significantly reduced the migration speed of new neurons (Figure 2E, H), indicating that blood flow changes directly impair neuronal migration in the adult brain. 

We have included these new data in Figure S4 and updated the corresponding text in the Results, Discussion, Figure legend, and Methods as follows:

Figure 3. About ghrelin signaling. It is unclear whether its transcytosis occurs in endomucin-negative because of the high bloodstream flow. How can this be explained? What happens upon BCAS, is there still a close relation between ghrelin transcytosis, blood flow, and neuron migration? 

As correctly noted, our initial explanation and data did not provide sufficient evidence that higher blood flow delivers a larger amount of ghrelin into the brain parenchyma. We found that some vessels had particularly strong fluorescent signals in the parenchymal area adjacent to the abluminal surface of vascular endothelial cells, as visualized by CD31 immunostaining (Feng et al., 2004) (Figure 3A′, A′′). On the basis of our observation that strong fluorescent signals were detected in vascular endothelial cells of endomucin-negative (high-flow) vessels (Figure 3C, D), we hypothesized that ghrelin transcytosis may occur more frequently in high-flow vessels due to increased endocytosis at the vessel endothelium. 

To test this hypothesis, we quantified signal gradients in the extra-vessel regions by calculating fold changes in fluorescent intensity between two zones: Area I (0–10 μm from the abluminal surface of the endothelium) and Area II (10–20 μm away), as illustrated in Figure 3E. Area I corresponds to the perivascular region where new neurons are frequently found (Figure 1). We found that the proportion of vessel segments with >1.5-fold signal increase in Area I relative to Area II was significantly higher in endomucin-negative vessels than endomucin-positive ones (Figure 3F). Furthermore, vessel segments with >2-fold increases were observed exclusively in the endomucin-negative group (6.48% ± 1.18%). These results support the idea that higher blood flow increases the amount of ghrelin that reaches the luminal surface of vascular endothelial cells, thereby increasing the possibility of ghrelin transcytosis into the brain parenchyma.

We also examined whether blood flow inhibition–induced by BCAS or photothrombotic clot formation–affects the relationship between ghrelin transcytosis, blood flow, and neuronal migration. The above results suggest that blood flow reduction may decrease ghrelin transcytosis, thereby contributing to impaired neuronal migration. To further explore this, we analyzed the distribution of new neurons around high- versus low-flow vessels under BCAS conditions. In the BCAS group, we still observed a higher density of new neurons in the region of high-flow (endomucin-negative) vessels compared with in low-flow (endomucin-positive) ones (Figure S9C). This suggests that even under reduced blood flow, neuronal migration preferentially occurs near high-flow vessels. Taken together, these results suggest that ghrelin transcytosis, blood flow and neuronal migration are connected, and that this relationship persists under conditions of blood flow reduction.

Figure 4. Is ghrelin controlling both individual Dcx+ neuron migration as well as chain migration (cells moving more together)? This should be assessed and clarified. 

How is ghrelin controlling actin dynamics in newborn migrating neurons? Since somal translocation speed and somal stride length are both modulated by ghrelin, this factor may also control MT remodeling, could that be checked? 

We have revised the manuscript to better explain the role of ghrelin in both modes of neuronal migration–chain and individual. Initially, we demonstrated that ghrelin enhances the migration of new neurons in V-SVZ culture (Figure 4A, B), where these neurons migrate outward as chains, indicating that ghrelin facilitates chain migration. In subsequent in vitro experiments (Figure 4C–M), we showed that ghrelin also enhances the migration of individual neurons. To examine this in vivo, we injected Ghsr1a-KD and control lentiviruses into two different anatomical regions: the V-SVZ, where chain migration originates, and the OB core, where new neurons migrate individually. These experiments enabled us to assess the role of ghrelin signaling in each mode of migration independently. We found that ghrelin enhanced both chain migration in the RMS and individual migration in the OB. These results indicate that ghrelin signaling facilitates both forms of neuronal migration. We added the following text in the Results section:

“To assess the direct effect of ghrelin on neuronal migration, we applied recombinant ghrelin to V-SVZ cultures, in which new neurons emerge and migrate as chains (Figure 4A). Ghrelin significantly increased the migration distance of these neurons (Figure 4B), indicating enhanced chain migration. We then used super-resolution time-lapse imaging to examine individually migrating neurons with or without knockdown (KD) of growth hormone secretagogue receptor 1a (GHSR1a), a ghrelin receptor expressed in V-SVZ-derived new neurons (Li et al., 2014) (Figure 4C). Ghrelin enhanced the migration speed of control cells (lacZ-KD) cells, indicating that it also facilitates individual migration (Figure 4D).” (Results, Page 9, lines 5–12)

“Of the total labeled Dcx+ cells, the percentage of Dcx+ cells reaching the GL was significantly lower in the Ghsr1a-KD group than in the control group (Figure 5B, C), suggesting that ghrelin enhances individual radial migration of new neurons in the OB.” (Results, Page 10, lines 5–8) “These data indicate that ghrelin signaling facilitates both individual migration in the OB and chain migration in the RMS.” (Results, Page 10, lines 17–18)

We also added discussion on how ghrelin may regulate cytoskeletal dynamics in migrating neurons. Ghrelin signaling has been reported to control actin cytoskeletal remodeling in astrocytoma cells (Dixit et al., 2006), which led us to investigate similar effects in migrating neurons. Rac, a member of the Rho GTPase family, was shown to mediate this actin remodeling in astrocytoma migration, suggesting it may also be involved in ghrelin-induced actin cup formation in new neurons. Furthermore, because somal translocation depends not only on actin but also on microtubule dynamics (Kaneko et al., 2017), it is possible that ghrelin influences both systems. Supporting this idea, ghrelin signaling was shown to modulate microtubule behavior via SFK-dependent phosphorylation of α-tubulin (Slomiany and Slomiany, 2017). These findings suggest that ghrelin may enhance somal translocation through coordinated regulation of both the actin and microtubule systems. We added following text in the Results and Discussion sections:

“Ghrelin signaling has been reported to regulate actin cytoskeletal dynamics in astrocytoma cells (Dixit et al., 2006), which led us to examine whether a similar mechanism operates in migrating neurons.”(Results, Page 9, lines 23–25)

“Further studies are needed to elucidate how ghrelin promotes actin cup formation in migrating neurons. Given that Rac, a Rho family GTPase, mediates actin remodeling downstream of ghrelin in astrocytoma cells (Dixit et al., 2006), it is possible that Rac may also be involved in ghrelininduced cytoskeletal regulation in new neurons.” (Discussion, Page 13, lines 28–31)

“In addition to actin remodeling, ghrelin may regulate microtubule dynamics. Ghrelin signaling was shown to modulate microtubules via SFK-dependent phosphorylation of α-tubulin (Slomiany and Slomiany, 2017), raising the possibility that ghrelin promotes somal translocation of new neurons through coordinated regulation of both actin and microtubule networks (Kaneko et al., 2017).” (Discussion, Page 13, line 31–Page 14, line 2)

It would also be informative to provide immunolabeling of Ghsr1 in the V-SVZ / RMS/ OB to have a clear picture of the expression pattern of this receptor. Newborn neurons migrate along blood vessels, which are surrounded by astrocytes that have also been reported to express Ghsr1, thus could newborn neuron migration change may also arise from activation of Ghsr1 in their surrounding astrocytes? 

A previous study reported that GHSR1a is expressed in DCX+ new neurons in the RMS and OB, and in V-SVZ neural progenitor cells (Li et al., 2014). To visualize the spatial expression pattern of Ghsr1a, we performed RNAscope in situ hybridization because specific anti-GHSR1a antibodies suitable for immunohistochemistry were not available. Consistent with the previous report, we detected Ghsr1a mRNA in DCX+ new neurons in the VSVZ, RMS, and OB (Figure S5A), indicating that new neurons directly receive ghrelin signaling. 

Moreover, our KD experiments demonstrated that ghrelin enhanced the migration of new neurons in a cell-autonomous manner via GHSR1a (Figure 4, 5). Nevertheless, a recent study (Stark et al., 2024) showed that GHSR1a was expressed in various cell types, including glutamatergic and GABAergic neurons, suggesting that ghrelin may also exert non-cellautonomous effects on neuronal migration. Given the presence of diverse cell types, including neurons, microglia, pericytes, and astrocytes, along the migratory route, it remains possible that GHSR1a activation in these neighboring cells contributes to the overall regulation of neuronal migration. 

Figure 5. About the in vivo knockdown of Ghsr1a. The results section (page 9, line 3) mentioned that mice were either injected with one or the other construct but Figure 5 shows coincidence of GFP and dsRed positive cells. Were control and Ghsr1a shRNAs injected together into the same mouse? Could you quantify the number of cells in green (control), red (Ghsr1a KD), and yellow (both)? Won't they mostly be yellow? Have you tried injecting control and Ghsr1a separately? If yes, do you get the same result? Such analysis would be important to separate cell autonomous from noncell autonomous effects. 

To minimize variability in injection conditions, we initially coinjected control and Ghsr1a-KD lentiviruses into the same mice and analyzed their migration using a paired design. As the reviewer correctly noted, some cells were coinfected and expressed both EmGFP and DsRed (18.7% ± 2.86% of EmGFP+ cells and 10.8% ± 0.533% of DsRed+ cells). To ensure that this overlap did not affect our analysis, we excluded EmGFP+/DsRed+ double-positive cells and focused solely on EmGFP+/DsRed− (control) and EmGFP−/DsRed+ (Ghsr1a-KD) single-positive cells. 

We agree with the reviewer that coinjection could lead to reciprocal interactions between control and Ghsr1a-KD cells, potentially masking cell-autonomous effects. To address this, we performed an independent experiment in which control and Ghsr1a-KD lentiviruses were injected separately into different mice (Figure S7A), as suggested. Consistent with the results of the coinjection experiment, we found that the Ghsr1a-KD cells showed significantly reduced distribution in the GL compared with that in control cells (Figure S7B). Although we cannot exclude the possibility of a non-cell-autonomous effect of ghrelin, this result supports the conclusion that ghrelin signaling enhances neuronal migration in a cell-autonomous manner. 

Who is expressing Ghsr1a, newborn neurons, and or their progenitors? The production and survival of newborn V-ZVS cells should be assessed upon knockdown of the ghrelin receptor too. 

To determine whether the altered distribution of new neurons observed upon Ghsr1aKD is due to impaired migration rather than decreased cell production or survival, we examined the effects of Ghsr1a-KD on the proliferation and survival of new neurons and their progenitors, which express GHSR1a (Li et al., 2014). 

We compared the proportion of cleaved caspase-3+ cells and Ki67+ cells from the total labeled cells in the V-SVZ and RMS between the control and Ghsr1a-KD groups. There was no significant difference in the proportion of cleaved caspase-3+ cells between the groups (Control: 874 cells from 5 mice; Ghsr1a-KD: 678 cells from 7 mice), suggesting that ghrelin signaling does not affect the survival of new neurons and their progenitors. 

Similarly, the proportion of Ki67+ cells in the RMS did not differ significantly between the two groups (Figure S8), indicating that Ghsr1a-KD does not impair cell proliferation in the RMS. However, it remains technically difficult to evaluate whether Ghsr1a-KD affects proliferation in the VSVZ, because lentivirus injection into the VSVZ may interfere with GHSR1a expression not only in new neurons and neural progenitors, but also in other cell types known to express GHSR1a (Zigman et al., 2006). A previous study reported that ghrelin signaling promoted cell proliferation in the V-SVZ (Li et al., 2014), thus we cannot exclude the possibility that Ghsr1a-KD may affect V-SVZ proliferation.

To overcome this limitation, we assessed the effects of Ghsr1a-KD on neuronal migration using in vitro KD experiments (Figure 4C–J) and in vivo OB-core lentivirus injections (Figure 5A–C), both of which did not interfere with proliferation in the V-SVZ. These complementary approaches consistently demonstrated that Ghsr1a-KD reduces the migration speed of new neurons. 

“To determine whether the altered distribution of new neurons after Ghsr1a-KD is due to impaired migration rather than changes in cell production or survival, we assessed the effects of Ghsr1aKD on the proliferation and survival of new neurons and their progenitors, which express GHSR1a (Li et al., 2014). We quantified the proportion of cleaved caspase-3+ cells and Ki67+ cells from the total labeled cells in the V-SVZ and RMS in both control and Ghsr1a-KD groups. We found no significant difference in cleaved caspase-3+ cell proportions between the groups (Control: 874 cells from 5 mice; Ghsr1a-KD: 678 cells from 7 mice), suggesting that ghrelin signaling does not influence the survival of new neurons and their progenitors. Similarly, the percentage of Ki67+ cells in the RMS was similar between the two groups (Figure S8), indicating that Ghsr1a-KD does not impair cell proliferation in the RMS. However, technical limitations prevented a reliable evaluation of proliferation in the V-SVZ, as lentivirus injection into this region may interfere with GHSR1a expression in not only neural progenitors and new neurons, but also other GHSR1aexpressing cell types (Zigman et al., 2006). Although ghrelin signaling has been reported to promote cell proliferation in the V-SVZ (Li et al., 2014), our complementary in vitro KD experiments (Figure 4C–J) and in vivo OB-core lentivirus injections (Figure 5A–C), which did not affect the V-SVZ, consistently demonstrated that Ghsr1a-KD reduces neuronal migration. Taken together, our results suggest that blood-derived ghrelin enhances neuronal migration in the RMS and OB by stimulating actin cytoskeleton contraction in the cell soma, rather than by altering cell proliferation or survival.” (Results, Page 10, line 19–Page 11, line 4)

“rat anti-Ki67 (1:500, #14-5698-82, eBioscience); and rabbit anti-cleaved caspase-3 (1:200, #9661, Cell Signaling Technology)” (Methods, Page 48, lines 14–16)

How much is ghrelin/Ghsr1 signaling conserved in marmosets? 

How ghrelin signaling is conserved between mice and common marmosets is important to clarify. A previous study reported the existence of a ghrelin homolog in common marmoset, which shares high sequence similarity with that in mice (Takemi et al., 2016). Moreover, the GHSR1a homolog in the common marmoset (https://www.ncbi.nlm.nih.gov/protein/380748978) shares 95.36% amino acid identity with its mouse counterpart. These findings suggest that blood-derived ghrelin may similarly promote neuronal migration in the marmoset brain, as observed in mice. 

We have added the following text in the Discussion section:

“Our data showed that new neurons preferentially migrate along arteriole-side vessels rather than venule-side vessels in both mouse and common marmoset brains, suggesting that the mechanism of blood flow-dependent neuronal migration is conserved across rodent and primate species, as well as across brain regions. A previous study identified a ghrelin homolog in the common marmoset with high sequence similarity to the murine version (Takemi et al., 2016). In addition, the marmoset GHSR1a homolog shares 95.36% amino acid identity with that of the mouse (https://www.ncbi.nlm.nih.gov/protein/380748978). These findings suggest that bloodderived ghrelin promotes neuronal migration in the common marmoset brain in a manner similar to that in mice.” (Discussion, Page 15, lines 8–16)

Page 9. Starvation has been shown to boost ghrelin blood levels. What is the exact protocol used in this experiment and is this indeed increasing Ghrelin release from blood vessels in the V-SVZ? What about Ghsr1 expression level in newborn neurons? 

We have clarified the calorie restriction (CR) protocol used in our experiments. We adopted a 70% CR protocol, which was previously shown to enhance hippocampal neurogenesis when administered for 14 days (Hornsby et al., 2016). In our study, the daily food intake under ad libitum (AL) conditions was first measured, and CR mice were then fed 70% of that amount for 5 consecutive days (see Figure 5I and Figure S10A). 

To assess whether CR enhances ghrelin transcytosis into the brain parenchyma, we performed ELISA to quantify ghrelin levels in the OB and RMS. However, ghrelin concentrations were below the detection limit in both groups, precluding a direct comparison.

We also considered whether CR modulates the expression level of the ghrelin receptor GHSR1a. A recent study reported that fasting increased GHSR1a expression in the OB (Stark et al., 2024), raising the possibility that CR may exert a similar effect. To test this, we performed in situ hybridization and quantified Ghsr1a mRNA puncta in Dcx+ cells in the OB. No significant difference was found between the AL and CR groups (Figure S5B), suggesting that CR does not alter GHSR1a expression levels in new neurons. 

Although we cannot exclude the possibility that CR increases GHSR1a expression in other OB cell types, our combined CR and Ghsr1a-KD experiments strongly support a cellautonomous contribution of ghrelin signaling to the enhanced neuronal migration observed under CR conditions. Corresponding data and text have been added to Figure S5 and the Results, Discussion, and the Figure legend sections as follows:

Minor 

Page 4 

Line 19 In Supplemental movies 1 and 2, it is unclear where to see the GFP+ new neurons interact with BV. Can you add arrows as an indication for the readers? It will be better to add the anatomy term for orientation, caudal, or rostral in the video. (The same for Supplemental movies 3, 4, and 5).  

To clarify the regions of interest in Supplemental Movies 1 and 2, where neuron–vessel interactions in the RMS are highlighted, we added dotted lines indicating the RMS boundaries. In addition, we created a new movie (Supplemental Movie S1′) showing a high-magnification view of Supplemental Movie S1, in which arrows mark EGFP+ new neurons interacting with blood vessels. We also added orientation indicators (e.g., caudal and rostral) and arrows to highlight new neuron–vessel interactions in Supplemental Movies S1–S5. 

The following descriptions have been added to the Figure legends:

“Supplemental Movie S1′ 

High-magnification view extracted from Supplemental Movie S1. Arrows indicate EGFP+ cells interacting with blood vessels.” (Figure legend, Page 46, lines 6–8)

“Arrows indicate EGFP+ cells interacting with blood vessels.” (Figure legend, Supplemental Movie S3, Page 46, lines 16–17)

“Arrows indicate Dcx+ cells interacting with blood vessels.” (Figure legend, Supplemental Movies S4 and S5, Page 46, lines 21–22, 26–27)

Blood vessels are labeled in the Supplemental movies 2 and 3 by employing Flt1DsRed transgenic mice instead of RITC-Dex-GMA. However, Flt1-DsRed transgenic mice are not mentioned in the results section. 

We have now included an explanation regarding the use of Flt1-DsRed mice, in which vascular endothelial cells were labeled with DsRed.

“To visualize blood vessels, we also used Flt1-DsRed transgenic mice, in which vascular endothelial cells were specifically labeled with DsRed (Matsumoto et al., 2012). Using DcxEGFP/Flt1-DsRed double transgenic mice, we observed close spatial relationships between new neurons and blood vessels (Supplemental Movies S2 and S3).” (Results, Page 4, lines 22– 26)

Figure 5. Can you indicate (in the figure legend and the result section) the stage of the adult brain used for this experiment? 

We used 6- to 12-week-old adult male mice in all experiments in this study. To specify this, we have added the age of animals to both the Results and the relevant Figure legends as follows:

“Therefore, we first studied blood vessel-guided neuronal migration in the RMS and OB using three-dimensional imaging in 6- to 12-week-old adult mice, which enabled analysis of the in vivo spatial relationship between new neurons and blood vessels.” (Results, Page 4, lines 14–16)

“Figure 1 New neurons migrate along blood vessels with abundant flow in the adult brain.” (Figure legend, Page 25, line 4)

“(B, C) Three-dimensional reconstructed images of a new neuron (green) and blood vessels (red) in the rostral migratory stream (RMS) (B) and glomerular layer (GL) (C) of 6- to 12-weekold adult mice.” (Figure legend, Page 25, lines 6–8)

“(E) Transmission electron microscopy image of a new neuron (green) in close contact with a blood vessel (red) in the GL of a 6- to 12-week-old adult mouse.” (Figure legend, Page 26, lines 4–5)

“(F) Time-lapse images of a migrating neuron (indicated by asterisks) in the GL of a 6- to 12week-old Dcx-EGFP mouse.” (Figure legend, Page 26, lines 6–7)

“Figure 3 Ghrelin is delivered from the bloodstream to the RMS and OB in the adult brain (A) Representative images of the OB and cortex of a fluorescent ghrelin-infused mouse (6 to 12 weeks old).” (Figure legend, Page 30, lines 1–3)

“Lentivirus injection into the OB core (A) and the VSVZ (D) was performed in 6- to 12-week-old adult mice.” (Figure legend, Page 33, lines 3–4)

Reviewer #2 (Recommendations for author):

Major:

Ghsr1KD and blood flow 2-photon experiments to directly measure migratory speed. Could also do the same with fasting with or without Ghsr1KD.  

We thank the reviewer for the valuable suggestion to strengthen our study. As pointed out in the Public Review, we agree that direct in vivo measurement of neuronal migration speed under Ghsr1a-KD conditions is important to clarify the link between ghrelin signaling and blood flow. 

Two-photon imaging is the most suitable method for this purpose. Although we attempted two-photon imaging of Ghsr1a-KD new neurons, the number of virus-infected cells observed in vivo was too low to yield reliable data. Therefore, we chose an alternative strategy, combining Ghsr1a-KD with blood flow reduction using the BCAS model (Figure S9A), in which migration speed can be quantified based on the percentage of labeled cells reaching the OB. As stated in the Public Review response, BCAS significantly decreased the migration speed of Ghsr1a-KD new neurons (Figure S9B), indicating that Ghsr1a-KD does not abolish the influence of blood flow reduction. These findings suggest that ghrelin signaling is involved, but is not essential, for blood flow-dependent neuronal migration. 

As suggested by the reviewer, direct observation of migration dynamics (e.g., somal translocation, leading process extension, stationary and migratory phases) is needed, especially in calorie restriction experiments. Although our data indicate that ghrelin signaling is required for fasting-induced increases in migration speed of new neurons, calorie restriction could also change concentrations of other factors in blood (Bonnet et al., 2020; Wu et al., 2024; Alogaiel et al., 2025), which may independently affect behavior of migrating neurons. Given that ghrelin is not the sole factor contributing to blood flow-dependent neuronal migration, other circulating factors could affect behavior of migrating neurons in a different manner during fasting. In vivo twophoton imaging would be a powerful approach to determine whether fasting-induced neuronal migration is caused by upregulated somal translocation speed, which would further support a role for ghrelin in this process.

We have added the following text in the Discussion:

“Although our data indicate that ghrelin signaling is essential for fasting-induced acceleration of neuronal migration, calorie restriction may also alter the concentrations of other circulating factors (Bonnet et al., 2020; Wu et al., 2024; Alogaiel et al., 2025), which could independently influence the behavior of migrating neurons.” (Discussion, Page 14, lines 25–29)

Minor: 

(1) Show fluorescent Ghreliin in Figure 3 for all brain areas measured in Figure 1 (GL, EPL, GCL, and RMS) for direct comparison.  

To allow for direct comparison across brain regions, we added a new Supplemental figure showing the distribution of fluorescently labeled ghrelin in the OB, including the GL, EPL, GCL and RMS. This comprehensive view highlights ghrelin localization relative to vasculature and migrating neurons in the regions analyzed in Figure 1.

(1) Figure 1, panel I is presented in a confusing manner. High blood flow points to 0 degrees, low blood flow to 180 degrees. It implies (unintentionally, I am sure) that low blood flow results in migration away from OB. Maybe plot separately?

We agree that the original presentation of Figure 1I could be misinterpreted as referring to anatomical orientation (i.e., toward or away from the OB). To avoid confusion, we revised the figure to categorize new neuron–vessel interactions into four groups according to (1) the angle between the migration direction and vessel axis (small or large), and (2) whether the new neuron is migrating toward or away from the direction of higher blood flow. This new presentation avoids implying a fixed anatomical direction and better reflects the relationship between local blood flow and neuronal migration behavior. The revised figure is presented as Supplemental Figure S1.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1(Public review):

We deeply appreciate the reviewer comments on our manuscript. Following up the revisions, our manuscript has been improved thanks to their insightful remarks. We have proceeded with all the required changes.  

Weaknesses:

The authors have still not addressed the inconsistent/missing description for sample size, the appropriate number of * for each figure panel, and the statistical tests used.

Description of sample size, specific P value and statistical test used has been added it both in the main text, figures and figure legends.

The authors assign 5% oxygen as hypoxia. This is not the case as the in vivo environment is close to this value. 5% is normoxia. Clinical IVF/embryo culture occurs at 5% O2. Please adjust your narrative around this.

We define in our manuscript “normoxia” as the standard atmospheric oxygen levels in tissue culture incubators, which range from about 20–21% oxygen. Our definition of hypoxia is 5% concentration of oxygen, taking into consideration the standard levels of oxygen in the IVF clinics. Physiological oxygen in mouse varies from ~1.5% to 8% (Alva et al 2022). Considering that these levels of oxygen are the standard levels in tissue culture practices, a paragraph has been added to the discussion and materials and methods for further clarification   

Reviewer #2 (Public review):

Weakness:

Given that this is a study on the induction of aneuploidy, it would be meaningful to assess aneuploidy immediately after induction, and then again before implantation. This is also applicable to the competition experiments on page 7/8. What is shown is the competitiveness of treated cells. Because the publication centers around aneuploidy, inclusion of such data in the main figure at all relevant points would strengthen it. There is some evaluation of karyotypes only in the supplemental - why? Would be good not to rely on a single assay that the authors appear to not give much importance.

This is an excellent point. However, due to the stochasticity of the arising of aneuploidies when embryos are treated with AZ3146 and reversine (Bolton et al 2016), every treatment is likely to generate different levels of aneuploidy. Due to this, and to the technical limitations of generating single-cell genomic DNA sequencing at the blastocyst stage, we were unable to determine the karyotype of all cells after different conditions. Nevertheless, Regin et al 2024 (eLife) showed similar results on the overall transcriptome changes of different dosages of aneuploidy: high dosage embryos overexpress p53, like reversine-treated embryos; meanwhile, low dosage embryos overexpress the hypoxic pathway, including HIF1A, similar to embryos treated with AZ3146.  

Reviewer #1 (Recommendations for the authors):

Corrections required before final publishing:

Please ensure that the number of asterisks is in alignment with standard convention (* <0.05; ** <0.01; *** <0.001; **** <0.0001). If you want to describe an exact P -vale it should be presented as P = 0.0004. line 108 *** is <0.0004. line 263 * P<0.0044

Same issue appears in lines 697, 711, 722, 753, 685

Specific values have been added in the figures and modified in the text. 

Line 199: "...viable E9.5 embryos" missing "Figure S1D"

Modified in manuscript

Line 120: "...decidua" please add "Figure S1C"

Modified in manuscript

Line 126-127: Please add a description for the results (morula) in Fig 1D, e.g., It appears that YH2Ax persists from 8-cell to morula when treated with Reversine but not AZ3146"

At the morula stage, the levels of γH2A.X in reversine- and AZ3146-treated embryos are similar (Fig. 1E). However, at the blastocyst stage, high levels of γH2A.X are maintained in reversine-treated embryos and reduced in AZ3146-treated embryos, suggesting some level of DNA repair between the morula-to-blastocyst stages (Fig. S2A). In contrast, in hypoxia, the levels of γH2A.X are low in the three treatments at the morula stages, suggesting that DNA repair can be enhanced under hypoxic conditions. Similar results have been reported in somatic cells (Marti et al., 2021; Pietrzak et al., 2018).

Line 213: PARP1 levels were also similar under all conditions; but Fig3E, top right shows PARP1 was significantly lower with Reversine treatment; also please correct me if i am wrong, but does the phrase "all conditions" cross reference yH2AX and PARP1 between Fig 3 and Fig 1 to show the impact of hypoxia? Because from my understanding Fig 1 was done in 20% oxygen, but Fig 3 was done in 5% oxygen – hypoxia.

This is correct. Modification in the manuscript has been performed for clarification

Line 264: extra forward dash? "Reversine/AZ3146/ aggregation"

Modified in manuscript

Line 644: you don't have a control for IDF treatment, so how did you differentiate between impact of aneuploid drugs vs IDF treatment alone? Would the impact observed be due to compounding effect of aneuploidy drugs + IDF?

This is a great observation. We previously demonstrated that IDF-1174 treatments in embryos do not affect pre-implantation development (Fig. S3).

Line 681: change their behaviour is a vague statement. Be specific.

Modified in manuscript

Line 676 missing bracket "E)"

Modified in manuscript

Line 680: "...significantly on" should be "for"

Modified in manuscript

Line 682-685: "...hypoxia favours the survival of reversine-induced aneuploid cells." does it? the statement before this says in Rev/AZ chimeras, AZ blastomeres contribute similarly to reversine-blastomeres to the TE and PE but significantly increase contributions to the EPI.Wouldn't this mean hypoxia favours survival of AZ aneuploid cells in EPI?

In normoxic conditions, AZ3146 treated cells in Rev/AZ chimeras contributed mostly to the EPI and TE but not PE. In contrast, in normoxic conditions, Rev-treated cells contributed similarly to all the lineages. This result seems to be due to a better survival of Rev-treated cells under normoxic conditions (Fig. 4D-E)

Line 720: (b) shows blastocyst staining from what group? DMSO? Rev/AZ? Or are the 3 blastocysts shown here, 3 separate examples of Reversine-treated blastocysts? Would require labelling Fig S2B, and adding a short description in the corresponding figure legend

Figure (B) shows the expression pattern of PARP1 at the blastocyst stage. Modified in manuscript

Figure 2, Figure S3 and Figure S6: were these experiments performed at 5% or 20% O2, please add detail.

Modified in manuscript

Reviewer #2 (Recommendations for the authors):

Lines 45-46 understanding of reduction of aneuploidy should mention/discuss the paper of attrition/selection, of the kind by the Brivanlou lab for instance, or others. As well as allocation to specific lineages, including the authors' work.

A section in the discussion has been added in response to this recommendation. Comparison between models is debatable.

The response does not clarify whether other papers were cited instead, or the authors own work that has shown preferential allocation to TE.

Author response:

There was a common theme across the reviews to provide a more cautious interpretation and to consider the key question of whether peer reviewers who include citations are being purely self-serving or are highlighting important missing context. I will include a suggested new text analysis to cover this and will expand the discussion on this key question. Reviewers highlighted some confusion around the sample sizes for the different analyses, and I will clarify all sample sizes in the next version.

Author response:

Reviewer #1 (Public Review):

This study presents an exploration of PPGL tumour bulk transcriptomics and identifies three clusters of samples (labeled as subtypes C1-C3). Each subtype is then investigated for the presence of somatic mutations, metabolism-associated pathways and inflammation correlates, and disease progression. The proposed subtype descriptions are presented as an exploratory study. The proposed potential biomarkers from this subtype are suitably caveated and will require further validation in PPGL cohorts together with a mechanistic study.

The first section uses WGCNA (a method to identify clusters of samples based on gene expression correlations) to discover three transcriptome-based clusters of PPGL tumours. The second section inspects a previously published snRNAseq dataset, and labels some of the published cells as subtypes C1, C2, C3 (Methods could be clarified here), among other cells labelled as immune cell types. Further details about how the previously reported single-nuclei were assigned to the newly described subtypes C1-C3 require clarification.

Thank you for your valuable suggestion. In response to the reviewer’s request for further clarification on “how previously published single-nuclei data were assigned to the newly defined C1-C3 subtypes,” we have provided additional methodological details in the revised manuscript (lines 103-109). Specifically, we aggregated the single-nucleus RNA-seq data to the sample level by summing gene counts across nuclei to generate pseudo-bulk expression profiles. These profiles were then normalized for library size, log-transformed (log1p), and z-scaled across samples. Using genesets scores derived from our earlier WGCNA analysis of PPGLs, we defined transcriptional subtypes within the Magnus cohort (Supplementary Figure. 1C). We further analyzed the single-nucleus data by classifying malignant (chromaffin) nuclei as C1, C2, or C3 based on their subtype scores, while non-malignant nuclei (including immune, stromal, endothelial, and others) were annotated using canonical cell-type markers (Figure. 4A).

The tumour samples are obtained from multiple locations in the body (Figure 1A). It will be important to see further investigation of how the sample origin is distributed among the C1-C3 clusters, and whether there is a sample-origin association with mutational drivers and disease progression.

Thank you for your valuable suggestion. In the revised manuscript (lines 74-79), Figure. 1A, Table S1 and Supplementary Figure. 1A, we harmonized anatomic site annotations from our PPGL cohort and the TCGA cohort and analyzed the distribution of tumor origin (adrenal vs extra-adrenal) across subtypes. The site composition is essentially uniform across C1-C3—approximately 75% pheochromocytoma (PC) and 25% paraganglioma (PG)—with only minimal variation. Notably, the proportion of extra-adrenal origin (paraganglioma origin) is slightly higher in the C1 subtype (see Supplementary Figure 1A), which aligns with the biological characteristics of tumors from this anatomical site, which typically exhibit more aggressive behavior.

Reviewer #2 (Public Review):

A study that furthers the molecular definition of PPGL (where prognosis is variable) and provides a wide range of sub-experiments to back up the findings. One of the key premises of the study is that identification of driver mutations in PPGL is incomplete and that compromises characterisation for prognostic purposes. This is a reasonable starting point on which to base some characterisation based on different methods. The cohort is a reasonable size, and a useful validation cohort in the form of TCGA is used. Whilst it would be resource-intensive (though plausible given the rarity of the tumour type) to perform RNA-seq on all PPGL samples in clinical practice, some potential proxies are proposed.

We sincerely thank the reviewer for their positive assessment of our study’s rationale. We fully agree that RNA sequencing for all PPGL samples remains resource-intensive in current clinical practice, and its widespread application still faces feasibility challenges. It is precisely for this reason that, after defining transcriptional subtypes, we further focused on identifying and validating practical molecular markers and exploring their detectability at the protein level.

In this study, we validated key markers such as ANGPT2, PCSK1N, and GPX3 using immunohistochemistry (IHC), demonstrating their ability to effectively distinguish among molecular subtypes (see Figure. 5). This provides a potential tool for the clinical translation of transcriptional subtyping, similar to the transcription factor-based subtyping in small cell lung cancer where IHC enables low-cost and rapid molecular classification.

It should be noted that the subtyping performance of these markers has so far been preliminarily validated only in our internal cohort of 87 PPGL samples. We agree with the reviewer that larger-scale, multi-center prospective studies are needed in the future to further establish the reliability and prognostic value of these markers in clinical practice.

The performance of some of the proxy markers for transcriptional subtype is not presented.

We agree with your comment regarding the need to further evaluate the performance of proxy markers for transcriptional subtyping. In our study, we have in fact taken this point into full consideration. To translate the transcriptional subtypes into a clinically applicable classification tool, we employed a linear regression model to compare the effect values (β values) of candidate marker genes across subtypes (Supplementary Figure. 1D-F). Genes with the most significant β values and statistical differences were selected as representative markers for each subtype.

Ultimately, we identified ANGPT2, PCSK1N, and GPX3—each significantly overexpressed in subtypes C1, C2, and C3, respectively, and exhibiting the most pronounced β values—as robust marker genes for these subtypes (Figure. 5A and Supplementary Figure. 1D-F). These results support the utility of these markers in subtype classification and have been thoroughly validated in our analysis. 

There is limited prognostic information available.

Thank you for your valuable suggestion. In this exploratory revision, we present the available prognostic signal in Figure. 5C. Given the current event numbers and follow-up time, we intentionally limited inference. We are continuing longitudinal follow-up of the PPGL cohort and will periodically update and report mature time-to-event analyses in subsequent work.

Author response:

Reviewer #1 (Public Review):

Summary:

The present study evaluates the role of visual experience in shaping functional correlations between extrastriate visual cortex and frontal regions. The authors used fMRI to assess "resting-state" temporal correlations in three groups: sighted adults, congenitally blind adults, and neonates. Previous research has already demonstrated differences in functional correlations between visual and frontal regions in sighted compared to early blind individuals. The novel contribution of the current study lies in the inclusion of an infant dataset, which allows for an assessment of the developmental origins of these differences.

The main results of the study reveal that correlations between prefrontal and visual regions are more prominent in the blind and infant groups, with the blind group exhibiting greater lateralization. Conversely, correlations between visual and somato-motor cortices are more prominent in sighted adults. Based on these data, the authors conclude that visual experience plays an instructive role in shaping these cortical networks. This study provides valuable insights into the impact of visual experience on the development of functional connectivity in the brain.

Strengths:

The dissociations in functional correlations observed among the sighted adult, congenitally blind, and neonate groups provide strong support for the study's main conclusion regarding experience-driven changes in functional connectivity profiles between visual and frontal regions.

In general, the findings in sighted adult and congenitally blind groups replicate previous studies and enhance the confidence in the reliability and robustness of the current results.

Split-half analysis provides a good measure of robustness in the infant data.

Weaknesses:

There is some ambiguity in determining which aspects of these networks are shaped by experience.

This uncertainty is compounded by notable differences in data acquisition and preprocessing methods, which could result in varying signal quality across groups. Variations in signal quality may, in turn, have an impact on the observed correlation patterns.

The study's findings could benefit from being situated within a broader debate surrounding the instructive versus permissive roles of experience in the development of visual circuits.

Reviewer #2 (Public Review):

Summary:

Tian et al. explore the developmental organs of cortical reorganization in blindness. Previous work has found that a set of regions in the occipital cortex show different functional responses and patterns of functional correlations in blind vs. sighted adults. In this paper, Tian et al. ask: how does this organization arise over development? Is the "starting state" more like the blind pattern, or more like the adult pattern? Their analyses reveal that the answer depends on the particular networks investigated; some functional connections in infants look more like blind than sighted adults; other functional connections look more like sighted than blind adults; and others fall somewhere in the middle, or show an altogether different pattern in infants compared with both sighted and blind adults. 

Strengths:

The question raised in this paper is extremely important: what is the starting state in development for visual cortical regions, and how is this organization shaped by experience? This paper is among the first to examine this question, particularly by comparing infants not only with sighted adults but also blind adults, which sheds new light on the role of visual (and cross-modal) experience. Another clear strength lies in the unequivocal nature of many results. Many results have very large effect sizes, critical interactions between regions and groups are tested and found, and infant analyses are replicated in split halves of the data. 

Weaknesses:

A central claim is that "infant secondary visual cortices functionally resemble those of blind more than sighted adults" (abstract, last paragraph of intro). I see two potential issues with this claim. First, a minor change: given the approaches used here, no claims should be made about the "function" of these regions, but rather their "functional correlations". Second (and more importantly), the claim that the secondary visual cortex in general resembles blind more than sighted adults is still not fully supported by the data. In fact, this claim is only true for one aspect of secondary visual area functional correlations (i.e., their connectivity to A1/M1/S1 vs. PFC). In other analyses, the infant secondary visual cortex looks more like sighted adults than blind adults (i.e., in within vs. across hemisphere correlations), or shows a different pattern from both sighted and blind adults (i.e., in occipito-frontal subregion functional connectivity). It is not clear from the manuscript why the comparison to PFC vs. non-visual sensory cortex is more theoretically important than hemispheric changes or within-PFC correlations (in fact, if anything, the within-PFC correlations strike me as the most important for understanding the development and reorganization of these secondary visual regions). It seems then that a more accurate conclusion is that the secondary visual cortex shows a mix of instructive effects of vision and reorganizing effects of blindness, albeit to a different extent than the primary visual cortex.

Relatedly, group differences in overall secondary visual cortex connectivity are particularly striking as visualized in the connectivity matrices shown in Figure S1. In the results (lines 105-112), it is noted that while the infant FC matrix is strongly correlated with both adult groups, the infant group is nonetheless more strongly correlated with the blind than sighted adults. I am concerned that these results might be at least partially explained by distance (i.e., local spread of the bold signal), since a huge portion of the variance in these FC matrices is driven by stronger correlations between regions within the same system (e.g., secondary-secondary visual cortex, frontal-frontal cortex), which are inherently closer together, relative to those between different systems (e.g., visual to frontal cortex). How do results change if only comparisons between secondary visual regions and non-visual regions are included (i.e., just the pairs of regions within the bold black rectangle on the figure), which limits the analysis to long-rang connections only? Indeed, looking at the off-diagonal comparisons, it seems that in fact there are three altogether different patterns here in the three groups. Even if the correlation between the infant pattern and blind adult pattern survives, it might be more accurate to claim that infants are different from both adult groups, suggesting both instructive effects of vision and reorganizing effects of blindness. It might help to show the correlation between each group and itself (across independent sets of subjects) to better contextualize the relative strength of correlations between the groups. 

It is not clear that differences between groups should be attributed to visual experience only. For example, despite the title of the paper, the authors note elsewhere that cross-modal experience might also drive changes between groups. Another factor, which I do not see discussed, is possible ongoing experience-independent maturation. The infants scanned are extremely young, only 2 weeks old. Although no effects of age are detected, it is possible that cortex is still undergoing experience-independent maturation at this very early stage of development. For example, consider Figure 2; perhaps V1 connectivity is not established at 2 weeks, but eventually achieves the adult pattern later in infancy or childhood. Further, consider the possibility that this same developmental progression would be found in infants and children born blind. In that case, the blind adult pattern may depend on blindness-related experience only (which may or may not reflect "visual" experience per se). To deal with these issues, the authors should add a discussion of the role of maturation vs. experience and temper claims about the role of visual experience specifically (particularly in the title). 

The authors measure functional correlations in three very different groups of participants and find three different patterns of functional correlations. Although these three groups differ in critical, theoretically interesting ways (i.e., in age and visual/cross-modal experience), they also differ in many uninteresting ways, including at least the following: sampling rate (TR), scan duration, multi-band acceleration, denoising procedures (CompCor vs. ICA), head motion, ROI registration accuracy, and wakefulness (I assume the infants are asleep).

Addressing all of these issues is beyond the scope of this paper, but I do feel the authors should acknowledge these confounds and discuss the extent to which they are likely (or not) to explain their results. The authors would strengthen their conclusions with analyses directly comparing data quality between groups (e.g., measures of head motion and split-half reliability would be particularly effective).

Response #1: We appreciate the reviewer’s comments. In response, we have revised the paper to provide a more balanced summary of the data and clarified in the introduction which signatures the paper focuses on and why. Additionally, we have included several control analyses to account for other plausible explanations for the observed group differences. Specifically, we randomly split the infant dataset into two halves and performed split-half cross-validation. Across all comparisons, the results from the two halves were highly similar, suggesting that the effects are robust (see Supplementary Figures S3 and S4).

Furthermore, we compared the split-half noise ceiling across the groups (infants, sighted adults, and blind adults) and found no significant differences between them (details in response #6). Finally, we repeated our analysis after excluding infants with a radiology score of 4 or 5, and the results remained consistent, indicating that our findings are not confounded by potential brain anomalies (details in response #2).

We hope these control analyses help strengthen our conclusions.

Reviewer #3 (Public Review):

Summary:

This study aimed to investigate whether the differences observed in the organization of visual brain networks between blind and sighted adults result from a reorganization of an early functional architecture due to blindness, or whether the early architecture is immature at birth and requires visual experience to develop functional connections. This question was investigated through the comparison of 3 groups of subjects with resting-state functional MRI (rs-fMRI). Based on convincing analyses, the study suggests that: 1) secondary visual cortices showed higher connectivity to prefrontal cortical regions (PFC) than to non-visual sensory areas (S1/M1 and A1) in sighted infants like in blind adults, in contrast to sighted adults; 2) the V1 connectivity pattern of sighted infants lies between that of sighted adults (stronger functional connectivity with non-visual sensory areas than with PFC) and that of blind adults (stronger functional connectivity with PFC than with non-visual sensory areas); 3) the laterality of the connectivity patterns of sighted infants resembled those of sighted adults more than those of blind adults, but sighted infants showed a less differentiated fronto-occipital connectivity pattern than adults.

Strengths:

The question investigated in this article is important for understanding the mechanisms of plasticity during typical and impaired development, and the approach considered, which compares different groups of subjects including, neonates/infants and blind adults, is highly original.

-Overall, the analyses considered are solid and well-detailed. The results are quite convincing, even if the interpretation might need to be revised downwards, as factors other than visual experience may play a role in the development of functional connections with the visual system.

Weaknesses:

While it is informative to compare the "initial" state (close to birth) and the "final" states in blind and sighted adults to study the impact of post-natal and visual experience, this study does not analyze the chronology of this development and when the specialization of functional connections is completed. This would require investigating when experience-dependent mechanisms are important for the setting- establishment of multiple functional connections within the visual system. This could be achieved by analyzing different developmental periods in the same way, using open databases such as the Baby Connectome Project. Given the early, "condensed" maturation of the visual system after birth, we might expect sighted infants to show connectivity patterns similar to those of adults a few months after birth.

The rationale for mixing full-term neonates and preterm infants (scanned at term-equivalent age) from the dHCP 3rd release is not understandable since preterms might have a very different development related to prematurity and to post-natal (including visual) experience. Although the authors show that the difference between the connectivity of visual and other sensory regions, and the one of visual and PFC regions, do not depend on age at birth, they do not show that each connectivity pattern is not influenced by prematurity. Simply not considering the preterm infants would have made the analysis much more robust, and the full-term group in itself is already quite large compared with the two adult groups. The current study setting and the analyses performed do not seem to be an adequate and sufficient model to ascertain that "a few weeks of vision after birth is ... insufficient to influence connectivity".

In a similar way, excluding the few infants with detected brain anomalies (radiological scores higher or equal to 4) would strengthen the group homogeneity by focusing on infants supposed to have a rather typical neurodevelopment. The authors quote all infants as "sighted" but this is not guaranteed as no follow-up is provided.

Response #2: We appreciate the reviewer’s suggestion. We re-analyzed the infant cohort after excluding all cases with radiological scores ≥4 (n =39 infants excluded). The revised analysis confirmed that the connectivity patterns reported in the main text remain statistically unchanged (see Supplementary Fig. S11). This demonstrates the robustness of our findings to potential confounding effects from potential brain anomalies. We have explicitly clarified this in the revised Methods section (page 14, line 391in the manuscript).

In our dataset, newborns (average age at scan = 2.79 weeks) have very limited and immature vision. We agree with the reviewer that long-term visual outcomes cannot be guaranteed without follow-up data. The term "sighted infants" was used operationally to distinguish this cohort from congenitally blind populations.

The post-menstrual age (PMA) at scan of the infants is also not described. The methods indicate that all were scanned at "term-equivalent age" but does this mean that there is some PMA variability between 37 and 41 weeks? Connectivity measures might be influenced by such inter-individual variability in PMA, and this could be evaluated.

The rationale for presenting results on the connectivity of secondary visual cortices before one of the primary cortices (V1) was not clear to understand. Also, it might be relevant to better justify why only the connectivity of visual regions to non-visual sensory regions (S1-M1, A1) and prefrontal cortex (PFC) was considered in the analyses, and not the ones to other brain regions.

In relation to the question explored, it might be informative to reposition the study in relation to what others have shown about the developmental chronology of structural and functional long-distance and short-distance connections during pregnancy and the first postnatal months.

The authors acknowledge the methodological difficulties in defining regions of interest (ROIs) in infants in a similar way as adults. The reliability and the comparability of the ROIs positioning in infants is definitely an issue. Given that brain development is not homogeneous and synchronous across brain regions (in particular with the frontal and parietal lobes showing delayed growth), the newborn brain is not homothetic to the adult brain, which poses major problems for registration. The functional specialization of cortical regions is incomplete at birth. This raises the question of whether the findings of this study would be stable/robust if slightly larger or displaced regions had been considered, to cover with greater certainty the same areas as those considered in adults. And have other cortical parcellation approaches been considered to assess the ROIs robustness (e.g. MCRIB-S for full-terms)?

Recommendations for the Authors:

Reviewer #1(Recommendations for the authors):

Further consideration should be given to the underlying changes in network architecture that may account for differences in functional correlations across groups. An increase (or decrease) in correlation between two regions could signify an increase (decrease) in connection or communication between those regions. Alternatively, it might reflect an increase in communication or connection with a third region, while the physical connections/interactions between the two original regions remain unchanged. These possibilities lead to distinct mechanistic interpretations. For example, there are substantial changes in connectivity during early visual (e.g. Burkhalter A. 1993, Cerebral Cortex) and visuo-motor development (e.g., Csibra et al. 2000 Neuroreport). It's not clear whether increases in communication within the visual network and improvements in visuo-motor behavior (e.g., Yizhar et al. 2023 Frontiers in Neuroscience) wouldn't produce a qualitatively similar pattern of results.

Relatedly, the within-network correlation patterns between visual ROIs and frontal ROIs appear markedly different between sighted adults and infants (Supplementary Figure S1). To what extent do the differences in long-range correlations between visual and frontal regions reflect these within-network differences in functional organization?

Response #3: The reviewer is raising some interesting questions about possible mechanisms and network changes. Resting state studies are indeed always subject to possibility that some effects are mediated by a third, unobserved region. Prior whole-cortex connectivity analyses have observed primarily changes in occipito-frontal connectivity in blindness, so there is not a clear cortical ‘third region’ candidate (Deen et al., 2015). However, some thalamic affects have also been observed and could contribute to the phenomenon (Bedny et al., 2011). Resting state changes in correlation between two areas do not imply changes in strength of long-range anatomical connectivity. Indeed, in the current case they may well reflect differential functional coupling, rather than strengthening or weakening of anatomical connections. We now discuss this in the Discussion section on page 12, line 301 as follows:

“Despite these insights, many questions remain regarding the neurobiological mechanisms underlying experience-based functional connectivity changes and their relationship to anatomical development. Long-range anatomical connections between brain regions are already present in infants—even prenatally—though they remain immature (Huang et al., 2009; Kostović et al., 2019, 2021; Takahashi et al., 2012; Vasung, 2017). Functional connectivity changes may stem from local synaptic modifications within these stable structural pathways, consistent with findings that functional connectivity can vary independently of structural connection strength (Fotiadis et al., 2024). Moreover, functional connectivity has been shown to outperform structural connectivity in predicting individual behavioral differences, suggesting that experience-based functional changes may reflect finer-scale synaptic or network-level modulations not captured by macrostructural measures (Ooi et al., 2022). Prior studies also suggest that, even in adults, coordinated sensory-motor experience can lead to enhancement of functional connectivity across sensory-motor systems, indicating that large-scale changes in functional connectivity do not necessarily require corresponding changes in anatomical connectivity (Guerra-Carrillo et al., 2014; Li et al., 2018).”

It is not clear how changes in correlation patterns among visual areas would produce the connectivity between visual areas and prefrontal areas reported in the current study. Activity in visual areas drives correlations both among visual areas and between visual and prefrontal areas and the same is true of prefrontal corticies.

The findings from this study should be more closely linked to the extensive literature surrounding the debate on whether experience plays an instructive or permissive role in visual development (e.g., Crair 1999 Current Opin Neurobiol; Sur et al. 1999 J Neurobiol; Kiorpes 2016 J Neurosci; Stellwagen & Shatz 2002 Neuron; Roy et al. 2020 Nature Communications).

Response #4: The instructive role suggests that specific experiences or patterns of neural activity directly shape and organize neural circuitry, while the permissive role indicates that such experiences or activity merely enable other factors, such as molecular signals, to influence neural circuit formation(Crair, 1999; Sur et al., 1999). To distinguish whether experience plays an instructive or permissive role, it is essential to manipulate the pattern or information content of neural activity while maintaining a constant overall activity level (Crair, 1999; Roy et al., 2020; Stellwagen & Shatz, 2002). However, both the sighted and blind adult groups have had extensive experience and neural activity in the visual cortices. For the sighted group, activity in the visual cortex is partly driven by bottom-up input from the external environment, through the retina, LGN, and ultimately to the cortex. In contrast, the blind group’s visual cortex activity is partially driven by top-down input from non-visual networks. The precise role of this activity in shaping the observed connectivity patterns remains unclear. Although our study cannot speak to this issue directly, we now link to the relevant literature on page 12,line 320 of the manuscript in the Discussion section as follows:

“The current findings reveal both effects of vision and effects of blindness on the functional connectivity patterns of the visual cortex. A further open question is whether visual experience plays an instructive or permissive role in shaping neural connectivity patterns. An instructive role suggests that specific sensory experiences or patterns of neural activity directly shape and organize neural circuitry. In contrast, a permissive role implies that sensory experience or neural activity merely facilitates the influence of other factors—such as molecular signals—on the formation and organization of neural circuits (Crair, 1999; Sur et al., 1999). Studies with animals that manipulate the pattern or informational content of neural activity while keeping overall activity levels constant could distinguish between these hypotheses (Crair, 1999; Roy et al., 2020; Stellwagen & Shatz, 2002).”

The assertion that a few weeks of vision after birth is insufficient to influence connectivity is provocative. Though supported by the study's results, it would benefit from integration with research in animal models showing considerable malleability of networks from early experience (e.g., Akerman et al. 2002 Neuron; Li et al. 2006 Nature Neuroscience; Stacy et al. 2023 J Neuroscience).

Response #5: We thank the reviewer for their suggestion. The present study found that several weeks of postnatal visual experience is insufficient to significantly alter the long-term connectivity patterns of the visual cortices. While animal studies have shown that acute visual experience, or even exposure to visual stimuli through unopened eyelids, can robustly influence visual system development(Akerman et al., 2002; Li et al., 2008; Van Hooser et al., 2012). We think this discrepancy may be attributed to the substantial differences in developmental timelines between species. The human lifespan is much longer, and so is the human critical period, making it unclear how to map duration from one species to another. We briefly touched upon the time course issue in page 11 line 289 in the Discussion section as follows:

“The present results reveal the effects of experience on development of functional connectivity between infancy and adulthood, but do not speak to the precise time course of these effects. Infants in the current sample had between 0 and 20 weeks of visual experience. Comparisons across these infants suggests that several weeks of postnatal visual experience is insufficient to produce a sighted-adult connectivity profile. The time course of development could be anywhere between a few months and years and could be tested by examining data from children of different ages.”

Substantial differences between the groups are evident in several key aspects of the study, including the number of subjects, brain sizes, imaging parameters, and data preprocessing, all of which are likely to have an impact on the overall signal quality. To clarify how these differences might have impacted correlation differences between groups, it would be essential to include information on the noise ceilings for each correlation analysis within each group.

Response #6: We thank the reviewer for their suggestion. We now report the split-half noise ceiling for adult and infant groups. For each participant, we first split the rs-fMRI time series into two halves, then calculated the ROI-wise rsFC pattern from the two splits. The split-half noise ceiling was estimated according to Lage-Castellanos et al (2019). The noise ceilings of the three groups (infants: 0.90 ± 0.056,blind adults: 0.88 ± 0.041, sighted adults: 0.90 ± 0.055) showed no significant difference (One-way ANOVA_, F(2,552) = 2.348, p = 0.097). Therefore, we believe that overall signal quality is unlikely to impact our results. We also add the relevant context in the Method section in page 16 Line 447 as follows:

“Substantial differences between the groups exist in this study, including the number of subjects, brain sizes, imaging parameters, and data preprocessing, all of which are likely to have an impact on the overall signal quality. To address this concern, we compared the split-half noise ceiling across the groups (infants, sighted adults, and blind adults). For each participant, we first split the rs-fMRI time series into two halves, then calculated the ROI-wise rsFC pattern from the two splits. The split-half noise ceiling was estimated according to Lage-Castellanos et al (Lage-Castellanos et al., 2019). The noise ceilings of the three groups (infants: 0.90 ± 0.056, blind adults: 0.88 ± 0.041, sighted adults: 0.90 ± 0.055) showed no significant difference (One-way ANOVA, F (2,552) = 2.348, p = 0.097). Therefore, overall signal quality is unlikely to impact our results.”

In general, it appears that the infant correlations are stronger compared to the other groups. While this could reflect increased coherence or lack of differentiation, it is also possible that it is simply due to the presence of a non-neuronal global signal. Such a signal has the potential to substantially limit the effective range of functional correlations and comparisons with adults. To address this, it is advisable to conduct control analyses aimed at assessing and potentially removing global signals.

Response #7: We agree with the reviewer that global signal regression (GSR) may help reduce non-neuronal artifacts, such as motion, cardiac, and respiratory signals, which are known to correlate with the global signal. However, the global signal also contains neural signals from gray matter, and removing it can introduce unwanted artifacts, especially for the current study. First, GSR can reduce the physiological accuracy of functional connectivity (FC); second, GSR may have differential effects across groups, potentially introducing additional artifacts in between-group comparisons, as noted by Murphy et al (Murphy & Fox, 2017). The CompCor method (Behzadi et al., 2007; Whitfield-Gabrieli & Nieto-Castanon, 2012) is capble to estimate the global non-neuronal artifacts like the GSR method. Meanwhile as it estimate global non-neuronal artifacts from signals within the white matter (WM) and cerebrospinal fluid (CSF) masks, but not the gray matter (GM), CompCor could introduce minimal unwanted bias to the GM signal.

Was there a difference in correlations for preterm vs term neonates? Recent research has suggested that preterm births can have an impact on functional networks, particularly in frontal cortices. e.g., Tokariev et al. 2019, Li et al. 2021 elife; Zhang et al. 2022 Fronteirs in Neuroscience.

Response #8: We have compared preterm and term neonates for all the main results, including the connectivity from the secondary visual cortex/V1 to non-visual sensory cortices versus prefrontal cortices, the laterality of occipito-frontal connectivity, and the specialization across different fronto-occipital networks. This information is reported in Page 6 line 169 and Supplementary Figure S7. The connectivities of full-term infants are generally higher than those of preterm infants. However, the connectivity patterns of term and preterm infants are very similar.

The consistency between the current results and prior work (e.g., Burton et al. 2014) is notable, particularly in the observed greater correlations in prefrontal regions and weaker correlations in somato-motor regions for early blind individuals compared to sighted. However, almost all visual-frontal correlations in both groups were negative in that prior study. Some discussion on why positive correlations were found in the current study could help to clarify.

Response #9: Many other papers have reported positive correlations similar to those found in our study (e.g., Deen et al., 2015; Kanjlia et al., 2021). In contrast, Burton's study identified predominantly negative visual-frontal correlations, we think this is likely because the global signal was regressed out during preprocessing. This methodological choice can lead to an increase in negative connections (Murphy & Fox, 2017).

The term "secondary visual areas" used throughout the paper lacks specificity, and its usage in terms of underlying anatomical and functional areas has been inconsistent in the literature. It would be advisable to adopt a more precise characterization based on functional and/or anatomical criteria.

Response #10: We specified in the article that Tthe occipital ROIs were defined in the current study are functional areas in people born blind identified in prior studies as regions that respond to three non-visual tasks such as language, math, or executive function, and show functional connectivity changes in blind adults in previous studies (Kanjlia et al., 2016, 2021; Lane et al., 2015). These regions respond to language, math and executivie function in the congenitally blind population (see Figure 1.) The are refered collectively as ‘secondary visual areas’ to destinguish them from V1. Anatomically, these three regions cover the majority of the lateral occipital cortex and part of the ventral occipital cortex, providing a good sample of the connectivity profile of higher-order visual areas. Thus, we are using the term "secondary visual areas" to refer to these regions. In blind individuals, although these regions respond to non-visual tasks, their exact functions are unknown.

The inclusion of the ventral temporal cortex in the visual ROIs is currently only depicted in Supplementary Figure S7. To enhance the clarity of the areas of interest analyzed, it would be advisable to illustrate the ventral temporal areas in the main text. Were there notable differences in the frontal correlations between the lateral occipital visual areas and ventral temporal areas?

Response #11: We thank the reviewer for pointing out this issue. We added a statement about the ventral visual cortex in describing the location of the ROI and added the ventral view of ROIs in the Figure 1. The language-responsive and math -responsive ROIs covers both the lateral and ventral visual cortex, whereas executive function (response-conflict) regions cover only the lateral visual cortex. We compared the connectivity patterns of these three regions and found no differences (see supplementary Fig S2).

The blind group results are characterized as reflecting a reorganization in comparison to sighted adults while the results for sighted adults compared to infants are discussed more as a maturation ("adult pattern isn't default but requires experience to establish"). Both the sighted and blind adult groups showed differences from the infant group, and these differences are attributed to the role of experience. Why use "reorganization" for one result and maturation for another?

Response #12: We agree with the reviewer that both of the adult groups should be thought of as equal in relation to the infants. In other words, the brain develops under one set of experiential conditions or another. We do not think that the adult sighted pattern reflects maturation. Rather, the sighted adult pattern reflects the combined influence of maturation and visual experience. The adult blind pattern reflects the combined influence of maturation and blindness. We use the term ‘reorganization’ to label differences in the blind adults relative to sighted infants. We do so for the purpose of clarity and to remain consistent with terminology in prior liaterature. However, we agree with the reviewer that the blind group does not reflect ‘reorganization’ intrinsically any more than the sighted adult group.

The statement that "visual experience is required to set up long-range functional connectivity" is unclear, especially since the infant and blind groups showed stronger long-range functional correlations with PFC.

Response #13: We revised this sentence to specifically as “visual experience establishes elements of the sighted-adult long-range connectivity” in tha Abstract line 17.

The statement that the visual ROIS roughly correspond to "the anatomical location of areas such as V5/MT+, LO, V3a, and V4v" appears imprecise. From Supplementary Figure S7, these areas cover anterior portions of ventral temporal cortex (do these span the anatomical location of putative category-selective areas?) and into the intraparietal sulcus.

Response #14: Thanks to the reviewer for the clarification. The ventral ROIs cover the middle and part of the anterior portion of the ventral temporal lobe, including the putative category-selective areas. Additionally, the dorsal ROIs extend beyond the occipital lobe to the intraparietal sulcus and superior parietal lobule. We have added a more detailed description of the anatomical location of the ROI in the Methods section Page 17 line 489 as follows:

“Each functional ROI spans multiple anatomical regions and together the secondary visual ROIs tile large portions of lateral occipital, occipito-temporal, dorsal occipital and occipito-parietal cortices. In sighted people, the secondary visual occipital ROIs include the anatomical locations of functional regions such as motion area V5/MT+, the lateral occipital complex (LO), category specific ventral occipitotemporal cortices and dorsally, V3a and V4v.  The occipital ROI also covers the middle of the ventral temporal lobe. Dorsally, it extended to the intraparietal sulcus and superior parietal lobule.”

The motivation for assessing correlations with motor and frontal regions was briefly discussed in the introduction. It would be helpful to reiterate this motivation when first introducing the analyses in the results.

Response #15: Thank you for the thoughtful suggestion. Upon reflection, we chose to substantially revise the Introduction to more clearly and comprehensively explain the rationale for examining the couplings with motor and frontal regions, rather than reiterating it in the Results section. We believe this revised framing provides a stronger foundation for the analyses that follow, while avoiding redundancy across sections. We hope this addresses the reviewer’s concern.

Reviewer #2 (Recommendations for the authors):

Congratulations on a well-written paper and an interesting set of results.

Reviewer #3 (Recommendations for the authors):

Abstract:

Mentioning "sighted infants" does not seem adequate.

Response #16: In our dataset, newborns (average age at scan = 2.79 weeks) have very limited and immature vision. We agree with the reviewer that long-term visual outcomes cannot be guaranteed without follow-up data. The term "sighted infants" was used operationally to distinguish this cohort from congenitally blind populations.

In sentences after "Specifically...", it was not clear whether the authors referred to V1 connectivity.

Response #17: We thank the reviewer for this comment. In the revised abstract, we have removed the original "Specifically..." phrasing and clarified the results.

Introduction

Talking about the "instructive effects" of vision might be confusing or misleading. Visual experiences like exposure to oral language are part of the normal/spontaneous environment that allows the infant behavioral acquisitions (contrarily with learnings that occur later during development with instruction like for reading).

Response #18: We appreciate the reviewer’s concern and would like to clarify that the term “instructive effect” is used here derived from neurodevelopmental studies (Crair, 1999; Sur et al., 1999). In this context, “instructive” refers to activity-dependent mechanisms where patterns of neural activity actively guide the organization of synaptic connectivity, emphasizing that spontaneous or sensory-driven activity (e.g., retinal waves, visual experience) can directly shape circuit refinement, as seen in ocular dominance column formation. In the context of our study, we emphasize that vision plays an instructive role in setting up the balance of connectivity between occipital cortex and non-visual networks.

For references on the development of connectivity, I would advise citing MRI studies but also studies based on histological approaches (see for example the detailed review by Kostovic et al, NeuroImage 2019).

Response #19: We thank the reviewer for this suggestion. We have incorporated a discussion on the long-range anatomical connections that emerge as early as infancy, referencing studies that employed diffusion MR imaging and histological methods, as detailed below.

“Many long-range anatomical connections between brain regions are already established in infants, even before birth, although they are not yet mature (Huang et al., 2009; Kostović et al., 2019, 2021; Takahashi et al., 2012; Vasung, 2017).” (Page 12, line 303 in the manuscript)

Results

P7 l170: It might be helpful to be precise that this is "compared with inter-hemispheric connectivity".

Response #20: We thank the reviewer for this suggestion. To align with our established terminology, we have revised the statement to explicitly contrast within-hemisphere connectivity with between-hemisphere connectivity. The modified text now reads (page 7, line 183 in the manuscript):

“Compared to sighted adults, blind adults exhibited a stronger dominance of within-hemisphere connectivity over between-hemisphere connectivity. That is, in people born blind, left visual networks are more strongly connected to left PFC, whereas right visual networks are more strongly connected to right PFC.

L176-181: It was not clear to me what was the difference between "across" and "between hemisphere connectivity". Would it be informative to test the difference between blind and sighted adults?

Response #21: We clarify that there is no distinction between the terms “across” and “between hemisphere connectivity”—they refer to the same concept. To ensure consistency, we have revised the text to exclusively use “between hemisphere connectivity” throughout the manuscript. Regarding the comparison between blind and sighted adults, we conducted statistical comparisons between these groups in our analysis, and the results have been incorporated into the revised version (Page 7, line 187 in the manuscript).

Adding statistics on Figure 3, but also on Figures 1 and 2 might help the reading.

Response #22: We have added the statistics in Figure 1-4.

Adding the third comparison in Figure 4 would be possible in my view.

Response #23: We explored integrating the response-conflict region into Figure 4, but this would require a 3x3 bar chart with pairwise statistical significance markers, which introduced excessive visual complexity that hindered readers’ ability to grasp our intended message. To ensure clarity, we retained the original Figure 4 while providing the complete three-region analysis (including all statistical comparisons) in Supplementary Figure S8 to ensure completeness.

Methods

The authors might have to specify ages at birth, and ages at scan (median + range?).

Response #24: We have added that information in the Methods section as follows:

“The average age from birth at scan = 2.79 weeks (SD = 3.77, median = 1.57, range = 0 – 19.71); average gestational age at scan = 41.23 weeks (SD = 1.77, median = 41.29, range = 37 – 45.14); average gestational age at birth = 38.43 weeks (SD = 3.73, median = 39.71, range = 23 – 42.71).” (Page 14, line 379 in the manuscript)

It might be relevant to comment on the range of available fMRI volumes, and the fact that connectivity measures might then be less robust in infants.

Response #25: We report the range of fMRI volumes in the Methods section (Page 16, Line 449). Adult participants (blind and sighted) underwent 1–4 scanning sessions, each containing 240 volumes (mean scan duration: 710.4 seconds per participant). For infants, all subjects had 2300 fMRI volumes, and we retained a subset of 1600 continuous volumes per subject with the minimum number of motion outliers. While infant connectivity measures may inherently exhibit lower robustness due to developmental and motion-related factors, our infant cohort’s large sample size (n=475) and stringent motion censoring criteria enhance the reliability of group-level inferences. We have integrated this clarification into the Methods section (Page 16, Line 444) as follows:

"While infant connectivity estimates may be less robust at the individual level compared to adults due to shorter scan durations and higher motion, our cohort’s large sample size (n=475) and rigorous motion censoring mitigate these limitations for group-level analyses. "

The mention of dHCP 2nd release should be removed from the paragraph on data availability.

Response #26: We have removed it.

Author response:

Response to Comments from reviewer #1

Many thanks for appreciating that SZN-043 can promote hepatocyte proliferation via the Wnt-signaling pathway.

(1) The reviewer is concerned with using only CYP1A2 expression as an endpoint to make a conclusion about the effect of SZN-043 on Wnt activity in human ALD samples. The reviewer raises a good point as the more commonly used Wnt target gene, AXIN2, is not consistantly changed in both cohorts. We were at first also surprised by this finding. However, upon closer analysis we found that the expression of hepatocyte-specific target genes such as CYP1A2 (Figure 2), CYP2E1, OAT, LGR5, GLUL (Table 1) and ZNRF3 were mostly expressed in hepatocytes and ductal cells were all down-regulated in ALD samples. Others Wnt target genes expressed in epithelial and mesenchymal liver cell populations, such as AXIN2, CCND1 and NOTUM are indeed not consistently and significantly changed. Given that SZN-043 is not active on mesenchymal cells, this discrepancy could be best explained by the large increase in mesenchymal cells in ALD tissue samples, thereby confounding the results. We have now clarified this in the discussion. Another method to assess Wnt activity is to measure b-catenin phosphorylation and nuclear transfer. In our hands, this method was found to be better suited for tissue culture than histological sections from in vivo studies. We have also amended the manuscript title to refer to expression of Wnt target genes, rather than Wnt activity.

(2) We have now added a supplemental figure to show the lack of Ki-67+ human hepatocytes in the cirrhotic tissue samples to confirm the absence of hepatocyte proliferation (Figure S1).

(3) The differences in amino acid sequence between SZN-043 and its precursor, αASGR1-RSPO2-RAIgG, can be found in the material and method section. These changes in amino acid sequences improved the biophysical properties of the final clinical candidate, such as oxidation and nonspecific binding. The biochemical analysis of those differences exceeds the scope of the current manuscript. We present here the pharmacokinetic properties of SZN-043 only, as this was the only molecule advanced to clinical trial and used in the studies presented here.

(4) The reviewer suggests to assess the effect of SZN-043 in Ctnnb1-KO mice to confirm that SZN043 acts via a canonical Wnt pathway. Indeed, there were several reports on the ability of Rspondin to act on other pathways besides the Wnt signaling pathway (for recent review, Niehrs et al, 2024, Bioessays). However, while an interesting suggestion, this line of investigation belongs to MOA studies and exceeds the scope of the current manuscript. An additional manuscript presenting MOA studies for SZN-043 was recently submitted elsewhere. Still, we have added this possibility in the discussion section.

(5) The reviewer is asking how SZN-043 is affecting liver functions in general. Indeed, we have observed a consistent reduction in the international normalized ratio of prothrombin time using the thioacetamide (TAA)-induced fibrosis model and previously published those findings (Zhang, 2020). In our hands, the TAA is the only liver injury model that significantly increases INR. This increase is modest compared to that observed in clinical patients. Therefore, we do not report INR findings for other models. We have not seen any effects of SZN-043 on hepatocyte differentiation markers such as HNF4A (data not shown) and the hepatocyte specific ASGR1/2 as shown in Figure 5. Rather we focused on proliferation as the main potentially beneficial endpoint, to restore the parenchymal mass in injured livers. Finally, consistent with what was reported in the literature, we have observed a transient and reciprocal effect on albumin and alfa-fetoprotein expression during the proliferative phase of liver regeneration. These results are detailed in an additional manuscript presenting MOA studies for SZN-043, which was recently submitted elsewhere.

(6) We have used females only in the ethanol-induced injury models because there are numerous reports in the literature stating that males are not as susceptible to those injuries.  

(7) The reviewer questions the relevance of the ethanol-induced injury model used to evaluate SZN043 efficacy. Indeed, none of the disease model developed to date reproduce the severity and complexity of alcohol-associated liver diseases, although some, such as the ethanol supplemented Lieber DeCarli diet, are more commonly used than others – which is the reason why this model was selected. 

(8) The reviewer questions the relevance of the fibrosis model used to evaluate SZN-043 efficacy. Indeed, none of the fibrosis models developed to date reproduce the severity and complexity of cirrhosis in human livers. While combining ethanol with CCl4 would lead to more severe fibrotic livers, CCl4 itself is not involved in ALD in humans. Both models are likely to result in similar pericentral fibrosis with central-to-central bridging. In this study, we were mostly interested in addressing the effects of SZN-043 in a tissue affected by fibrotic scars.  

(9) The sex of CCl4-treated mice is male. We added this information in the methods section.

(10) A summary of histology and fibrosis assessment data for alcohol-fed mice was added in supplemental Table S3. In our hands, the use of aging mice did not induce the presence of fibrosis, in contrast to published results.  

(11) The rationale for using 13.5-month-old mice in the alcohol studies and scid mice in the CCl4 studies has been clarified in the results and discussion sections. 

a. Briefly, aging mice were reported to be more susceptible to ethanol-induced injury than young mice and to include induction of fibrosis. However, we were unable to reproduce the presence of fibrosis reported in the literature.  

b. Scid mice were used in the CCl4 studies to test whether a stronger response could be observed in the absence of a potential anti-drug antibodies response. While a modest reduction in fibrosis was observed in both B6 and scid mice following the SZN-043 treatment, the effect size did not seem affected by the mouse strain. 

Response to Comments from reviewer #2

Many thanks for appreciating that the use of multiple disease models to identify SZN-043 as a potential novel drug for liver regeneration.

(1) The importance of restoring liver regeneration capacity to reduce the need for liver transplantation had been emphasized in the introduction.

(2) There is continuous damage to the mouse hepatocytes in the FRG mice, due to the Fah mutation. They undergo repair mechanisms favoring the proliferation of human hepatocytes during the production period. Injury models that affect the human hepatocytes population have been developed in these mice. However, the primary goal of this study was to confirm that SZN043 was efficacious in inducing human hepatocytes proliferation, a feature difficult to reproduce in primary hepatocyte cultures. Given the artefactual nature of the chimeric liver in FRG mice and the high cost of these mice, further studies were not judged to be necessary.

(3) Corrected

(4) A figure including DAPI staining has now been included in supplemental Figure S2.

(5) Clarification that the 8 weeks alcohol feeding used in our study design is a modification of the NIAAA model. While some ASGR1 has been reported on the surface of macrophages, additional data from MOA studies strongly suggest that the effect of SZN-043 is mediated via a hepatocytespecific mechanism (submitted manuscript).

(6) The reviewer inquired about the potential role of macrophages in promoting an antiinflammatory state in response to SZN-043. While a direct effect is unlikely, a potential effect of macrophages in response to SZN-043 is plausible. Wnt activation is known to induce the secretion of hepatokines, such as LECT2, which in turn can influence macrophage activity. This possibility is discussed in the discussion section.

(7) The potential off-target effects of SZN-043 such as stellate cell activation is discussed in the discussion section.

(8) The discussion of the limitations of current models has been included in the discussion section of the manuscript.

(9) We have now included a discussion of prior RSPO-based therapies, such as OMP-131R10. We explain why the hepatocyte-targeting of RSPO activity minimizes undesired effects.

Author response:

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

Reviewer #1 (Public review):

Summary:

The work from this paper successfully mapped transcriptional landscape and identified EA-responsive cell types (endothelial, microglia). Data suggest EA modulates BBB via immune pathways and cell communication. However, claims of "BBB opening" are not directly proven (no permeability data).

(1) No in vivo/in vitro assays confirm BBB permeability changes (e.g., Evans blue leakage, TEER).

(2) Only male rats were used, ignoring sex-specific BBB differences.

(3) Pericytes and neurons, critical for the BBB, were not captured, likely due to dissociation artifacts.

(4) Protein-level validation (Western blot, IHC) absent for key genes (e.g., LY6E, HSP90).

(5) Fixed stimulation protocol (2/100 Hz, 40 min); no dose-response or temporal analysis.

We sincerely apologize for the oversight regarding the description of changes in blood-brain barrier permeability. In fact, our team conducted a series of preliminary studies that verified this aspect, and we hace provided a more detailed introduction in the introduction section, in lines 60-71 of the manuscript.

We are very grateful to the reviewers for pointing out the important and meaningful issue of "gender-specific BBB differences." We will make this a focal point in our future research.

As for pericytes and neurons, we acknowledge their importance in the function of the blood-brain barrier. We acknowledge the importance of pericytes and neurons in the blood-brain barrier. However, neurons are absent because our sample processing method involves dissociation. During the dissociation procedure, neuronal axons, which are relatively long, are filtered out during the frequent cell suspension steps and cannot enter the downstream microfluidic system for analysis, so they are not present in our data. Since this experiment is primarily focused on non-neuronal cells, we did not choose to use nucleus extraction for sample processing. As for pericytes, we believe they are not captured because their proportion in our samples is extremely low, which is why they are not present in the data. Further research may require single-nucleus transcriptomics or the separate isolation of these two cell types for study. Of course, in our current mechanistic studies, we are also fully considering the important roles these two cell types play in BBB function.

In addition, to validate the results at the protein level, we have recently conducted some experiments. However, as several proteins are currently at a critical stage of further experimental validation, it is not appropriate to present them in the manuscript at this time. Instead, we have uploaded the relevant data as an appendix for your review. This includes a figure of several protein markers we examined, as well as a table of the antibodies used.

This section is also further elaborated in the introduction and its references.

Reviewer #2 (Public review):

Summary:

This study uses single-cell RNA sequencing to explore how electroacupuncture (EA) stimulation alters the brain's cellular and molecular landscape after blood-brain barrier (BBB) opening. The authors aim to identify changes in gene expression and signaling pathways across brain cell types in response to EA stimulation using single-cell RNA sequencing. This direction holds promise for understanding the consequences of noninvasive methods of BBB opening for therapeutic drug delivery across the BBB.

(1) The work falls short in its current form. The experimental design lacks a clear justification, and readers are not provided with sufficient background information on the extent, timing, or regional specificity of BBB opening in this EA model. These details, established in prior work, are critical to understanding the rationale behind the current transcriptomic analyses.

(2) Further, the results are often presented with minimal context or interpretation. There is no model of intercellular or molecular coordination to explain the BBB-opening process, despite the stated goal of identifying such mechanisms. The statement that EA induces a "unique frontal cortex-specific transcriptome signature" is not supported, as no data from other brain regions are presented. Biological interpretation is at times unclear or inaccurate - for instance, attributing astrocyte migration effects to endothelial cell clusters or suggesting microglial tight junction changes without connecting them meaningfully to endothelial function.

(3) The study does include analyses of receptor-ligand signaling and cell-cell communication, which could be among its most biologically rich outputs. However, these are relegated to supplementary material and not shown in the leading figures. This choice limits the utility of the manuscript as a hypothesis-generating resource.

(4) Overall, while the dataset may be of interest to BBB researchers and those developing technologies for drug delivery across the BBB, the manuscript in its current form does not yet fulfill its interpretive goals. A more integrated and biologically grounded analysis would be beneficial.

This section is also further elaborated in the introduction and its references.

Our current study is actually based on previous findings that electroacupuncture can open the BBB, with a more pronounced effect observed in the frontal lobe (this aspect should be further described in the research background). Building on this foundation, our aim is to delineate the potential biological mechanisms involved. Therefore, we selected frontal lobe tissue as our primary choice for sequencing and have not yet investigated differences across other brain regions, although this may become a focus of future research. Additionally, we recognize that the mechanism underlying BBB opening is complex, and at present, we cannot determine whether it is driven by a single direct factor or by coordinated actions between cells or molecules. As such, our results are presented only briefly for now, and we will carefully consider whether to supplement our findings by incorporating insights from other studies.

Considering the overall data layout and the length of the article, we ultimately decided not to make any changes to the presentation of the article's data. The images included in the supplementary materials are also thoroughly described and referenced in the manuscript, allowing readers to selectively view any data they are interested in.

Indeed, our current dataset and analysis tend to present objective data results. We are also conducting a series of validations that may be related to the biology of the blood-brain barrier, and we look forward to sharing and discussing any future research findings with you and everyone.

Reviewer #1 (Recommendations for the authors):

(1) Figures 3-7: Label treatment groups (CON vs. EA) consistently in legends.

(2) Methods: Specify rat strain (Sprague-Dawley) in the abstract.

(3) Clarify Limitations: Explicitly state that BBB opening is inferred, not proven.

This section has been revised at lines 743-733, 748, 949, 754-755, and 759-760 of the manuscript.

Revised at line 31 of the manuscript.

Thank you for your feedback. The background information on the open evidence of BBB has been added to the introduction.

Reviewer #2 (Recommendations for the authors):

(1) Abstract and Introduction

• Include specific key findings in the abstract to improve clarity and reader engagement.

• Expand the introduction to situate this work in the context of other BBB-opening methods (e.g., ultrasound) and the known consequences of BBB disruption.

• Clarify the rationale for choosing electroacupuncture.

• Include information (perhaps summarized from previous studies) about the extent, timeline, and functional assessment of BBB opening in this model to help justify the single-cell RNA-seq design.

(2) Experimental Rationale and Context

• Reiterate experimental design and rationale in each results section, rather than relying exclusively on the Methods section.

• Specify the time point of tissue collection relative to the EA intervention.

• Describe the anatomical sites of acupuncture stimulation and their physiological relevance.

(3) Data Presentation

• Replace the human brain cartoon in Figure 1 with an anatomically appropriate rat brain schematic.

• Reevaluate which data are presented in the main versus supplementary figures. Highlight biologically meaningful results, such as cell-cell communication and ligand-receptor interactions, in the main figures rather than supplementary data.

(4) Interpretation and Modeling

• More carefully link transcriptional changes (e.g., Wnt signaling in microglia) to biologically plausible mechanisms of BBB regulation-e.g., microglial signaling to endothelial cells.

• Clarify whether the presence of granulocytes and T cells might result from a lack of perfusion prior to brain dissection.

• Consider proposing a model (even speculative) of how EA leads to BBB opening based on observed transcriptional changes.

First, for the sake of brevity in the abstract, we did not present specific results in this section. Second, since BBB opening via EA is a unique strategy, our previous studies have examined the opening time window and the recovery of the BBB after EA intervention (as mentioned in the introduction). We believe its characteristics differ from those of ultrasound-induced BBB opening and BBB disruption, so we did not conduct comparative discussions, but objectively presented our research findings. In further functional validation experiments, we may consider integrating other opening strategies in our studies. Additionally, the choice of electroacupuncture was based on our previous series of studies, which have already been outlined in the research background. Finally, we did indeed determine the experimental design of this study based on prior research, as described in the background section of the introduction.

We decided not to make changes to this section in the manuscript after careful consideration. The setup of electroacupuncture intervention and controls has been thoroughly discussed in our previous studies (as referenced in the introduction), so we have not repeated it in this manuscript. Overall, building on all our previous findings, this study focuses primarily on the potential mechanisms of EA intervention. The anatomical sites of acupuncture stimulation and their physiological relevance are another key area of our research, and we are currently conducting a series of related studies. We look forward to sharing these findings with you in the future.

We have already changed the human brain diagram in Figure 1 to a rat brain diagram, and have replaced Figure 1 in the files with the revised version. However, considering the overall data layout and the length of the article, we ultimately decided not to make changes to the data presentation in the manuscript. The images in the supplementary materials are also thoroughly described and referenced in the manuscript, allowing readers to selectively view the data they are interested in.

This section has provided us with excellent suggestions for further exploration, although no changes have been made to the manuscript at this time. In the future, we may conduct more detailed transcriptomic studies focusing on sex differences and different brain regions, which will allow for a more comprehensive analysis of the biological mechanisms involved in BBB regulation.

Author response:

Reviewer #1 (Public review): 

Summary: 

The authors analyzed the expression of ATAD2 protein in post-meiotic stages and characterized the localization of various testis-specific proteins in the testis of the Atad2 knockout (KO). By cytological analysis as well as the ATAC sequencing, the study showed that increased levels of HIRA histone chaperone, accumulation of histone H3.3 on post-meiotic nuclei, defective chromatin accessibility and also delayed deposition of protamines. Sperm from the Atad2 KO mice reduces the success of in vitro fertilization. The work was performed well, and most of the results are convincing. However, this manuscript does not suggest a molecular mechanism for how ATAD2 promotes the formation of testis-specific chromatin. 

We would like to take this opportunity to highlight that the present study builds on our previously published work, which examined the function of ATAD2 in both yeast S. pombe and mouse embryonic stem (ES) cells (Wang et al., 2021). In yeast, using genetic analysis we showed that inactivation of HIRA rescues defective cell growth caused by the absence of ATAD2. This rescue could also be achieved by reducing histone dosage, indicating that the toxicity depends on histone over-dosage, and that HIRA toxicity, in the absence of ATAD2, is linked to this imbalance.

Furthermore, HIRA ChIP-seq performed in mouse ES cells revealed increased nucleosome-bound HIRA, particularly around transcription start sites (TSS) of active genes, along with the appearance of HIRA-bound nucleosomes within normally nucleosome-free regions (NFRs). These findings pointed to ATAD2 as a major factor responsible for unloading HIRA from nucleosomes. This unloading function may also apply to other histone chaperones, such as FACT (see Wang et al., 2021, Fig. 4C).

In the present study, our investigations converge on the same ATAD2 function in the context of a physiologically integrated mammalian system—spermatogenesis. Indeed, in the absence of ATAD2, we observed H3.3 accumulation and enhanced H3.3-mediated gene expression. Consistent with this functional model of ATAD2— unloading chaperones from histone- and non-histone-bound chromatin—we also observed defects in histone-toprotamine replacement.

Together, the results presented here and in Wang et al. (2021) reveal an underappreciated regulatory layer of histone chaperone activity. Previously, histone chaperones were primarily understood as factors that load histones. Our findings demonstrate that we must also consider a previously unrecognized regulatory mechanism that controls assembled histone-bound chaperones. This key point was clearly captured and emphasized by Reviewer #2 (see below).

Strengths: 

The paper describes the role of ATAD2 AAA+ ATPase in the proper localization of sperm-specific chromatin proteins such as protamine, suggesting the importance of the DNA replication-independent histone exchanges with the HIRA-histone H3.3 axis. 

Weaknesses: 

(1) Some results lack quantification. 

We will consider all the data and add appropriate quantifications where necessary.

(2) The work was performed well, and most of the results are convincing. However, this manuscript does not suggest a molecular mechanism for how ATAD2 promotes the formation of testis-specific chromatin. 

Please see our comments above.

Reviewer #2 (Public review): 

Summary: 

This manuscript by Liakopoulou et al. presents a comprehensive investigation into the role of ATAD2 in regulating chromatin dynamics during spermatogenesis. The authors elegantly demonstrate that ATAD2, via its control of histone chaperone HIRA turnover, ensures proper H3.3 localization, chromatin accessibility, and histone-toprotamine transition in post-meiotic male germ cells. Using a new well-characterized Atad2 KO mouse model, they show that ATAD2 deficiency disrupts HIRA dynamics, leading to aberrant H3.3 deposition, impaired transcriptional regulation, delayed protamine assembly, and defective sperm genome compaction. The study bridges ATAD2's conserved functions in embryonic stem cells and cancer to spermatogenesis, revealing a novel layer of epigenetic regulation critical for male fertility. 

Strengths: 

The MS first demonstration of ATAD2's essential role in spermatogenesis, linking its expression in haploid spermatids to histone chaperone regulation by connecting ATAD2-dependent chromatin dynamics to gene accessibility (ATAC-seq), H3.3-mediated transcription, and histone eviction. Interestingly and surprisingly, sperm chromatin defects in Atad2 KO mice impair only in vitro fertilization but not natural fertility, suggesting unknown compensatory mechanisms in vivo. 

Weaknesses:

The MS is robust and there are not big weaknesses 

Reviewer #3 (Public review): 

Summary: 

The authors generated knockout mice for Atad2, a conserved bromodomain-containing factor expressed during spermatogenesis. In Atad2 KO mice, HIRA, a chaperone for histone variant H3.3, was upregulated in round spermatids, accompanied by an apparent increase in H3.3 levels. Furthermore, the sequential incorporation and removal of TH2B and PRM1 during spermiogenesis were partially disrupted in the absence of ATAD2, possibly due to delayed histone removal. Despite these abnormalities, Atad2 KO male mice were able to produce offspring normally. 

Strengths: 

The manuscript addresses the biological role of ATAD2 in spermatogenesis using a knockout mouse model, providing a valuable in vivo framework to study chromatin regulation during male germ cell development. The observed redistribution of H3.3 in round spermatids is clearly presented and suggests a previously unappreciated role of ATAD2 in histone variant dynamics. The authors also document defects in the sequential incorporation and removal of TH2B and PRM1 during spermiogenesis, providing phenotypic insight into chromatin transitions in late spermatogenic stages. Overall, the study presents a solid foundation for further mechanistic investigation into ATAD2 function. 

Weaknesses:

While the manuscript reports the gross phenotype of Atad2 KO mice, the findings remain largely superficial and do not convincingly demonstrate how ATAD2 deficiency affects chromatin dynamics. Moreover, the phenotype appears too mild to elucidate the functional significance of ATAD2 during spermatogenesis. 

We respectfully disagree with the statement that our findings are largely superficial. Based on our investigations of this factor over the years, it has become evident that ATAD2 functions as an auxiliary factor that facilitates mechanisms controlling chromatin dynamics (see, for example, Morozumi et al., 2015). These mechanisms can still occur in the absence of ATAD2, but with reduced efficiency, which explains the mild phenotype we observed.

This function, while not essential, is nonetheless an integral part of the cell’s molecular biology and should be studied and brought to the attention of the broader biological community, just as we study essential factors. Unfortunately, the field has tended to focus primarily on core functional actors, often overlooking auxiliary factors. As a result, our decade-long investigations into the subtle yet important roles of ATAD2 have repeatedly been met with skepticism regarding its functional significance, which has in turn influenced editorial decisions.

We chose eLife as the venue for this work specifically to avoid such editorial barriers and to emphasize that facilitators of essential functions do exist. They deserve to be investigated, and the underlying molecular regulatory mechanisms must be understood.

(1) Figures 4-5: The analyses of differential gene expression and chromatin organization should be more comprehensive. First, Venn diagrams comparing the sets of significantly differentially expressed genes between this study and previous work should be shown for each developmental stage. Second, given the established role of H3.3 in MSCI, the effect of Atad2 knockout on sex chromosome gene expression should be analyzed. Third, integrated analysis of RNA-seq and ATAC-seq data is needed to evaluate how ATAD2 loss affects gene expression. Finally, H3.3 ChIP-seq should be performed to directly assess changes in H3.3 distribution following Atad2 knockout.  

(1) In the revised version, we will include Venn diagrams to illustrate the overlap in significantly differentially expressed genes between this study and previous work. However, we believe that the GSEAs presented here provide stronger evidence, as they indicate the statistical significance of this overlap (p-values). In our case, we observed p-value < 0.01 (**) and p < 0.001 (***).

(2) Sex chromosome gene expression was analyzed and is presented in Fig. 5C.

(3) The effect of ATAD2 loss on gene expression is shown in Fig. 4A, B, and C as histograms, with statistical significance indicated in the middle panels.

(4) Although mapping H3.3 incorporation across the genome in wild-type and Atad2 KO cells would have been informative, the available anti-H3.3 antibody did not work for ChIP-seq, at least in our hands. The authors of Fontaine et al., 2022, who studied H3.3 during spermatogenesis in mice, must have encountered the same problem, since they tagged the endogenous H3.3 gene to perform their ChIP experiments.

(2) Figure 3: The altered distribution of H3.3 is compelling. This raises the possibility that histone marks associated with H3.3 may also be affected, although this has not been investigated. It would therefore be important to examine the distribution of histone modifications typically associated with H3.3. If any alterations are observed, ChIP-seq analyses should be performed to explore them further.  

Based on our understanding of ATAD2’s function—specifically its role in releasing chromatin-bound HIRA—in the absence of ATAD2 the residence time of both HIRA and H3.3 on chromatin increases. This results in the detection of H3.3 not only on sex chromosomes but across the genome. Our data provide clear evidence of this phenomenon. The reviewer is correct in suggesting that the accumulated H3.3 would carry H3.3-associated histone PTMs; however, we are unsure what additional insights could be gained by further demonstrating this point.

(3) Figure 7: While the authors suggest that pre-PRM2 processing is impaired in Atad2 KO, no direct evidence is provided. It is essential to conduct acid-urea polyacrylamide gel electrophoresis (AU-PAGE) followed by western blotting, or a comparable experiment, to substantiate this claim. 

Figure 7 does not suggest that pre-PRM2 processing is affected in Atad2 KO; rather, this figure—particularly Fig. 7B—specifically demonstrates that pre-PRM2 processing is impaired, as shown using an antibody that recognizes the processed portion of pre-PRM2. ELISA was used to provide a more quantitative assessment; however, in the revised manuscript we will also include a western blot image.

(4) HIRA and ATAD2: Does the upregulation of HIRA fully account for the phenotypes observed in Atad2 KO? If so, would overexpression of HIRA alone be sufficient to phenocopy the Atad2 KO phenotype? Alternatively, would partial reduction of HIRA (e.g., through heterozygous deletion) in the Atad2 KO background be sufficient to rescue the phenotype? 

These are interesting experiments that require the creation of appropriate mouse models, which are not currently available.

(5)The mechanism by which ATAD2 regulates HIRA turnover on chromatin and the deposition of H3.3 remains unclear from the manuscript and warrants further investigation. 

The Reviewer is absolutely correct. In addition to the points addressed in response to Reviewer #1’s general comments (see above), it would indeed have been very interesting to test the segregase activity of ATAD2 (likely driven by its AAA ATPase activity) through in vitro experiments using the Xenopus egg extract system described by Tagami et al., 2004. This system can be applied both in the presence and absence (via immunodepletion) of ATAD2 and would also allow the use of ATAD2 mutants, particularly those with inactive AAA ATPase or bromodomains. However, such experiments go well beyond the scope of this study, which focuses on the role of ATAD2 in chromatin dynamics during spermatogenesis

Reference

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Author response:

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

Reviewer #1 (Public review):

Summary:

Desveaux et al. describe human mAbs targeting protein from the Pseudomonas aeruginosa T3SS, discovered by employing single cell B cell sorting from cystic fibrosis patients. The mAbs were directed at the proteins PscF and PcrV. They particularly focused on two mAbs binding the T3SS with the potential of blocking activity. The supplemented biochemical analysis was crystal structures of P3D6 Fab complex. They also compared the blocking activity with mAbs that were described in previous studies, using an assay that evaluated the toxin injection. They conducted mechanistic structure analysis and found that these mAbs might act through different mechanisms by preventing PcrV oligomerization and disrupting PcrVs scaffolding function.

Strengths:

The antibiotic resistance crisis requires the development of new solutions to treat infections caused by MDR bacteria. The development of antibacterial mAbs holds great potential. In that context, this report is important as it paves the way for the development of additional mAbs targeting various pathogens that harbor the T3SS. In this report, the authors present a comparative study of their discovered mAbs vs. a commercial mAb currently in clinical testing resulting in valuable data with applicative implications. The authors investigated the mechanism of action of the mAbs using advanced methods and assays for the characterization of antibody and antigen interaction, underlining the effort to determine the discovered mAbs suitability for downstream application.

Weaknesses:

Although the information presented in this manuscript is important, previous reports regarding other T3SS structures complexed with antibodies, reduce the novelty of this report. Nevertheless, we provide several comments that may help to improve the report. The structural analysis of the presented mAbs is incomplete and unfortunately, the authors did not address any developability assessment. With such vital information missing, it is unclear if the proposed antibodies are suited for diagnostic or therapeutic usage. This vastly reduces the importance of the possibly great potential of the authors' findings. Moreover, the structural information does not include the interacting regions on the mAb which may impede the optimization of the mAb if it is required to improve its affinity.

As described in the manuscript (Fig. 6), our mAbs are markedly less effective in every in vitro T3SS inhibition assay than the mAbs recently described by Simonis et al. They are therefore very unlikely to outperform these mAbs in in vivo animal models of P. aeruginosa infection. Considering the high cost of animal experiments and ethical concerns-and in accordance with the Reduction principal of the 3Rs guidelines-we chose not to pursue in vivo experiments. Instead, we focused on leveraging the new isolated mAbs to investigate the mechanisms of action and structural features of anti-PcrV mAbs.

Following the reviewer's suggestion, we have now added mAb interaction features into the structural data presented in the manuscript. However, based on the efficiency data, the structural analysis and the mechanistic insights presented, we do not consider further therapeutic use and optimization of our mAbs to be warranted.

Reviewer #2 (Public review):

Summary:

Desveaux et al. performed Elisa and translocation assays to identify among 34 cystic fibrosis patients which ones produced antibodies against P. aeruginosa type three secretion system (T3SS). The authors were especially interested in antibodies against PcrV and PcsF, two key components of the T3SS. The authors leveraged their binding assays and flow cytometry to isolate individual B cells from the two most promising sera, and then obtained monoclonal antibodies for the proteins of interest. Among the tested monoclonal antibodies, P3D6 and P5B3 emerged as the best candidates due to their inhibitory effect on the ExoS-Bla translocation marker (with 24% and 94% inhibition, respectively). The authors then showed that P5B3 binds to the five most common variants of PcrV, while P3D6 seems to recognize only one variant. Furthermore, the authors showed that P3D6 inhibits translocon formation, measured as cell death of J774 macrophages. To get insights into the P3D6PcrV interaction, the authors defined the crystal structure of the P3D6-PcrV complex. Finally, the authors compared their new antibodies with two previous ones (i.e., MEDI3902 and 30-B8).

Strengths:

(1) The article is well written.

(2) The authors used complementary assays to evaluate the protective effect of candidate monoclonal antibodies.

(3) The authors offered crystal structure with insights into the P3D6 antibody-T3SS interaction (e.g., interactions with monomer vs pentamers).

(4) The authors put their results in context by comparing their antibodies with respect to previous ones.

Weaknesses:

The authors used a similar workflow to the one previously reported in Simonis et al. 2023 (antibodies from cystic fibrosis patients that included B cell isolation, antibody-PcrV interaction modeling, etc.) but the authors do not clearly explain how their work and findings differentiate from previous work.   

We employed a similar mAb isolation pipeline to that used by Simonis et al., beginning with the screening of a cohort of cystic fibrosis patients chronically infected with P. aeruginosa. As in Simonis et al., we isolated specific B cells using a recombinant PcrV bait, followed by single-cell PCR amplification of immunoglobulin genes. The main differences in methodology between the two studies are as follows: i) the use of individuals from different cohorts, and therefore having different Ab repertoires; ii) the nature of the screening assays, although in both cases the screening was focused on the inhibition of T3SS function; iii) the PcrV labeling strategy, with Simonis et al. employing direct labeling, whereas we used a biotinylated tag combined with streptavidin;

The number of specific mAbs obtained and produced was higher in Simonis et al. (47 versus 9 in our study). They sorted B cells from three individuals compared to two in our work and possibly started with a larger amount of PBMCs per donor, which may account for the higher number of specific B cells and mAbs isolated. Considering that the strategies were overall very similar, the greater number of mAbs isolated in Simonis et al. likely explains, to a large extent, why they identified mAbs targeting different epitopes compared to ours, including highly potent mAbs that we did not recover. 

Our modeling study, unlike that of Simonis et al., which relied on an AlphaFold prediction of the multimeric structure of P. aeruginosa PcrV, was based on the experimentally determined structure of the homologous Salmonella SipD pentamer, as described in the manuscript. Furthermore, we compared our mAb P3D6 not only with 30-B8 from Simonis et al., but also with MEDI3902. Finally, in contrast to the approach of Simonis et al., we used functional assays to investigate the differences in mechanisms of action among these mAbs, which target three distinct epitopes.

(2) Although new antibodies against P. aeruginosa T3SS expand the potential space of antibodybased therapies, it is unclear if P3D6 or P5B3 are better than previous antibodies. In fact, in the discussion section authors suggested that the 30-B8 antibody seems to be the most effective of the tested antibodies.  

As explained above and shown in the Results section (Figure 6), the 30-B8 mAb is markedly more effective at inhibiting T3SS activity in both in vitro assays used.

(3) The authors should explain better which of the two antibodies they have discovered would be better suited for follow-up studies. It is confusing that the authors focused the last sections of the manuscript on P3D6 despite P3D6 having a much lower ExoS-Bla inhibition effect than P5B3 and the limitation in the PcrV variant that P3D6 seems to recognize. A better description of this comparison and the criteria to select among candidate antibodies would help readers identify the main messages of the paper. 

The P3D6 mAb shows stronger inhibitory activity than P5B3 in the two assays used, as shown in Supplementary Figure 1. An error in the table in Figure 2B was corrected and this table now reflects the results presented in Supplementary Figure 1. 

The final sections of the manuscript focus on P3D6, which is more potent than P5B3, and for which we successfully determined a co-crystal structure with PcrV*. All parallel attempts to obtain a structure of P5B3 in complex with PcrV* failed. The P3D6-PcrV* structure was used to analyze epitope recognition and mechanisms of action in comparison to previously described mAbs. As previously mentioned, we do not consider further studies aimed at therapeutic development and optimization of our mAbs to be justified given the current data. Therefore, we believe that the main message of the paper is adequately captured in the title.

(4) This work could strongly benefit from two additional experiments:

(a) In vivo experiments: experiments in animal models could offer a more comprehensive picture of the potential of the identified monoclonal antibodies. Additionally, this could help to answer a naïve question: why do the patients that have the antibodies still have chronic P. aeruginosa infections? 

As explained above, the mAbs we isolated are significantly less potent than those described by Simonis et al., and are therefore unlikely to outperform the best anti-PcrV candidates in vivo. In light of the data, and considering ethical concerns related to animal use in research and budgetary constraints, we decided not to proceed with in vivo experiments.

There are a number of reasons that may explain why patients with anti-PcrV Abs blocking the T3SS can still be chronically infected with Pa. First these Abs may be at limiting concentration, particularly in sites where Pa replicates, and thus unable to clear infection. in addition, it has been described that the T3SS is downregulated in chronic infection in cystic fibrosis patients. This suggests that a therapeutic intervention with T3SS inhibiting Abs may be more efficient if done early in cystic fibrosis patients to prevent colonization when Pa possesses an active T3SS. Finally, T3SS is not the only virulence mechanism employed by P. aeruginosa during infection. Indeed, multiple protein adhesins and polysaccharides are important factors facilitating the formation of bacterial biofilms that are crucial for establishing chronic persistent infection. In this regard, a combination of Abs targeting different factors on the P. aeruginosa surface may be needed to treat chronic infections.  

(b) Multi-antibody T3SS assays (i.e., a combination of two or more monoclonal antibodies evaluated with the same assays used for characterization of single ones). This could explore the synergistic effects of combinatorial therapies that could address some of the limitations of individual antibodies. 

Given the high potency of the Simonis mAbs and the mechanisms of action highlighted by our analysis, it is unlikely that our mAbs would synergize with those described by Simonis. Additionally, since our two mAbs cross-compete for binding, synergy between them is also improbable.

Reviewer #1 (Recommendations for the authors):

Line 166: How was the serum-IgG purified? (e.g., protein A, protein G). 

Protein A purification was used, as now mentioned in the manuscript. Purified Igs were thus predominantly IgG1, IgG2 and IgG4, as indicated.

(2) Line 196: When mentioning affinities, it is preferable to present in molar units. 

To facilitate comparisons, Ab concentrations were presented in µg/mL as in Simonis et al.

(3) Line 206: The author states that P3D6 displays significantly reduced ExoS-Bla injection (Figure 2B), but according to the presented table, ExoS-Bla inhibition was higher for P5B3. Additionally, when using "significantly", what was the statistical test that was used to evaluate the significance? Please clarify.

We thank the reviewer for pointing out this inconsistency. Indeed, the names of P3D6 and P5B3 were exchanged when building the table related to Figure 2B. The corrected version of this figure is now presented in the new version of the manuscript. An ANOVA was performed to evaluate the significance of the observed difference (adjusted p-values < 0.001) and it is now mentioned in the figure caption.  

(4) Line 215: "P3B3" typo.

This was corrected.

(5) Figure 3B: Could the author explain the higher level of ExoS-Bla injection when using VRCO1 antibody compared to no antibody.  

A slightly higher level of the median is observed in the case of three variants out of five. However, this difference is not statistically significant (p-value > 0.05).

(6) Supplement Figure 1: the presented grey area is not clear (is it the 95%CI?) and how was the IC50 calculated? With what model was it projected? Are the values for IC50 beyond the 100µg/mL mark a projection? It seems that projecting such greater values (such as the IC50 of over 400µg/mL for variant 5) is prone to high error probability.

The grey area represents the 95% confidence interval (95% CI) and it is now mentioned in the figure caption. The IC50 and 95% CI were both inferred by the dose-response drc R package based on a three-parameters log-logistic model and it is now explained in the Materials & Methods section. The p-values for IC50 beyond the 100µg/mL were below 0.05 but we agree that such extrapolation should be considered with precaution (see below our response to comment number 7).

(7) Line 227: The author describes that P5B3 has similar IC50 values towards variants 1-4, but the  IC50 towards variant 5 is substantially higher with 400µg/mL, albeit the only difference between variant 4 and 5 is the switch position 225 Arg -> Lys which are very similar in their properties. Please provide an explanation. 

As explained in our response to comment number 6, we agree that the comparison of IC50 that are estimated to be close or higher than the highest experimental concentration is somehow speculative. Indeed, we performed further statistical analysis that showed no significant difference between the IC50 toward the five PcrV variants of mAb P5B3. In contrast, the difference between the IC50 of mAbs P5B3 and P3D6 toward variant 1 is statistically significant. This is now explained in the manuscript.

(8) Line 233: Pore assembly: It is not clear how the data was normalized. The authors mention the methods normalization against the wildtype strain in the absence of antibodies, but did not elaborate clearly if the mutant strain has the same base cytotoxicity as the wild type. It would be helpful to show the level of cytotoxicity of the wild type compared to the mutant in the absence of antibodies to understand the baseline of cytotoxicity of both strains.  

In these experiments we did not use the wild-type strain. As explained, the only strain that allows the measurement of pore formation by translocators PopB/PopD is the one lacking all effectors. All the experiments were done with this strain, and all the measurements were normalized accordingly. 

(9) Figure 4: The explanation is redundant as it is clearly stated in the results. It would be better for the caption to describe the figure and leave interpretation to the results section. Overall, this comment is relevant to all figure captions, as it will reduce redundancy. My suggestion is to keep the figure caption as a road map to understand what is shown in the figure. For example, the Figure 4 caption should include that the concentration is presented in logarithmic scale, what is the dashed line, what is the grey area (what interval does it represent?), what each circle represents, and what is the regression model used? 

Figure captions have been improved as suggested. 

(10) Line 432: The authors apparently misquoted the original article describing the chimeric form PcrV* by describing the fusion of amino acids 1-17 and 136-249. I quote the original article by Tabor et al. "[...] we generated a truncated PcrV fragment (PcrVfrag) comprising PcrV amino acids 1-17 fused to amino acids 149-236 [...]". Additionally, how does the absence of amino acid 21 in the variant affect the conclusion? 

Our construct was inspired by the one described in Tabor et al. but was not identical. We have therefore replaced "was constructed based on a construct by Tabor et al." for "whose design was inspired by the construct described in Tabor et al."

Amino acid 21 is only absent in the construct used for crystallization experiments; all other experiments looking at Ab activity were performed with bacteria bearing full-length PcrV. The difference in P3D6 activity between variants V1 and V2-appears to be explained by the nature of the residue at position 225, according to the structural data, as explained now in more detail in the manuscript. Accordingly, the difference in efficiency of P3D6 against the V1 and V2  variants is explained by the residue at position 225, as both variants have the same residue at position 21. However, while the nature of the residue at position 225 appears to explain the absence of efficiency of the Ab for the variants studied, an impact of residue 21 could not be totally ruled out in putative variants with a Ser at 225 but different amino acids at 21.

(11) Line 569: Missing word - ESRF stands for European Synchrotron Radiation Facility. 

This has been corrected.

(12) Line 268-269 (Figure 5A): The description of the alpha helices in relation to the figure is incomplete. Helices 2,3 and 5 are not indicated. 

Indeed, since the structure is well-known and in the interest of visibility and simplicity, we only included the most relevant secondary structure features.

(13) Line 271-272: It would be good to elaborate on the exact binding platform between LC and HC of the Fab and the residues on the PcrV side. For example, the author could apply the structure to PDBePISA (EMBL-EBI) which will provide details about the interface between the PcrV and the antibody. It is very interesting to learn what regions of the antibody are in charge of the binding, such as: is the H-CDR3 the major contributor of the binding or are other CDRs more involved? Additionally, in line 275 they state that the substitution of Ser 225 with Arg or Lys is consistent with the P3D6 insufficient binding. What contributed to this result on the antibodies side? 

In order to address this question, we are now providing a LigPlot figure (supplementary Figure 3) in which specific interactions between PcrV* and the Fab are shown.

(14) Line 291: It is unclear from what data the authors concluded that anti-PscF targets 3 distinct regions of PscF. 

The data are shown in Supplementary Table 2, as mentioned in the manuscript. We have now modified the order of the anti-PcrV mAbs in the table to better illustrate the three identified epitope clusters (Sup table 2). Similarly, the anti-PscF mAbs appear to group into three clusters as P3G9 and P5E10 only compete with themselves, while mabs P3D6 and P5B3 compete with themselves and each other.

(15) Line 315: It is preferable to introduce results in the results section instead of the discussion. 

While preparing the manuscript, we initially included these results as a separate paragraph in the Results section, but ultimately chose the current format to improve flow and avoid redundancy.

(16) Supplement Figure 2: What was the regression model used to evaluate IC50, and what is presented in the graph? What is the dashed line (see comment for Figure 4 above)? 

The regression is based on a three-parameters log-logistic model and the light-colors area correspond to the 95% IC. The dashed lines visually represents 100% of ExoS-Bla injection. These information are now mentioned in the figure caption.

(17) Figure 6B: It would be better to show an additional rotation of the PcrV bound by Fab 30-B8 that corresponds to the same as the one represented with Fab MEDI3092. This would clear up the differences in binding regions. Same for Fab P3D6. 

Figure 6 already depicts two orientations. Despite the fact that we agree that additional orientations could be of interest, we believe that this would add unnecessary complexity to the figure, and would prefer to maintain the figure as is, if possible.

(18) Line 356-358: The author proposes an experiment to support the suggested mechanism of P3D6, it would follow up with a bio-chemical analysis showing the prevention of PcrV oligomerization in its presence. 

We understand the reviewers’ comment regarding the potential use of biochemical approaches to test our hypothesis. However, this not currently feasible as we have been unable to achieve in vitro oligomerization of PcrV alone, possibly due to the absence of other T3SS components, such as the polymerized PscF needle.

(19) Line 456: Missing details about how the ELISA was conducted including temperature, how the antigen was absorbed, plate type, etc. 

Experimental details have been added.

(20) Line 460: Missing substrate used for alkaline phosphatase. 

The nature of the substrate was added to the methods.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This research takes a novel theoretical and methodological approach to understanding how people estimate the level of control they have over their environment and how they adjust their actions accordingly. The task is innovative and both it and the findings are well-described (with excellent visuals). They also offer thorough validation for the particular model they develop. The research has the potential to theoretically inform understanding of control across domains, which is a topic of great importance.

We thank the Reviewer for their favorable appraisal and valuable suggestions, which have helped clarify and strengthen the study’s conclusion. 

In its revised form, the manuscript addresses most of my previous concerns. The main remaining weakness pertains to the analyses aimed at addressing my suggesting of Bayesian updating as an alternative to the model proposed by the authors. My suggestion was to assume that people perform a form of function approximation to relate resource expenditure to success probability. The authors performed a version of this where people were weighing evidence for a few canonical functions (flat, step, linear), and found that this model underperformed theirs. However, this Bayesian model is quite constrained in its ability to estimate the function relating resources. A more robust test would be to assume a more flexible form of updating that is able to capture a wide range of distributions (e.g., using basis functions, gaussian processes, or nonparametric estimators); see, e.g., work by Griffiths on human function learning). The benefit of testing this type of model is that it would make contact with a known form of inference that individuals engage in across various settings and therefore could offer a more parsimonious and generalizable account of function learning, whereby learning of resource elasticity is a special case. I defer to the authors as to whether they'd like to pursue this direction, but if not I think it's still important that they acknowledge that they are unable to rule out a more general process like this as an alternative to their model. This pertains also to inferences about individual differences, which currently hinge on their preferred model being the most parsimonious.

We thank the Reviewer for this thoughtful suggestion. We acknowledge that more flexible function learning approaches could provide a stronger test in favor of a more general account. Our Bayesian model implemented a basis function approach where the weights of three archetypal functions (flat, step, linear) are learned from experience Testing models with more flexible basis functions would likely require a task with more than three levels of resource investment (1, 2, or 3 tickets). This would make an interesting direction for future work expanding on our current findings. We now incorporate this suggestion in more detail in our updated manuscript (335-341):

“Second, future models could enable generalization to levels of resource investment not previously experienced. For example, controllability and its elasticity could be jointly estimated via function approximation that considers control as a function of invested resources. Although our implementation of this model did not fit participants’ choices well (see Methods), other modeling assumptions drawn from human function learning [30] or experimental designs with continuous action spaces may offer a better test of this idea.”

Reviewer #2 (Public review):

This research investigates how people might value different factors that contribute to controllability in a creative and thorough way. The authors use computational modeling to try to dissociate "elasticity" from "overall controllability," and find some differential associations with psychopathology. This was a convincing justification for using modeling above and beyond behavioral output and yielded interesting results. Notably, the authors conclude that these findings suggest that biased elasticity could distort agency beliefs via maladaptive resource allocation. Overall, this paper reveals important findings about how people consider components of controllability. The authors have gone to great lengths to revise the manuscript to clarify their definitions of "elastic" and "inelastic" and bolster evidence for their computational model, resulting in an overall strong manuscript that is valuable for elucidating controllability dynamics and preferences. 

We thank the Reviewer for their constructive feedback throughout the review process, which has substantially strengthened our manuscript and clarified our theoretical framework.

One minor weakness is that the justification for the analysis technique for the relationships between the model parameters and the psychopathology measures remains lacking given the fact that simple correlational analyses did not reveal any significant associations.

We note that the existence of bivariate relationships is not a prerequisite for the existence of multivariate relationships. Conditioning the latter on the former, therefore, would risk missing out on important relationships existing in the data. Ultimately, correlations between pairs of variables do not offer a sensitive test for the general hypothesis that there is a relationship between two sets of variables. As an illustration, consider that elasticity bias correlated in our data (r = .17, p<.001) with the difference between SOA (sense of agency) and SDS (self-rating depression). Notably, SOA and SDS were positively correlated (r = .47, p<.001), and neither of them was correlated with elasticity bias (SOA: r=.04 p=.43, SDS: r=-.06, p=.16). It was a dimension that ran between them that mapped onto elasticity bias. This specific finding is incidental and uncorrected for multiple comparisons, hence we do not report it in the manuscript, but it illustrates the kinds of relationships that cannot be accounted for by looking at bivariate relationships alone.  

Reviewer #3 (Public review):

A bias in how people infer the amount of control they have over their environment is widely believed to be a key component of several mental illnesses including depression, anxiety, and addiction. Accordingly, this bias has been a major focus in computational models of those disorders. However, all of these models treat control as a unidimensional property, roughly, how strongly outcomes depend on action. This paper proposes---correctly, I think---that the intuitive notion of "control" captures multiple dimensions in the relationship between action and outcome.

In particular, the authors identify one key dimension: the degree to which outcome depends on how much *effort* we exert, calling this dimension the "elasticity of control". They additionally argue that this dimension (rather than the more holistic notion of controllability) may be specifically impaired in certain types of psychopathology. This idea has the potential to change how we think about several major mental disorders in a substantial way and can additionally help us better understand how healthy people navigate challenging decision-making problems. More concisely, it is a very good idea.

We thank the Reviewer for their thoughtful engagement with our manuscript. We appreciate their recognition of elasticity as a key dimension of control that has the potential to advance our understanding of psychopathology and healthy decision-making.

Starting with theory, the authors do not provide a strong formal characterization of the proposed notion of elasticity. There are existing, highly general models of controllability (e.g., Huys & Dayan, 2009; Ligneul, 2021) and the elasticity idea could naturally be embedded within one of these frameworks. The authors gesture at this in the introduction; however, this formalization is not reflected in the implemented model, which is highly task-specific.

Our formal definition of elasticity, detailed in Supplementary Note 1, naturally extends the reward-based and information-theoretic definitions of controllability by Huys & Dayan (2009) and Ligneul (2021). We now further clarify how the model implements this formalized definition (lines 156-159).

“Conversely, in the ‘elastic controllability model’, the beta distributions represent a belief about the maximum achievable level of control (𝑎_Control, 𝑏_Control) coupled with two elasticity estimates that specify the degree to which successful boarding requires purchasing at least one (𝑎_elastic≥1, 𝑏_elastic≥1) or specifically two (𝑎_elastic2, 𝑏_elastic2) extra tickets. As such, these elasticity estimates quantify how resource investment affects control. The higher they are, the more controllability estimates can be made more precise by knowing how much resources the agent is willing and able to invest (Supplementary Note 1).”

Moreover, the authors present elasticity as if it is somehow "outside of" the more general notion of controllability. However, effort and investment are just specific dimensions of action; and resources like money, strength, and skill (the "highly trained birke") are just specific dimensions of state. Accordingly, the notion of elasticity is necessarily implicitly captured by the standard model. Personally, I am compelled by the idea that effort and resource (and therefore elasticity) are particularly important dimensions, ones that people are uniquely tuned to. However, by framing elasticity as a property that is different in kind from controllability (rather than just a dimension of controllability), the authors only make it more difficult to integrate this exciting idea into generalizable models.

We respectfully disagree that we present elasticity as outside of, or different in kind from, controllability. Throughout the manuscript, we explicitly describe elasticity as a dimension of controllability (e.g., lines 70-72, along many other examples). This is also expressed in our formal definition of elasticity (Supplementary Note 1). 

The argument that vehicle/destination choice is not trivial because people occasionally didn't choose the instructed location is not compelling to me-if anything, the exclusion rate is unusually low for online studies. The finding that people learn more from non-random outcomes is helpful, but this could easily be cast as standard model-based learning very much like what one measures with the Daw two-step task (nothing specific to control here). Their final argument is the strongest, that to explain behavior the model must assume "a priori that increased effort could enhance control." However, more literally, the necessary assumption is that each attempt increases the probability of success-e.g. you're more likely to get a heads in two flips than one. I suppose you can call that "elasticity inference", but I would call it basic probabilistic reasoning.

We appreciate the Reviewer’s concerns but feel that some of the more subjective comments might not benefit from further discussion. We only note that controllability and its elasticity are features of environmental structure, so in principle any controllability-related inference is a form of model-based learning. The interesting question is whether people account in their world model for that particular feature of the environment.   

The authors try to retreat, saying "our research question was whether people can distinguish between elastic and inelastic controllability." I struggle to reconcile this with the claim in the abstract "These findings establish the elasticity of control as a distinct cognitive construct guiding adaptive behavior". That claim is the interesting one, and the one I am evaluating the evidence in light of.

In real-world contexts, it is often trivial that sometimes further investment enhances control and sometimes it does not. For example, students know that if they prepare more extensively for their exams they will likely be able to achieve better grades, but they also know that there is uncertainty in this regard – their grades could improve significantly, modestly, or in some cases, they might not improve at all, depending on the type of exams their study program administers and the knowledge or skills being tested. Our research question was whether in such contexts people learn from experience the degree to which controllability is elastic to invested resources and adapt their resource investment accordingly. Our findings show that they do. 

The authors argue for CCA by appeal to the need to "account for the substantial variance that is typically shared among different forms of psychopathology". I agree. A simple correlation would indeed be fairly weak evidence. Strong evidence would show a significant correlation after *controlling for* other factors (e.g. a regression predicting elasticity bias from all subscales simultaneously). CCA effectively does the opposite, asking whether-with the help of all the parameters and all the surveys-one can find any correlation between the two sets of variables. The results are certainly suggestive, but they provide very little statistical evidence that the elasticity parameter is meaningfully related to any particular dimension of psychopathology.

We agree with the Reviewer on the relationship between elasticity and any particular dimension of psychopathology. The CCA asks a different question, namely, whether there is a relationship between psychopathology traits and task parameters, and whether elasticity bias specifically contributes to this relationship. 

I am very concerned to see that the authors removed the discussion of this limitation in response to my first review. I quote the original explanation here:

- In interpreting the present findings, it needs to be noted that we designed our task to be especially sensitive to overestimation of elasticity. We did so by giving participants free 3 tickets at their initial visits to each planet, which meant that upon success with 3 tickets, people who overestimate elasticity were more likely to continue purchasing extra tickets unnecessarily. Following the same logic, had we first had participants experience 1 ticket trips, this could have increased the sensitivity of our task to underestimation of elasticity in elastic environments. Such underestimation could potentially relate to a distinct psychopathological profile that more heavily loads on depressive symptoms. Thus, by altering the initial exposure, future studies could disambiguate the dissociable contributions of overestimating versus underestimating elasticity to different forms of psychopathology.

The logic of this paragraph makes perfect sense to me. If you assume low elasticity, you will infer that you could catch the train with just one ticket. However, when elasticity is in fact high, you would find that you don't catch the train, leading you to quickly infer high elasticity eliminating the bias. In contrast, if you assume high elasticity, you will continue purchasing three tickets and will never have the opportunity to learn that you could be purchasing only one-the bias remains.

The authors attempt to argue that this isn't happening using parameter recovery. However, they only report the *correlation* in the parameter, whereas the critical measure is the *bias*. Furthermore, in parameter recovery, the data-generating and data-fitting models are identical-this will yield the best possible recovery results. Although finding no bias in this setting would support the claims, it cannot outweigh the logical argument for the bias that they originally laid out. Finally, parameter recovery should be performed across the full range of plausible parameter values; using fitted parameters (a detail I could only determine by reading the code) yields biased results because the fitted parameters are themselves subject to the bias (if present). That is, if true low elasticity is inferred as high elasticity, then you will not have any examples of low elasticity in the fitted parameters and will not detect the inability to recover them.

The logic the Reviewer describes breaks down when one considers the dynamics of participants’ resource investment choices. A low elasticity bias in a participant’s prior belief would make them persist for longer in purchasing a single ticket despite failure, as compared to a person without such a bias. Indeed, the ability of the experimental design to demonstrate low elasticity biases is evidenced by the fact that the majority of participants were fitted with a low elasticity bias (μ = .16 ± .14, where .5 is unbiased). 

Originally, the Reviewer was concerned that elasticity bias was being confounded with a general deficit in learning. The weak inter-parameter correlations in the parameter recovery test resolved this concern, especially given that, as we now noted, the simulated parameter space encompassed both low and high elasticity biases (range=[.02,.76]). Furthermore, regarding the Reviewer's concern about bias in the parameter recovery, we found no such significant bias with respect to the elasticity bias parameter (Δ(Simulated, Recovered)= -.03, p=.25), showing that our experiment could accurately identify low and high elasticity biases.

The statistical structure of the task is inconsistent with the framing. In the framing, participants can make either one or two second boarding attempts (jumps) by purchasing extra tickets. The additional attempt(s) will thus succeed with probability p for one ticket and 2p – p^{^}2 for two tickets; the p^{^}2 captures the fact that you only take the second attempt if you fail on the first. A consequence of this is buying more tickets has diminishing returns. In contrast, in the task, participants always jumped twice after purchasing two tickets, and the probability of success with two tickets was exactly double that with one ticket. Thus, if participants are applying an intuitive causal model to the task, they will appear to "underestimate" the elasticity of control. I don't think this seriously jeopardizes the key results, but any follow-up work should ensure that the task's structure is consistent with the intuitive causal model.

We thank the Reviewer for this comment, and agree the participants may have employed the intuitive understanding the Reviewer describes. This is consistent with our model comparison results, which showed that participants did not assume that control increases linearly with resource investment (lines 677-692). Consequently, this is also not assumed by our model, except perhaps by how the prior is implemented (a property that was supported by model comparison). In the text, we acknowledge that this aspect of the model and participants’ behavior deviates from the true task's structure, and it would be worthwhile to address this deviation in future studies. 

That said, there is no reason that this will make participants appear to be generally underestimating elasticity. Following exposure to outcomes for one and three tickets, any nonlinear understanding of probabilities would only affect the controllability estimate for two tickets. This would have contrasting effects on the elasticity estimated to the second and third tickets, but on average, it would not change the overall elasticity estimated. On the other hand, such a participant is only exposed to outcomes for two and three tickets, they would come to judge the difference between the first and second tickets too highly, thereby overestimating elasticity.  

The model is heuristically defined and does not reflect Bayesian updating. For example, it overestimates maximum control by not using losses with less than 3 tickets (intuitively, the inference here depends on what your beliefs about elasticity). Including forced three-ticket trials at the beginning of each round makes this less of an issue; but if you want to remove those trials, you might need to adjust the model. The need to introduce the modified model with kappa is likely another symptom of the heuristic nature of the model updating equations.

Note that we have tested a fully Bayesian model (lines 676-691), but found that this model fitted participants’ choices worse. 

You're right; saying these analyses provides "no information" was unfair. I agree that this is a useful way to link model parameters with behavior, and they should remain in the paper. However, my key objection still holds: these analyses do not tell us anything about how *people's* prior assumptions influence behavior. Instead, they tell us about how *fitted model parameters* depend on observed behavior. You can easily avoid this misreading by adding a small parenthetical, e.g.

Thus, a prior assumption that control is likely available **(operationalized by \gamma_controllability)** was reflected in a futile investment of resources in uncontrollable environments.

We thank the Reviewer for the suggestion and have added this parenthetical (lines 219, 225).

Author response:

Reviewer #1 (Public review): 

Summary: 

This study addresses the important question of how top-down cognitive processes affect tactile perception in autism - specifically, in the Fmr1-/y genetic mouse model of autism. Using a 2AFC tactile task in behaving mice, the study investigated multiple aspects of perceptual processing, including perceptual learning, stimulus categorization and discrimination, as well as the influence of prior experience and attention.  

We appreciate the reviewer’s statement highlighting the importance of our study. 

Strengths: 

The experiments seem well performed, with interesting results. Thus, this study can/will advance our understanding of atypical tactile perception and its relation to cognitive factors in autism. 

We thank the reviewer for recognizing the quality of our experiments and the relevance of our findings for understanding tactile perception and cognition in autism.

Weaknesses: 

Certain aspects of the analyses (and therefore the results) are unclear, which makes the manuscript difficult to understand. Clearer presentation, with the addition of more standard psychometric analyses, and/or other useful models (like logistic regression) would improve this aspect. The use of d' needs better explanation, both in terms of how and why these analyses are appropriate (and perhaps it should be applied for more specific needs rather than as a ubiquitous measure). 

We thank the reviewer for the helpful comments. We understand that the analyses were difficult to follow, and we will work on the clarity of the Results section. However, we would like to emphasize that every d′ measure is accompanied by analyses of response rates (i.e., correct and incorrect choice rates). In addition, we applied standard psychometric analyses whenever possible. Specifically, psychometric functions were fitted to the data using logistic regression. We will rework the text to clarify these points.

During training, only two stimulus amplitudes were presented, which precluded the construction of psychometric curves. For the categorization task, however, psychometric analyses were feasible and conducted (Figure 2). These analyses revealed no evidence of categorization bias (as measured by threshold) or accuracy (as measured by the slope) across stimulus strengths.

The calculation of d’ is included in the Methods, but we will also report and explain its use in each part of the Results section where it has been included.

Reviewer #2 (Public review): 

Summary: 

This manuscript presents a tactile categorization task in head-fixed mice to test whether Fmr1 knockout mice display differences in vibrotactile discrimination using the forepaw. Tactile discrimination differences have been previously observed in humans with Fragile X Syndrome, autistic individuals, as well as mice with loss of Fmr1 across multiple studies. The authors show that during training, Fmr1 mutant mice display subtle deficits in perceptual learning of "low salience" stimuli, but not "high salience" stimuli, during the task. Following training, Fmr1 mutant mice displayed an enhanced tactile sensitivity under low-salience conditions but not high-salience stimulus conditions. The authors suggest that, under 'high cognitive load' conditions, Fmr1 mutant mouse performance during the lowest indentation stimuli presentations was affected, proposing an interplay of sensory and cognitive system disruptions that dynamically affect behavioral performance during the task. 

Strengths: 

The study employs a well-controlled vibrotactile discrimination task for head-fixed mice, which could serve as a platform for future mechanistic investigations. By examining performance across both training stages and stimulus "salience/difficulty" levels, the study provides a more nuanced view of how tactile processing deficits may emerge under different cognitive and sensory demands. 

We thank the reviewer for emphasizing the strengths of our task design and analysis approach, and we appreciate that the potential of this platform for future mechanistic investigations is recognized.

Weaknesses: 

The study is primarily descriptive. The authors collect behavioral data and fit simple psychometric functions, but provide no neural recordings, causal manipulations, or computational modeling. Without mechanistic evidence, the conclusions remain speculative. 

We thank the reviewer for the careful reading of our manuscript and for the constructive feedback. The reviewer raises a valid point. We agree that our study is primarily descriptive and focused on behavioral data, and we appreciate the opportunity to clarify the scope and interpretation of our findings. Our primary goal was to characterize behavioral patterns during tactile discrimination and categorization, and the psychometric analyses were intended to provide a detailed description of these patterns. We do not claim to provide direct neural, causal, or computational evidence. 

Second, the authors repeatedly make strong claims about "categorical priors," "attention deficits," and "choice biases," but these constructs are inferred indirectly from secondary behavioral measures. Many of the effects are based on non-significant trends, and alternative explanations (such as differences in motivation, fatigue, satiety, stereotyped licking, and/or reward valuation) are not considered. 

Alternative explanations of our findings, such as differences in motivation, fatigue, satiety, stereotyped licking, and reward valuation have indeed been considered. We will revise the manuscript to present these points more clearly. 

Third, the mapping of the behavioral results onto high-level cognitive constructs is tenuous and overstated. The authors' interpretations suggest that they directly tested cognitive theories such as Load Theory, Adaptive Resonance Theory, or Weak Central Coherence. However, the experiments do not manipulate or measure variables that would allow such theories to be tested. More specific comments are included below.

This was not done intentionally. We do not claim to have tested the Load Theory; rather, inspired by it, we assessed behavioral patterns in our tactile categorization task. We agree that referring to the Adaptive Resonance Theory, which is based on artificial neural network models, might be misleading since we focus on behavioral results, and we will revise the text accordingly. However, our task allowed us to examine the impact of categorization on discrimination, confirming that Fmr1^-/yation can amplify perceptual differences between stimuli belonging to different categories and reduce perceived differences within a category in WT mice but not in the mice when low-salience stimuli were experienced. Finally, we do not claim to have tested the Weak Central Coherence theory, although our results suggest reduced use of categories in low-salience tactile discrimination. 

(1) The authors employ a two-choice behavioral task to assess forepaw tactile sensitivity in Fmr1 knockout mice. The data provide an interesting behavioral observation, but it is a descriptive study. Without mechanistic experiments, it is difficult to draw any conclusions, especially regarding top-down or bottom-up pathway dysfunctions. While the task design is elegant, the data remain correlational and do not advance our mechanistic understanding of Fmr1-related sensory and/or cognitive alterations. 

We agree with the reviewer that our current experiments are behavioral in nature and do not provide direct mechanistic evidence for top-down pathway dysfunction. Our goal was to carefully characterize tactile responses and behavioral patterns in Fmr1^-/y mice. The notion of “top-down” is used at the behavioral level, referring to the influence of higher-level cognitive processes (e.g., categorization, attention) on perception, rather than to underlying neural circuits. We will revise the manuscript to more clearly emphasize that our conclusions are based on behavioral observations, and we will frame mechanistic inferences as hypotheses rather than established findings. We will also explicitly note that future work using neural recordings or causal manipulations will be required to directly test these hypotheses.

We also note that identifying the precise top-down circuits involved will require extensive additional experimentation. For example, one would first need to pinpoint the specific top-down pathway that modulates the influence of categorization on discrimination without directly altering categorization itself. After such a circuit is identified, further work would then be needed to rescue or manipulate this pathway in the Fmr1^-/y model. These steps represent a substantial program of mechanistic research that, while important, goes well beyond the scope of the present study.

(2) The conclusions hinge on speculative inferences about "reduced top-down categorization influence" or "choice consistency bias," but no neural, circuit-level, or causal manipulations (e.g., optogenetics, pharmacology, targeted lesions, modeling) are used to support these claims. Without mechanistic data, the translational impact is limited. 

We recognize that “reduced top-down categorization influence” and “choice consistency bias” are based on behavioral observations. However, we respectfully disagree that this makes these constructs inherently speculative. Similar behavioral inferences have been applied in previous clinical studies to characterize cognitive tendencies (Soulières et al., 2007; Feigin et al., 2021). The translational impact of our work lies in the highly translational platform we have developed – and in highlighting the complexity of tactile measures and additional analyses that can be conducted in clinical studies.

We agree with the reviewer that the neural-based experiments would indeed provide valuable mechanistic insight into our observed behavioral alterations, and we believe future studies should therefore focus on their underlying neurobiological substrate.

We will revise the language throughout the manuscript to clarify that all conclusions are based on behavioral measures.  

(3) Statistical analysis: 

(a) Several central claims are based on "trends" rather than statistically significant effects (e.g., reduced task sensitivity, reduced across-category facilitation). Building major interpretive arguments on nonsignificant findings undermines confidence in the conclusions.  

Several trends are evident in complex measures, such as d’ analyses on task sensitivity or responses pooled across different amplitudes. Additional analyses revealed which component of these measures showed a statistically significant difference across genotypes, namely the low-salience incorrect choices accounting for low task sensitivity. We chose to present all analyses to be transparent and to highlight that commonly used complex measures (like d’ analyses) may mask important findings. In the text, we described p-values between 0.05 and 0.1 as observed trends without over-interpreting their significance. 

(b) The n number for both genotypes should be increased. In several experiments (e.g., Figure 1D, 2E), one animal appears to be an outlier. Considering the subtle differences between genotypes, such an outlier could affect the statistical results and subsequent interpretations. 

The number of mice used in each genotype group is consistent with standard practices in behavioral studies using mice and sensory tasks. We have performed effect size measures (e.g., Cohen’s d) alongside some of the statistical comparisons, showing a medium effect size (>0.5). 

As the reviewer correctly noted, no mice were excluded based on outlier analyses, since the observed variability reflects true biological differences rather than experimental or technical errors. We will reexamine our dataset for potential outliers. If any are identified, we will perform analyses both with and without the outlier and report any effects that are sensitive to single animals. These procedures and results will be explicitly described in the Methods and Results sections.

(c) The large number of comparisons across salience levels, categories, and trial histories raises concern for false positives. The manuscript does not clearly state how multiple comparisons were controlled.  

We thank the reviewer for raising this important point and we will include a clear statement on multiple comparisons in the Methods section. 

(d) The data in Figure 5, shown as separate panels per indentation value, are analyzed separately as ttests or Mann-Whitney tests. However, individual comparisons are inappropriate for this type of data, as these are repeated stimulus applications across a given session. The data should be analyzed together and post-hoc comparisons reported. Given the very subtle difference in miss rates across control and mutant mice for 'low-salience' stimulus trials, this is unlikely to be a statistically meaningful difference when analyzed using a more appropriate test. 

We thank the reviewer for raising this point. This was not done intentionally. A repeated-measures ANOVA on miss rates for low-salience stimuli during categorization confirmed that there are statistically significant differences both across stimulus amplitudes and between genotypes. Additional correction for multiple comparisons will be performed and explained in the Methods section.  

(4) Emphasis on theoretical models: The paper leans heavily on theories such as Adaptive Resonance Theory, Load Theory of Attention, and Weak Central Coherence, but the data do not actually test these frameworks in a rigorous way. The discussion should be reframed to highlight the potential relevance of these frameworks while acknowledging that the current data do not allow them to be assessed. 

As mentioned above, our goal was not to directly test these theories but rather to apply them within our translational framework. The Discussion section will be reframed to highlight that our findings are consistent with predictions from certain cognitive theories rather than implying that these frameworks were directly tested.

Reviewer #3 (Public review): 

Summary: 

Developing consistent and reliable biomarkers is critically important for developing new pharmacological therapies in autism spectrum disorders (ASDs). Altered sensory perception is one of the hallmarks of autism and has been recently added to DSM-5 as one of the core symptoms of autism. Touch is one of the fundamental sensory modalities, yet it is currently understudied. Furthermore, there seems to be a discrepancy between different studies from different groups focusing on tactile discrimination. It is not clear if this discrepancy can be explained by different experimental setups, inconsistent terminology, or the heterogeneity of sensory processing alterations in ASDs. The authors aim to investigate the interplay between tactile discrimination and cognitive processes during perceptual decisions. They have developed a forepaw-based 2-alternative choice task for mice and investigated tactile perception and learning in Fmr1-/y mice 

Strengths: 

There are several strengths of this task: translational relevance to human psychophysical protocols, including controlled vibrotactile stimulation. In addition to the experimental setup, there are also several interesting findings: Fmr1-/y mice demonstrated choice consistency bias, which may result in impaired perceptual learning, and enhanced tactile discrimination in low-salience conditions, as well as attentional deficits with increased cognitive load. The increase in the error rates for low salience stimuli is interesting. These observations, together with the behavioral design, may have a promising translational potential and, if confirmed in humans, may be potentially used as biomarkers in ASD. 

We appreciate the reviewer’s positive assessment of our study’s translational value and the importance of our behavioral findings.

Weaknesses: 

Some weaknesses are related to the lack of the original raster plots and density plots of licks under different conditions, learning rate vs time, and evaluation of the learning rate at different stages of learning. Overall, these data would help to answer the question of whether there are differences in learning strategies or neural circuit compensation in Fmr1-/y mice. It is also not clear if reversal learning is impaired in Fmr1-/y mice.  

We thank the reviewer for these helpful suggestions. We agree that visualizing behavioral patterns, such as raster and density plots of licks, as well as learning rate over time, could provide additional insights into learning dynamics. This analysis will be conducted and added into the revised manuscript.

There was no assessment of reversal learning in Fmr1^-/y mice in this study. While it is an interesting and important question based on previous findings in preclinical and clinical studies, it falls outside the scope of the current manuscript.    

Feigin H, Shalom-Sperber S, Zachor DA, Zaidel A (2021) Increased influence of prior choices on perceptual decisions in autism. Elife 10.

Soulières I, Mottron L, Saumier D, Larochelle S (2007) At ypical categorical perception in autism: Autonomy of discrimination? J Autism Dev Disord 37:481–490.

Author response:

Reviewer #1 (Public review):

Cognitive Load and Task-Switching Components:

We agree that cognitive load is multi-faceted and encompasses dimensions not fully captured in our present models, including domain and rule switching. For the revision, we will explicitly model these components in the statistical analyses by incorporating predictors reflecting attended domain switching and rule complexity, as suggested. We will also explain our inclusion of n-back reaction predictors and justify their relationship with theoretical constructs of executive function. Full details of coding schemes will be provided.

Modeling Entropy and Surprisal:

We appreciate the reviewer’s suggestion to further explain the distinction between entropy (predictive uncertainty) and surprisal (integration difficulty), and acknowledge that our treatment of entropy warrants extension. In the revision, we will expand the results and discussion on entropy, providing clearer theoretical motivation for its inclusion and conducting supplementary analyses to examine its role alongside surprisal.

Replicability of Findings:

We note the concern regarding two-way vs. three-way interactions in model replication. In the revised manuscript, we will report robustness analyses on subsets of our data (e.g., matched age and education groups), clarify degrees of freedom and group sizes, and transparently report any discrepancies.

Predictors and Statistical Modeling:

We will add clarifications on predictor selection, data structure, and rationale for model hierarchy. The functions of d-prime, comprehension accuracy, and performance modeling will be described in more detail, including discussion of block-level vs. participant-level effects.

Reviewer #2 (Public review):

Distinction Between Prediction and Predictability:

We recognize the importance of clearly communicating the difference between prediction and predictability, as well as integration-based vs. prediction-based effects. We will clarify these distinctions throughout the introduction, methods, and discussion sections, citing the relevant theoretical literature (e.g., Pickering & Gambi 2018; Federmeier 2007; Staub 2015; Frisson 2017).

Aging, Corpus Predictability, and Individual Differences:

We appreciate the critical point regarding age, corpus-based predictability, and potential cohort effects in language model estimates. In the revision, we will provide conceptual clarifications on how surprisal and entropy might differ for different age groups and discuss limitations in extrapolating these metrics to participant-specific predictions. The limitations inherent in relying on LLM-derived estimates and text materials will be more directly addressed.

Coverage of Literature and Paradigms:

We will broaden the literature review as requested, particularly on the N400 effects and behavioral traditions in prediction research. These additions should help contextualize the present work within both neuroscience and psycholinguistics.

Experimental Context and Predictability Metrics:

We will address concerns regarding the context window for prediction estimation, describing more precisely how context was defined and whether broader textual cues may improve predictability metrics.

References

Pickering, M.J. & Gambi, C. (2018). Predicting while comprehending language: A theory and review. Psychol. Bull., 144(10), 1002–1044.

Federmeier, K.D. (2007). Thinking ahead: The role and roots of prediction in language comprehension. Psychophysiology, 44(4), 491–505.

Frisson, S. (2017). Can prediction explain the lexical processing advantage for short words? J. Mem. Lang., 95, 121–138.\

Staub, A. (2015). The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation. Lang. Linguist. Compass, 9(8), 311–327.Huettig, F. & Mani, N. (2016). Is prediction necessary to understand language? Probably not. Trends Cogn. Sci., 20(10), 484–492.We appreciate the reviewers’ constructive comments and believe their suggestions will meaningfully strengthen the paper. Our planned revisions will address each of the above points with additional analyses, clarifications, and expanded discussion.

Author response:

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

Reviewer #1 (Public review):

(1) The manuscript is quite dense, with some concepts that may prove difficult for the non-specialist. I recommend spending a few more words (and maybe some pictures) describing the difference between task-relevant and task-irrelevant planes. Nice technique, but not instantly obvious. Then we are hit with "stimulus-related", which definitely needs some words (also because it is orthogonal to neither of the above). 

We agree that the original description of the planes was too terse and have expanded on this in the revised manuscript.

Line 85 - To test the influence of attention, trials were sorted according to two spatial reference planes, based on the location of the stimulus: task-related and task-unrelated (Fig. 1b). The task-related plane corresponded to participants’ binary judgement (Fig 1b, light cyan vertical dashed line) and the task-unrelated plane was orthogonal to this (Fig 1b, dark cyan horizontal dashed line). For example, if a participant was tasked with performing a left-or-right of fixation judgement, then their task-related plane was the vertical boundary between the left and right side of fixation, while their task-unrelated plane was the horizontal boundary. The former (left-right) axis is relevant to their task while the latter (top-bottom) axis is orthogonal and task irrelevant. This orthogonality can be leveraged to analyze the same data twice (once according to the task-related plane and again according to the taskunrelated plane) in order to compare performance when the relative location of an event is either task relevant or irrelevant.

Line 183 - whereas task planes were constant, the stimulus-related plane was defined by the location of the stimulus on the previous trial, and thus varied from trial to trial. That is, on each trial, the target is considered a repeat if it changes location by <|90°| relative to its location on the previous trial, and an alternate if it moves by >|90°|.

(2) While I understand that the authors want the three classical separations, I actually found it misleading. Firstly, for a perceptual scientist to call intervals in the order of seconds (rather than milliseconds), "micro" is technically coming from the raw prawn. Secondly, the divisions are not actually time, but events: micro means one-back paradigm, one event previously, rather than defined by duration. Thirdly, meso isn't really a category, just a few micros stacked up (and there's not much data on this). And macro is basically patterns, or statistical regularities, rather than being a fixed time. I think it would be better either to talk about short-term and long-term, which do not have the connotations I mentioned. Or simply talk about "serial dependence" and "statistical regularities". Or both. 

We agree that the temporal scales defined in the current study are not the only way one could categorize perceptual time. We also agree that by using events to define scales, we ignore the influence of duration. In terms of the categories, we selected these for two reasons: 1) they conveniently group previous phenomena, and 2) they loosely correspond to iconic-, short- and long-term memory. We agree that one could also potentially split it up into two categories (e.g., short- and long-term), but in general, we think any form of discretization will have limitations. For example, Reviewer 1 suggests that the meso category is simply a few micros stacked together. However, there is a rich literature on phenomena associated with sequences of an intermediate length that do not appear to be entirely explained by stacking micro effects (e.g., sequence learning and sequential dependency). We also find that when controlling for micro level effects, there are clear meso level effects. Also, by the logic that meso level effects are just stacked micro effects, one could also argue the same for macro effects. We don’t think this argument is incorrect, rather we think it exemplifies the challenge of discretising temporal scales. Ultimately, the current study was aimed to test whether seemingly disparate phenomena identified in previous work could be captured by unifying principles. To this end we found that these categories were the most useful. However, we have included a “Limitations and future directions” section in the Discussion of the revised manuscript that acknowledges both the alternative scheme proposed by Reviewer 1, and the value of extending this work to consider the influence of duration (as well as events).

Line 488 - Limitations and future directions. One potential limitation of the current study is the categorization of temporal scales according to events, independent of the influence of event duration. While this simplification of time supports comparison between different phenomena associated with each scale (e.g., serial dependence, sequential dependencies, statistical learning), future work could investigate the role of duration to provide a more comprehensive understanding of the mechanisms identified in the current study.

Related to this, while the temporal scales applied here conveniently categorized known sensory phenomena, and partially correspond to iconic-, short-, and long-term memory, they are but one of multiple ways to delineate time. For example, temporal scales could alternatively be defined simply as short- and long-term (e.g., by combining micro and meso scale phenomena). However, this could obscure meaningful differences between phenomena associated with sensory persistence and short-term memory, or qualitative differences in the way that shortsequences of events are processed.

(3) More serious is the issue of precision. Again, this is partially a language problem. When people use the engineering terms "precision" and "accuracy" together, they usually use the same units, such as degrees. Accuracy refers to the distance from the real position (so average accuracy gives bias), and precision is the clustering around the average bias, usually measured as standard deviation. Yet here accuracy is percent correct: also a convention in psychology, but not when contrasting accuracy with precision, in the engineering sense. I suggest you change "accuracy" to "percent correct". On the other hand, I have no idea how precision was defined. All I could find was: "mixture modelling was used to estimate the precision and guess rate of reproduction responses, based on the concentration (k) and height of von Mises and uniform distributions, respectively". I do not know what that means.

In the case of a binary decision, is seems reasonable to use the term “accuracy” to refer to the correspondence between the target state and the response on a task. However, we agree that while our (main) task is binary, the target is not and nor is the secondary task. We thank the reviewer for bringing this to our attention, as we agree that this will be a likely cause of confusion. To avoid confusion we have specifically referred to “task accuracy” throughout the revised manuscript.

With regards to precision, our measure of precision is consistent with what Reviewer 1 describes as such, i.e., the clustering of responses. In particular, the von Mises distribution is essentially a Gaussian distribution in circular space, and the kappa parameter defines the width of the distribution, regardless of the mean, with larger values of kappa indicating narrower (more precise) distributions. We could have used standard deviation to assess precision; however, this would incorrectly combine responses on which participants failed to encode the target (e.g., because of a blink) and were simply guessing. To account for these trials, we applied mixture modelling of guess and genuine responses to isolate the precision of genuine responses, as is standard in the visual working memory literature. However, we agree that this was not sufficiently described in the original manuscript and have elaborated on this method in the revised version.

Line 598 - From the reproduction task, we sought to estimate participant’s recall precision. It is likely that on some trials participants failed to encode the target and were forced to make a response guess. To isolate the recall precision from guess responses, we used mixture modelling to estimate the precision and guess rate of reproduction responses, based on the concentration (k) and height of von Mises and uniform distributions, respectively (Bays et al., 2009). The k parameter of the von Mises distribution reflects its width, which indicates the clustering of responses around a common location.

(4) Previous studies show serial dependence can increase bias but decrease scatter (inverse precision) around the biased estimate. The current study claims to be at odds with that. But are the two measures of precision relatable? Was the real (random) position of the target subtracted from each response, leaving residuals from which the inverse precision was calculated? (If so, the authors should say so..) But if serial dependence biases responses in essentially random directions (depending on the previous position), it will increase the average scatter, decreasing the apparent precision. 

Previous studies have shown that when serial dependence is attractive there is a corresponding increase in precision around small offsets from the previous item (citations). Indeed, attractive biases will lead to reduced scattering (increased precision) around a central attracter. Consistent with previous studies, and this rational, we also found an attractive bias coupled with increased precision. To clarify, for the serial dependency analysis, we calculated bias and precision by binning reproduction responses according to the offset between the current and previous target and then performing the same mixture modelling described above to estimate the mean (bias) and kappa (precision) parameters of the von Mises distribution fit to the angular errors. This was not explained in the original manuscript, so we thank Reviewer 1 for bringing this to our attention and have clarified the analysis in the revised version.

Line 604 - For the serial dependency analysis, we calculated bias and precision by binning reproduction responses according to the angular offset between the current and previous target and then performing mixture modelling to estimate the mean (bias) and k (precision) parameters of the von Mises distribution.

(5) I suspect they are not actually measuring precision, but location accuracy. So the authors could use "percent correct" and "localization accuracy". Or be very clear what they are actually doing. 

As explained in our response to Reviewer 1’s previous comment, we are indeed measuring precision.

Reviewer #2 (Public review):

(1) The abstract should more explicitly mention that conclusions about feedforward mechanisms were derived from a reanalysis of an existing EEG dataset. As it is, it seems to present behavioral data only.

It is not clear what relevance the fact that the data has been analyzed previously has to the results of the current study. However, we do think that it is important to be clear that the EEG recordings were collected separately from the behavioural and eyetracking data, so we have clarified this in the revised abstract.

Line 7 - By integrating behavioural and pupillometry recordings with electroencephalographical recordings from a previous study, we identify two distinct mechanisms that operate across all scales.

(2) The EEG task seems quite different from the others, with location and color changes, if I understand correctly, on streaks of consecutive stimuli shown every 100 ms, with the task involving counting the number of target events. There might be different mechanisms and functions involved, compared to the behavioral experiments reported. 

As stated above, we agree that it is important that readers are aware that the EEG recordings were collected separately to the behavioural and eyetracking data. We were forthright about this in the original manuscript and how now clarified this in the revised abstract. We agree that collecting both sets of data in the same experiment would be a useful validation of the current results and have acknowledged this in a new Limitations and future directions section of the Discussion of the revised manuscript.

Line 501 - Another limitation of the current study is that the EEG recordings were collected in the separate experiment to the behavioural and pupillometry data. The stimuli and task were similar between experiments, but not identical. For example, the EEG experiment employed coloured arc stimuli presented at a constant rate of ~3.3 Hz and participants were tasked with counting the number of stimuli presented at a target location. By contrast, in the behavioural experiment, participants viewed white blobs presented at an average rate of ~2.8 Hz and performed a binary spatial task coupled with an infrequent reproduction task. An advantage of this was that the sensory responses to stimuli in the EEG recordings were not conflated with motor responses; however, future work combining these measures in the same experiment would serve as a validation for the current results.

(3) How is the arbitrary choice of restricting EEG decoding to a small subset of parieto-occipital electrodes justified? Blinks and other artifacts could have been corrected with proper algorithms (e.g., ICA) (Zhang & Luck, 2025) or even left in, as decoders are not necessarily affected by noise. Moreover, trials with blinks occurring at the stimulus time should be better removed, and the arbitrary selection of a subset of electrodes, while reducing the information in input to the decoder, does not account for trials in which a stimulus was missed (e.g., due to blinks).

Electrode selection was based on several factors: 1) reduction of eye movement/blink artifacts (as noted in the original manuscript), 2) consistency with the previous EEG study (Rideaux, 2024) and other similar decoding studies (Buhmann et al., 2024; Harrison et al., 2023; Rideaux et al., 2023), 3) improved signal-to-noise by including only sensors that carry the most position information (as shown in Supplementary Figure 1a and the previous EEG study). We agree that this was insufficiently explained in the original manuscript and have clarified our sensor selection in the revised version.

Line 631 - We only included the parietal, parietal-occipital, and occipital sensors in the analyses to i) reduce the influence of signals produced by eye movements, blinks, and non-sensory cortices, ii) for consistency with similar previous decoding studies (Buhmann et al., 2024; Rideaux, 2024; Rideaux et al., 2025), and iii) to improve decoding accuracy by restricting sensors to those that carried spatial position information (Supplementary Fig. 1a).

(4) The artifact that appears in many of the decoding results is puzzling, and I'm not fully convinced by the speculative explanation involving slow fluctuations. I wonder if a different high-pass filter (e.g., 1 Hz) might have helped. In general, the nature of this artifact requires better clarification and disambiguation.

We agree that the nature of this artifact requires more clarification and disambiguation. Due to relatively slow changes in the neural signal, which are not stimulus-related, there is a degree of temporal autocorrelation in the recordings. This can be filtered out, for example, by using a stricter high-pass filter; however, we tried a range of filters and found that a cut-off of at least 0.7 Hz is required to remove the artifact, and even a filter of 0.2 Hz introduces other (stimulus-related) artifacts, such as above-chance decoding prior to stimulus onset. These stimulus-related artifacts are due to the temporal smearing of data, introduced by the filtering, and have a more pronounced and complex influence on the results and are more difficult to remove through other means, such as the baseline correction applied in the original manuscript.

The temporal autocorrelation is detected by the decoder during training and biases it to classify/decode targets that are presented nearby in time as similar. That is, it learns the neural pattern for a particular stimulus location based on the activity produced by the stimulus and the temporal autocorrelation (determined by slow stimulus unrelated fluctuations). The latter only accounts for a relatively smaller proportion of the variance in the neural recordings under normal circumstances and would typically go undetected when simply plotting decoding accuracy as a function of position. However, it becomes weakly visible when decoding accuracy is plotted as a function of distance from the previous target, as now the bias (towards temporally adjacent targets) aligns with the abscissa. Further, it becomes highly visible when the stimulus labels are shuffled, as now the decoder can only learn from the variance associated with the temporal autocorrelation (and not from the activity produced by the stimulus).

In the linear discriminant analysis, this led to temporally proximal items being more likely to be classified as on the same side. This is why there is above-chance performance for repeat trials (Supplementary Figure 2b), and below-chance performance for alternate trials, even when the labels are shuffled – the temporal autocorrelation produces a general bias towards classifying temporally proximate stimuli as on the same side, which selectively improves the classification accuracy of repeat trials. Fortunately, the bias is relatively constant as a function of time within the epoch and is straightforward to estimate by shuffling the labels, which means that it can be removed through a baseline correction. However, to further demonstrate that the autocorrelation confound cannot account for the differences observed between repeat and alternate trials in the micro classification analysis, we now additionally show the results from a more strictly filtered version of the data (0.7 Hz). These results show a similar pattern as the original, with the additional stimulusrelated artifacts introduced by the strict filter, e.g., above chance decoding prior to stimulus onset.

In the inverted encoding analysis, the same temporal autocorrelation manifests as temporally proximal trials being decoded as more similar locations. This is why there is increased decoding accuracy for targets with small angular offsets from the previous target, even when the labels are shuffled (Supplementary Figure 3c), because it is on these trials that the bias happens to align with the correct position. This leads to an attractive bias towards the previous item, which is most prominent when the labels are shuffled.

To demonstrate the phenomenon, we simulated neural recordings from a population of tuning curves and performed the inverted encoding analysis on a clean version of the data and a version in which we introduced temporal autocorrelation. We then repeated this after shuffling the labels. The simulation produced very similar results to those we observed in the empirical data, with a single exception: while precision in the simulated shuffled data was unaffected by autocorrelation, precision in the unshuffled data was clearly affected by this manipulation. This may explain why we did not find a correlation between the shuffled and unshuffled precision in the original manuscript. 

These results echo those from the classification analysis, albeit in a more continuous space. However, whereas in the classification analysis it was straightforward to perform a baseline correction to remove the influence of general temporal dependency, the more complex nature of the accuracy, precision, and bias parameters over the range of time and delta location makes this approach less appropriate. For example, the bias in the shuffled condition ranged from -180 to 180 degrees, which when subtracted from the bias in the unshuffled condition would produce an equally spurious outcome, i.e., the equal opposite of this extreme bias. Instead for the inverted encoding analysis, we used the data high-pass filtered at 0.7 Hz. As with the classification analysis, this removed the influence of general temporal dependencies, as indicated by the results of the shuffled data analysis (Supplementary Figure 3f), but it also temporally smeared the stimulus-related signal, resulting in above chance decoding accuracy prior to stimulus onset (Supplementary Figure 3d). However, given thar we were primarily interested in the pattern of accuracy, precision, and bias as a function of delta location, and less concerned with the precise temporal dynamics of these changes, which appeared relatively stable in the filtered data. Thus, this was the more suitable approach to removing the general temporal dependencies in the inverted encoding analysis and the one that is presented in Figure 3.

We have updated the revised manuscript in light of these changes, including a fuller description of the artifact and the results from the abovementioned control analyses.

Figure 3 updated.

Figure 3 caption - e) Decoding accuracy for stimulus location, from reanalysis of previously published EEG data (17). Inset shows the EEG sensors included in the analysis (blue dots), and black rectangles indicate the timing of stimulus presentations (solid: target stimulus, dashed: previous and subsequent stimuli). f) Decoding accuracy for location, as a function of time and D location. Bright colours indicate higher decoding accuracy; absolute accuracy values can be inferred from (e). g-i) Average location decoding  (g) accuracy, (h) precision, and (h) bias from 50 – 500 ms following stimulus onset. Horizontal bar in (e) indicates cluster corrected periods of significance; note, all time points were significantly above chance due to temporal smear introduced by strict high-pass filtering (see Supplementary Figure 3 for full details). Note, the temporal abscissa is aligned across (e & f). Shaded regions indicate ±SEM.

Line 218 - To further investigate the influence of serial dependence, we applied inverted encoding modelling to the EEG recordings to decode the angular location of stimuli. We found that decoding accuracy of stimulus location sharply increased from ~60 ms following stimulus onset (Fig. 3e). Note, to reduce the influence of general temporal dependencies, we applied a 0.7 Hz high-pass filter to the data, which temporally smeared the stimulus-related information, resulting in above chance decoding accuracy prior to stimulus presentation (for full details, see Supplementary Figure 3). To understand how serial dependence influences the representation of these features, we inspected decoding accuracy for location as a function of both time and D location (Fig. 3f). We found that decoding accuracy varied depending not only as a function of time, but also as a function of D location. To characterise this relationship, we calculated the average decoding accuracy from 50 ms until the end of the epoch (500 ms), as a function of D location (Fig. 3g). This revealed higher accuracy for targets with larger D location. We found a similar pattern of results for decoding precision (Fig. 3h). These results are consistent with the micro temporal context (behavioural) results, showing that targets that alternated were recalled more precisely. Lastly, we calculated the decoding bias as a function of D location and found a clear repulsive bias away from the previous item (Fig. 3i). While this result is inconsistent with the attractive behavioural bias, it is consistent with recent studies of serial dependence suggesting an initial pattern of repulsion followed by an attractive bias during the response period (20–22).

Line 726 - As shown in Supplementary Figure 3, we found the same general temporal dependencies in the decoding accuracy computed using inverted encoding that were found using linear discriminant classification. However, as a baseline correction would not have been appropriate or effective for the parameters decoded with this approach, we instead used a high-pass filter of 0.7 Hz to remove the confound, while being cautious about interpreting the timing of effects produced by this analysis due to the temporal smear introduced by the filter.

Supplementary Figure 2 updated.

Supplementary Figure 2 caption - Removal of general micro temporal dependencies in EEG responses. We found that there were differences in classification accuracy for repeat and alternate stimuli in the EEG data, even when stimulus labels were shuffled. This is likely due to temporal autocorrelation within the EEG data due to low frequency signal changes that are unrelated to the decoded stimulus dimension. This signal trains the decoder to classify temporally proximal stimuli as the same class, leading to a bias towards repeat classification. For example, in general, the EEG signal during trial one is likely to be more similar to that during trial two than during trial ten, because of low frequency trends in the recordings. If the decoder has been trained to classify the signal associated with trial one as a leftward stimulus, then it will be more likely to classify trial two as a leftward stimulus too. These autocorrelations are unrelated to stimulus features; thus, to isolate the influence of stimulus-specific temporal context, we subtracted the classification accuracy produced by shuffling the stimulus labels from the unshuffled accuracy (as presented in Figure 2e, f). We confirmed that using a stricter high-pass filter (0.7 Hz) removes this artifact, as indicated by the equal decoding accuracy between the two shuffled conditions. However, the stricter high-pass filter temporally smears the stimulus-related signal, which introduces other (stimulus-related) artifacts, e.g., above-chance decoding accuracy prior to stimulus presentation, that are larger and more complex, i.e., changing over time. Thus, we opted to use the original high pass filter (0.1 Hz) and apply a baseline correction. a) The uncorrected classification  accuracy along task related and unrelated planes. Note that these results are the same as the corrected version shown in Figure 2e, because the confound is only apparent when accuracy is grouped according to temporal context.

b) Same as (a), but split into repeat and alternate stimuli, along (left) task-related and (right) unrelated planes. Classification  accuracy when labels are shuffled is also shown. Inset in (a) shows the EEG sensors included in the analysis (blue dots). (c, d) Same as (a, b), but on data filtered using a 0.7 Hz high-pass filter. Black rectangles indicate the timing of stimulus presentations (solid: target stimulus, dashed: previous and subsequent stimuli). Shaded regions indicate ±SEM.

Supplementary Figure 3 updated.

Supplementary Figure 3 caption - Removal of general temporal dependencies in EEG responses for inverted encoding analyses. As described in Methods - Neural Decoding, we used inverted encoding modelling of EEG recordings to estimate the decoding accuracy, precision, and bias of stimulus location. Just as in the linear discriminant classification analysis, we also found the influence of general temporal dependencies in the results produced by the inverted encoding analysis. In particular, there was increased decoding accuracy for targets with low D location. This was weakly evident in the period prior to stimulus presentation, but clearly visible when the labels were shuffled. These results are mirror those from the classification analysis, albeit in a more continuous space. However, whereas in the classification analysis it was straightforward to perform a baseline correction to remove the influence of general temporal dependency, the more complex nature of the accuracy, precision, and bias parameters over the range of time and D location makes this approach less appropriate. For example, the bias in the shuffled condition ranged from -180° to 180°, which when subtracted from the bias in the unshuffled condition would produce an equally spurious outcome, i.e., the equal opposite of this extreme bias. Instead for the inverted encoding analysis, we used the data high-pass filtered at 0.7 Hz. As with the classification analysis, this significantly reduced the influence of general temporal dependencies, as indicated by the results of the shuffled data analysis, but it also temporally smeared the stimulus-related signal, resulting in above chance decoding accuracy prior to stimulus onset. However, we were primarily interested in the pattern of accuracy, precision, and bias as a function of D location, and less concerned with the precise temporal dynamics of these changes. Thus, this was the more suitable approach to removing the general temporal dependencies in the inverted encoding analysis and the one that is presented in Figure 3. (a) Decoding accuracy as a function of time for the EEG data filtered using a 0.1 Hz high-pass filter. Inset shows the EEG sensors included in the analysis (blue dots), and black rectangles indicate the timing of stimulus presentations (solid: target stimulus, dashed: previous and subsequent stimuli). (b, c) The same as (a), but as a function of time and D location for (b) the original data and (c) data with shuffled labels. (d-f) Same as (a-c), but for data filtered using a 0.7 Hz high-pass filter. Shaded regions in (a, d) indicate ±SEM. Horizontal bars in (a, d) indicate cluster corrected periods of significance; note, all time points in (d) were significantly above chance. Note, the temporal abscissa is vertically aligned across plots (a-c & d-f).

In the process of performing these additional analyses and simulations, we became aware that the sign of the decoding bias in the inverted encoding analyses had been interpreted in the wrong direction. That is, where we previously reported an initial attractive bias followed by a repulsive bias relative to the previous target, we have in fact found the opposite, an initial repulsive bias followed by an attractive bias relative to the previous target. Based on the new control analyses and simulations, we think that the latter attractive bias was due to general temporal dependencies. That is, in the filtered data, we only observe a repulsive bias. While the bias associated with serial dependence was not a primary feature of the study, this (somewhat embarrassing) discovery has led to reinterpretation of some results relating to serial dependence. However, it is encouraging to see that our results now align with those of recent studies (Fischer et al., 2024; Luo et al., 2025; Sheehan et al. 2024).

Line 385 - Our corresponding EEG analyses revealed better decoding accuracy and precision for stimuli preceded by those that were different and a bias away from the previous stimulus. These results are consistent with finding that alternating stimuli are recalled more precisely. Further, while the repulsive pattern of biases is inconsistent with the observed behavioural attractive biases, it is consistent with recent work on serial dependence indicating an initial period of repulsion, followed by an attractive bias during the response period (20–22). These findings indicate that serial dependence and first-order sequential dependencies can be explained by the same underlying principle.

(5) Given the relatively early decoding results and surprisingly early differences in decoding peaks, it would be useful to visualize ERPs across conditions to better understand the latencies and ERP components involved in the task.

A rapid presentation design was used in the EEG experiment, and while this is well suited to decoding analyses, unfortunately we cannot resolve ERPs because the univariate signal is dominated by an oscillation at the stimulus presentation frequency (~3 Hz). We agree that this could be useful to examine in future work.

(6) It is unclear why the precision derived from IEM results is considered reliable while the accuracy is dismissed due to the artifact, given that both seem to be computed from the same set of decoding error angles (equations 8-9).

This point has been addressed in our response to point (4).

(7) What is the rationale for selecting five past events as the meso-scale? Prior history effects have been shown to extend much further back in time (Fritsche et al., 2020). 

We used five previous items in the meso analyses to be consistent with previous research on sequential dependencies (Bertelson, 1961; Gao et al., 2009; Jentzsch & Sommer, 2002; Kirby, 1976; Remington, 1969). However, we agree that these effects likely extend further and have acknowledged this in the revied version of the manuscript.

Line 240 - Higher-order sequential dependences are an example of how stimuli (at least) as far back as five events in the past can shape the speed and task accuracy of responses to the current stimulus (9, 10); however, note that these effects have been observed for more than five events (20).

(8) The decoding bias results, particularly the sequence of attraction and repulsion, appear to run counter to the temporal dynamics reported in recent studies (Fischer et al., 2024; Luo et al., 2025; Sheehan & Serences, 2022). 

This point has been addressed in our response to point (4).

(9) The repulsive component in the decoding results (e.g., Figure 3h) seems implausibly large, with orientation differences exceeding what is typically observed in behavior. 

As noted in our response to point (4), this bias was likely due to the general temporal dependency confound and has been removed in the revised version of the manuscript.

(10) The pattern of accuracy, response times, and precision reported in Figure 3 (also line 188) resembles results reported in earlier work (Stewart, 2007) and in recent studies suggesting that integration may lead to interference at intermediate stimulus differences rather than improvement for similar stimuli (Ozkirli et al., 2025).

Thank you for bringing this to our attention, we have acknowledged this in the revised manuscript.

Line 197 - Consistent with our previous binary analysis, and with previous work (19), we also found that responses were faster and more accurate when D location was small (Fig. 3b, c).

(11) Some figures show larger group-level variability in specific conditions but not others (e.g., Figures 2b-c and 5b-c). I suggest reporting effect sizes for all statistical tests to provide a clearer sense of the strength of the observed effects. 

Yes, as noted in the original manuscript, we find significant differences between the variance task-related and -unrelated conditions. We think this is due to opposing forces in the task-related condition: 

“The increased variability of response time differences across the taskrelated plane likely reflects individual differences in attention and prioritization of responding either quickly or accurately. On each trial, the correct response (e.g., left or right) was equally probable. So, to perform the task accurately, participants were motivated to respond without bias, i.e., without being influenced by the previous stimulus. We would expect this to reduce the difference in response time for repeat and alternate stimuli across the taskrelated plane, but not the task-unrelated plane. However, attention may amplify the bias towards making faster responses for repeat stimuli, by increasing awareness of the identity of stimuli as either repeats or alternations (17). These two opposing forces vary with task engagement and strategy and thus would be expected produce increased variability across the task-related plane.” We agree that providing effect sizes may provided a clearer sense of the observed effects and have done so in the revised version of the manuscript.

Line 739 - For Wilcoxon signed rank tests, the rank-biserial correlation (r) was calculated as an estimate of effect size, where 0.1, 0.3, and 0.5 indicate small, medium, and large effects, respectively (54). For Friedman’s ANONA tests, Kendal’s W was calculated as an estimate of effect size, where 0.1, 0.3, and 0.5 indicate small, medium, and large effects, respectively (55).

(12) The statement that "serial dependence is associated with sensory stimuli being perceived as more similar" appears inconsistent with much of the literature suggesting that these effects occur at post-perceptual stages (Barbosa et al., 2020; Bliss et al., 2017; Ceylan et al., 2021; Fischer et al., 2024; Fritsche et al., 2017; Sheehan & Serences, 2022). 

In light of the revised analyses, this statement has been removed from the manuscript.

(13) If I understand correctly, the reproduction bias (i.e., serial dependence) is estimated on a small subset of the data (10%). Were the data analyzed by pooling across subjects?

The dual reproduction task only occurred on 10% of trials. There were approximately 2000 trials, so ~200 reproduction responses. For the micro and macro analyses, this was sufficient to estimate precision within each of the experimental conditions (repeat/alternate, expected/unexpected). However, it is likely that we were not able to reproduce the effect of precision at the meso level across both experiments because we lacked sufficient responses to reliably estimate precision when split across the eight sequence conditions. Despite this, the data was always analysed within subjects.

(14) I'm also not convinced that biases observed in forced-choice and reproduction tasks should be interpreted as arising from the same process or mechanism. Some of the effects described here could instead be consistent with classic priming. 

We agree that the results associated with the forced-choice task (response time task accuracy) were likely due to motor priming, but that a separate (predictive) mechanism may explain the (precision) results associated with the reproduction task. These are two mechanisms we think are operating across the three temporal scales investigated in the current study.

Reviewing Editor Comments:

(1) Clarify task design and measurement: The dense presentation makes it difficult to understand key design elements and their implications. Please provide clearer descriptions of all task elements, and how they relate to each other (EEG vs. behaviour, stimulus plane vs. TR and TU plane, reproduction vs. discrimination and role of priming), and clearly explain how key measures were computed for each of these (e.g., precision, accuracy, reproduction bias).

In the revised manuscript, we have expanded on descriptions of the source and nature of the data (behavioural and EEG), the different planes analyzed in the behavioural task, and how key metrics (e.g., precision) were computed.

(2) Offer more insight into underlying data, including original ERP waveforms to aid interpretation of decoding results and the timing of effects. In particular, unpack the decoding temporal confound further.

In the revised manuscript, we have considerably offered more insight into the decoding results, in particular, the nature of the temporal confound. We were unable to assess ERPs due to the rapid presentation design employed in the EEG experiment.

(3) Justify arbitrary choices such as electrode selection for EEG decoding (e.g., limiting to parieto-occipital sensors), number of trials in meso scale, and the time terminology itself.

In the revised manuscript, we have clarified the reasons for electrode selection.

(3) Discuss deviations from literature: Several findings appear to contradict or diverge from previous literature (e.g., effects of serial dependence). These discrepancies could be discussed in more depth. 

Upon re-analysis of the serial dependence bias and removal of the temporal confound, the results of the revised manuscript now align with those from previous literature, which has been acknowledged.

Reviewer #1 (Recommendations for the authors):

(1) would like to use my reviewer's prerogative to mention a couple of relevant publications. 

Galluzzi et al (Journal of Vision, 2022) "Visual priming and serial dependence are mediated by separate mechanisms" suggests exactly that, which is relevant to this study.

Xie et al. (Communications Psychology, 2025) "Recent, but not long-term, priors induce behavioral oscillations in peri-saccadic vision" also seems relevant to the issue of different mechanisms. 

Thank you for bringing these studies to our attention. We agree that they are both relevant have referenced both appropriately in the revised version of the manuscript.

Reviewer #2 (Recommendations for the authors): 

(1) I find the discussion on attention and awareness (from line 127 onward) somewhat vague and requiring clarification.

We agree that this statement was vague and referred to “awareness” without operationation. We have revised this statement to improve clarity.

Line 135 - However, task-relatedness may amplify the bias towards making faster responses for repeat stimuli, by increasing attention to the identity of stimuli as either repeats or alternations (17).

(2) Line 140: It's hard to argue that there are expectations that the image of an object on the retina is likely to stay the same, since retinal input is always changing. 

We agree that retinal input is often changing, e.g., due to saccades, self-motion, and world motion. However, for a prediction to be useful, e.g., to reduce metabolic expenditure or speed up responses, it must be somewhat precise, so a prediction that retinal input will change is not necessarily useful, unless it can specify what it will change to. Given retinal input of x at time t, the range of possible values of x at time t+1 (predicting change) is infinite. By contrast, if we predict that x=x at time t+1 (no change), then we can make a precise prediction. There is, of course, other information that could be used to reduce the parameter space of predicted change from x at time t, e.g., the value of x at time t-1, and we think this drives predictions too. However, across the infinite distribution of changes from x, zero change will occur more frequently than any other value, so we think it’s reasonable to assert that the brain may be sensitive to this pattern.

(3) Line 564: The gambler's fallacy usually involves sequences longer than just one event.

Yes, we agree that this phenomenon is associated with longer sequences. This section of the manuscript was in regards to previous findings that were not directly relevant to the current study and has been removed in the revised version.

(4) In the shared PDF, the light and dark cyan colors used do not appear clearly distinguishable. 

I expect this is due to poor document processing or low-quality image embeddings. I will check that they are distinguishable in the final version.

References: 

Barbosa, J., Stein, H., Martinez, R. L., Galan-Gadea, A., Li, S., Dalmau, J., Adam, K. C. S., Valls-Solé, J., Constantinidis, C., & Compte, A. (2020). Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory. Nature Neuroscience, 23(8), Articolo 8. https://doi.org/10.1038/s41593-020-0644-4

Bliss, D. P., Sun, J. J., & D'Esposito, M. (2017). Serial dependence is absent at the time of perception but increases in visual working memory. Scientific reports, 7(1), 14739. 

Ceylan, G., Herzog, M. H., & Pascucci, D. (2021). Serial dependence does not originate from low-level visual processing. Cognition, 212, 104709. https://doi.org/10.1016/j.cognition.2021.104709

Fischer, C., Kaiser, J., & Bledowski, C. (2024). A direct neural signature of serial dependence in working memory. eLife, 13. https://doi.org/10.7554/eLife.99478.1

Fritsche, M., Mostert, P., & de Lange, F. P. (2017). Opposite effects of recent history on perception and decision. Current Biology, 27(4), 590-595. 

Fritsche, M., Spaak, E., & de Lange, F. P. (2020). A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception. eLife, 9, e55389. https://doi.org/10.7554/eLife.55389

Gekas, N., McDermott, K. C., & Mamassian, P. (2019). Disambiguating serial effects of multiple timescales. Journal of vision, 19(6), 24-24. 

Luo, M., Zhang, H., Fang, F., & Luo, H. (2025). Reactivation of previous decisions repulsively biases sensory encoding but attractively biases decision-making. PLOS Biology, 23(4), e3003150. https://doi.org/10.1371/journal.pbio.3003150

Ozkirli, A., Pascucci, D., & Herzog, M. H. (2025). Failure to replicate a superiority effect in crowding. Nature Communications, 16(1), 1637. https://doi.org/10.1038/s41467025-56762-5

Sheehan, T. C., & Serences, J. T. (2022). Attractive serial dependence overcomes repulsive neuronal adaptation. PLoS biology, 20(9), e3001711. 

Stewart, N. (2007). Absolute identification is relative: A reply to Brown, Marley, and

Lacouture (2007).  Psychological  Review, 114, 533-538. https://doi.org/10.1037/0033-295X.114.2.533

Treisman, M., & Williams, T. C. (1984). A theory of criterion setting with an application to sequential dependencies. Psychological review, 91(1), 68. 

Zhang, G., & Luck, S. J. (2025). Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals. NeuroImage, 316, 121304. https://doi.org/10.1016/j.neuroimage.2025.121304

Author response:

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

Reviewer #1 (Recommendations for the authors):

Although this study is rigorous and the paper is well-written, I have a few concerns that the authors should address before publication.

(1) Cellular levels of protein ADP-ribosylation should be analyzed using anti-ADPR antibodies following infection, both with and without Mac1 and AVI-4206 treatment. While the authors have provided impressive in vivo data, these experiments could ideally be conducted in mice. However, I would be amenable to these analyses being performed in human airway organoids, as they demonstrate clear phenotypes following AVI-4206 treatment post-infection. For a more in-depth exploration, the authors could consider affinity purifying ADP-ribosylated proteins and identifying them via mass spectrometry. I would find it particularly compelling if this approach revealed components of the NF-kB signaling pathway, given the intriguing results presented in Fig. 5. I am also curious if there are differences in ADP ribosylated proteins when comparing Mac1 KO SARS-C0V-2 to AVI-4206 treatment.

We note that despite the recent flurry of activity around Mac1, there is a surprising lack of public data on overall ADPr levels or targets. While we will address the literature precedence for PARP14 signals specifically below (Reviewer 2 point (h)) by immunofluorescence, we note that overall levels have not been characterized biochemically previously. Recent PARP14 papers and the ASAP AViDD preprint show changes by immunofluorescence only: and the evidence in that preprint is quite modest - see Figure 7B - https://pmc.ncbi.nlm.nih.gov/articles/PMC11370477/.

We suspect the difficulty in tracking changes biochemically is due to multiple factors that influence the overall detectability and reproducibility. First, with regard to detectability - it is quite possible that only a small change in the ADPr status of a small number of targets is responsible for the phenotypes in vivo. Virus levels are very low in the organoid system and the variability in ADPr levels from tissue samples from in vivo experiments is high. Given the difficulty in translating back to cellular models, this problem is therefore magnified further. Second, with regard to reproducibility - we observe a great deal of reagent dependence on ADPr signals by Western blot+/- Mac1 expression in both cellular and tissue lysates (including when stimulated with H2O2, interferon, or during viral infection). Similarly, we do not observe reproducible proteins that pulldown with Mac1 when assayed by mass spectrometry. It is quite likely that these issues are a result of tissue/sample preparation that results in a loss of the ADPr modification during preparation (especially for acidic residue modifications). This also explains the reliance on IF assays in the PARP14 literature. A very good discussion of these issues is also contained in this paper: https://doi.org/10.1042/BSR20240986.

Nonetheless we have attempted one final experiment. Here, we have measured ADPr modification of cellular lysates upon uninfected conditions as well as upon infection with either WT or N40D mutant virus. For all conditions, this was done with or without treatment of cells with 100 μM of AVI-4206. Measurement of ADPr modifications by western blot using a  pan-ADPr antibody revealed a single prominent band with a molecular weight of ~130kDa, that showed a uniform increase in signal upon treatment of cells with AVI-4206 regardless of infection status. While this general trend was also observed with the mono-ADPr antibody, it was not statistically significant in its regulation upon AVI-4206 treatment. We suspect that the major band observed in these western blots is PARP1, as upon enrichment of ADPr proteins from these lysates by Af1521 immunoprecipitation, we find PARP1 to be among the most abundant proteins detected within this molecular weight range. We note that there is a baseline increase in polyADPr detection upon infection of virus with WT Mac1 (relative to uninfected and virus with N40D) and further increase when treated with AVI-4206. This compound-dependent increase is paralleled in the uninfected and N40D conditions. The counterintuitive increase upon WT Mac1 virus infection, which should erase ADPr marks, and the compound-dependent increase in the uninfected condition suggest that there are many indirect effects on ADPr signalling dynamics in this experiment. These results are difficult to reconcile with the specificity profiling of AVI-4206 (Supplementary Figure5: Thermal proteome profiling in A549 cellular lysates). As mentioned above, the lack of consistent signal across reagents for ADPr detection and the timing of monitoring ADPr levels are additional complicating factors.

We added to the results:

“However, we observed no strong consistent signals of global pan-ADP-ribose (panADPr) or mono-ADP-ribose (monoADPr) accumulation in infected cells treated with AVI-4206 in immunoblot analyses (Supplementary Figure 8).”

Methods for experiment:

Calu3 cells were obtained from ATCC and cultured in Advanced DMEM (Gibco) supplemented with 2.5% FBS, 1x GlutaMax, and 1x Penicillin-Streptomycin at 37°C and 5% CO₂. 5x10⁶ cells were plated in 15-cm dishes and media was changed every 2-3 days until the cells were 80% confluent. The cells were treated with INFy 50 ng/mL (R&D Systems) w/without AVI-4206 100 μM. After 6 hours, the cells were infected with WA1 or WA1 NSP3 Mac1 N40D at a multiplicity of infection (MOI) of 1 for 36 hours. The cells were washed with PBS x 3 and scraped in Pierce IP Lysis Buffer (ThermoFisher) containing 1x HALT protease and phosphatase inhibitor mix (ThermoFisher) on ice. The lysate was stored at -80C until further processing.

The cell lysate was incubated for 5 minutes at room temperature with recombinant benzonase. Following incubation, the lysate was centrifuged at 13,000 rpm at 4°C for 20 minutes, and the supernatant was collected. The samples were then boiled for 5 minutes at 95°C in 1x NuPAGE LDS sample buffer (Invitrogen) with a final concentration of 1X NuPAGE sample reducing agent (Invitrogen). For the detection of ADPr levels in whole-cell lysates, the samples were subjected to SDS-PAGE and Immunoblotting. All primary and secondary antibodies (pan-ADP-ribose antibody (MABE1016, Millipore), Mono-ADP-ribose antibody (AbD33204, Bio-Rad), HRP-conjugated (Cell signaling), used at a 1:1000 dilution were diluted in 5% non-fat dry milk in TBST. Signals were detected by chemiluminescence (Thermo) and visualized using the ChemiDoc XRS+ System (Bio-Rad). Densitometric analysis was performed using Image Lab (Bio-Rad). Quantification was normalized to Actin. The data are expressed as mean ± SD. Statistical differences were determined using an unpaired t-test in GraphPad Prism 10.3.1.

(2) SARS-CoV-2 escape mutants for AVI-4206 should be generated, sequenced, and evaluated for both ADP-ribosyl hydrolase activity and their susceptibility to inhibition by AVI-4206.

We thank the reviewer for this suggestion. These are indeed key experiments which are currently hampered by the lack of a cell line that is fully responsive to drug treatment. Although infected organoids and macrophages show an effect in response to AVI-4206, viral levels are ~3 logs lower than in cell lines and difficult to sequence. In the absence of a system that would allow meaningful screening for outgrowth of resistant viruses, we have conducted mass spectrometry studies that showed that Mac1 is the only significant hit for AVI-4206 (SupplementaryFigure 5). The suggested outgrowth experiments will be conducted once a responsive cell line model has been established.

(3) Given that Mac1 is found in several coronaviruses, it would be insightful for the authors to test a selection of Mac1 homologs from divergent coronaviruses to assess whether AVI-4206 can inhibit their activity in vitro.

As mentioned above, inconsistencies in ADPr staining limit our ability to directly measure cellular activity. As an alternative approach to measure AVI-4206 selectivity in cells, we have adapted our CETSA assay for SARS-1 and MERs macrodomain proteins and find evidence that AVI-4206 can shift the melting temperature of both proteins, albeit to a lesser degree than that seen for Mac1. In line with MERS being more structurally divergent than SARS-1 from SARS CoV2, the ΔTagg for SARS-1 and MERS are 4℃ and 1℃, respectively, compared to 9℃ for Mac1.  These data have been added as Supplementary Fig S3C. Development of broader spectrum pan-inhibitors is on our radar for future work which will more thoroughly assess homologs from divergent coronaviruses.

We added the following sentence to the main results:

“Encouragingly, we were also able to adapt our CETSA assay for SARS-1 and MERs macrodomain proteins and find that AVI-4206 can shift the melting temperature of both proteins, albeit to a lesser degree than that seen for Mac1 (Supplementary Figure 3C).”

We also added this supplementary figure 3:

Minor

(1) Line 88, "respectively.heir potency"

Fixed, thank you!

(2) Line 149 add a period after proteome

Fixed, thank you!

Reviewer #2 (Recommendations for the authors):

(a) The authors assess inhibition of MacroD2 and Targ1 as of-targets for AVI-4206. However, Mac1 belongs to the MacroD-type class of macrodomains of which MacroD1, MacroD2 and MOD1s of PARP9 and PARP14 are the human members. In contrast Targ1 belongs to the ALC1-like class, which is only very distantly related to Mac1. Furthermore, recent studies have shown that the first macrodomains of PARP9 and PARP4 (MOD1 of PARP9/14) are much closer related to Mac1 and PARP9/14 were implicated in antiviral immunity. As such the authors should include assays showing the activity of their compounds against MacroD1 and MOD1s of PARP9/14.

We emphasize that we detect no significant shift for any protein other than Mac1 in A549 cells by CETSA-MS (Supplementary Figure 6). For Mac1 CESTA, we see an average of 6 PARP14 spectral counts across conditions and did not detect PARP9.  In addition, for separate work in MPro, we ran similar CETSA experiments where we observed an average of 2 PARP9 and 15 PARP14 spectral counts across conditions. Although PARP9 and PARP14 massively increase expression upon IFN treatment in A549 cells, both proteins have been detected by Western Blot in A549 cells previously at baseline.

Nonetheless, we have included modeling of more diverse macrodomains as a supplemental figure and added to the text:

Modeling of other diverse macrodomains, including those within human PARP9 and PARP14 further suggests that AVI-4206 is selective for Mac1 (Supplementary Figure 4)

(b) In the context of SARS-CoV-2 superinfection are a known major complication of infections. These superinfections are associated with lung damage and therefore it would be good if the authors could assess lung damage, e.g. by histology, to see if their treatment has a positive impact on lung damage and thus may help to suppress complications.

We performed histology and the results are inconclusive, but suggest that AVI-4206 treatment could lower apoptosis.There is no difference in pathology between the N40D cohort and vehicle with these markers. This could suggest that AVI-4206 provides an additional mechanism that results in protection.  We added to the results:

Caspase 3 staining shows that AVI-4206 treatment reduces apoptosis in the lungs compared to vehicle controls. Additionally, Masson's Trichrome staining reveals  a significant reduction in collagen deposition, a surrogate for lung pathology, in the lungs of AVI-4206 treated animals.(Supplementary Figure 9).

Histology:

Mouse lung tissues were fixed in 4% PFA (Sigma Aldrich, Cat #47608) for 24 hours, washed three times with PBS and stored in 70% ethanol. All the stainings were performed at Histo-Tec Laboratory (Hayward, CA). Samples were processed, embedded in paraffin, and sectioned at 4μm. The slides were dewaxed using xylene and alcohol-based dewaxing solutions. Epitope retrieval was performed by heat-induced epitope retrieval (HIER) of the formalin-fixed, paraffin-embedded tissue using citrate-based pH 6 solution (Leica Microsystems, AR9961) for 20 mins at 95°C. The tissues were stained for H&E, caspase-3 (Biocare #CP229c 1:100), and trichrome, dried, coverslipped (TissueTek-Prisma Coverslipper), and visualized using Axioscan 7 slide scanner (ZEISS) at 40X. Image quantification was performed with Image J software and GraphPad Prism.

(c) Fig. 1D labelling is wrong

Thank you - fortunately the data were plotted correctly and it was just the inset table of values that was incorrect. This is now fixed!

(d) Line 88: "T" missing at start of sentence

Fixed, thank you!

(e) Line 118: NudT5/AMP-Glo assay was developed in https://doi.org/10.1021/acs.orglett.8b01742

We have added this foundational reference, thank you!

(f) Line 147ff: It would be good if the authors could highlight that the TPP methodology has known limitations (e.g. detection of low abundance proteins and low thermal shift of some binders) and thus is not an absolute proof that AVI-4206 "engage with high specificity for Mac1"

We added this important context to the concluding sentence of this paragraph:

“While this assay may not be sensitive to detection of proteins with low abundance proteins or low thermal shift upon ligand binding, collectively, these results indicate that AVI-4206 can cross cellular membranes and engage with high specificity for Mac1.”

(g) The authors use their well established in vitro Mac1 model as well as the SARS-CoV-2 WA strain. Given the ongoing diversification of SARS-CoV-2 and the current prevalence of the Omicron VOC it would be good if the authors could investigate whether alteration in Mac1 occurred or are detected which could influence the efficacy of their inhibitor. Similarly, it would be interesting to know how effective their drug is on other clinically relevant beta-CoV Mac1, e.g. from MERS or SARS1.

We thank the reviewer for the suggestion. Mac1 is one of the more conserved areas of the SARS-CoV-2 genome as there has only been one nonsynonymous mutation V34L (Orf1a:V1056L) that recently emerged in the BA.2.86 lineage and is now in all of the JN.1 derivatives. Currently, the mutation is only ~80% penetrant in circulating SARS-CoV-2 sequences suggesting that it might revert to wild-type and is not associated with a fitness benefit. Based on our structural analysis (shown in Supplementary Figure4D above), we do not believe this mutation affects AVI-4206 binding, but we are including this variant in our future in vitro and in vivo studies as well as other beta-CoV.  For SARS and MERS, see response to Reviewer 1 using CETSA to show that these targets are engaged by AVI-4206.

(h) As methods to detect PARP14-derived ADP-ribosylation are available and it was shown that Mac1 can reverse this modification in cells. It would be good if the authors could investigate the impact of AVI-4206 on ADP-ribosylation in vivo.

To test this idea we adapted the IF assay used by others in the field and show an effect of AVI-4206. We have added to the text:

Although the IFN response was not sufficient to control viral replication, it is possible that the changes in ADP-ribosylation, in particular marks catalyzed by PARP14, downstream of IFN treatment could serve as a marker for Mac1 efficacy  (Ribeiro et al. 2025). To investigate whether downstream signals from PARP14 were specifically erased by Mac1, we used an immunofluorescence assay that showed that Mac1 could remove IFN-γ-induced ADP-ribosylation that is mediated by PARP14 (Kar et al. 2024).  We stably expressed wild-type Mac1 and the N40D mutant Mac1 in A549 cells. The data showed that Mac1 expression decreased IFN-γ-induced ADP-ribosylation, whereas the Mac1-N40D mutant did not (Figure 3E, F), indicating that Mac1 mediates the hydrolysis of IFN-γ-induced ADP-ribosylation. The PARP14 inhibitor RBN012759 completely blocked IFN-γ-induced ADP-ribosylation (Figure 3E, F), further confirming that IFN-γ-induced ADP-ribosylation is mediated by PARP14. AVI-4206 reversed the Mac1-induced hydrolysis of ADP-ribosylation and enhanced the ADP-ribosylation signal in Mac1-overexpressing cells (Figure 3E, F), further demonstrating its ability to inhibit the hydrolase activity of Mac1. We further validated this result using different ADP-ribosylation antibodies for immunofluorescence (Supplementary Figure 7). However, we observed no strong consistent signals of global pan-ADP-ribose (panADPr) or mono-ADP-ribose (monoADPr) accumulation in infected cells treated with AVI-4206 in immunoblot analyses (Supplementary Figure 8). Collectively, these results provide further evidence that simple cellular models are insufficient to explore the effects of Mac1 inhibition and that monitoring specific PARP14-mediated ADP-ribosylation patterns can provide an accessible biomarker for the efficacy of Mac1 inhibition.

A549 Mac1 expression cell construction

Mac1 wild-type (Mac1) and N1062D mutant (Mac1 N1062D) gene fragments were loaded into pLVX-EF1α-IRES-Puro (empty vector, EV) using Gibson cloning kit (NEB E5510). Lentivirus was prepared as previously described (PMID: 30449619; DOI: 10.1016/j.cell.2018.10.024). Briefly, 15 million HEK293T cells were grown overnight on 15 cm poly-L-Lysine coated dishes and then transfected with 6 ug pMD2.G (Addgene plasmid # 12259 ; http://n2t.net/addgene:12259 ; RRID:Addgene_12259), 18 ug dR8.91 (since replaced by second generation compatible pCMV-dR8.2, Addgene plasmid #8455) and 24 ug pLVX-EF1α-IRES-Puro (EV, Mac1, Mac1-N1062D) plasmids using the lipofectamine 3000 transfection reagent per the manufacturer’s protocol (Thermo Fisher Scientific, Cat #L3000001). pMD2.G and dR8.91 were a gift from Didier Trono. The following day, media was refreshed with the addition of viral boost reagent at 500x as per the manufacturer’s protocol (Alstem, Cat #VB100). Viral supernatant was collected 48 hours post transfection and spun down at 300 g for 10 minutes, to remove cell debris. To concentrate the lentiviral particles, Alstem precipitation solution (Alstem, Cat #VC100) was added, mixed, and refrigerated at 4°C overnight. The virus was then concentrated by centrifugation at 1500 g for 30 minutes, at 4°C. Finally, each lentiviral pellet was resuspended at 100x of original volume in cold DMEM+10%FBS+1% penicillin-streptomycin and stored until use at -80°C. To generate Mac1 overexpressing cells, 2 million A549 cells were seeded in 10 cm dishes and transduced with lentivirus in the presence of 8 μg/mL polybrene (Sigma, TR-1003-G). The media was changed after 24h and, after 48 hours, media containing 2μg/ml puromycin was added. Cells were selected for 72 hours and then expanded without selection. The expression of Mac1 was confirmed by Western Blot.

Immunofluorescence assay:

To assess the effect of Mac1 on IFN-induced ADP-ribosylation. A549-pLVX-EV, A549-pLVX-Mac1 and A549-pLVX-Mac1-N1062D cells were seeded in 96-well plate (10,000 cells/well). Cells were pre-treated with medium or 100 unit/mL IFN-γ (Sigma, SRP3058) for 24 hours to induce the expression of ADP-ribosylation. These 3 cell lines were then treated the next day with the indicated concentrations of AVI-4206 or RBN012759 (Medchemexpress, HY-136979). After 24 hours of exposure to drugs, treated cells were fixed in pre-cooled methanol at -20°C for 20 min, blocked in 3% bovine serum albumin for 15 min, incubated with Poly/Mono-ADP Ribose (E6F6A) Rabbit mAb (CST, 83732S) or Poly/Mono-ADP Ribose (D9P7Z) Rabbit mAb (CST, 89190S) antibodies for 1 h, and then incubated with Goat anti-Rabbit IgG Secondary Antibody, Alexa Fluor 488 (ThermoFisher, A-11008) secondary antibodies for 30 min and stained with DAPI for 10 minutes. Fluorescent cells were imaged with an IN Cell Analyzer 6500 System (Cytiva) and analyzed using IN Carta software (Cytiva).

Reviewer #3 (Recommendations for the authors):

Just a couple of observations/details that might help strengthen the article:

(1) The caco-1 data for AVI4206 would suggest that there is some sort of efflux going on, yet there is no mention of it in the paper. This might be useful in the optimization paradigm moving forward.

We thank the reviewer for this observation and suggestion.  Indeed, we believe that efflux is behind the low oral bioavailability of AVI-4206.  We are working specifically to remove this liability in next-generation analogs, using the caco2 assay to guide this ongoing effort. Keep an eye out for a preprint on this soon!  We have added to the discussion:

“In addition to dissecting such molecular mechanisms of macrodomain function and inhibition, future efforts will focus on improving pharmacokinetic properties, including a cellular efflux liability that results in low oral bioavailability of AVI-4206. ”

(2) There are some spectroscopic anomalies/mistakes in the NMR data. The carbon NMR for 1-((8-amino-9H-pyrimido[4,5-b]indol-4-yl)amino)pyrrolidin-2-one should only have 14 unique carbons, but the authors report 15. The HNMR for AVI1500 should only have 19 H's, but the authors list 20. The HNMR data for AVI3762/3763 should have 16 H's, but the authors only report 13. The CNMR for AVI4206 should only have 19 unique carbons, but the authors report 20.

Thank you for noting these inconsistencies regarding the reported NMR spectra. We have rectified them by more closely examining the spectra and in some cases acquiring new data. We identified one peak (47.9) in the 13C NMR of 1-((8-amino-9H-pyrimido[4,5-b]indol-4-yl)amino)pyrrolidin-2-one that is apparently an artifact of the automated peak picking in the data analysis software.  In the 1H NMR of AVI-1500, the triplet peak at 7.20 integrates to 1H, but was erroneously reported as 2H in the original manuscript.  This error has been corrected.  Spectra were re-acquired for AVI-3762, AVI-3763, and AVI-4206 with longer acquisition times, and/or on a 600 MHz spectrometer to afford the complete line lists now reported in the revised manuscript. Please note AVI-4206 has 18 distinct 13C resonances due to the equivalence of the gem-dimethyl methyl groups.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

(1) The use of single-cell RNA and TCR sequencing is appropriate for addressing potential relationships between gene expression and dual TCR.

Thank you for your detailed review and suggestions. The main advantages of scRNA+TCR-seq are as follows: (1) It enables comparative analysis of features such as the ratio of single TCR paired T cells to dual TCR paired T cells at the level of a large number of individual T cells, through mRNA expression of the α and β chains. In the past, this analysis was limited to a small number of T cells, requiring isolation of single T cells, PCR amplification of the α and β chains, and Sanger sequencing; (2) While analyzing TCR paired T cell characteristics, it also allows examination of mRNA expression levels of transcription factors in corresponding T cells through scRNA-seq.

(2) The data confirm the presence of dual TCR Tregs in various tissues, with proportions ranging from 10.1% to 21.4%, aligning with earlier observations in αβ T cells.

Thank you very much for your detailed review and suggestions. Early studies on dual TCR αβ T cells have been very limited in number, with reported proportions of dual TCR T cells ranging widely from 0.1% to over 30%. In contrast, scRNA+TCR-seq can monitor over 5,000 single and paired TCRs, including dual paired TCRs, in each sample, enabling more precise examination of the overall proportion of dual TCR αβ T cells. It is important to note that our analysis focuses on T cells paired with functional α and β chains, while T cells with non-functional chain pairings and those with a single functional chain without pairing were excluded from the total cell proportion analysis. Previous studies generally lacked the ability to determine expression levels of specific chains in T cells without dual TCR pairings.

(3) Tissue-specific patterns of TCR gene usage are reported, which could be of interest to researchers studying T cell adaptation, although these were more rigorously analyzed in the original works.

Thank you very much for your detailed review and suggestions. T cell subpopulations exhibit tissue specificity; thus, we conducted a thorough investigation into Treg cells from different tissue sites. This study builds upon the original by innovatively analyzing the differences in VDJ rearrangement and CDR3 characteristics of dual TCR Treg cells across various tissues. This provides new insights and directions for the potential existence of “new Treg cell subpopulations” in different tissue locations. The results of this analysis suggest the necessity of conducting functional experiments on dual TCR Treg cells at both the TCR protein level and the level of effector functional molecules.

(4) Lack of Novelty: The primary findings do not substantially advance our understanding of dual TCR expression, as similar results have been reported previously in other contexts.

Thank you for your detailed review and suggestions. Early research on dual TCR T cells primarily relied on transgenic mouse models and in vitro experiments, using limited TCR alpha chain or TCR beta chain antibody pairings. Flow cytometry was used to analyze a small number of T cells to estimate dual TCR T cell proportion. No studies have yet analyzed dual TCR Treg cell proportion, V(D)J recombination, and CDR3 characteristics at high throughput in physiological conditions. The scRNA+TCR-seq approach offers an opportunity to conduct extensive studies from an mRNA perspective. With high-throughput advantages of single-cell sequencing technology, researchers can analyze transcriptomic and TCR sequence characteristics of all dual TCR Treg cells within a study sample, providing new ideas and technical means for investigating dual TCR T cell proportions, characteristics, and origins under different physiological and pathological states.

(5) Incomplete Evidence: The claims about tissue-specific differences lack sufficient controls (e.g., comparison with conventional T cells) and functional validation (e.g., cell surface expression of dual TCRs).

Thank you for your detailed review and suggestions. This study indeed only analyzed dual  TCR Treg cells from different tissue locations based on the original manuscript, without a comparative analysis of other dual TCR T cell subsets corresponding to these tissue locations. The main reason for this is that, in current scRNA+TCR-seq studies of different tissue locations, unless specific T cell subsets are sorted and enriched, the number of T cells obtained from each subset is very low, making a detailed comparative analysis impossible. In the results of the original manuscript, we observed a relatively high proportion of dual TCR Treg cell populations in various tissues, with differences in TCR composition and transcription factor expression. Following the suggestions, we have included additional descriptions in R1, citing the study by Tuovinen et al., which indicates that the proportion of dual TCR Tregs in lymphoid tissues is higher than other T cell types. This will help understand the distribution characteristics of dual TCR Treg cells in different tissues and provide a basis for mRNA expression levels to conduct functional experiments on dual TCR Treg cells in different tissue locations.

(6) Methodological Weaknesses: The diversity analysis does not account for sample size differences, and the clonal analysis conflates counts and clonotypes, leading to potential misinterpretation.

We thank you for your review and suggestions. In response to your question about whether the diversity analysis considered the sample size issue, we conducted a detailed review and analysis. This study utilized the inverse Simpson index to evaluate TCR diversity of Treg cells. A preliminary analysis compared the richness and evenness of single TCR Treg cell and dual TCR Treg cell repertoires. The two datasets analyzed were from four mouse samples with consistent processing and sequencing conditions. However, when analyzing single TCR Tregs and dual TCR Tregs from various tissues, differences in detected T cell numbers by sequencing cannot be excluded from the diversity analysis. Following recommendations, we provided additional explanations in R1: CDR3 diversity analysis indicates TCR composition of dual TCR Treg cells exhibits diversity, similar to single TCR Treg cells; however, diversity indices of single TCR Tregs and dual TCR Tregs are not suitable for statistical comparison. Regarding the "clonal analysis" you mentioned, we define clonality based on unique TCR sequences; cells with identical TCR sequences are part of the same clone, with ≥2 counts defined as expansion. For example, in Blood, there are 958 clonal types and 1,228 cells, of which 449 are expansion cells. In R1, we systematically verified and revised clonal expansion cells across all tissue samples according to a unified standard.

(7) Insufficient Transparency: The sequence analysis pipeline is inadequately described, and the study lacks reproducibility features such as shared code and data.

Thank you for your review and suggestions. Based on the original manuscript, we have made corresponding detailed additions in R1, providing further elaboration on the analysis process of shared data, screening methods, research codes, and tools. This aims to offer readers a comprehensive understanding of the analytical procedures and results.

(8) Weak Gene Expression Analysis: No statistical validation is provided for differential gene expression, and the UMAP plots fail to reveal meaningful clustering patterns.

Thank you very much for your review and suggestions. Based on your recommendations, we conducted an initial differential expression analysis of the top 10 mRNA molecules in single TCR Treg and dual TCR Treg cells using the DESeq2 R package in R1, with statistical significance determined by Padj < 0.05. Regarding the clustering patterns in the UMAP plots, since the analyzed samples consisted of isolated Treg cell subpopulations that highly express immune suppression-related genes, we did not perform a more detailed analysis of subtypes and expression gene differences. This study primarily aims to explore the proportions of single TCR and dual TCR Treg cells from different tissue sources, as well as the characteristics of CDR3 composition, with a focus on showcasing the clustering patterns of samples from different tissue origins and various TCR pairing types.

(9) A quick online search reveals that the same authors have repeated their approach of reanalysing other scientists' publicly available scRNA-VDJ-seq data in six other publications,In other words, the approach used here seems to be focused on quick re-analyses of publicly available data without further validation and/or exploration.

Thank you for your review and suggestions. Most current studies utilizing scRNA+TCR-seq overlook analysis of TCR pairing types and related research on single TCR and dual TCR T cell characteristics. Through in-depth analysis of shared scRNA+TCR-seq data from multiple laboratories, we discovered a significant presence of dual TCR T cells in high-throughput T cell research results that cannot be ignored. In this study, we highlight the higher proportion of dual TCR Tregs in different tissue locations, which exhibits a certain degree of tissue specificity, suggesting these cells may participate in complex functional regulation of Tregs. This finding provides new ideas and a foundation for further research into dual TCR Treg functions. However, as reviewers pointed out, findings from scRNA+TCR-seq at the mRNA level require additional functional experiments on dual TCR T cells at the protein level. We have supplemented our discussion in R1 based on these suggestions.

Reviewer #2 (Public review):

(1) The existence of dual TCR expression by Tregs has previously been demonstrated in mice and humans (Reference #18 and Tuovinen. 2006. Blood. 108:4063; Schuldt. 2017. J Immunol. 199:33, both omitted from references). The presented results should be considered in the context of these prior important findings.

Thank you very much for your review and suggestions. Based on the original manuscript, we have supplemented our reading, understanding, and citation of closely related literature (Tuovinen, 2006, Blood, 108:4063 (line 44,line175 in R1); Schuldt, 2017, J Immunol, 199:33 (line 44,line178 in R1)). We once again appreciate the valuable comments from the reviewers, and we will refer to these in our subsequent dual TCR T cell research.

(2) This demonstration of dual TCR Tregs is notable, though the authors do not compare the frequency of dual TCR co-expression by Tregs with non-Tregs. This limits interpreting the findings in the context of what is known about dual TCR co-expression in T cells.

Thank you very much for your review and suggestions. This analysis is primarily based on the scRNA+TCR-seq study of sorted Treg cells, where we found the proportions and distinguishing features of dual TCR Treg cells in different tissue sites. Given the diversity and complexity of Treg function, conducting a comparative analysis of the origins of dual TCR Treg cells and non-T cells with dual TCRs will be a meaningful direction. Currently, peripheral induced Treg cells can originate from the conversion of non-Treg cells; however, little is known about the sources and functions of dual TCR Treg cell subsets in both central and peripheral sites. In R1, we have supplemented the discussion regarding the possible origins and potential applications of the "novel dual TCR Treg" subsets.

(3) Comparison of gene expression by single- and dual TCR Tregs is of interest, but as presented is difficult to interpret. Statistical analyses need to be performed to provide statistical confidence that the observed differences are true.

Thank you very much for your review and suggestions. Based on your recommendations, we performed an initial differential expression analysis of the top 10 mRNA molecules in single TCR Treg and dual TCR Treg cells using the DESeq2 R package in R1, with a statistical significance threshold of Padj<0.05 for comparisons.

(4) The interpretations of the gene expression analyses are somewhat simplistic, focusing on the single-gene expression of some genes known to have a function in Tregs. However, the investigators miss an opportunity to examine larger patterns of coordinated gene expression associated with developmental pathways and differential function in Tregs (Yang. 2015. Science. 348:589; Li. 2016. Nat Rev Immunol. Wyss. 2016. 16:220; Nat Immunol. 17:1093; Zenmour. 2018. Nat Immunol. 19:291).

Thank you for your review and suggestions. This study is based on publicly available scRNA+TCR-seq data from different organ sites generated by the original authors, focusing on sorted and enriched Treg cells within each tissue sample. However, there was no corresponding research on other cell types in each tissue sample, preventing analysis of other cells and factors involved in development and differentiation of single TCR Treg and dual TCR Treg. The literature suggested by the reviewer indicates that development, differentiation, and function of Treg cells have been extensively studied, resulting in significant advances. It also highlights complexity and diversity of Treg origins and functions. This research aims to investigate "novel dual TCR Treg cell subpopulations" that may exhibit tissuespecific differences found in the original authors' studies of Treg cells across different organ sites. This suggests further experimental research into their development, differentiation, origin, and functional gene expression as an important direction, which we have supplemented in the discussion section of R1.

Reviewer #3 (Public review):

(1) Definition of Dual TCR and Validity of Doublet Removal:This study analyzes Treg cells with Dual TCR, but it is not clearly stated how the possibility of doublet cells was eliminated. The authors mention using DoubletFinder for detecting doublets in scRNA-seq data, but is this method alone sufficient?We strongly recommend reporting the details of doublet removal and data quality assessment in the Supplementary Data.

Thank you very much for your review and suggestions. In the analysis of the shared scRNA+TCR-seq data across multiple laboratories, as you mentioned, this study employed the DoubletFinder R package to exclude suspected doublets. Additionally, we used the nCount values of individual cells (i.e., the total sequencing reads or UMI counts for each cell) as auxiliary parameters to further optimize the assessment of cell quality. Generally, due to the possibility that doublet cells may contain gene expression information from two or more cells, their nCount values are often abnormally high. In this study, all cells included in the analysis had nCount values not exceeding 20,000. Among the five tissue sample datasets, we further utilized hashtag oligonucleotide (HTO) labeling (where HTO labeling provides each cell with a unique barcode to differentiate cells from different tissue sources. By analyzing HTO labels, doublets and negative cells can be accurately identified) to eliminate doublets and negative cells.After the removal of chimeric cells, all samples exhibited T cells that possessed two or more TCR clones. This phenomenon validates the reliability of the methodological approach employed in this study and indicates that the analytical results accurately reflect the proportion of dual TCR T cells. Based on the recommendations of the reviewers, we have supplemented and clarified the methods and discussion sections in the manuscript. It is particularly noteworthy that in our analysis, the discussed dual TCR Treg cells and single TCR Treg cells specifically refer to those T cells that possess both functional α and β chains, which are capable of forming TCR. We have excluded from this analysis any Treg cells that possess only a single functional α or β chain and do not form TCR pairs, as well as those Treg cells in which the α or β chains involved in TCR pairing are non-functional.

(2) In Figure 3D, the proportion of Dual TCR T cells (A1+A2+B1+B2) in the skin is reported to be very high compared to other tissues. However, in Figure 4C, the proportion appears lower than in other tissues, which may be due to contamination by non-Tregs. The authors should clarify why it was necessary to include non-Tregs as a target for analysis in this study. Additionally, the sensitivity of scRNA-seq and TCR-seq may vary between tissues and may also be affected by RNA quality and sequencing depth in skin samples, so the impact of measurement bias should be assessed.

We deeply appreciate your review and constructive comments. Based on the original manuscript, we have further supplemented and elaborated on the uniqueness and relative proportions of double TCR T cell pairs in skin tissue samples in Section R1. Due to the scarcity of T cells in skin samples, we included some non-Treg cells during single-cell RNA sequencing and TCR sequencing to obtain a sufficient number of cells for effective analysis. The presence of non-regulatory T cells may indeed impact the statistical representation of double TCR T cells as well as the related comparative analyses, as noted by the reviewer. T cells with A1+A2+B1+B2 type double TCR pairings are primarily found within the non-regulatory T cell population in the skin. In response to this point, we have provided a detailed explanation of this analytical result in the revised manuscript R1. Furthermore, concerning the two datasets included in the study, we conducted a comparative analysis in R1, exploring how factors such as sequencing depth at different tissue sites might introduce biases in our findings, which we have thoroughly elaborated upon in the discussion section. We thank you once again for your valuable suggestions. 

(3) Issue of Cell Contamination:In Figure 2A, the data suggest a high overlap between blood, kidney, and liver samples, likely due to contamination. Can the authors effectively remove this effect? If the dataset allows, distinguishing between blood-derived and tissue-resident Tregs would significantly enhance the reliability of the findings. Otherwise, it would be difficult to separate biological signals from contamination noise, making interpretation challenging.

We thank you for your review and suggestions. We have carefully verified data sources for tissues such as blood, kidneys, and liver. In the study by Oliver T et al., various techniques were employed to differentiate between leukocytes from blood and those from tissues, ensuring accurate identification of leukocytes from tissue samples. First, anti-CD45 antibody was injected intravenously to label cells in the vasculature, verifying that analyzed cells were indeed resident in the tissue. Second, prior to dissection and cell collection, authors performed perfusion on anesthetized mice to reduce contamination of tissue samples by leukocytes from the vasculature. Additionally, during single-cell sequencing, authors utilized HTO technology to avoid overlap between cells from different tissues.

Analysis of the scRNA+TCR-seq data shared by the original authors revealed highly overlapping TCR sequences in blood, kidney, and liver, despite distinct cell labels associated with each tissue. While these techniques minimize overlap of cells from different sources, they cannot completely rule out the potential impact of this technical issue. As suggested, we have provided additional clarification in R1 of the manuscript regarding this phenomenon of high overlap in the kidney, liver, and blood, indicating that the possibility of Treg migration from blood to kidney and liver cannot be entirely excluded.

(4) Inconsistency Between CDR3 Overlap and TCR Diversity:The manuscript states that Single TCR Tregs have a higher CDR3 overlap, but this contradicts the reported data that Dual TCR Tregs exhibit lower TCR diversity (higher 1/DS score). Typically, when TCR diversity is low (i.e., specific clones are concentrated), CDR3 overlap is expected to increase. The authors should carefully address this discrepancy and discuss possible explanations.

Thank you for your review and suggestions. Regarding the potential relationship between CDR3 overlap and TCR diversity, in samples with consistent sequencing depth, lower diversity indeed corresponds to a higher proportion of CDR3 overlap. In our analysis of scRNA+TCR-seq data, we found that single TCR Tregs exhibit both higher diversity and CDR3 overlap, seemingly presenting contradictory analytical results (i.e., dual TCR Tregs show lower TCR diversity and CDR3 overlap). In R1, we supplemented the analysis of possible reasons: the presence of multiple TCR chains in dual TCR Treg cells may lead to a higher uniqueness of CDR3 due to multiple rearrangements and selections, resulting in lower CDR3 overlap; the lower diversity of dual TCR Tregs may be related to the number of T cells sequenced in each sample. The CDR3 diversity analysis in this study merely suggests that the TCR composition of dual TCR Treg cells is diverse, similar to that of single TCR Tregs. However, the diversity indices of single TCR Tregs and dual TCR Tregs are not suitable for statistical comparative analysis. A more in-depth and specific analysis of the diversity and overlap of the VDJ recombination mechanisms and CDR3 composition in dual TCR Tregs during development will be an important technical means to elucidate the function of dual TCR Treg cells.

(5) Functional Evaluation of Dual TCR Tregs:This study indicates gene expression differences among tissue-resident Dual TCR T cells, but there is no experimental validation of their functional significance. Including functional assays, such as suppression assays or cytokine secretion analysis, would greatly enhance the study's impact.

We sincerely appreciate your review and suggestions: In this analysis of scRNA+TCR-seq data, we innovatively discovered a higher proportion of dual TCR Treg cells in different tissue sites, which exhibited differences in tissue characteristics. Furthermore, we conducted a comparative analysis of the homogeneity and heterogeneity between single TCR Treg and dual TCR Treg cells. This result provides a foundation for further research on the origin and characteristics of dual TCR Treg cells in different tissue sites, offering new insights for understanding the complexity and functional diversity of Treg cells. Based on your suggestions, we have supplemented R1 with the feasibility of further exploring the functions of tissue-resident dual TCR T cells and the necessity for potential application research.

(6) Appropriateness of Statistical Analysis:When discussing increases or decreases in gene expression and cell proportions (e.g., Figure 2D), the statistical methods used (e.g., t-test, Wilcoxon, FDR correction) should be explicitly described. They should provide detailed information on the statistical tests applied to each analysis.

Thank you for your review and suggestions: Based on the original manuscript, we have supplemented the specific statistical methods for the differences in cell proportions and gene expression in R1.

Author response:

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

Reviewer #1 (Public review):

“Alternative possibilities are discussed regarding the prior and likelihood of the model. Given that the second case study inspired the introduction of the zero-inflation likelihood, it is not clear how applicable the general methodology is to various datasets. If every unique dataset requires a tailored prior or likelihood to produce the best results, the methodology will not easily replace more traditional statistical analyses that can be applied in a straightforward manner. Furthermore, the differences between the results produced by the two Bayesian models in case study 2 are not discussed. In specific regions, the models provide conflicting results (e.g., regions MH, VPMpc, RCH, SCH, etc.), which are not addressed by the authors. A third case study would have provided further evidence for the generalizability of the methodology.”

We hope in this paper to propose a ‘standard workflow’ for these data; this standard workflow uses the horseshoe prior and we propose that this is the approach used to describe cell count data instead of the better established, but to our thinking, inefficient, t-testing approach.

The horseshoe prior is robust and allows a partially-pooled model to used while weighing-up the contribution of different data points. This is an analogue of excluding outliers and, in any analysis it is normal to investigate further if there are points being excluded as outliers. Often this reveals a particular challenge with the data, in the case of the data here, there are a lot of zeros, indicating that some samples should be excluded because the preparation failed to tag cells rather than because there were no cells to tag. This idea behind the ZIP example is to show that the Bayesian method can allow for this sort of further investigation and, indeed, as the reviewer notes this sort of extended analysis is often bespoke, tailored to the data.

We have clearly failed to explain that the ‘standard workflow’ we propose replace the more traditional methods is the first one we describe, with the horseshoe prior; this produces better results on both datasets than the traditional approach. However, we also feel it is useful to show how a more tailored follow-on can be useful; we need to make it clear that this is intended as an illustration of an ‘optional extra’ rather than a part of the more straightforward ‘standard workflow’.

To make this clearer we have made altered the text in several locations:

• end of Introduction: added clarifying sentence “Here, our aim is to introduce a ‘standard’ Bayesian model for cell count data. We illustrate the application of this model to two datasets, one related to neural activation and the other to developmental lineage. For the second dataset, we also demonstrate a second example extension Bayesian model.”

• Section Hierarchical modeling: “Our goal in both cases is to quantify group differences in the data. We present a ‘standard’ hierarchical model. This model reflects the experimental features common to cell count experiments and reflects the hierarchical structure of cell count data; the standard model is designed to deal robustly and efficiently with noise. On some occasions, to reflect a specific hypotheses, the structure of a particular experiment or an observed source of noise, this model can be further refined or changed to target the analysis. We will give an example of this for our second dataset.”

• Section Horseshoe prior: “The alternative is via a flexible prior such as the horseshoe Carvalho et al., 2010; Piironen and Vehtari, 2017. This more generic option may be suitable as a default ‘standard’ approach in the typical case where outliers are poorly understood.”

• Discussion: word ‘standard’ added to sentence: “Our standard workflow uses a horseshoe prior, along with the partial pooling, this allows our model to deal effectively with outliers.”

• Discussion: modified sentence “The horseshoe prior model workflow we have exhibited here is intended as a standard approach.”

Indeed, because the horseshoe prior deals robustly with outliers, whereas the ZIP is intended to model the outliers, any substantial difference between the two should be examined carefully. The referee is right to point out that we have not explained this in any detail and has helpfully listed a few brain regions were there are differences. This is useful, particularly since the examples listed illustrate in a useful way the opportunities and hazards this sort of data presents. To address this, we have added a new version of Figure 6 to the revised manuscript

Previously Figure 6 showed two example brain regions: MPN and TMd. We have now added MH and SCH to the figure, and new text commenting on the insights the plots provide, both in the Results and Discussion.

Reviewer #2 (Public review):

“A clearer link between the experimental data and model-structure terminology would be a benefit to the non-expert reader.”

This is a very good point and we are acutely aware through our own work how difficult it can be moving between fields with different research goals, different scientific cultures and different technical vocabularies. Just as it can be difficult translating from one language to another without losing nuance and meaning, it can be a real challenge finding technical terms that are useful for the non-expert reader while retaining the precision the application requires! In the long run, we hope that, just as some of the very specialized vocabulary that surrounds frequentist statistics has become familiar to to the working experimental scientists, the precise terminology involved in Bayesian modelling will become familiar and transparent. However, in advance of that day, we have included a glossary of terms at the end of the main text, and have made numerous small tweaks to make sure that link between data and model terminology is clearer and better explained.

Reviewer #1 (Recommendations fro the authors):

(1) “I would strongly recommend that the authors include more case studies in the manuscript, and address the qualitative differences between the different versions of the model.”

We agree that our method will only become established when it is applied to more datasets, we hope to contribute to further analysis and we know other people are already using the approach on their own data. We do, however, feel that adding more datasets to this paper will make it longer and more complex; the plan, instead, is to use the method on novel datasets to test specific hypotheses, so that the results will include novel scientific findings as well as adding another illustration of the Bayesian approach applied to data that is already well studied.

(2) “Figure 6 is not discussed in the main text.”

We had discussed the results presented in Figure 6 in the second paragraph of the section “Case study two – Ontogeny of inhibitory interneurons of the mouse thalamus”, however the reviewer is right in that we did not directly refer to the Figure – this was an oversight. In any case, in the revised manuscript we present a new version of Figure 6 (in response to above comment), which is now explicitly cited in the text.

Revised Figure 6: Example data and inferences highlighting model discrepancies. On the left under ‘data’: boxplots with medians and interquartile ranges for the raw data for four example brain regions. The shape of each point pairs left and right hemisphere readings in each of the five animals. On the right under ‘inference’: HDIs and confidence intervals are plotted. Purple is the Bayesian horseshoe model, pink is the Bayesian ZIP model, and orange is the sample mean. The Bayesian estimates are not strongly influenced by the zero-valued observations (MPN, SCH, TMd) or large-valued outliers (MH) and have means close to the data median. This explains the advantage of the Bayesian results over the confidence interval.

Reviewer #2 (Recommendations from the authors):

(1) “This is a generally well-written methodology paper that also provides the underlying code as a resource. As a reviewer outside both cell-count modelling and hierarchical-Bayesian approaches (though with a general interest in the topics) I found the method a little difficult to follow and would have liked to have been left with a better understanding of how the method is applied to the data. For example, in Figure 1 we are introduced to brain region count, animal count, and “items”. Then in the next line: pooling, model, structure, population and etc in subsequent lines. It is not clear what the subscripts (the pools?) are referring to: are they different regions R or animals N? These terms need to be better linked to the data and/or trimmed. Having said that, the later results look like a solid contribution to the field with a significant reduction in uncertainty from the Bayesian approach over the frequentist one. A future version of the manuscript, therefore, would benefit from greater precision of language as well as an economy and greater focus of terms linking the method to the biology. This is particularly the case around the exposition parts in Figure 1, Figure 2, and the “Hierarchical modelling” section.”

This is another important point. We have now made numerous small changes to tighten up the text in the paper, in response to both this point and the next point.

(2) “Language throughout could be sharpened. Subjectivity like “surprising outliers” could be removed and quirky grammar like “often small, ten is a typical” improved. There are also typos “an rate” etc that should be tidied up.”

As per previous response, we have made numerous tweaks and small improvements and feel that the paper is stronger in this respect.

(3) “Figure 1 caption. “It is a spectrum that depends” Is spectrum the right word here? Also, “thicker stroke” what does this refer to? Wasn’t immediately clear. In A, why is the whole animal within the R bracket that signifies brain regions, and then the brain regions are within the N bracket that signifies whole animals? Apart from the teal colouring, what are the other coloured regions in the image referring to? Improving this first figure would greatly help a reader unfamiliar with the context of the approach.”

We have replaced the word “spectrum” with “continuum”. We have replaced “ Observed quantities have been highlighted with a thicker stroke in the graphical model.” with “The observed data quantities, y_i to y_n, are highlighted with a thick line in the model diagrams”. We have added the following text to describe the red and green lines in panel A: “green and red lines indicate regions labeled as damaged”.

(4) “On P2 there is no discussion of priors when running through the advantage of the Bayesian approach. Is this a choice or an oversight? Priors do have a role in the later analysis.”

A short additional paragraph has been added to the introduction outlining the advantage of having a prior, but also noting that the obligation to pick a prior can be intimidating and that suggesting priors is one of the contributions of our paper: “A Bayesian model also includes a set of probability distributions, referred to as the prior, which represent those beliefs it is reasonable to hold about the statistical model parameters before actually doing the experiment. The prior can be thought of as an advantage, it allows us to include in our analysis our understanding of the data based on previous experiments. The prior also makes explicit in a Bayesian model assumptions that are often implicit in other approaches. However, having to design priors is often considered a challenge and here we hope to make this more straightforward by suggesting priors that are suitable for this class of data.”

(5) “On P4 more explanation would help greatly. Formulas like 23*10*4 or 50*6+50*4 are presented without explanation. What are the various numbers being multiplied? Regions, animals? Again, a clearer link between biological data and model structure would be advantageous.”

We have now modified this line to clearly state the numbers’ sources: “The index i runs over the full set of samples, which in this case comprises 23 brain regions ×10 animals ×4 groups ≈920 datapoints in the first study, and 50 brain regions × 6 HET animals + 50 brain regions × 4 KO animals ≈500 datapoints in the second.”

(6) “P6 and Results. Is it possible to show examples of the data set sampled from? Perhaps an image or two for the two experiments. Both Figures 4 and 5 as they currently are could be made slightly smaller to provide space for a small explanatory sub-panel. This would help ground the results.”

This is a good idea. We have now added heatmap visualisations of both entire datasets to revised versions of Figures 4 and 5 (assuming that this is what the reviewer was suggesting).

Author response:

Reviewer #1 (Public review):

Weaknesses:

(1) Data:

a) The main weakness in the data is the lack of functional and anatomical data from mouse hair bundles. While the authors compensate in part for this difficulty with bullfrog crista bundles, those data are also fragmentary - one TEM and 2 exemplar videos. Much of the novelty of the EM depends on the different appearance of stretches of a single kinocilium - can we be sure of the absence of the central microtubule singlets at the ends?

Our single-cell RNA-seq findings show that genes related to motile cilia are specifically expressed in vestibular hair cells. This has not been demonstrated before. We have also provided supporting evidence using electrophysiology and imaging from bullfrogs and mice. Although no ultrastructural images of mouse vestibular kinocilia were provided in our study, transmission electron micrograph of mouse vestibular kinocilia has been published (O’Donnell and Zheng, 2022). The mouse vestibular kinocilia have a “9+2” microtubule configuration with nine doublet microtubules surrounding two central singlet microtubules. This finding contrasts with a previous study, which demonstrated that the vestibular kinocilia from guinea pigs lack central singlet microtubules and inner dynein arms, whereas outer dynein arms and radial spokes are present (Kikuchi et al., 1989). The central pair of microtubules is absent at the end of the bullfrog saccular kinocilium (Fig. 7A).  We would like to point out that the dual identity of primary and motile cilia is not just based on the TEM images. The kinocilium has long been considered a specialized cilium, and its role as a primary cilium during development has been demonstrated before (Moon et al., 2020; Shi et al., 2022).  

In most motile cilia, the central pair complex (CPC) does not originate directly from the basal body; instead, it begins a short distance above the transition zone, a feature that already illustrates variation in CPC assembly across systems (Lechtreck et al., 2013). The CPC can also show variation in its spatial extent: for example, in mammalian sperm axonemes, it can terminate before reaching the distal end of the axoneme (Fawcett and Ito, 1965). In addition, CPC orientation differs across organisms: in metazoans and Trypanosoma, the CPC is fixed relative to the outer doublets, whereas in Chlamydomonas and ciliates it twists within the axoneme (Lechtreck et al., 2013). Such variation has been described in multiple motile cilia and flagella and is therefore not unique to vestibular kinocilia. What appears more unusual in our data is the organization at the distal tip, where a distinct distal head is present, similar to cilia tip morphologies recently described in human islet cells (Polino et al., 2023). Although this feature is intriguing, we interpret it primarily as a structural signature rather than as evidence for a specialized motile adaptation, and we will moderate our interpretation accordingly in the revision.

b) While it was a good idea to compare ciliary motility expression in published P2 datasets for mouse cochlear and vestibular hair cells for comparison with the authors' adult hair cell data, the presentation is too superficial to assess (Figure 6C-E; text from line 336) - it is hard to see the basis for concluding that motility genes are specifically lower in P2 cochlear hair cells than vestibular hair cells. Visually, it is striking that CHCs have much darker bands for about 10 motility-related genes.

We aimed to show that kinocilia in neonatal cochlear and vestibular hair cells are largely similar, except that neonatal cochlear hair cells lack key genes and proteins required for the motile apparatus. While these genes (e.g., Dynll1, Dynll2, Dynlrb1, Cetn2, and Mdh1) appear more highly expressed in P2 cochlear hair cells, they are not uniquely associated with the axoneme. For example, Dynll1/2 and Dynlrb1 are components of the cytoplasmic dynein-1 complex (Pfister et al., 2006), Cetn2 has multiple basic cellular functions beyond cilia (e.g., centrosome organization, DNA repair), and Mdh1 encodes a cytosolic malate dehydrogenase involved in central metabolic pathways such as the citric acid cycle and malate–aspartate shuttle. This contrasts with axonemal dyneins, which are uniquely required for cilia motility. To avoid ambiguity, we will mark such cytoplasmic or multifunctional genes with stars in both Figure 5G and Figure 6D together with legend in the revised manuscript.

Although those genes (i.e., Dynll1, Dynll2, Dynlrb1, Cetn2, and Mdh1) are highly expressed in neonatal cochlear hair cells, key genes for motile machinery are not detected. For example, Dnah6, Dnah5, and Wdr66 are not expressed in the P2 cochlear hair cells.  Dnah6 and Dnah5 encode axonemal dynein and are part of inner and outer dynein arms while Wdr66 is a component of radial spokes. Importantly, we did not detect the expression of CCDC39 and CCDC40 in kinocilia of P2 cochlear hair cells.  Axonemal CCDC39 and CCDC40 are the molecular rulers that organize the axonemal structure in the 96-nm repeating interactome and are required for the assembly of IDAs and N-DRC for ciliary motility (Becker-Heck et al., 2011; Merveille et al., 2011; Oda et al., 2014). We will modify Figure 6D to highlight the key difference between P2 cochlear and vestibular hair cells in the revised manuscript. We will also revise the text so that the key differences will clearly be described.

(2) Interpretation:

The authors take the view that kinociliary motility is likely to be normally present but is rare in their observations because the conditions are not right. But while others have described some (rare) kinociliary motility in fish organs (Rusch & Thurm 1990), they interpreted its occurrence as a sign of pathology. Indeed, in this paper, it is not clear, or even discussed, how kinociliary motility would help with mechanosensitivity in mature hair bundles. Rather, the presence of an autonomous rhythm would actively interfere with generating temporally faithful representations of the head motions that drive vestibular hair cells.

Spontaneous flagella-like rhythmic beating of kinocilia in vestibular HCs in frogs and eels (Flock et al., 1977; Rüsch and Thurm, 1990) and in zebrafish early otic vesicle (Stooke-Vaughan et al., 2012; Wu et al., 2011) has been reported previously. Based on Rüsch and Thurm (1990), spontaneous kinocilia motility occurred under non-physiological conditions and was interpreted as a sign of cellular deterioration rather than a normal feature. We speculate that deterioration under non-physiological conditions may lead to the disruption of lateral links between the kinocilium and the stereociliary bundle, effectively unloading the kinocilium and allowing it to move more freely. Additionally, fluctuations in intracellular ATP levels may contribute, as ciliary motility is highly ATP-dependent; when ATP is depleted, beating ceases. Similar phenomena have been documented in respiratory epithelia, where ciliary activity can temporarily pause. Nevertheless, the fact that kinocilia can exhibit spontaneous motility under these conditions indicates that they possess the motile machinery necessary for such beating. Irrespective of the condition, cilia without the molecular machinery required for motility will not be able to move.

We agree with the reviewer that, based on the present data, it is difficult to know the functional role of kinocilia and whether the presence of such autonomous rhythm would interfere with temporal fidelity. Spontaneous bundle motion, driven by the active process associated with mechanotransduction, was observed in bullfrog saccular hair cells (Benser et al., 1996; Martin et al., 2003). We will revise the discussion to clarify this important point of the reviewer. Specifically, we will emphasize that our observations of ciliary beating in the ex vivo conditions may not reflect its properties in the mature in vivo context, but rather a byproduct of motile machinery clearly present in the kinocilia. We speculate that this machinery in mature hair cells could operate in a more subtle mode—modulating the rigor state of dynein arms or related axonemal structures to influence kinociliary mechanics and, in turn, bundle stiffness in response to stimuli or signaling cues. Such a mechanism could either enhance sensitivity or introduce filtering properties, thereby contributing to the fine control of mechanosensory function without compromising temporal fidelity. Future studies using loss-of-function approach will be needed to reveal the unexplored role(s) of kinocilia for vestibular hair cells in vertebrates. 

Could kinociliary beating play other roles, possibly during development - for example, by interacting with forming accessory structures (but see Whitfield 2020) or by activating mechanosensitivity cell-autonomously, before mature stimulation mechanisms are in place? Then a latent capacity to beat in mature vestibular hair cells might be activated by stressful conditions, as speculated regarding persistent Piezo channels that are normally silent in mature cochlear hair cells but may reappear when TMC channel gating is broken (Beurg and Fettiplace 2017). While these are highly speculative thoughts, there is a need in the paper for more nuanced consideration of whether the observed motility is normal and what good it would do.

We thank the reviewer for these excellent suggestions. We agree that kinociliary motility could plausibly serve roles during development, for example by guiding hair bundle formation or by contributing to early mechanosensitivity and spontaneous activity before mature stimulation mechanisms are established. It is also possible that the motility machinery represents a latent capacity in mature vestibular hair cells that could be reactivated under stress or pathological conditions. We will revise the Discussion to address these possibilities and to provide a more nuanced consideration of whether the observed motility is normal and what potential functions it might serve.

Reviewer #2 (Public review):

Summary:

In this study, the authors compared the transcriptomes of the various types of hair cells contained in the sensory epithelia of the cochlea and vestibular organs of the mouse inner ear. The analysis of their transcriptomic data led to novel insights into the potential function of the kinocilium.

Strengths:

The novel findings for the kinocilium gene expression, along with the demonstration that some kinocilia demonstrate rhythmic beating as would be seen for known motile cilia, are fascinating. It is possible that perhaps the kinocilium, known to play a very important role in the orientation of the stereocilia, may have a gene expression pattern that is more like a primary cilium early in development and later in mature hair cells, more like a motile cilium. Since the kinocilium is retained in vestibular hair cells, it makes sense that it is playing a different role in these mature cells than its role in the cochlea.

Another major strength of this study, which cannot be overstated, is that for the transcriptome analysis, they are using mature mice. To date, there is a lot of data from many labs for embryonic and neonatal hair cells, but very little transcriptomic data on the mature hair cells. They do a nice job in presenting the differences in marker gene expression between the 4 hair cell types. This information is very useful to those labs studying regeneration or generation of hair cells from ES cell cultures. One of the biggest questions these labs confront is what type of hair cells develop in these systems. The more markers available, the better. These data will also allow researchers in the field to compare developing hair cells with mature hair cells to see what genes are only required during development and not in later functioning hair cells.

We would like to thank reviewer 2 for his/her comments and hope that the datasets provided in this manuscript will be a useful resource for researchers in the auditory and vestibular neuroscience community.

Joint Recommendations:

We will make changes in the revision based on the joint recommendations of the two reviewers.

References

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Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

The authors aim to explore the effects of the electrogenic sodium-potassium pump (Na<SUP>+</SUP>/K<SUP>+</SUP>ATPase) on the computational properties of highly active spiking neurons, using the weakly-electric fish electrocyte as a model system. Their work highlights how the pump's electrogenicity, while essential for maintaining ionic gradients, introduces challenges in neuronal firing stability and signal processing, especially in cells that fire at high rates. The study identifies compensatory mechanisms that cells might use to counteract these effects, and speculates on the role of voltage dependence in the pump's behavior, suggesting that Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase could be a factor in neuronal dysfunctions and diseases

Strengths:

(1) The study explores a less-examined aspect of neural dynamics-the effects of Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase electrogenicity. It offers a new perspective by highlighting the pump's role not only in ion homeostasis but also in its potential influence on neural computation.

(2) The mathematical modeling used is a significant strength, providing a clear and controlled framework to explore the effects of the Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase on spiking cells. This approach allows for the systematic testing of different conditions and behaviors that might be difficult to observe directly in biological experiments.

(3) The study proposes several interesting compensatory mechanisms, such as sodium leak channelsand extracellular potassium buffering, which provide useful theoretical frameworks for understanding how neurons maintain firing rate control despite the pump's effects.

Weaknesses:

(1) While the modeling approach provides valuable insights, the lack of experimental data to validate the model's predictions weakens the overall conclusions.

(2)The proposed compensatory mechanisms are discussed primarily in theoretical terms without providing quantitative estimates of their impact on the neuron's metabolic cost or other physiological parameters.

Comments on revisions:

The revised manuscript is notably improved.

We thank the reviewer for their concise and accurate summary and appreciate the constructive feedback on the article’s strengths and weaknesses. Experimental work is beyond the scope of our modeling-based study. However, we would like our work to serve as a framework for future experimental studies into the role of the electrogenic pump current (and its possible compensatory currents) in disease, and its role in evolution of highly specialized excitable cells (such as electrocytes).

Quantitative estimates of metabolic costs in this study are limited to the ATP that is required to fuel the Na<SUP>+</SUP>/K<SUP>+</SUP> pump. By integrating the net pump current over time and dividing by one elemental charge, one can find the rate of ATP that is consumed by the Na<SUP>+</SUP>/K<SUP>+</SUP> pump for either compensatory mechanism. The difference in net pump current is thus proportional to ATP consumption, which allows for a direct comparison of the cost efficiency of the Na<SUP>+</SUP>/K<SUP>+</SUP> pump for each proposed compensatory mechanism. The Na<SUP>+</SUP>/K<SUP>+</SUP> pump is however not the only ATP-consuming element in the electrocyte, and some of the compensatory mechanisms induce other costs related to cell ‘housekeeping’ or presynaptic processes. We now added a section in the appendix titled ‘Considerations on metabolic costs of compensatory mechanisms’ (section 11.4), where we provide rough estimates on the influence of the compensatory mechanisms on the total metabolic costs of the cell and membrane space occupation. Although we argue that according these rough estimates, the impact of discussed compensatory mechanisms could be significant, due to the absence of more detailed experimental quantification, a plausible quantitative cost estimate on the whole cell level remains beyond the scope of this article.

Reviewer #1 (Recommendations for the authors):

I just have a few recommendations on the updated manuscript.

(1) When exploring the different roles of Na<SUP>+</SUP>/K<SUP>+</SUP>-ATPase in the Results section, the authors employed many different models. For instance, the voltage equation on page 15, voltage equation (2) on page 22, voltage equation (12) on page 24, voltage equation (30) on page 32, and voltage equation (38) on page 35 are presented as the master equations for their respective biophysical models. Meanwhile, the phase models are presented on page 29 and page 33. I would recommend that the authors clearly specify which equations correspond to each subsection of the Results section and explicitly state which equations were used to generate the data in each figure. This would help readers more easily follow the connections between the models, the results, and the figures.

We thank the reviewer for pointing out that the links of the different voltage equations to the results could be expressed more explicitly in the article. All simulations were done using the ‘master equation’  expressed in Eq. 2, and the other voltage equations that are specified in the article (in the new version of the article Eqs. 13, 31, and 39) are reformulations of Eq. 2 to analytically show different properties of the voltage equation (Eq. 2). This has now been mentioned in the article when formulating the voltage equations, and the equation for the total leak current (in the new version Eq. 3) has been added for completeness.

(2) The authors may want to revisit their description and references concerning Eigenmannia virescens. For example, wave-type weakly electric fish (e.g., Eigenmannia) and pulse-type weakly electric fish (e.g., Gymnotus carapo) exhibit large differences, making references 52-55 may be inappropriate for subsection 4.3.1, as these studies focus on Gymnotus carapo. Additionally, even within wave-type species, chirp patterns vary. For example, Eigenmannia can exhibit short "pauses"-type chirps, whereas Apteronotus leptorhynchus (another waver-form fish) does not (https://pubmed.ncbi.nlm.nih.gov/14692494/).

We thank the reviewer for pointing this out. The citations and phrasing in sections 4.3.1 and 4.3.2 have been updated to specifically refer to the weakly electric fish e. Virescens.

(3) Table on page 21: Please explain why the parameter value (13.5mM) of [Na<SUP>^</SUP>+]_{in} is 10 timeslarger than its value (1.35mM) in reference [26]? How does this value (13.5mM) compare with the range of variable [Na<SUP>^</SUP>+]_{in} in equation (6)?

The intracellular sodium concentration in reference [26] was reported to be 1.35 mM, but the authors also reported an extracellular sodium concentration of 120 mM, and a sodium reversal potential of 55 mV. Upon calculating the sodium reversal potential, we found that an intracellular sodium concentration of 1.35 mM would give a sodium reversal potential of 113 mV. An intracellular sodium concentration of 13.5 mM, on the other hand, leads to the reported and physiological reversal potential of 55 mV. This has now been clarified in the article, and the connection between this value and Eq. 6 (Eq. 7 in the new version) has also been clarified.

Reviewer #2 (Public review):

Summary:

The paper by Weerdmeester, Schleimer, and Schreiber uses computational models to present the biological constraints under which electrocytes - specialized, highly active cells that facilitate electro-sensing in weakly electric fish-may operate. The authors suggest potential solutions that these cells could employ to circumvent these constraints.

Electrocytes are highly active or spiking (greater than 300Hz) for sustained periods (for minutes to hours), and such activity is possible due to an influx of sodium and efflux of potassium ions into these cells after each spike. The resulting ion imbalance must be restored, which in electrocytes, as with many other biological cells, is facilitated by the Na-K pumps at the expense of biological energy, i.e., ATP molecules. For each ATP molecule the pump uses, three positively charged sodium ions from the intracellular space are exchanged for two positively charged potassium ions from the extracellular space. This creates a net efflux of positive ions into the extracellular space, resulting in hyperpolarized potentials for the cell over time. For most cells, this does not pose an issue, as their firing rate is much slower, and other compensatory mechanisms and pumps can effectively restore the ion imbalances. However, in the electrocytes of weakly electric fish, which spike at exceptionally high rates, the net efflux of positive ions presents a challenge. Additionally, these cells are involved in critical communication and survival behaviors, underscoring their essential role in reliable functioning.

In a computational model, the authors test four increasingly complex solutions to the problem of counteracting the hyperpolarized states that occur due to continuous NaK pump action to sustain baseline activity. First, they propose a solution for a well-matched Na leak channel that operates in conjunction with the NaK pump, counteracting the hyperpolarizing states naturally. Their model shows that when such an orchestrated Na leak current is not included, quick changes in the firing rates could have unexpected side effects. Secondly, they study the implications of this cell in the context of chirps-a means of communication between individual fish. Here, an upstream pacemaking neuron entrains the electrocyte to spike, which ceases to produce a so-called chirp - a brief pause in the sustained activity of the electrocytes. In their model, the authors demonstrate that including the extracellular potassium buffer is necessary to obtain a reliable chirp signal. Thirdly, they tested another means of communication in which there was a sudden increase in the firing rate of the electrocyte, followed by a decay to the baseline. For this to occur reliably, the authors emphasize that a strong synaptic connection between the pacemaker neuron and the electrocyte is necessary. Finally, since these cells are energy-intensive, they hypothesize that electrocytes may have energy-efficient action potentials, for which their NaK pumps may be sensitive to the membrane voltages and perform course correction rapidly.

Strengths:

The authors extend an existing electrocyte model (Joos et al., 2018) based on the classical Hodgkin and Huxley conductance-based models of sodium and potassium currents to include the dynamics of the sodium-potassium (NaK) pump. The authors estimate the pump's properties based on reasonable assumptions related to the leak potential. Their proposed solutions are valid and may be employed by weakly electric fish. The authors explore theoretical solutions to electrosensing behavior that compound and suggest that all these solutions must be simultaneously active for the survival and behavior of the fish. This work provides a good starting point for conducting in vivo experiments to determine which of these proposed solutions the fish employ and their relative importance. The authors include testable hypotheses for their computational models.

Weaknesses:

The model for action potential generation simplifies ion dynamics by considering only sodium and potassium currents, excluding other ions like calcium. The ion channels considered are assumed to be static, without any dynamic regulation such as post-translational modifications. For instance, a sodium-dependent potassium pump could modulate potassium leak and spike amplitude (Markham et al., 2013).

This work considers only the sodium-potassium (NaK) pumps to restore ion gradients. However, in many cells, several other ion pumps, exchangers, and symporters are simultaneously present and actively participate in restoring ion gradients. When sodium currents dominate action potentials, and thus when NaK pumps play a critical role, such as the case in Eigenmannia virescens, the present study is valid. However, since other biological processes may find different solutions to address the pump's non-electroneutral nature, the generalizability of the results in this work to other fast-spiking cell types is limited. For example, each spike could include a small calcium ion influx that could be buffered or extracted via a sodium-calcium exchanger.

We thank the reviewer for the detailed summary and the updated identified strengths and weaknesses. The current article indeed focuses on and isolates the interplay between sodium currents, potassium currents, and sodium-potassium pump currents. As discussed in section 5.1, in excitable cells where these currents are the main players in action-potential generation, the results presented in this article are applicable. The contribution of post-translational effects of ion channels, other ionic currents, and other active transporters and pumps, could be exciting avenues for further studies

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Reviewer #2 (Recommendations for the authors):

Thank you for addressing my comments.

All the figures are now consistent. The color schema used is clear.

The methods and discussions expansions improve the paper.

Including the model assumptions and simplifications is appreciated.

Including internal references is helpful.

The equations are clear, and the references have been fixed.

I am content with the changes. I have updated my review accordingly.

We thank the reviewer for their initial constructive comments that lead to the significant improvement of the article.

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Although this is technically correct, the article is about electrocommunication signals and does not focus on sensing.

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electrocommunication

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Judging from the cited article, I think this should be a sodium-dependent potassium current.