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Reply to the reviewers

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

In this study, it is shown that cofilin severs actin filaments slowly when fascin is present. Authors show that this is due to slower cluster nucleation of cofilin on fascin-induced actin bundles. Interestingly, the authors show that cofilin binding promotes helicity in actin filament bundles which in turn promotes fascin exclusion and more cofilin clustering in adjacent filament bundles; thus, inducing local transmission of structural changes.

The authors use an elegant approach, and the data is nicely presented. Overall, I

consider that this manuscript is in good shape to be published. It might benefit from language editing, though.

We thank the reviewer for their positive comments. We have edited the manuscript to improve its readability (changes are in blue in the manuscript).

Reviewer #1 (Significance (Required)):

According to me the significance of this manuscript is that elegantly shows the molecular details of the cofilin severing effect of fascin-induced actin filament bundles. The authors show that cofilin binding promotes helicity in actin filament bundles which in turn promotes fascin exclusion and more cofilin clustering in adjacent filament bundles; thus, inducing local transmission of structural changes.

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

Summary:

In this study, Chikireddy et al. perform a series of experiments in which they compare the efficiency of cofilin-mediated severing and actin filament disassembly on individual filaments versus bundles of different sizes from by the actin-bundling protein fascin. The key outcome, quite distinct from previously published conclusions by the authors themselves and other authors, is that fascin bundling actually reduces cofilin-mediated severing mostly because of much slower "nucleation" of cofilin clusters on fascin-bound filament bundles. Cofilin cluster formation is followed by local fascin removal, and the nucleation of a cofilin cluster on an adjacent bundle in the absence of fascin is strongly enhanced. The reason for the latter surprising observation is not entirely clear, but proposed to arise from cofilin-mediated changes in filament helicity of neighboring filaments. To my understanding, the main reason why fascin protects from cofilin severing here rather than enhancing it (as reported previously) is due to the lack of constraining of the induced, cofilin-mediated twist, because if this twist is constrained e.g. by anchoring of the bundles to the surface chamber, then severing by cofilin is accelerated.

We thank the reviewer for their positive feedback on the manuscript. We have substantially edited the manuscript in light of the insightful comments of the reviewer (changes are in blue).

Major comments:

I think the study is very well done, most experiments are super-elegant and controlled; I really don't have any objections against the conclusions drawn, as most of what I have seen is totally justified and reasonable. So from a scientific point of view, I can easily agree with all the major conclusions drawn, and so in my view, this should be published fast.

Minor comments:

There are two minor points that could be addressed:

1) I am not entirely convinced by the conclusions drawn from the EM images shown in Figure 6A, and in particular by the filaments in two-filament bundles locally twisting around each other (without breaking) at spatial sites lacking fascin and decorated by cofilin. This is hard to imagine for me, and the evidence for something like this happening is not very strong, as in the EM, only larger bundles could be observed. In addition, I am not sure that the braiding of filaments seen in the presence of cofilin is really occurring just locally on cofilin-decorated bundle segments and thus indeed coincides with loss of fascin as proposed in the scheme in Fig. 6B.

Can the authors exclude that the braiding is not caused by some experimental artefact, as induced perhaps by sample preparation for negative staining?

We thank the reviewer for raising this point. We have repeated the negative staining EM experiments several times and now show new images and quantification (new Supp Fig. 13). In our new series of experiments, the braiding that was previously shown in Fig. 6 proved difficult to reproduce and to quantify. We therefore decided to remove EM observations from the main Fig. 6, and we no longer present them as evidence supporting the mechanism that we propose for inter-filament cooperativity.

From EM images, we now quantify the frequency of fragmentation of large actin filament bundles. We observed that bundles often terminate with the ends of their filaments in close proximity, consistent with sharp breaks due to co-localized cofilin clusters.

We have rewritten this part of the result section in the manuscript which now reads : ‘To further investigate larger bundles, we imaged them using negative staining electron microscopy. In the absence of cofilin, filaments in bundles are arranged in a parallel manner, as previously reported in vitro (Jansen et al, 2011). Compared with the control, filament bundles exposed to cofilin show numerous sharp breaks (65 breaks per 122 µm of bundles, versus 4 breaks per 68 µm in the control. Supp. Fig. 13). This is consistent with bundle fragmentation occurring at boundaries of co-localized cofilin clusters.’

Did the authors quantify the occurrence of such braided bundle segments with and without cofilin?

How large are these braided segments on average when you quantify them? Would you also see them if you prepared the bundles for an alternative EM-technique, such as Cryo-EM, for instance?

As mentioned in the answer to the previous point, the braided segments proved difficult to reproduce and quantify, and we have removed EM experiments from the main figure 6. Instead of the braided segments, we now quantify the severing of the bundles, and the distribution of filament ends at the extremities of the bundles (new Supp. Fig. 13).

We have not tried Cryo-EM due to limited access to such experimental tools within the timeframe of the study.

This may admittedly all be experimentally challenging, but would it be possible to combine the negative staining of filaments with staining for cofilin and/or fascin using immunogold technology, to prove that the braided segments do indeed correlate with high cofilin and low fascin concentrations? In the absence of such data, and in particular in the absence of a clear quantification, the proposal is too strong in my view. Finally, it would be nice (albeit not essential I guess) to also look at two-filament bundles. The authors stated these can not be easily generated due to the tendency of fascin to promote the formation of larger bundles, but can this not be titrated/tuned somehow by lowering fascin concentrations, to come closer in reality to what is proposed to occur in the scheme in Figure 6B? In any case, the way the data are presented right now appears to constitute a pretty large gap between experimental evidence and theoretical model.

We agree with the reviewer that EM observations are limited and, alone, do not provide strong evidence in favor of braiding/super-twisting being the mechanism responsible for inter-filament cooperativity (please see our answers to the points above). We have performed negative staining EM assays at higher cofilin-1 concentration (500 nM) compared to microfluidics assays, in order for cofilin to quickly bind to filaments, even in large bundles, so that our chances to capture bundles targeted by cofilin would be high.

Nevertheless, both microfluidics and EM observations point in the same direction : bundle fragmentation by cofilin is caused by the co-localized cooperative nucleation of cofilin clusters.

2) I think that the proposal of cofilin-decorated filaments to "transfer" the resulting cofilin-induced changes in filament helicity onto neighboring filaments in the bundle, which is proposed to occur locally and in the absence of fascin is a bit vague, and difficult to understand mechanistically. Can the authors speculate, at least, how they think this would occur? Are there no alternative possibilities for explaining obtained results? Maybe I am missing something here, but with considering cofilin to be monomeric and only harboring one actin-binding site, this proposal of helicity transfer onto neighboring filaments seems inconclusive.

On single actin filaments, the change of helicity induced by cofilin binding has been observed by many groups using EM and cryoEM (e.g. McGough et al, JBC 1997 10.1083/jcb.138.4.771; Egelman et al, PNAS 2011 10.1073/pnas.1110109108 ; Huehn et al, JBC 2018 10.1074/jbc.AC118.001843). These studies have revealed that actin subunits get ‘tilted’ relative to their original orientation along the filament long axis. This leads to the shortening of the helical pitch for cofilin-saturated actin filament segments.

In our assays, the progressive binding of cofilin along a single filament creates a cluster where all actin subunits are tilted and the helical pitch of the filaments within the cluster is shortened (from a half pitch of 36 nm down to 27 nm). This change of helicity in a cluster induces the rotation of one end of the filament relative to the other (as we have shown previously in Wioland et al, PNAS 2019). Therefore, if two parallel filaments are stapled together, the local twisting of one filament causes the twisting of the other in the overlapping region.

We have rephrased this point to more clearly explain this in the last paragraph of the results section:

“From our kinetic analysis, we propose the following model that recapitulates the binding of cofilin to fascin-induced 2-filament bundles (Fig. 6D). Initially, actin filaments in fascin-induced bundles are in conformations that are less favorable for cofilin binding than isolated actin filaments. Once a cofilin cluster has nucleated, its expansion locally triggers fascin unbinding and prevents it from rebinding. The increase of filament helicity induced by cofilin causes a local twisting of the entire bundle, thereby changing the helicity of the adjacent filament in the fascin-free region facing the cofilin cluster. In this region, the increase in filament helicity enhances cofilin affinity, and thus locally promotes the nucleation of a cofilin cluster (inter-filament cooperativity).”

We have tried to think of other alternative scenarios that might explain our observations, but none appeared to be valid.

Reviewer #2 (Significance (Required)):

General assessment:

The strength of this study is that owing, at least in part, to the microfluidics devices employed and the careful biochemistry, the experimental setups are super-controlled and clean, and they are used in a highly innovative and elegant fashion. The simulations are also nice! A limitation is that it is not entirely clear how precisely the main observations can be translated to what's happening in vivo. The results are largely dependent on the bundles not being constrained I understand, so to what extent would bundles be unconstrained in vivo? Perhaps this is not so important, because the experimental setup allows the authors to dissect specific biochemical behaviors and inter-dependencies between distinct actin binding proteins, but the latter view (if correct) could be stated more clearly!

We thank the reviewer for their remarks. We have updated the part where we discuss the biological implications of our in vitro observations to better explain how the twist-constraints expected for fascin bundles in cells would accelerate cofilin bundle disassembly.

Advance:

As stated above, the results are opposite to the proposed synergistic activities of fascin and cofilin observed for bundles previously, perhaps because they were not constrained. So although touched in part and in a very polite fashion in the discussion, the authors could specify more clearly what the differences between the studies are, and which of the distinct activities observed either here or in previous literature will be dominant or more relevant to consider in the future? This will be hard to discern as is now, in particular for non-experts.

We agree with the reviewer that the manuscript will benefit from discussing more in depth the plausible reasons why our experimental observations are in disagreement with the earlier interpretation by Breitsprecher and colleagues. We have extended our discussion on this point, which now reads: “Previously, using pyrene-actin bulk experiments, Breitsprecher and colleagues observed a diminished cofilin binding to fascin-induced filament bundles (Breitsprecher et al, 2011). In spite of this, their observation of fluorescently labeled actin filament bundles seemed to indicate an efficient severing activity. Since cofilin was not fluorescently labeled, they could not observe cofilin clusters, and they proposed that severing was enhanced because fascin served as anchors along filaments and impeded cofilin-induced changes in filament helicity”

Audience:

This manuscript will be most influential for a specialized audience interested in the complexities of biochemical activities of specific actin binding proteins when looking at them in combination. Although specialized, this is still a quite relevant audience though, since prominent actin binding proteins like cofilin are highly important in virtually any cell type and various actin structures, hence of broad relevance again in this respect.

Expertise:

I am a cell biologist and geneticist interested in actin dynamics and actin-based, motile processes.

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

My only major concern that is that although the authors provide data that strongly supports interfilament cooperativity in two filament bundles for cofilin binding, the evidence to support that this induces filament twist on the opposing filament is not strong enough to conclusively establish this as the mechanism for the observed interfilament cooperativity. This is stated as such in the results section as a proposed model, but stated with more certainty than the presented data supports in the discussion. It might be better, based on the data presented, to state this as one possible mechanism for the observed cooperativity.

We thank the reviewer for their remark. We have edited our discussion section to clearly say that inter-filament cooperativity arises from cofilin-induced filament twisting is a proposed model that would best account for what we observed: “Indeed, we report here the exclusion of fascin from within cofilin clusters, and a strong increase in the nucleation of cofilin clusters on adjacent filaments. This inter-filament cooperativity mechanism leads to the co-localized nucleation of cofilin clusters, and permits bundle fragmentation faster than if the nucleation of cofilin clusters on adjacent filaments were purely random. To our knowledge, this is the first time such inter-filament cooperativity is ever reported. To explain this mechanism, we propose that the cofilin-induced change of helicity produced locally on one filament can be transmitted to the adjacent filaments within the bundle (Fig. 6D).”

So far, we have been unable to propose alternative mechanisms that could explain our observations in light of what is known for cofilin at the single filament level (a similar point was raised by reviewer #2, please see above).

Areas within the paper, if addressed, will improve the arguments presented as well as the readability of the paper.

(1) The authors use both the terms cofilin binding (in section I of the results) as well as cofilin nucleation (in section III of the results). It is unclear if these terms are meant to indicate the same, or different, processes. The manuscript would benefit from a clear explanation of the steps of cofilin-mediated disassembly measured and quantified in the experiments, namely nucleation (or binding), cluster growth, and filament or bundle fragmentation. A clear description of these steps would also allow the reader to follow the logic of the experiments from Figure 3 to Figure 5.

We have edited the introduction to better describe the different steps of cofilin activity, and to remove any ambiguity whereas we are referring to cofilin binding or cofilin nucleation.

2) Throughout the paper, the authors move from single filaments, to 2-filament bundles, to multifilament bundles, using different concentrations of fascin and cofilin. Given the biphasic behavior of cofilin, namely that low concentrations favor severing and high concentrations can favor coating and filament stabilization, I think it is important that concentrations for the components are consistent across experiments, and if changes of concentrations of important components (such as cofilin and fascin) are changed, a clear explanation as to why is included.

As explained in the beginning of the result section, most of our experiments and quantification of cofilin activity using the microfluidics assay were done using 200 nM fascin and 200 nM cofilin as a standard. This is the case, in particular, for all the data shown in Fig 2, 3 and 4, where we compare the behavior of single filaments, 2-filament bundles, and larger bundles, exposed to the same protein concentrations.

We have also explored higher fascin and cofilin concentrations to document their respective impact, always mentioning any change in concentration. We agree with the reviewer that cofilin activity is biphasic at the single filament level (in the range of 0 to 1 µM for mammalian ADF/cofilin, at physiological pH 7.4). In the case of fascin-induced bundles (already for two-filament bundles), filament saturation by cofilin, and thus their stabilization, will occur at higher cofilin concentration. This is mainly due to the lower nucleation activity of cofilin on fascin-induced bundles, preventing the nucleation of numerous cofilin clusters that will eventually fuse together, thus preventing saturation of filament bundles by cofilin before bundle fragmentation.

(3) In Figure 2, it is mentioned that for the spectrin seeds with the microfluidics, the filaments consisting of larger bundles were not analyzed along with the single filament and 2-filament bundles. Instead, a different experiment with seeds attached to beads is used to assess larger filament bundles. Why were larger bundles not analyzed in the microfluidic experiment?

We appreciate the insightful observation by the reviewer. When elongating actin filaments from spectrin-actin seeds, the seeds are randomly located on the glass coverslip of the microfluidics chamber. Upon exposure to fascin, only a subsection of any filament will be in contact with one or multiple filaments, ultimately forming a bundle due to the presence of fascin. In the case of high filament densities leading to large bundles, it is very difficult to identify the exact subsection of each filament which is engaged in a bundle or not. Despite our attempts to image individual filaments before and after exposure to fascin for enhanced clarity, the inherent difficulty persisted.

This limitation hindered our ability to quantify cofilin activity on large bundles when using spectrin-actin seeds randomly distributed on glass. To address this, we opted for an alternative approach involving micron-sized beads coated with spectrin-actin seeds. This modification not only circumvents the aforementioned limitation but also aids in the formation of larger bundles (up to 10 filaments per bundle). This adjustment significantly enhances our ability to study and quantify cofilin activity on larger bundles, contributing to a more robust and comprehensive understanding of cofilin activity on bundles.

And conversely, why were 2 filament bundles not assessed with the beads? Comparing the findings on two filament bundles with the findings on multifilament bundles would be easier for the reader if the small and large bundles were evaluated in the same experiments. If this is not experimentally feasible, the authors need to provide clearer explanation as to why this analysis is not included.

Actually, we did assess 2-filament bundles in the bead assay. The cofilin activity on 2-filament bundles from beads are reported, along with larger bundles, in figure 3E-F for nucleation, and in figure 4C for cofilin cluster growth rates.

(4) The authors indicate that at increased fascin concentration (1uM) that single filaments decrease the nucleation rate of cofilin clusters. The authors should comment on the mechanism for fascin (at 1uM concentration) for affecting cofilin binding.

We thank the review for this comment. We now comment on this mechanism in the result section:

“This observation is consistent with the low affinity of fascin for the side of single actin filaments. Furthermore, this indicates that cofilin and fascin may have overlapping binding sites, or that a more complex competition may exist between the two proteins, where the binding of one protein would induce conformational changes on neighboring actin subunits affecting the binding of the other protein.”

(5) The authors should determine and include the dissociation rate for the labeled cofilin used in this study, especially given the proposed mechanism for cofilin excluding fascin within the bundles.

• If the reviewer means that we need to characterize the behavior of the labeled cofilin: in Wioland et al 2017, we have previously reported that cofilin dissociates slowly from cluster boundaries (at 0.7 s-1 for cofilin-1 on alpha-skeletal rabbit actin, as used in the present study) and extremely slowly from inside a cofilin cluster (~2.10-5 s-1).

• If the reviewer means that we should investigate the competition between fascin and cofilin along bundles: we agree that this is indeed an interesting question. However this is quite complex because many unknown parameters are involved. In addition to the on/off-rates of each protein and how it is affected by the presence or the proximity of the other protein, we need to consider that fascin has fewer binding sites than cofilin, and that their accessibility changes as the helicity of the filament evolves as cofilin binds. Investigating this question would require many experiments, which we would need to confront to a model. We believe that this is out of the scope of this manuscript.

(6) For Figure 4, D and E, what do the dynamics of fascin and cofilin signal look like on a larger filament bundle? It would be informative to provide the cofilin cluster nucleation rate on larger filament bundles with a range of fascin concentrations (as in 3D for a two filament bundle).

It would be interesting indeed to investigate the dynamics of fascin and cofilin on larger bundles. However, this experiment is quite challenging due to the fluorescence background of fluorescently-labeled fascin in our microfluidics assay (regardless of bundle size). We have been unable to perform this assay with success on large bundles. Moreover, it is difficult for us to carry out more of these experiments now that the first author of the study has left the lab.

However, based on our results, we would expect that, for large bundles, increasing fascin concentration would also have a limited impact on the reduction of cofilin nucleation. Indeed, for 2-filament bundles, we can note that the increase of fascin concentration has a more limited impact on the nucleation of cofilin clusters (fig. 3D, roughly ~2 fold decrease for fascin from 100 to 500 nM), than the number of filaments per bundle (fig. 3F, a 10-fold decrease when increasing the size of a bundle from 2 to 10 filaments).

(7) Additionally, it would be useful to report the cofilin severing rate at a range of cofilin concentrations, at least for the 2 filament bundles.

Cofilin severing rate is not dependent on cofilin concentration in solution. This has been reported previously by several groups, including ours (e.g. Suarez et al, Current Biology 2011 ; Gressin et al, Current Biology 2015; Wioland et al, Current Biology 2017).

Below is the comparison of cofilin cluster severing at 100 and 200 nM cofilin, on single actin filaments, which we added to supplementary figure 10.

At 100 nM cofilin, we measured a similar cofilin cluster severing rate on 2-filament bundles, by measuring the survival fraction of overlapping cofilin clusters that lead to 2-filament bundle fragmentation over time. The figure pasted below is new Supp. Fig. 11.

When the severing occurs in the two filament bundles, does this severing occur mostly at boundaries with cofilin-actin and bare actin or does this severing occur at cofilin-actin/fascin-actin boundaries?

This is an interesting point. In the presence of a saturating amount of fascin, on 2-filament bundles, one fascin protein is bound every 13 actin subunits along each filament of a bundle. Most of the time, a cofilin boundary will not be in contact with a fasin-bound actin subunit. The limited spatial resolution of optical microscopy does not allow to say whether fascin was present at the boundary of a cofilin cluster or not when severing occurred. Nonetheless, we show that cofilin cluster severing is unaffected by fascin-bundling (i.e. severing rates per cofilin cluster boundary are similar on single filaments and on 2-filament bundles). Overall, bundling by fascin probably does not change the way cofilin severs, i.e. it occurs at the boundary between cofilin-decorated and bare actin regions.

(8) For the images of large bundles appearing braided in figure 6A, the lower left panel the braided appearance is not obvious. Additionally, what is the number of filaments in the bundles shown? Finally, given that in Figure 3F it is indicated that cofilin cluster nucleation events are rare on large bundles, and the cluster growth rate is reduced on large bundles (Figure 4C), the authors need to indicate how frequently this braided appearance is observed as well as what the nucleation rate, growth rate and severing rate is for 500nM cofilin on bundles.

We have repeated the negative staining EM experiments several times and now show new images and quantification (new Supp. Fig. 13). In our new series of experiments, the braiding that was previously shown in Fig. 6 proved difficult to reproduce and to quantify. We therefore decided to remove EM observations from the main fig 6, and we no longer present them as evidence supporting the mechanism that we propose for inter-filament cooperativity.

As stated in point (7) above, the severing rate is independent of cofilin concentration. We’ve used 500 nM cofilin, which is a rather high cofilin concentration, to investigate bundle fragmentation in EM, as in solution we mostly form large bundles and they are more slowly targeted by cofilin than individual or 2-filament bundles (figure 3F & 4C). At the single filament and 2-filament bundle level, the nucleation of cofilin clusters is extremely fast at 500 nM cofilin (> 10-4 s-1 per binding site).

(9) The authors indicate that the rapid fragmentation of twist constrained 2-filament bundles prevented them from directly quantifying the nucleation rate of the subsequent cofilin clusters that overlapped the initial ones. I'm unclear why this is the case, and if this is the case, I don't understand how the authors can be sure that a second nucleation event occurred in the twist constrained bundles. From the experimental data in 7C, it appears that the fragmentation rate for two filament bundles is similar to the fragmentation rate for twist constrained single filaments. The authors need to clearly state what they were able to observe and quantify as well as include the timing for this severing. If the authors could not observe a second nucleation event prior to severing, this should be clearly stated.

Fragmentation of a 2-filament bundle requires the severing of two co-localized cofilin clusters, one on each filament. When 2-filament bundles are twist-constrained the sequence of events leading to bundle fragmentation is fast, thus it is difficult to separate the events within the resolution of our experiment. In this case, cofilin clusters sever quickly, thus the size of the clusters is small, which translates into a low fluorescence intensity. Therefore, the quantification of the increase of cofilin fluorescence intensity along a bundle did not allow us to unambiguously identify the ‘cooperative’ nucleation of two-overlapping cofilin clusters before the bundle is fragmented. So, apart from the quantification of the nucleation of cofilin clusters, which we show is unaffected by twist-constraining the bundles, we were unable to measure the growth rate nor the severing rate of cofilin clusters.

Numerical simulations, using similar severing rates for cofilin clusters on both twist-constrained single filaments and 2-filament bundles, satisfactorily reproduce our experimental observations (dashed lines in Fig. 3C).

We have edited the ‘Twist-constrained bundle fragmentation’ section to clearly say what we measured and what could not be measured : “We observed that the nucleation rate of cofilin clusters was similar for both twist-constrained and twist-unconstrained fascin bundles (Supp. Fig. 15), in agreement with observations on single actin filaments (Wioland et al, 2019b).

The rapid fragmentation of twist-constrained 2-filament bundles prevented us from directly quantifying the nucleation rate of the subsequent cofilin clusters that overlapped with the initial ones, as well as cluster growth and severing rates.”

This could be due to the rapid fragmentation, but it could also be due to severing occurring in the absence of a second cofilin nucleation event. It would be informative to compare the time from cofilin nucleation to severing event for two filament bundles in twist constrained and unconstrained. Clarification of the dynamics of nucleation and spreading of cofilin and the timing of fragmentation of the twist constrained filament bundles is needed.

As explained in the previous point, cofilin-induced severing occurs significantly faster on twist-constrained single actin filaments compared to unconstrained filaments.

For twist-unconstrained filament bundles, we never observed bundle fragmentation that originated from only one cofilin cluster. For twist-constrained bundles, while our observation is limited by the rapid fragmentation of the bundles, it is hard to imagine that a single cofilin cluster on one filament would induce the fragmentation of the neighboring filament. Recently, Bibeau et al, PNAS 2023, using magnetic tweezers to twist single actin filaments, showed that, without cofilin, applying up to 1 rotation/µm to an actin filament does not cause its fragmentation. It is thus reasonable to say that cofilin binding is required to fragment twist-constrained filaments.

Moreover, in our numerical simulations (without inter-filament cooperativity, faithfully reproducing the kinetic of 2-filament fragmentation observed in microfluidics), 75% of bundle fragmentation resulted from a sequential nucleation of cofilin clusters, with the nucleation of the second cofilin cluster occurring after the first cofilin cluster has already severed one filament of the bundle.

(10) Discussion of how twist constrained fragmentation dynamics might affect the dynamics of larger bundles in structures such as filopodia would be useful.

We had substantially edited the discussion section of the manuscript, attempting to better discuss the physiological implications of our in vitro observations (bundle size & twist-constraints).

Minor changes that would improve the paper:

(11) In Figure 1C, Figure 2B and Figure 2E, the indication, on the graph, of the fold-change between the rates is confusing as it is not clear from the labeling on the graph that the x15 is referring to the slope of the lines, keeping this information in the legend is appropriate, but if it is to be included on the graph, perhaps adding in the linear fit on the graph is also needed.

We have edited the figures accordingly, and included fit lines in figure 1.

(12) Figure 7A, lining up the diagram with the kymographs below would help improve interpretation of the diagram and simulation. Alternatively, if the diagram (upper) in A does not diagram the kymographs below, this needs to be clearly stated, and it would be preferable that the diagram above matches the kymographs below.

We have edited the figure layout accordingly.

(13) Despite referencing the Breitsprecher, 2011 paper in the introduction, the authors do not explain how their results showing that cofilin fragments filament bundles slower than single actin filaments correspond with the Breitsprecher findings that fascin bundles favors cofilin filament severing. While the authors do not need to explain the Breitsprecher data, if they reference these findings that run counter to their results, an explanation for the discrepancy would be reasonable to include in the discussion.

We agree with the reviewer comments, which was also a comment made by reviewer #2. We now more directly discuss possible discrepancies between Breitsprecher and our studies : “Previously, using pyrene-actin bulk experiments, Breitsprecher and colleagues reported a diminished cofilin binding to fascin-induced filament bundles (Breitsprecher et al, 2011). In spite of this, their observation of fluorescently labeled actin filament bundles seemed to indicate an efficient severing activity. Since cofilin was not fluorescently labeled, they could not observe cofilin clusters, and they proposed that severing was enhanced because fascin served as anchors along filaments and impeded cofilin-induced changes in filament helicity. This proposed mechanism bears resemblance to our previously reported findings for artificially twist-constrained single actin filaments (Wioland et al, 2019b). Here, we show that this mechanism does not occur in fascin-induced bundles.”

Reviewer #3 (Significance (Required)):

The research presented in "Fascin-induced bundling protects actin filament from disassembly by cofilin" is relevant and of interest to the field as it directly addresses a limitation in our understanding of how cofilin-induced severing occurs in F-actin bundles. Bundled F-actin may constitute the majority of linear F-actin within the cell and is specifically important in F-actin-based structures such as filopodia and stress-fibers. The data supports a model for interfilament cooperativity that provides a molecular mechanism for cofilin-mediated severing of fascin-bundled filaments.

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Reply to the reviewers

Planned Revisions based on comments from Reviewer #1

• The introductory material and the title of the paper emphasize the ring canal scaling question. This problem is somewhat obscured in the text by the side problem of nuclear scaling, which comes up frequently even though the results are not as thoroughly explored. Could the authors think about moving these data into a different, single figure for the sake of coherence? This is not a required revision. Just a thought.
• *We have moved the nuclear scaling data from Fig. 5 into Fig. S3, and once we have analyzed the data from the planned experiments (over-expressing either HtsRC or the active form of myosin), then we will have a better idea of whether we should move the rest of the nuclear scaling data out of the main part of the paper, consolidate it into a single figure (as Reviewer #1 suggests), or keep some of it in the main figures. *

Planned Revisions based on comments from Reviewer #3

• I cannot see differences in RC size in the panel A images. More importantly, this method altering ring canal size is limited. A more direct way is overexpression of HtsRC (https://doi.org/10.1534/genetics.120.303629).
• We have requested and just recently received the line to over-express HtsRC in the germline. We plan to cross this UAS line to the mataTub-GAL4 which expresses GAL4 beginning around stage 3 of oogenesis. Because crossing this UAS line with this GAL4 line produced egg chambers with larger ring canals in the original study2*, we do not anticipate any technical issues with this experiment. We will incorporate the results from analysis of these egg chambers in the revised manuscript. *

• To further explore the effect of ring canal size on scaling, we will also be testing a condition that we hope will have the opposite effect on ring canal size; expression of a phosphomimetic version of the non-muscle myosin II regulatory light chain, encoded by spaghetti squash (Sqh)(UAS-sqhE20E21). We plan to cross this UAS line to two different GAL4 drivers (nos-GAL4, which expresses GAL4 in a pulse during early oogenesis and then in another pulse in mid-oogenesis and the mataTub-GAL4 which expresses GAL4 beginning around stage 3 of oogenesis). We know that expression of sqhE20E21will reduce the size of the ring canals that connect the nurse cells to each other, but it is possible that the posterior ring canals will not show a strong phenotype. In a study that looked at egg chambers homozygous for a mutation in the myosin binding subunit of the myosin phosphatase, DMYPT, which should also increase sqh phosphorylation, it was shown that the posterior ring canals were larger than those connecting nurse cells 1*. Therefore, it is possible that this condition may not allow us to consistently reduce the size of all ring canal types; however, if we do see a significant reduction in posterior ring canal size in these egg chambers, we will include these data in the revised manuscript. *

• In panel 2E, it would be helpful to plot the y-intercepts separately, too.

• Based on the analysis of the data from the proposed experiments, we will consider plotting the y-intercepts separately for the various conditions.

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

Revisions made based on comments from Reviewer #1

• One way to think about the dhc-64C experiments presented in Figure 2 is that they are meant to test the hypothesis that ring canal size impacts scaling in such a way that transport across the four ring canals tends towards equilibrium over time. One possibility would therefore be that ring canals aren't programmed to grow to a particular final size but rather they grow at different rates until their diameters are the same. This seems to me an important distinction. It might be made by analysis of the arpC2-RNAi cells, since those ring canals are meant to be initially larger. Unfortunately, I can't see the answer.
• *Reviewer #3 suggested determining the ratio of the diameter of the M1 ring canal to the M4 ring canal. If ring canals grow toward equilibrium (to achieve a similar final size), then we would expect to see this ratio approach 1; when we performed this analysis, we saw that the ratio did decrease as the egg chambers increased in volume, but it never quite reaches a ratio of 1. We have added a supplemental figure (Fig. S1) showing these data and incorporated this idea into the text within the results and discussion sections. *
• *Although it would be informative to determine whether ring canals that all started with a similar diameter would grow at the same rate, we have not found a condition that would provide the opportunity to test this hypothesis. We hope that the planned experiments will provide us with a way to test this hypothesis; we will determine the M1/M4 ratio in egg chambers over-expressing either HtsRC or sqhE20E21 and see whether this ratio still decreases as egg chamber volume increases. *

• *Once we perform the planned experiments to either increase or decrease ring canal size, then we can determine whether we need to further modify Fig. 3 to highlight these size differences between ring canals in the arpC2-RNAi egg chambers or whether we will instead focus more on the results of the planned experiments. *

• The authors write that arpC2-RNAi "ring canals tended to be larger than those in similarly-sized control egg chambers," but that conclusion isn't obvious to me from the data in Figure 3B. The only difference I can see is that the M4 ring canals look to be consistently smaller in the experimental versus control egg chambers, especially at the final timepoint.

• *To further clarify the difference in ring canal size between the control and the arpC2-RNAi egg chambers, we have added additional explanation to the results section to highlight that the y-intercepts of the lines of best fit are significantly higher in the arpC2-RNAi egg chambers at each stage. This demonstrates that given an egg chamber volume, the ring canals will be larger in egg chambers depleted of ArpC2 than in the controls. *

• The authors write that "there was a consistent, but not significant decrease in the scaling exponents for the arpC2-RNAi egg chambers compared to controls," but I don't see this in the M1 (identical) or M2 (almost the same) ring canals. The scaling decrease is most pronounced at M4. All the other ring canals seem to reach a final size that's equivalent to controls. What does this tell us about scaling? Is the M4 more sensitive to the effect of arpC2-RNAi? I note and appreciate that the data for M4 show a wide distribution and might have been impacted by outliers, which could be discussed.

• *We have separated the arpC2-RNAI ring canal scaling data by lineage (Fig. S2), and we have color-coded the data in Fig. 3B (as suggested by Reviewer #3). *

• We have expanded the discussion of these results and their implications, and we have added a line in the results section to address this wide distribution of the M4 ring canal sizes.

• The possibility that ring canal scaling "could generate eggs of different sizes" could use some elaboration (at least) as it does not seem to be especially well supported:

• Only one of the small egg lines had lower scaling exponents than the big egg lines, and it's a struggle for me to understand the extent of that difference based on the data shown. (Is it significant?).

• *We have restructured this section of the results and modified Fig. 5 to highlight similarities and differences between the four lines. In the results section (and in the figure legend), we have stated that when we compared the slopes of the regression lines for all four lines, there was a significant difference for M1, M2, and M4 (Fig. 5C, D, and F). We have also modified the results section to highlight that although the slopes for line 9.31.4 was not different from the two big egg lines, the intercepts were significantly different for M1, M2, M3, and M4 ring canals. We moved the nuclear scaling data to Fig. S3 to simplify the figure. *

• The authors conclude that "the effect of lineage on ring canal scaling is conserved, and it suggests that at least in one line, reducing posterior ring canal scaling could provide a mechanism to produce a smaller mature egg." The first part of this sentence is confusing for me since I don't know what is meant here by "conserved." The second part of the sentence is technically correct but disguises what I would consider the more meaningful and exciting finding. The 9.31.4 line produces the smallest eggs but does not demonstrate scaling differences in comparison to the big egg lines examined (1.40.1 and 3.34.1). The authors have therefore avoided/solved a "chicken and egg" ("fruit fly and egg"?) problem by showing that scaling and egg size can be decoupled!

• We have modified the first part of the sentence to clarify our point. We appreciate this suggestion and have modified the text in the results section to further elaborate on the results.

• This point is not made very clearly in the discussion, which concludes with the suggestion that scaling could help explain why some insects produce much larger or much smaller eggs that fruit flies. I can only understand this to be the case if - as the authors point out - scaling "affect the directed transfer of materials into the oocyte." That argument seems predicated on the possibility that these insects make the same amount of initial material then regulate how much is transferred. Seems like a costly way to go about it.

• *We have modified this section of the discussion. *

• I really had to look very closely to distinguish the little blue boxes from the little blue circles in panels 2C and especially 2D. I suggest using a different color instead of a different shape, or maybe splitting the graphs up.

• *We have made the shapes larger in Fig. 2C (nuclear sizes), and we have split the ring canal size data into Fig. 2D, E and made the shapes larger. The legend has been modified to reflect this change. *

• "Depletion of the linker protein, Short stop (Shot), or dynein heavy chain (Dhc64C), significantly reduced the biased transport at the posterior, which reduced oocyte size (Lu et al., 2021)." I suggest this sentence might be clearer if it was rewritten as "Depletion of either dynein heavy chain (Dhc64C) or the linker protein Short stop (Shot) significantly reduces biased transport at the posterior, in turn reducing oocyte size (Lu et al., 2021).

• We have made this change.

• "Because nuclear growth has been shown to be tightly coupled to cell growth (Diegmiller et al., 2021), we can use nuclear size as a proxy for nurse cell size." I think it would help the reader to know that the Diegmiller study was performed using germline cysts in the Drosophila ovary; I paused when I got to this sentence because I initially read it as overly broad. I suggest "Recent work in demonstrates that nuclear growth is tightly coupled to cell growth in this system (Diegmiller et al., 2021), and we can therefore use nuclear size as a proxy for nurse cell size" or similar. This is certainly not a required revision, just a suggestion.

• We have made this change.

Planned Revisions based on comments from Reviewer #3

• Reviewer #3 asked: Does the ratio of the diameter of M1 to M4 stay the same?

• *We have performed this analysis in the control egg chambers (from Fig. 1), and we found that the ratio does not stay the same, but that it tends to decrease as the egg chamber increases in volume. We plotted the log of egg chamber volume versus this ratio, and the equation for the regression line was y = -0.166x + 2.32, which was significantly different from a slope of 0 (included in Fig. S1). *

• It would be helpful to explain that the log-log plots were used to derive a line equation (y=mx + b) and why that is useful in this context. In the case of a log-log plot, what does the y-intercept mean biologically? Is it simply a way to compare two things or does it indicate real measurements such as volume or ring canal size? Also, the slope of the line is being used as a scaling value. Be careful to define the terms "scaling" and "scaling exponent".

• We have added additional explanation in the results section.

• Are four significant digits called for in calculating slope? The figure has 4 significant digits, the text has three.

• *We have modified all figures and text to include only 3 significant digits. *

• Why is isometric scaling 0.66 - is that microns squared over microns cubed? Please explain.

• We have added additional explanation to the results section.

• Were all four posterior nuclei measured? The figure indicates just M1 and M4.

• We apologize that it was not clear that all four posterior nuclei were measured in Fig. 1. For the sake of space, we only showed images of the M1, M4, and Anterior ring canals and nuclei (in Fig. 1A), but all four nuclear measurements were included in the graph in Fig. 1B. We have added M1-M4 to the legend to clarify and revised the text of the legend.

• It is hard to explain why all four posterior nuclei are bigger than anterior when one of the four is the same age as the anterior nucleus.

• The posterior nuclei are larger than the anterior nuclei due to their proximity to the oocyte. Multiple recent studies have described this hierarchical nurse cell size relationship in which the nurse cells closest to the oocyte are larger than those separated from the oocyte by additional intercellular bridges 3–5*. *

• In panel D, a conclusion is, "Further, the scaling exponent [slope] for the anterior ring canals, which are also formed during the fourth mitotic division, was not significantly different from that of the posterior M4 ring canals". Anterior is 0.23, M4 is 0.25. These seem different to me. How is significance determined? Were any of the scaling exponents in M1, M2, M3, M4 or Anterior significantly different?

• *Significance was determined within the Prism software using a method equivalent to an ANCOVA. If the slopes are compared, M1 is significantly different from M2, M3, and M4, and M2 is significantly different from M4. M4 is not significantly different from the slope for the anterior ring canals, which supports the correlation between scaling and lineage. *

• References are needed for the statements about biased transport to the oocyte.

• *There was a reference to the Lu (2021) paper in that paragraph, but we have added an additional reference to that paper to this part of the results section. *

• In panel 2C, why are the scaling exponents (slopes) of the controls bigger than in Figure 1B? The controls look hyper allometric in Fig. 2.

• *This experiment was done with a different GAL4 driver, so it is possible that there are some differences in scaling based on genetic background. *

• In panel 2D it is impossible to pick out the control posterior vs anterior lines - use different colors as in Figure 1. Why do the control lines for posterior and anterior merge?

• *We have split the ring canal scaling data from Fig. 2D into different separate panels (Fig. 2D,E), as suggested by Reviewer #1. *

• These lines likely approach each other because the slope of the line for the anterior ring canals (M4 type) is always larger than the slope for the combined posterior ring canals.

• Re: Fig. 3: Scaling of what? RC size?

• *We assume that this comment is related to the heading for this section of the results, so we have added “ring canal to the end of this title, so that it now reads: “Increasing initial ring canal size does not dramatically alter ring canal scaling” *

• Since there was no effect, "dramatically" should be deleted from the section title.

• This change has been made.

• Clarify this sentence: If ring canal size inversely correlates with scaling, then increasing initial ring canal diameter should reduce the scaling exponent.

• We have made this change in the text.

• How does panel B show that RCs are larger in arpC2 KD? Fig. S1A has smaller y-intercept for control. Again, it is impossible to see which lines go with which M and which genotype.

• *As mentioned above, we have modified Fig. 3 to highlight these differences and added additional explanation to the results section. *

• Panels 4D & 4G are clear - should include significance indications.

• *We have added asterisks to indicate significant differences. *

• The conclusion from panels 5B and 5C that reducing RC scaling could lead to smaller mature eggs is a stretch. Without looking at the rest of the lines these data are preliminary and detract from the rest of the paper.

• *As suggested by Reviewer #1, we have modified the results and discussion sections, and we have added a statement about the need for analysis of additional lines. *

2. Description of analyses that authors prefer not to carry out

Comment from Reviewer #2

• I am surprised that the author has not considered controlling the impact of cell cycle regulation on this scaling process, especially as the work of Dorherty et al. has shown that this type of regulation is essential for regulating the size of nurse cell nuclei. The authors should test the impact of at least dacapo and cyclin E in this process.
• We have attempted to deplete Dacapo from the germline by crossing two different RNAi lines to multiple germline drivers; however, we have been unable to see a consistent effect on nurse cell nuclear size, which suggests that these RNAi lines may not effectively reduce Dacapo protein in the germline. Although we agree with the reviewer that this is an obvious mechanism that should be explored, we believe that it is not necessary for it to be included in this manuscript, because altering Dacapo levels in the germline would not provide a mechanism to explain our model that ring canal lineage impacts ring canal scaling. Dacapo has been shown to contribute to the hierarchical pattern of nurse cell size observed in the germline. Dacapo mRNA produced in the nurse cells is transported into the oocyte, where it is translated. Then, the Dacapo protein diffuses back into the nurse cells, producing a posterior to anterior gradient 4. Doherty (2021) showed that reducing the levels of the Dacapo protein using the deGradFP system eliminated the nurse cell size hierarchy. If our data had supported a model in which proximity to the oocyte was a strong predictor of ring canal size and scaling (as shown for the nurse cells and their nuclei3,5*), then this would have been an excellent way to dig further into the mechanism. Instead, our data supported a role for ring canal lineage in predicting ring canal growth, since the M4 ring canals at the posterior and anterior showed similar scaling with egg chamber volume. *
• We believe that performing the proposed experiments (over-expressing HtsRC to increase ring canal size or expressing the phosphomimetic form of the myosin regulatory light chain, sqhE20,E21 to reduce ring canal size) will allow us to determine how ring canal size affects scaling, which will provide additional mechanistic insight into this scaling behavior.*

*

Comment from Reviewer #3

• Panel 3E is interesting and would fit better in Figure 1.
• *This panel is from a different genetic background than the data in Fig. 1. Therefore, we do not think it would be appropriate to move it to Fig. 1. *

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

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Reply to the reviewers

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

*

*Major comments: 1. Mirc56_2 and 4 showed lower integration rates, and the authors suggest that this could be due to sgRNA pool imbalance. The authors should validate this by performing sequencing of the input sgRNA and cassettes. *

→Thank you very much for your comment, and we agree with your suggestion.

We are going to confirm sgRNA pool imbalance in donor vector library by amplicon short-read NGS.

In addition, to confirm another possibility that we raised, we re-sequenced sgRNA donor vector for Mirc56_2 and 4, and will add the following sentences:

“We firstly doubted that their low integration frequencies were caused by any mutations on PB transposon of sgRNA donor vector, on especially ITR or ID that are important for integration efficiency [PMID: 15663772]. Therefore, we sequenced PB transposons for Mirc56_2 and 4 again. However, we could not find any mutations on their PB transposon.” following to “…efficiency or cell growth.” in the Discussion (page14, line 346)

*2. Clonal analysis in Figure 5c is unclear a. Figure 5c indicates that all changes were homozygous (e.g. both alleles were deleted). Was this the case in all clones? Or were some mutations heterozygous? *

→Thank you very much for your comment.

We apologize for the misleading context.

We targeted mono allele on X chromosome in male mES cells so that all mutations should be hemizygous as mentioned in the Result (page11, line 259-260)

To enhance our study is monoallelic assessment, we will add the following sentence:

“This study targeted mono allele on X chromosome in male mES cells so that all genotype on Mirc56 should be hemizygous and these mutations induced might be cis-mutation.” following to“…owing to six tandem repeats [37]” in the Result (page12, line 302)

*b. Many clones in Figure 5c show that the entire region was deleted (all black dots). Could this be due to some experimental error or misinterpretation of the sequencing data, or could it be validated using some orthogonal method? This is especially surprising for clones in which the final guide (Mirc56_13) was not detected yet the final site (Mirc56_13) was reported as "Regional deletion". *

→Thank you very much for your comment.

We apologize for the misleading context.

Firstly, we just confirmed and sequenced the mature-miRNA genomic regions by amplifying approximately 200 bp around the target sites. Therefore, we defined unamplified regions as “miRNA deletion”. In addition, to make the Figure 5C easy to understand, we added “predicted regional deletion” and each name of clones as attached.

In fact, only 4 clones harbored entire Mirc56_X deletions on all analysed Mirc56_X genomic region (Mirc56_1 to 13). Besides, these clones could be PCR-amplified by sgRNA cassettes and Sry on Y chromosome so that these results suggested we could successfully obtain their genomic DNA and at least mature-miRNA genomic regions were deleted.

Moreover, Mirc56_13 deletions without target sites on Mirc56_13 are always within predicted regional deletions that are induced from upstream and downstream of sgRNA target sites. Therefore, it could be estimated that these deletions were induced from the target sites on Mirc56_14, 15, 16, or 17 and upstream of Mirc56_13.

To clarify them, we will add the following sentences:

• “Four clones (#2_066, #1_021, #1_029 and #1_046) harboured entire Mirc56_X deletions on all analysed Mirc56_X genomic region. In addition to these clones, only 3 pairs (#2_019 and #2_084, #2_038 and #1_023, #1_016 and #1_027) harboured same combination of mutations.” following to “…combinations of mutations (Figure 5C).” in the Result (page16, line 378-380)
• “Meanwhile, focusing on relationship between mutations and target sites that targeted by sgRNA cassettes in each clone, all Mirc56_X genomic regions harbouring Indel mutations were target Micr56_X In addition, if sequential Mirc56_Xs on the genome were deleted, the most upstream and downstream of Mirc56_Xs deleted were always on the target Mirc56_X sites except for #2_025 and #1_41.” following to “…combinations of mutations (Figure 5C).” in the Result (page12, line 304)
• “Genotyping PCR amplified approximately 200 bp around the mature-miRNA genomic region. Unamplified region is defined as miRNA deletion (Black circle) and amplified region was determined as Indel mutation (Gray circle) or Intact by short-NGS. If sequential Mirc56_Xs on the genome were deleted, black translucent square indicates predicted regional deletion assumed that the genomic region flanked by miRNA deletions was also deleted. Besides, if miRNA deletion was induced in Mirc56_13 and the clone have target Mirc56_X on Mirc56_14, 15, 16, or 17” following to “…in each PB mES clone.” in the Figure legend (page23, line 575) Moreover, because we defined “miRNA deletion”, we will change ”regional deletion” to “miRNA deletion” where I mean “deletion of the mature-miRNA genomic regions” in the Result (page13, line 312) and the Discussion (page14, line 363)

*3. Next-generation targeted sequencing of clones should be made publicly accessible. *

→Thank you very much for your comment. We apologize for the inconvenience.

We already informed Review commons that we made publicly available.

We already described BioProject ID PRJNA996747 in the Data Availability (page16, line 383-384)

4. OPTIONAL - Cassette integration number is understudied. One important aspect of tiling mutagenesis is the control over how many guides are present in each cell. The authors report an average of 4.7 cassettes/cell. This could be modulated by the amount of donor vector added, and indeed the authors performed titration experiments, but only with a fluorescent reporter readout. It would be very useful to know how the concentration of donor vector corresponds to the number of cassettes/cell - perhaps genotyping of clones from one or two additional experiments would be sufficient.

→ Thank you very much for your suggestion.

We agree that cassette integration number is one important aspect of tiling mutagenesis.

To investigate how many copies our concentration of donor vector could integrate, we are going to check actual copy numbers in several clones by qPCR.

We think that it is other research to confirm “how the concentration of donor vector corresponds to the number of cassettes/cell”. The correlation might not be liner due to transposase overproduction inhibition (OPI) so that it would require huge amounts of experiments to confirm it. Our research is how CTRL-Mutations induce diverse mutations but not how property PB system have.

Minor comments: 1. The background fails to acknowledge the work of CRISPR-Cas tiling screens (e.g. https://doi.org/10.1038/nbt.3450) or CRISPR-Cas in creating mutagenesis in cell lines (e.g. https://doi.org/10.1007/978-1-0716-0247-8_29*) *

→ Thank you very much for your suggestion, and we agree with your suggestion.

We will add to acknowledge previous studies for CRISPR-Cas tilling screens.

• “Recently, targeted mutagenesis combined forward genetics and reverse genetics has been developed such as saturating mutagenesis and tiling mutagenesis that induce random mutation within target gene(s) [PMID: 25141179, 31586052, 27260157, 28118392]. This targeted mutagenesis can construct a mutant library harbouring subtly different mutations within a target gene(s) so that comparative analysis through the mutant library can screen out critical mutation(s) for biological processes. These random mutagenesises have also revealed the function of numerous coding genes” following to “…list of coding genes [6–8].” in the Introduction (page3, line 55-56)
• “In addition, the saturating mutagenesis are limited in the length of target region due to an approach basing homology-directed repair although it could introduce random mutations on donor template library harbouring any combination and variety of mutations [PMID: 25141179]. On the other hand, the tiling mutagenesis could expand target length in principle because the length depends on multiplex guide RNA (gRNA) designed to target genomic region. Therefore, tiling mutagenesis has been employed to identify critical regions embedded in cis-elements [PMID: 26375006, 30612741, 26751173, 27708057, 28416141, 31784727]. Tiling mutagenesis requires editor such as Cas9 or epigenetic modifier fused to catalytically dead Cas9 (e.g. KRAB-dCas9), and a library containing multiplex gRNA tiling across target genomic region. In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system.” following to “…within a narrow region.” in the Introduction (page4, line 82) However, we do not agree that we have to acknowledge previous report about KI or KO by single or double cut in cell lines (as you suggested that https://doi.org/10.1007/978-1-0716-0247-8_29) because it is obvious knowledge. Therefore, we will not add this paper.

2. Figure 1 left 'ROI random mutant PB mES cell' should be horizontally aligned so Mir_1, Mir_2 and MirX align with the upper figure.

→ Thank you very much for your kind comment, and we agree with your suggestion.

Therefore, we changed it in the Figure 1.

*3. It is interesting and unexpected that some guides never induce indels, even in the absence of a regional deletion (e.g. Mirc56_3, Mirc56_7). Why might this be? Was there perhaps an error in the assignment of these guides to these cells? *

→ Thank you very much for your comment.

As you mentioned, Mirc56_3, 4 and 7 had no indel. We appreciate that we can correct our mistakes by your suggestion. We corrected Figure 5D as attached. In addition, we will correct average Mirc56_X site as 22.6 from 22.7.

These sgRNA also induced miRNA deletion with low frequency (Mirc56_3: 38.9%, Mirc56_4: 25.0% and Mirc56_7: 68.0%, Figure 5D). Moreover, every deleted Mirc56_3, 4 and 7 was within predicted regional deletion except for target Mirc56_3 of PB mES clone #2_080 (revised Figure 5C).

Therefore, we raised why some guides never induce indels even in the absence of a regional deletion, as “In addition to low frequencies, Indel mutation might disappear due to regional deletion if these sgRNAs could induce Indel mutation”.

To clarify them, we will add the following sentences:

• “In particularly, middle target sites such as Mirc56_3, 4 and 7 were induced only miRNA deletion or Intact (Figure 5D)” following to “…in our mutant library (Figure 5C, D).” in the Discussion (page14, line 364)
• “In fact, every deleted Mirc56_3, 4 and 7 was within predicted regional deletion except for target Mirc56_3 of PB mES clone #2_080 (Figure 5C). In addition, these sgRNA induced mutation with low frequency (Mirc56_3: 38.9%, Mirc56_4: 25.0% and Mirc56_7: 68.0%, Figure S6). Therefore, we suspected that regional deletion and their low mutation introduction rate facilitated to disappear Indel mutation.” following to “…induced at target sites.” in the Discussion (page14, line 366)

  *4. Regarding Mirc56_2 and 4 integration, on line 34 the authors suggest that "We suspect this was caused by a technical error, such as an unequal amount of sgRNA donor vector or the sequence in sgRNA cassettes affecting integration efficiency or cell growth." sgRNA library imbalance would be a technical error, but integration affecting cell growth is not a technical error. This sentence should be reworded. *

→ Thank you very much for your comment.

We apologize for the misleading sentence even though this paper was already English-reviewed by English language editor.

We will reword that “We suspect this was caused by the sequence in sgRNA cassettes affecting integration efficiency or cell growth, or a technical error such as an unequal amount of sgRNA donor vector.” following to “…PB mES clones via FACS..” in the Discussion (page14, line 344)

*5. Line 540 "ration" is the incorrect word - perhaps "ratio"? *

→ Thank you very much for your kind comment, and we are sorry for the typo.

We will correct it in the Figure legend (page22, line 540).

6. Plot 5b should be shown as a histogram rather than a swarm plot to show how many clones were in each category.

→ Thank you very much for your suggestion.

In Figure 5B, we aimed to indicate the number of sgRNA cassette varieties in each clone but not distribution of the number of integrated sgRNA cassettes. Distribution of the number of integrated sgRNA cassettes in clone library matched with the frequency of target sites in Figure 5D.

We already described the distribution data as “In addition, an average of 22.7 Mirc56_X sites … the same frequency except for the Mirc56_2- and 4-targeting cassettes.” in the Result (page13, line 312-315)

*Reviewer #1 (Significance (Required)):

1. General assessment: The authors are successful in creating clonal cell lines bearing a variety of mutations. Unfortunately, the cell lines also have transposase-mediated insertion events of the sgRNA cassettes at unknown positions in the genome, which will hamper the interpretability of any experiment using these cell lines. The authors fail to justify the use of the transposase and integration of the sgRNA, especially compared to lentiviral transfection or RNPs which would produce edits at the region of interest. Alternately, integrated sgRNA cassettes could have been excised with Flp recombinase as in https://doi.org/10.1007/978-1-0716-0247-8_29. *

→ Thank you very much for your suggestion.

We agree that we did not mention why we choose PiggyBac system compared to lentiviral delivery.

Therefore, we will add the following sentences:

• “In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system. To identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis [PMID: 23435812].” in the Introduction.
• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction. However, we are not going to mention comparison to RNPs because it is obvious that random sgRNA expressions is important key for random mutagenesis and design of random sgRNA treatments by RNP is difficult. The reason is that the target region might be cleaved by almost all sgRNA incorporated into cells. On the other hand, it is easier to design the number of sgRNA expression variety using the delivery system via integration into the chromosome because only integrate sgRNA are expressed.

In addition, we could not agree that “integrated sgRNA cassettes could have been excised with Flp recombinase as in https://doi.org/10.1007/978-1-0716-0247-8_29.”

This paper reports the concept that one EM7>neoR expression cassette flanked by Frt within KI allele could select intended-KI clone and then the cassette could remove by Flp recombinase. However, this approach is not suitable for our method because it causes structural mutation by recombination of multi Frt cassettes that are integrated into nearby genomic regions. Therefore, we will not mention it.

*2. Additionally, the genotyping analysis is unclear, and seems to indicate that each clone bears homozygous mutations, with several clones showing deletions of the entire region. *

→ Thank you very much for your suggestion.

We will revise them in Reviewer #1 Major comment 2a and b.

3. Advance: The authors are motivated to create clones using tiling mutagenesis. Tiling mutagenesis has already been performed without transposases (e.g. https://doi.org/10.1038/nbt.3450, https://doi.org/10.1371/journal.pone.0170445, https://doi.org/10.1038/s41467-019-12489-8*) in the context of a screen, and clones have already been created using CRISPR/Cas9 mutagenesis so the advance presented in this manuscript over previous published work is unclear. *

→Thank you very much for your suggestion, and we agree with your suggestion.

We will add to acknowledge previous studies for CRISPR-Cas tilling screens.

We will add the following sentences:

• “Recently, targeted mutagenesis combined forward genetics and reverse genetics has been developed such as saturating mutagenesis and tiling mutagenesis that induce random mutation within target gene(s) [PMID: 25141179, 31586052, 27260157, 28118392]. This targeted mutagenesis can construct a mutant library harbouring subtly different mutations within a target gene(s) so that comparative analysis through the mutant library can screen out critical mutation(s) for biological processes. These random mutagenesises have also revealed the function of numerous coding genes” following to “…list of coding genes [6–8].” in the Introduction (page3, line 55-56)
• “In addition, the saturating mutagenesis are limited in the length of target region due to an approach basing homology-directed repair although it could introduce random mutations on donor template library harbouring any combination and variety of mutations [PMID: 25141179]. On the other hand, the tiling mutagenesis could expand target length in principle because the length depends on multiplex guide RNA (gRNA) designed to target genomic region. Therefore, tiling mutagenesis has been employed to identify critical regions embedded in cis-elements [PMID: 26375006, 30612741, 26751173, 27708057, 28416141, 31784727]. Tiling mutagenesis requires editor such as Cas9 or epigenetic modifier fused to catalytically dead Cas9 (e.g. KRAB-dCas9), and a library containing multiplex gRNA tiling across target genomic region. In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system.” following to “…within a narrow region.” in the Introduction (page4, line 82) The paper you raised as DOI: https://doi.org/10.1038/nbt.3450 applied CRISPRko tiling mutagenesis to find out critical region embedded 2 kb of p53 binding enhancer region by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions. In addition, we expand the length of target region to more than 50 kb. This is one of the advances.

The paper you raised as DOI: https://doi.org/10.1371/journal.pone.0170445 applied CRISPRko tiling mutagenesis to find out critical mutation on MAP2K1 and BRAF protein coding sequence by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions.

The paper you raised as DOI: https://doi.org/10.1038/s41467-019-12489-8 applied CRISPRko tiling mutagenesis for to find out critical domain from protein coding sequence by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions.

To clarify the advantages, we will add the following sentences:

• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction.
• “CRISPRko tiling mutagenesis is conducted for less than 15 kb target genomic region so far [PMID: 26375006, 30612741], while CRISPRi tiling mutagenesis can target more than 70 kb [PMID: 27708057], it is reported that. Hence, it remains unknown unclear how length CRISPRko tiling mutagenesis could expand” in the Introduction. In addition, we are going to conduct transposon removal by exicision-only-PBase treatment with several PB mES clones, for the proof of concept that CTRL-Mutagenesis can generate mutant library with no sgRNA cassettes.

*4. Audience: The manuscript is written for the basic research audience, and the method could be applied to the study of regions of interest in many diseases. However, the unexcised use of transposases make the method less desirable than other methods. *

→ Thank you very much for your suggestion.

We do not agree that the PiggyBac make the method less desirable than other methods.

As mentioned in our response for reviewer #1 Significance 3, only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. However, sgRNA cassettes by lentiviral delivery is never removed from the genome. In addition, other approaches such as Flp recombinase that reviewer #1 proposed in Significance 1 is not better than PiggyBac because Flp recombinase causes stratal mutation by recombination of multi Frt cassettes that are integrated into nearby genomic regions.

To clarify them, we will add the following sentences:

• “However, multiple integration of single guide RNA (sgRNA) cassettes has higher risk of non-targeted endogenous gene disruptions and may impair functional analysis.” in the Abstract.
• “However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis [PMID: 23435812]. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome.” in the Introduction.
• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction. In addition, we are going to conduct transposon removal by exicision-only-PBase treatment with several PB mES clones, for the proof of concept that CTRL-Mutagenesis can generate mutant library with no sgRNA cassettes.

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

Major concerns:

1) Concern about the Novelty of Functional Analysis Platforms: The authors claim that there are no established platforms for the study of cis-elements or microRNA clusters. This assertion seems inaccurate, as previous studies have utilized Cas9 tiling screens to investigate cis-regulatory elements (CREs) and large-scale screens to probe microRNA functions, as exemplified by the works of Canver et al. in Nature 2015, Gasperini et al. in Cell 2019, and others. *

→ Thank you very much for your suggestion, and we agree with your suggestion.

We apologize our false claim so that we will delete the following sentences:

• “In contrast, no functional analysis platforms have been established for the study of cis-elements or microRNA cluster regions consisting of multiple microRNAs with functional overlap” in the Abstract (page2, line 28-30)
• “While loss-of-function analysis has been conducted for numerous coding genes, very limited progress has been made on non-coding genes and cis-elements.” in the Introduction (page3, line 47-49) The paper you raised as DOI: https://doi.org/10.1038/nature15521 (Canver et al. in Nature 2015) applied CRISPRko tiling mutagenesis to find out critical region embedded 12 kb of BCL11A enhancer region by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions. In addition, we expand the length of target region to more than 50 kb. This is one of the advances.

The paper you raised as DOI: https://doi.org/10.1016/j.cell.2018.11.029 (Gasperini et al. in Cell 2019) applied CRISPRko tiling mutagenesis to find out critical region embedded maximum 12 kb enhancer candidates, in addition to CRISPRi tilling candidate screening through one sgRNA by one candidate enhancer, by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions. In addition, we expand the length of target region to more than 50 kb. This is one of the advances. Additionally, to identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome. Thus, to identify combinations of critical region embedded in target regions with no artifact owing to no footprint by removal of sgRNA cassettes, CRISPRko tiling mutagenesis rather than CRISPRi is better method because CRISPRi requires integrated cassettes that stably expressed sgRNA and epigenetic modifier fused to dCas9. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions to find out combinations of critical region embedded in target regions.

Therefore, to add to acknowledge previous studies and clarify the advantages, we will add the following sentences:

• “In addition, the saturating mutagenesis are limited in the length of target region due to an approach basing homology-directed repair although it could introduce random mutations on donor template library harbouring any combination and variety of mutations [PMID: 25141179]. On the other hand, the tiling mutagenesis could expand target length in principle because the length depends on multiplex guide RNA (gRNA) designed to target genomic region. Therefore, tiling mutagenesis has been employed to identify critical regions embedded in cis-elements [PMID: 26375006, 30612741, 26751173, 27708057, 28416141, 31784727]. Tiling mutagenesis requires editor such as Cas9 or epigenetic modifier fused to catalytically dead Cas9 (e.g. KRAB-dCas9), and a library containing multiplex gRNA tiling across target genomic region. In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system.” following to “…within a narrow region.” in the Introduction (page4, line 82)
• “To identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis [PMID: 23435812]. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome. Thus, to identify combinations of critical region embedded in target regions with no artifact owing to no footprint by removal of sgRNA cassettes, CRISPRko tiling mutagenesis rather than CRISPRi is better method because CRISPRi requires integrated cassettes that stably expressed sgRNA and epigenetic modifier fused to dCas9.” in the Introduction.
• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction.
• “CRISPRko tiling mutagenesis is conducted for less than 15 kb target genomic region so far [PMID: 26375006, 30612741], while CRISPRi tiling mutagenesis can target more than 70 kb [PMID: 27708057], it is reported that. Hence, it remains unknown unclear how length CRISPRko tiling mutagenesis could expand” in the Introduction. On the other hand, we could not find previous studies employing Cas9 tiling mutagenesis to investigate miRNA functions. The application for miRNA cluster is also one of the advances.

2) Advantages of PiggyBack System Over Lentiviral Integration: The paper does not clearly articulate the advantages of their proposed PiggyBack-based system for sgRNA integration over traditional lentiviral integration. Both methods facilitate the random integration of multiple gRNAs, but the paper lacks a comparative analysis or justification for choosing the PiggyBack system.

→ Thank you very much for your suggestion, and we agree with your suggestion.

We agree that we did not mention why we choose PiggyBac system compared to lentiviral delivery.

Therefore, we will add the following sentences:

• “In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system. To identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis [PMID: 23435812].” in the Introduction.
• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction.

  *3) Lack of Comparative Analysis with Alternative Methods: The authors did not provide a comparison of CTRL-Mutagenesis with other existing screening methods. Such a comparison is crucial for understanding the effectiveness and efficiency of the new method in relation to established techniques. *

→ Thank you very much for your suggestion.

We agree with the comparison is one of important experiments.

However, our main claim is validation of tiling mutagenesis using PiggyBac that is only integration system with no footprint. Therefore, we propose our novelty without the comparison and not argue higher / lower efficiency of CTRL-Mutagenesis compared to exiting methods.

*4) Limitations in Library Resolution: The paper acknowledges the limited resolution of their proposed library. The authors might have explored the use of base editors for enhanced resolution in such screens, as base editing could potentially offer more precise and controlled mutagenesis as briefly mentioned in the discussion. *

→ Thank you very much for your suggestion.

We agree with your suggestion.

Base editing is occurred within only editing window. In addition, a major limitation of prime editing is low efficiency (https://doi.org/10.1016/j.tibtech.2023.03.004). Therefore, design of sgRNA for base editor or pegRNA and its editing efficiency requires huge amounts of experiments.

Our study is proof of concept to validate PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions. Thus, we just discussed limited resolution of our mutant library and proposed the use of base editors for enhanced resolution in the Discussion (page14, line 366-370).

5) Absence of Functional Data Post-Mutagenesis: A significant limitation of the study is the absence of functional data following the creation of cells with different mutations. While the authors speculate about using differentiation systems or organoids for practical applications, they do not provide empirical data to demonstrate the utility of the CTRL-Mutagenesis approach. This lack of functional validation raises questions about the practical applicability of the method.

→ Thank you very much for your suggestion.

We agree with your suggestion.

We would make functional analysis future research.

In this paper, we just validated PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions.

To change our tone that claiming usability of our method for functional analysis, we will change the following sentences:

• Change “to identify functionally important elements in non-coding regions” to “to induce diverse combination and variety of mutations within more than 50 kb non-coding region” in the Title.
• Add “However, not much loss-of-function screens of non-coding regulatory elements has been conducted due to ambiguous annotations compared with protein-coding genes. Tiling mutagenesis has been employed to identify critical regions embedded in non-coding regulatory elements by comparative analysis through a mutant library harbouring subtly different regions mutated within less than 15 kb region. Conventional tiling mutagenesis construct a mutant library integrated multiple sgRNA cassettes by retroviral delivery. However, multiple integration of single guide RNA (sgRNA) cassettes has higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. Herein, combining tiling mutagenesis and PiggyBac transposon that can be removed with no footprint on integrated sites, we established an expanded tilling mutagenesis method named CRISPR- & Transposase-based RegionaL Mutagenesis (CTRL-Mutagenesis). We demonstrated that PiggyBac system could integrated diverse combinations and varieties of sgRNA cassettes.and then CTRL-Mutagenesis randomly induces diverse combination and variety of mutations within more than 50 kb non-coding region in murine embryonic stem cells. CTRL-Mutagenesis would apply for wider non-coding regulatory elements with no risk of non-targeted endogenous gene disruptions.” in the Abstract.
• Delete “Comparative analysis of mutants harbouring subtly different mutations within the same region would facilitate the further study of cis-element and microRNA clusters.” in the Abstract (page2, line 38-40).
• Change “The generated random mutant mES clone library could facilitate further functional analyses of non-coding regulatory elements within the genome.” to “The generated random mutant mES clone library could develop to investigate critical regions of non-coding regulatory elements within the genome.” In the Introduction (page4, line 88-90)

  *Reviewer #2 (Significance (Required)):

  1. In summary, while the idea to integrate sgRNA in the genome by the PiggyBack system is interesting the claim of novelty is questionable due to existing methods in the field. The advantages of their system over existing technologies are not clearly articulated, and a lack of comparative analysis with other methods leaves the efficiency of CTRL-Mutagenesis uncertain. *

→ Thank you very much for your suggestion.

Previous studies about CRISPRko and CRISPRi tiling mutagenesis employ lentiviral delivery of sgRNA cassettes into the genome. However, multi sgRNA cassette integrations have higher risk to disrupt non-targeted endogenous functions. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome. Nevertheless, lentiviral transposon, one of retrotransposon, cannot be removed from the chromosome. On the other hand, only PiggyBac transposon can be removed with no footprint. Therefore, we aimed to validate PiggyBac system for tiling mutagenesis. Moreover, there is no report that CRISPRko tiling mutagenesis apply for more than 15 kb genomic region. Therefore, we aimed to expand the length of target region.

Therefore, we will change our claim that our method could expand CRISPRko tiling mutagenesis to more than 50 kb with no risk of non-targeted endogenous gene disruption.

We will add the novelty and advantage of our method.

• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction.
• “CRISPRko tiling mutagenesis is conducted for less than 15 kb target genomic region so far [PMID: 26375006, 30612741], while CRISPRi tiling mutagenesis can target more than 70 kb [PMID: 27708057], it is reported that. Hence, it remains unknown unclear how length CRISPRko tiling mutagenesis could expand” in the Introduction. However, our main claim is validation of tiling mutagenesis using PiggyBac that is only integration system with no footprint. Therefore, we will propose our novelty without the comparison and not argue higher / lower efficiency of CTRL-Mutagenesis compared to exiting methods.

In addition, we are going to conduct transposon removal by exicision-only-PBase treatment with several PB mES clones, for the proof of concept that CTRL-Mutagenesis can generate mutant library with no sgRNA cassettes.

2. Moreover, the limited resolution of their library and the absence of functional data post-mutagenesis are significant drawbacks that need to be addressed in future research to ascertain the method's practical utility.

→ Thank you very much for your suggestion.

We agree with your suggestion.

We would make functional analysis future research.

Base editing is occurred within only editing window. In addition, a major limitation of prime editing is low efficiency (https://doi.org/10.1016/j.tibtech.2023.03.004). Therefore, design of sgRNA for base editor or pegRNA and its editing efficiency requires huge amounts of experiments.

Our study is proof of concept to validate PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions. Thus, we just discussed limited resolution of our mutant library and proposed the use of base editors for enhanced resolution in the Discussion (page14, line 366-370).

Therefore, we just claimed that we validated PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions.

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

Major comments: 1. Authors claim that "CTRL-mutagenesis randomly induces diverse mutations only within the targeted regions in murine embryonic stem (mES) cells.", however, the outcome of mutations is not entirely random since most of the mutations are regional deletions. For example, despite the random distribution of gRNAs per cell, the inner regions like Mirc56_5 or Mirc56_8 are mutated with >80% efficiency.*

→ Thank you very much for your comment.

We agree that middle regions are tending to be deleted and mutation type induced is not entirely random. However, we do not agree that “the outcome of mutations is not entirely random since most of the mutations are regional deletions.” Focusing on the combinations of mutations as mentioned in the Result (page12, line 302-304), CTRL-Mutagenesis could induce diverse mutation combinations randomly at a moderate degree. In fact, 79.2% of clones harboring multiple mutations were induced different combinations of mutations. In addition, to confirm how mutations occurred within Mirc56 by CTRL-Mutagenesis, we constructed only 87 mutant clones though single cloning. Therefore, it is not completely understanded due to fewer clones compared with conventional CRISPRko tiling mutant library. Of course, we should improve the randomness of mutation combinations, but we already discussed it and proposed solutions in the Discussion (page14, line 366-370).

Certainly, CTRL-Mutagenesis would be difficult to identify necessary and sufficient genomic region due to incomplete randomness. Nevertheless, there is no report to induce diverse combination and variety of mutations within more than 50 kb genomic region. Hence, CTRL-Mutagenesis should be worth screening out critical regions within more than 50 kb regions.

To clarify them, will add the following sentences:

• “CRISPRko tiling mutagenesis is conducted for less than 15 kb target genomic region so far [PMID: 26375006, 30612741], while CRISPRi tiling mutagenesis can target more than 70 kb [PMID: 27708057], it is reported that. Hence, it remains unknown unclear how length CRISPRko tiling mutagenesis could expand” in the Introduction.
• “Four clones (#2_066, #1_021, #1_029 and #1_046) harboured entire Mirc56_X deletions on all analysed Mirc56_X genomic region. In addition to these clones, only 3 pairs (#2_019 and #2_084, #2_038 and #1_023, #1_016 and #1_027) were induced same combination of mutations. Besides, 26 clones had only one mutation from Mirc56_1 to Mirc56_13. On the other hand, there was no mutation on Mirc56_1 to 13 in 11 clones including 5 clones (#2_012, #2_015, #2_054, #2_092 and #2_102) carried no sgRNA cassette for Mirc56 _1 to 13 and 6 clones (#2_017, #2_053, #2_098, #1_003, #1_012 and #1_044) even carried any one of sgRNA cassettes for Mirc56 _1 to 13. Among 48 clones carrying multiple mutations except for clones carrying only one mutation or Intact, 38 clones (79.2%) harboured different combinations of mutations. These results suggested that CTRL-Mutagenesis could induce diverse combinations of mutations.” following to “…different combinations of mutations (Figure 5C).” in the Result (page12, line 304)
• “Note that CTRL-Mutagenesis would be difficult to identify necessary and sufficient genomic region due to incomplete randomness. Nevertheless, CTRL-Mutagenesis should be worth screening out critical regions within more than 50 kb regions” following to “…to induce regional deletions.” in the Discussion (page15, line 378)
• Change “diverse mutations” to “diverse combination and variety of mutations” in the Title, Abstract (page2, line 37), Introduction (page4, line 87), Result (page13, line 318), Discussion (page13, line 325), (page14, line 363) Additionally, we do not agree with your suggestions that “the inner regions like Mirc56_5 or Mirc56_8 are mutated with >80% efficiency”. We apologize for the misleading context. These high mutation rates were calculated on only the target sites. Actually, maximum mutation rate on all MIrc56_X genomic regions are 44.8% on Mirc56_10, minimum is 14.9% on Mirc56_2 and an average is 30.9% (attached Figure).

We appreciate that we can recognize our misleading context by your suggestion. It is more important that the analysis focusing all Mirc56_X genomic regions rather than target Mirc56_X. Therefore, we newly made figure about event occurrence in Mirc56_X genomic regions (attached Figure) as Figure 5D and replaced previous Figure 5D about event occurrence in target Mirc56_X to Supplemental Figure S6.

To clarify them, we will add the following sentences:

• “As for event occurrences on each Mirc56_X genomic region, miRNA deletions were dominant and an average of 26.7 Mirc56_X genomic region were induced mutations in 87 clones (Figure 5D). Maximum mutation rate on all MIrc56_X genomic regions was 44.8% (39/87) on Mirc56_10, minimum was 14.9% (13/87) on Mirc56_2” following to “…on the same strand” in the Result (page12, line 309)
• “__D, __Mutations in 87 Mirc56 random mutant clones. The target sites do not include Mirc56_14, 15, 16, and The vertical axis and bar graphs show event occurrence on each Mirc56 genomic region in 87 Mirc56 random mutant clones. The bar colour indicates each event (Black: Regional deletion, Gray: Indel mutation, White: Intact).” in the Figure legend.

  2. Also, although the authors discuss that the lower mutation frequency observed for Mirc56_2 and 4 may be due to a technical error, confirming this by repeating the experiment would be important to prove the usability of this method.

→Thank you very much for your comment, and we agree with your suggestion.

We had already constructed bulk PB mES cells twice and showed Figure 4B combined these experimental replicates.

To clarify that we constructed bulk PB mES cells twice, we changed Figure 4B as attached and will add the following sentences:

• “Even though these bulk PB mES cells were constructed twice, it seemed that sgRNA cassettes for Mirc56_2 and 4 were difficult to integrate into the genome.” following to “…were rarely detected” in the Result (page11, line 273)
• “In addition, we suspected technical errors so that we constructed bulk PB mES cells twice. Unfortunately, their low integration frequencies were not improved.” following to “…efficiency or cell growth.” in the Discussion (page14, line 346)
• “Bulk1 and Bulk2 indicate the experimental replicate.” following to “…next-generation sequencing (NGS).”in the Figure legend (page22 line 563) In addition, we re-sequenced sgRNA donor vector for Mirc56_2 and 4, and will add the following sentences:

“We firstly doubted that their low integration frequencies were caused by any mutations on PB transposon of sgRNA donor vector, on especially ITR or ID that are important for integration efficiency [PMID: 15663772]. Therefore, we sequenced PB transposons for Mirc56_2 and 4 again. However, we could not find any mutations on their PB transposon.” following to “…efficiency or cell growth.” in the Discussion (page14, line 346)

Moreover, to confirm the technical error, we are going to confirm sgRNA pool imbalance in donor vector library by amplicon short-read NGS.

*3. Additionally, the experiments were performed on the haploid X chromosome of a male cell line. It is questionable whether this method can be generalized to other regions located in the other chromosomes. Clarifying These points would be essential especially because the focus of this manuscript is to describe the efficiency of this novel methodology. *

→Thank you very much for your comment.

We expect that CTRL-Mutagenesis could be valid on other biallelic locus.

Therefore, we raised predicted issue such as complex genotyping and proposed one solution.

When we target other biallelic locus, we must determine whether the combination of mutations induced are cis- or trans-mutations. Haplotype phasing, combined long-read sequencing with SNP markers within ROI on maternal/paternal chromosome, assembles each allele via SNP markers on each read [PMID: 35710642]. Therefore, combining CTRL-Mutagenesis on heterozygotic alleles of cells derived from such as human or murine hybrid with haplotype phasing might simplify genotyping.

We will add the following sentences:

“In this study, CTRL-Mutagenesis was validated by genotyping on mono allele in male mES cells to avoid investigating whether the combination of mutations induced are cis- or trans-mutations. All genotypes on Mirc56 should be hemizygous and these mutations induced might be cis-mutations so that we determined the genotypes by amplifying approximately 200 bp around the target sites. However, we did not confirm large mutations such as deletion of the genomic region between target sites and inversion. Long-read sequencing might capture their large mutations. Besides, we also expect that CTRL-Mutagenesis could be valid for ROI on biallelic autosome and X chromosome in female. Therefore, it is required to determine whether the combination of mutations induced are cis- or trans-mutation. Haplotype phasing, combined long-read sequencing with SNP markers within ROI on maternal/paternal chromosome, assembles each allele via SNP markers on each read [PMID: 35710642]. Therefore, combining CTRL-Mutagenesis on heterozygotic alleles of cells derived from such as human or murine hybrid with haplotype phasing might simplify genotyping.” in the Discussion.

*4. The limitations of the methods seem not to be fully described in the manuscript and must be clarified. Compared to the previous studies (see "significance" section for details), this method is inferior in that (1) it is time-consuming because it requires clonal expansion of single cells and (2) it has low throughput because it requires genome sequencing due to the occurrence of deletions. These points should be described for the potential users of this methodology. For example, it may be useful to detail the time consumption in each experimental step in Fig. 4A. *

→Thank you very much for your comment.

We do not agree that (1) it is time-consuming because it requires clonal expansion of single cells.

To confirm the mutations that CTRL-Mutagenesis induced, we did not conduct phenotyping screening such as dropout screening in this study. For further high-throughput screening, CTRL-Mutagenesis could apply bulk mutant mES cells, that is treated with Cas9 and EGFP-positive, for phenotyping screening.

Additionally, we do not agree that (2) it has low throughput because it requires genome sequencing due to the occurrence of deletions. In this study, to prove our concept that CTRL-Mutagenesis could induce diverse combinations and varieties of mutations such as Indel and regional deletion, we conducted genotyping in all random mutant clones. On the other hand, there are alternative comparative method to improve throughput without genotyping. Combination of phenotyping screening and gene expression assay for target miRNAs or transcript regulated by target cis-element help us obtain clones harboring mutations on functionally critical regions within target region. Finally, we should conduct genotyping to identify critical regions embedded in non-coding regulatory elements.

Even so, we will add the time consumption in Figure 4A as attached because the information may be useful for potential users as you mentioned.

*Minor comments: 1. Data and methods are well-presented for reproducibility. The EGFP-positive ratio may be added to Fig. 4C for clarity. *

→Thank you very much for your kind comment.

We added the EGFP-positive ratio to Figure 4C and will add the following sentence:

“The percentage above the box indicates the EGFP-positive ratio.” following to “…the gates of the EGFP filter.” in the Figure legends (page23, line 567)

2. Enhance referencing accuracy, rectify DOI format in ref 21, and ensure consistency in citation formatting, e.g., ref 32.

→Thank you very much for your kind comment.

Along with the transfer, we will modify the style of references and have already confirmed the referencing accuracy in the Reference.

3. It seems that the experimental condition (e.g. The amount of vectors used for transfection) should be re-considered every time the researcher wants to set up an experiment changing target genomic regions, cell types etc. If so, this also should be described in the text for potential users of this method.

→Thank you very much for your comment, and we agree with your suggestion.

We will add the following sentences:

“This study just validated CTRL-Mutagenesis for 17 target sites in mES cells. Therefore, it might be better to adjust the number of integrated sgRNA cassettes according to the number of target sites and cell types.” following to “…sgRNA cassettes to be integrated.” in the Discussion (page14, line 355)

*Reviewer #3 (Significance (Required)):

There were various methods described in the late 2010's which aimed to screen for the functional non-coding regions using approaches such as KO-based, HDR-based, and epigenetic silencing using dCas9 (for example, PMID: 25141179, 26751173, 27708057, 28416141, 31784727). The authors should summarize what would be the strength of their method compared to these previously described methodologies. The strength of this methodology seems to be moderate complexity and cost-effectiveness compared to these previous techniques. It may be difficult for this methodology to become a state-of-the-art method to evaluate cis-element combinations, but it can be beneficial to researchers wanting to set up a low-cost system that can produce moderately complex cell libraries.*

→Thank you very much for your suggestion.

We will add to acknowledge previous studies for CRISPR-Cas tilling screens.

• “Recently, targeted mutagenesis combined forward genetics and reverse genetics has been developed such as saturating mutagenesis and tiling mutagenesis that induce random mutation within target gene(s) [PMID: 25141179, 31586052, 27260157, 28118392]. This targeted mutagenesis can construct a mutant library harbouring subtly different mutations within a target gene(s) so that comparative analysis through the mutant library can screen out critical mutation(s) for biological processes. These random mutagenesises have also revealed the function of numerous coding genes” following to “…list of coding genes [6–8].” in the Introduction (page3, line 55-56)
• “In addition, the saturating mutagenesis are limited in the length of target region due to an approach basing homology-directed repair although it could introduce random mutations on donor template library harbouring any combination and variety of mutations [PMID: 25141179]. On the other hand, the tiling mutagenesis could expand target length in principle because the length depends on multiplex guide RNA (gRNA) designed to target genomic region. Therefore, tiling mutagenesis has been employed to identify critical regions embedded in cis-elements [PMID: 26375006, 30612741, 26751173, 27708057, 28416141, 31784727]. Tiling mutagenesis requires editor such as Cas9 or epigenetic modifier fused to catalytically dead Cas9 (e.g. KRAB-dCas9), and a library containing multiplex gRNA tiling across target genomic region. In general, random single guide RNA (sgRNA) expression cassette are integrated into chromosomes of host cells by retrotransposon system.” following to “…within a narrow region.” in the Introduction (page4, line 82) The paper you raised as DOI: https://doi.org/10.1038/nature13695 (PMID: 25141179) applied saturation mutagenesis to find out critical mutation on BRCA1 and DBR1 protein coding sequence by HDR-based strategy using donor template library. This method based homologous recombination repair, so that the length of target region is limited. Our method employs tiling mutagenesis whose target length depends on sgRNA designed. We expand the length of target region to more than 50 kb from less than 15 kb previously reported. This is our strength compared with this report.

The paper you raised as DOI: https://doi.org/10.1038/nbt.3450 (PMID: 25141179) applied CRISPRko tiling mutagenesis to find out critical region from 2 kb of p53 binding enhancer region by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions. In addition, we expand the length of target region to more than 50 kb. This is one of the advances.

The paper you raised as DOI: https://doi.org/10.1126/science.aag2445 (PMID: 27708057) applied CRISPRi tiling mutagenesis to find out critical region from 74 kb genomic region around GATA1 and MYC by lentiviral delivery of sgRNA cassettes. Our method employs CRISPRko and PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. To identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome. Thus, to identify combinations of critical region embedded in target regions with no artifact owing to no footprint by removal of sgRNA cassettes, CRISPRko tiling mutagenesis rather than CRISPRi is better method because CRISPRi requires integrated cassettes that stably expressed sgRNA and epigenetic modifier fused to dCas9. PiggyBac system can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions to find out combinations of critical region embedded in target regions.

The paper you raised as DOI: https://doi.org/10.1016/j.molcel.2017.03.007 (PMID: 28416141) reported applied CRISPRi tiling mutagenesis to find out critical region from TAD scale (about 200 kb) with low magnification by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions.

The paper you raised as DOI: https://doi.org/10.1038/s41588-019-0538-0 (PMID: 31784727) reported applied CRISPRi tiling mutagenesis to develop method that can find out novel regulatory element around protein coding by lentiviral delivery of sgRNA cassettes. Our method employs PiggyBac system that can remove the sgRNA cassettes from the chromosome with no footprint. Therefore, our method should be novel method that generates mutant library with no risk of non-targeted endogenous gene disruptions.

To clarify the advantages, we will add the following sentences:

• “To identify combinations of critical region embedded in target regions, it would require diverse combinations of mutations or inactivation sites. To induce multiple mutations or inactivated sites, it requires multiple sgRNA cassettes integration. However, multiple integration of sgRNA cassettes have higher risk of non-targeted endogenous gene disruptions and may impair functional analysis [PMID: 23435812]. To eliminate the risk that integrated sgRNA cassettes disrupt non-targeted endogenous genes, it is best way to remove the sgRNA cassettes from the chromosome. Thus, to identify combinations of critical region embedded in target regions with no artifact owing to no footprint by removal of sgRNA cassettes, CRISPRko tiling mutagenesis rather than CRISPRi is better method because CRISPRi requires integrated cassettes that stably expressed sgRNA and epigenetic modifier fused to dCas9.” in the Introduction.
• “Here, we proposed that DNA transposon system rather than retrotransposon system is more suitable to remove sgRNA cassettes from a mutant library. Transposons are genetic elements that can relocate between genomic sites and there are two types of transposons: (1) DNA transposon is transferred by a "cut and paste" mechanism in which the transposon sequence is cut directly from the genome, and (2) retrotransposon is transferred by a "copy and paste" mechanism in which the transposon sequence is transcribed into RNA and then integrated by reverse transcribed [PMID: 21958341]. Therefore, retrotransposon is never removed from the genome. DNA transposon such as PiggyBac, Sleeping Beauty and Tol2 systems are also used as gene transfer tools in vertebrates [PMID: 26481584]. Especially, PiggyBac leaves no footprint on integrated sites after transposons relocated while other DNA transposon system leaves small insertion on integrated sites [PMID: 34064900]. In addition, excision-only-PiggyBac transposase that can remove transposons but not integrate them, is developed [PMID: 27929521]. Only PiggyBac system can remove transposons carrying sgRNA cassettes from mutant library with no footprint. Therefore, we aimed to validate PiggyBac system for CRISPRko tilling mutagenesis.” in the Introduction.
• “CRISPRko tiling mutagenesis is conducted for less than 15 kb target genomic region so far [PMID: 26375006, 30612741], while CRISPRi tiling mutagenesis can target more than 70 kb [PMID: 27708057], it is reported that. Hence, it remains unknown unclear how length CRISPRko tiling mutagenesis could expand” in the Introduction. In addition, we are going to conduct transposon removal by exicision-only-PBase treatment with several PB mES clones, for the proof of concept that CTRL-Mutagenesis can generate mutant library with no sgRNA cassettes.

*Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Major concerns, 1, Authors claim "to identify functionally important elements in non-coding regions in the title but there is no evidence of any functional analysis in the manuscript.*

→ Thank you very much for your suggestion, and we agree with your suggestion.

In this paper, we just validated PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions.

To change our tone that claiming usability of our method for functional analysis, we will change the following sentences:

• Change “to identify functionally important elements in non-coding regions” to “to induce diverse combination and variety of mutations within more than 50 kb non-coding region” in the Title.
• Add “However, not much loss-of-function screens of non-coding regulatory elements has been conducted due to ambiguous annotations compared with protein-coding genes. Tiling mutagenesis has been employed to identify critical regions embedded in non-coding regulatory elements by comparative analysis through a mutant library harbouring subtly different regions mutated within less than 15 kb region. Conventional tiling mutagenesis construct a mutant library integrated multiple sgRNA cassettes by retroviral delivery. However, multiple integration of single guide RNA (sgRNA) cassettes has higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. Herein, combining tiling mutagenesis and PiggyBac transposon that can be removed with no footprint on integrated sites, we established an expanded tilling mutagenesis method named CRISPR- & Transposase-based RegionaL Mutagenesis (CTRL-Mutagenesis). We demonstrated that PiggyBac system could integrated diverse combinations and varieties of sgRNA cassettes.and then CTRL-Mutagenesis randomly induces diverse combination and variety of mutations within more than 50 kb non-coding region in murine embryonic stem cells. CTRL-Mutagenesis would apply for wider non-coding regulatory elements with no risk of non-targeted endogenous gene disruptions.” in the Abstract.
• Delete “Comparative analysis of mutants harbouring subtly different mutations within the same region would facilitate the further study of cis-element and microRNA clusters.” in the Abstract (page2, line 38-40).
• Change “The generated random mutant mES clone library could facilitate further functional analyses of non-coding regulatory elements within the genome.” to “The generated random mutant mES clone library could develop to investigate critical regions of non-coding regulatory elements within the genome.” In the Introduction (page4, line 88-90)

  2, Genotypes of mutant library, especially Mirc56, 14,15, 16, 17 were not determined due to six tandem repeats. Thus, analysis of the relationship between genotype and biological functions is not possible. Moreover, the authors did not show any phenotypic analysis.

→ Thank you very much for your suggestion.

The 6 tandem repeats consisted of each approximately 3.3 kb are hard to determine mutations and are uncommon.

Therefore, we skipped genotyping Mirc56_14, 15, 16, and 17

Certainly, it is drawback that we did not determine all mutations induced by CRTL-mutagenesis.

Even so, we could determine the properties of mutant library within 37 kb genomic region from Mirc56_1 to Mirc56_13.

Therefore, we could conclude that CTRL-mutagenesis could induce diverse combinations and variations of mutations into more than 50 kb.

3, Multiple gRNA may cause deletion and inversion to targeted loci. With local PCR based amplification, detection of large deletion and inversion can be very difficult. I think the authors should examine and address this possibility more carefully. The definition of indel in Fig 5C should be explained in more detail.

→ Thank you very much for your comment, and we agree with your suggestion.

We did not confirm inversion and large deletion.

To confirm whether inversions were happened, we are going to perform PCR walking in several clones and long-read sequencing.

4, Although the authors showed a variety of PB cassettes (Max is 17), more importantly would be to determine the actual copy number of PB cassettes. Difference between the highest and the lowest EGFP intensities in Fig 2C (Donor 300ng Effector 350ng) is approximately ~100 fold, thus ES clone bearing highest PB vector may contain ~100 copies of PB vector. PB transposon prefers insertion in active genes compared to other transposon system such as Sleeping Beauty and Tol2 transposon. (Yoshida J et al Sci Rep. 2017 Mar 2;7:43613. doi: 10.1038/srep43613.). Higher integration rates of PB vectors have a higher chance of endogenous gene disruptions and may impair functional analysis.

→ Thank you very much for your suggestion.

We agree that cassette integration number is one important aspect of tiling mutagenesis. To determine actual copy number of PB transposon is useful information when potential user consider optimizing our method for own target region. However, to confirm whether the relationship between mutations induced and sgRNA cassettes integrated, the number of integrated cassette variety is more important because the diversity of sgRNAs variety expressed is more related to the diversity of mutations induced. Therefore, we identified the number of integrated cassette variety.

To clarify this point, we will add we the following sentences:

“rather than the copy number of sgRNA cassettes because the diversity of sgRNAs variety expressed is more related to the diversity of mutations induced” following to “…the number of sgRNA cassette varieties.” in the Result (page12, line 297)

Certainly, we apologize that it is not accurate that “EGFP signal intensity correlated with the copy number of EGFP cassettes integrated into genomes[23]” in the Result (page11, line 249-250). EGFP expression levels are affected by cell cycle so that the paper reported that “Median EGFP intensities correlated with the copy number of EGFP cassettes integrated into genomes”.

Therefore, we will delete the following sentence:

“EGFP signal intensity correlated with the copy number of EGFP cassettes integrated into genomes[23]” in the Result (page11, line 249-250).

To investigate how many copies our concentration of donor vector could integrate, we are going to check actual copy numbers in several clones by qPCR.

Besides, we agree with your suggestion that “PB transposon prefers insertion in active genes compared to other transposon system such as Sleeping Beauty and Tol2 transposon. (Yoshida J et al Sci Rep. 2017 Mar 2;7:43613. doi: 10.1038/srep43613.)”

Therefore, we will change the following sentence:

“random TTAA sites across genomes [24]” to “random TTAA sites of transcribed region rather than intergenic region [PMID: 28252665]” in the Discussion (page14, line 357).

However, Sleeping Beauty and Tol2 transposon remain footprint at integration sites when these transposons move [PMID: 15133768, 23143102]. Especially, SB transposon leaves canonical 5 bp insertion at integration sites so that the canonical 5bp insertion into coding sequence could disrupt the function of endogenous protein frequently. On the other hand, PB transposon remains no footprint. Therefore, excision-only-PBase can remove the PB transposon from mutant library clearly. Thus, it is no worry about that PB transposon disrupt non-targeted endogenous gene impair functional analysis if PB mutant library is treated with excision-only-PBase.

In addition, we are going to conduct transposon removal by exicision-only-PBase treatment with several PB mES clones, for the proof of concept that CTRL-Mutagenesis can generate mutant library with no sgRNA cassettes.

5, Most non-coding regions are located at autosomes. Genotyping would be very difficult or even impossible by the current PCR based strategy.

→ Thank you very much for your comment, and we agree with your suggestion.

This is one of our issues.

We expect that CTRL-Mutagenesis could be valid on other biallelic locus.

Therefore, we raised predicted issue such as complex genotyping and proposed one solution.

When we target other biallelic locus, we must determine whether the combination of mutations induced are cis- or trans-mutations. Haplotype phasing, combined long-read sequencing with SNP markers within ROI on maternal/paternal chromosome, assembles each allele via SNP markers on each read [PMID: 35710642]. Therefore, combining CTRL-Mutagenesis on heterozygotic alleles of cells derived from such as human or murine hybrid with haplotype phasing might simplify genotyping.

We will add the following sentences:

“In this study, CTRL-Mutagenesis was validated by genotyping on mono allele in male mES cells to avoid investigating whether the combination of mutations induced are cis- or trans-mutations. All genotypes on Mirc56 should be hemizygous and these mutations induced might be cis-mutations so that we determined the genotypes by amplifying approximately 200 bp around the target sites. However, we did not confirm large mutations such as deletion of the genomic region between target sites and inversion. Long-read sequencing might capture their large mutations. Besides, we also expect that CTRL-Mutagenesis could be valid for ROI on biallelic autosome and X chromosome in female. Therefore, it is required to determine whether the combination of mutations induced are cis- or trans-mutation. Haplotype phasing, combined long-read sequencing with SNP markers within ROI on maternal/paternal chromosome, assembles each allele via SNP markers on each read [PMID: 35710642]. Therefore, combining CTRL-Mutagenesis on heterozygotic alleles of cells derived from such as human or murine hybrid with haplotype phasing might simplify genotyping.” in the Discussion.

Moreover, genome-wide NGS and nanopore Cas9-treated sequencing (nCATs) could also help us to read the mutations without PCR-amplification. However, both methods can obtain reads of target regions with low frequency. Therefore, it is difficult to perform multiplex samples for mutant library.

*6, Fig 4C, large amounts of Cas9 independent EGFP positive cells suggest the current system is not efficient. *

→ Thank you very much for your comment.

We cannot agree with your indication.

In fact, by the cutoff set in Cas9-untreated cells, the EGxxFP system successfully selected at least 76 mutant clones (87.4%) harboring mutations within Mirc56_1 to Mirc56_13. Moreover, we could seed 180 single-cells for single cloning by FACS once.

To enhance this point, we added the following sentences:

“Moreover, at least 76 out of 87 PB mES clones have mutations within all analysed Mirc56_Xs (Figure 5C). Therefore, the EGxxFP system could selected ROI mutant mES clones efficiently.” following to “…depended on integrated sgRNA cassettes.” in the Discussion (page13, line 355)

*Reviewer #4 (Significance (Required)):

The authors claim "Functional analysis" in the manuscript title but there is no evidence of functional analysis in the manuscript.*

→ Thank you very much for your suggestion, and we agree with your suggestion.

In this paper, we just validated PiggyBac system for CRISPRko tilling mutagenesis and expanded the length of target regions.

To change our tone that claiming usability of our method for functional analysis, we will change the following sentences:

• Change “to identify functionally important elements in non-coding regions” to “to induce diverse combination and variety of mutations within more than 50 kb non-coding region” in the Title.
• Add “However, not much loss-of-function screens of non-coding regulatory elements has been conducted due to ambiguous annotations compared with protein-coding genes. Tiling mutagenesis has been employed to identify critical regions embedded in non-coding regulatory elements by comparative analysis through a mutant library harbouring subtly different regions mutated within less than 15 kb region. Conventional tiling mutagenesis construct a mutant library integrated multiple sgRNA cassettes by retroviral delivery. However, multiple integration of single guide RNA (sgRNA) cassettes has higher risk of non-targeted endogenous gene disruptions and may impair functional analysis. Herein, combining tiling mutagenesis and PiggyBac transposon that can be removed with no footprint on integrated sites, we established an expanded tilling mutagenesis method named CRISPR- & Transposase-based RegionaL Mutagenesis (CTRL-Mutagenesis). We demonstrated that PiggyBac system could integrated diverse combinations and varieties of sgRNA cassettes.and then CTRL-Mutagenesis randomly induces diverse combination and variety of mutations within more than 50 kb non-coding region in murine embryonic stem cells. CTRL-Mutagenesis would apply for wider non-coding regulatory elements with no risk of non-targeted endogenous gene disruptions.” in the Abstract.
• Delete “Comparative analysis of mutants harbouring subtly different mutations within the same region would facilitate the further study of cis-element and microRNA clusters.” in the Abstract (page2, line 38-40).
• Change “The generated random mutant mES clone library could facilitate further functional analyses of non-coding regulatory elements within the genome.” to “The generated random mutant mES clone library could develop to investigate critical regions of non-coding regulatory elements within the genome.” In the Introduction (page4, line 88-90).

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

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Reply to the reviewers

Review Commons - Revision Plan

Manuscript number: RC-2023-02228

Corresponding author(s): Gatfield, David

1. General Statements

We are grateful to the three Reviewers for their detailed assessment of our manuscript and are delighted about their very constructive and positive evaluations, highlighting the study’s novelty and rigor.

Briefly, the main points raised by Reviewers 1 and 3 do not involve additional experiments and are mostly about rethinking manuscript structure (e.g. moving data/analyses to the supplement or removing them altogether, as they distract from the main thrust of the story) and making the text overall less dense and more readable.

Reviewer 3 also raises a number of additional interesting points that we should discuss in our manuscript, which would allow us placing our findings more effectively into the context of the existing literature.

All these points are very well taken and will be implemented (see below, under 2).

Reviewer 2 is overall also rather positive – speaking of “a very careful and detailed study that addresses an important issue” and the study being “really rigorous and the logic […] very well explained”; moreover, this Reviewer also shares the view of both other Reviewers that parts of the manuscript (i.e., in particular its beginning) should be shortened.

Importantly, this Reviewer remarks in addition under “Significance”: “Without additional mechanistic insights suggesting that there is something particular different about the regulation of these mRNAs the manuscript is not of extremely high significance.” – an important point of criticism that we wish to address in our revision, as detailed below.

2. Description of the planned revisions

In the following, we detail how we plan to address the points raised by the Reviewers. The order in which we treat the points follows their – in our view – relative importance according to the Reviewers’ feedback. In particular the first item below, under (A), is the main point of criticism that we feel we should address carefully for the future revised version.

(A) Major point raised by Reviewer 2: “However, the study falls short on addressing the mechanism of this regulation and if it is different of other feeding regulated mRNA oscillations. This diminishes the significance of the study unless additional mechanistic details are provided.” , which is cross-commented both by Reviewer 1: “More importantly, clues to the mechanism (e.g. iron, heme) regulating the rhythmic translation of IRP1 and IRP2 IRE-mRNAs in liver would increase the significance of the work.” as well as by Reviewer 3: “Reading the comment from Reviewer #2 over the lack of a mechanism to explain why only four transcripts with IREs amongst a larger pool are subject to circadian regulation by IRPs somehow reduces the significance of the study, one has to agree that a discovery - likely another component in the system - is wanting. I remain of the view that the present work exposes this "weakness" of the entire field in a global as opposed to a partial manner and in doing so, makes a significant contribution, especially by further sub-classifying the IRE-containing transcripts according to their responsiveness in the diurnal occupancy of their IREs.”

Our response and revision plan: Indeed, in the original version of our manuscript we established the link to feeding, yet we did not pinpoint the precise molecular cue that could underlie the rhythmic regulation observed on certain IRE-containing mRNAs. We did discuss the molecular candidates quite extensively in the Discussion section of the manuscript (Fe2+; oxygen; reactive oxygen species), and it remains quite obviously the main question whether the observed diurnal control could be mediated directly by changes in intracellular iron availability.

Of note, the preprint by Bennett et al., for which we cite the initial biorXiv version in our manuscript, was updated very recently (https://doi.org/10.1101/2023.05.07.539729 – see version submitted December 18, 2023). It now includes new data that analyses around-the-clock iron levels also in liver. Briefly, the preprint shows, first, that serum iron is rhythmic with a peak during the dark phase at ZT16 (Figure 1D in Bennett et al.) yet loses rhythmicity when feeding is restricted to the light phase (Bennett et al., Figure 2E), indicating both feeding-dependence and circadian gating. Moreover, liver total non-heme iron – quantified using a method that measures both ferrous Fe(II) and ferric Fe(III) – shows low-amplitude diurnal variations which, however, do not meet the threshold for rhythmicity significance (Bennett et al., Figure 3G). Still, the difference between timepoints ZT4 (lower iron; light phase) and ZT16 (higher iron; dark phase) is reported as significant, with a fold-change that is not very pronounced (not compatible with the observed direction of regulation of Tfrc mRNA, whose higher abundance in the dark phase would rather be in line with lower *cytoplasmic iron levels, as pointed out by the authors.

Thus, at first sight the analyses by Bennett et al. would appear to answer part of the Reviewer’s question and point towards other mechanisms of regulation than iron levels themselves. However, it should be pointed out that the particular methodology for iron measurements used by the authors includes the use of reducing reagents and hence quantifies the sum of Fe2+ and Fe3+ iron. Large amounts of iron are stored in the liver in the form of ferritin-bound Fe3+, yet the bioactive, low-complexity iron that is considered relevant for IRP regulation is in the Fe2+ form. Therefore, the question whether bioactive ferrous iron levels follow a daily rhythm, compatible with the observed IRP/IRE rhythms described in our manuscript, still remains an open question and warrants a dedicated set of experiments that we are proposing to conduct in response to the Reviewers’ comments.

Briefly, for the revision we propose to use liver pieces from the two relevant timepoints of our study (i.e., ZT5 and ZT12) and apply a method that allows the separate quantification of Fe2+ and Fe3+ (Abcam iron assay ab83366; this assay can be adapted to liver iron measurements, see e.g. PMID31610175, Fig. 4A). This experiment will provide novel and decisive data on the molecular mechanism that may regulate the IRP/IRE system in a rhythmic fashion and therefore add to the significance of our findings, as requested by the reviewers.

Moreover, we believe that the outcome of the experiment would be very interesting either way, i.e. if we find rhythms in Fe2+ that are compatible with rhythmic IRP/IRE regulation, we would be able to provide excellent evidence in term of likely molecular mechanism and rhythmicity cue. If, by contrast, we find that Fe2+ is not rhythmic, it will point towards a mechanism that is distinct from simple Fe2+ concentrations.

In the latter case, collecting additional evidence on relevant alternative molecular cues would be beyond our capabilities for this particular manuscript, as it would require quite sophisticated methodological setup and preparation. For example, one could imagine that measuring around-the-clock liver oxygen levels in vivo – another candidate cue – would be highly interesting, yet we would not be able to conduct these experiments in a reasonable time frame (to start with, we would first need to request ethics authorisation from the Swiss veterinary authorities, which would in itself take ca. 4-6 months before we could even start an experiment). Thus, in the case of non-rhythmic iron levels, we would leave the question of other responsible cues open, but still think that with a balanced discussion of the resulting hypotheses we could provide significant added value to our work.

(B) Major comment raised by Reviewer 1: “Alas2 is expressed mainly in erythroid cells and not liver, whereas Alas1 is ubiquitously expressed. Therefore, it is possible that Alas2 in this study may originate from red cells/reticulocytes in the liver, and not from hepatocytes.”

Our response and revision plan: We would like to thank the Reviewer for the comment that is indeed pertinent. It is well established that Alas1 is the main transcript encoding delta-aminolevulinate synthase activity in hepatocytes, and Alas2 is about 10-fold less abundant in total liver RNA-seq data (quantified form own RNA-seq data, not shown).

We are nevertheless relatively sure that the Alas2 signal comes from low expression in hepatocytes; the best argument in support of this hypothesis is the analysis of single-cell RNA-seq data, as shown in the following Revision Plan Figure 1, which we would be happy to include in a revised version of the manuscript if the reviewers wish:

(C) Minor comment raised by Reviewer 1: “The paper is dense and not easy to read. For example, the section on Tfrc regulation and NMD regulation is lengthy and perhaps not necessary for the paper and the section on "Previous observations in IRE-IRP regulation...." could be included in the discussion rather in than in the Results section. Some figures could be included in a supplement.” continued in Referee cross-commenting “I agree with Reviewer 2 that the first sections in the manuscript are lengthy and not needed.”; moreover, Reviewer 2: “Also, the manuscript first sections (which mainly describe negative results) seem too long and descriptive.”

Our response and revision plan: We shall reorganize the paper accordingly, with the aim of making it an easier, shorter, clearer read. Many thanks for the input.

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(D) Minor comment raised by Reviewer 1: “A description of the new anti-IREB2 antibody is needed. What IRP2 sequence was used to generate antibodies?”

Our response and revision plan: The following information will be included in the manuscript: “Rat monoclonal antibodies against ACO1/IRP1 and IREB2/IRP2 were generated at the Antibodies Core Facility of the DKFZ. Briefly, full-length murine ACO1/IRP1 and IREB2/IRP2 proteins, fused to a poly-histidine tag, were expressed in E. coli and purified on Ni-NTA columns using standard protocols. Purified His-tagged proteins were used to immunize rats and generate hybridomas. Hybridoma supernatants were first screened by ELISA against His-tagged ACO1/IRP1 and His-tagged IREB2/IRP2. As an additional control, supernatants were tested against full-length His-tagged murine ACO2 (mitochondrial aconitase), which shares 27 and 26% identity with ACO1/IRP1 and IREB2/IRP2, respectively. Supernatants reacting specifically with ACO1 or IREB2 were validated by western blotting using extracts from wild-type versus ACO1- or IREB2-null mice.”

(E) Minor comment raised by Reviewer 1: “A model summarizing the data would be useful.”

• *Our response and revision plan: Thank you for the suggestion – this will be done.

(F) “Optional” idea raised by Reviewer 3: “One nuance in the field of circadian biology is that a rhythm is deemed to be genuinely "circadian" when it continues in the absence of zeitgebers. In this sense, although all experiments are valuable, the "collapse" of the rhythm in the paradigms where dietary rhythms have been disrupted makes the phenomenology a candidate "epiphenomenon" rather than being closer related to the biological clock(s). Likewise, in the manuscript we never learn how the liver IRE-binding activity behaves in constant darkness.”

Our response and revision plan: This is an important aspect that we can clarify more specifically in our manuscript. It is true that constant (darkness) conditions are used to call a phenomenon circadian. We would nevertheless argue that for a rhythmic feature that is specifically found in liver, the constant darkness definition to distinguish circadian from non-circadian is not fully valid because even in constant darkness, the liver clocks are not in a free-running state but continue to be entrained by the SCN clock (it is only the latter that is free-running under these conditions).

In our manuscript, we actually suggest that the observed rhythms are not a core output of the circadian machinery (Fig. 6 of our manuscript), but indirectly engendered through feeding rhythms, which are coupled to sleep-wake cycles and thus connect in an indirect way to the central circadian clock activity in the SCN.

In wild-type mice we would therefore expect that irrespective of constant darkness or light-dark entrainment (and assuming ad libitum feeding), the hepatic rhythms of the relevant IRE-containing transcripts would persist in a similar fashion.

(G) “Optional” idea raised by Reviewer 3: “Where the authors mention in a parenthesis "moreover, there are documented links between iron and the circadian timekeeping mechanism itself", I invite them to take a closer look to the paper Konstantinos Mandilaras and I coauthored in 2012 "Genes for iron metabolism influence circadian rhythms in Drosophila melanogaster". In that work, we showed that RNA interference of genes that are required for iron sulfur cluster formation (including on IRP1) in the central clock neurons of the fly result in loss of the circadian rhythm when flies were kept at constant darkness (not so when they were kept under light:dark oscillation). So this point should probably remain open..”

Our response and revision plan: We would like to thank the Reviewer for pointing out this interesting connection that would fit well into the context of our manuscript. It should be cited in the context of our current Figure 3, where we measure in vivo and in tissue explants whether IRP-deficiency affects the clock itself.

To follow Reviewer 3’s idea, we have gone a little further in our analyses of around-the-clock expression data to see if any of the components of the Fe-S assembly machinery is rhythmic itself, which could have the potential to add novel information.

Briefly, we have used for this purpose our around-the-clock RNA-seq and ribo-seq data from PMID 26486724. In summary, we find that the expression at RNA and/or footprint level is non-rhythmic for the vast majority of genes involved in FeS biogenesis, assembly or transport, with the exception of low-amplitude rhythms for Glrx5 and Iba57 (Revision Plan Figure 2).

By contrast, all of the following other genes are non-rhythmic throughout (list of Fe-S-relevant genes from PMID34660592): Cytoplasmic/nuclear, all non-rhythmic: Cfd1=Nubp2, Nbp35=Nubp1 , Ciapin1, Ndor1, Iop1=Ciao3=Narfl, Ciao1, Ciao2b=Fam96b, Mms19, Ciao2a=Fam96a; mitochondrial, all non-rhythmic: Iscu, Nfs1, Isd11=Lyrm4, Acpm=Ndufab1, Fdx1, Fdx2=Fdx1l, Fxn, Hspa9 Hsc20=Hscb, Abcb7, Alr=Gfer, Isca1, Isca2, Nfu1

As these are mainly “negative results”, and as we are also unable to propose a solid possible mechanistic connection between the Glrx5 and/or Iba57 rhythms and the rest of the story of our manuscript, we do not intend to include such data in our manuscript, but are only putting it for the record into this rebuttal.

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

NONE

4. Description of analyses that authors prefer not to carry out

NONE – we think we can address all points as described above.

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Reply to the reviewers

Manuscript number: RC-2023-02224R

Corresponding author(s): Austin Smith

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for constructive comments and helpful suggestions which we have adopted to clarify and improve the manuscript. In addition, we have added a link to a web portal that will allow readers to visualise gene expression profiles and create their own plots using our early human embryo UMAP embedding (https://bioinformatics.crick.ac.uk/shiny/users/boeings/radley2024umap_app/). Stefan Boeing created this tool and is added to the author list with agreement of other authors.

2. Point-by-point description of the revisions

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

Summary In this manuscript, Arthur Radley and Austin Smith designed a new feature selection method for scRNA-Seq, which is a successor to ESFW previously proposed by the same authors. As an evolution of this earlier framework, cESFW is also based on the idea that informative genes share information with other genes, whereas non-informative genes have a more random relative expression. The authors emphasize the key importance of feature selection in the scRNA-Seq workflow and assess the current state of the art for this step. They also propose that better feature selection leads to less data transformation. They show that cESFW outperforms Scran and Seurat feature selection in most cases of synthetic datasets. cESFW is then used in the context of early human development, re-analysing data from several published datasets where they show that they do not require batch correction. They also further strengthen the conclusion that a "2-step" model for TE-ICM and EPI-Hyp differentiation is also present in human embyros. Finally, they map several types of in vitro pluripotent stem cells, in particular primed and naive, to their manifold and study the evolution of the gene signatures during early human development. Overall, the manuscript is well written and presents a solid methodology. The re-analysis of human early development is convincing and justified. The main critic is that the quality of figures can be greatly improved: their resolution is too low and they are hard to read. For instance, more contrasted color schemes could be used to improve clarity, and given the high number of clusters for some UMAPs, indicating the name of some cluster near their centroids should improve clarity.

We agree that the resolution of the figures should be improved. We had to compress the images to satisfy the size limit for uploaded documents to bioRxiv. Our final submission will be of higher quality (original figures are at 900dpi). With regards to colour schemes, this is a surprisingly difficult problem. We tried multiple colour palettes but could not achieve greater contrast. The suggestion to add key cluster names near to their centroids on the UMAPs is an excellent idea, which we have implemented.

Comments: Page 2 I think the criticism of PCA is unfair because it is not a true feature selection method, and it is mainly used for computational purposes. I believe that for most workflows, between 30 and 50 PCs are retained, which do not significantly change the results in the downstream analyses. The citation (Yeung and Ruzzo 2001) does not seem appropriate, as they examine cases where only a small number of PCs are retained, outside the context of scRNA-seq.

We agree that the criticism of PCA is insufficiently justified by the citation. We thank the reviewer for pointing this out and have removed the comment.

"Furthermore, HVG selection has been found to be biased toward selecting highly expressed genes over low expressed genes." Could the author justify or remove this statement, as the Seurat and Scran methods are specifically designed to consider average expression to determine HVG? The cited article (Yip, Sham, and Wang 2019) raises this issue for methods other than Seurat and scran.

The reviewer is correct that the provided citation highlights Seurat and Scran HVG selection as relatively insensitive to the average gene expression levels compared with other HVG selection methods. We again thank the reviewer and have deleted the comment.

More generally, we have shortened the introduction, focusing on cESFW as a new approach to feature selection rather than critiquing alternative methods.

Page 6 I might have missed it, but I do not understand the number of cells in the early human development dataset also shown in Figure S2B. The Petropoulos et al. dataset alone is larger than the sum of cells from different cell types. Is there some filtering step that is not described?

We have added text in the data availability section to clarify the cells used in our analysis:

“The pre-implantation raw counts scRNA-seq data from Yan et al. 2013, Petropoulos et al. 2016, Fogarty et al. 2017, and Meistermann et al. 2021, were compiled into a single gene expression matrix by Meistermann et al. 2021. For information regarding quality control and cell filtering of these 4 datasets, please refer to Meistermann et al. 2021.”

The unsupervised clustering used to annotate cell types is unconventional (especially with the high number of clusters chosen), which is not a problem, but should be clarified. Improving the figure 3D to make it clearer and providing a cell cluster correlation plot might help to better appreciate the relationship between cell types.

We agree that the gene expression heatmap in figure 3D contributed little to the interpretation of the data/results. As suggested, we have replaced this heatmap with a cell cluster correlation plot to help appreciate cell state similarities. (Changes in figure 3.)

It could be emphasized that the ICM/TE branch cell type is a major difference with the mouse topology, as the readers might not be aware that the ICM/TE is an unspecified blastocyst state that only exists in humans.

There appears to be some misunderstanding around the use of “ICM/TE branch”. The cluster comprises an uncommitted population at the branching point from morula to either ICM or TE, as also described in the mouse embryo. We have adjusted the discussion to make more clear that the two branching point clusters are heterogeneous populations, not unitary cell types or states:

“The branching populations reside at critical junctures in blastocyst formation, the partitioning of extraembryonic and embryonic lineages. These branchpoint clusters do not define unitary states. On the contrary, cells in these clusters are heterogeneous and may become specified to alternative fates. For example, PDGFRA, a hypoblast marker (Corujo-Simon et al. 2023), and NANOG, an epiblast marker (Allegre et al. 2022), are heterogeneously distributed in the Epi/Hyp branching population. Furthermore, branch cluster boundaries extend beyond the topological bifurcation, potentially indicating that cells remain plastic and may be redirected. This would be consistent with the demonstration in mouse embryos that cells expressing ICM genes remain capable of generating TE up to the late 32-cell stage (Posfai et al. 2017).”

Page 9 To further substantiate the stepwise ICM/TE and EPI/PrE specification events, authors could project cells from each embryo on the UMAP, and analyze what are the co-occurrence of cells (as performed for instance in Meistermann et al 2021). This should show as reported (and cited by the authors) that some GATA3 positive cells (TE fated) start appearing from late morula stage and that ICM cells almost never co-exist with EPI nor Hyp in embryos.

We appreciate this suggestion. We have generated the requested plots showing where cells from individual embryos at different developmental timepoints are positioned on our UMAP embedding. (new supplemental figure (New figure, Figure S6). We present a summary heatmap of cell co-occurrence in revised Figure 4. These results offer greater insight than the RNA velocity analysis, which we have moved to supplemental Figure S6. We have added discussion of these analyses in the “Lineage branching blastocyst development” Results section.

Reviewer #1 (Significance (Required)):

The presented methodology shows significant value especially in the field of scRNA-Seq, where the critical step of feature selection is often inadequately addressed. Furthermore, this field is characterized by a limited set of feature selection methodologies. cESFW appears to be an important alternative to HVG methods that could improve scRNA-Seq analysis in certain contexts.

The new findings on early human development are somehow incremental, but a welcome addition to solidify the two-step model and refine the concept of reject cells. The audience for this early development context is specialized, but cESFW will most likely have an impact to the entire field of scRNA-Seq analysis.

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

Here, Radley and Austin present a novel approach for feature weighting in scRNAseq data based on entropy sorting. Feature selection is a central part of scRNAseq analysis, and it is most likely the case that there is no single approach that outperforms all others across all datasets. Hence, innovation in this space is needed for the field. The cESFW method presented here has several appealing properties from a theoretical point of view, and it also performs well on the synthetic and real datasets considered. Nevertheless, there are several major issues that need to be addressed before I can recommend the manuscript for publication:

1 The original entropy sorting (eq 1 in SI 1) is based on only two discrete states. However, calculating entropy for continuous distributions can be more tricky and it is unclear to me what assumptions are made regarding the gene expression. Could the authors clarify what properties of the distribution are required for the updated ESE equation to be valid? Is the only assumption that values are drawn from the [0, 1] interval? What happens if values are highly skewed, ie forming a bimodal or power-law distribution rather than something close to a uniform distribution?

We agree that it is beneficial to clarify these points. We have added a section titled “Assumed properties of underlying sample distributions” to the supplemental information. Briefly, we show that the ESS correlation metric is directly linked to the commonly used correlation metric, Mutual Information (MI). A desirable properly of MI is that it is able to capture non-linear/skewed relationships between features. The ES framework and ESS share this property with MI, allowing the ES framework to be relatively robust to presence of non-uniform distributions.

The main assumption for applying ES is that the features can be meaningfully scaled between values of 0 and 1. For gene expression, an intuitive way of achieving this is to inspect each gene and designate 0 count values as having 0 expression activity, and the maximum counts as having activities of 1, and all values in between existing within the [0,1] interval. A useful property of ES is that we do not need to assume a particular shape or distribution of the samples within the [0, 1] interval. The ES framework is non-parametric and does not require an assumed distribution to calculate the conditional entropy (CE), even in the continuous form. This is possible because the ES framework is formulated by turning the probabilistic form of CE into an ordinary differential equation (ODE), where the only dependent variable, x, is the overlap between the minority state activities of each individual sample. This calculation is explicitly identifiable/calculable, and is permutation invariant, meaning the shape of the distributions of a reference feature (RF) and query feature (QF) does not need to be assumed/defined. In other words, the ES framework quantifies to what degree active expression states enrich/overlap with one another in a manner that is robust to different distribution shapes.

2 How robust is the procedure for the choice of percentile for normalizing the gene expression scores? Does one get roughly the same results for 90-99th percentile or is it sensitive to this choice?

We have carried out a sensitivity analysis on the choice of percentile for each of the synthetic datasets and added it to the manuscript. (New figure, Figure S11). We find that on each of our 4 synthetic datasets the final results of cESFW are robust to a wide range of normalisation percentiles.

3 Similarly, I am concerned about the procedure for how to choose the number of significant genes. How robust is this process? Also, it is not altogether clear how to generalize the procedure outlined on p19. Most potential users would benefit from more quantitative guidelines. In particular, having to rely on interpretation of GO terms typically requires a considerable amount of understanding about the system at hand which could make it challenging to apply the procedure for others. For most users it would be helpful to know how robust the procedure is to this step and also if there could be more stringent guidelines for how to decide which genes to include.

We understand the reviewers concern regarding the robustness of feature selection on real scRNA-seq datasets. We have now applied our cESFW workflow to peripheral blood mononuclear cells (PBMC) scRNA-seq data, and found cESFW feature selection to be comparable, and by one metric more robust, than Seurat and Scran HVG selection (New Figure S2).

As cESFW is applied to more scRNA-seq data, we will learn more about how results compare to highly variable gene selection, and how workflows may be adapted to optimise results in different scenarios. For example, we have found that supervising the selection of gene clusters using a small set of markers known to be important in the system of study can help identify which clusters of genes should be retained during gene selection. We have added this to the materials and methods with the following paragraph:

“Furthermore, we suggest supervising the selection of gene clusters using a small set of markers known to be important in the system of study. In this work, we found that genes known to be important during early human embryo development (FigS4) are enriched in the dark blue cluster of genes, further suggesting that this cluster of genes is more likely to separate cell type identities in downstream analysis.”

While gene cluster selection supervision in this manner requires a degree of domain expertise, we believe this is not unreasonable for most applications, and is the case for many scRNA-seq analysis pipelines.

Our primary software contribution is the cESFW algorithm which calculates the ESS and EP matrices. With this manuscript we provide 6 commented workflows for applying cESFW to different datasets (4 synthetic data, human embryo data, PBMC data). We believe these workflows provide a good balance of documented use cases and user flexibility for cESFW usage. This is important because it is advantageous to be able easily to adapt workflows to incorporate domain expertise and different methodologies. Although workflows such as Seurat and Scran are user-friendly, their rigidity can be difficult when wanting to deviate from their standard workflows. In summary, we believe that our provided workflows are suitable for users to implement cESFW, while providing the flexibility to apply adapted pipelines.

4 The comparison of the clusterings on p6 is not really fair is it? If I understand it correctly, the 3,012 genes identified by cESFW was used to define clusters in fig 3c through unsupervised clustering. The authors then use HVG methods to identify 3,012 genes and then carries out clustering based on those. To evaluate the methods the silhouette score is used, but the labels from the cESFW clustering is used as ground truth. This does not sound like a fair way to compare. Could the authors please clarify, and if needed come up with an approach where the three methods have a more level playing field if needed.

The reviewer raises a fair point regarding the comparison of cluster identities and ranked gene lists. This issue is a chicken and egg problem, in that we require a baseline to benchmark different methodologies but lack an explicitly defined ground truth. For that reason we used synthetic datasets for initial comparison.

For the human embryo data, we have presented substantial evidence that our cluster annotations are biologically coherent and consistent with prior knowledge. We therefore consider it legitimate to compare the ranked lists of Seurat, Scran and cESFW. However, we acknowledge the potential bias and have mentioned this in the “Limitations of the study” section.

In addition, we have now analysed the peripheral blood mononuclear cells (PBMC) scRNA-seq dataset that is used in the tutorial workflows of Seurat and Scran. This PBMC dataset is arguably better defined since it has more discrete populations of cells, and by using the Seurat generated cell type labels we bias the analysis towards Seurat rather than cESFW. The results show that cESFW performs comparably to Seurat and Scran, and that the cESFW ranked gene list may be more stable than Seurat and Scran. These results suggest that cESFW can be widely applicable as a suitable alternative for feature selection. We have included this analysis in the Results and as a supplemental figure (New figure, Figure S2).

5 The main cESFW.py file in the github repository is clearly well structured and commented. However, I would like to see a much better documentation so that one does not have to go through the source code to understand what functions there are and what they do. In particular, I would like to see a vignette to make it easier for others to incorporate cESFW into their workflows.

We thank the reviewer for the positive comments regarding our cESFW.py commenting. We accept that our initial submission failed to point the reader directly towards our example workflows that provide step by step, well commented vignettes for using cESFW to analyse scRNA-seq data. In our initial submission we provided 5 workflows (4 synthetic data and the human embryo data), and in the re-submission we have added a workflow for analysing PBMC data. We have updated our cESFW Github to guide users to these example workflows (https://github.com/aradley/cESFW/tree/main).

Please note, the embryo workflow will be easily accessible through GitHub, whereas the synthetic data and PBMC workflows will be provided through a Mendeley data link (referenced in the manuscript and on our GitHub). However, the content of the Mendeley link cannot be made public until the paper is finalised, as it cannot be changed after publication. We provide a temporary public Dropbox link for the reviewers so that they may access the additional workflows (https://www.dropbox.com/scl/fo/xr5o9xm6490ftjsa55wxg/h?rlkey=maindrxwdqnirsw1en3my5qsr&dl=0).

Minor:

Why are the figures not always in order? For example, fig S10 is mentioned before fig S2 on p 6

Thank you for pointing this out; we have amended the text.

I am not sure if the indexing in eq 1 (p 18) is correct. j is both on the LHS and it is also being summed over on the RHS. Should one of these be i instead?

The indexing is correct. Each column j of a matrix refers to gene/feature on the RHS, and in the calculation on the RHS we take the column averages, leading to vector on the LHS that is still indexed by genes/features j. We have clarified this in the text.

Reviewer #2 (Significance (Required)):

The work presents a new method for feature selection in scRNAseq. Feature selection is a very important step and can have a big impact on findings. The method presented here is theoretically sound and it seems to provide interesting result when applied to early embryo development. However, as cESFW is only tested for one dataset it is unclear how well the method generalizes to other problems and datasets.

Appreciation of the utility of cESFW will grow as it is applied to more datasets. However, we would like to highlight that the human embryo dataset consists of 6 independent scRNA-seq datasets from different laboratories, and that cESFW was able to identify common and differing structure between them without any batch correction, smoothing or feature extraction. We have added to our summary that we propose cESFW may be best suited to analysis of transcriptome trajectories in time course and developmental data. However, we have also now performed comparison of Seurat, Scran and cESFW feature selection in a different context, using a reference PMBC scRNA-seq dataset. The results demonstrate that cESFW is a viable alternative for feature selection in that static system also (New figure, Figure S2).

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Reply to the reviewers

We thank the three reviewers for their thoughtful and constructive comments. The changes to the text and figures made in response to the questions raised have made this a clearer and stronger manuscript. The additional citations suggested by the reviewers helped to further anchor our study within the growing literature on facultative parthenogenesis. Below we have responded to each comment in blue. We have added new data to the manuscript (Fig. 4C, Fig. S10B and Fig. S10D).

Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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

1. Summary: Here Ho et al. provide strong molecular evidence for the production of facultatively parthenogenetic whiptail lizards, through a gametic duplication. As evidenced through multiple routes, including microsatellites, WGS, RADseq, and RBC ploidy, and lines of evidence from multiple specimens, this study is timely in furthering our understanding of the mechanisms underlying FP. The findings are conclusive.

That said, I have several comments that should be addressed prior to publication. The introduction which addresses FP in other systems fails to cite several key studies that provide strongly molecular support for terminal fusion automixis. Similarly, the study pushes the idea that this is an adaptive trait, however without proving that the parthenogens can themselves reproduce, this is a moot point at this stage.

That said, my comments are minor. I found this to be an excellent study, well written, comprehensive in methodology, and one that I strongly advocate for publication.

We thank reviewer 1 for referring to our manuscript as an excellent study and strongly advocating for its publication. We concur with his/her points that evidence for automixis in other systems was not sufficiently referenced and that the adaptive trait hypothesis for FP is somewhat speculative. The text has been modified accordingly (see below).

Major comments - None.

Minor Comments: Should be addressed.

Line 36 - However, data that supports terminal fusion are no longer restricted to microsat data. Studies utilizing RADseq and whole-genome sequencing in snakes and crocodiles have now provided further evidence supporting terminal fusion.

See: Booth et al. 2023. Discovery of facultative parthenogenesis in a new world crocodile. Biology Letters. 19, 20230129.

Card et al. 2021. Genome-wide data implicate terminal fusion automixis in king cobra facultative parthenogenesis. Scientific Reports. 11, 1-9

Allen et al. 2018. Molecular evidence for the first records of facultative parthenogenesis in elapid snakes. R. Soc. Open. Sci. 5, 171901.

We have now included that automixis in other systems is supported by both microsatellite and NGS data in the abstract of our manuscript. The references have been included in the main text.

Ln 42 - Evidence suggesting that isolation from males was not a pre-requisite for FP has previously been reported in snakes.

See: Booth et al. 2011. Evidence for viable, non-clonal but fatherless Boa constrictors. Biology Letters. 7, 253-256.

Booth et al. Facultative parthenogenesis discovered in wild vertebrates. Biology Letters. 8, 983-985.

Booth et al. 2014. New insights on facultative parthenogenesis in pythons. Biol J Linn Soc. 112, 461-468.

Despite the prior evidence to the contrary cited by the reviewer, it is still a commonly held belief among scientists and science journalists that isolation from males promotes or triggers FP. We have placed our findings in the context of other studies, including those mentioned above, that came to the same conclusion that isolation from mating partners is not a requirement for FP. We thank the reviewer for the additional citations, which are now included in the discussion section.

Ln 48 - Is this really an argument. While an immediate transition to homozygosity will purge some deleterious alleles, given the genome-wide nature of this, there will also conversely have been strong selection for mildly deleterious alleles.

Even though many FP animals have congenital defects, our data, combined with that of others, show that seemingly healthy animals arise as well. Even if these healthy animals harbor slightly deleterious alleles, the most detrimental alleles would have therefore been purged especially for subsequent generations. We have modified the abstract to be clearer: “Conversely, for animals that develop normally, FP exerts strong purifying selection as all lethal recessive alleles are purged in one generation.”

Ln 56 - I would recommend the inclusion of both Allen et al. 2018. R. Soc. Open Sci, and Card et al. 2021. Sci Reports, here, as they are members of the elapids, not represented in the other examples.

These two citations have been added.

Ln 60 - Recent studies have highlighted the significance of sperm storage in reptiles. For example, Levine et al. 2021. Exceptional long-term sperm storage by a female vertebrate. PLos ONE. 16(6).e0252049, describe the storage of sperm by a female rattlesnake for ~70 months, with two instances of its utilization to produce healthy offspring during that period. Clearly, molecular tools are providing both support for long-term sperm storage, and an understanding of its utilization.

Recent work has indeed provided new evidence for instances of long-term sperm storage and the two mechanisms are no longer competing hypotheses, but it is clear that both mechanisms exist in nature. We have modified the text accordingly to include “Nevertheless, clear examples of long-term sperm storage have also been documented in the recent literature (29), underscoring the need for molecular methods such as MS analysis or sequencing data to elucidate the underlying mechanisms.”

Ln 68 - American Crocodile would also be suitable to include here.

This has now been included in the list of examples of endangered species.

Ln71 - The problem with this hypothesis is that parthenogens produced through FP tend to have very low viability. For example, Adams et al. 2023. Endangered Species Research, follow a cohort of sharks produced through FP and all survive. Similarly low levels of survival are reported across other systems for which FP was reported. More likely, FP is simply a neutral trait. The mother is not negatively impacted through producing parthenogens and can go on to produce sexual offspring. Few instances report successful reproduction of a parthenogen. See pers. Comm in Card et al. 2021. And Straube et al. 2016.

We thank the reviewer for the comment and agree that more data on the successful reproduction of parthenotes are needed to claim that FP is an adaptive trait. We have modified the text to include that studies on “the successful reproduction by FP offspring” are needed to support this hypothesis and have included the Straube et al. 2016 citation. We decided to omit the Card et al. 2021 citation as the reports of second-generation FP was through personal communication mentioned in this study and the results themselves have not yet been published.

Ln 79 - I doubt that there is a desperate need for this for conservation. However, I think there is a need to simply further our understanding of basic biological function, given that it is not uncommon, and is phylogenetically widespread in species lacking genomic imprinting.

We agree that understanding FP as a basic biological function is important in light of the realization that it occurs more commonly than previously thought. We have added this aspect to the text: “A better understanding of the triggers and molecular mechanisms underlying FP and the fitness of the resulting offspring are therefore needed in a variety of contexts. These include: to understand a fundamental biological mechanism and its significance in vertebrate evolution, to aid in conservation efforts including captive breeding programs, and to possibly harness FP in an agricultural context (28).”

Ln 85 - It would be worth citing Card et al. 2021., here given that they used genome-wide ddRAD markers to show support for terminal fusion.

The citation has been added.

Ln 91 - Better citations here are Card et al. 2021. Allen et al. 2018, and Booth et al. 2023, which all utilize either RADseq or WGS.

These citations have been added.

Ln 95 - The conclusion of genome duplication here was supported only by a small number of microsatellite loci. As such, given that terminal fusion has been supported through genome-wide markers in other species of snakes and crocodiles, the conclusion of genome duplication is likely incorrect.

In light of the other examples that show terminal fusion in snakes, we have removed this sentence.

Ln 96 - I would strongly disagree with this statement. Allen et al. 2018, Card et al. 2021, Booth et al. 2023, all provide evidence of heterozygous loci and thus support terminal fusion. While no species-specific chromosome level reference genome is available for any of these species, the fact that levels of heterozygosity are below 33% percent supports terminal fusion. Rates over 33% support central fusion, but have not been reported in any vertebrate to date. AS such, I would recommend the removal of this statement.

We agree that the studies listed by the reviewer all support terminal fusion in snakes and crocodiles and therefore, we have removed the statement.

Ln 121 - Recent work in Drosophila mercatorum and D. melanogaster suggest that three genes play a role in the activation of FP in unfertilized eggs. In this case, through the fusion of meiotic products. That said, it is plausible to assume that FP in these lizards has an underlying genomic mechanism that is not related to isolation from males. See Sperling et al. 2023. Current Biology. 33, P3545-P3560.E13.

Clearly isolation from males is not a key trigger in FP in whiptail lizards and other vertebrate species. With recent work from Sperling et al. 2023 and the fact that selection has led to increases in parthenogenesis in birds, an underlying genetic mechanism may well be at play. We have cited and addressed this in the discussion and propose identifying the genetic basis for FP in whiptail lizards in future studies.

“Recent work identifying key cell cycle genes inducing FP in two species of Drosophila (71) and selection resulting in higher incidences of parthenogenesis in birds (24, 33) suggest a genetic basis for the initiation of FP. [...] Additional whole-genome sequencing data for species with documented FP will aid in the understanding the genetic basis, propensity, and evolutionary significance of FP.”

Ln 126 - While these data strongly support FP of the two unusual A. marmoratus appearing offspring, can long term sperm storage be ruled out. Either through captive history or allelic exclusion of other males in the group?

We have added the following sentence to the text: “Given that all of these offspring are female, inherited only maternal alleles, and animal 122 had no history of being housed with a conspecific male during its lifetime, both interspecific hybridization and long-term sperm storage are all but ruled out and FP is strongly supported.”

Ln 171 - 191 - Given that the topic of this manuscript is the genomic mechanism underlying FP in this species, are these data necessary? These are not discussed later and as such I would recommend that they are moved supplemental material. Otherwise, they simply clutter that manuscript and detract from the key question. Indeed, they are important to show that the genome constructed is of high quality, but online Supp Mat is the place for that here.

We chose to keep this section in the main text for the following reasons: There is still a lack of published reference quality genomes for many reptile species and therefore we want to highlight that this A. marmoratus reference adds not only to the understanding of FP, but also expands the small list of reptile genomes and makes the first Aspidoscelis genome available to the community. The high quality and contiguity of the genome (as indicated by the high N50 value and BUSCO score) is important to emphasize in the main text because the absence of any heterozygous regions in FP animals supports a mechanism of post-meiotic genome duplication. We would not want to bury these key points in the supplement.

Ln 296 - Comparable estimates were made for parthenogenetic production in wild populations of two North American pitviper species. See Booth et al. 2012. Biology Letters.

In Booth et al. 2012, 2 out of 59 litters of the two pitvipers (3.39%) were identified to contain FP offspring and these results are very similar to our reported rate of FP in whiptail lizards. We have now included this similarity in our discussion. “Interestingly, these rates are similar to what has been reported for wild populations of two North American pitviper species (10)”.

Ln 312 - Again, can this really be suggested? Above, the authors state that most FP animals that hatched had congenital defects, and a large number failed to hatch. This does not sound like strong support for generating individuals that counter the effects of population bottlenecks and inbreeding depression. The authors need to take this study further and monitor the long-term viability of the FP individuals that survive.

We agree with the reviewer that the adaptive advantages of FP reproduction are dependent on the fitness and reproductive potential of FP offspring and present data is insufficient to clearly support this notion. We have modified the text to include that long-term studies are needed to support or refute this hypothesis: “However, support for this hypothesis is predicated on the fitness and reproduction of FP offspring and therefore more long-term studies on seemingly healthy individuals of FP origin are needed.”

Ln 348 - To be able to provide support for this, you need to track animals long term to understand their reproductive competence, and that of their offspring.

We have added the text: “To assess whether the co-occurrence of sexual and FP reproduction in vertebrates can indeed be considered a reproductive strategy rather than biological noise will require further studies to assess the reproductive competence and fecundity of offspring produced by either mode of reproduction.”

Ln 358 - But, the caveat is that the parthenogens must themselves reproduce. This must me stated.

The statement that parthenogens must be able to reproduce to support a hypothesis of FP as an adaptive trait has been added: “One must now consider the possibility that FP is an adaptive trait and that low rates of successful FP could contribute significantly to genome purification. Such a role for FP hinges on further studies demonstrating the ability of parthenogens to reproduce themselves either through further FP or sexually.”

Ln 359 - Note that FP can also fix mildly deleterious alleles. Only if it is strongly deleterious will it be lost.

We now make it clearer that selection only applies to strongly deleterious alleles.

Ln 361 - See above comments.

We have modified the text to include that “FP offspring will have low genetic load and only pass on neutral and mildly-deleterious alleles to the next generation.”

Reviewer #1 (Significance (Required)):

1. Significance:

While reports of parthenogenesis have been reported as far back as the early 1900's, it has only been over the last decade that reports are become common. Such that facultative parthenogenesis is no longer considered a rarity, but is recognized now as being relatively common and phylogenetically widespread in species that lack genomic imprinting - particularly reptiles, birds, and sharks. Reasons for this are both an increased understanding that the trait can occur, hence recognizing it as an alternative mechanism to long-term sperm storage, and the ease of using molecular approaches.

The fundamental questions of recent times have been understanding the mechanisms driving FP. Recent papers utilizing whole genome sequencing and ddRADseq have provided support for terminal fusion automixis in snakes and sharks. Here, this study provides evidence of gametic duplication in whiptails, a mechanism with an alternative outcome in regards to the levels of retained heterozygosity. As such, this study compares to the recent work of Card et al. 2021 (Scientific Reports), and Booth et al. 2023 (Biology Letters), in providing substantive advances in the field.

The audience for this will be broad. Parthenogenesis is a fascinating topic that attracts significant media attention. See the Altmetric score of recent papers on the topic, particularly Booth et al. 2023 (Altmetric score - ~3100). As such, the study will be of interest to both a broad readership, but will also be of great significance to a specialized group working on parthenogenesis. All round, an excellent paper that has promise to advance the field.

We thank reviewer 1 for this positive assessment and for putting our work into context.

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

Summary: The researchers bring together microsatellite and whole-genome sequencing data from long-term laboratory cultures of lizards to discover occasional production of parthenogenetic offspring by several species of otherwise sexually producing whiptail lizards ("facultative parthenogenesis, "FP") and to show that these FP-produced lizards have patterns of genomic homozygosity that are incompatible with currently held assumptions about mechanisms of FP. Instead, the FP lizards seem to have been produced by a mechanism that results in almost complete homozygosity, likely a consequence of post-meiotic duplication of genomes from haploid unfertilized oocytes. They also show that FP offspring were produced by females housed with males and along with sexually produced offspring, counter to prevailing assumptions that FP offspring are only produced in situations where mates are not available. Many of the FP-produced offspring did not survive to hatching or had major abnormalities, consistent with a situation where this high homozygosity exposes harmful alleles. Finally, the authors used reduced-representation sequencing (RAD-seq) to survey heterozygosity in 321 wild-collected whiptail lizards from 15 species, showing evidence for strikingly low homozygosity in at least one individual and perhaps up to 5, consistent with the potential for FP in nature. These data are of broad interest in demonstrating several exciting new possibilities. Most importantly, the data hint at a different mechanism of FP than previously assumed, and one that causes immediate near-complete homozygosity. This scenario would likely lead to immediate purging of harmful recessive alleles. If the selective load of this purging wasn't insurmountably high, a lineage with a history of purging could produce FP offspring of relatively high fitness. Other exciting possibilities suggested by the data include the existence of FP even in a setting where mating occurs and in natural populations, versus just captivity.

Major Comments:

I found it difficult to impossible to sort out exactly what the researchers did and with what lizards. For example, in line 107, they refer to a "systematic MS analysis" for all individuals of gonochoristic species in their laboratory, but where are these data? Indeed, at this early spot in the paper, the introduction from here on out suddenly reads like a discussion. What would be better here would be to summarize what was known and wasn't known about the system and questions involved, why gaps in knowledge were important, and what the researchers actually did for this paper. In my opinion, the paper would be a much easier read if the researchers left the results and interpretation for later in the paper.

As a consequence of the reviewers’ comments, the text of the manuscript has undergone major revision, and we trust that reviewer 2 will find this new version far more accessible. The MS data collection of more than 1000 individuals is the subject of another ongoing study and was only mentioned peripherally here to put the identification of FP into context. As most of the MS data relates to gonochoristic reproduction and interspecific hybridization, we are only presenting the data that are directly relevant to this manuscript as part of this study. To our knowledge, there is no common repository to upload raw MS data, but we have provided the data for the FP animals and controls discussed in this paper in the Github repository (see section “Data availability”).

Even with this suggested fix, however, the data are still too inaccessible and analyses too opaque. For example, in line 202, a critical definition is laid out regarding heterozygous sites as those having "equal support" for two alleles. What do the researchers mean by "equal support"? My presumption is that this is something about equal or close to equal numbers of reads, but this definition needs to be spelled out and justified because it underpins much of the downstream analyses. A similar problem occurs in line 208-209, where the authors make a statement about limiting further analysis to positions in the genome where the coverage is "equal" to the mean sequencing depth.

We have changed the text to “we defined heterozygous sites as those having two alleles supported by an equal number of reads. This stringent requirement was chosen to limit the search to apparent heterozygous sites with strong support, decreasing the chance of false positives.”. We further look at only sites where the coverage is equal to the average sequencing depth to exclude regions where over-assembly and collapse of repetitive elements would artificially increase the coverage.

Another data/analysis issue emerges with the components of the manuscript that deal with mixoploidy. As far as I can tell, these data come from one sexually produced lizard, one FP A. marmoratus, and one FP A. arizonae. While the reports of bimodality of nuclear size are certainly interesting, the data and discussion are no more than an anecdotal case study in the absence of careful replication across multiple FP lizards and comparison to sexually produced lizards. Without these data, the conclusion that “Animals produced by facultative parthenogenesis are characterized by mixoploidy” (Figure 4 caption; also see lines 324-331) is far too strong.

We have added animal IDs to figure legends 4 and S10 to clarify that these erythrocyte staining come from two FP A. marmoratus, and one FP A. arizonae. In addition, imaging from two sexually produced control animals (1 A. marmoratus and 1 A. arizonae) have now been included in S10 (as S10B and S10D). We also have included an extra panel of flow cytometry data (new Figure 4C) as a complementary methodology for ploidy determination. Both imaging and flow cytometry support similar amounts of haploid cells. With the additional data and clarification, we hope that the reviewer agrees that the observations of mixoploidy are well beyond “anecdotal”. Nevertheless, we have changed the title for Figure 4 to “Detection of mixoploidy associated with facultative parthenogenesis.” We hope that our observations here will indeed inspire future studies to see if mixoploidy is a widespread phenomenon in FP outside of whiptails as indicated by earlier work in birds.

I had a similar reaction to the discussion of developmental abnormalities and embryonic lethality of embryos of FP origin presented in lines 263-281 (also lines 307-309). What is the baseline level of such abnormalities and the frequency of lethality in sexually produced eggs/embryos/hatchlings, and especially those produced via inbreeding? These comparisons are needed to interpret the significance of the patterns observed in the FP eggs/embryos/hatchings. Analogously, the comparison of the ovaries and germinal vesicles from one FP individual relative to one sexual individual do not tell us anything nearly so definitive as the text in lines 279-281 (also see Fig. S12 title, which is too broad of a conclusion for N = 1). This overly ambitious conclusion also underpins the discussion regarding the potentially adaptive nature of FP with respect to genome purification (lines 341-363; also see lines 47-50). If FP does not actually increase the rate of purging in FP lizards relative to inbred sexual counterparts (sounds like inbreeding is common from line 339), it seems less likely that we can view FP as adaptive at least from this perspective.

We have now included a comparison between defects seen in sexually produced animals vs FP animals: “six out of 16 FP animals (37.5%) hatched with no discernable developmental defects (Fig. S11A-B). This is in stark contrast to sexually produced animals, where over 98% of hatchlings showed no abnormalities. Additionally, most of the defects noted in sexually produced animals were less severe than in FP animals including bulges in tails or truncated digits.”

We agree that our statement on the lack of differences between sexually produced and FP animals was too general. We have modified the title of Fig. S12 from “No differences between ovaries and germinal vesicles of Aspidoscelis marmoratus produced by facultative parthenogenesis or fertilization” to "Ovaries of Aspidoscelis marmoratus FP animal 8450 and germinal vesicles of FP sister 8449 revealed no differences in structure and anatomy compared to fertile sexually reproducing animals.” Due to instant complete homozygosity, FP would indeed have a higher rate of purging than inbreeding. While one hypothesis is that FP is adaptive (in large enough populations), our intentions were to highlight the alternative that FP could be detrimental in smaller populations (that already would likely experience high inbreeding rates). We would expect inbreeding to not be common in whiptails relative to other lizards given that they tend to have large population sizes and actively range across generalist habitats.

A final data concern is with the use of liver tissue for whole-genome sequencing and reference genome assembly (lines 389-390) and then using these data and the reference genome to make conclusions about ploidy/coverage. Liver tissue is very commonly endopolyploid, meaning that coverage could be artificially high for animals for which liver (vs. tail) tissue was used for DNA extraction. In particular, it would be helpful if the researchers consider whether endopolyploidy could have affected their ability to make accurate estimation of coverage and thus, heterozygosity, when libraries generated from diploid (tail) tissues are aligned to a reference genome generated from a polyploid tissue as was done here.

This is an interesting point and indeed hepatic cells in various organisms have been documented to be polyploid. The proportion of polyploid cells though vary and as far as we are aware, all published studies on polyploid hepatocytes are in mammals (DOI: 10.1016/j.tcb.2013.06.002). Reference genomes have been generated from a variety of tissue sources and liver is commonly used. As most assemblies are for haploid genomes, polyploidy (unlike aneuploidy) does not impact the assembly quality. The reference genome was also from an animal of FP origin and therefore has genome-wide homozygosity that aids in a more contiguous genome assembly by eliminating the phasing problem. For the 10 animals sequenced, genomic DNA was derived from liver for three animals and the rest from tail tissue. The sequencing data generated from either liver or tail resulted in similar coverage levels (Figure S6) and similar levels of heterozygosity (Figure 2A). Minor Comments:

Line 410: Please explain why the BLAST cutoff was changed from the default.

The BLAST cutoff was changed from the default 1e-03 to 1e-06 to be more stringent and thereby increase confidence in the BUSCO results.

Lines 441-443: Please explain why this dataset was seemingly larger than expected.

Animal 122 was sequenced on one flow cell without any multiplexing with other samples and therefore yielded more reads than other animals sequenced. We subsampled the reads from this animal for analysis, so it is directly comparable with the other WGS data.

Line 510: The link to the Github repository was broken, so I was unable to access the code and data denoted as available here.

We apologize for the unavailability of the link at the time of review. Review Commons did not request a reviewer token. The repository will be made public upon journal acceptance. We would be happy to provide a reviewer token in the meantime upon request by Review Commons.

Figure 1, and other figures featuring comparisons of MS data across parents and offspring: The authors need to engage here with the alleles that do not match either parent here (e.g., allele 282 at MS7), explaining the likelihood that these alleles indeed represent a binning error (or, perhaps, stepwise mutation from parental allele), and these alleles should be flagged. Instead, they bin these unique alleles with the most similar parental allele without any explanation or flagged. The authors do bring this point up in Figure S1, but this issue needs to be addressed in the main text (related point: the mix of red/green in MS16 offspring appear more green than red. Is this meant to denote a probability different than 50:50? If not, the authors should adjust the shading so that this shape is half green, half red).

We have added to the figure legend that single nucleotide differences are most likely binning errors and are therefore not considered “de novo” alleles. Instead, they are assigned it to the most similar parental allele, consistent with Figure S1. The shading at MS16 has been removed so that it is consistent with Figure 3.

Figure 3: Indicate that white background for alleles means that allelic inheritance is not determinable, or use the mix of colors applied in Fig. 1 to indicate as such. Unique offspring alleles should be flagged rather than just automatically assigned to the most similar parental allele. Finally, it would be helpful if the alleles were presented within loci from the shorter to the longer alleles.

We have included in the figure legend that non-shaded alleles are those for which multiple potential parents share the same allele and the inheritance therefore remains ambiguous for this locus. Single nucleotide differences are also now addressed, and sizes are ordered from smallest to largest.

Figure S7. Indicate visually which panels indicate FP animals.

We have now indicated which animals are FP and included this in Figure S6 as well.

Fig. S13. The 5 animals that had especially low heterozygosity should be flagged. The title of this figure should be toned down in light of the tentative nature of the conclusions regarding FP in nature: low heterozygosity could instead reflect, for example, a long history of inbreeding. My reaction to the data is also that the % heterozygosity distribution for many of the species looks continuous rather than the bimodality one might expect under FP vs. sexual reproduction.

Since FP has not been further confirmed in these animals, unlike those examples from our captive colony, there could indeed be other reasons for low heterozygosity. We have changed the title of the figure from “Facultative parthenogenesis in whiptail lizards collected in nature” to the more neutral “Heterozygosity estimates of whiptail lizards collected in nature.” Since there are so relatively few animals, one would not necessarily expect a bimodal distribution to be apparent in the current data. We did show that the animal with the lowest calculated level of heterozygosity (deppii LDOR30) was a statistical outlier when compared to other individuals of the same species though. Since these animals were sampled across different locations and habitats, the effective population sizes would be assumed to be different as well, reflecting the range of heterozygosity estimates seen here. This has been made clear in the text.

Reviewer #2 (Significance (Required)):

General assessment: strengths and limitations. The paper's strengths include the combination of data from lab and natural populations, the characterization of an unexpected means of achieving FP, with dramatic genetic consequences, and the data suggesting that this type of FP is fairly common and occurs even in the context of mating.

Audience: The biological questions of relevance to these discoveries are of broad interest, and the paper is likely to garner some attention from the life sciences community as whole and the popular press.

Advance: These data fill an important knowledge gap regarding the mechanisms potentially driving FP in vertebrates, how often FP is likely to occur, and its genetic consequences. The discoveries are potentially conceptual/fundamental, though the extent to which they are ground breaking is not clear in the absence of functional characterization of how FP occurs as well as the need for more rigorous comparisons and replication that I outlined above.

We thank reviewer 2 for summarizing the strengths of this manuscript, pointing out the broad interest and stating that this work fills an important knowledge gap.

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

Summary: The occurrence of facultative parthenogenesis has been described in a number of vertebrate lineages but the underlying cytological mechanism(s) have remained largely speculative due to sparsity of data. Here, Ho & Tormey et al. provide a detailed analysis of facultative parthenogenesis in gonochoristic species of the lizard genus Aspidoscelis. They show that parthenogenesis leads to a complete loss of heterozygosity (LOH) within a single generation. They attribute the LOH to diploidization through duplication of the oocytes haploid genome after completion of meiosis. This mechanism is consistent with their finding of mixoploidy in erythrocytes of asexually produced offspring. Based on LOH the authors additionally show that facultative parthenogenesis in Aspidoscelis is not condition dependent (no developmental switch): it can occur in the presence of males, alongside with sexual reproduction in the same clutch, and both in captivity and the wild. Finally, the authors show that facultative parthenogenesis is associated with developmental aberrations, likely caused by expression of homozygous recessive deleterious mutations.

Major comments: In my opinion, this study presents a very comprehensive, careful documentation of mechanistic aspects and consequences of facultative parthenogenesis in a vertebrate. The genomic and microsatellite results leave little to no doubt that facultative parthenogenesis has led to complete LOH in Aspidoscelis. I am particularly impressed by the meticulous analysis of genomic coverage to exclude e.g. false positive heterozygosity due to merged paralogs in the assembly. I also follow the authors conclusion that a post-meiotic "gamete duplication"-like mechanism is likely causative for the LOH (and the mixoploidy of erythrocytes; but I am no expert on that). I was wondering if terminal fusion automixis together with a complete absence of recombination would be worth mentioning as an (probably very unlikely) alternative in the discussion. It would be exciting to corroborate the conclusion of diploidization by genome duplication in the future, e.g. via early embryonic DNA stainings to show the duplication "in action" (if that is practically possible)...? As for this manuscript, I suggest emphasizing the indirect nature of the evidence for the mechanism of parthenogenesis a little bit more.

We thank the reviewer for highlighting the effort that went into the genomic analysis that led us to our conclusions. In terms of terminal fusion without recombination, we argue that this is not an obvious alternative explanation as a large body of work has established that at least one crossover per homologous chromosome pair is required to advance into meiosis I in many organisms (e.g. see https://doi.org/10.3389/fcell.2021.681123) and therefore the absence of recombination would likely not produce the polar bodies necessary for automixis.

We have added to the text: “In whiptail lizards, we have not been able to examine post-meiotic oocytes as locating the post-meiotic nucleus within a large yolked egg is inherently difficult. The difficulty is compounded by the unpredictability of which eggs will undergo FP development and the need to sacrifice animals to remove eggs.”

While the genome duplication mechanism we propose is indeed indirect because we are unable to visualize developing FP embryos, the most parsimonious explanation from the whole-genome sequencing analysis is genome duplication because of the lack of heterozygous regions associated with automixis. In the text, we have made sure to state genome-wide homozygosity as the basis for our conclusion.

I agree that facultative parthenogenesis in the presence of males hints at a baseline rate of parthenogenesis without requiring a developmental switch. However, this makes it difficult to rule out that sperm played a role in activation of embryonal development (gynogenesis; however I am only aware of gynogenesis in fishes and amphibians)... maybe, the authors want to take this up in the discussion. Were the five parthenogenetic individuals for whole genome sequencing actually produced in the presence of males, too?

FP has been reported to occur in isolated females for other reptile and bird species, suggesting that sperm activation is at least not a general requirement in FP of amniotes. (Watts, et al. 2006, W. W. Olsen, S. J. Marsden 1954). In all cases in this study, the female mothers were housed with conspecific or heterospecific males. While we cannot completely rule out a non-genetic contribution of sperm in these cases, it would seem to be an unlikely explanation in light of the sperm-independent reproduction by obligate parthenogenesis in other species of whiptail lizards (unlike the sperm-dependence of all unisexual reproduction in amphibians and fish). We decided to not include speculation on sperm-dependence in this manuscript as we have no evidence in favor of it, nor is there any evidence for this in the literature relating to other amniotes. In fact, most examples of FP were reported from isolated females, most likely because offspring were not expected in those cases and prompted further analysis as to their origin.

I agree with the interpretation of the LOH in the RADseq data as a likely case of facultative parthenogenesis in the wild. However, when looking at figure S13 I noticed some bimodal looking distributions (e.g. in A. guttatus). It may be interesting for future studies to look into what factors influence heterozygosity in natural populations of Aspidoscelis (e.g. inbreeding vs parthenogenesis). Could there be different mechanisms of facultative parthenogenesis in different Aspidoscelis species explaining different LOH intensities?

The continuous nature of the data may reflect natural variation between individuals and collection at various locations with possibly different effective population sizes and levels of hybridization. Low levels of heterozygosity could be indicative of inbreeding or FP in some cases. This is important to note in future studies and we have added this to the manuscript (“Further fieldwork and analysis will be required to assess the level of FP in natural populations of gonochoristic Aspidoscelis species (and other factors that could influence the observed heterozygosity such as population size, levels of hybridization, and inbreeding) …”). While there are different mechanisms of FP in other vertebrate groups, the most parsimonious hypothesis is that within a genus, the mechanism would be the same.

The manuscript is well written, the introduction nicely explains the significance of the study, the methods are fully appropriate and the results (and supplementary results) displayed comprehensibly and in great detail. The discussion might benefit from going a bit more generally into the occurrence and mechanism of obligate asexuality in Aspidoscelis. One might e.g. speculate on whether the ability for facultative parthenogenesis in gonochoristic species has facilitated the transitions to obligate parthenogenesis in the hybrid lineages and what peculiarities might predispose Aspidoscelis to parthenogenesis (e.g. are centrioles contributed by sperm required?). In addition, I think the occurrence of LOH due to gamete duplication (facultative and obligate) in invertebrates (e.g. due to Wolbachia) is worth mentioning in the discussion: e.g. there is a similar case in facultative asexual Bacillus rossius stick insects, where the early dividing cells are haploid. Some of them diploidize via duplication later and form the embryo.

Thank you for complimenting each section of the manuscript and referring to it as well-written. Our lab has a long-standing interest in obligate parthenogenesis. While it is interesting that both obligate and facultative parthenogenesis occur alongside each other in this genus, the mechanisms appear to be fundamentally different, and we would like to focus the discussion on FP in a variety of systems and its potential implications in conservation and evolution. Parthenogenesis in general is a fascinating topic for a broad audience and not discussing another form of parthenogenesis (obligate in this case), the focus remains on FP and keeps the manuscript more accessible for non-specialists. We have included the stick insect as another example of diploid restoration through genome duplication in the discussion.

Minor comments:

39-41: I am a bit puzzled by the usage of the term "post-meiotic" to contrast the diploidization through duplication with automixis. Wouldn't one consider polar body fusion after completion of meiosis II also post-meiotic? Maybe I am just not aware of how the term is usually used in this context here...

We use the term “post-meiotic” because the restoration of an entirely homozygous diploid cell can only occur after the completion of both meiotic divisions. It is our understanding that polar body fusion and meiotic restitution after meiosis I or meiosis II are generally considered meiotic mechanisms in the specialized literature, even though polar body fusion would also occur after the meiotic divisions.

65: isn't that gynogenesis (sperm-dependent parthenogenesis) in the amazon molly?

While sperm is required for parthenogenesis in the Amazon Molly, it is an all-female species that exclusively reproduces through gynogenesis. In this case, it is considered an example of obligate parthenogenesis rather than FP.

78: the term "economically viable" may be a bit puzzling for a biologist's audience. "Economically sustainable" could be an alternative.

This has been changed.

129: the Arizona male was referred to as ID 4272 above. Here it is ID 4238?

This has been corrected. The correct ID is 4272.

218: please define over-assembly (see line 207)

The definition of “over-assembly” is collapsing paralogous loci into a single representative sequence. This is now explained in the text.

263-281: please, indicate a hatching rate/ rate of malformations of sexually produced offspring for comparison.

A comparison has been added: “This is in stark contrast to sexually produced animals, where over 98% of hatchlings had no abnormalities noted.”

333: in the haploid cells recessive deleterious mutations would be exposed in the hemizygous state but in the diploid cells in the homozygous state.

The text has been modified to reflect the difference between haploid and diploid cells.

470: please, provide more detail for the RADseq analyses (variant calling, calculation of heterozygosity etc.)

We have elaborated on the analysis in the methods.

Figure 1B: please, mention in the legend that the shown mechanisms are not exhaustive, e.g. first polar body fusion could occur right after meiosis 1 or polar body formation could be skipped completely.

This has been added.

Figure 1C: it may be interesting for non-specialists to name the distinctive morphological characters setting apart the three species in the figure legend and highlight them e.g. with arrows in the figure.

We have now included in the figure legend characteristic color patterns for each species: “(C) Photographs of Aspidoscelis arizonae with characteristic blue ventral coloration (top), A. gularis with light spots in dark fields that separate light stripes on dorsum (middle), and A. marmoratus with light and dark reticulated pattern on dorsum (bottom).” Since the descriptions are specific and apparent, we did not add arrows to the pictures.

Reviewer #3 (Significance (Required)):

Significance: The study by Ho & Tormey et al. substantially enhances the understanding of (facultative) asexuality in vertebrates. In particular, while most reports of facultative parthenogenesis in vertebrates have been attributed to a form of automixis, the authors conclusively show an instance of diploidization through genome duplication, a mechanism functionally similar to "gamete duplication". The study is novel, very comprehensive and of interest for a general audience within the field of evolutionary biology.

We thank reviewer 3 for pointing out that our study substantially enhances the understanding of asexuality in vertebrates, is very comprehensive and of interest for a general audience within

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Reply to the reviewers

We appreciate the thoughtful comments of the reviewers. We have revised the manuscript according to these comments as detailed below.

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

Efficient proteostasis in cells demands efficient clearing of damaged or misfolded proteins, and an important pathway involved in such clearance is the ubiquitin-proteasome pathway. In this system, proteins are tagged with ubiquitin to target them for degradation by the 26S proteasome complex. The conventional 26S proteasome complex consists of a core particle (CP or 20S proteasome) and one or two regulatory particles (RP, or 19S proteasome) to form the singly or doubly-capped proteasome, respectively. Proteasome assembly is a well-orchestrated process that requires proper stoichiometry of proteasome subunits and dedicated proteasome assembly chaperones. This is maintained by fine-tuning their transcriptional and translational regulation.

This manuscript elucidates an important aspect of how the different proteasome components are transcriptionally regulated upon denervation in mouse muscles for timely and efficiently assembling 26S proteasome. The authors present data that point out towards the model whereby a two-phase transcriptional program (early: day 3-7 and late: day 10-14) activates genes encoding proteasome subunits and assembly chaperones to boost an increase in proteasome content. This involves the coordinated functions of two transcription factors, PAX4 and alpha-PAL(Nrf1) which were important for both early and late phase of the transcriptional program. Their roles were not redundant as loss of one transcription factor was sufficient to prevent induction of various proteasome genes in muscle after denervation.

In summary, the authors report a novel bi-phasic mechanism elevating proteasome production in vivo, which involves the coordinated functions of two transcription factors, PAX4 and alpha-PAL(Nrf1).

Major points: 1) It is not clear why PAX4 and alpha-PAL(Nrf1) are both fully required for the transcriptional induction of some proteasome genes upon denervation (with good overlap), while only PAX4 is important for increased proteasome assembly. The authors speculate that this could be due to a stoichiometry problem but an alternative scenario where translation is increased upon alpha-PAL(Nrf1) inhibition would also be possible. This would explain why, for example, the induction of PSMC1 gene expression upon denervation is abolished upon alpha-PAL(Nrf1) inhibition (Fig. 5C) while the protein level is still increased (Fig. 6H). Is that also true for PSMD5 and Rpn9? Could it also be that the loss of function of alpha-PAL(Nrf1) is too detrimental for the muscle so that they induce an alternative stress response pathway increasing proteasome subunit translation?

We thank the reviewer for this comment. To better clarify this important point, we conducted further experiments to examine the differential effects between PAX4 vs. α-PALNRF1 on proteasome assembly chaperons (Fig. S4b). Our new data show that PAX4 promotes the induction of the assembly chaperone, PSMD5 (S5b) at 3 days after denervation (Fig. S4B). This induction is critical for the increase in PSMD5 protein levels because PAX4 knockout results in decreased PSMD5 protein levels at both 3 and 10 days after denervation (Fig. 4K). α-PALNRF1, however, does not affect the mRNA levels of this chaperone (Fig. S4A). This new result strengthens our conclusion that induced expression of assembly chaperones by PAX4 is key to raising proteasome levels after denervation.

We cannot rule out an indirect effect of α-PALNRF1 knock-down on protein synthesis, and therefore this potential alternative mechanism is now discussed in the text. It appears unlikely, however, that α-PALNRF1 knock-down is too detrimental to muscle as we do not find any evidence phenotypically for any type of stress or abnormalities.

2) Pax4 controls Rpt1-2 transcription and these two Rpt proteins form a pair. As Rpt4 is also regulated by Pax4, is Rpt5 also controlled by Pax4?

We believe the reviewer meant to request the data for Rpt4, because the data for Rpt5 was already included in original Fig. 4G-H. Therefore, we repeated the RT-PCR analysis of PAX4 KO mouse muscles for Rpt4 and now show that its induction requires PAX4 at 10 d after denervation, just when proteasome content is increased (Fig. 4G). At 3 d after denervation, Rpt4 induction is probably regulated by other transcription factors because its mRNA levels at this early phase were similar in muscles from WT and PAX4 KO mice (Fig. 4H). These data, strengthen our conclusions that coordinated functions of multiple transcription factors control proteasome gene expression in vivo. In future studies, we will investigate the specific mode of cooperation and mechanisms by which various transcription factors and co-factors collaborate to enhance the expression of proteasome genes in the early and delayed stages of gene expression within a living organism.

What about the assembly chaperone for these two pairs: PSMD5 and p27? It would be very interesting to know if there is a transcriptional coregulation based on proteasome assembly intermediates.

The referee raises an important point, which we also discuss in the text. We now present data showing that PAX4 promotes the induction of the assembly chaperon PSMD5 at 3 d after denervation (Fig. S4B), correlating nicely with the observed changes in protein levels of this chaperon (Fig. 4K). The expression of PSMD9 (p27) however, does not require neither PAX4 nor α-PALNRF1 (Fig. S4). Consequently, we conclude that PAX4 promotes proteasome biogenesis by promoting PSMD5 induction, and in the absence of α-PALNRF1 proteasome subunits can still efficiently assemble into the proteasomes (even though their expression is reduced), due to the induced expression and increased action of the assembly chaperone PSMD5. Our data highlight the intricacy in controlling proteasome levels, through transcriptional regulation of proteasome genes and assembly chaperones during muscle atrophy. We now further document and discuss the regulation of proteasome biogenesis by these two transcription factors in the text and Discussion (p.28).

3) Fig. 4J: PSMD5 and PSMD13 are not tested in Fig. 4A, G and H. This needs to be done if the authors want to draw the parallel mRNA-protein levels, as in their conclusion. Moreover, the protein levels seem to be much more induced than the mRNA levels, could that be due to increased translation? This could be discussed.

We accepted this thoughtful suggestion and now present the mRNA levels for PSMD5 and PSMD13 in Figs. 4A, G and H and Fig. S4. The new data does not change our conclusion that protein abundance largely correlate with the transcript levels (Figs. 2 and 4K).

The reviewer raises an important question that we hope to resolve in the future. As we point out in the revised Discussion section, “the substantial rise in protein levels compared to mRNA levels after denervation suggests potential increased protein translation due to PAX4 loss. Whether PAX4 regulates protein synthesis and thus can affect protein levels beyond gene expression are intriguing questions for future research”.

4) The conclusion is not correct in this sentence: "Moreover, analysis of innervated and 10 d denervated muscle homogenates from WT, alpha-PAL(Nrf1) KD or PAX4/alpha-PAL(Nrf1) KD mice by native gels and immunoblotting or LLVY-cleavage indicated that loss of both transcription factors is necessary to effectively block accumulation of active assembled proteasomes on denervation (Fig. 6H)". This is not correct, as the loss of PAX4 is sufficient to block accumulation of active assembled proteasomes on denervation (Fig. 4K). So, it could just be that alpha-PAL(Nrf1) KD has no effect on the induction of proteasome assembly after denervation and that all the effect of the double mutant is due to PAX4 loss. This needs to be corrected.

We thank the reviewer for this thoughtful comment. The text has been revised accordingly.

Minor points:

1) I would rephrase the sentence "baseline at 14 d after denervation and showed a sustained low mRNA levels until 28 d (Fig. 2A-F).", as the mRNA levels are still significantly higher that the basal levels for most proteasome genes. Same for the sentence: "RNA sequencing (RNA-Seq) analysis of TA muscles at 14 d after denervation indicated that expression of most proteasome genes is low at 14 d (Fig. S1)". Expression is low compared to what and not being induced doesn't mean they are low. This needs to be rephrased.

We revised the text accordingly and thank the reviewer for these suggestions.

2) Microscopy images need more explanation: define the green and red channel and what they are used for in the legend.

The legends have been updated as requested.

3) Columns have moved from the Table 2.

The tables have now been submitted as separate files.

4) Fig. S3: RT-PCR on NRF-1(NFE2L1) need to be performed to see the extent of inhibition by shRNA.

We thank the reviewer for this important comment. The data, which was added as new Fig. S3A, shows an efficient knockdown of NRF-1NFE2L1 with shNFE2L1.

5) In the sentence: "PAX4 maintaining subunit stoichiometry for increased proteasome assembly.", could it be due to the much higher levels of PSMB8, 9 and 10 immunoproteasome subunits upon alpha-PAL(Nrf1) KD (Fig. 6F)?

We addressed this aspect in Major Point #1, regarding the difference between PAX4 and α-PALNRF1; please see our response. As for the Reviewer’s comment concerning Fig. 6F, we think that the increased expression of PSMB 8, 9, and 10 in α-PALNRF1-KD compared to the double KD or PAX4 KO further suggests a distinct cooperative interaction between these transcription factors in promoting proteasome expression, assembly, and function, which we plan to thoroughly investigate in future separate studies. However, the increased expression of PSMB 8, 9, and 10 can affect the composition of the CP (by replacing their normal ounterpart), but not the RP assembly. CP and RP are known to assemble separately with their own dedicated chaperones; RP and CP then associate to complete the assembly of proteasome holoenzyme (RP-CP complex). Thus, it is unlikely that increased CP assembly alone would increase overall RP-CP assembly.

**Referees cross-commenting**

All other comments are relevant.

Reviewer #1 (Significance (Required)):

Overall, the work is impactful and timely, reporting the participation of a novel transcription factor, alpha-PAL(Nrf1), along with PAX4, in regulating the transcription of proteasome genes and the subsequent assembly of conventional proteasomes in mouse muscle upon denervation. One limitation is that alpha-PAL(Nrf1) kockdown is only inhibiting proteasome genes expression but proteasome assembly, the reason being still unknown. Most of the conclusions drawn in the manuscript are supported by the experimental data. Better understanding how proteasome homeostasis is regulated upon stressful conditions is an important fundamental aspect of proteasome biology. I would support publication of this manuscript providing the more specific concerns listed are addressed.

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

The main limitation of this study is that is based on a single model of muscle atrophy: that induced by cut of the sciatic nerve. Another one will nicely complement the findings as fasting atrophy or cancer cachexia model, to see if the two phase is recapitulated with regard to proteasome modulation.

The referee raises an interesting point, but as we explained throughout the manuscript, we did not use denervation in this study as a model for atrophy but rather as an in vivo model system to investigate mechanisms of protein degradation and proteasome homeostasis in a whole organism in vivo. The reason we selected denervation as an in vivo model for accelerated proteolysis is due to the gradual nature of muscle loss, which allows us to dissect the various phases of proteasome homeostasis effectively. Fasting, as an alternative model, is too rapid for addressing the specific questions that we asked in this study. In addition, in the rapid atrophy induced by fasting the primary physiological mechanism to increase protein degradation in vivo is believed to be through post-synthetic modification of proteasomes, rather than the production of new proteasomes (VerPlank et al., 2019). In future separate studies, we will thoroughly investigate whether the mechanisms discovered here are applicable to other types of atrophy (e.g. diabetes, aging, cancer). The obtained results will be published and fully discussed separately, in part because covering all types of atrophy within a single paper is impractical and goes beyond the scope of the current manuscript.

Another major concern is that the author do not measure over time during denervation atrophy the mRNA and protein content expression of the two transcription factors that they found crucial in the proteasome induction and assembly.

We agree with the reviewer that time course would strengthen our conclusions that the two transcription factors are important for proteasome gene induction and assembly. We have added these data showing that PAX4 (Fig. 4I) and α-PALNRF-1 (Fig. 6E) both accumulate in the nucleus at 7 d after denervation, just when proteasome content is maximal (Fig. 3A) and protein breakdown is accelerated (Cohen 2009; Volodin 2017; Aweida 2021). The mRNA levels of PAX4 were presented as original Fig. 4F and indicate that PAX4 is induced already at 3 d after denervation. We have added new RT-PCR data for α-PALNRF-1 showing that α-PALNRF-1 is induced at 7 d and 10 d after denervation (Fig. 6D).

Major and minor concerns are as follows:

Typos now and then are present all over the text, as holoemzyme shall be replaced with holoenzyme on page 9, on page 12 proteasome is misspelled on mid page, as well as cellls. By cotrast shall be corrected on page 19. References on page 22 shall be formatted.

We have corrected the typographical errors.

• reference 29 on page 7 seems out of context together with the sentences it is coupled with.

The reference is appropriately located within the text in terms of context, and precisely aligns with the sentence to which it is associated. Reference 29 (Boos 2019) describes a cellular state in which all proteasome genes rise simultaneously.

• muscle electroporation of plasmid shall be replaced by AAV9 injection that causes less inflammation and more expressing fibers

We do not understand and see no basis for the referee’s assertion that the “muscle electroporation of plasmid shall be replaced by AAV9 injection”. On the contrary, the electroporation methodology is widely used by many labs because of its many advantages. This in vivo gene transfection approach is extremely useful to study transient gene (or shRNA) effects in adult muscles, while avoiding the developmental effects of genes (or shRNA) that are often seen in transgenic or knockout animals (e.g., the inducible knockout of α-PALNRF-1 caused lethality, see Fig. 6B-C).

In addition, the electroporation technique offers great advantages from its speed and major cost savings. We have been using it routinely in our lab for in vivo studies, and articles using it from many laboratories worldwide have appeared in all major journals, e.g. see our papers in Nature Communications, J Cell Biol, PNAS, EMBO rep, and papers from late Alfred Goldberg (Harvard), Marco Sandri (Padova, Italy), Jeff Brault (Indiana Univ.) and others. In all studies included in this manuscript that involve electroporation, contrary to the reviewer’s impression, there was no damage or inflammation to the muscles, and we routinely examined histological sections. Finally, for our studies, we always use muscles that are at least ~70% transfected, which has proven adequate for observing gene effects in mouse muscle. In each experiment, transfected muscles are always compared and analyzed in parallel to control muscles (transfected with scrambled shLacz control). In fact, the validity of the in vivo electroporation technique is further confirmed herein by our investigations of transgenic inducible knock-down mice, showing similar effects on proteasome gene expression.

• the shGankyrin data shall be complemented with overexpression of the same chaperone to see the effects of proteasome expression and assembly.

We understand the reviewer’s concern but do not believe that such an experiment is necessary since it is well known and there is already extensive evidence in the literature showing that the chaperon Gankyrin is essential for proteasome assembly (Kaneko et al. Cell 137, 914–925, May 29, 2009 (DOI 10.1016/j.cell.2009.05.008). Thus, various Gankyrin mutants have often been used as an inactive control for proteasome assembly in vitro and in vivo (Kaneko et al. Cell 137, 914–925, May 29, 2009 (DOI 10.1016/j.cell.2009.05.008). In fact, Gankyrin’s known function in ensuring not only the proper subunit composition, but also proper conformation of the proteasome holoenzyme (Lu et al., Mol Cell. 2017 Jul 20;67(2):322-333.e6).

• another important transcription factor driving MuRF1 expression is Twist and it is totally ignored in the discussion, please add it.

We regret this oversight. We did not mean to slight any authors, although our major new discoveries and focus is on proteasome genes and not MuRF1. However, to satisfy the reviewer, we now discuss in the text Twist and other transcription factors (including SMAD2/3, glucocorticoid receptors and NFkB) capable of inducing the major atrophy-related genes (among them MuRF1).

• WB in Fig 2 shall be complemented by one in the Supp with more replicates per timepoint

We accepted this thoughtful suggestion and now present blots from additional normal and atrophying denervated mouse muscle samples as new Fig. S1B. This approach, however, does not change any of our conclusions.

• please justify why only PSMD10 (gankyrin) has been silenced and not any of the others (POMP, PSMD5, PSMD9)

We silenced PSMD10 (Gankyrin) as a representative RP assembly chaperone, since it is better characterized than the other RP assembly chaperones (PSMD5 and PSMD9). We kept POMP (a CP assembly chaperone) intact. Since the formation of one proteasome holoenzyme (RP2-CP) requires two RPs and one CP, increasing proteasome assembly is expected to be more demanding for RP assembly than CP. This led us to predict that disrupting RP assembly should be sufficient to block the induced proteasome assembly. This prediction is supported by our data (Fig. 3), and this justification was also added to the revised text to enhance clarity.

The originality is limited by the fact that Pax4 was already shown to have a role in muscle atrophy and drives the expression of p97 by the same authors. I would be curious to see if treatments in vitro know to induce the proteasome as starvation etc acts through the biphase mechanism showed in this paper, to understand how extendable to other kinds of atrophy is.

We respectfully disagree that the originally of the present findings is limited, because previously we validated a single proteasome subunit (Rpt1) as a target gene for PAX4 (Volodin 2017), and here we discover novel global coordination of proteasome gene expression by multiple transcription factors.

As we mention above, muscle denervation was used here as an in vivo model system of catabolic conditions. Unlike prior reports that were limited to cultured cells, our studies focus on the physiological setting in vivo to reveal mechanisms of proteasome homeostasis. In any case, regulation of proteasome gene expression by multiple transcription factors in other types of atrophy has not been investigated but is possible because common transcriptional adaptations activate protein breakdown in different types of muscle atrophy, including a coordinated induction of numerous components of the ubiquitin proteasome system (Jagoe 2002; Lecker 2004; Gomes 2001). In future independent research, we intend to investigate if the two-phase mechanism reported here can in fact be generalized to other atrophy (or stress) conditions.

Reviewer #2 (Significance (Required)):

The authors Gilda and co-workers made a great attempt to dissect the induction of proteasome activity during denervation muscle atrophy and discovered a two-phase process which involves two transcription factors Pax4 and NRF1. The manuscript is clearly written and the experiments fully delineated.

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

Using denervated mouse muscle as a model, Gilda et al. demonstrated that a two-step transcriptional program operates in the process of muscle atrophy after denervation and that proteasome expression-induced enhancement of protein degradation is important. Gilda et al. clarified that the transcription factors PAX4 and PAL/NRF-1 act on this proteasome expression induction and that the induction of these transcription factors and the expression induction of the proteasome gene cluster after denervation are necessary for muscle atrophy using an in vivo mouse model. The experiments were logically designed, and the results presented are considered clear and reliable. However, some of the descriptions in the text lack accuracy and courtesy, and some experiments require additional data to support and strengthen the author's claims. In particular, it is unclear whether PAX4, FOXO3, and NRF-1 work together or whether they have distinct functions. Although the authors claim that there are two stages of proteasome expression induction after denervation, this remains unclear. The authors should clarify the differences in target sequence or target genes and the substitutability of each transcription factor.

Major comments: 1: In Figure 3A, the results of the immunoblot of SDS-PAGE against 20S proteasome subunits should also be shown to confirm the increase in proteasome activity and amount.

We would like to clarify this aspect. We show the increased levels of proteasome holoenzyme complex (RP2-CP) by immunoblotting of the native gel, rather than SDS-PAGE gel. This is because the blots of the native gel can assess the levels of the actual proteasome complex, not simply subunit levels in their denatured state as in SDS-PAGE; SDS-PAGE cannot distinguish between free subunits and ones that are incorporated into the proteasome.

If proteasome activity was increased due to some other mechanisms, proteasome levels would remain relatively constant, while proteasome activity would have increased. However, this is not the case here since our data demonstrates that both RP2-CP activities and levels peak at day 7. Furthermore, the in-gel peptidase assay (Fig. 3A panel b) directly tests the 20S CP activity within the proteasome holoenzyme (RP2-CP complex) using the fluorogenic model substrate, LLVY-AMC. The 20S CP is activated for substrate degradation, only upon its association with RP (RP2-CP complex), since RP opens the substrate entry gate of the 20S. Free 20S itself is inactive, as its gate for substrate entry is closed; for this reason, free 20S can be detected, only after its substrate entry gate is artificially opened by SDS (see free 20S in panel b, but not in panel a).

2: In Figure 3, the reviewer assumed the conflict between the results of peptidase activity and SDS-PAGE in 14d. Therefore, quantification and statistical analysis should be performed on the results of proteasome peptidase activity and immunoblots to clarify the relationships between proteasome activity and amounts. Immunoblotting against ubiquitin is also needed to confirm the requirement and efficiency of proteasome induction.

As the reviewer pointed out, it might seem discrepant that peptidase activity at 14 d denervation is lower than its peak at 7d (Fig. 3A, panel a), but SDS-PAGE signal for proteasome subunits seems still high (Fig. 3A, panel d, Rpn2). SDS-PAGE detects total cellular content of proteasome subunits (free subunits as well as ones assembled within proteasomes). However, at any given moment, these subunits are not only in the proteasome holoenzyme complex, but also in different assembly intermediates. When proteasome subunits are transcriptionally induced as in this study, proteasome assembly process is also increased. However, proteasome assembly is a multi-step process, and the fold-induction for each specific subunit is different (Fig. 2A-B). This means that the rate of a certain assembly step would be differently affected for a given subunit, depending on their fold-induction. For this reason, some subunits seem to exist at a high level at 14d (e.g. Fig. 3A, panel d, Rpn2), but they are not yet incorporated into the proteasome complex, because they might be still undergoing assembly process.

As for the ubiquitin blot, it can be a good indicator for proteasome activity, when proteasome activity is decreased than normal. In such situations, ubiquitinated proteins accumulate (i.e. their signals increase as compared to control), due to their deficient degradation. However, our present study pertains to the opposite situation, where proteasome activity is increased in degrading ubiquitinated proteins. In normal cells, ubiquitinated proteins are hardly detectable due to their rapid degradation. Thus, when proteasome activity is greater than normal, ubiquitinated protein levels will be further decreased than normal. Data become unreliable when the signals are below the detection threshold. For this reason, we provided functional readouts involving the number of muscle fibers (for example, Fig. 3D).

3: In Figure 3C, the sample labels of shGankyrin and shLacZ are repeated. Would it be mislabeled?　In addition, NATIVE PAGE immunoblot analysis against Gankyrin and proteasome subunits are needed to prove the knockdown efficiency and to reveal the assembly defect of proteasome by Gankyrin knockdown.

To present our findings more clearly, we show one of each sample in the revised figure, rather than the duplicates as in the previous figure (Fig. 3C). We also included the immunoblot data to show that Gankyrin knockdown disrupts proteasome assembly, as seen by the reduced proteasome complex activity and level (Fig. 3C, panels a, b, c, lane 3, see RP2-CP). In Gankyrin knockdown samples, proteasome holoenzyme complex exhibited smeary appearance (Fig. 3C, panel c, see bracketed region in lane 3), as opposed to a discrete band in the controls (lanes 1, 2). This smeary appearance reflects more heterogeneous proteasome populations, due to defects in their composition and/or conformation. This is in line with Gankyrin’s known function in ensuring not only the proper subunit composition, but also proper conformation of the proteasome holoenzyme (Lu et al., Mol Cell. 2017 Jul 20;67(2):322-333.e6).

4: In Figures 4A, 4G, 4H, 4J, and 4K, the results of shPAX4 against innervated muscle should be shown to estimate the contribution of PAX4 in steady-state conditions. To clarify the innervated muscle-specific function of PAX4, histological analysis and quantification of proteasome gene expression in multiple organs in PAX4 KO mice are needed.

The reviewer raises an interesting point, but as we explained above, we concentrate here on the major new discovery that multiple transcription factors increase proteasome content in a catabolic condition in vivo, correlating directly with the accelerated protein loss. Regulation of the basal levels of proteasome in normal conditions in various types of cells and tissues is certainly an important issue meriting in depth study and will be the subject for future studies, but it is beyond the scope of this lengthy paper. This point is now discussed in the revised text.

The tissue distribution of PAX4 and the detailed description of the phenotype of KO mice are also needed to understand and evaluate the role of PAX4 in muscle.

We added the requested data about PAX4 distribution as Fig. 4I. These data shows that PAX4 accumulates in the nucleus already at 3 d after denervation. Furthermore, we are happy to add further information about the knock-out mouse model. The requested information and a detailed description of how PAX4 KO mice were generated were added to the text. The PAX4 KO mice showed no abnormalities and did not appear in any way different from the wild type littermates.

5: In Figure 4C, immunoblot analysis against PAX4 is essential to confirm the PAX4 protein knockout.

We agree and representative blots were added to Fig. 4C.

6: In Figure 5, peptidase activity and immunoblotting in NATIVE PAGE are needed to reveal the contribution of FOXO3 and NRF-1 in denervated muscle as shown in Figure 4.

The requested data for FOXO3 using FOXO3 dominant negative (as in Fig. 5A-B) were added as new Fig. 5C-D, showing no effect on proteasome content by FOXO3 inhibition. These new data are consistent with our findings that the expression of only two proteasome subunit genes was affected by FOXO3 inhibition at 10 d after denervation (Fig. 5B). The data for α-PALNRF-1 and the effects of its knockdown on proteasome content and activity were shown as original Fig. 6H (now Fig. 6J).

The expression of FOXO3 and NRF-1 should also be shown by RT-PCR and immunoblotting as shown in Figure 4.

We thank the reviewer for this thoughtful suggestion, and as requested, we now show representative blots of transfected muscles to support the graphical data (Figs. 5C-F). These data confirm the efficient expression of HA-FOXO3ΔC or FLAG-α-PALNRF-1 dominant negative inhibitors in transfected muscles. It is important to note that these inhibitors are mutant forms designed to interfere with the normal function of the wild-type endogenous FOXO3 or α-PALNRF-1 proteins, without affecting their transcript levels. Given this mechanism, we believe that Western blotting is a more appropriate technique for assessing their impact, as it provides direct insights into protein expression. In the revised main text and methods, we have now clarified this point.

Similar to previous comments, the expression of the dominant negative form of Foxo3 and NRF-1 should be performed in innervated muscles to reveal the significance and specificity of Foxo3 and NRF-1 function in denervated muscles.

As mentioned above, regulation of the normal basal levels of proteasomes is certainly an important issue meriting in depth study and will be the subject for future studies, but it is beyond the scope of this lengthy paper, which focuses on the mechanisms increasing protein content in catabolic conditions in vivo. With respect to FOXOs, there is a large literature on its regulation and roles in normal muscle (please see papers by late Alfred L Goldberg, Marco Sandri and others). Under normal conditions FOXO3 is largely inactive via phosphorylation by insulin-PI3K-AKT signaling (Stitt 2004; Latres 2005; Zhao 2007).

7: In Figure 6D, the list of genes should be served especially about 27 genes and 69 genes that show common features between NRF-1 KD and PAX4 KO.

The requested data is now presented as new Table 4.

8: In Figure 6F, the list of genes that change expression in PAX4 and NRF-1 KD mice is needed.

We agree and the requested data has now been added to table 5.

9: In Figure 6H, immunoblotting against ubiquitin is needed to evaluate the contribution of proteasome induction to protein degradation.

We clarified this aspect in the Major Point #2. Please see our response.

10: This study lacks the detailed mechanisms by which PAX4, Foxo3, and NRF-1 regulate the expression of proteasome genes. The contribution of these transcription factors is revealed by experiments, but the specific sequence that these transcription factors bind and how transcription factors are induced in denervated muscles is not clarified. As shown in the figures, the ChIP assay provides convincing results, but the detailed sequence or map of the promoter region of proteasome genes must be shown in the figures to clarify the target sequences of NFE2L1 and PAX4, FOXO3, and NRF-1. In addition, the luciferase assay would support the results of the ChIP assay.

Again, the reviewer raises an important question that we plan to resolve in the future. As mentioned, our findings strongly suggest a novel coordinated mechanism involving multiple transcription factors that control proteasome content in catabolic states in vivo. The enclosed revised manuscript primarily focuses on elucidating the contributions of individual transcription factors (α-PALNRF-1, PAX4, NRF-1NFE2L1 and FOXO3) to the induction of proteasome genes, revealing a significant overlap in genes regulated by multiple transcription factors. The specific mode of cooperation among these and other transcription factors and cofactors is certainly an important question for future studies, but it is beyond the scope of this lengthy paper. In the revised text we have now clarified this point (page 27). In addition, we agree that clarifying how the transcription factors are induced in denervated muscles merits some considerations and a paragraph was added to the Discussion (page 26) concerning possible mechanisms. For example, it is possible that the transcription factor STAT3 is involved in PAX4 induction because, based on previous microarray and ChIP data in cultured NIH3T3 cells, PAX4 was identified as a target gene of STAT3 (Snyder et al., 2008), and STAT3 becomes activated after denervation (Madaro et al., 2018).

We are delighted that the reviewer found the results obtained through the ChIP assay convincing. Given the extensive scope of our investigation and rigorous analyses of dozens of genes, it is not feasible to generate luciferase-encoding plasmids for all of them. However, in response to the reviewer's request, we have carried out predictions of the binding sites of the 4 transcription factors within the minimal promoter regions (300 up- and 1000 down-stream to TSS) of the 64 proteasome sequences. The predicted binding sites are now listed in Table 2A-D. These new data further support our key findings that multiple transcription factors control proteasome gene expression in a catabolic physiological state in vivo.

11: The results of the loss of transcription factors are well done, but the authors should also try to estimate the effect of overexpression of transcription factors in muscle. If the overexpressed transcription factors cause proteasome induction and muscle fiber mass reduction, these results strongly support the importance of transcription factor-mediated proteasome enhancement.

We understand the reviewer’s comment but do not believe that such an experiment is necessary to support our key findings about proteasome gene induction by multiple transcription factors in vivo. In fact, we have specifically refrained from pursuing overexpression studies in this context due to the apparent coordination and some potential interdependence between the functions of PAX4 and α-PALNRF-1 transcription factors in inducing proteasome genes. Manipulating one specific gene through overexpression could potentially disrupt this delicate coordination and yield misleading results.

In addition, there are several limitations of gene overexpression in mouse muscle, as it may not be as efficient and does not represent physiological conditions. Therefore, to validate gene functions in a physiological setting in vivo, we generated transgenic animals with the gene of interest specifically knocked-out or knocked-down. Utilizing transgenic mice lacking the gene of interest, though time-consuming, is a widely accepted and common approach that proves to be the most suitable method for specifically demonstrating the involvement of a particular gene in a physiological process, enabling a targeted and controlled investigation of its role and providing valuable insights into its contribution to the observed effects.

Minor comments:

12: The authors should describe the inducible KO mice more carefully and correctly. In the Results section on P12, the description of "whole body Cre+ mice" confuses the readers in understanding the mechanism of inducible Cre-mediated KO.

We agree and have added the information requested about the KO mice to the main text and a detailed description in the methods section.

13: In Figures 6B and 6C, the number of mice and the meaning of the asterisk should be described correctly. Is it statistically significant?

We agree. By accident the number of mice and sign for statistical significance were omitted during processing. The correct sign was added to Fig. 6B-C, and the number of mice used, and the meaning of asterisks were added to the corresponding legend. N=10 mice per condition. **, P

14: There is no description of Figure 6E in the manuscript. The authors should include it.

In the original version of this paper, we refer to Fig. 6E in the text on pages 21 and 25. Also, the presented illustration is fully described in the corresponding legend.

Reviewer #3 (Significance (Required)):

This paper clarified a novel mechanism of proteasome induction by transcription factors in denervated muscles other than Nrf1 (NFE2L1), which has been shown to contribute to the induction of proteasome gene expression in cultured cells. This is an important paper for expanding the understanding of the field. It is also important because it has demonstrated the potential for new therapeutic targets in diseases such as type 2 diabetes and cancer.

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

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Reply to the reviewers

1. General Statements [optional]

We would like to thank all three reviewers for their careful and comprehensive reviews of our manuscript. We have taken on board all the comments and have made appropriate changes to improve the manuscript. The more substantive changes are to the structuring of the text in Introduction section, and to improving the clarity of Figure 2 after reviewers’ comments (we have added extra panels to A, F and G). Other minor changes are individually signposted in each paragraph of the point-by-point response attached below.

We performed a number of pieces of additional analysis to address reviewer comments. To be as transparent as possible we make these and all other data analyses available in the form of .html files exported by Rmarkdown, hosted at https://joebowness.github.io/YY1-XCI-analysis/.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

• *

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

Summary: This manuscript uses differentiation of the highly informative inter-specific hybrid mouse ESC to follow features of genes that inactivate slowly. Resistance to silencing is reflected in reduced change in chromatin accessibility and the authors identify YY1 and CTCF as enriched amongst these 'slow' genes. This finding is provocative as these factors have been reported to enrich at both human and mouse escape genes. The authors go on to demonstrate that eviction of YY1 is slowly evicted from the X, and that removal of YY1 increases silencing.

Minor Comments: Overall, the manuscript's conclusions are well supported; however, the brevity of the presentation in some places made it difficult to follow, and in other places seemed a missed opportunity to more fully examine or present their data.

1. Introduction is only 2 paragraphs and half of the last is their new findings. First part of results/discussion is then forced to be very introductory. In addition, some discussion of escapees, even if predominantly human, seems warranted in the introduction. There are multiple studies that have tried to identify features enriched at genes that escape inactivation that could be mentioned.

We have now written the introduction as 3 paragraphs instead of 2. In doing this, we have moved the sentence introducing chromatin accessibility from the results section to the introduction. Additionally, we now discuss the studies that focus on escapees (in mouse XCI) in the second introduction paragraph.

Variation in silencing rates. 'Comparable rankings' cites multiple studies (oddly previous sentence cites only two) - how concurrent are they? Developing this further (perhaps a supplementary table) would inform whether the genes assessed are ones that routinely behave similarly across different studies/lines; and also serve as a resource for future studies.

To avoid double-citing, we have made this one sentence and have cited at the end of the sentence 7 studies which describe gene-by-gene variability in rates of silencing. The majority of these studies include comparisons of their categories of fast and slow-silencing gene with previous classifications, and they all conclude that there is substantial concurrence. Some examples:

• Marks et al, 2015, Table S3,
• Loda et al, 2017, Figure 5,
• Barros de Andrade E Sousa et al. 2019, Figure 2
• Pacini et al. 2021, Figure 6e,i We believe this is sufficient evidence for our claim that these studies report “comparable categories” (“ranking” changed to “categories” as not all studies strictly rank). A comprehensive gene-by-gene comparison table would likely serve only to highlight differences due the various silencing assays/model systems/classification approaches used in the studies. If required, however, we would be willing to include a supplemental table which collates where gene silencing categories are discussed in each publication, and links to any supplemental files which provide full lists of X-linked genes.

It would be helpful to give insight into informativity of cross - what proportion of ATAC-seq peaks were informative with allelic information (and similarly, what proportion of genes expressed had allelic information?

Of the 2042 consensus ATAC-seq peaks we defined on ChrX via aggregating macs2 peaks over all time course samples, n = 821 passed our initial criteria for allelic analysis in the iXist-ChrX-Dom model line (ie they are proximal to the Xist locus in ChrX 0-103Mb, overlap SNPs, and contain sufficient allelic reads). A small number of peaks were additionally filtered out during fitting of the exponential decay model, leaving a final ATAC-seq peak set of n = 790 elements (38.6%) which we focus on in this study. We have added this information to the text (first Results paragraph).

Our collections of ChrX genes amenable to allelic analysis were not redefined for this study. We used lists of genes defined in our previous ChrRNA-seq study (10.1016/j.celrep.2022.110830). In general, allelic analysis of gene expression is not as limited by the frequency of SNPs, because the sequence length of transcripts (including introns, which are a significant fraction of the reads in ChrRNA-seq data) is much greater than for ATAC-seq peaks. Only a few very lowly expressed genes are not amenable to allelic ChrRNA-seq analysis.

P5: "can be influenced by Xist RNA via a variety of mechanisms" seems like it this sweeping statement could use expansion, or at least a reference. Authors could also clarify that 'distal elements assigned by linear genomic proximity is their definition of nearest gene.

The statement that “both [chromatin accessibility and gene expression] can be influenced by Xist RNA via a variety of mechanisms” is intentionally broad to support a negative argument that we do not wish to mechanistically over-interpret the observation that Xi chromatin accessibility loss occurs slower than gene silencing. Nonetheless, we have added two references to studies which report mechanisms for how Xist may influence chromatin accessibility; via recruiting PRC1 (Pintacuda et al 2017) or antagonising BRG1 (Jegu et al 2019). That multiple molecular pathways simultaneously contribute towards the effect of Xist RNA on gene silencing is well established in the field (see reviews such as Brockdorff et al 2020, Boeren et al 2021, Loda et al 2022).

We have clarified in the text that our definition of “distal” is all REs which do not overlap with promoter regions (TSS+/-500bp). We have also made it clearer that our definition of “nearest” gene refers to linear genomic proximity in both the Results and Methods sections.

Figure S1 - there are 6-8 other regions that fail to become monoallelic - what are they?

The regions which stand out most by the colour scheme of the heatmap in Figure S1 are those where accessibility increases on Xi, most notably the loci of Firre, Dxz4 and Xist, which are known to have unique features related to the 3D superstructure of the inactive X chromosome. A few other regions which do not become monoallelic harbour classic “escapee” genes. We have now labelled the locations of escapees Ddx3x, Slc25a5 and Eif2s3x in FigS1.

The other regions noticeable in the heatmap have no obvious features which explain why they fail to become monoallelic. We have highlighted a region containing intragenic peaks within Bcor (a gene which is silenced in iXist-ChrX mESCs), but many other regions are not in the vicinity of genes. Some of the persistently Xi-accessible peaks within these regions contain strong YY1 or CTCF sites, although many others do not.

It is also possible that some Xi-accessible peaks are artefacts of mismatches between the Castaneous or Domesticus/129Sv strain SNP databases and ground truth iXist-ChrX genome sequence. The number of these cases are small, and if a misannotated SNP is the only SNP present in a single peak, the peak is discarded by our allelic filtering criteria as it will appear monoallelic in uninduced mESCs.

Is there any correlation between silencing speed and expression (as previously reported)? If yes, then is there also a correlation with YY1 presence - and is this correlation greater than or less than seen on autosomes?

The data we present here pertaining to gene silencing kinetics is reused from our previous study. In that work we did indeed observe a significant association between silencing rate and initial gene expression levels (10.1016/j.celrep.2022.110830, Supplemental Information Figure S5F), which has also been reported by multiple groups previously.

To correlate YY1 binding with gene expression levels, we calculated transcripts per million (TPM) for all genes from our genome-wide mRNA-seq data of uninduced iXist-ChrX-Dom cells (GSE185869). It is indeed true that, on average, X-linked genes classified as “direct” YY1-targets in our analysis have higher levels of initial expression (median TPM 70.8, n=64) compared to non-target genes (median TPM 30.7, n=346). Autosomal YY1 targets are also relatively higher expressed (median TPM 29.6, n=1882) than non-YY1 genes (median TPM 8.0, n=9983). Within the list of YY1-targets, there is no additional correlation between quantitative levels of YY1 ChIP enrichment (calculated in this study using BAMscale (Pongor et al, 2020)) and gene expression (R=-0.05, Spearman correlation).

Therefore, we appreciate that this correlation between YY1-binding and gene expression levels may be a covariate in the correlation we report in this study between YY1-target genes and slow-silencing. This does not invalidate a potential functional role for YY1 in impeding silencing, as it could affect both variables via common or distinct mechanisms. Nevertheless, in an attempt to account for initial expression level as a covariate, we compared the silencing halftimes of YY1-targets versus non-targets within genes grouped by similar expression levels (low, medium and high-expressed genes). YY1-targets have slower halftimes in each comparison, and this difference is highly significant (p=1.9e-05, Wilcoxon test) for the “medium-expressed” gene group. This implies that YY1 contributes towards slower gene silencing kinetics independently of initial gene expression levels. We have added this panel to Fig2 with an associated sentence in the Results section.

These new analyses are also appended to the documentation of the R scripts used to generate the main figures in this study (Figure2_YY1association.Rmd), which will all be published to Github.

It is also important to note that this analysis approach is complicated by the methodology we use to classify YY1 target genes. In this study, we define YY1 targets based on the presence of ChIP-seq peaks overlapping the gene promoters, which is reasonable and widely accepted practice when defining targets of transcription factors. However, as briefly discussed in the Methods, in YY1 ChIP-seq data samples with very high signal:noise (eg Fig3), minor peaks of YY1 enrichment can be detected at almost every active promoter. As enrichment at these peaks is typically much less than at peaks with occurrences of the YY1 consensus DNA motif, we hypothesise that these small peaks result from secondary YY1 cofactors enriched at promoters (eg P300, BAF, Mediator) rather than direct sites of binding to DNA/chromatin. Therefore, for annotating genes as “direct” YY1 targets, we chose to use the YY1 peak set defined from lower signal:noise ChIP-seq data in iXist-ChrX produced with the endogenous YY1 Ab. Nevertheless, this behaviour is likely to confound any analysis correlating YY1 ChIP binding with gene expression.

Figure 2: Have the authors considered using quartiles rather than an arbitrary division into depleted and persistent?

We primarily chose this binary classification of REs as either Xi-“persistent” or Xi-“depleted” to maximise the numbers of sequences that could be used in each group as input for the HOMER motif enrichment software.

It is also not trivial to separate REs into quartiles because our “Xi-persistent” classification includes peaks defined as “biallelically accessible in NPCs”, as well as peaks with slow accessibility halftimes. This is explained in both the Results and Methods but we now have edited Fig2A to make it clearer. Instead of quartiles, we have performed an analysis which keeps “biallelically accessible REs” as a separate category and subdivides the remaining peaks into three groups by halftimes (slow, intermediate and fast accessibility loss). The same trends are evident with this four-category approach as with the two-category approach.

Importantly, our follow-up analyses which confirm the association between YY1 binding and slow Xi accessibility loss (Fig2E) and slow silencing (Fig 2F-H) are independent from categorisations of REs which rely on arbitrary thresholds.

1. Could simplify secondary labels to solely YY1 and CTCF. D & F do not print in black and white. Overall the mESC versus NPC can be confusing, perhaps mESC (no diff) would be helpful?

We have simplified the secondary labels in Fig2B and modified the colour scheme of FIg2D and Fig2F as suggested. “mESC” is now modified to “mESC no diff” in Fig2H, FigS2B, Fig3C and Fig3E to reduce the potential for confusion.

The numbers appear to suggest YY1 is generally enriched on X, but not at promoters?? Is this true?

The explanation for this is that clear peaks of YY1 ChIP are found at young LINE1 elements in iXist-ChrX mESCs (specifically over L1Md_T subfamilies). These elements are highly enriched (>2-fold) on the mouse X chromosome compared to autosomes (Waterston 2002), and the majority are not promoter-associated. We chose not to include a discussion of YY1 enrichment at repetitive LINE1 elements in this study primarily because of a) issues related to multiple-mapping reads, such as difficulties distinguishing ChrX vs autosomal reads, and b) the absence of strain-specific SNPs within annotated ChrX L1Md_Ts means that none of these elements are amenable to allelic analysis so we cannot compare Xi versus Xa. However, these LINE1 peaks are a significant fraction (262/521) of the numbers of YY1 ChIP-seq peaks in Fig2C.

For Figure 2f, it might be helpful to show autosomal genes - are Fast depleted or Slow enriched for YY1 relative to autosomes?

We have calculated these numbers as part of the analysis of gene expression on ChrX and autosomes above. Overall, the fraction of genes defined as YY1-targets is the same on ChrX as on autosomes (~0.16). Accordingly, fast-silencing genes are depleted for YY1 compared to autosomes, whereas slow-silencing genes are enriched for YY1 compared to autosomes. Fig2F is now redesigned to include the total numbers of YY1-target genes on ChrX and autosomes.

More generally, is YY1 binding on the X lost more slowly than YY1 binding on autosomes, or is the slow loss a feature of YY1. While I agree YY1 could have direct up or down-regulatory roles, Figure S3 could also be reflecting a secondary impact.

We agree that many of the differentially regulated genes after 52 hours of YY1 degradation could be secondary effects and have added a sentence on this to the relevant paragraph in the text.

Figure 3, 4 and supplementary - the chromosome cartoon introduces the LOH in iXist, but this needs to be described in text. Describing the reciprocal as a biological replicate seems challenging given this LOH.

It is true that the reciprocal lines iXist-ChrX-Dom and iXist-ChrX-Cast are not true biological replicates, and we try to avoid referring to them as such. Writing this in the legend of Fig3 was an error which we have corrected. We have now also mentioned the recombination event in the iXist-ChrX-Dom cell line at the point where data from this line is first discussed (paragraph 1 of Results section).

For the latter parts this work (Figs 3 and 4), we made the conscious decision to proceed with two YY1-FKP12F36V cell lines from different reciprocal iXist-ChrX backgrounds (aF1 in iXist-ChrX-Dom, cC3 in iXist-ChrX-Cast), rather than “biological replicate” clones from either iXist-ChrX-Dom or iXist-ChrX-Cast. Our reasoning was to control against potential confounding effects of strain background on our experiments related to the role of YY1. Although there were some minor differences between the clones, aF1 and cC3 demonstrated essentially equivalent phenotypes in all analyses we performed.

Could a panel of TFs be used rather than OCT4 which has its own unique properties to emphasize that YY1 is unique?

This would indeed be worthwhile, and we did consider attempting to perform ChIP-seq for additional TFs other than OCT4 in order to collect more points of comparison for the slow rate of loss of YY1 binding to Xi. However, it is admittedly hard to identify appropriate candidate TFs in mESCs which a) have similar numbers of discrete peaks of binding in promoters and distal elements on ChrX and b) it is possible to reliably perform ChIP-seq for at sufficiently high signal:noise to allow for quantitative allelic analysis.

We have changed the text to acknowledge that our comparison only to OCT4 limits the scope of the statements we can make about unique properties of YY1 binding.

Figure 4 - by examining 'late' genes, a change in allelic ratio is observed, but what about escape genes (e.g. Kdm5c, Kdm6a)? Do they now become silent? It would be helpful to have all this data as a supplementary table so people could query their 'favourite' gene.

YY1 degradation experiments performed for Figure 4 were performed on mESCs without cellular differentiation (YY1-ablated cells do not survive in our mESC to NPC differentiation protocol). In undifferentiated mESCs, silencing of the inactive X does not reach completion, and in fact all X-linked genes are residually expressed at a higher level than in equivalent timepoints of Xist induction with NPC differentiation (see Figure 4D, Bowness et al 2022). We write in the text “slow-silencing genes are residually expressed from Xi” because genes of this category account for the majority of expression under these conditions, and indeed almost all slow genes would all be classed as “escape genes” in this setting by a conventional definition of >10% residual expression from Xi (see also Figure 4D, Bowness et al 2022). Our analysis in Fig4D (of this study) includes all genes, and we share processed .txt files of allelic ratio and allelic fold changes in GEO, so querying the behaviour of a favourite gene would be easy (GSE240680).

Incidentally, when we do perform NPC differentiation of iXist-ChrX NPC, at late stages very few genes show any expression from Xi (Ddx3x, Slc25a5, Eif2s3x and Kdm5c clearly escape, but even Kdm6a is entirely silenced). Unfortunately, with such a small number of “super” escapees it is hard to make any general conclusions, so in this study we can only make inferences about escape via the transitive property that many “slow-silencing” genes are facultative escapees in other settings without induced Xist overexpression. We now write about this consideration in the introduction and final paragraph of the main text.

It seems surprising that loss of YY1 has no demonstrative impact on the Xa. Figure S3B suggests that over 1000 genes are significantly impacted - primarily down regulated. How many of those are X-linked? Perhaps they could be colored differently?

For the broad-brush differential expression testing in FigS3B, we use all the ChrRNA-seq samples (6 x untreated, 6 x dTAG) as “pseudo-replicates”, disregarding any confounding effects related to induced Xist-silencing as effecting untreated and dTAG sample groups equivalently. We did specifically investigate the behaviour of X-linked genes in this volcano plot, however only a very small number of genes were differentially expressed (n=22 X-linked genes appeared significantly downregulated compared to n=4 genes upregulated). This can be seen in our analysis records uploaded to Github.

Additionally, there is actually a minor effect of YY1 loss on expression of YY1-target genes on Xa. This can be seen in Fig4F, where the median lines of YY1-target boxes lie below the horizontal line of 0-fold change.

Since XIST+/undifferentiated cells retain YY1, is YY1 binding sensitive to DNAme? Indeed, are X chromosome bound sites in islands that become methylated? Figure S4 shows YY1-targetted X genes in SMCHD1 knockout; can CTCF targets also be shown? While identified in Figure 2, CTCF was not examined the way YY1 was, although it has also been identified in somatic studies of genes that escape X inactivation.

Binding of YY1 is indeed sensitive to DNA methylation; specifically it is reported to be blocked by CpG methylation (see refs (Kim et al, 2003; Makhlouf et al, 2014; Fang et al, 2019). Thus, crosstalk with the DNA methylation pathways, which deposit de novo CpG island methylation as a late event of XCI (Lock 1987, Gendrel 2012), did appeal to us as a potential mechanism of YY1 “eviction”. However, preliminary analysis we performed to investigate this revealed limited overlap between YY1 binding sites and de novo meythlated CpG islands in the iXist-ChrX model cell line.

FigS4 presents ATAC-seq data from two iXist-ChrX SmcHD1 KO clonal cell lines, comparing the accessibility loss kinetics between YY1-binding and non-YY1 REs in these cells.

Although FigS4 in this paper does not show genes, we have previously published ChrRNA-seq data from these SmcHD1 KO lines over a similar Xist induction + NPC differentiation time course (Figure 6, Bowness et al, 2022). A reanalysis of this ChrRNA-seq data by YY1-target vs non-target genes shows a similar trend to the accessibility data, although this is expected from the strong overlap of both “YY1-target” and “SmcHD1-dependent” genes with slow-silencing genes in our model.

With respect to CTCF, we have performed a similar analysis of this data separating ATAC-seq peaks by CTCF-binding rather than YY1-binding. This shows a similar trend to YY1, but is overall less pronounced, and is now included in our analysis records. We have reported previously that loss of CTCF from many binding sites on Xi requires SmcHD1 (Gdula et al, 2019).

When the authors use cf. do they simply mean see also, or as wikipedia suggests: "the cited source supports a different claim (proposition) than the one just made, that it is worthwhile to compare the two claims and assess the difference". Perhaps it would be worth spelling out to clarify for the audience.

We used “cf.” in the text to mean “compare with”, when referring to a plot/observation/piece of data outside of the figure being immediately discussed (either in another study or different section of the paper). We were not aware of the recommendation to only use the cf abbreviation when the two items are intended to be contrasted. We do not believe this to be a universal grammatical convention, but nevertheless have changed incidences of cf. to “see also”.

Reviewer #1 (Significance (Required)):

General assessment: An important question in human biology is how much the sex chromosome contributes to sex differences in disease frequency. Genes that escape X inactivation in humans seem to have considerable impact on gene expression genome-wide. While there are not as many genes in mouse that escape inactivation, the use of the mESC cell differentiation approach allows detailed assessment of the timing of silencing during inactivation. The authors utilize an inter-specific cross and it would be interesting to know the limitations of such a system (in terms of informative DHS/genes that are informative).

Advance: As the authors note, there are multiple studies of similar systems that have revealed differences in the speeds of silencing of genes. However, this is the first study to my knowledge that has then tried to assess timing with gene-specific factors. There are multiple studies in humans comparing escape and subject genes for TFs, but lacking the developmental timing that this study incorporates.

Audience: While generally applicable to a basic research audience interested in gene regulation, the applicability to human genes that escape inactivation may interest cancer researchers or clinical audiences interested in sex differences.

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

The authors studied the molecular basis of a variation in the rate of individual gene silencing on the X undergoing inactivation. They took advantage of ATAC-seq to observe the kinetics of chromatin accessibility along the inactive X upon induction of Xist expression in mESCs. They demonstrated a clear correspondence between the decrease in chromatin accessibility and the silencing of nearby genes. Furthermore, they found that persistently accessible regulatory elements and slow-silencing were associated with binding of YY1. YY1 tended to associate longer with genes that required more time to be silenced than those that became silenced fast on the inactive X during XCI. The acute loss of YY1 facilitated silencing of slow genes in a shorter period. They suggest that whether or not the transcription factors stay associated longer is another factor that impacts the variation in the rate of gene silencing on the Xi.

Reviewer #2 (Significance (Required)):

It has been suggested that the rate of gene silencing during XCI is varies depending on the distance of individual genes from the Xist locus or the entry site of Xist RNA on the X, as well as their initial expression levels before silencing. This study provides another perspective on this issue. The persistent association of transcription factors during XCI affects the rate of gene silencing. Although the issued addressed here might draw attention from only the limited fields of specialists, their finding advances our understanding of how the efficiency of silencing is controlled during the process of XCI. The experimental data essentially support their conclusion, and the manuscript was easy to follow. However, I still have some comments, which I would like the authors to consider before further consideration.

Major concerns 1. Based on the results shown in Figure 3E and F, the authors concluded that YY1 was more resistant than other TFs against the eviction from the X upon Xist induction. I am not still convinced with this. YY1 binds DNA via the zinc finger domain, while Oct4 binds DNA via the homeodomain. The difference in the binding module between them might affect their dissociation or the response to Xist RNA-mediated chromatin changes. In addition, given that YY1 has been reported to bind RNA, including Xist, as well, Oct4 might not be a good TF to compare.

We acknowledge and agree that our singular comparison between YY1 and OCT4 is insufficient to support a general conclusion that YY1 is unique with respect to its binding properties on Xi. This was also alluded to by Reviewer #1 (see 10.), where in response we write about the difficulties of selecting other appropriate/feasible candidate TFs for ChIP-seq in order to widen the comparison beyond OCT4. In consideration of this concern, we have re-phrased our conclusions regarding this point in the text, both at the point where it is first presented (Fig3F) and in the first discussion paragraph.

Furthermore, the difference in allelic ratio change between YY1 and OCT4 is admittedly not dramatic, and this metric can be influenced somewhat by the properties of the sets of peaks used (which is also why we have not tried to add statistical significance to this comparison in Fig3F). In order to make the comparison with OCT4 (a classic pluripotency factor), we were also limited to using mESC culture without differentiation conditions. It is possible that more pronounced differences between YY1 and other TFs would be observed under conditions where XCI is able to proceed further.

Even so, we contend that our observation that YY1 binding is lost from the Xi relatively slowly likely stands without a requirement for a comparison with OCT4 or other transcription factors. The decrease in allelic ratio for YY1 ChIP occurs more slowly than overall loss of chromatin accessibility from REs, which is arguably a more general proxy for TF binding, and much slower than kinetics of gene silencing (Fig3D and FigS2C). In addition, no other TF motifs (except CTCF, which has its own unique properties) were found significantly enriched within persistently-accessible REs, which would be an expectation if a different factor had similar properties of late-retained Xi binding as YY1.

Thus, overall we have tried to write the paper without overstating in isolation the importance of our claim that YY1 binding on Xi is relatively resistant to Xist-mediated inactivation, instead emphasising that it should be considered alongside the other pieces of data in the study.

I don't think that Kinetics of YY1 eviction upon Xist induction in SmcHD1 KO cells during NSC differentiation fit the phenotype of Smchd1mutant cells. Although their previous study by Bowness et al (2022) showed that Smchd1-KO cells fail to establish complete silencing of SmcHD1-dependnet genes, their silencing still reached rather appreciable levels according to Figure 6 of Bowness et al (2022). This is, in fact, consistent with the idea that XCI initially takes place in the mutant embryos, at least to an extent that does not compromise early postimplantation development. On the other hand, a significant portion of YY1 appears to remain associated with the target genes on both active and inactive X (Figure S4), which I think suggests that the presence of YY1 is compatible with silencing of SmdHD1-dependent genes. This is contradictory to the proposed role of YY1 that sustains the expression of X-linked genes in this context.

At any given timepoint of XCI, our data sets of gene silencing (ChrRNA-seq) consistently show a more pronounced allelic skew compared to chromatin accessibility (ATAC-seq). This behaviour is discussed in relation to Figure 1 in the text (see Results paragraph 2). We do not wish to overinterpret this quantitative difference because the assays are technically different and accessibility is not linearly correlated with gene expression. With this in consideration, we interpret the ATAC-seq data presented in Figure S4 to be fully consistent with the iXist-ChrX SmcHD1 KO ChrRNA-seq data in Figure 6 of our previous publication ie. a small increase in residual Xi gene expression from SmcHD1 KO NPCs is accompanied by a more appreciable increase in residual Xi chromatin accessibility. In line with this, it would not be contradictory for substantially increased Xi YY1 binding to sustain a quantitively small (but nonetheless meaningful) increase in residual gene expression from Xi.

Additionally, the context in which we include this SmcHD1 KO ATAC-seq data in the current paper is to hypothesise a potential role for SmcHD1 in contributing towards the eventual removal of YY1 binding from Xi. This hypothesis is essentially based on two observations; 1.) There is substantially more residual YY1 binding to Xi in mESC no diff conditions (Figure 3) and 2.) One difference between no diff and diff conditions is absence of SmcHD1 recruitment in the former (Figure 5 in our previous study). The new SmcHD1 KO ATAC-seq data adds a third observation which supports the hypothesis - that YY1-bound REs are appreciably more accessible from Xi in SmcHD1 KO. However, none of these observations are direct evidence of a link between SmcHD1 and YY1, and more experiments would be required to substantiate this potential mechanism. If confirmed, it would be logically reasonable to suggest a role for YY1 in contributing towards the residual expression of X-linked in the context of SmcHD1 KO, but we do not yet claim this, and a potential link with SmcHD1 KO is not the main focus of the paper.

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

In this manuscript, Bowness and colleagues describe the interesting finding that the transcription factor YY1 is associated with slow silencing genes in induced X-Chromosome Inactivation (XCI). The authors have conducted a comprehensive characterization of X-linked gene silencing and the loss of chromatin accessibility of regulatory elements in induced XCI in ESCs and during NPC differentiation. X-linked gene silencing was classified into four categories, ranging from fast-silenced genes to genes that escape silencing. Motif enrichment analysis of regulatory elements associated with slowly silenced genes identified YY1 as the transcription factor most significantly enriched. The separation of YY1-target and non-target genes confirmed that most genes bound by YY1 indeed exhibit slower silencing kinetics. A comparison of the binding kinetics of YY1 to another transcription factor, OCT4, during XCI revealed that YY1 is evicted more slowly compared to OCT4 on the inactive X, suggesting that slower eviction is a unique property of YY1. Conditional knock-outs of YY1 using protein degradation during induced XCI in mESCs demonstrated that the loss of YY1 at target genes enhances silencing. This supports the hypothesis that YY1 serves as a crucial barrier for slow-silenced genes during XCI. Finally, the authors propose a hypothesis regarding the mechanism of YY1 eviction, suggesting a potential connection to the role of SmcHD1 during XCI.

The authors provide an in-depth analysis of the role of YY1 in gene silencing kinetics during induced XCI and believe this manuscript should be published if our comments are addressed.

Major comment:

Based on the allelic ratio in figure 3C only minor loss of YY1 binding occurs in induced XCI in mESCs on the Xi, while silencing is established properly as shown in figure 4C (left panel, red boxplots). This suggests that YY1 eviction is not necessarily required for these genes to be effectively silenced. Could the authors explain this discrepancy in the data regarding their manuscript conclusions? It seems this is true for XCI happening during differentiation towards NPCs, but not if cells are stuck in the pluripotency stage?

Whilst indeed substantial, we do not consider the silencing seen for 6-day mESCs in Fig4C to be “established properly”. We refer to our previous publication (Figure 4 Bowness et al., 2022), which shows that silencing at equivalent timepoints under differentiation conditions (d5-d7) is significantly more pronounced (near-“complete”). Indeed, the level of silencing reached by YY1-FKBP mESCs (Xist induced but no dTAG treatment) aligns with the plateau of silencing in undifferentiated mESCs we describe in our previous study (median allelic ratio of approximately 0.1).

We conclude that YY1 contributes somewhat to sustaining this residual expression in mESCs, because a) substantial YY1 binding remains on Xi at these timepoints in mESCs and b) silencing increases with degradation of YY1 (the latter is more direct evidence). Notably, silencing does not progress to completion (allelic ratio of 0) in the absence of YY1, so we do not claim that YY1 is the only factor sustaining residual Xi gene expression in mESCs.

We interpret this comment to be a fundamentally similar concern to that raised by Reviewer #2 (2.), but in the context of undifferentiated mESCs rather than SmcHD1 KO. As stated above, we do not think it inherently contradictory for substantially increased Xi YY1 binding to sustain a quantitively small (but nonetheless meaningful) increase in residual gene expression from Xi.

Minor comments:

1. In the abstract lines 7-8, the authors state that the experiments were performed in mouse embryonic stem cell lines, but much of the data shown is acquired in NPC differentiations. Please adjust abstract.

We have adjusted this sentence in the abstract to include that many of the experiments in the paper involved differentiation of iXist-ChrX mESCs.

The last sentence of the abstract states that YY1 acts as a barrier to silencing but as stated in my major comment, that does seem to be the case in ESC differentiation towards NPCs, but not in ESCs themselves. Please tone down this sentence. Moreover, we do not fully understand where the 'is removed only at late stages' comes from? Is this because of the Smchd1 link? We find this link quite weak with the data presented. We would tone down that last abstract sentence.

We have toned down the final sentence of the abstract accordingly. We agree that “removed only at late stages” is unsubstantiated since YY1 binding on Xi decreases over the entire time course (albeit slowly). However, we maintain that a connection between YY1 and late stages of the XCI process is reasonable to infer from the various pieces of evidence we provide in the study (egs YY1 is persistently enriched in accessible REs, it is associated with slow-silencing genes, and it remains bound to Xi in undifferentiated mESCs).

Several comparisons to human XCI have been made in the article. We do agree that there are similarities between mouse and human XCI. However, there is insufficient data that substantiates that these genes are regulated in a similar manner in humans. We believe the comparisons should be removed altogether or attenuated.

We agree that there is nothing in our data that directly pertains to human XCI. Comparisons to human are only made twice in the paper: Initially in the introduction to make a broad statement that many mechanisms of Xist function are conserved between species, and finally as speculation in the last discussion paragraph. We think it is relevant to acknowledge the parallels between our study, which links YY1 binding with resistance to Xist-silencing in a mouse ESC model, and literature describing a similar association between YY1 and XCI escape in humans.

At bottom of page 4, the authors say that for any given gene, the allelic ration of accessibility at its promoter decreased more slowly than it silenced and then write Fig 1B. They probably mean S1C? Since 1B only shows 4 genes.

The phrase “any given” was used colloquially (ie imprecisely), so we have replaced it with “individual”.

Figure 1B shows the average allelic ratio of multiple clones for genes representing different silencing speeds. Each data point is the average of multiple clones for these representative genes, could the authors show the individual data points or the standard deviation?

Fig1B predominantly shows the averages of only two replicate time-courses of Xist induction with NPC differentiation using the same parental clonal cell line, iXist-ChrX-Dom, but performed on different dates and passages. We regenerated the panel without merging the replicate data points, but this has little effect on the plot (see the Rmarkdown html file of Figure 1 on Github).

Figure 1B. Loss of promoter accessibility lags behind loss of chromatin-associated RNA expression for these 4 genes. What about distal REs? Do the allelic ratios for the distal REs more closely follow chromatin-associated RNA expression? Could the authors show this in a supplemental figure?

We comment from FigS1C on the general trend that accessibility decrease from Xi occurs slower than gene silencing (measured by ChrRNA-seq). We then find in FigS1D that distal elements lose accessibility slightly faster than promoters. Although overall the allelic ratio decrease of distal (non-CTCF) RE accessibility is slightly closer to the trajectory to that of gene silencing, it remains substantially slower (see again the Rmarkdown .html file of Figure 1 on Github).

An equivalent plot to Fig1B showing distal REs would rely on our simplistic assignment of distal elements to their nearest genes. We believe this is reasonable generalisation for investigating chromosome-wide trends but unlikely to be sufficiently accurate at the level of specific genes.

Figure 1B: gene silencing trajectory is depicted left while the legend says right. Same for promoter accessibility.

The legend is now corrected.

Figure S1A shows only part of the X chromosome. The area downstream of Xist is missing. Is this because the iXist-ChrXDom cell line is missing allelic resolution as shown in figure S2A? Could the authors explain in the figure legend that part of the X-Chromosome is missing?

We have now included a reference to the recombination event in the iXist-ChrXDom cell line both when we present data from this background in the first paragraph of the Results section, and in the legend of FigS1A.

Figure 2C shows that 94 TSSs bear a YY1 peak, yet Fig 2F shows 62 are targets of YY1. Is this because the rest are not properly silenced or are escapees?

Fig2C shows the numbers of ChrX YY1 ATAC-seq peaks which overlap with “promoters” (ie regions +/- 500bp of a TSS). By contrast, Fig2F shows ChrX genes classified as direct YY1-targets for allelic silencing analysis. The discrepancy between these numbers is due to a number of reasons:

1. It is possible for multiple YY1 peaks to overlap the same promoter (eg one peak overlaps 500bp upstream, a separate peak overlaps 500bp downstream).
2. The count in Fig2C is not restrictive to one TSS per gene in cases where there are multiple transcript isoforms in the gene annotation, thus multiple YY1 peaks can overlap different promoters for the same gene.
3. A few genes do not pass our filters for allelic silencing analysis (eg they are too lowly expressed). Some YY1 peaks may overlap these genes. We hope the revised version of Fig2F, which includes numbers of direct YY1 target genes on autosomes and ChrX, makes the distinction between these two numbers clearer.

Moreover, YY1 has ~4-fold more peaks on the X chromosome on distal elements compared to promoters. Yet figure 2F exclusively shows the proportion of YY1 binding sites on TSSs. Would distal REs show similar proportions for the silencing categories? Could the authors show the differences in a Supplemental figure?

As discussed in the response to Reviewer #1 (point 8.), a large fraction of distal YY1 peaks on ChrX are at LINE1 elements, which are not amenable to allelic analysis. Excluding these peaks results in a smaller number of distal elements bound by YY1. The application of our filters for allelic analysis reduces the number of distal YY1-bound REs even more, and our assignment of distal REs to their nearest gene is imprecise. For these reasons, we do not think a comparison of genes classified by whether they are putative targets of distal YY1-bound enhancers is informative.

The authors switch between different model systems in the figures, which makes quite confusing which type of XCI is being discussed. We would like to see clearly stated above all panels which cell culture condition is being studied (mESCs or NPCs).

We have tried to improve this potential source of confusion by modifying “mESC” to “mESC no diff” in the relevant figure panels (see response to Reviewer #1 comment 7B), and adding “in mESCs without differentiation” to the title of Figure 4.

In Figure 3E and 3F the authors look at the binding retention of OCT4 during XCI in ESCs. However, it is not clear why the authors choose OCT4. Could the authors explain why specifically OCT4 was chosen for these analyses?

In our responses to the other reviewers, we discuss the limitations of only having one other TF to compare to YY1. The choice of OCT4 was primarily dictated by our experience and confidence in being able to generate high quality ChIP-seq data of this factor.

As it was essentially arbitrary for the purposes of this paper, we have added a comment to this effect in the text (“with that of a different arbitrary TF, OCT4”).

What is the expression level of YY1 in NPCs compared to mESCS? In Supplemental S2A, it seems that YY1 protein levels decrease over time during NPC differentiation. Is part of the increased eviction a result of lower protein levels of YY1? Probably not since you calculate ratios between Xi and Xa. Can you please comment on this?

We were similarly intrigued by this apparent decrease in YY1 protein levels in NPCs (there is no decrease on the RNA level) and initially considered if it could contribute to the relative.

In FigS2A specifically, the d18 NPC band is probably just a poor quality sample extraction. Our ChIP-seq data generated from the same sample is similar poor compared to the others (FigS2B). In other YY1-FKBP12F36V clones we derived and characterised by Western (not described further in this study, but will likely be published as raw source data for the cropped blots we show in FigS2A), the apparent difference in YY1 protein levels in NPCs is less pronounced. Although a minor decrease in YY1 protein in NPCs seems to be robust, we do not think it relevant in the context of our analysis of YY1 and XCI, as we almost always use Xa as internal comparison for any observations made about Xi.

On page 7 the authors state that degrading YY1 does not affect Xist spreading and/or localisation. Indeed, it has been previously shown by other groups that YY1 is required for Xist localisation during XCI. Could the authors elaborate further on the why their cells behave differently compared to the Jeon 2011 paper?

We are working with a mouse ESC model of inducible Xist from its endogenous locus on ChrX and using the dTAG system to degrade YY1 protein. By contrast, Jeon 2011 worked with an Xist transgene integrated at random in the genome of mouse embryonic fibroblasts (MEFs) and siRNA knockdown of YY1. The difference in our observations could be linked to any of these 4 differences (ie cellular context, Xist genomic location, Xist introns, knockdown strategy), but we cannot identify a specific explanation.

In figure 4G and figure S3D elevated levels of Xist are observed in the dTAG conditions. As the authors point out, this could then result in accelerated silencing of the X seen upon YY1 loss. Are these elevated Xist levels that result in enhanced silencing in figure 4 relevant for the kinetics of silencing? Moreover, YY1 could act as transcriptional regulator of those genes in the X and by removing YY1, one would expect decreased transcription, which would be read as accelerated silencing. The authors could see whether the genes that show accelerated silencing are regulated by YY1 in ESCs (+ dTAG, - Dox).

We agree that these points are important to consider when interpreting the results of the YY1-FKBP12F36V ChrRNA-seq we present in Figure 4. However, we believe they are covered in the text during our discussion of the data.

In relation to the final suggestion, the silencing of almost all X-linked genes is increased upon YY1 removal so separating a specific set of genes which show accelerated silencing would be difficult. Nevertheless, in Fig4F we report that the increases in Xi silencing are strongest for direct YY1 target genes. In fact, these genes also show a minor decrease in expression in the + dTAG - Dox condition (see response to Reviewer #1 point 12.). However, by-and-large the differences in Xa log2FCs between YY1-target and non-target genes are less statistically significant. Non-significant p-values are not shown on Fig4F, but can be found in our Rmarkdown analysis records.

Can the authors explain why they decided to put the Smchd1 part after the conclusion? Before the conclusion would have been better? The probable link between YY1 and SmcHD1 is definitely something important to investigate.

Supplemental FigS4 relating to SmcHD1 is more speculative and we lack direct mechanistic evidence linking YY1 and SmcHD1. It would require more experiments to substantiate this as a mechanism. We think these experiments could potentially be very interesting, but are beyond the scope of this study.

In the paper the authors cite Bowness et al., 2022. In it, Figure 5F studies silencing times with respect to silencing dependency on SmcHD1. What is the overlap between SmcHD1 target genes and YY1 target genes? This would provide more data about the correlation between YY1 and SmcHD1.

There is an association between YY1 target genes and our previous categories of genes based on SmcHD1 dependence (13/56 SmcHD1_dependent genes are YY1 targets compared to only 8/101 of SmchD1_not_dependent genes). However, this enrichment of YY1 targets in SmcHD1 dependent genes is not so striking to warrant inclusion into the (very short) discussion of SmcHD1 in this paper. This association is also expected from the fact that both YY1-target genes and SmcHD1-dependent genes associate with the set of slow-silencing genes.

Of note, our categories of SmcHD1 dependency were in fact defined in a previous study (Gdula et al., 2019) from a different cellular model (SmcHD1 KO MEFs).

The authors hypothesise that SmcHD1 might play a role in the eviction of YY1 in NPC differentiation. The current data shows impaired silencing of slow silencing genes and YY1-dependent genes in the SmcHD1 knock-out. However, it doesn't show SmcHD1 is required for YY1 eviction. Could the authors provide direct evidence for their hypothesis by performing NPC differentiation in wild type and SmcHD1 knock-out cells and investigate YY1 binding using ChIP-seq?

The data we show in FigS4 is ATAC-seq data. It shows that YY1 target REs are particularly more accessible from the Xi in SmcHD1 KO, which is not direct evidence but does align with a potential role for SmcHD1 in mediating removal of YY1 binding from Xi (see our response to Reviewer #2’s comment 2.). We agree that YY1 ChIP-seq over the same time course would be an interesting experiment, but arguably this would also only be indirect evidence (ie increased Xi YY1 enrichment may be due to a confounding consequence of SmcHD1 KO). We therefore believe the full suite of experiments needed to rigorously test the hypothesis are beyond the scope of this paper.

In figure S4A and S4B no significance is indicated among the different conditions across the different differentiation days. Could the authors add this?

At all timepoints, differences of Xi accessibility between YY1-binding vs non-YY1 REs are significant. P values are now added to FigS4 and the statistical test is described in the legend.

Finally, we would like the authors to elaborate in the conclusion about the order of events. As they correctly state at the top of page 5 (and we agree), delayed loss of promoter accessibility compared to gene silencing does not automatically mean that it is downstream of gene silencing. Can you elaborate on this? Also, in light of Fig S2C where loss of YY1 binding seems to happen after gene silencing.

We mention in the text and in the above response to Reviewer #2 (point 2.) that we do not wish to overinterpret this quantitative difference because the assays are technically different and accessibility is not linearly correlated with gene expression.

It is possible to speculate plausible biological explanations for this discrepancy in kinetics between accessibility loss, TF binding and gene silencing. For example, a change in the landscape of histone modifications at a promoter may have little effect on its accessibility to TFs but directly hinder RNA Polymerase II in initiation and/or elongation of transcription of the gene. However, we prefer to keep this speculation out of the main text of the paper.

Reviewer #3 (Significance (Required)):

This manuscript highlights a novel role for YY1 in XCI. The manuscript provides an analysis of the correlation and causation of YY1 in gene silencing during XCI. There is a clear correlation between YY1 and delayed silencing of genes on the Xi. To our knowledge, this is the first time such an analysis has been performed for YY1. It advances our conceptual and mechanistic understanding of gene silencing kinetics and what the factors involved in it are. We believe it is an important contribution to the XCI field and will be of great value to the XCI community.

Strength:

This study presents a comprehensive and in-depth characterization of X-linked gene silencing during XCI.

Two different types of inducible XCI are studied and compared (ESCs vs differentiation towards NPCs), which we are grateful for.

Systematic and stepwise analysis of the data is very strong.

Many data points have been collected which provide stronger conclusions.

Weakness:

Some sentences in the abstract should be toned down.

YY1 eviction on the inactive X doesn't seem crucial to establish X-linked gene silencing in mESCs.

The mechanistic approach at the end of the manuscript with relation to SmcHD1 could be studied further.

This paper will be suited for a specialised audience in XCI and transcription factor control of gene expression, i.e. basic research.

Field of expertise: XCI, epigenetics, Xist, gene silencing, X chromosome biology.

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

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Reply to the reviewers

General Statement We very much appreciate the reviewers' thorough comments and are sincerely grateful for their kind remarks on the novelty and interest of our manuscript. We are confident to have addressed all the points that they have raised including new data, as well as revised figures and text.

Point-by-point description of revisions All the revisions have been already carried out and included in the transferred manuscript.

Reviewer #1

Major comments:

> The number of the replicates/animals for the experiments described in Figures 1 and 2 should be reported either in the figure legends or in the methods (statistical analysis). We have added the required numbers to the corresponding revised figures, as requested.

> A relevant part of the discussion repeats what the authors have already said in the results. I would recommend to reorganize this section, emphasizing the importance of these results in the context of human brain tumors.

Following our own style, we have written a very short (46 lines in length!) Discussion. We dedicate a few lines to highlighting two points: (1) the suggestion, derived from our allograft experiments, that the initial stages of tumour development and long-term tumour growth may be molecularly distinct events, and (2), the unique effect of the combined loss of TrxT and dhd on mbt tumour transcriptomics -unique because none of the suppressors of mbt reported before are as effective in erasing both the MBTS and SDS mbt signatures. Neither of these points are raised in Results. In the remaining few lines we put our results in the context of human Cancer/Testis and elaborate on the fact that the TrxT and dhd pair qualify as head-to-head, CT-X genes, like those reported in human oncology. This is as far as we are willing to go at this stage at emphasizing the importance of our results in the context of human tumours.

Reviewer #2

> 1. Figures should include information regarding the sex of the larvae, particularly as there has been a previously reported sex-linked effect in the phenotypes analysed. (e.g. in Figure 2 and Figure S1, where Indication of the sex of the animals should be provided in the figure OK and not just in the figure legend). We fully agree. Sex must always be taken into account as a biological variable. All the experiments reported in the manuscript were carried out with sexed samples, and were annotated accordingly in the original text. In compliance with the reviewer's request we have added this information also to the revised figure.

*> 2. Data regarding fertility. Can this be shown in a table format? Are dhdKO females fully sterile? What are the fertility levels of Df(1)J5? * Please note that we are not discovering anything here but merely corroborating what has been published before: the lack of TrxT does not affect fertility in either sex; the lack of Dhd results in female sterility (Torres-Campana et al., 2022, Tirmarche et al., 2016, Svensson et al., 2003, Pellicena-Palle et al., 1997). Adding a table would not be justified. Moreover, it would be a rather simple table: all single-pair mating tests (n=10 for each genotype) with Trxt KO and Dhd KO males, and TrxT KO females were as fertile as control flies, while all single-pair mating tests (n=10) with Dhd KO females were sterile.

> 3. Are dhd and TrxT the only genes affected by Df(1)J5? Is there transcriptional data from Df(1)J5 animals to suggest that nearby genes are not affected by the deficiency? Of particular interest would be to assess if snf is affected or not as it is a known regulator of gene expression and splicing. Yes dhd and TrxT are the only genes affected by Df(1)J5. That is the case according to Flybase (citing Svensson et al., 2003, and Salz et al., 1994) and confirmed by our own RNAseq data. No other transcripts, including snf, are affected by Df(1)J5.

> 4. In Figure 1C, statistical test plus indication of significance is not presented. The requested statistical test and significance data have been added as required to the revised figure and figure legend.

> 5. Related to Figure 1D. Additional neural markers could be assessed in dhdKO and TrxTKO flies. Whilst the gross morphology of the brain does not seem to be affected, there is a possibility that cell specification is affected. Specific markers for the NE, MED and CB could be used to assess this in more detail, particularly as the DE-cad images shown for dhdKO and TrxTKO flies seem to differ slightly from the control. We believe that there may be a small misunderstanding here. We have made this point clear in the revised version by referring to substantial published data showing that expression of these two genes is restricted to the germline and that, female fertility aside, TrxT and dhd deficient flies' development and life span are perfectly normal. If anything, Figure 1D is redundant. However, we would rather keep it as a control that our CRISPR KO mutants behave as expected.

> 6. Related to Figure 2A, images from TrxTKO; l(3)mbtts1, dhdKO and l(3)mbtts1 should be added at the very least in a supplementary figure. Additionally, data for NE/BL ratio should be provided for dhdKO, TrxTKO and Df(1)J5 in the absence of l(3)mbtts1 tumours. Related to Figure S1, quantification of NE/BL ratio for female lobes should be added to the figure. All the requested images and data have been included in the revised version in new figures Figure S2B, Figure S2A, and Figure S1A.

> 7. Related to Figure 2B and Figure S1, three rows of images are presented for each genotype. It is unclear whether these correspond to brain lobes from different larvae or different confocal planes from the same animal. This should be clarified in the figure and/or figure legend. This point has been clarified as requested in the revised figure legend. Each group of three rows correspond to brain lobes from different larvae of the same genotype.

> 7 cont. Related to this, in addition to the anti-DE-cadherin data, it would be informative to include immunofluorescence data using antibodies such as anti-Dachshund (lamina), anti-Elav (medulla cortex) and anti-Prospero (central brain and boundary between central brain and medulla cortex) (as assessed in e.g. Zhou and Luo, J Neurosci 2013) in the mbt tumour situation to accurately describe regions disrupted by the tumours. There is no denying that taking advantage of the many cell-type specific markers that are readily available in Drosophila could be of interest. The same applies to cell cycle markers like PH3, FUCCI, and many others. However, we believe that interesting as they may be, none of this markers will give us the clue on the molecular basis of TrxT and Dhd tumour function that is, of course, the open burning question that we are trying to address now.

> 8. Authors should clarify how the NE was defined when mbt tumours are generated, as it is severely affected. From the images provided, it is unclear which region corresponds to NE or how the NE/BL ratio was measured. It would be helpful to outline these regions in the images or, as mentioned above, use antibodies to define them. The figure has been modified to include the requested outlines defining the NE that indeed is correspond to the channel showing DE-Cadh staining.

> 9. Figure 2C does not have indication of statistical significance for the comparisons stated in the text. Potential explanations for the different roles of Dhd and TrxT in long-term tumour development should be explored in the discussion. The requested statistical significance data for these comparisons were stated in the second last paragraph of that section. To make these data more prominent we have also added this information to revised Figure 2C.

>9 cont. Related to this, does the analysis of the RNA-seq data from TrxTKO; l(3)mbtts1 and dhdKO; l(3)mbtts1 animals reveal why they have similar effect on mbt tumour development but do not synergistically contribute to long-term growth? Unfortunately our analysis of the RNA-seq data from TrxTKO; l(3)mbtts1 and dhdKO; l(3)mbtts1 animals does not give us any clue that could help us understand why they have similar effect on mbt tumour development, but not in long-term growth (allografts). To further explore this point, we have added new Figure S3 that includes a Venn diagramme showing the overlap between the affected mMBTS genes in TrxTKO; l(3)mbtts1 and dhdKO; l(3)mbtts1, together with the lists of enriched GOs among overlapping and non-overlapping genes. GO differences are tantalising, indeed, However, they do not immediately suggest any direct explanation for the different roles of Dhd and TrxT in long-term tumour development.

> 10. Authors should clarify if there is any overlap between the affected M-tSDS and F-tSDS in the TrxTKO; l(3)mbtts1 and dhdKO; l(3)mbtts1 conditions. Would the limited overlap suggest that TrxT and dhd act in parallel rather than synergistically? This might also explain the differential effects on long-term tumour development. Additionally, the stronger effect observed in Df(1)J5 animals may be due to TrxT and dhd functional redundancy. Currently, there is limited evidence to suggest that TrxT and dhd act synergistically to regulate mbt tumour growth based on the presented data. See below.

> 11. Authors should include a Venn diagram depicting affected genes (M-tSDS and F-tSDS) in the TrxTKO; l(3)mbtts1, dhdKO; l(3)mbtts1 and Df(1)J5; l(3)mbtts1 genotypes as this could clarify the percentage of overlap of gene signatures in these different conditions. Related to this point, authors could provide results from GO analysis to investigate whether specific functional clusters are altered in the different conditions. We have taken the liberty of fusing points 10 and 11 that are conceptually similar. The requested Venn diagrams showing the overlap between the affected M-tSDS and F-tSDS genes in the TrxTKO; l(3)mbtts1, dhdKO; l(3)mbtts1, and Df(1)J5; l(3)mbtts1 conditions, and GO analysis are now shown in new Figure S5. Unfortunately, these new data do not suggest any obvious explanation for the differential effects of these two genes, nor do they allow us to derive any further conclusions regarding the nature of the pathways through which TrxT and dhd cooperate to sustain mbt tumour growth. However, our analyses demonstrate that efficient suppression of mbt phenotypic traits (in larval brains) and transcriptome requires the combined elimination of both germline thioredoxins, while the effect of individual removal of either of them is only partial. These data demonstrate the synergistic nature of TrxT and dhd function in mbt tumour growth.

> 12. In Figure 3E, authors should indicate more explicitly in the figure panel and/or figure legend which genes display significant differences in expression in the different samples. We apologise for not having made this point clear in the original version: All (21) genes shown in this Table are significantly downregulated in DfJ5;ts1 vs ts1. From these, nanos and Ocho are also significantly downregulated in TrxTKO;ts1 vs ts1, and Ocho, HP1D3csd, hlk, fj, Lcp9, CG43394, and CG14968 are significantly downregulated in dhdKO;ts1 vs ts1. These data have been included in the revised figure legend. Data on all other comparisons are included in Table S1.

> 13. In Figure S2C-F it is not clear if the graphs represent data from all tissues or data from male and female tissues separately, as shown in Figure 4. Apologies for the confusion. All samples were from male tissues as indicated in the original figure legend. To make it more clear, we have labelled all four panels in the revised figure.

> 14. Are TrxT and dhd also deregulated in other tumour types? Or is this specific for mbt tumours? This information could be provided to enhance the scope of the manuscript. Thank you for raising this point. TrxT and dhd are not dysregulated in the other tumour types that were analysed in Janic et al., 2010 (i.e pros, mira, brat, lgl and pins).

> 15. Authors conclude that TrxT and dhd cooperate in controlling gene expression between wild-type and tumour samples and that they act synergistically in the regulation of sex-linked gene expression in male tumour tissue. However, the link between the two observations (if indeed there is a link) has not been well explained. Is the effect on gene expression in tumours simply a result of the regulation of sex-linked transcription? Our data show that TrxT and dhd synergistically contribute to the emergence of both the MBTS (i.e tumour versus wild type) and SDS (i.e. male tumour versus female tumour). The only certainty at this time regarding the interconnection between both signatures is that they overlap, but only partially, which answers one the questions raised by the reviewer: the effect on gene expression in tumours is not simply a result of the regulation of sex-linked transcription. Beyond that, the link (if indeed there is a link) between these two signatures has not been investigated. The lack of insight on this issue is not surprising taking into account that, in contrast to classical tumour signatures (tumour versus healthy tissue), the concept of sex-linked tumour signatures is relatively new and only a handful of such signatures have been published. Moreover, the vast majority of classical tumour signatures have not been worked out in a sex-dependent manner.

Reviewer #3 Comments: > - In the first section of the results, as a first step to study the role of TrxT and dhd genes on mbt tumors the authors generate CRISPR knock outs of these genes and correctly validate them. However, afterwards, the experiment where the authors test the KO of these genes in a wild-type larva brain is not contextualized with the rest of the section. It might be best to first address the role of these genes in a tumor context and only then complement with the experiments in wild-type (in supplementary material). We do appreciate the reviewer's view, but respectfully disagree. In our opinion, the manuscript flows better by presenting the tools that we have generated in Figure 1, By corroborating published data showing that these two germline genes do not affect soma development (Torres-Campana et al., 2022, Tirmarche et al., 2016, Svensson et al., 2003, Pellicena-Palle et al., 1997) this first figure not only validates our CRISPR KO mutants, but also sets the stage to highlight their significant effect on a somatic tumour like mbt.

> - Fig 2 B - To back up the quantifications in Fig 2A the authors could include images of l(3)mbt ts1 tumors with TrxT KO and dhd KO also. The requested images are shown in new figure Figure S2B.

> Fig 2 B and C - Indeed, the results suggest that TrxT seems to be responsible for most tumor lethality upon l(3)mbt allografts, but not dhd. This is curious since l(3)mbt; dhd KO brain tumors have the same partial phenotype as l(3)mbt; TrxT KO (fig 1A). It would be interesting to further explore these phenotypes by staining l(3)mbt; TrxT KO and l(3)mbt; dhd KO brains with, for instance, PH3 to understand if the number of dividing cells of these tumors could be different. In addition, to back up this information, the authors could look at what happens to l(3)mbt tumors with TrxT KO and dhd KO at a later stage of development (or to larva or pupa lethality if that is the case) and compare it with l(3)mbt brains. We did explore the possibility of looking at later stages. Unfortunately, the onset of the lethality phase compounded by major tissue reshaping from larval to adult brain make these stages unsuitable to reach any meaningful conclusion. With regards to staining for PH3, we think that like FUCCI and a long list of other useful labels that could be explored, it is potentially interesting, but hardly likely to give us the clue on the molecular basis of TrxT and Dhd tumour function, that is of course the one important question that we are addressing now.

> - Fig 2 B - What happens to the medulla in a l(3)mbt brain tumor? Although the ratio of NE/BL is the same for wild-type and D(1)J5; l(3)mbt, it still seems that the medulla in D(1)J5; l(3)mbt brains is substantially bigger, although quantifications would be required. Do the authors know if the NE in D(1)J5; l(3)mbt brains is either proliferating less or differentiating more? There are no significant differences in medulla/BL nor in CB/BL ratios. The corresponding quantifications have been added to the revised version. As for the question on proliferation versus differentiation, the simple answer is that we do not know.

> Figure S1 - Although the effects of TrxT KO and dhd KO in male mbt tumors seem to be enhanced in relation to female tumors, the authors should include some form of tumor quantification for female tumors like in Fig 2 A. We have carried out the requested quantifications and added the results in a new panel in revised Figure S1A.

Moreover in the 2nd section of the results, relative to Fig 1S in "...Df(1)J5; l(3)mbtts1 female larvae although given the much less severe phenotype of female mbt tumours, the effect caused by Df(1)J5 is quantitatively minor." to say "quantitatively" minor, the authors should include not only quantifications, but a form of comparison between female tumors vs. male tumors. The requested quantification was published in Molnar et al., 2019. However, we agree on the convenience of doing it again with our new samples. The new data, that confirm published results, are now shown as a new panel in revised Figure S1C.

> - Fig 3D - The hierarchical clustering was done according to which parameters? A brief explanation could help a better interpretation of this results section. The requested information has been added to the Methods section. Hierarchical clustering was done using the function heatmap.2 in R to generates a plot in which samples (columns) are clustered (dendogram); genes (rows) are scaled by “rows"; distance = Euclidean; and hclust method = complete linkage. Expression levels are reported as Row Z-score.

> - Fig 3D - It could be beneficial for the authors to include an analysis of the downregulated genes shared between TrxT KO mbt tumors and dhd KO mbt tumors, as well as the genes that are not shared (besides MBTS genes). Could be something like a Venn diagram. Thanks for pointing this out. New Figure S3 shows the requested Venn diagram, as well as the list of enriched GOs for each group.There are no enriched GOs in the list of overlapping genes. TrxTKO; l(3)mbtts1-specific genes are enriched for GOs related to game generation, sexual reproduction, germ cell development and simlar GOs. dhdKO; l(3)mbtts1 -specific genes are enriched for GOs related to chitin, molting and cuticle development. Tantalising as they are, these observations do not immediately suggest any direct explanation for the different roles of Dhd and TrxT in long-term tumour development. We are happy to add these supplemental information, but we do not deem it worth of any further discussion at this point.

> - Results section 3 - "Expression of nanos is also significantly down-regulated upon TrxT loss, but remains unaffected by loss of dhd" - to corroborate the idea that TrxT and dhd work as a pair, but contribute to different functions within the tumor, it would be interesting for the authors to do an allograft experiment of dhd KO; l(3)mbt male tissue with nanos knock down in the brain, if genetically possible. The suggested experiment is published. The gene in question (nanos) is a suppressor of mbt tumour growth: In a nanos knock down background, l(3)mbt allografts do not grow (Janic 2010).

Minor comments: * > - In the first section of the results, the authors claim that "Consistent with the reported phenotypes of Df(1)J5...", but then the study is not mentioned.* The corresponding references (Salz et al., 1994; Svensson et al., 2003; Tirmarche et al., 2016) have been added.

> - Fig 1 B - It is a bit confusing to follow where TrxT and dhd are in the Genome browser view. I am guessing we should follow the TrxT-dhd locus from A, but the authors could make it clearer. Figure 1 has been changed to make this point more clear.

> - In the same section, in the next sentence, the homozygous and hemizygous is a bit confusing. "...homozygous TrxTKO females, dhdKO males, and TrxTKO males", should be corrected. We appreciate the suggestion, but would rather stick to classical terminology and refer to KO/KO females as homozygous and to KO/Y males as hemizygous.

>- In the same section (Fig 1C): "RNA-seq data also shows that TrxT is significantly upregulated in l(3)mbtts1 males compared to females (FC=7.06; FDR=1.10E-44) while dhd is not (FC=1.89; FDR=2.00E-14)." - But dhd is nevertheless upregulated, although less, in l3mbt males, right? The authors might need to rephrase. We refer to comparing males versus females, not wild type versus tumours. The text has been rephrased in the revised version to make this point clear.

> - Fig 2 A (quantifications), should be after the confocal images (Fig 2 B). We respectfully disagree on this minor point. We initially organised this figure in the order recommended by the reviewer, but we eventually found it easier to write the article using the order shown in the submitted figure. We would rather stick to this version.

> - Fig 2 B and Fig S1 - Please include an outline of at least neuroepithelia and, if possible, Central brain or medulla so that these regions can more clearly identified. Moreover, these results will be easier to interpret if you add a male symbol in this image and a female symbol in Figure S1, otherwise, it might seem like the same figure Outlines and symbols have been added to the revised figure, as required.

> - In results, section 2, "Consequently, in spite of the strong sex dimorphism of mbt tumours, the phenotype of Df(1)J5; l(3)mbtts1 larval brains is not sexually dimorph" - to back this up, quantifications of Df(1)J5; l(3)mbtts1 female vs male tumor size, as well as statistical analysis are needed, like previously said. The requested the new data is now shown in revised Figure S1C.

> - In results section 2 - "For allografts derived from, female larvae, we found that differences in lethality rate caused by TrxTKO; l(3)mbtts1, dhdKO; l(3)mbtts1, Df(1)J5; l(3)mbtts1, and l(3)mbtts1 tissues (7-23%) were not significant (Figure 2C)" - there is no statistical analysis to conclude that the lethality rate is not significant, from 7% to 23% still seems like a difference. Thanks for pointing this out. We did of course generate the requested statistical analysis data, but failed to include it in the manuscript. Chi-square statistical test gives a p value=0.2346. These data have been added to the revised version.

> - Last paragraph of section 2 of results - very long and confusing sentence. Please rephrase text. We have rephrased this sentence to make it shorter and clearer.

> - On section 3 of results: "The vas, piwi and CG15930 transcripts are not significantly down-regulated following either TrxT or dhd depletion alone." - in Fig 3E, not only these transcripts seem to suffer a slight downregulation, but there is also no statistical analysis supporting this. There seems to be a misunderstanding here. The requested statistical data for each gene were shown in Table S1

> - First paragraph of section 3 results - the first sentence is written in a confusing way. Moreover, more context is needed in the sentence afterwards: "we first focused on transcripts that are up-regulated in male mbt tumour samples compared to male wild-type larval brains (mMBTS)." but using which data? The RNA seq data? Agreed; this paragraph has been amended in the revised version.

> - Brief conclusion missing on the second paragraph of the last section of results. As far as the results presented in this paragraph are concerned, we can only mention the two potentially interesting observations, which were pointed out in the original version: (i) the suggestion that nanos upregulation could be critical for sustained mbt tumour growth upon allograft, and (ii) the fact that three genes (vas, piwi and CG15930), also known to be required for mbt tumour growth, are downregulated in Df(1)J5; l(3)mbtts1, but remain unaffected following either TrxT or dhd depletion alone. We are unable to derive any other conclusion from these observations.

> - In the end of 3rd paragraph of last section of results: "...M-tSDS and F-tSDS genes is partially reduced in l(3)mbtts1 brains lacking either TrxT or dhd, but it is completely suppressed upon the lack of both." - "completely" might not be a correct word to use in this case, as there is still some small differences As requested, we have changed "completely" for "strongly".

> - 4th paragraph of last section of results: Either mention the male results and then female (to be in order with the figure, as the female graphs come after the male graphs) or change the order in the figure. Also, this paragraph is not very clear, could benefit from a better explanation of the results and conclusions. Point taken. Figure 4 has been changed and female graphs come before male graphs. The paragraph is clearer now. The conclusion from this paragraph is included in the final paragraph of this section.

> - Fig 4 C,D,E,F: to make it more clear, please write the name of the genotypes in question in the figure. At the reviewer's request, the genotypes in question are now written in each panel. Please note that we did not do so before because all four panels correspond to the same genotype: Df(J5); l(3)mbtts1 vs l(3)mbtts1, as we mentioned in the original figure legend.

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Reply to the reviewers

Reviewer #1

The paper by Yammine et al addresses a major problem in peculiarities of genotype to phenotype manifestation in collagen II and chondrodysplasia. It is a lucid and comprehensive study detailing what they see as the fundamental mechanism of Gly1170 Ser mutated Col2a1 gene.

At the heart of the matter is the debunking of the results from a mouse model generated by liang et al (Plos one 2014) paper in which the authors suggested that the phenotype seen only homozygous mice (heterozygous mice appear normal), was related to ER stress -UPR-apoptosis cascade resulting in the chondrodysplasia. Yammine et al paper uses a different model, a robust human iPSC-based tissue, with a CRISPRed variant show that despite the ability of the variant chondrocytes to deposit a Gly1170Ser-substituted collagen II in both the hetero- and homozygous models, is not accompanied by any substantive UPR. The authors of this current paper also argue that their model system is most closely resemble the human context, where heterozygous individual show pathology.

We appreciate the Reviewer highlighting the significance of this manuscript addressing a major issue in the field.

I have sieved all the data related to this topic and have gone back to examine the data and what struck me was the repeated use of the phrase "slow to fold" in the current paper and wondered whether the element of "TIME" is as important in the chondrogenesis of either models and it is this element that generate the difference between the two results? While iPSC-based tissue takes up to 44 days, a female mouse would have had two litters in this time and made many more growth plates. Could it be by "slowing" the chondrogenesis pathway, which is part of the procedure of differentiation of iPS cells into chondrocytes, the ER is not as "stressed" as in mouse development? I would like the authors to reflect and comment and put forward their view given UPR signaling pathways play a crucial role in chondrocytes in phases of high protein synthesis, e.g., during bone development by endochondral ossification (Journal of Bone Metabolism, vol. 24, no. 2, pp. 75-82, 2017).

The Reviewer here emphasizes a likely benefit of the human model that we had not previously considered, as differentiation and growth in our model are indeed far more similar to humans than the rapid timeline in mice. We also note that the evidence that collagen is slow to fold comes from the gold standard assay in the field for collagen folding rate, a point discussed in greater detail in response to Reviewer 2’s query (see below).

The literature evidence does indicate that transient UPR signaling is relevant for chondrogenesis. We selected UPR timepoints that do not interface with the differentiation process, but rather the tissue deposition process while chondrocytes are still actively depositing and maintaining the extracellular matrix, to be able to distinguish a physiological transient UPR during differentiation from a potential chronic and possibly pathologic one. We now clarify this point in the manuscript (see text below).

“These timepoints were selected to reflect an early and a late stage of cartilage maturation, but with both timepoints harvested post-chondrogenesis so as not to interfere with the physiologic transient UPR activation that can be important in that process.”

This is not withstanding the good argument given by the authors in defending their robust results, namely that the there is no evidence that the hydrophilic triple-helical domain of pro-collagen binds BiP, the main detector of accumulated misfolded proteins. What then do they make out of the immunostaining and qPCR with ER stress related genes in Liang et al paper?? I know that the data is not theirs but a comment on the indisputable data gives the reader a better understanding.

It is critical to note that the evidence for ER stress that induces the UPR is, at best, exceptionally weak for heterozygotes in the Liang et al paper, and arguably also weak for homozygotes. Liang et al observed, via quantitative PCR, that the mRNA levels of just Chop (which is also a marker of the integrated stress response and not a good readout for UPR activity) and ATF6 (whose RNA-level upregulation is not a standard marker of the UPR) were significantly upregulated in the disease-relevant heterozygous mice – given that other (more valid) UPR markers were not altered, this is not so different from our observation of a lack of UPR in the disease-relevant heterozygotes.

A somewhat more comprehensive set of UPR markers, including Chop, Xbp1(Total and Spliced), Grp78 (BiP), ATF4, and ATF6, was significantly upregulated only in homozygous mice compared to wild-type. PERK is one of many kinases upstream of ATF4 and Chop that can be activated by a variety of processes (the pathway is part of the integrated stress response, for example). Moreover, transcriptional upregulation of ATF4 (which is actually induced translationally, not transcriptionally) and ATF6 (which is actually induced proteolytically, not transcriptionally) are not normally used to read out UPR activation, so it is not so clear to us that a robust UPR was induced even in homozygotes. Moreover, there was not a substantial increase in Xbp1-S (S = spliced) relative to Xbp1-T (T= total) in the study of homozygous mice, which is the most appropriate measure of UPR activation – rather than change in Xbp1-T and Xbp1-S. The use of mostly non-standard genes to assess UPR induction, the weak upregulation of BiP (2.5-fold), and the unchanged ratio of Xbp1-S to Xbp1-T raise some questions regarding UPR induction even in the homozygotes. Regardless of these homozygote data, as noted above, the evidence for a UPR in heterozygotes is very weak, despite ER stress being the focus of the Liang et al paper.

With respect to immunostaining, Liang et al observed that tissue from homozygous mice (but not heterozygotes) contained significantly more apoptotic cells. Apoptosis could be a result of chronic, unresolved UPR signaling, but it could also result from any number of other pathways and is certainly not direct evidence for UPR-inducing ER stress. Additionally, for the homozygote apoptosis assay, Liang et al do not note how many mice were analyzed for each genotype, a value they did report for their other assays. While examining multiple sections for each genotype is valuable (they state ≥10), the assessment of biological replicates (additional mice) seems critical to confidently reach a conclusion.

Although I understand the choice of cell lines for overexpression, the transfection of the HT-1080 cells using wild-type and Gly1170Ser COL2A1encoding plasmids are not a match to the in vivo model (variation of efficiency, etc.) the appearance of BiP even at a lower fold increase does not negate ER stress, as the authors acknowledge but more important is what other paracrine signals which triggers the UPR signally pathway which is not linked to BiP? or an iPS system may lack? Is there anything else not only ATF6α (activating transcription factor 6 alpha), but IRE1α (inositol-requiring enzyme 1 alpha), and PERK (protein kinase RNA-like endoplasmic reticulum kinase).

Our finding that the UPR is not activated is based on comprehensive RNA-sequencing performed in the physiologically more relevant iPSC-derived chondrocyte, as opposed to the tumor cell line HT-1080. Our interactomic finding that BiP interacts to the same extent with wild-type and Gly1170Ser procollagen-II (in HT-1080 cells) strongly supports our proposal that the reason the UPR is not activated is that BiP fails to recognize unfolded triple-helical domains.

We note that, although HT-1080 cells are not a perfect match, they are the most accessible option for interactome-based studies. Because there is no MS-grade antibody for collagen-II IP, we need to IP a transfected, tagged collagen. We cannot do this in chondronoids, or in isolated chondrocytes that transfect poorly and rapidly dedifferentiate. Critically, Prockop and co-workers extensively validated HT-1080 cells as a platform for fibrillar collagen biochemical studies in Matrix 1993, 13, 399. Our own lab further characterized their capacity to properly handle fibrillar collagen variants in great molecular detail in ACS Chem Biol 2016, 11__, __1408.

Since our chondronoid system contains only chondrocyte cells, as is the case in cartilage, the cells can receive paracrine signals from other chondrocytes, but not other cell types. In joints within a whole animal, it is true that paracrine crosstalk occurs between different cell types of different tissues, including inflammatory cells for example. The chondronoid is very useful for elucidating the defects that occur at the chondrocyte-level, without confounding secondary effects. At the chondrocyte-level, Gly1170Ser-substituted procollagen-II does not activate the UPR.

The Reviewer’s comment regarding the absence of paracrine signals in an iPSC-based system is well-taken, and we added discussion as follows:

“These observations indicate that the chondrocytes were not raising such stress responses, at least when examined in the absence of paracrine signals from other cell types in the joint.”

The authors have given us plenty of alternatives that are relevant, and they prepared us for yet another paper on articular cartilage using iPS tissue model which I am looking forward to.

We are also excited about the upcoming potential of this model system!

Significance

I think this paper is publishable and it is important in understanding the mechanism by which mutation in collagen type II affect chondrogenesis and therefore bone formation. This paper will appeal to musculoskeletal scientist especially those who are interested in bone and its pathology. It would be important for the authors to respond to the critique of "TIME" and speed of protein synthesis which create a duress in the ER pathway.

We greatly appreciate the Reviewer’s comment again on the significance of this work, and their scholarly input which has substantially improved the paper. We hope they will agree that the manuscript is now ready for publication.

Reviewer #2

Evidence, reproducibility and clarity

*System: The investigators have used a human iPSC chondrocyte model system to investigate the biochemistry of the Chondrodysplasia caused by the p.Gly1170Ser mutation in the type II collagen gene (COL2A1). They studied presumably homogeneous chondronoids formed by 3 cell lines they previously reported in which the chondrocytes were either homozygous wild type for the gene, homozygous for the Cas Crispr induced mutation or heterozygous for the two alleles (their refs 42-45). In addition, they utilized cultured HT1080 human fibrosarcoma cells transfected with wild type and mutant Col2A1 to study differences in the interactomes of the two proteins.

*

*Analytic Parameters: They investigated the extracellular matrix formed by the three cells using collagen and proteoglycan staining and TEM and the transcriptional responses in chondronoids expressing the wild type and mutant genes.

*

*Observations: matrix formation was defective in the two mutation bearing cell populations, reflecting defective fibril formation proportional to the abnormal gene dose. They found increased accumulations of post-translational modifications (hydroxylation, and O-glycosylation) on the mutant collagen extracted from the chondronoids and EM evidence of collagen retention in the ER. They studied the comparative transcriptional profiles in the three phenotypes and failed to find a profound UPR response late in culture and only a mild upregulation of UPR genes in the young cultures. They could not find evidence for activation of the ISR except in the homozygous mutant cells.

*

*Using transfected HT-1080 cells (previously shown by these investigators not to express endogenous pro-collagen II but able to synthesize transfected pro-collagen genes) they were able to study the comparative wt and mutant pro-collagen interactomes.

*

Conclusions: They conclude that the p.gly1170ser mutation in Col2A1 results in abnormal folding which results in trapping of the protein in the ER and some interaction with cellular elements of the proteostatic response. They concluded that the cellular proteostasis machinery can recognize slow-folding Gly1170Ser through increased interactions with certain ER network components but not in the same fashion that has been described for liver cells producing mutated versions of high volume secreted proteins.

We appreciate this careful summary of our work.

*Major comments:

*

Their first conclusion, stated in the abstract, "Biochemical characterization reveals that Gly1170Ser procollagen-II is notably slow to fold and secrete." that the mutant polypeptide chain is slower folding than the wild type chain is based on the premise that the longer the chains are in the ER the greater the degree of lysine hydroxylation and O-glycosylation. Although this may be true, they do not provide a reference and I could not find a definitive description of the phenomenon. Their reference 48 only discusses the occurrence of intracellular post-translational modification of the lysines and continuing modification extracellularly but does not relate these phenomena to the rate at which the peptides traverse the cell. I think the reader would benefit from seeing experiments in which the rate of folding and secretion of the wild type and mutant chains are measured and the degree of post-translational modification are compared. Cabral WA et al showed differences in collagen folding and secretion rates in cyclophilin wt, knockouts and heterozygotes osteoblasts and fibroblasts by western blots. (2014) Abnormal Type I Collagen Post-translational Modification and Crosslinking in a Cyclophilin B KO Mouse Model of Recessive Osteogenesis Imperfecta. PLoS Genet 10(6): e1004465. doi:10.1371 / journal.pgen. 1004465). Performing such experiments in their chondronoids would confirm the authors' interpretation that the increased post-translational modification portrayed in their figure 4 reflects slowed folding and secretion related to the mutation.

We apologize for failing to provide essential background references and information to assess our assay for slow folding/secretion of procollagen. In fact, slow migration on SDS-PAGE is not only a widely used assay for comparing the rate of folding of procollagens, it has also remained the gold standard in the field for the past forty years. The studies cited below are some of the seminal papers in the field linking collagen’s rate of folding with its extent of posttranslational modifications and its electrophoretic mobility. We have now updated our citations accordingly.

1. Bateman, J.F.; Mascara, T.; Chan, D.; Cole, W.G. “Abnormal type I collagen metabolism by cultured fibroblasts in lethal perinatal osteogenesis imperfecta” Biochem J 1984, 217, 103.
2. Bonadio, J.; Holbrook, K.A.; Gelinas, R.E.; Jacob, J.; Byers, P.H. “Altered triple helical structure of type I procollagen in lethal perinatal osteogenesis imperfecta” J Biol Chem 1985, 260, 1734.
3. Bateman, J.F.; Chan, D.; Mascara, T.; Rogers, J.G.; Cole, W.G. “Collagen defects in lethal perinatal osteogenesis imperfecta” Biochem J 1986, 240, 699.
4. Godfrey, M.; Hollister, D.W. “Type II achondrogenesis-hypochondrogenesis: Identification of abnormal type II collagen” Am J Hum Genet 1988, 43, 904. The basis for this collagen-specific assay of folding rate is that the ER-localized procollagen proline and lysine hydroxylases require monomeric collagen strands as substrates, and cannot accommodate a folded triple helix in their active sites. Thus, accumulation of post-translational modifications on collagen depends on the procollagen triple-helical domain’s residence time as an unfolded monomeric region of the assembling triple-helical trimer within the ER. Some fraction of the hydroxylated lysines are later glycosylated, which slows migration on SDS-PAGE gels. We have now clarified our slow folding conclusion with more precise references and discussion in the manuscript.

Pulse-chase experiments like those suggested by the Reviewer would indeed be beneficial if they were possible in this system, but they simply are not. Although it might be possible to soak in a radiolabeled amino acid over a short time period, the assay still relies on separating the cell fraction from the secreted fraction. This is possible in monolayer cultures, but in a chondronoid composed of complex cartilage and cells we have no way to do it. One could propose that we extract the chondrocytes and then do the pulse-chase in a monolayer culture, but this unfortunately is also not possible as chondrocytes do not behave well outside the tissue setting and rapidly differentiate into other cell types. Fortunately, the procollagen overmodification assay is a widely used and well-accepted measure of slow folding, and thus addresses the issue.

I think Figure 4 needs more explanation for the reader. While, as expected, the homozygous mutant band is much slower than the homozygous wild type band, in the heterozygotes the band is intermediate rather than showing a discrete mixture of wild type and mutant proteins, reflecting different degrees of post-translational modification. Is this a function of mixed triple helices with heterogeneous degrees of post-translational modification? It deserves more comment, since the argument relating the degree of post-translational modification to the rate of folding is dependent on this observation. It would also be helpful to show the whole gel with collagen II markers.

We modified Figure 4 __to show the whole gel (in the SI, see __Fig. S3) and molecular weight markers. It also shows the wild-type collagen-II band. Most of the procollagen produced by the heterozygote is heterotrimeric for the disease-causing substitution (>87% of trimers will contain at least one mutant chain and thus experience delayed folding) and, therefore, the diffuse banding structure is to be expected. Further, we would speculate that in these challenged ER, even the folding of wild-type only trimers is impaired. The Reviewer’s comment suggests there may be some basis for that speculation. We added a note to this effect.

“The presence of a single broad, slow-migrating band as opposed to distinctive overmodified mutant versus normally modified wild-type strands is due to fact that the vast majority of trimers formed in heterozygotes (>85%) contain at least one Gly1170Ser strand that delays triple-helix folding.”

Another approach to the question of intracellular accumulation due to a slow rate of folding of the mutant collagen would be to perform pulse chase labeling of the three types of chondronoids with radiolabeled amino acids and sugars and processing the media and lysates with analysis using antibodies specific for the two collagen chain types. Given the authors extensive experience in studying collagen biosynthesis (e.g. Chan et al J. Biochem. Biophys. Methods 36 (1997) 11-29), such a supporting study would firmly establish whether the rate of folding/secretion differs between the wt and the homozygous and heterozygous chondroidinomas. Until the slow folding can be directly demonstrated in a quantitative fashion rather than by monitoring the secondary phenomenon of post-translational modification the hypothesis remains unproven.

Discussed above in response to the Reviewer’s earlier suggestion of pulse-chase and question regarding the post-translational modification assay, unfortunately the pulse-chase experiment is infeasible. Fortunately, the modification-based assay is already the gold standard in the collagen field.

Another issue that does not appear to be addressed is the consequence of having misfolded collagen chains in the dilated ER. Liang et al, using mice transgenic for one or two copies of the mutant human gene showed apoptosis in the homozygotes but not in the hets a finding similar to that of Kimura et al using transgenics carrying a different human COL2A1 mutation. Okada et al, using chondrocytes converted from human fibroblasts with clinical collagenopathy (heterozygous), although not the same mutation as in the present study, showed dilated ER and some level of apoptosis in the cultured cells. Hintze et al, examining chondrocytes expressing different mutants associated with different forms of spondyloepiphyseal dysplasia, suggested that the degree of stability of the mutations might determine whether apoptosis occurred, i.e. the thermolabile p.R989C was associated with apoptosis while cells expressing the more thermostable mutants p.275C, P.719C and p.G853E did not reveal any evidence for ongoing apoptosis R989. Is it possible that the smaller size of the homozygous chondronoids reflect fewer cells rather than less matrix (or both) as result of apoptosis? Examination of the chondronoids with reagents for caspase 3 or Tunel staining. One could also measure by Col/DNA ratio in wt, hets and homos. It might also have been useful for these experiments been more quantitative, i.e. by cell sorting rather than by eye. Would ImageJ software been helpful?

We greatly appreciate this suggestion. We now added results of TUNEL assays performed on sections of the chondronoids (see Fig. 8), including quantification of the results. Notably, we do not observe a significant difference in apoptosis between genotypes at the timepoint considered. This result is also supported by our transcriptional data, where we do not observe upregulation of apoptosis-related pathways, via the UPR or otherwise.

It is also unclear as to the conformation of chains trapped in the ER. There are many examples in which the natural tendency of misfolded proteins is to aggregate. This is certainly true in the neurodegenerative diseases. While at the magnification used here in the TEM's the ER inclusions appear homogeneous and amorphous, perhaps at higher magnification/resolution a more discrete structure might be seen.

From collagen-II immunohistochemistry confocal images, the intracellular collagen appears sometimes as aggregated puncta, and in other cases more diffuse and amorphous. Given this heterogeneity, we were not able to readily obtain clear additional structural characterization of the intracellular procollagen-II fraction.

*While the choice of time points for the transcriptional analysis, i.e. early and late seems well thought out, the lack of a significant response may be due to the timing and it might have been useful to do earlier or later time points or intermediate time points in case the response was transient, particularly since other laboratories have reported UPR activation and abnormalities in the context of the silencing of Xbp1, the spliced form of which is a major driver of at least one arm of the UPR. *

While our RNA-sequencing results at the specific timepoints we chose cannot rule out a transient activation of the UPR, they do indicate that chronic, unresolved UPR signaling is not the underlying cause of pathology, which is the main point we are making.

The notion that pro-collagen is largely hydrophilic without the potential for exposure of hydrophobic regions that might engage BiP, thus is not sensitive to BiP sensing, is interesting. Is it possible that the tendency of the mutant polypeptides to form the triple helix which in itself acts as kind of a self chaperoning structure? Looking at the kinetics of assembly inside the cell, see suggestions above, might provide further insight into the process beyond that obtained by looking at the modified state of the lysines.

We believe this notion is very strongly supported by the interactomic experiment showing that BiP fails to preferentially engage the poorly folding triple-helical variant. There are, however, many other chaperones and folding enzymes that assist collagen folding, including prolyl isomerases and Hsp47. Hence, it is not clear to us that substantial self-chaperoning occurs. Still, the self-chaperoning idea is intriguing, and we will note that prior work does indicate that triple-helical domains of individual procollagen polypeptides are strongly pre-organized for triple-helix formation (for a review, see Annu Rev Biochem 2009, 78, 929). That said, we hesitate to speculate here on the self-chaperoning idea without additional evidence.__

__Minor comments:

As I mentioned above, while the transcriptional interactome experiments are computationally sophisticated the cell biology and biochemistry would benefit from more and better quantitation.

We have included quantitation of the extent of intracellular procollagen accumulation and the extent of apoptotic cells, which we hope helps to address this point.

The paper is written in a style in which results and discussion are intermingled. Personally I prefer that the introductions are short, the results clearly and briefly presented and the discussion deals with the interpretation and conclusions. I thought that whole paragraphs could have been omitted. e.g. in the introduction *Omit paragraph "The fibrillar.........achondrogenesis type II" Omit paragraph "Conventional and... for example." Omit "Excitingly........in vitro and in vivo (36)." Results: First paragraph repeats last paragraph of introduction and not necessary in one place or the other, condense. *

We appreciate this feedback and have accordingly edited the manuscript for clarity and brevity, which includes deleting or significantly shortening all the paragraphs indicated by the Reviewer. These improvements are indicated in the track-changes version of the manuscript we resubmitted.

Figure 2 by eye MGP (Matrix gla protein inhibits vascular calcification of type II collagen) seems highly over-expressed in the homozygous mutants; MGP is supposedly an inhibitor of calcification, does its over-expression here reflect something about the adequacy of the matrix

Overexpression of MGP could indeed reflect a defect in the matrix of the homozygous variants. It is also likely a reflection of the delayed hypertrophy and maturation observed in the homozygous variants, as matrix calcification is a step in the endochondral ossification process. We did not follow-up on this particular observation, as it is exclusively observed in the less clinically relevant homozygous variant. We added a note to the manuscript to capture the Reviewer’s point about MGP, as below:

“The upregulation in the homozygous system of Matrix Gla Protein (MGP) (Fig. 2A), which inhibits vascular calcification of the matrix in vivo, further supports the delay in hypertrophy, and could lead to differences in the biomechanical properties of the matrix.”

Figure 5 is good but can it be confirmed by quantitative biochemistry?

We have included quantitation of the extent of intracellular procollagen accumulation and the extent of apoptotic cells.

__ __Did you stain with antibodies to other ER resident chaperones other than calreticulin?

Yes, we also stained the ER with PDI. However, the chondronoids require extensive optimization for immunostaining and we could obtain much better images using the ER marker for calreticulin, hence our choice of images to present in the manuscript.__

__Do cells with large amounts of intracellular G1170S die?

As indicated by the newly included TUNEL data, interestingly, even cells expressing exclusively the Gly1170Ser variant of procollagen-II do not seem to apoptose at a significantly higher rate than wild-type, at least at the timepoint considered. As mentioned above, we added these data as Fig. 8, and added discussion of these results and methodology in the relevant sections of the manuscript.__

__Does higher magnification EM reveal any structure of the material within the dilated ER?

We have so far not been able to use EM to obtain higher-resolution insight into intracellular procollagen structures, but we will work on this idea in future studies.__

__Are there any inflammatory cells in the Chondronoids? To respond to aberrant proteins?

There should not be any such cells present in the chondronoids, and we indeed do not observe any inflammatory response. As noted in the response to Reviewer 1, we added discussion regarding the absence of paracrine signals in this type of model system, which we do believe has major advantages for biochemical studies like those performed here.__

__Paragraph

* "Bypassing the UPR.......often do not" Is discussion not results*

Corrected, thanks.

Significance

The experimental system described here is clearly the wave of the present. Generating human ipSC's of different lineages is now being exploited to study a variety of disorders, to achieve better understanding of pathogenesis at the molecular level to serve as appropriate models for drug development, particularly in the context of high throughput screening. In addition, as in this case, relatively rare autosomal dominant disorders with phenotypes that resemble more common sporadic disease, may allow the development of treatments that are relevant for the sporadic disorder. While it is likely that the osteoarthritis that develops in the carriers of the COL2A1 mutations is a function of the host response to the aberrant mechanics resulting from the defective extra-cellular matrix caused by the mutation, having a pure system in which the primary defect can be corrected and the predisposing matrix deficit reversed, could allow normal reparative processes to mitigate the functional joint disability. While the transgenic mice are useful as a disease model, they represent not only the expression of the primary defect but the host pathophysiologic response to that defect, i.e. in this case how the mouse responds to the defective matrix state and whether those responses add additional pathogenic factors to the disease course. Having a tool in which to relatively assess the pure chondrocyte effect should allow more granular analysis of the primary process.

We appreciate the Reviewer’s careful and enthusiastic assessment of the significance of our work.__

__

Their findings reinforce the notion that involvement of the UPR as well as the other arms of the proteostatic response in chondrocytes expressing a variety of mutant collagens suggests a degree of heterogeneity, perhaps depending on the mutation involved. While I do not believe that their current data prove or rigorously test their proposed hypothesis, i.e. that "perhaps due to the pathologic substitution occurring within a triple-helical domain that lacks hydrophobic character, this ER protein accumulation is not recognized by cellular stress responses, such as the unfolded protein response", it is worth considering.

We provide that hypothesis as a reasonable explanation for the absence of a UPR, and it is strongly supported by our interactomic studies. Furthermore, neither we nor others have found evidence for BiP binding the triple helical domain of procollagen in any other studies. Still, that hypothesis is not the core point of the paper and we do appreciate the Reviewer’s perspective.

Given the fact that this is a relatively small field with a variety of observations concerning the role of proteostasis and the UPR in particular which seem to vary depending on the system, i.e. transgenic mice, transfected fibroblasts, the chondroidomas, these observations particularly with additional biochemistry to confirm their notions regarding folding rates etc, represent a useful technical addition to the field and should be interesting for people working on collagen biology, arthritis and protein folding.

I am not a collagen biologist hence my knowledge of some of the nuances of collagen biology may not be extensive. My own areas of interest include the assembly of multi-peptide proteins (such as immunoglobulins) for secretion; the mechanisms that allow them to exit the cell and the aggregation of misfolded proteins as exemplified by the amyloidoses and other forms of clinically relevant protein aggregation. Hence, I am very familiar with tissue culture, transgenic animals as disease models, studies of protein aggregation, and as a former rheumatologist, osteoarthritis.

We greatly appreciate the Reviewer providing such valuable and scholarly input from the perspective of a scientist with deep expertise in the secretory pathway and other diseases of protein misfolding, as well as from rheumatology. Specifically from the perspective of expertise in collagen biology/biochemistry, we hope that our detailed explanations of assays that are possible versus not possible with collagen in this system, the additional context for why our assessment of the modification of procollagen is correlated with folding/secretion rate, and the further analyses added to the paper, now make a convincing case that the improved manuscript is of high significance and is ready for publication.

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Reply to the reviewers

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

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

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

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

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

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

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

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

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

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

We edited the sentence as suggested.

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

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

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

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

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

Corrected.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The legends for Figure S4 are now as follows.

“Figure S4. Additional STED images for Figure 4.

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

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

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

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

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

EndoA1/2 are localized with Dyn1xA.

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

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

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

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

In the legend of Figure S3,

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

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

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

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

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

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

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

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

Legends for Figure S4C,

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Significance (Required)):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Legends for Figure S11

Figure S11. Data variability in Figure 4.

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

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

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

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

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

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

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

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

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

Figure legend Figure 7,

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

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

Reviewer #2 (Significance (Required)):

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

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

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

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

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

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Reply to the reviewers

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

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

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

Thank you for reading and appreciating our work

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Odds Ratio comparison synthesis vs total

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

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

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

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

Genome size=2528

Contingency Table:

notA inA

notB 2265 114

inB 115 34

Overlapping p-value=5.4e-13

Odds ratio=5.9

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

Jaccard Index=0.1

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

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

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

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

Genome size=2528

Contingency Table:

notA inA

notB 2395 50

inB 67 16

Overlapping p-value=9.7e-11

Odds ratio=11.4

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

Jaccard Index=0.1

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

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

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

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

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

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

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

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

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

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

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

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

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

Page 11:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

*Reviewer #1 (Significance (Required)):

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

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

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

"Circadian regulation of macromolecular complex turnover and proteome renewal"

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

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

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

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

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

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

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

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

Thank you for reading and appreciating our work

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

*S4e? true regression lines? *

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

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

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

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

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

Reviewer #2 (Significance (Required)):

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

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

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

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

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

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

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

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

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

**Major questions/points:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

To communicate this we have added the following text:

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

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

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

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

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

*Minor:

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

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

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

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

The following has been revised in the text:

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

*

**p10 3rd paragraph: S2e not S3e *

Thank you, this has been fixed.

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

Thank you, this has been added.

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

xxxxx

Author Response

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

Public Reviews:

Reviewer #2 (Public Review):

I would like to express my appreciation for the authors' dedication to revising the manuscript. It is evident that they have thoughtfully addressed numerous concerns I previously raised, significantly contributing to the overall improvement of the manuscript.

Response: We appreciate the reviewers’ recognition of our efforts in revising the manuscript.

My primary concern regarding the authors' framing of their findings within the realm of habitual and goal-directed action control persists. I will try explain my point of view and perhaps clarify my concerns. While acknowledging the historical tendency to equate procedural learning with habits, I believe a consensus has gradually emerged among scientists, recognizing a meaningful distinction between habits and skills or procedural learning. I think this distinction is crucial for a comprehensive understanding of human action control. While these constructs share similarities, they should not be used interchangeably. Procedural learning and motor skills can manifest either through intentional and planned actions (i.e., goal-directed) or autonomously and involuntarily (habitual responses).

Response: We would like to clarify that, contrary to the reviewer’s assertion of a scientific consensus on this matter, the discussion surrounding the similarities and differences between habits and skills remains an ongoing and unresolved topic of interest among scientists (Balleine and Dezfouli, 2019; Du and Haith, 2023; Graybiel and Grafton, 2015; Haith and Krakauer, 2018; Hardwick et al., 2019; Kruglanski and Szumowska, 2020; Robbins and Costa, 2017). We absolutely agree with the reviewer that “Procedural learning and motor skills can manifest either through intentional and planned actions (i.e., goal-directed) or autonomously and involuntarily (habitual responses)”. But so do habits. Some researchers also highlight the intentional/goal-directed nature of habits (e.g., Du and Haith, 2023, “Habits are not automatic” (preprint) or Kruglanski and Szumowska, 2020, “Habitual behavior is goal-driven”: “definitions of habits that include goal independence as a foundational attribute of habits are begging the question; they effectively define away, and hence dispose of, the issue of whether habits are goal-driven (p 1258).” Therefore, there is no clear consensus concerning the concept of habit.

While we acknowledge the meaningful distinctions between habits and skills, we also recognize a substantial body of literature supporting the overlap between these concepts (cited in our manuscript), particularly at the neural level. The literature clearly indicates that both habits and skills are mediated by subcortical circuits, with a progressive disengagement of cognitive control hubs in frontal and cingulate cortices as repetition evolves. We do not use these concepts interchangeably. Instead, we simply present evidence supporting the assertion that our trained app sequences meet several criteria for their habitual nature.

Our choice of Balleine and Dezfouli (2018)'s criteria stemmed from the comprehensive nature of their definitions, which effectively synthesized insights from various researchers (Mazar and Wood, 2018; Verplanken et al., 1998; Wood, 2017, etc). Importantly, their list highlights the positive features of habits that were previously overlooked. However, these authors still included a controversial criterion ("habits as insensitive to changes in their relationship to their individual consequences and the value of those consequences"), even though they acknowledged the problems of using outcome devaluation methods and of relying on a null-effect. According to Kruglanski and Szumowska (2020), this criterion is highly problematic as “If, by definition, habits are goalindependent, then any behavior found to be goal-dependent could not be a habit on sheer logical grounds” (p. 1257). In their definition, “habitual behavior is sensitive to the value of the reward (i.e., the goal) it is expected to mediate and is sensitive to the expectancy of goal attainment (i.e., obtainment of the reward via the behavior, p.1265). In fact, some recent analyses of habitual behavior are not using devaluation or revaluation as a criterion (Du and Haith, 2023). This article, for example, ascertains habits using different criteria and provides supporting evidence for trained action sequences being understood as skills, with both goal-directed and habitual components.

In the discussion of our manuscript, we explicitly acknowledge that the app sequences can be considered habitual or goal-directed in nature and that this terminology does not alter the fact that our overtrained sequences exhibit clear habitual features.

Watson et al. (2022) aptly detailed my concerns in the following statements: "Defining habits as fluid and quickly deployed movement sequences overlaps with definitions of skills and procedural learning, which are seen by associative learning theorists as different behaviors and fields of research, distinct from habits."

"...the risk of calling any fluid behavioral repertoire 'habit' is that clarity on what exactly is under investigation and what associative structure underpins the behavior may be lost." I strongly encourage the authors, at the very least, to consider Watson et al.'s (2022) suggestion: "Clearer terminology as to the type of habit under investigation may be required by researchers to ensure that others can assess at a glance what exactly is under investigation (e.g., devaluationinsensitive habits vs. procedural habits)", and to refine their terminology accordingly (to make this distinction clear). I believe adopting clearer terminology in these respects would enhance the positioning of this work within the relevant knowledge landscape and facilitate future investigations in the field.

Response: We would like to highlight that we have indeed followed Watson et al (2022)’s recommendations on focusing on other features/criteria of habits at the expense of the outcome devaluation/contingency degradation paradigm, which has been more controversial in the human literature. Our manuscript clearly aligns with Watson et al. (2022) ‘s recommendations: “there are many other features of habits that are not captured by the key metrics from outcome devaluation/contingency degradation paradigms such as the speed at which actions are performed and the refined and invariant characteristics of movement sequences (Balleine and Dezfouli, 2019). Attempts are being made to develop novel behavioral tasks that tap into these positive features of habits, and this should be encouraged as should be tasks that are not designed to assess whether that behavior is sensitive to outcome devaluation, but capture the definition of habits through other measures”.

Regarding the authors' use of Balleine and Dezfouli's (2018) criteria to frame recorded behavior as habitual, as well as to acknowledgment the study's limitations, it's important to highlight that while the authors labelled the fourth criterion (which they were not fulfilling) as "resistance to devaluation," Balleine and Dezfouli (2018) define it as "insensitive to changes in their relationship to their individual consequences and the value of those consequences." In my understanding, this definition is potentially aligned with the authors' re-evaluation test, namely, it is conceptually adequate for evaluating the fourth criterion (which is the most accepted in the field and probably the one that differentiate habits from skills). Notably, during this test, participants exhibited goaldirected behavior.

The authors characterized this test as possibly assessing arbitration between goal-directed and habitual behavior, stating that participants in both groups "demonstrated the ability to arbitrate between prior automatic actions and new goal-directed ones." In my perspective, there is no justification for calling it a test of arbitration. Notably, the authors inferred that participants were habitual before the test based on some criteria, but then transitioned to goal-directed behavior based on a different criterion. While I agree with the authors' comment that: "Whether the initiation of the trained motor sequences in experiment 3 (arbitration) is underpinned by an action-outcome association (or not) has no bearing on whether those sequences were under stimulus-response control after training (experiment 1)." they implicitly assert a shift from habit to goal-directed behavior without providing evidence that relies on the same probed mechanism. Therefore, I think it would be more cautious to refer to this test as solely an outcome revaluation test. Again, the results of this test, if anything, provide evidence that the fourth criterion was tested but not met, suggesting participants have not become habitual (or at least undermines this option).

Response: In our previously revised manuscript, we duly acknowledged that the conventional (perhaps nowadays considered outdated) goal devaluation criterion was not met, primarily due to constraints in designing the second part of the study. We did cite evidence from another similar study that had used devaluation app-trained action sequences to demonstrate habitual qualities (but the reviewer ignored this).

The reviewer points out that we did use a manipulation of goal revaluation in one of the follow-up tests conducted (although this was not a conventional goal revaluation test inasmuch that it was conducted in a novel context). In this test, please note that we used 2 manipulations: monetary and physical effort. Although we did show that subjects, including OCD patients, were apparently goaldirected in the monetary reward manipulation, this was not so clear when goal re-evaluation involved the physical effort expended. In this effort manipulation, participants were less goaloriented and OCD patients preferred to perform the longer, familiar, to the shorter, novel sequence, thus exhibiting significantly greater habitual tendencies, as compared to controls. Hence, we cannot decisively conclude that the action sequence is goal-directed as the reviewer is arguing. In fact, the evidence is equivocal and may reflect both habitual and goal-directed qualities in the performance of this sequence, consistent with recent interpretations of skilled/habitual sequences (Du and Haith, 2023). Relying solely on this partially met criterion to conclude that the app-trained sequences are goal-directed, and therefore not habitual, would be an inaccurate assessment for several reasons: 1) the action sequences did satisfy all other criteria for being habitual; 2) this approach would rest on a problematic foundation for defining habits, as emphasized by Kruglanski & Szumowska (2020); and 3) it would succumb to the pitfall of subscribing to a zero-sum game perspective, as cautioned by various researchers, including the review by Watson et al. (2022) cited by the referee, thus oversimplifying the nuanced nature of human behavior.

While we have previously complied with the reviewer’s suggestion on relabelling our follow-up test as a “revaluation test” instead of an “arbitration test”, we have now explicitly removed all mentions of the term “arbitration” (which seems to raise concerns) throughout the manuscript. As the reviewer has suggested, we now use a more refined terminology by explicitly referring to the measured behavior as "procedural habits", as he/she suggested. We have also extensively revised the discussion section of our manuscript to incorporate the reviewer’s viewpoint. We hope that these adjustments enhance the clarity and accuracy of our manuscript, addressing the concerns raised during this review process.

In essence, this is an ontological and semantic matter, that does not alter our findings in any way. Whether the sequences are consider habitual or goal directed, does not change our findings that 1) Both groups displayed equivalent procedural learning and automaticity attainment; 2) OCD patients exhibit greater subjective habitual tendencies via self-reported questionnaires; 3) Patients who had elevated compulsivity and habitual self-reported tendencies engaged significantly more with the motor habit-training app, practiced more and reported symptom relief at the end of the study; 4) these particular patients also show an augmented inclination to attribute higher intrinsic value to familiar actions, a possible mechanism underlying compulsions.

Reviewer #2 (Recommendations For The Authors):

A few more small comments (with reference to the point numbers indicated in the rebuttal):

(14) I am not entirely sure why the suggested analysis is deemed impractical (i.e., why it cannot be performed by "pretending" participants received the points they should have received according to their performance). This can further support (or undermine) the idea of effect of reward on performance rather than just performance on performance.

Response: We have now conducted this analysis, generating scores for each trial of practices after day 20, when participants no longer gained points for their performance. This analysis assesses whether participants trial-wise behavioral changes exhibit a similar pattern following simulated relative increases or decrease in scores, as if they had been receiving points at this stage. Note that this analysis has fewer trials available, around 50% less on average.

Before presenting our results, we wish to emphasize the importance of distinguishing between the effects of performance on performance and the effects of reward on performance. In response to a reviewer's suggestion, we assessed the former in the first revision of our manuscript. We normalized the movement time variable and evaluated how normalized behavioral changes responded to score increments and decrements. The results from the original analyses were consistent with those from the normalized data.

Regarding the phase where participants no longer received scores, we believe this phase primarily helps us understand the impact of 'predicted' or 'learned' rewards on performance. Once participants have learned the simple association between faster performance and larger scores, they can be expected to continue exhibiting the reward sensitivity effects described in our main analysis. We consider it is not feasible to assess the effects of performance on performance during the reward removal phase, which occurs after 20 days. Therefore, the following results pertain to how the learned associations between faster movement times and scores persist in influencing behavior, even when explicit scores are no longer displayed on the screen.

Results: The main results of the effect of reward on behavioral changes persist, supporting that relative increases or decreases in scores (real or imagined/inferred) modulate behavioral adaptations trial-by-trial in a consistent manner across both cohorts. The direction of the effects of reward is the same as in the main analyses presented in the manuscript: larger mean behavioral changes (smaller std) following ∆R- . First, concerning changes in “normalized” movement time (MT) trial-by-trial, we conducted a 2 x 2 factorial analysis of the centroid of the Gaussian distributions with the same factors Reward, Group and Bin. This analysis demonstrated a significant main effect of Reward (P = 2e-16), but not of Group (P = 0.974) or Bin (P = 0.281). There were no significant interactions between factors. The main Reward effect can be observed in the top panel of the figure below. The same analysis applied to the spread (std) of the Gaussian distributions revealed a significant main effect of Reward (P = 0.000213), with no additional main effects or interactions.

Author response image 1.

Next, conducting the same 2 x 2 factorial analyses on the centroid and spread of the Gaussian distributions fitted to the Consistency data, we also obtained a robust significant main effect of Reward. For the centroid variable, we obtained a significant main effect of Reward (P = 0.0109) and Group (P = 0.0294), while Bin and the factor interactions were non-significant. See the top panel of the figure below.

On the other hand, Reward also modulated significantly the spread of the Gaussian distributions fitted to the Consistency data, P = 0.00498. There were no additional significant main effects or interactions. See the bottom panel in the figure below.

Note that here the factorial analysis was performed on the logarithmic transformation of the std.

Author response image 2.

(16) I find this result interesting and I think it might be worthwhile to include it in the paper.

Response: We have now included this result in our revised manuscript (page 28)

(18) I referred to this sentence: "The app preferred sequence was their preferred putative habitual sequence while the 'any 6' or 'any 3'-move sequences were the goal-seeking sequences." In my understanding, this implies one choice is habitual and another indicates goal-directedness.

One last small comment:
In the Discussion it is stated: "Moreover, when faced with a choice between the familiar and a new, less effort-demanding sequence, the OCD group leaned toward the former, likely due to its inherent value. These insights align with the theory of goal-direction/habit imbalance in OCD (Gillan et al., 2016), underscoring the dominance of habits in particular settings where they might hold intrinsic value."

This could equally be interpreted as goal-directed behavior, so I do not think there is conclusive support for this claim.

Response: The choice of the familiar/trained sequence, as opposed to the 'any 6' or 'any 3'-move sequences cannot be explicitly considered goal-directed: firstly, because the app familiar sequences were associated with less monetary reward (in the any-6 condition), and secondly, because participants would clearly need more effort and time to perform them. Even though these were automatic, it would still be much easier and faster to simply tap one finger sequentially 6 times (any6) or 3 times (any-3). Therefore, the choice for the app-sequence would not be optimal/goaldirected. In this sense, that choice aligns with the current theory of goal-direction/habit imbalance of OCD. We found that OCD patients prefer to perform the trained app sequences in the physical effort manipulation (any-3 condition). While this, on one hand cannot be explicitly considered a goal-directed choice, we agree that there is another possible goal involved here, which links to the intrinsic value associated to the familiar sequence. In this sense the action could potentially be considered goal-directed. This highlights the difficulty of this concept of value and agrees with: 1) Hommel and Wiers (2017): “Human behavior is commonly not driven by one but by many overlapping motives . . . and actions are commonly embedded into larger-scale activities with multiple goals defined at different levels. As a consequence, even successful satiation of one goal or motive is unlikely to also eliminate all the others(p. 942) and 2) Kruglanski & Szumowska (2020)’s account that “habits that may be unwanted from the perspective of an outsider and hence “irrational” or purposeless, may be highly wanted from the perspective of the individual for whom a habit is functional in achieving some goal” (p. 1262) and therefore habits are goal-driven.

References:

Balleine BW, Dezfouli A. 2019. Hierarchical Action Control: Adaptive Collaboration Between Actions and Habits. Front Psychol 10:2735. doi:10.3389/fpsyg.2019.02735

Du Y, Haith A. 2023. Habits are not automatic. doi:10.31234/osf.io/gncsf Graybiel AM, Grafton ST. 2015. The Striatum: Where Skills and Habits Meet. Cold Spring Harb Perspect Biol 7:a021691. doi:10.1101/cshperspect.a021691

Haith AM, Krakauer JW. 2018. The multiple effects of practice: skill, habit and reduced cognitive load. Current Opinion in Behavioral Sciences 20:196–201. doi:10.1016/j.cobeha.2018.01.015

Hardwick RM, Forrence AD, Krakauer JW, Haith AM. 2019. Time-dependent competition between goal-directed and habitual response preparation. Nat Hum Behav 1–11. doi:10.1038/s41562019-0725-0

Hommel B, Wiers RW. 2017. Towards a Unitary Approach to Human Action Control. Trends Cogn Sci 21:940–949. doi:10.1016/j.tics.2017.09.009

Kruglanski AW, Szumowska E. 2020. Habitual Behavior Is Goal-Driven. Perspect Psychol Sci 15:1256– 1271. doi:10.1177/1745691620917676

Mazar A, Wood W. 2018. Defining Habit in Psychology In: Verplanken B, editor. The Psychology of Habit: Theory, Mechanisms, Change, and Contexts. Cham: Springer International Publishing. pp. 13–29. doi:10.1007/978-3-319-97529-0_2

Robbins TW, Costa RM. 2017. Habits. Current Biology 27:R1200–R1206. doi:10.1016/j.cub.2017.09.060

Verplanken B, Aarts H, van Knippenberg A, Moonen A. 1998. Habit versus planned behaviour: a field experiment. Br J Soc Psychol 37 ( Pt 1):111–128. doi:10.1111/j.2044-8309.1998.tb01160.x

Watson P, O’Callaghan C, Perkes I, Bradfield L, Turner K. 2022. Making habits measurable beyond what they are not: A focus on associative dual-process models. Neurosci Biobehav Rev 142:104869. doi:10.1016/j.neubiorev.2022.104869

Wood W. 2017. Habit in Personality and Social Psychology. Pers Soc Psychol Rev 21:389–403. doi:10.1177/1088868317720362

Author Response

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

Major comments (Public Reviews)

Generality of grid cells

We appreciate the reviewers’ concern regarding the generality of our approach, and in particular for analogies in nonlinear spaces. In that regard, there are at least two potential directions that could be pursued. One is to directly encode nonlinear structures (such as trees, rings, etc.) with grid cells, to which DPP-A could be applied as described in our model. The TEM model [1] suggests that grid cells in the medial entorhinal may form a basis set that captures structural knowledge for such nonlinear spaces, such as social hierarchies and transitive inference when formalized as a connected graph. Another would be to use eigen-decomposition of the successor representation [2], a learnable predictive representation of possible future states that has been shown by Stachenfield et al. [3] to provide an abstract structured representation of a space that is analogous to the grid cell code. This general-purpose mechanism could be applied to represent analogies in nonlinear spaces [4], for which there may not be a clear factorization in terms of grid cells (i.e., distinct frequencies and multiple phases within each frequency). Since the DPP-A mechanism, as we have described it, requires representations to be factored in this way it would need to be modified for such purpose. Either of these approaches, if successful, would allow our model to be extended to domains containing nonlinear forms of structure. To the extent that different coding schemes (i.e., basis sets) are needed for different forms of structure, the question of how these are identified and engaged for use in a given setting is clearly an important one, that is not addressed by the current work. We imagine that this is likely subserved by monitoring and selection mechanisms proposed to underlie the capacity for selective attention and cognitive control [5], though the specific computational mechanisms that underlie this function remain an important direction for future research. We have added a discussion of these issues in Section 6 of the updated manuscript.

(1) Whittington, J.C., Muller, T.H., Mark, S., Chen, G., Barry, C., Burgess, N. and Behrens, T.E., 2020. The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell, 183(5), pp.1249-1263.

(2) Dayan, P., 1993. Improving generalization for temporal difference learning: The successor representation. Neural computation, 5(4), pp.613-624.

(3) Stachenfeld, K.L., Botvinick, M.M. and Gershman, S.J., 2017. The hippocampus as a predictive map. Nature neuroscience, 20(11), pp.1643-1653.

(4) Frankland, S., Webb, T.W., Petrov, A.A., O'Reilly, R.C. and Cohen, J., 2019. Extracting and Utilizing Abstract, Structured Representations for Analogy. In CogSci (pp. 1766-1772).

(5) Shenhav, A., Botvinick, M.M. and Cohen, J.D., 2013. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), pp.217-240. Biological plausibility of DPP-A

We appreciate the reviewers’ interest in the biological plausibility of our model, and in particular the question of whether and how DPP-A might be implemented in a neural network. In that regard, Bozkurt et al. [1] recently proposed a biologically plausible neural network algorithm using a weighted similarity matrix approach to implement a determinant maximization criterion, which is the core idea underlying the objective function we use for DPP-A, suggesting that the DPP-A mechanism we describe may also be biologically plausible. This could be tested experimentally by exposing individuals (e.g., rodents or humans) to a task that requires consistent exposure to a subregion, and evaluating the distribution of activity over the grid cells. Our model predicts that high frequency grid cells should increase their firing rate more than low frequency cells, since the high frequency grid cells maximize the determinant of the covariance matrix of the grid cell embeddings. It is also worth noting that Frankland et al. [2] have suggested that the use of DPPs may also help explain a mutual exclusivity bias observed in human word learning and reasoning. While this is not direct evidence of biological plausibility, it is consistent with the idea that the human brain selects representations for processing that maximize the volume of the representational space, which can be achieved by maximizing the DPP-A objective function defined in Equation 6. We have added a comment to this effect in Section 6 of the updated manuscript.

(1) Bozkurt, B., Pehlevan, C. and Erdogan, A., 2022. Biologically-plausible determinant maximization neural networks for blind separation of correlated sources. Advances in Neural Information Processing Systems, 35, pp.13704-13717.

(2) Frankland, S. and Cohen, J., 2020. Determinantal Point Processes for Memory and Structured Inference. In CogSci.

Simplicity of analogical problem and comparison to other models using this task

First, we would like to point out that analogical reasoning is a signatory feature of human cognition, which supports flexible and efficient adaptation to novel inputs that remains a challenge for most current neural network architectures. While humans can exhibit complex and sophisticated forms of analogical reasoning [1, 2, 3], here we focused on a relatively simple form, that was inspired by Rumelhart’s parallelogram model of analogy [4,5] that has been used to explain traditional human verbal analogies (e.g., “king is to what as man is to woman?”). Our model, like that one, seeks to explain analogical reasoning in terms of the computation of simple Euclidean distances (i.e., A - B = C - D, where A, B, C, D are vectors in 2D space). We have now noted this in Section 2.1.1 of the updated manuscript. It is worth noting that, despite the seeming simplicity of this construction, we show that standard neural network architectures (e.g., LSTMs and transformers) struggle to generalize on such tasks without the use of the DPP-A mechanism.

Second, we are not aware of any previous work other than Frankland et al. [6] cited in the first paragraph of Section 2.2.1, that has examined the capacity of neural network architectures to perform even this simple form of analogy. The models in that study were hardcoded to perform analogical reasoning, whereas we trained models to learn to perform analogies. That said, clearly a useful line of future work would be to scale our model further to deal with more complex forms of representation and analogical reasoning tasks [1,2,3]. We have noted this in Section 6 of the updated manuscript.

(1) Holyoak, K.J., 2012. Analogy and relational reasoning. The Oxford handbook of thinking and reasoning, pp.234-259.

(2) Webb, T., Fu, S., Bihl, T., Holyoak, K.J. and Lu, H., 2023. Zero-shot visual reasoning through probabilistic analogical mapping. Nature Communications, 14(1), p.5144.

(3) Lu, H., Ichien, N. and Holyoak, K.J., 2022. Probabilistic analogical mapping with semantic relation networks. Psychological review.

(4) Rumelhart, D.E. and Abrahamson, A.A., 1973. A model for analogical reasoning. Cognitive Psychology, 5(1), pp.1-28.

(5) Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

(6) Frankland, S., Webb, T.W., Petrov, A.A., O'Reilly, R.C. and Cohen, J., 2019. Extracting and Utilizing Abstract, Structured Representations for Analogy. In CogSci (pp. 1766-1772).

Clarification of DPP-A attentional modulation

We would like to clarify several concerns regarding the DPP-A attentional modulation. First, we would like to make it clear that ω is not meant to correspond to synaptic weights, and thank the reviewer for noting the possibility for confusion on this point. It is also distinct from a biasing input, which is often added to the product of the input features and weights. Rather, in our model ω is a vector, and diag (ω) converts it into a matrix with ω as the diagonal of the matrix, and the rest entries are zero. In Equation 6, diag(ω) is matrix multiplied with the covariance matrix V, which results in elementwise multiplication of ω with column vectors of V, and hence acts more like gates. We have noted this in Section 2.2.2 and have changed all instances of “weights (ω)” to “gates (ɡ)” in the updated manuscript. We have also rewritten the definition of Equation 6 and uses of it (as in Algorithm 1) to depict the use of sigmoid nonlinearity (σ) to , so that the resulting values are always between 0 and 1.

Second, we would like to clarify that we don’t compute the inner product between the gates ɡ and the grid cell embeddings x anywhere in our model. The gates within each frequency were optimized (independent of the task inputs), according to Equation 6, to compute the approximate maximum log determinant of the covariance matrix over the grid cell embeddings individually for each frequency. We then used the grid cell embeddings belonging to the frequency that had the maximum within-frequency log determinant for training the inference module, which always happened to be grid cells within the top three frequencies. Author response image 1 (also added to the Appendix, Section 7.10 of the updated manuscript) shows the approximate maximum log determinant (on the y-axis) for the different frequencies (on the x-axis).

Author response image 1.

Approximate maximum log determinant of the covariance matrix over the grid cell embeddings (y-axis) for each frequency (x-axis), obtained after maximizing Equation 6.

Third, we would like to clarify our interpretation of why DPP-A identified grid cell embeddings corresponding to the highest spatial frequencies, and why this produced the best OOD generalization (i.e., extrapolation on our analogy tasks). It is because those grid cell embeddings exhibited greater variance over the training data than the lower frequency embeddings, while at the same time the correlations among those grid cell embeddings were lower than the correlations among the lower frequency grid cell embeddings. The determinant of the covariance matrix of the grid cell embeddings is maximized when the variances of the grid cell embeddings are high (they are “expressive”) and the correlation among the grid cell embeddings is low (they “cover the representational space”). As a result, the higher frequency grid cell embeddings more efficiently covered the representational space of the training data, allowing them to efficiently capture the same relational structure across training and test distributions which is required for OOD generalization. We have added some clarification to the second paragraph of Section 2.2.2 in the updated manuscript. Furthermore, to illustrate this graphically, Author response image 2 (added to the Appendix, Section 7.10 of the updated manuscript) shows the results after the summation of the multiplication of the grid cell embeddings over the 2d space of 1000x1000 locations, with their corresponding gates for 3 representative frequencies (left, middle and right panels showing results for the lowest, middle and highest grid cell frequencies, respectively, of the 9 used in the model), obtained after maximizing Equation 6 for each grid cell frequency. The color code indicates the responsiveness of the grid cells to different X and Y locations in the input space (lighter color corresponding to greater responsiveness). Note that the dark blue area (denoting regions of least responsiveness to any grid cell) is greatest for the lowest frequency and nearly zero for the highest frequency, illustrating that grid cell embeddings belonging to the highest frequency more efficiently cover the representational space which allows them to capture the same relational structure across training and test distributions as required for OOD generalization.

Author response image 2.

Each panel shows the results after summation of the multiplication of the grid cell embeddings over the 2d space of 1000x1000 locations, with their corresponding gates for a particular frequency, obtained after maximizing Equation 6 for each grid cell frequency. The left, middle, and right panels show results for the lowest, middle, and highest grid cell frequencies, respectively, of the 9 used in the model. Lighter color in each panel corresponds to greater responsiveness of grid cells at that particular location in the 2d space.

Finally, we would like to clarify how the DPP-A attentional mechanism is different from the attentional mechanism in the transformer module, and why both are needed for strong OOD generalization. Use of the standard self-attention mechanism in transformers over the inputs (i.e., A, B, C, and D for the analogy task) in place of DPP-A would lead to weightings of grid cell embeddings over all frequencies and phases. The objective function for the DPP-A represents an inductive bias, that selectively assigns the greatest weight to all grid cell embeddings (i.e., for all phases) of the frequency for which the determinant of the covariance matrix is greatest computed over the training space. The transformer inference module then attends over the inputs with the selected grid cell embeddings based on the DPP-A objective. We have added a discussion of this point in Section 6 of the updated manuscript.

We would like to thank the reviewers for their recommendations. We have tried our best to incorporate them into our updated manuscript. Below we provide a detailed response to each of the recommendations grouped for each reviewer.

Reviewer #1 (Recommendations for the authors)

(1) It would be helpful to see some equations for R in the main text.

We thank the reviewer for this suggestion. We have now added some equations explaining the working of R in Section 2.2.3 of the updated manuscript.

(2) Typo: p 11 'alongwith' -> 'along with'

We have changed all instances of ‘alongwith’ to ‘along with’ in the updated manuscript.

(3) Presumably, this is related to equivariant ML - it would be helpful to comment on this.

Yes, this is related to equivariant ML, since the properties of equivariance hold for our model. Specifically, the probability distribution after applying softmax remains the same when the transformation (translation or scaling) is applied to the scores for each of the answer choices obtained from the output of the inference module, and when the same transformation is applied to the stimuli for the task and all the answer choices before presenting as input to the inference module to obtain the scores. We have commented on this in Section 2.2.3 of the updated manuscript.

Reviewer #2 (Recommendations for the authors)

(1) Page 2 - "Webb et al." temporal context - they should also cite and compare this to work by Marc Howard on generalization based on multi-scale temporal context.

While we appreciate the important contributions that have been made by Marc Howard and his colleagues to temporal coding and its role in episodic memory and hippocampal function, we would like to clarify that his temporal context model is unrelated to the temporal context normalization developed by Webb et al. (2020) and mentioned on Page 2. The former (Temporal Context Model) is a computational model that proposes a role for temporal coding in the functions of the medial temporal lobe in support of episodic recall, and spatial navigation. The latter (temporal context normalization) is a normalization procedure proposed for use in training a neural network, similar to batch normalization [1], in which tensor normalization is applied over the temporal instead of the batch dimension, which is shown to help with OOD generalization. We apologize for any confusion engendered by the similarity of these terms, and failure to clarify the difference between these, that we have now attempted to do in a footnote on Page 2.

Ioffe, S. and Szegedy, C., 2015, June. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr.

(2) page 3 - "known to be implemented in entorhinal" - It's odd that they seem to avoid citing the actual biology papers on grid cells. They should cite more of the grid cell recording papers when they mention the entorhinal cortex (i.e. Hafting et al., 2005; Barry et al., 2007; Stensola et al., 2012; Giocomo et al., 2011; Brandon et al., 2011).

We have now cited the references mentioned below, on page 3 after the phrase “known to be implemented in entohinal cortex”.

(1) Barry, C., Hayman, R., Burgess, N. and Jeffery, K.J., 2007. Experience-dependent rescaling of entorhinal grids. Nature neuroscience, 10(6), pp.682-684.

(2) Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M.B. and Moser, E.I., 2012. The entorhinal grid map is discretized. Nature, 492(7427), pp.72-78.

(3) Giocomo, L.M., Hussaini, S.A., Zheng, F., Kandel, E.R., Moser, M.B. and Moser, E.I., 2011. Grid cells use HCN1 channels for spatial scaling. Cell, 147(5), pp.1159-1170.

(4) Brandon, M.P., Bogaard, A.R., Libby, C.P., Connerney, M.A., Gupta, K. and Hasselmo, M.E., 2011. Reduction of theta rhythm dissociates grid cell spatial periodicity from directional tuning. Science, 332(6029), pp.595-599.

(3) To enhance the connection to biological systems, they should cite more of the experimental and modeling work on grid cell coding (for example on page 2 where they mention relational coding by grid cells). Currently, they tend to cite studies of grid cell relational representations that are very indirect in their relationship to grid cell recordings (i.e. indirect fMRI measures by Constaninescu et al., 2016 or the very abstract models by Whittington et al., 2020). They should cite more papers on actual neurophysiological recordings of grid cells that suggest relational/metric representations, and they should cite more of the previous modeling papers that have addressed relational representations. This could include work on using grid cell relational coding to guide spatial behavior (e.g. Erdem and Hasselmo, 2014; Bush, Barry, Manson, Burges, 2015). This could also include other papers on the grid cell code beyond the paper by Wei et al., 2015 - they could also cite work on the efficiency of coding by Sreenivasan and Fiete and by Mathis, Herz, and Stemmler.

We thank the reviewer for bringing the additional references to our attention. We have cited the references mentioned below on page 2 of the updated manuscript.

(1) Erdem, U.M. and Hasselmo, M.E., 2014. A biologically inspired hierarchical goal directed navigation model. Journal of Physiology-Paris, 108(1), pp.28-37.

(2) Sreenivasan, S. and Fiete, I., 2011. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nature neuroscience, 14(10), pp.1330-1337.

(3) Mathis, A., Herz, A.V. and Stemmler, M., 2012. Optimal population codes for space: grid cells outperform place cells. Neural computation, 24(9), pp.2280-2317.

(4) Bush, D., Barry, C., Manson, D. and Burgess, N., 2015. Using grid cells for navigation. Neuron, 87(3), pp.507-520

(4) Page 3 - "Determinantal Point Processes (DPPs)" - it is rather annoying that DPP is defined after DPP-A is defined. There ought to be a spot where the definition of DPP-A is clearly stated in a single location.

We agree it makes more sense to define Determinantal Point Process (DPP) before DPP-A. We have now rephrased the sentences accordingly. In the “Abstract”, the sentence now reads “Second, we propose an attentional mechanism that operates over the grid cell code using Determinantal Point Process (DPP), which we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space.” We have also modified the second paragraph of the “Introduction”. The modified portion now reads “b) an attentional objective inspired from Determinantal Point Processes (DPPs), which are probabilistic models of repulsion arising in quantum physics [1], to attend to abstract representations that have maximum variance and minimum correlation among them, over the training data. We refer to this as DPP attention or DPP-A.” Due to this change, we removed the last sentence of the fifth paragraph of the “Introduction”.

(1) Macchi, O., 1975. The coincidence approach to stochastic point processes. Advances in Applied Probability, 7(1), pp.83-122.

(5) Page 3 - "the inference module R" - there should be some discussion about how this component using LSTM or transformers could relate to the function of actual brain regions interacting with entorhinal cortex. Or if there is no biological connection, they should state that this is not seen as a biological model and that only the grid cell code is considered biological.

While we agree that the model is not construed to be as specific about the implementation of the R module, we assume that — as a standard deep learning component — it is likely to map onto neocortical structures that interact with the entorhinal cortex and, in particular, regions of the prefrontal-posterior parietal network widely believed to be involved in abstract relational processes [1,2,3,4]. In particular, the role of the prefrontal cortex in the encoding and active maintenance of abstract information needed for task performance (such as rules and relations) has often been modeled using gated recurrent networks, such as LSTMs [5,6], and the posterior parietal cortex has long been known to support “maps” that may provide an important substrate for computing complex relations [4]. We have added some discussion about this in Section 2.2.3 of the updated manuscript.

(1) Waltz, J.A., Knowlton, B.J., Holyoak, K.J., Boone, K.B., Mishkin, F.S., de Menezes Santos, M., Thomas, C.R. and Miller, B.L., 1999. A system for relational reasoning in human prefrontal cortex. Psychological science, 10(2), pp.119-125.

(2) Christoff, K., Prabhakaran, V., Dorfman, J., Zhao, Z., Kroger, J.K., Holyoak, K.J. and Gabrieli, J.D., 2001. Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage, 14(5), pp.1136-1149.

(3) Knowlton, B.J., Morrison, R.G., Hummel, J.E. and Holyoak, K.J., 2012. A neurocomputational system for relational reasoning. Trends in cognitive sciences, 16(7), pp.373-381.

(4) Summerfield, C., Luyckx, F. and Sheahan, H., 2020. Structure learning and the posterior parietal cortex. Progress in neurobiology, 184, p.101717.

(5) Frank, M.J., Loughry, B. and O’Reilly, R.C., 2001. Interactions between frontal cortex and basal ganglia in working memory: a computational model. Cognitive, Affective, & Behavioral Neuroscience, 1, pp.137-160.

(6) Braver, T.S. and Cohen, J.D., 2000. On the control of control: The role of dopamine in regulating prefrontal function and working memory. Control of cognitive processes: Attention and performance XVIII, (2000).

(6) Page 4 - "Learned weighting w" - it is somewhat confusing to use "w" as that is commonly used for synaptic weights, whereas I understand this to be an attentional modulation vector with the same dimensionality as the grid cell code. It seems more similar to a neural network bias input than a weight matrix.

We refer to the first paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(7) Page 4 - "parameterization of w... by two loss functions over the training set." - I realize that this has been stated here, but to emphasize the significance to a naïve reader, I think they should emphasize that the learning is entirely focused on the initial training space, and there is NO training done in the test spaces. It's very impressive that the parameterization is allowing generalization to translated or scaled spaces without requiring ANY training on the translated or scaled spaces.

We have added the sentence “Note that learning of parameter occurs only over the training space and is not further modified during testing (i.e. over the test spaces)” to the updated manuscript.

(8) Page 4 - "The first," - This should be specific - "The first loss function"

We have changed it to “The first loss function” in the updated manuscript.

(9) Page 4 - The analogy task seems rather simplistic when first presented (i.e. just a spatial translation to different parts of a space, which has already been shown to work in simulations of spatial behavior such as Erdem and Hasselmo, 2014 or Bush, Barry, Manson, Burgess, 2015). To make the connection to analogy, they might provide a brief mention of how this relates to the analogy space created by word2vec applied to traditional human verbal analogies (i.e. king-man+woman=queen).

We agree that the analogy task is simple, and recognize that grid cells can be used to navigate to different parts of space over which the test analogies are defined when those are explicitly specified, as shown by Erdem and Hasselmo (2014) and Bush, Barry, Manson, and Burgess (2015). However, for the analogy task, the appropriate set of grid cell embeddings must be identified that capture the same relational structure between training and test analogies to demonstrate strong OOD generalization, and that is achieved by the attentional mechanism DPP-A. As suggested by the reviewer’s comment, our analogy task is inspired by Rumelhart’s parallelogram model of analogy [1,2] (and therefore similar to traditional human verbal analogies) in as much as it involves differences (i.e A - B = C - D, where A, B, C, D are vectors in 2D space). We have now noted this in Section 2.1.1 of the updated manuscript.

(1) Rumelhart, D.E. and Abrahamson, A.A., 1973. A model for analogical reasoning. Cognitive Psychology, 5(1), pp.1-28.

(2) Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

(10) Page 5 - The variable "KM" is a bit confusing when it first appears. It would be good to re-iterate that K and M are separate points and KM is the vector between these points.

We apologize for the confusion on this point. KM is meant to refer to an integer value, obtained by multiplying K and M, which is added to both dimensions of A, B, C and D, which are points in ℤ2, to translate them to a different region of the space. K is an integer value ranging from 1 to 9 and M is also an integer value denoting the size of the training region, which in our implementation is 100. We have clarified this in Section 2.1.1 of the updated manuscript.

(11) Page 5 - "two continuous dimensions (Constantinescu et al._)" - this ought to give credit to the original study showing the abstract six-fold rotational symmetry for spatial coding (Doeller, Barry and Burgess).

We have now cited the original work by Doeller et al. [1] along with Constantinescu et al. (2016) in the updated manuscript after the phrase “two continuous dimensions” on page 5.

(1) Doeller, C.F., Barry, C. and Burgess, N., 2010. Evidence for grid cells in a human memory network. Nature, 463(7281), pp.657-661.

(12) Page 6 - Np=100. This is done later, but it would be clearer if they right away stated that Np*Nf=900 in this first presentation.

We have now added this sentence after Np=100. “Hence Np*Nf=900, which denotes the number of grid cells.”

(13) Page 6 - They provide theorem 2.1 on the determinant of the covariance matrix of the grid code, but they ought to cite this the first time this is mentioned.

We have cited Gilenwater et al. (2012) before mentioning theorem 2.1. The sentence just before that reads “We use the following theorem from Gillenwater et al. (2012) to construct :”

(14) Page 6 - It would greatly enhance the impact of the paper if they could give neuroscientists some sense of how the maximization of the determinant of the covariance matrix of the grid cell code could be implemented by a biological circuit. OR at least to show an example of the output of this algorithm when it is used as an inner product with the grid cell code. This would require plotting the grid cell code in the spatial domain rather than the 900 element vector.

We refer to our response above to the topic “Biological plausibility of DPP-A” and second, third, and fourth paragraphs of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contain our responses to this issue.

(15) Page 6 - "That encode higher spatial frequencies..." This seems intuitive, but it would be nice to give a more intuitive description of how this is related to the determinant of the covariance matrix.

We refer to the third paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(16) Page 7 - log of both sides... Nf is number of frequencies... Would be good to mention here that they are referring to equation 6 which is only mentioned later in the paragraph.

As suggested, we now refer to Equation 6 in the updated manuscript. The sentence now reads “This is achieved by maximizing the determinant of the covariance matrix over the within frequency grid cell embeddings of the training data, and Equation 6 is obtained by applying the log on both sides of Theorem 2.1, and in our case where refers to grid cells of a particular frequency.”

(17) Page 7 - Equation 6 - They should discuss how this is proposed to be implemented in brain circuits.

We refer to our response above to the topic “Biological plausibility of DPP-A” under “Major comments (Public Reviews)”, which contains our response to this issue.

18) Page 9 - "egeneralize" - presumably this is a typo?

Yes. We have corrected it to “generalize” in the updated manuscript.

(19) Page 9 - "biologically plausible encoding scheme" - This is valid for the grid cell code, but they should be clear that this is not valid for other parts of the model, or specify how other parts of the model such as DPP-A could be biologically plausible.

We refer to our response above to the topic “Biological plausibility of DPP-A” under “Major comments (Public Reviews)”, which contains our response to this issue.

(20) Page 12 - Figure 7 - comparsion to one-hots or smoothed one-hots. The text should indicate whether the smoothed one-hots are similar to place cell coding. This is the most relevant comparison of coding for those knowledgeable about biological coding schemes.

Yes, smoothed one-hots are similar to place cell coding. We now mention this in Section 5.3 of the updated manuscript.

(21) Page 12 - They could compare to a broader range of potential biological coding schemes for the overall space. This could include using coding based on the boundary vector cell coding of the space, band cell coding (one dimensional input to grid cells), or egocentric boundary cell coding.

We appreciate these useful suggestions, which we now mention as potentially valuable directions for future work in the second paragraph of Section 6 of the updated manuscript.

(22) Page 13 - "transformers are particularly instructive" - They mention this as a useful comparison, but they might discuss further why a much better function is obtained when attention is applied to the system twice (once by DPP-A and then by a transformer in the inference module).

We refer to the last paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(23) Page 13 - "Section 5.1 for analogy and Section 5.2 for arithmetic" - it would be clearer if they perhaps also mentioned the specific figures (Figure 4 and Figure 6) presenting the results for the transformer rather than the LSTM.

We have now rephrased to also refer to the figures in the updated manuscript. The phrase now reads “a transformer (Figure 4 in Section 5.1 for analogy and Figure 6 in Section 5.2 for arithmetic tasks) failed to achieve the same level of OOD generalization as the network that used DPP-A.”

(24) Page 14 - "statistics of the training data" - The most exciting feature of this paper is that learning during the training space analogies can so effectively generalize to other spaces based on the right attention DPP-A, but this is not really made intuitive. Again, they should illustrate the result of the xT w inner product to demonstrate why this work so effectively!

We refer to the second, third, and fourth paragraphs of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(25) Bibliography - Silver et al., go paper - journal name "nature" should be capitalized. There are other journal titles that should be capitalized. Also, I believe eLife lists family names first.

We have made the changes to the bibliography of the updated manuscript suggested by the reviewer.

Author Response

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

eLife assessment

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

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

Reviewer #1 (Public Review):

Summary:

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

Strengths:

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

Thank you for your positive assessment of our work.

Weaknesses:

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

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

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

Reviewer #2 (Public Review):

Summary:

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

Strengths:

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

We appreciate your positive assessment of our work.

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Recommendations For The Authors):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The same criticism applies to these other models that follow.

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

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

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

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

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

Reviewer #2 (Recommendations For The Authors):

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

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

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

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

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

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

Suggestion well taken. We have changed the wording accordingly:

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

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

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

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

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

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

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

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

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

Reviewer #3 (Recommendations For The Authors)

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

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

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

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

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

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

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

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

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

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

Author Response

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

Reviewer #1 (Recommendations For The Authors):

I only have a few minor suggestions:

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

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

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

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

Fixed.

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

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

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

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

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

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

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

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

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

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

σ(t)2 = (t-toffset) * σ̇ 2diff * N

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

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

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

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

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

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

Reviewer #2 (Recommendations For The Authors):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We have added the following to the manuscript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thank you for this comment.

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

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

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

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

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

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

Reviewer #3 (Recommendations For The Authors):

(1) Dynamics of sensory signals during perception

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(2) Effectivity of retro-cues at long delays

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

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

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

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

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

(3) Swap errors

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

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

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

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

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

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

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

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

(4) Direct sensory readout

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

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

(5) Encoding of distractors

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

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

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

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zhang et al., investigated the relationship between monocular and binocular responses of V1 superficial-layer neurons using two-photon calcium imaging. They found a strong relationship in their data: neurons that exhibited a greater preference for one eye or the other (high ocular dominance) were more likely to be suppressed under binocular stimulation, whereas neurons that are more equivalently driven by each other (low ocular dominance) were more likely to be enhanced by binocular stimulation. This result chiefly demonstrates the relationship between ocular dominance and binocular responses in V1, corroborating what has been shown previously using electrophysiological techniques but now with greater spatial resolution (albeit less temporal resolution). The binocular responses were well-fitted by a model that institutes divisive normalization between the eyes that accounts for both the suppression and enhancement phenomena observed in the subpopulation of binocular neurons. In so doing, the authors reify the importance of incorporating ocular dominance in computational models of binocular combination.

The conclusions of this paper are mostly well supported by the data, but there are some limitations of the methodology that need to be clarified, and an expansion of how the results relate to previous work would better contextualize these important findings in the literature.

Strengths:

The two-photon imaging technique used to resolve the activity of individual neurons within intact brain tissue grants a host of advantages. Foremost, two-photon imaging confers considerably high spatial resolution. As a result, the authors were able to sample and analyze the activity from thousands of verified superficial-layer V1 neurons. The animal model used, awake macaques, is also highly relevant for the study of binocular combination. Macaques, like humans, are binocular animals, meaning they have forward-facing eyes that confer overlapping visual fields. Importantly, macaque V1 is organized into cortical columns that process specific visual features from the separate eyes just like in humans. In combination with a powerful imaging technique, this allowed the authors to evaluate the monocular and binocular response profiles of V1 neurons that are situated within neighboring ocular dominance columns, a novel feat. To this aim, the approach was well-executed and should instill further confidence in the notion that V1 neurons combine monocular information in a manner that is dependent on the strength of their ocular dominance.

Weaknesses:

While two-photon imaging provides excellent spatial resolution, its temporal resolution is often lower compared to some other techniques, such as electrophysiology. This limits the ability to study the fast dynamics of neuronal activity, a well-understood trade-off of the method. The issue is more so that the authors draw comparisons to electrophysiological studies without explicit appreciation of the temporal difference between these techniques. In a similar vein, two-photon imaging is limited spatially in terms of cortical depth, preferentially sampling from neurons in layers 2/3. This limitation does not invalidate any of the interpretations but should be considered by readers, especially when making comparisons to previous electrophysiological reports using microelectrode linear arrays that sample from all cortical layers. Indeed, it is likely that a complete picture of early cortical binocular processing will require high spatial resolution (i.e., sampling from neurons in neighboring ocular dominance columns, from pia mater to white matter) at the biophysically relevant timescales (1ms resolution, capturing response dynamics over the full duration of the stimulus presentation, including the transient onset and steady-state periods).

To address the same concern from all three reviewers, we discussed the technical limitations of two photon calcium imaging at the end of Discussion, including limited imaging depth, low temporal resolution, and nonlinearity. The relevant texts are copied here:

(Ln 304) “Limitations of the current study

Although capable of sampling a large number of neurons at cellular resolution and with low sampling bias, two-photon calcium imaging has its known limitations that may better make it a complementary research tool to electrophysiological recordings.

For example, two-photon imaging can only sample neurons from superficial-layers, while binocular neurons also exist in deeper layers, and even neurons in the input layer are affected by feedback from downstream binocular neurons to exhibit binocular response properties (Dougherty, Cox, Westerberg, & Maier, 2019). Furthermore, calcium signals are relatively slow and cannot reveal the fast dynamics of neuronal responses. Due to these spatial and temporal limitations, a more complete picture of the neuronal mechanisms underlying binocular combination of monocular responses may come from studies using both technologies.

In addition, calcium signals may exaggerate the nonlinear properties of neurons. Although calcium signals indicated by GCaMP5, our favored choice of calcium indicator, displays a linear relationship to neuronal spike rates within a range of 10-150 Hz (Li, Liu, Jiang, Lee, & Tang, 2017), weak and strong signals out of this range are more nonlinear, and may appear poorer and stronger, respectively, than electrode-recorded effects. Consequently, the differences in population responses between monocular and binocular stimulations revealed by this study might be less pronounced.”

(Recommendations For The Authors):

Overall, my main suggestion for the authors to improve the paper is to revise some of the interpretations of their results in relation to previous research. The purpose of the present study was to illustrate a more complete picture of the binocular combination of monocular responses by taking into consideration the ocular dominance of V1 cells (lines 34-36). A study published earlier this year had an identical purpose (Mitchell et al., Current Biology, 2023) and arrived at a highly similar conclusion (and also applied divisive normalization to fit their data). I would ask that this paper be mentioned in the introduction and discussed.

The Mitchell et al 2023 paper is added to the Introduction and Discussion:

(Ln 50) “In addition (to the Dougherty et al 2019 paper from the same group), Mitchell, Carlson, Westerberg, Cox, and Maier (2023) reported that binocular combination of monocular stimuli with different contrasts is also affected by neurons’ eye preference.”

(Ln 286) “The critical roles of ocular dominance have been largely overlooked by extant binocular vision models to our knowledge, except that Anderson and Movshon (1989) demonstrated that a model consisting of multiple ocular dominance channels can better explain their psychophysical adaptation data, and that Mitchell et al. (2023) revealed that binocular combination of different contrasts presented to different eyes are affected by neurons’ ocularity preference.”

Nevertheless, the results of the present study are very valuable. They add substantial spatial resolution and sophisticated relational analysis of monocular and binocular responses that Mitchell et al., 2023 did not include. Therefore, my suggestion is to emphasize the advantages of two-photon imaging in the introduction, focusing on the ability to image neurons in neighboring ocular dominance columns. The rigorous modeling of the relationship between nearby neurons with a range of eye preferences, in tandem with the incredible yield of two-photon imaging, is what sets this paper apart from previous electrophysiological work.

The finding that binocular responses were dependent on ocular dominance is largely consistent with previous electrophysiological results. However, there should be a paragraph in the discussion section that speaks to the limitations of comparing two-photon imaging data to electrophysiological data. Namely, there are two limitations:

(1) These two techniques confer different temporal resolutions. It is conceivable that some of the electrophysiology relationships (for example, described by Dougherty et al., 2019) may be dependent on the temporal window over which the data was averaged, typically over 50-100ms around stimulus onset, or 100-250ms comprising the neurons' sustained response to the stimulus. This possible explanation of the difference in obtained results would be especially useful for the discussion paragraph starting at line 232. It would also be helpful to readers for there to be some mention of the advantage of having high temporal resolution (i.e., the benefits of electrophysiology) since (a) recent work has distinguished between sequential stages of binocular combination (Cox et al., 2019) and (b) modern models of V1 neurons emphasize recurrent feedback to explain V1 temporal dynamics (see Heeger et al., 2019; Rubin et al., 2015), which could prove to be relevant for combination of stimuli in the two eyes (Fleet et al., 1997).

Our discussion regarding the technical limitations of 2-p calcium imaging has been listed earlier. Specific to the Dougherty et 2019 paper, we added the following discussion to address the issue of temporal resolution difference between two technologies.

(Ln 266) “In addition, it is unclear whether the discrepancies are caused by different temporal resolutions of electrode recording and calcium imaging. The results of Dougherty et al. (2019) represent changes of neuronal spike activities over a period of approximately 50-200 ms after the stimulus onset, which may reflect the sustained neuronal responses to the stimulus and possible feedback signals. Calcium signals are much slower and indicative of the aggregated neuronal responses over a longer period (up to 1000 ms in the current study). They should have smeared, rather than exaggerated, the differences between monocular and binocular responses, although we cannot exclude the possibility that some neuronal response changes beyond 200 ms are responsible for the discrepancies.”

(2) The sample of V1 neurons in this study is limited to cells in the most superficial layers of the cortex (layers 2/3). This limitation is, of course, well understood, but it should be mentioned at least in the context of studying the formative mechanisms of binocular combination in V1 (since we know that binocular neurons also exist in layers 5/6, and there is now substantial evidence that even layer 4 neurons are not as "monocular" as we previously thought (Dougherty et al., 2019)).

See our discussion regarding the technical limitations of 2-p calcium imaging listed earlier.

In short, I believe the paper would be improved by (1) adding the above citations in the appropriate places, (2) acknowledging in the introduction that this question has been investigated electrophysiologically but emphasizing the advantages of two-photon imaging, and (3) adding a paragraph to the discussion section that discusses the temporal and spatial limitations when using two-photon imaging to study binocular combination, particularly when comparing the results to electrophysiology.

Reviewer #2 (Public Review):

Summary:

This study examines the pattern of responses produced by the combination of left-eye and right-eye signals in V1. For this, they used calcium imaging of neurons in V1 of awake, fixating monkeys. They take advantage of calcium imaging, which yields large populations of neurons in each field of view. With their data set, they observe how response magnitude relates to ocular dominance across the entire population. They analyze carefully how the relationship changed as the visual stimulus switched from contra-eye only, ipsi-eye only, and binocular. As expected, the contra-eye-dominated neurons responded strongly with a contra-eye-only stimulus. The ipsi-eye-dominated neurons responded strongly with an ipsi-eye-only stimulus. The surprise was responses to a binocular stimulus. The responses were similarly weak across the entire population, regardless of each neuron's ocular dominance. They conclude that this pattern of responses could be explained by interocular divisive normalization, followed by binocular summation.

Strengths:

A major strength of this work is that the model-fitting was done on a large population of simultaneously recorded neurons. This approach is an advancement over previous work, which did model-fitting on individual neurons. The fitted model in the manuscript represents the pattern observed across the large population in V1, and washes out any particular property of individual neurons. Given the large neuronal population from which the conclusion was drawn, the authors provide solid evidence supporting their conclusion. They also observed consistency across 5 fields of view.

The experiments were designed and executed appropriately to test their hypothesis. Their data support their conclusion.

Weaknesses:

One weakness of their study is that calcium signals can exaggerate the nonlinear properties of neurons. Calcium imaging renders poor responses poorer and strong responses stronger, compared to single-unit recording. In particular, the dramatic change in the population response between monocular stimulation and binocular stimulation could actually be less pronounced when measured with single-unit recording methods. This means their choice of recording method could have accidentally exaggerated the evidence of their finding.

We discussed the nonlinearity of calcium signals as part of the technical limitations of 2-p imaging calcium. The calcium indicator we use, GCaMP5, has a reasonable range of linear relationship with spike rates. But out of this range, the nonlinearity is indeed a concern.

(Ln 314) “In addition, calcium signals may exaggerate the nonlinear properties of neurons. Although signals indicated by GCaMP5, our favored choice of calcium indicator, displays a linear relationship to neuronal spike rate within a range of 10-150 Hz (Li et al., 2017), weak and strong signals out of this range are more nonlinear, and may appear poorer and stronger, respectively, than electrode-recorded effects. Consequently, the changes in population responses between monocular and binocular stimulations revealed by this study might be less pronounced.”

The implication of their finding is that strong ocular dominance is the result of release from interocular suppression by a monocular stimulus, rather than the lack of binocular combination as many traditional studies have assumed. This could significantly advance our understanding of the binocular combination circuitry of V1. The entire population of neurons could be part of a binocular combination circuitry present in V1.

This is a very good insight. We added the following sentences to the end of the first paragraph of Discussion:

(Ln 242) “These findings implicate that at least for neurons in superficial layers of V1, significant ocular dominance may result from a release of interocular suppression during monocular stimulation, an unusual viewing condition as our vision is typically binocular, rather than a lack of binocular combination of inputs from upstream monocular neurons.”

(Recommendations For The Authors):

Line 150: "To model interocular response suppression, responses from each eye in Eq. 2 were further normalized by an interocular suppression factor wib or wcb," I recommend the authors improve their explanation of how they arrived at Eq. 3 from Eq. 2. As it stands, my impression is that they have one model for the responses to monocular stimulation, and another model for the responses to binocular stimulation. What I think is missing is that both equations are derived from the same model. Monocular stimulation is a situation in which the stimulus in one eye's contrast is zero. Could the authors clarify whether this situation produces an interocular suppression of zero, and how that leads to Eq. 2?

We rewrote the modeling part to show that Equations 1-3 are sequential steps of development for the same model. We also added a brief paragraph to discuss how Eq. 3 could lead to Eq. 2 under monocular viewing:

(Ln 166) “Although not shown in Eq. 3, we also assumed that the nonlinear exponent b also depends on the contrast of the stimulus presented to the other eye (i.e., Sc or Si). Consequently, when Sc or Si = 0 under monocular stimulation, Rc or Ri = 0 (Eq. 1), and interocular suppression wib or wcb = 1, so Eq. 3 changes back to Eq. 2. It is only when Sc and Si are equal and close to 1, as in the current study, that interocular suppression and binocular combination would be in the current Eq. 3 format.”

Line 225: "However, individually, compared to monocular responses, responses of monocular neurons more preferring the stimulated eye are actually suppressed, and only responses of binocular neurons are increased by binocular stimulation." This sentence is difficult to follow. I recommend the authors improve clarity by breaking up the sentence into several sentences. If I understand correctly, they summarize the pattern in the data that is indicative of interocular divisive normalization, i.e., their final conclusion.

This sentence no longer exists in the Discussion.

Line 426: "Third, for those showing significant orientation difference, the trial-based orientation responses of each neuron were fitted with a Gaussian model with a MATLAB nonlinear least squares function:" The choice of using a Gaussian function to fit orientation tuning was probably suboptimal. A Gaussian function provides an adequate fit only for neurons whose tuning is very sharp. The responses outside of the peak fall down to the baseline and the two ends meet. Otherwise, the two ends do not meet. An adequate fit would be achieved with a function of a circular variable, which wraps around 180 deg. I recommend using a Von Mises function for fitting orientation tuning.

We agree with the reviewer that the Von Mises function is more accurate than Gaussian for fitting orientation tuning functions. Indeed we are using it to fit orientation tuning of V4 neurons, many of which have two peaks. For the current V1 data, the differences between Von Mises and Gaussian fittings are very small, as shown in the orientation functional maps from three macaques below. Because we also use the same Gaussian fitting of orientation tuning in several published and current under-review papers, we prefer to keep the Gaussian fitting results in the manuscript.

Author response image 1.

Reviewer #3 (Public Review):

The authors have made simultaneous recordings of the responses of large numbers of neurons from the primary visual cortex using optical two-photon imaging of calcium signals from the superficial layers of the cortex. Recordings were made to compare the responses of the cortical neurons under normal binocular viewing of a flat screen with both eyes open and monocular viewing of the same screen with one eye's view blocked by a translucent filter. The screen displayed visual stimuli comprising small contrast patches of Gabor function distributions of luminance, a stimulus that is known to excite cortical neurons.

This is an important data set, given the large numbers of neurons recorded. The authors present a simple model to explain the binocular combination of neuronal signals from the right and left eyes.

The limitations of the paper as written are as follows. These points can be addressed with some additional analysis and rewriting of sections of the paper. No new experimental data need to be collected.

(1) The authors should acknowledge the fact that these recordings arise from neurons in the superficial layers of the cortex. This limitation arises from the usual constraints on optical imaging in the macaque cortex. This means that the sample of neurons forming this data set is not fully representative of the population of binocular neurons within the visual cortex. This limitation is important in comparing the outcome of these experiments with the results from other studies of binocular combination, which have used single-electrode recording. Electrode recording will result in a sample of neurons that is drawn from many layers of the cortex, rather than just the superficial layers.

See our discussion regarding the technical limitations of 2-p calcium imaging listed earlier.

(2) Single-neuron recording of binocular neurons in the primary visual cortex has shown that these neurons often have some spontaneous activity. Assessment of this spontaneous level of firing is important for accurate model fitting [1]. The paper here should discuss the level of spontaneous neuronal firing and its potential significance.

We have noticed previously that at non-optimal spatial frequencies, calcium responses to a moving Gabor grating are close to zero (Guan et al., Prog Neurobiology, 2021, Fig. 1B), but we cannot tell whether this is due to calcium response nonlinearity, or a close-to-zero level of spontaneous neuronal activity. Prince et al (2002) reported low spontaneous responses of V1 neurons with moving grating stimuli (e.g., about 3 spikes/sec in one exemplar neuron, their Fig. 1B), so this appears not a big effect. In our data fitting, we do have an orientation-unspecific component in the Gaussian model, which represents the neuronal response at a non-preferred orientation, but not necessarily the spontaneous activity.

(3) The arrangements for visual stimulation and comparison of binocular and monocular responses mean that the stereoscopic disparity of the binocular stimuli is always at zero or close to zero. The animal's fixation point is in the centre of a single display that is viewed binocularly. The fixation point is, by definition, at zero disparity. The other points on the flat display are also at zero disparity or very close to zero because they lie in the same depth plane. There will be some small deviations from exactly zero because the geometry of the viewing arrangements results in the extremities of the display being at a slightly different distance than the centre. Therefore, the visual stimulation used to test the binocular condition is always at zero disparity, with a slight deviation from zero at the edges of the display, and never changes. [There is a detail that can be ignored. The experimenters tested neurons with visual stimulation at different real distances from the eyes, but this is not relevant here. Provided the animals accurately converged their eyes on the provided binocular fixation point, then the disparity of the visual stimuli will always be at or close to zero, regardless of viewing distance in these circumstances.] However, we already know from earlier work that neurons in the visual cortex exhibit a range of selectivity for binocular disparity. Some neurons have their peak response at non-zero disparities, representing binocular depths nearer than the fixation depth or beyond it. The response of other neurons is maximally suppressed by disparities at the depth of the fixation point (so-called Tuned Inhibitory [TI] neurons). The simple model and analysis presented in the paper for the summation of monocular responses to predict binocular responses will perform adequately for neurons that are tuned to zero disparity, so-called tuned excitatory neurons [TE], but is necessarily compromised when applied to neurons that have other, different tuning profiles. Specifically, when neurons are stimulated binocularly with a non-preferred disparity, the binocular response may be lower than the monocular response[2, 3]. This more realistic view of binocular responses needs to be considered by the authors and integrated into their modelling.

We agree and include the following texts when discussing the future work:

(Ln 298) “In addition, in our experiments, binocular stimuli were presented with zero disparity, which best triggered the responses of neurons with zero-disparity tuning. A more realistic model of binocular combination also requires the consideration of neurons with other disparity-tuning profiles.”

(4) The data in the paper show some features that have been reported before but are not captured by the model. Notably for neurons with extreme values of ocular dominance, the binocular response is typically less than the larger of the two monocular responses. This is apparent in the row of plots in Figure 2D from individual animals and in the pooled data in Figure 2E. Responses of this type are characteristic of tuned inhibitory [TI] neurons[2]. It is not immediately clear why this feature of the data does not appear in the summary and analysis in Figure 3.

This difference is indeed captured by the model, which can be more easily appreciated in Fig. 4A where monocular and binocular model simulations are plotted in the same panel. In the text, we also wrote: (Ln 195) “It is apparent that binocular responses cannot be explained by the sum of monocular responses, as binocular responses are substantially lower than the summed monocular responses for both monocular and binocular neurons. Nor can binocular responses be explained by the responses to the preferred eye, as binocular responses are also lower than those to the preferred eye (the larger of the two monocular responses) for monocular neurons.”

The paper text states that the responses were "first normalized by the median of the binocular responses". This will certainly get rid of this characteristic of the data, but this step needs better justification, or an amendment to the main analysis is needed.

The relevant sentence has been rewritten as “Monocular and binocular data of each FOV/depth, as well as the pooled data, were first normalized by the respective median of the binocular responses of all neurons in the same FOV/depth.” This normalization would render the overall binocular responses to be around unity, for the purpose of facilitating comparisons among all FOV/depth, but it would not affect the overall characteristic of the data.

In the present form, the model and analysis do not appear to fit the data in Figure 2 as accurately as needed.

Thanks for pointing out the problem, as data fitting for FOV C_270 and the pooled data were especially inaccurate. The issue has been mostly fixed when each datum was weighted by its standard deviation (please see the updated Fig. 3).

Author Response

Public Reviews:

Reviewer #1 (Public Review):

Summary:

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

Strengths:

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

We thank the reviewer for highlighting these strengths.

Weaknesses:

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

We thank the reviewer for this overall positive assessment.

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

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

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

Reviewer #3 (Public Review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work describes the mechanism of protein disaggregation by the ClpL AAA+ protein of Listeria monocytogenes. Using several model subtrate proteins the authors first show that ClpL possesses a robust disaggregase activity that does not further require the endogenous DnaK chaperone in vitro. In addition, they found that ClpL is more thermostable than the endogenous L. monocytogenes DnaK and has the capacity to unfold tightly folded protein domains. The mechanistic basis for the robust disaggregase activity of ClpL was also dissected in vitro and in some cases, supported by in vivo data performed in chaperonedeficient E. coli strains. The data presented show that the two AAA domains, the pore-2 site and the N-terminal domain (NTD) of ClpL are critical for its disaggregase activity. Remarkably, grafting the NTD of ClpL to ClpB converted ClpB into an autonomous disaggregase, highlighting the importance of such a domain in the DnaK-independent disaggregation of proteins. The role of the ClpL NTD domain was further dissected, identifying key residues and positions necessary for aggregate recognition and disaggregation. Finally, using sets of SEC and negative staining EM experiments combined with conditional covalent linkages and disaggregation assays the authors found that ClpL shows significant structural plasticity, forming dynamic hexameric and heptameric active single rings that can further form higher assembly states via their middle domains.

Strengths:

The manuscript is well-written and the experimental work is well executed. It contains a robust and complete set of in vitro data that push further our knowledge of such important disaggregases. It shows the importance of the atypical ClpL N-terminal domain in the disaggregation process as well as the structural malleability of such AAA+ proteins. More generally, this work expands our knowledge of heat resistance in bacterial pathogens.

Weaknesses:

There is no specific weakness in this work, although it would have helped to have a drawing model showing how ClpL performs protein disaggregation based on their new findings. The function of the higher assembly states of ClpL remains unresolved and will need further extensive research. Similarly, it will be interesting in the future to see whether the sole function of the plasmid-encoded ClpL is to cope with general protein aggregates under heat stress.

We thank the reviewer for the positive evaluation. We agree with the reviewer that it will be important to test whether ClpL can bind to and process non-aggregated protein substrates. Our preliminary analysis suggests that the disaggregation activity of ClpL is most relevant in vivo, pointing to protein aggregates as main target.

We also agree that the role of dimers or tetramers of ClpL rings needs to be further explored. Our initial analysis suggests a function of ring dimers as a resting state. It will now be important to study the dynamics of ClpL assembly formation and test whether substrate presence shifts ClpL assemblies towards an active, single ring state.

Reviewer #2 (Public Review):

The manuscript by Bohl et al. is an interesting and carefully done study on the biochemical properties and mode of action of potent autonomous AAA+ disaggregase ClpL from Listeria monocytogenes. ClpL is encoded on plasmids. It shows high thermal stability and provides Listeria monocytogenes food-pathogen substantial increase in resistance to heat. The authors show that ClpL interacts with aggregated proteins through the aromatic residues present in its N-terminal domain and subsequently unfolds proteins from aggregates translocating polypeptide chains through the central pore in its oligomeric ring structure. The structure of ClpL oligomers was also investigated in the manuscript. The results suggest that mono-ring structure and not dimer or trimer of rings, observed in addition to mono-ring structures under EM, is an active species of disaggregase.

Presented experiments are conclusive and well-controlled. Several mutants were created to analyze the importance of a particular ClpL domain.

The study's strength lies in the direct comparison of ClpL biochemical properties with autonomous ClpG disaggregase present in selected Gram-negative bacteria and well-studied E. coli system consisting of ClpB disaggregase and DnaK and its cochaperones. This puts the obtained results in a broader context.

We thank the reviewer for the detailed comments. There are no specific weaknesses indicated in the public review.

Reviewer #3 (Public Review):

Summary:

This manuscript details the characterization of ClpL from L. monocytogenes as a potent and autonomous AAA+ disaggregase. The authors demonstrate that ClpL has potent and DnaKindependent disaggregase activity towards a variety of aggregated model substrates and that this disaggregase activity appears to be greater than that observed with the canonical DnaK/ClpB co-chaperone. Furthermore, Lm ClpL appears to have greater thermostability as compared to Lm DnaK, suggesting that ClpL-expressing cells may be able to withstand more severe heat stress conditions. Interestingly, Lm ClpP can provide thermotolerance to E. coli that have been genetically depleted of either ClpB or in cells expressing a mutant DnaK103. The authors further characterized the mechanisms by which ClpL interacts with protein aggregates, identifying that the N-terminal domain of ClpL is essential for disaggregase function. Lastly, by EM and mutagenesis analysis, the authors report that ClpL can exist in a variety of larger macromolecular complexes, including dimer or trimers of hexamers/heptamers, and they provide evidence that the N-terminal domains of ClpL prevent dimer ring formation, thus promoting an active and substrate-binding ClpL complex. Throughout this manuscript the authors compare Lm ClpL to ClpG, another potent and autonomous disaggregase found in gram-negative bacteria that have been reported on previously, demonstrating that these two enzymes share homologous activity and qualities. Taken together this report clearly establishes ClpL as a novel and autonomous disaggregase.

Strengths:

The work presented in this report amounts to a significant body of novel and significant work that will be of interest to the protein chaperone community. Furthermore, by providing examples of how ClpL can provide in vivo thermotolerance to both E. coli and L. gasseri the authors have expanded the significance of this work and provided novel insight into potential mechanisms responsible for thermotolerance in food-borne pathogens.

Weaknesses:

The figures are clearly depicted and easy to understand, though some of the axis labeling is a bit misleading or confusing and may warrant revision. While I do feel that the results and discussion as presented support the authors' hypothesis and overall goal of demonstrating ClpL as a novel disaggregase, interpretation of the data is hindered as no statistical tests are provided throughout the manuscript. Because of this only qualitative analysis can be made, and as such many of the concluding statements involving pairwise comparisons need to be revisited or quantitative data with stats needs to be provided. The addition of statistical analysis is critical and should not be difficult, nor do I anticipate that it will change the conclusions of this report.

We thank the reviewer for the valid criticism. We addressed the major concern of the reviewer and added the requested statistical analysis to all relevant figures. The analysis confirms our conclusions. We also followed the advice of the reviewer and revised axis labeling to increase clarity.

Reviewer #1 (Recommendations For The Authors):

• It would really help to have a model showing how ClpL performs protein disaggregation based on their findings.

We show that ClpL exerts a threading activity that is fueled by ATP hydrolysis in both AAA domains and executed by pore-located aromatic residues. The basic disaggregation mechanism of ClpL therefore does not differ from ClpB and ClpG disaggregases. Similarly, the specificity of ClpL towards protein aggregates is based on simultaneous interactions of multiple N-terminal domains with the aggregate surface. We could recently describe a similar mode of aggregate recognition for ClpG [1]. We therefore prefer not to add a model to the manuscript. We are currently in preparation of a review that includes the characterization of the novel bacterial disaggregases and will present models there as we consider a review article as more appropriate for such illustrations.

• AAA2 domain of ClpL in Fig 3E should be the same color as in Fig 1A.

We used light grey instead of dark grey for the ClpL AAA2 domain in Fig 3E, to distinguish between ClpL and ClpB AAA domains. This kind of illustration allows for clearer separation of both AAA+ proteins and the fusion construct LN-ClpB*. We therefore prefer keeping the color code.

• Partial suppression of the dnaK mutant could be added in the main manuscript Figure.

The main figure 3 is already very dense and we therefore prefer showing respective data as part of a supplementary figure.

• It would have been interesting to know if the robust autonomous disaggregation activity of ClpL would be sufficient to rescue the growth of more severe E. coli chaperone mutants, like dnaK tig for example. Did the authors test this?

We tested whether expression of clpL can rescue growth of E. coli dnaK103 mutant cells at 40°C on LB plates. This experiment is different from the restoration of heat resistance in dnaK103 cells (Figure 3, figure supplement 2A), as continuous growth at elevated temperatures (40°C) is monitored instead of cell survival upon abrupt severe heat shock (49°C). We did not observe rescue of the temperature-sensitive growth phenotype (40°C) of dnaK103 cells upon clpL expression, though expression of clpG complemented the temperature-sensitive growth phenotype (see Author response image 1 below). This finding points to differences in chaperone activities of ClpL and ClpG. It also suggests that ClpL activity is largely restricted to heat-shock generated protein aggregates, enabling ClpL to complement the missing disaggregation function of DnaK but not other Hsp70 activities including folding and targeting of newly synthesized proteins. We believe that dissecting the molecular reasons for differences in ClpG and ClpL complementation activities should be part of an independent study and prefer showing the growth-complementation data only in the response letter.

Author response image 1.

Serial dilutions (10-1 – 10-6) of E. coli dnaK103 mutant cells expressing E. coli dnaK, L. monocytogenes clpL or P. aeruginosa clpG were spotted on LB plates including the indicated IPTG concentrations. Plates were incubated at 30°C or 40°C for 24 h. p: empty vector control.

Reviewer #2 (Recommendations For The Authors):

Based on results presented in Fig. 2B the authors conclude "that stand-alone disaggregases ClpL and ClpG but not the canonical KJE/ClpB disaggregase exhibit robust threading activities that allow for unfolding of tightly folded domains" (page 5 line 209). In this experiment, the threading power of disaggregases was assessed by monitoring YFP fluorescence during the disaggregation of aggregates formed by fusion luciferase-YFP protein. In my opinion, the results of the experiment depend not only on the threading power of disaggregases but also on the substrate recognition by analyzed disaggregating systems and/or processivity of disaggregases. N-terminal domain in the case of ClpL and KJE chaperones in the case of the KJE/ClpB system are involved in recognition. This is not discussed in the manuscript and the obtained result might be misinterpreted. The authors have created the LN-ClpB* construct (N-terminal domain of ClpL fused to derepressed ClpB) (Fig. 3 E and F). In my opinion, this construct should be used as an additional control in the experiment in Fig. 2 B. It possesses the same substrate recognition domain and therefore the direct comparison of disaggregases threading power might be possible.

We performed the requested experiment (new Figure 3 - figure supplement 2D). We did not observe unfolding of YFP by LN-ClpB. Sínce ClpL and LN-ClpB do not differ in their aggregate targeting mechanisms, this finding underlines the differences in threading power between ClpL and activated (derepressed) ClpB. It also suggests that the AAA threading motors and the aggregate-targeting NTD largely function independently.

Presented results suggest that tetramer and dimer of rings might be a "storage form" of disaggregase. It would be interesting to analyze the thermotolerance and/or phenotype of ClpL mutants that do not form tetramer and dimer (E352A). This variant possesses similar to WT disaggregation activity but does not form dimers and tetramers. If in vivo the differences are observed (for example toxicity of the mutant), the "storage form" hypothesis will be probable.

When testing expression of clpL-MD mutants (E352A, F354A), which cannot form dimers and tetramers of ClpL rings, in E. coli ∆clpB cells, we observed reduced production levels as compared to ClpL wildtype and speculated that reduced expression might be linked to cellular toxicity. We therefore compared spotting efficiencies of E. coli ∆clpB cells expression clpL, ∆NclpL or the clpL-MD mutants at different temperatures. Expression of clpL at high levels abrogated colony formation at 42°C (new Figure 6 - figure supplement 3). ClpL toxicity was dependent on its NTD as no effect was observed upon expression of ∆N-clpL. ClpL-MD mutants (E352A, F354A) were expressed at much lower levels and exhibited strongly increased toxicity as compared to ClpL-WT when produced at comparable levels (new Figure 6 – figure supplement 3). This implies a protective role of ClpL ring dimers and tetramers in the cellular environment by downregulating ClpL activity. We envision that the formation of ClpL assemblies restricts accessibility of the ClpL NTDs and reduces substrate interaction. Increased toxicity of ClpL-E352A and ClpL-F354A points to a physiological relevance of the dimers and tetramers of ClpL rings and is in agreement with the proposed function as storage forms. We added this potential role of ClpL ring assemblies to the discussion section. Due to the strongly reduced production levels of ClpL MD mutants and their enhanced toxicity at elevated temperatures we did not test for their ability to restore thermotolerance in E. coli ∆clpB cells.

Figure 6G and Figure 6 -figure supplement 2 - it is not clear what is the difference in the preparation of WT and WTox forms of ClpL.

ClpL WT was purified under reduced conditions (+ 2 mM DTT), whereas WTox was purified in absence of DTT, thus serving as control for ClpL-T355C, which forms disulfide bonds upon purification without DTT. We have added respective information to the figure legend and the materials and methods section.

Page 5 line 250 - wrong figure citation. Instead of Figure 1 - Figure Supplement 2A should be Figure 3 - Figure Supplement 2A.

Page 5 line 251 - wrong figure citation. Instead of Figure 1 - Figure Supplement 2B/C should be Figure 3 - Figure Supplement 2B/C.

Page 7 line 315 - wrong figure citation. Instead of Figure 4F, it should be Figure 4G Figure 1 - Figure Supplement 2E - At first glance, this Figure does not correspond to the text and is confusing. It would be nice to have bars for Lm ClpL activity in the figure. Alternatively, the description of the y-axis might be changed to "relative to Lm ClpL disaggregation activity" instead of "relative disaggregation activity". One has to carefully read the figure legend to find out that 1 corresponds to Lm ClpL activity.

We have corrected all mistakes and changed the description of y-axis (Figure 1 - figure Supplement 2E) as suggested.

Reviewer #3 (Recommendations For The Authors):

(1) While the authors make many experimental comparisons throughout their study, no statistical tests are described or presented with their results or figures, nor are these statistical tests described in the methods. While the data as presented does appear to support the author's conclusions, without these statistical tests no meaningful conclusions from paired analysis can be drawn. Critically, please report these statistical tests. As a general suggestion please include the statistics (p-values) in the results section when presenting this data, as well as in the figure legends, as this will allow the reader to better understand the authors' presentation and interpretation of the data.

We have added statistical tests to all relevant figures. The analysis is confirming our former statements. We have further clarified our approach for the statistical analysis in the methods section. We report p-values in the results section, however, due to the volume of comparisons we did not add individual p-values to the figure legends but used standard labeling with stars.

(2) Some of the axis labels for the presented graphs are a bit misleading or confusing. Many describe a relative (%) disaggregation rate, but it is not clear from the methods or figure legends what this rate is relative to. Is it relative to non-denatured substrates, to no chaperone conditions, etc.? Is it possible to present the figures with the raw data rates/activity (ex. luciferase activity / time) vs. relative rates? I think that labeling these figure axes with "disaggregation rate" is a bit misleading as none of these experiments measure the actual rate of disaggregation of these model substrates per se (say by SEC-MALS or other biophysical measurements), but instead infer the extent of disaggregation by measuring a property of these substrates, i.e. luciferase activity or fluorescence intensity over time. Thus, labeling these figures with the appropriate axis for what is being measured, and then clarifying in the methods and results what is being inferred by these measurements, will help solidify the author's conclusions.

Relative (%) disaggregation rate usually refers to the disaggregation activity of ClpL wildtype serving as reference. We clarified this point in the revised text and respective figure legends. We now also refer to the process measured (e.g. relative refolding activity of aggregated Luciferase instead of relative disaggregation activity) as suggested by the reviewer and added clarifications to text and materials and methods.

Since we have many measurements for our most frequently used assays and have a reasonable estimate for the general variance within these assays, we found it reasonable to show activity data in relation to fixed controls. This reduces the impact of unspecific variance and thereby makes more accurate comparisons between different repetitions. The reference is now indicated in the axis title.

(3) The figures are well presented, clutter-free, and graphically easy to understand. Figure legends have sufficient information aside from the aforementioned statistical information and should include the exact number of independent replicates for each panel/experiment (ex. n=4), not just a greater than 3. While the figures do show each data point along with the mean and error, in some figures it is difficult to determine the number of replicate data points. Example figures 2c, 2d, and 3a. Also, please state whether the error is std. error or SEM.

While we agree, that this is valuable information, we fear that overloading the figure legends with information may take a toll on the readability. We therefore decided to append the number of replicates for each experiment in a separate supplementary table (Table S2). The depicted error is showing the SD and not the SEM, which we also specified in the figure legends.

(4) There are various examples throughout the results where qualitative descriptors are used to describe comparisons. Examples of this are "hardly enhanced" (Figure 1) and "partially reduced" (Figure 6). While this is not necessarily wrong, qualitative descriptions of comparisons in this manner would require further explanation. What is the definition of "hardly" or "partially"? My recommendation is to just state the data quantitatively, such as "% enhanced" or "reduced by x", this way there is no misinterpretation. Examples of this can be found in Figures 6C-G. This would require a full statistical overview and presentation of these stats in the results.

We followed the reviewer`s advice and no longer use the terms criticized (e.g. “hardly enhanced”). We instead provide the requested quantifications in the text.

Questions for Figures:

Figures 1B and 1C:

(1) Is the disaggregase activity of ClpL towards heat-denatured luciferase and GFP ATPdependent? While the authors later in the manuscript show that mutations within the Walker B domains dramatically impair reactivation (disaggregation) of denatured luciferase, this does not rule out an ATP-independent effect of these mutations. Thus, the authors should test whether disaggregase activity is observed when wild-type ClpL is incubated with denatured substrates without ATP present or in the presence of ADP only.

We tested for ClpL disaggregation activity in absence of nucleotide and presence of ADP only (new Figure 1 – figure supplement 2A). We did not observe any activity, demonstrating that ClpL activity depends on ATP binding and hydrolysis (see also Figure 3 – figure supplement 1D: ATPase-deficient ClpL-E197A/E530A is lacking disaggregation activity).

(2) The authors suggest that a reduction in disaggregase activity observed in samples combining Lm ClpL and KJE (Figure 1C, supp. 1C-E) could be due to competition for protein aggregate binding as observed previously with ClpG. Did the authors test this directly by pulldown assay or another interaction-based assay? While ClpL and ClpG appear to work in a similar manner, it would be good to confirm this. Also, clarification on how this competition operates would be useful. Is it that ClpL prevents aggregates from interacting with KJE, or vice versa?

We probed for binding of ClpL to aggregated Malate Dehydrogenase in the presence of L. monocytogenes or E. coli Hsp70 (DnaK + respective J-domain protein DnaJ) by a centrifugation-based assay. Here, we used the ATPase-deficient ClpL-E197A/E530A (ClpLDWB) mutant, ensuring stable substrate interaction in presence of ATP. We observe reduced binding of ClpL-DWB to protein aggregates in presence of DnaK/DnaJ (new Figure 1 – figure supplement 2G). This finding indicates that both chaperones compete for binding to aggregated proteins and explains inhibition of ClpL disaggregation activity in presence of Hsp70.

(3) Related to the above, while incubation of aggregated substrates with ClpL and KJE does appear to reduce aggregase activity towards GFP (Figure 1c), α-glucosidase (Supp. 1C), and MDH (Supp. 1D), this doesn't appear to be the case towards luciferase (Figure 1b, Supp. 1b). Furthermore, ClpL aggregase activity is reduced towards luciferase when combined with E. coli KJE (Supp. 1e) but not with Lm KJE (Figure 1b). The authors provide no commentary or explanation for these observations. Furthermore, these results complicate the concluding statement that "combining ClpL with Lm KJE always led to a strong reduction in disaggregation activity ... ".

We suggest that the differing inhibitory degrees of the KJE system on ClpL disaggregation activities reflect diverse binding affinities of KJE and ClpL to the respective aggregates. While we usually observe strong inhibition of ClpL activity in presence of KJE, this is different for aggregated Luciferase. This points to specific structural features of Luciferase aggregates or the presence of distinct binding sites on the aggregate surface that favour ClpL binding. We have added a respective comment to the revised manuscript.

The former statement that “combining ClpL with Lm KJE always led to a strong reduction in disaggregation activity” referred to aggregated GFP, MDH and α-Glucosidase for which a strong inhibition of ClpL activity was observed. We have specified this point.

Figures 1D and 1E:

(1) The authors conclude that the heat sensitivity of ΔClpL L. gasseri cells is because they do not express the canonical ClpB disaggregase. A good test to validate this would be to express KJE/ClpB in these Lg ΔClpL cells to see if heat-sensitivity could be fully or partially rescued.

We agree that such experiment would further strengthen the in vivo function of ClpL as alternative disaggregase. However, such approach would demand for co-expression of E. coli ClpB with the authentic E. coli DnaK chaperone system (KJE), as ClpB and DnaK cooperate in a species-specific manner [2-4]. This makes the experiment challenging, also because the individual components need to be expressed at a correct stochiometry. Furthermore, the presence of the authentic L. gasseri KJE system, which is likely competing with the E. coli KJE system for aggregate binding, will hamper E. coli KJE/ClpB disaggregation activity in L. gasseri. In view of these limitations, we would like to refrain from conducting such an experiment.

(2) The rationale for investigating Lg ClpL, and the aggregase activity assays are compelling and support the hypothesis that ClpL contributes to thermotolerance in multiple grampositive species. Though, from Figure 1d, why was only Lg ClpL investigated? It appears that S. thermophilus also lacks the canonical ClpB disaggregase and demonstrates ΔClpL heat sensitivity. There is also other Lactobacillus sp. presented that lack ClpB but were not tested for heat sensitivity. Why only test and move forward with L. gasseri? Lastly, L. mesenteroides is ClpB-negative but doesn't demonstrate ΔClpL heat sensitivity. Why?

We wanted to document high, partner-independent disaggregation activity for another ClpL homolog. We chose L. gasseri, as (i) this bacterial species lacks a ClpB homolog and (ii) a ∆clpL mutant exhibit reduced survival upon severe heat shock (thermotolerance phenotype), which is associated with defects in cellular protein disaggregation. The characterization of L. gasseri ClpL as potent disaggregase in vitro represents a proof-of-concept and allows to generalize our conclusion. We therefore did not further test S. thermophilus ClpL. L. mesenteroides encodes for ClpL but not ClpB, yet, a ∆clpL mutant has not yet been characterized in this species to the best of our knowledge. As we wanted to link ClpL in vitro activity with an in vivo phenotype, we did not characterize L. mesenteroides ClpL.

We agree with the reviewer that the characterization of additional ClpL homologs is meaningful and interesting, however, we strongly believe that such analysis should be part of an exhaustive and independent study.

Figures 2A and 2B:

(1) Figure 2B demonstrates that both ClpL and ClpG, but not the canonical KJE/ClpB, are able to unfold YFP during the luciferase disaggregation process, suggesting that ClpL and ClpG exhibit stronger threading activity. A technical question, can luciferase activity be measured alongside in the same assay sample? If so, would you expect to observe a concomitant increase in luciferase activity as YFP fluorescence decreases?

KJE/ClpB can partially disaggregate and refold aggregated Luciferase-YFP without unfolding YFP during the disaggregation reaction [5]. YFP unfolding is therefore not linked to refolding of aggregated Luciferase-YFP. On the other hand, unfolding of YFP during disaggregation can hamper the refolding of the fused Luciferase moiety as observed for the AAA+ protein ClpC in presence of its partner MecA [5]. These diverse effects make the interpretation of LuciferaseYFP refolding experiments difficult as the degree of YFP unfolding activity does not necessarily correlate with the extend of Luciferase refolding. We therefore avoided to perform the suggested experiment.

Figure 2C and 2D:

(1) Thermal shift assays for ClpL, ClpG, and DnaK were completed with various nucleotides. Were these experiments also completed with samples in their nucleotide-free apo state? Also, while all these chaperones are ATPases, the nucleotides used differ, but no explanation is provided. Comparison should be made of these ATPases bound to the same molecules.

We did not monitor thermal stabilities of chaperones without nucleotide as such state is likely not relevant in vivo. We used ATPγS in case of ClpL to keep the AAA+ protein in the ATPconformation. ATP would be rapidly converted to ADP due to the high intrinsic ATPase activity of ClpL. In case of DnaK ATPγS cannot be used as it does not induce the ATP conformation [6]. The low intrinsic ATPase activity of DnaK allows determining the thermal stability of its ATP conformation in presence of ATP. This is confirmed by calculating a reduced thermal stability of ADP-bound DnaK.

(2) The authors suggest that incubation at 55⁰C will cause unfolding of Lm DnaK, but not ClpL, providing ClpL-positive Lm cells disaggregase activity at 55⁰C. While the thermal shift assays in Figures 2C and 2D support this, an experiment to test this would be to heat-treat Lm DnaK and ClpL at 55⁰C then test for disaggregase activity using either aggregated luciferase or GFP as in Figure 1.

We followed the suggestion of the reviewer and incubated Lm ClpL and DnaK at 55-58°C in presence of ATP for 15 min prior to their use in disaggregation assays. We compared the activities of pre-heated chaperones with controls that were incubated at 30°C for 15 min. Notably, we did not observe a loss of DnaK disaggregation activity, suggesting that thermal unfolding of DnaK at this temperature is reversible. We provide these data as Figure 2 -figure supplement 1 and added a respective statement to the revised manuscript.

Figure 3B:

(1) The authors state that ATPase activity of ΔN-ClpL was "hardly affected", but from the data provided it appeared to result in an approximate 35% reduction. As discussed above, no stats are provided for this figure, but given the error bars, it is highly likely that this reduction is significant. Please perform this statistical test, and if significant, please reflect this in the written results as well as the figure. Lastly, if this reduction in ATPase activity is significant, why would this be so, and could this contribute to the reduction in aggregase activity towards luciferase and MDH observed in Figure 3A?

We applied statistical tests as suggested by the reviewer, showing that the reduction in ATPase activity of ∆N-ClpL is statistically significant. N-terminal domains of Hsp100 proteins can modulate ATPase activity as shown for the family member ClpB, functioning as auxiliary regulatory element for fine tuning of ClpB activity [7]. We speculate that the impact of the ClpL-NTD on the assembly state (stabilization of ClpL ring dimers) might affect ClpL ATPase activity. We would like to point out that other ClpL mutants (e.g. NTD mutant ClpL-Y51A; MDmutant ClpL-F354A) have a similarly reduced ATPase activity, yet exhibit substantial disaggregation activity (approx. 2-fold reduced compared to ClpL wildtype). In contrast ∆NClpL does not exhibit any disaggregation activity. This suggests that the loss of disaggregation activity is caused by a substrate binding defect but not by a partial reduction in ATPase activity. We added a comment on the reduced ATPase activity and also discuss its potential reasons in the discussion section.

(2) I think the authors' conclusion that deletion of the ClpL NTD does not contribute to structural defects of ClpL is premature given the apparent reduction in ATPase activity. Did the authors perform any biophysical analysis of ΔN-ClpL to confirm this conclusion? Thermal shift assays, Native-PAGE, or size-exclusion chromatography for aggregates would all be good assays to demonstrate that the wild-type and ΔN-ClpL have similar structural properties. Surprisingly, Figure 6 describes significant macromolecular changes associated with ΔN-ClpL such that it preferentially forms a dimer of rings. Furthermore, in Supp. Figure 6D the authors report that ΔN-ClpL appears to have an increased Tm as compared to WT- or ΔM-ClpL. The authors should reflect these observations as deletion of the ClpL NTD does appear to contribute to structural changes, though perhaps only at the macromolecular scale, i.e. dimerization of the rings.

We have characterized the oligomeric state of ∆N-ClpL by size exclusion chromatography (Figure 6 – figure supplement 1A) and negative staining electron microscopy (Figure 6C), both showing that it forms assemblies similar to ClpL wildtype. We did not observe an increased tendency of ∆N-ClpL to form aggregates and the protein remained fully soluble after several cycles of thawing and freezing. EM data reveal that ∆N-ClpL exclusively form ring dimers, suggesting that the NTDs destabilize MD-MD interactions. The stabilized interaction between two ∆N-ClpL rings can explain the increased thermal stability (Figure 6 – figure supplement 1D). We speculate that the ClpL NTDs either affect MD-MD interactions through steric hindrance or by directly contacting MDs. We have added a respective statement to the discussion section.

Figure 3C and 3D:

(1) Given the larger error in samples expressing ClpG (100) or ClpL (100) statistical analysis with p-values is required to make conclusions regarding the comparison of these samples vs. plasmid-only control. The effect of ΔN-ClpL vs. wild-type ClpL looks compelling and does appear to attenuate the ClpL-induced thermotolerance. This is nicely demonstrated in Figure 3D.

We quantified respective spot tests (new Figure 3E) and tested for statistical significance as suggested by the reviewer. We show that restoration of heat resistance is significant for the first 30 min. While we always observe rescue at later timepoints significance is lost here due to larger deviations in the number of viable cells and thus the degree of complementation.

Figure 3F:

(1) What is the role of the ClpB NTD? It appears to be dispensable for disaggregase activity, assuming that ClpB is co-incubated with KJE. A quick explanation of this domain in ClpB could be useful.

The ClpB NTD is not required for disaggregation activity, as ClpB is recruited to protein aggregates by DnaK, which interacts with the ClpB MDs. Still, two functions have been described for the ClpB NTD. First, it can bind soluble unfolded substrates such as casein [8]. This substrate binding function can increase ClpB disaggregation activity towards some aggregated model substrates (e.g. Glucose-6-phosphate dehydrogenase) [9]. However, NTD deletion usually does not decrease ClpB disaggregation activity and can even lead to an increase [7, 10, 11]. An increased disaggregation activity of ∆N-ClpB correlates with an enhanced ATPase activity, which is explained by NTDs stabilizing a repressing conformation of the ClpB MDs, which function as main regulators of ClpB ATPase activity [7]. We added a short description on the role of the ClpB NTD to the respective results section.

(2) The result of fusing the ClpL NTD to ClpB supports a role for this NTD in promoting autonomous disaggregase activity. What would you expect to observe if the fused Ln-ClpB protein was co-incubated with KJE? Would this further promote disaggregase activity, or potentially impair through competition? This experiment could potentially support the authors' hypothesis that ClpL and ClpB/KJE can compete with each other for aggregated substrates as suggested in Figure 1.

We have performed the suggested experiment using aggregated MDH as model substrate. We did not observe an inhibition of LN-ClpB disaggregation activity in presence of KJE. In contrast ClpL disaggregation activity towards aggregated MDH is inhibited upon addition of KJE due to competition for aggregate binding (Figure 1 – figure supplement 2D/F). Disaggregation activity of LN-ClpB in presence of KJE can be explained by functional cooperation between both chaperone systems, which involves interactions between aggregate-bound DnaK and the ClpB MDs of the LN-ClpB fusion construct. We prefer showing these data only in the response letter but not including them in the manuscript, as respective results distract from the main message of the LN-ClpB fusion construct: the ClpL NTD functions as autonomous aggregatetargeting unit that can be transferred to other Hsp100 family members.

Author response image 2.

LN-ClpB cooperates with DnaK in protein disaggregation. Relative MDH disaggregation activities of indicated disaggregation systems were determined. KJE: DnaK/DnaJ/GrpE. The disaggregation activity of Lm ClpL was set to 1. Statistical Analysis: Oneway ANOVA, Welch’s Test for post-hoc multiple comparisons. Significance levels: **p < 0.001. n.s.: not significant.

Figures 4E and 4F:

(1) While the effect of various NTD mutations follows a similar trend in regard to the impairment of ClpL-mediated disaggregation of luciferase and MDH, the degree of these effects does appear different. For example, patch A and C mutations reduce ClpL disaggregase activity towards luciferase (~60% / 50% reduction) vs. MDH (>90%) respectively. While these results do suggest a critical role for residues in patches A and C of ClpL, these substrate-specific differences are not discussed. Why would we expect a difference in the effect of these patch A/C ClpL mutations on different substrates?

We speculate that the aggregate structure and the presence or distributions of ClpL NTD binding sites differ between aggregated Luciferase and MDH. A difference between both aggregated model substrates was also observed when testing for an inhibitory effect of Lm KJE (and Ec KJE) on ClpL disaggregation activity (see comment above). We speculate that the mutated NTD residues make specific contributions to aggregate recognition. The severity of binding defects (and reduction of disaggregation activities) of these mutants will depend on specific features of the aggregated model substrates. We now point out that ClpL NTD patch mutants can differ in disaggregation activities depending on the aggregated model substrate used and refer to potential differences in aggregate structures.

(2) The authors suggest that the loss of disaggregation activity of selected NTD mutants could be linked to reduced binding to aggregated luciferase. While this is likely given that these mutations do not appear to affect ATPase activity (Supp. 4), it could be possible that these mutants can still bind to aggregated luciferase and some other mechanism may impair disaggregation. A pull-down assay would help to prove whether reduced binding is observed in these NTD ClpL mutants. This also needs to be confirmed for Supp. Figure 4.2H.

We have shown a strong correlation between loss of aggregate binding and disaggregation activity for several NTD mutants (Fig. 4G, Figure 4 – figure supplement 2H). We decided to perform the aggregate binding assay only with mutants that show a full but not a partial disaggregation defect as we made the experience that the centrifugation-based assay provides clear and reproducible results for loss-of-activity mutants but has limitations in revealing differences for partially affected mutants. This might be explained by the use of nonhydrolyzable ATPγS in these experiments, which strongly stabilizes substrate interactions, potentially covering partial binding defects. We agree with the reviewer that some ClpL NTD mutants might have additional effects on disaggregation activity by e.g. controlling substrate transfer to the processing pore site. We have added a respective comment to the revised manuscript.

(3) Supp. Figure 4.2H has no description in the figure legend. The Y-axes states % aggregate bound to chaperone. How was this measured? See the above comments for Figures 4E and 4F.

We apologize and added the description to the figure legend. The determination of % aggregate bound chaperone is based on the quantifications of chaperones present in the supernatant and pellet fractions after sample centrifugation. Background levels of chaperones in the pellet fractions in absence of protein aggregates were subtracted. We added this information to the materials and methods section.

Figure 6G:

The authors observed reduced disaggregase activity and ATPase activity of mutant T355C under both oxidative and reducing conditions. While this observation under oxidative conditions supports the authors' hypothesis, under reducing conditions (+DTT) we would expect the enzyme to behave similarly to wild-type ClpL unless this mutation has other effects. Can the authors please comment on this and provide an explanation or hypothesis?

The reviewer is correct, ClpL-T355C exhibit a reduced disaggregation activity (Figure 6 – figure supplement 2B). We observe a similar reduction in disaggregation activity for the ClpL MD mutant F354A, pointing to an auxiliary function of the MD in protein disaggregation. We have made a respective comment in the discussion section of the revised manuscript. How exactly ClpL MDs support protein disaggregation is currently unclear and will be subject of future analysis in the lab. We strongly believe that such analysis should be part of an independent study.

Discussion:

In the fourth feature, it is discussed that one disaggregase feature of ClpL is that it does not cooperate with the ClpP protease. While a reference is provided for the canonical ClpB, no data in this paper, nor a reference, is provided demonstrating that ClpL does not interact with ClpP. As discussed, it is highly unlikely that ClpL interacts with ClpP given that ClpL does not contain the IGL/F loops that mediate the interaction of ClpP with cochaperones, such as ClpX, but data or a reference is needed to make such a factual statement.

The absence of the IGL/F loop makes an interaction between ClpL and ClpP highly unlikely. However, the reviewer is correct, direct evidence for a ClpP-independent function of ClpL, though very likely, is not provided. We have therefore rephrased the respective statement: “Forth, novel disaggregases lack the specific IGL/F signature motif, which is essential for cooperation of other Hsp100 proteins with the peptidase ClpP. This feature is shared with the canonical ClpB disaggregase [12] suggesting that protein disaggregation is primarily linked to protein refolding.”.

References

(1) Katikaridis P, Simon B, Jenne T, Moon S, Lee C, Hennig J, et al. Structural basis of aggregate binding by the AAA+ disaggregase ClpG. J Biol Chem. 2023:105336.

(2) Glover JR, Lindquist S. Hsp104, Hsp70, and Hsp40: A novel chaperone system that rescues previously aggregated proteins. Cell. 1998;94:73-82.

(3) Krzewska J, Langer T, Liberek K. Mitochondrial Hsp78, a member of the Clp/Hsp100 family in Saccharomyces cerevisiae, cooperates with Hsp70 in protein refolding. FEBS Lett. 2001;489:92-6.

(4) Seyffer F, Kummer E, Oguchi Y, Winkler J, Kumar M, Zahn R, et al. Hsp70 proteins bind Hsp100 regulatory M domains to activate AAA+ disaggregase at aggregate surfaces. Nat Struct Mol Biol. 2012;19:1347-55.

(5) Haslberger T, Zdanowicz A, Brand I, Kirstein J, Turgay K, Mogk A, et al. Protein disaggregation by the AAA+ chaperone ClpB involves partial threading of looped polypeptide segments. Nat Struct Mol Biol. 2008;15:641-50.

(6) Theyssen H, Schuster H-P, Bukau B, Reinstein J. The second step of ATP binding to DnaK induces peptide release. J Mol Biol. 1996;263:657-70.

(7) Iljina M, Mazal H, Goloubinoff P, Riven I, Haran G. Entropic Inhibition: How the Activity of a AAA+ Machine Is Modulated by Its Substrate-Binding Domain. ACS chemical biology. 2021;16:775-85.

(8) Rosenzweig R, Farber P, Velyvis A, Rennella E, Latham MP, Kay LE. ClpB N-terminal domain plays a regulatory role in protein disaggregation. Proc Natl Acad Sci U S A. 2015;112:E6872-81.

(9) Barnett ME, Nagy M, Kedzierska S, Zolkiewski M. The amino-terminal domain of ClpB supports binding to strongly aggregated proteins. J Biol Chem. 2005;280:34940-5.

(10) Beinker P, Schlee S, Groemping Y, Seidel R, Reinstein J. The N Terminus of ClpB from Thermus thermophilus Is Not Essential for the Chaperone Activity. J Biol Chem. 2002;277:47160-6.

(11) Mogk A, Schlieker C, Strub C, Rist W, Weibezahn J, Bukau B. Roles of individual domains and conserved motifs of the AAA+ chaperone ClpB in oligomerization, ATP-hydrolysis and chaperone activity. J Biol Chem. 2003;278:15-24.

(11) Weibezahn J, Tessarz P, Schlieker C, Zahn R, Maglica Z, Lee S, et al. Thermotolerance Requires Refolding of Aggregated Proteins by Substrate Translocation through the Central Pore of ClpB. Cell. 2004;119:653-65.

Author Response:

Reviewer #1 (Public Review):

Summary:

The authors identified that genetically and pharmacological inhibition of CERS1, an enzyme implicated in ceramides biosynthesis worsen muscle fibrosis and inflammation during aging.<br /> Strengths:

The study points out an interesting issue on excluding CERS1 inhibition as a therapeutic strategy for sarcopenia. Overall, the article it's well written and clear.<br /> Weaknesses:

Many of the experiments confirmed previous published data, which also show a decline of CERS1 in ageing and the generation and characterization of a muscle specific knockout mouse line. The mechanistic insights of how the increased amount of long ceramides (cer c24) and the decreased of shorter ones (cer c18) might influence muscle mass, force production, fibrosis and inflammation in aged mice have not been addressed.

We thank the reviewer for the assessment and would like to point out that Cers1 had not previously been studied in the context of aging. Moreover, our unbiased pathway analyses in human skeletal muscle implicate CERS1 for the first time with myogenic differentiation, which we validate in cell culture systems. To improve mechanistic insights, as suggested by Reviewer #1, we performed more experiments to gain insights how Cers1 derived c18, and Cers2 derived c24 ceramide species affect myogenesis. We recently showed that knocking out Cers2 reduces c24:0/c24:1 and promotes muscle cell maturation (PMID: 37118545, Fig. 6m-r and Supplementary Fig. 5e). This suggests that the very long chain ceramides c24 might indeed be driving the effect we see upon Cers1 inhibition because we observe an accumulation of c24 ceramides upon Cers1 (c18) inhibition (Fig 2B, Fig 3B, Fig 4A, Fig S3E), which is associated with impaired muscle maturation (Fig 4B-C, Fig S3G-I, Fig S4G-I). To study whether impaired muscle cell differentiation upon Cers1 inhibition is dependent on Cers2, we knocked-down Cers1 alone, or in combination with the knockdown of Cers2. Results show that reduced muscle cell maturation mediated by Cers1KD is rescued by the simultaneous knockdown of Cers2 as shown by gene expression analyses and immunohistochemical validation and quantification. Hence, we believe that reducing Cers1 function during aging might lead to an increase in sphingosine levels as has been shown previously (PMID: 31692231). Increased sphingosine triggers cell apoptosis due to its toxicity (PMID: 12531554). Therefore, channeling accumulating sphingosine towards C24 ceramides may avoid toxicity but, as we show in this manuscript, will reduce the myogenic potential in muscle. However, if also C24 production is blocked by Cers2 inhibition, sphingosine is forced towards the production of other, potentially less toxic or myogenesis-impairing ceramides. We added these new data to the revised manuscript as new Fig 5D-E and new Fig S5G-I.

Reviewer #2 (Public Review):

Summary:

The manuscript by Wohlwend et al. investigates the implications of inhibiting ceramide synthase Cers1 on skeletal muscle function during aging. The authors propose a role for Cers1 in muscle myogenesis and aging sarcopenia. Both pharmacological and AAV-driven genetic inhibition of Cers1 in 18month-old mice lead to reduced C18 ceramides in skeletal muscle, exacerbating age-dependent features such as muscle atrophy, fibrosis, and center-nucleated fibers. Similarly, inhibition of the Cers1 orthologue in C. elegans reduces motility and causes alterations in muscle morphology.<br /> Strengths:

The study is well-designed, carefully executed, and provides highly informative and novel findings that are relevant to the field.

Weaknesses:

The following points should be addressed to support the conclusions of the manuscript.

(1) It would be essential to investigate whether P053 treatment of young mice induces age-dependent features besides muscle loss, such as muscle fibrosis or regeneration. This would help determine whether the exacerbation of age-dependent features solely depends on Cers1 inhibition or is associated with other factors related to age- dependent decline in cell function. Additionally, considering the reported role of Cers1 in whole-body adiposity, it is necessary to present data on mice body weight and fat mass in P053treated aged-mice.

We thank the reviewer to suggest that we study Cers1 inhibition in young mice. In fact, a previous study shows that muscle-specific Cers1 knockout in young mice impairs muscle function (PMID: 31692231). Similar to our observation, these authors report reduced muscle fiber size and muscle force. Therefore, we do not believe that our observed effects of Cers1 inhibition in aged mice are specific to aging, although the phenotypic consequences are accentuated in aged mice. As requested by the reviewer, we attached the mice body weights and fat mass (Author response image 1A-B). The reduced fat mass upon P053 treatment is in line with previously reported reductions in fat mass in chow diet or high fat diet fed young mice upon Cers1 inhibition (PMID: 30605666, PMID: 30131496), again suggesting that the effect of Cers1 inhibition might not be specific to aging.

Author response image 1.

(A-B) Body mass (A) and Fat mass as % of body mass (B) were measured in 22mo C57BL/6J mice intraperitoneally injected with DMSO or P053 using EchoMRI (n=7-12 per group). (C-D) Grip strengh measurements in all limbs (C) or only the forelimbs (D) in 24mo C57BL/6J mice intramuscularly injected with AAV9 particles containing scramble, or shRNA targeting Cers1 (n=8 per group). (E-F) Pax7 gene expression in P053 or AAV9 treated mice (n=6-7 per group) (E), or in mouse C2C12 muscle progenitor cells treated with 25nM scramble or Cers1 targeting shRNA (n=8 per group) (F). (G) Proliferation as measured by luciferase intensity in mouse C2C12 muscle muscle cells treated with 25nM scramble or Cers1 targeting shRNA (n=24 per group). Each column represents one biological replicate. (H) Overlayed FACS traces of Annexin-V (BB515, left) and Propidium Iodide (Cy5, right) of mouse C2C12 muscle myotubes treated with 25nM scramble or Cers1 targeting shRNA (n=3 per group). Quantification right: early apoptosis (Annexin+-PI-), late apoptosis (Annexin+-PI+), necrosis (Annexin--PI+), viability (Annexin--PI-). (I) Normalized Cers2 gene expression in mouse C2C12 muscle muscle cells treated with 25nM scramble or Cers1 targeting shRNA (n=6-7 per group). (J-K) Representative mitochondrial respiration traces of digitonin-permeablized mouse C2C12 muscle muscle cells treated DMSO or P053 (J) with quantification of basal, ATP-linked, proton leak respiration as well as spare capacity and maximal capacity linked respiration (n=4 per group). (L) Reactive oxygen production in mitochondria of mouse C2C12 muscle muscle cells treated DMSO or P053. (M) Enriched gene sets related to autophagy and mitophagy in 24mo C57BL/6J mouse muscles intramuscularly injected with AAV9 particles containing scramble, or shRNA targeting Cers1 (left), or intraperitoneally injected with DMSO or P053 (right). Color gradient indicates normalized effect size. Dot size indicates statistical significance (n=6-8 per group). (N) Representative confocal Proteostat® stainings with quantifications of DMSO and P053 treated mouse muscle cells expressing APPSWE (top) and human primary myoblasts isolated from patients with inclusion body myositis (bottom). (O) Stillness duration during a 90 seconds interval in adult day 5 C. elegans treated with DMSO or 100uM P053. (P) Lifespan of C. elegans treated with DMSO or P053. (n=144-147 per group, for method details see main manuscript page 10).

(2) As grip and exercise performance tests evaluate muscle function across several muscles, it is not evident how intramuscular AAV-mediated Cers1 inhibition solely in the gastrocnemius muscle can have a systemic effect or impact different muscles. This point requires clarification.

The grip strength measurements presented in the manuscript come from hindlimb grip strength, as pointed out in the Methods section. We measured grip strength in all four limbs, as well as only fore- (Author response image 1C-D). While forelimb strength did not change, only hindlimb grip strength was significantly different in AAV-Cers1KD compared to the scramble control AAV (Fig 3I), which is in line with the fact that we only injected the AAV in the hindlimbs. This is similar to the effect we observed with our previous data where we saw altered muscle function upon IM AAV delivery in the gastrocnemius (PMID: PMID: 34878822, PMID: 37118545). The gastrocnemius likely has the largest contribution to hindlimb grip strength given its size, and possibly even overall grip strength as suggested by a trend of reduced grip strength in all four limbs (Author response image 1C). We also suspect that the hindlimb muscles have the largest contribution to uphill running as we could also see an effect on running performance. While we carefully injected a minimal amount of AAV into gastrocnemius to avoid leakage, we cannot completely rule out that some AAV might have spread to other muscles. We added this information to the discussion of the manuscript as a potential limitation of the study.

(3) To further substantiate the role of Cers1 in myogenesis, it would be crucial to investigate the consequences of Cers1 inhibition under conditions of muscle damage, such as cardiotoxin treatment or eccentric exercise.<br /> While it would be interesting to study Cers1 in the context of muscle regeneration, and possibly mouse models of muscular dystrophy, we think such work would go beyond the scope of the current manuscript.

(4) It would be informative to determine whether the muscle defects are primarily dependent on the reduction of C18-ceramides or the compensatory increase of C24-ceramides or C24-dihydroceramides.

To improve mechanistic insights, as suggested by Reviewer #2, we performed more experiments to gain insights how Cers1 derived c18, and Cers2 derived c24 ceramide species affect myogenesis. We recently showed that knocking out Cers2 reduces c24:0/c24:1 and promotes muscle cell maturation (PMID: 37118545, Fig. 6m-r and Supplementary Fig. 5e). This suggests that the very long chain ceramides c24 might indeed be driving the effect we see upon Cers1 inhibition because we observe an accumulation of c24 ceramides upon Cers1 (c18) inhibition (Fig 2B, Fig 3B, Fig 4A, Fig S3E), which is associated with impaired muscle maturation (Fig 4B-C, Fig S3G-I, Fig S4G-I). To study whether impaired muscle cell differentiation upon Cers1 inhibition is dependent on Cers2, we knocked-down Cers1 alone, or in combination with the knockdown of Cers2. Results show that reduced muscle cell maturation mediated by Cers1KD is rescued by the simultaneous knockdown of Cers2 as shown by gene expression analyses and immunohistochemical validation and quantification. We added these data to the manuscript as new Fig 5D-E, new Fig S5G-I. These data, together with our previous results showing that Degs1 knockout reduces myogenesis (PMID: 37118545, Fig. 6s-x and Fig. 7) suggest that C24/dhC24 might contribute to the age-related impairments in myogenesis. We added the new results to the revised manuscript.

(5) Previous studies from the research group (PMID 37118545) have shown that inhibiting the de novo sphingolipid pathway by blocking SPLC1-3 with myriocin counteracts muscle loss and that C18-ceramides increase during aging. In light of the current findings, certain issues need clarification and discussion. For instance, how would myriocin treatment, which reduces Cers1 activity because of the upstream inhibition of the pathway, have a positive effect on muscle? Additionally, it is essential to explain the association between the reduction of Cers1 gene expression with aging (Fig. 1B) and the age-dependent increase in C18-ceramides (PMID 37118545).

Blocking the upstream enzyme of the ceramide pathway (SPT1) shuts down the entire pathway that is overactive in aging, and therefore seems beneficial for muscle aging. While most enzymes in the ceramide pathway that we studied so far (SPTLC1, CERS2) revealed muscle benefits in terms of myogenesis, inflammation (PMID: 35089797; PMID: 37118545) and muscle protein aggregation (PMID: 37196064), the CERS1 enzyme shows opposite effects. This is also visible in the direction of CERS1 expression compared to the other enzymes in one of our previous published studies (PMID: 37118545, Fig. 1e and Fig. 1f). In the current study, we show that Cers1 inhibition indeed exacerbates age-related myogenesis and inflammation as opposed to the inhibition of Sptlc1 or Cers2. As the reviewer points out, both C18- and C24-ceramides seem to accumulate upon muscle aging. We think this is due to an overall overactive ceramide biosynthesis pathway. Blocking C18-ceramides via Cers1 inhibition results in the accumulates C24-ceramides and worsens muscle phenotypes (see reply to question #4). On the other hand, blocking C24-ceramides via Cers2 inhibition improves muscle differentiation. These observations together with the finding that Cers1 mediated inhibition of muscle differentiation is dependent on proper Cers2 function (new Fig 5D-E, new Fig S5G-I) points towards C24-ceramides as the main culprit of reduced muscle differentiation. Hence, at least a significant part of the benefits of blocking SPTLC1 might have been related to reducing very long-chain ceramides. We believe that reduced Cers1 expression in skeletal muscle upon aging, observed by us and others (PMID: 31692231), might reflect a compensatory mechanism to make up for an overall overactive ceramide flux in aged muscles. Reducing Cers1 function during aging might lead to an increase in sphingosine levels as has been shown previously (PMID: 31692231). Increased sphingosine triggers cell apoptosis due to its toxicity (PMID: 12531554). Therefore, channeling accumulating sphingosine towards C24 ceramides may avoid toxicity but, as we show in this manuscript, will reduce the myogenic potential in muscle. However, if also C24 production is blocked by Cers2 inhibition (new Fig 5E-D, new Fig S5G-I), sphingosine is forced towards the production of other, potentially less toxic, or myogenesis-impairing ceramides. These data are now added to the revised manuscript (see page 7). Details were added to the discussion of the manuscript (see page 8).

Addressing these points will strengthen the manuscript's conclusions and provide a more comprehensive understanding of the role of Cers1 in skeletal muscle function during aging.

Reviewer #1 (Recommendations For The Authors):

The authors identified that genetical and pharmacological inhibition of CERS1, an enzyme implicated in ceramides biosynthesis worsen muscle fibrosis and inflammation during aging.

Even though many of the experiments only confirmed previous published data (ref 21, 11,37,38), which also show a decline of CERS1 in ageing and the generation and characterization of a muscle specific knockout mouse line, the study points out an interesting issue on excluding CERS1 inhibition as a therapeutic strategy for sarcopenia and opens new questions on understanding how inhibition of SPTLC1 (upstream CERS1) have beneficial effects in healthy aging (ref 15 published by the same authors).

Overall, the article it's well written and clear. However, there is a major weakness. The mechanistic insights of how the increased amount of long ceramides (c24) and the decreased of shorter ones (cer c18) might influence muscle mass, force production, fibrosis and inflammation in aged mice have not been addressed. At the present stage the manuscript is descriptive and confirmatory of CERS1 mediated function in preserving muscle mass. The authors should consider the following points:

Comments:

(1) Muscle data

(a) The effect of CERS1 inhibition on myotube formation must be better characterized. Which step of myogenesis is affected? Is stem cell renewal or MyoD replication/differentiation, or myoblast fusion or an increased cell death the major culprit of the small myotubes? Minor point: Figure S1C: show C14:00 level at 200 h; text of Fig S2A and 1F: MRF4 and Myogenin are not an early gene in myogenesis please correct, Fig S2B and 2C: changes in transcript does not mean changes in protein or myotube differentiation and therefore, authors must test myotube formation and myosin expression.

Cers1 inhibition seems to affect differentiation and myoblast fusion. To test other suggested effects we performed more experiments as delineated. Inhibiting Cers1 systemically with the pharmacological inhibitor of Cers1 (P053) or with intramuscular delivery of AAV expressing a short hairpin RNA (shRNA) against Cers1 in mice did not affect Pax7 transcript levels (Author response image 1E). Moreover, we did also not observe an effect of shRNA targeting Cers1 on Pax7 levels in mouse C2C12 muscle progenitor cells (Author response image 1F). To characterize the effect of Cers1 inhibition on muscle progenitor proliferation/renewal, we used scramble shRNA, or shRNA targeting Cers1 in C2C12 muscle progenitors and measured proliferation using CellTiter-Glo (Promega). Results showed that Cers1KD had no significant effect on cell proliferation (Author response image 1G). Next, we assayed cell death in differentiating C2C12 myotubes deficient in Cers1 using FACS Analysis of Annexin V (left) and propidium iodide (right). We found no difference in early apoptosis, late apoptosis, necrosis, or muscle cell viability, suggesting that cell death can be ruled out to explain smaller myotubes (Author response image 1H). These findings support the notion that the inhibitory effect of Cers1 knockdown on muscle maturation are primarily based on effects on myogenesis rather than on apoptosis. Our data in the manuscript also suggests that Cers1 inhibition affects myoblast fusion, as shown by reduced myonucleation upon Cers1KD (Fig S3H right, Fig S5I).

(b) The phenotype of CESR1 knockdown is milder than 0P53 treated mice (Fig S5D and Figure 3F, 3H are not significant) despite similar changes of Cer18:0, Cer24:0, Cer 24:1 concentration in muscles . Why?

Increases in very long chain ceramides were in fact larger upon P053 administration compared to AAVmediated knockdown. For example, Cer24:0 levels increased by >50% upon P053 administration, compared to 20% by AAV injections. Moreover, dhC24:1 increased by 6.5-fold vs 2.5-fold upon P053 vs AAV treatment, respectively. These differences might not only explain the slightly attenuated phenotypes in the AA- treated mice but also underlines the notion that very long chain ceramides might cause muscle deterioration. We believe inhibiting the enzymatic activity of Cers1 (P053) as compared to degrading Cers1 transcripts is a more efficient strategy to reduce ceramide levels. However, we cannot completely rule out multi-organ, systemic effects of P053 treatment beyond its direct effect on muscle. We added these details in the discussion of the revised manuscript (see page 8 of the revised manuscript).

(c) The authors talk about a possible compensation of CERS2 isoform but they never showed mRNA expression levels or CERS2 protein levels aner treatment. Is CERS2 higher expressed when CERS1 is downregulated in skeletal muscle?

We appreciate the suggestion of the reviewer. We found no change in Cers2 mRNA levels upon Cers1 inhibition in mouse C2C12 myoblasts (Author response image 1I). We would like to point out that mRNA abundance might not be the optimal measurement for enzymes due to enzymatic activities. Therefore, we think metabolite levels are a better proxy of enzymatic activity. It should also be pointed out that “compensation” might not be an accurate description as sphingoid base substrate might simply be more available upon Cers1KD and hence, more substrate might be present for Cers2 to synthesize very long chain ceramides. This “re-routing” has been previously described in the literature and hypothesized to be related to avoid toxic (dh)sphingosine accumulation (PMID: 30131496). Therefore, we changed the wording in the revised manuscript to be more precise.

(d) Force measurement of AAV CERS1 downregulated muscles could be a plus for the study (assay function of contractility)

In the current study we measured grip strength in mice, which had previously been shown to be a good proxy of muscle strength and general health (PMID: 31631989). Indeed, our results of reduced muscle grip strength are in line with previous work that shows reduced contractility in muscles of Cers1 deficient mice (PMID: 31692231).

(e) How are degradation pathways affected by the downregulation of CERS1. Is autophagy/mitophagy affected? How is mTOR and protein synthesis affected? There is a recent paper that showed that CerS1 silencing leads to a reduction in C18:0-Cer content, with a subsequent increase in the activity of the insulin pathway, and an improvement in skeletal muscle glucose uptake. Could be possible that CERS1 downregulation increases mTOR signalling and decreases autophagy pathway? Autophagic flux using colchicine in vivo would be useful to answer this hypothesis

Cers1 in skeletal muscle has indeed been linked to metabolic homeostasis (see PMID: 30605666). In line with their finding in young mice we also find reduced fat mass upon P053 treatment in aged mice (Author response image 1A-B). We also looked into mitochondrial bioenergetics upon blocking Cers1 with P053 treatment using an O2k oxygraphy (Author response image 1J-L). Results show that Cers1 inhibition in mouse muscle cells increases mitochondrial respiration, similar to what has been shown before (PMID: 30131496). However, we also found that reactive oxygen species production in mouse muscle cells is increased upon P053 treatment, suggesting the presence of dysfunctional mitochondria upon inhibiting Cers1 with P053.We next looked into the mitophagy/autophagy degradation pathways suggested by the reviewer and do not find convincing evidence supporting that Cers1 has a major impact on autophagy or mitophagy derived gene sets in mice treated with shRNA against Cers1, or the Cers1 pharmacological inhibitor P053 (Author response image 1M).

We then assessed the effect of Cers1 inhibition on transcripts levels related to the mTORC1/protein synthesis, as suggested by the reviewer. Cers1 knockdown in differentiating mouse muscle cells showed only a weak trend to reduce mTORC1 and its downstream targets (new Fig S4A). In line with this, there was no notable difference in protein synthesis in differentiating, Cers1 deficient mouse C2C12 myoblasts as assessed by L-homopropargylglycine (HPG) amino acid labeling using confocal microscopy (new Fig S4B) or FACS analyses (new Fig S4C). However, Cers1KD increased transcripts related to the myostatin-Foxo1 axis as well as the ubiquitin proteasome system (e.g. atrogin-1, MuRF1) (new Fig S4D), suggesting Cers1 inhibition increases protein degradation. We added these details to the revised manuscript on page 7. We recently implicated the ceramide pathway in regulating muscle protein homeostasis (PMID: 37196064). Therefore, we assessed the effect of Cers1 inhibition with the P053 pharmacological inhibitor on protein folding in muscle cells using the Proteostat dye that intercalates into the cross-beta spine of quaternary protein structures typically found in misfolded and aggregated proteins. Interestingly, inhibiting Cers1 further increased misfolded proteins in C2C12 mouse myoblasts expressing the Swedish mutation in APP and human myoblasts isolated from patients with inclusion body myositis (Author response imageure 1N). These findings suggest that deficient Cers1 might upregulate protein degradation to compensate for the accumulation of misfolded and aggregating proteins, which might contribute to impaired muscle function observed upon Cers1 knockdown. Further studies are needed to disentangle the underlying mechanstics.

(f) The balances of ceramides have been found to play roles in mitophagy and fission with an impact on cell fate and metabolism. Did the authors check how are mitochondria morphology, mitophagy or how dynamics of mitochondria are altered in CERS1 knockdown muscles? (fission and fusion). There is growing evidence relating mitochondrial dysfunction to the contribution of the development of fibrosis and inflammation.

Previously, CERS1 has been studied in the context of metabolism and mitochondria (for reference, please see PMID: 26739815, PMID: 29415895, PMID: 30605666, PMID: 30131496). In summary, these studies demonstrate that C18 ceramide levels are inversely related to insulin sensitivity in muscle and mitochondria, and that Cers1 inhibition improves insulin-stimulated suppression of hepatic glucose production and reduced high-fat diet induced adiposity. Moreover, improved mitochondrial respiration, citrate synthase activity and increased energy expenditure were reported upon Cers1 inhibition. Lack of Cers1 specifically in skeletal muscle was also reported to improve systemic glucose homeostasis. While these studies agree on the effect of Cers1 inhibition on fat loss, results on glucose homeostasis and insulin sensitivity differ depending on whether a pharmacologic or a genetic approach was used to inhibit Cers1. The current manuscript describes the effect of CERS1 on muscle function and myogenesis because these were the most strongly correlated pathways with CERS1 in human skeletal muscle (Fig 1C) and impact of Cers1 on these pathways is poorly studied, particularly in the context of aging. Therefore, we would like to refer to the mentioned studies investigating the effect of CERS1 on mitochondria and metabolism.

(2) C.elegans data:

(a) The authors checked maternal RNAi protocol to knockdown lagr-1 and showed alteration of muscle morphology at day 5. They also give pharmacological exposure of P053 drug at L4 stage. Furthermore, the authors also used a transgenic ortholog lagr-1 to perform the experiments. All of them were consistent showing a reduced movement. It would be important to show rescue of the muscle phenotype by overexpressing CERS1 ortholog in knockdown transgenic animals.

We used RNAi to knockdown the Cers1 orthologue, lagr-1, in C.elegans. Therefore, we do not have transgenic animals. Overexpressing lagr-1 in the RNAi treated animals would also not be possible as the RNA from the overexpression would just get degraded.

(b) The authors showed data about distance of C.elegans. It would be interesting to specify if body bends, reversals and stillness are affected in RNAi and transgenic Knockdown worms.

As suggested, we measured trashing and stillness as suggested by the reviewer and found reduced trashing (new Fig S5B) and a trend towards an increase in stillness (Author response image 1O) in P053 treated worms on day 5 of adulthood, which is the day we observed significant differences in muscle morphology and movement (Fig 4D-E, Fig S5A). These data are now included in the revised manuscript.

(c) Is there an effect on lifespan extension by knocking down CERS1?

We performed two independent lifespan experiments in C.elegans treated with the Cers1 inhibitor P053 and found reduced lifespan in both replicate experiments (for second replicate, see Author response image 1P). We added these data to the revised manuscript as new Fig 4H.

How do the authors explain the beneficial effect of sptlc1 inhibition on healthy aging muscle? Discuss more during the article if there is no possible explanation at the moment.

We believe that blocking the upstream enzyme of the ceramide pathway (SPT1) shuts down the entire pathway that is overactive in aging, and therefore is more beneficial for muscle aging. Our current work suggests that at least a significant part of Sptlc1-KD benefits might stem from blocking very long chain ceramides. While SPTLC1 and CERS2 revealed muscle benefits in terms of myogenesis, inflammation (PMID: 35089797; PMID: 37118545) and muscle protein aggregation (PMID: 37196064), the CERS1 enzyme shows opposite effects, which is also visible in Fig 1e and Fig 1f of PMID: 37118545. In the current study, we show that Cers1 inhibition indeed exacerbates aging defects in myogenesis and inflammation as opposed to the inhibition of Sptlc1 or Cers2. The fact that the effect of Cers1 on inhibiting muscle differentiation is dependent on the clearance of Cers2-derived C24-ceramides suggests that reducing very long chain ceramides might be crucial for healthy muscle aging. We added details to the discussion.

Author Response

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

eLife assessment

This important study reports a novel measurement for the chemotactic response to potassium by Escherichia coli. The authors convincingly demonstrate that these bacteria exhibit an attractant response to potassium and connect this to changes in intracellular pH level. However, some experimental results are incomplete, with additional controls/alternate measurements required to support the conclusions. The work will be of interest to those studying bacterial signalling and response to environmental cues.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This paper shows that E. coli exhibits a chemotactic response to potassium by measuring both the motor response (using a bead assay) and the intracellular signaling response (CheY phosporylation level via FRET) to step changes in potassium concentration. They find increase in potassium concentration induces a considerable attractant response, with an amplitude larger than aspartate, and cells can quickly adapt (but possibly imperfectly). The authors propose that the mechanism for potassium response is through modifying intracellular pH; they find both that potassium modifies pH and other pH modifiers induce similar attractant responses. It is also shown, using Tar- and Tsr-only mutants, that these two chemoreceptors respond to potassium differently. Tsr has a standard attractant response, while Tar has a biphasic response (repellent-like then attractant-like). Finally, the authors use computer simulations to study the swimming response of cells to a periodic potassium signal secreted from a biofilm and find a phase delay that depends on the period of oscillation.

Strengths:

The finding that E. coli can sense and adapt to potassium signals and the connection to intracellular pH is quite interesting and this work should stimulate future experimental and theoretical studies regarding the microscopic mechanisms governing this response. The evidence (from both the bead assay and FRET) that potassium induces an attractant response is convincing, as is the proposed mechanism involving modification of intracellular pH.

Weaknesses:

The authors show that changes in pH impact fluorescent protein brightness and modify the FRET signal; this measurement explains the apparent imprecise adaptation they measured. However, this effect reduces confidence in the quantitative accuracy of the FRET measurements. For example, part of the potassium response curve (Fig. 4B) can be attributed to chemotactic response and part comes from the pH modifying the FRET signal. Measuring the full potassium response curve of the no-receptor mutants as a control would help quantify the true magnitude of the chemotactic response and the adaptation precision to potassium.

Response: We thank the reviewer for the suggestion. We have now measured the full potassium response curve for the no-receptor mutant (HCB1414-pVS88), as shown in Fig. S4. We characterized the pH effects on CFP and YFP channels at different concentrations of KCl, and the relationship between the ratio of the signal post- to pre-KCl addition and the KCl concentration was established for both channels, as shown in Fig. S4C. The pH-corrected signal after KCl addition for strains with receptors was obtained by dividing the original signal after KCl addition by this ratio at the specific KCl concentration. This was done for both CFP and YFP channels. The pH-corrected responses for the Tar-only and Tsr-only strains are represented by red dots in Fig. 5BC. The recalculated response curve and adaptation curve for the wild-type strain are shown in Fig. S5. The same correction was applied to Fig. 3 as well. We also re-performed the simulations using the corrected dose-response curve and replotted Fig. 6, though the simulation results did not change much.

We have now added a subsection “Revised FRET responses by correcting the pH effects on the brightness of eCFP and eYFP” at line 296 in “Results” to describe this.

The measured response may also be impacted by adaptation. For other strong attractant stimuli, the response typically shows a low plateau before it recovers (adapts). However, in the case of Potassium, the FRET signal does not have an obvious plateau following the stimuli. Do the authors have an explanation for that? One possibility is that the cells may have already partially adapted when the response reaches its minimum, which could indicate a different response and/or adaptation dynamics from that of a regular chemo-attractant? In any case, directly measuring the response to potassium in mutants without adaptation enzymes (CheR, CheB) and with the receptors in different methylation levels would shed more light on the problem.

Response: We appreciate the reviewer’s insightful questions. To observe the low plateau before adaptation, a saturating amount of attractant should be added in a stepwise manner. According to the dose-response curve we measured for potassium, a saturating amount of potassium would be close to 100 mM. In fact, there is a small segment of the low plateau in the step response to 30 mM KCl (Fig. 4C or Fig. S5A). To observe more of this low plateau, we could have used a higher concentration of KCl. However, a stimulation higher than 30 mM KCl will induce substantial physiological changes in the cell, resulting in a significant decrease in fluorescence for both channels (Fig. S7). Therefore, the range of KCl concentration that can be reliably applied in FRET measurements is limited.

The half-time of adaptation at 30 mM KCl was measured to be approximately 80 s, demonstrating a faster adaptation than 0.1 mM MeAsp, which induced a similar magnitude of response. Nevertheless, this is still significantly slower than the time required for medium exchange in the flow chamber, which takes less than 10 s to replace 99% of the medium. Thus, the effect on the measured response magnitude due to adaptation should be small (less than 10%).

We thank the reviewer for the suggestion of measuring the response to potassium in mutants without adaptation enzymes (CheR, CheB) and with the receptors in different methylation levels. However, these mutants are typically less sensitive than the wild-type, exhibiting higher values of K0.5 (Sourjik & Berg, PNAS 99:123, 2002), and thus require an even higher KCl concentration to see the low plateau. Consistent with this, we attempted to measure the response to potassium in a cheRcheB mutant (HCB1382-pVS88). As shown in Fig. R1 below, there is no response to up to 30 mM KCl, suggesting that the sensitive region of the mutant is beyond 30 mM KCl.

The relevant text was added at line 413-424.

Author response image 1.

The response of the cheRcheB mutant (HCB1382-pVS88) to different concentrations of KCl. The blue solid line denotes the original signal, while the red dots represent the pH-corrected signal. The vertical purple (green) dashed lines indicate the moment of adding (removing) 0.01 mM, 0.1 mM, 0.3 mM, 1 mM, 3 mM, 10 mM and 30 mM KCl, in chronological order.

There seems to be an inconsistency between the FRET and bead assay measurements, the CW bias shows over-adaptation, while the FRET measurement does not.

Response: We thank the reviewer for pointing this out. We have now demonstrated that the imprecise adaptation shown in the FRET assay primarily resulted from the pH-induced intensity change of the fluorescent proteins. As shown in Fig. S5A&C, the FRET signal also shows over-adaptation, similar to the bead assay, when we recalculated the response by correcting the CFP and YFP channels.

Now we clarified it at line 315.

The small hill coefficient of the potassium response curve and the biphasic response of the Tar-only strain, while both very interesting, require further explanation since these are quite different than responses to more conventional chemoattractants.

Response: We thank the reviewer for pointing this out. We have now recalculated the pH-corrected results for the dose-response curve (Fig. S5) and the biphasic response of the Tar-only strain (Fig. 5C). The new Hill coefficient is 0.880.14 (meanSD), which is close to the response to MeAsp (1.2) (ref. 46). We suspected that this Hill coefficient of slightly less than 1 resulted from the different responses of Tar and Tsr receptors to potassium.

The Tar-only strain exhibits a repellent response to stepwise addition of low concentrations of potassium less than 10 mM, and a biphasic response above (Fig. 5C). This biphasic response might result from additional pH-effects on the activity of intracellular enzymes such as CheRB and CheA, which may have a different timescale and response from the Tar receptor. We have now added the penultimate paragraph in “Discussion” to talk about the response of the Tar-only strain.

Reviewer #2 (Public Review):

Summary:

Zhang et al investigated the biophysical mechanism of potassium-mediated chemotactic behavior in E coli. Previously, it was reported by Humphries et al that the potassium waves from oscillating B subtilis biofilm attract P aeruginosa through chemotactic behavior of motile P aeruginosa cells. It was proposed that K+ waves alter PMF of P aeruginosa. However, the mechanism was this behaviour was not elusive. In this study, Zhang et al demonstrated that motile E coli cells accumulate in regions of high potassium levels. They found that this behavior is likely resulting from the chemotaxis signalling pathway, mediated by an elevation of intracellular pH. Overall, a solid body of evidence is provided to support the claims. However, the impacts of pH on the fluorescence proteins need to be better evaluated. In its current form, the evidence is insufficient to say that the fluoresce intensity ratio results from FRET. It may well be an artefact of pH change. Nevertheless, this is an important piece of work. The text is well written, with a good balance of background information to help the reader follow the questions investigated in this research work.

In my view, the effect of pH on the FRET between CheY-eYFP and CheZ-eCFP is not fully examined. The authors demonstrated in Fig. S3 that CFP intensity itself changes by KCl, likely due to pH. They showed that CFP itself is affected by pH. This result raises a question of whether the FRET data in Fig3-5 could result from the intensity changes of FPs, but not FRET. The measured dynamics may have nothing to do with the interaction between CheY and CheZ. It should be noted that CFP and YFP have different sensitivities to pH. So, the measurement is likely confounded by the change in intracellular pH. Without further experiments to evaluate the effect of pH on CFP and YFP, the data using this FRET pair is inconclusive.

Response: We thank the reviewer for pointing this out. We have now measured the full potassium response curve for the no-receptor mutant (HCB1414-pVS88), as shown in Fig. S4. We characterized the pH effects on CFP and YFP channels at different concentrations of KCl, and the relationship between the ratio of the signal post- to pre-KCl addition and the KCl concentration was established for both channels, as shown in Fig. S4C. The pH-corrected signal after KCl addition for strains with receptors was obtained by dividing the original signal after KCl addition by this ratio at the specific KCl concentration. This was done for both CFP and YFP channels. The pH-corrected responses for the Tar-only and Tsr-only strains are represented by red dots in Fig. 5BC. The recalculated response curve and adaptation curve for the wild-type strain are shown in Fig. S5. The same correction was applied to Fig. 3 as well. We also re-performed the simulations using the corrected dose-response curve and replotted Fig. 6, though the simulation results did not change much.

We have now added a subsection “Revised FRET responses by correcting the pH effects on the brightness of eCFP and eYFP” at line 296 in “Results” to describe this.

The data in Figure 1 is convincing. It would be helpful to include example videos. There is also ambiguity in the method section for this experiment. It states 100mM KCl was flown to the source channel. However, it is not clear if 100 mM KCl was prepared in water or in the potassium-depleted motility buffer. If KCl was prepared with water, there would be a gradient of other chemicals in the buffer, which confound the data.

Response: We apologize for the ambiguity. The KCl solution used in this work was prepared in the potassium-depleted motility buffer. We have now clarified this at both lines 116 and 497. We now provided an example video, Movie S1, with the relevant text added at line 123.

The authors show that the FRET data with both KCl and K2SO4, and concluded that the chemotactic response mainly resulted from potassium ions. However, this was only measured by FRET. It would be more convincing if the motility assay in Fig1 is also performed with K2SO4.

Response: We thank the reviewer for the suggestion. The aim of comparing the responses to KCl and K2SO4 was to determine the role of chloride ions in the response and to prove that the chemotactic response of E. coli to KCl comes primarily from its response to potassium ions. It is more sensitive to compare the responses to KCl and K2SO4 by using the FRET assay. In contrast, the microfluidic motility assay is less sensitive in revealing the difference in the chemotactic responses, making it difficult to determine the potential role of chloride ions.

Methods:

• Please clarify the promotes used for the constitutive expression of FliCsticky and LacI.

Response: The promoters used for the constitutive expression of LacIq and FliCsticky were the Iq promoter and the native promoter of fliC, respectively (ref. 57).

Now these have been clarified at line 471.

• Fluorescence filters and imaging conditions (exposure time, light intensity) are missing.

Response: Thank you for the suggestion. We have now added more descriptions at lines 535-546: The FRET setup was based on a Nikon Ti-E microscope equipped with a 40× 0.60 NA objective. The illumination light was provided by a 130-W mercury lamp, attenuated by a factor of 1024 with neutral density filters, and passed through an excitation bandpass filter (FF02-438/24-25, Semrock) and a dichroic mirror (FF458-Di02-25x36, Semrock). The epifluorescent emission was split into cyan and yellow channels by a second dichroic mirror (FF509-FDi01-25x36, Semrock). The signals in the two channels were then filtered by two emission bandpass filters (FF01-483/32-25 and FF01-542/32-25, Semrock) and collected by two photon-counting photomultipliers (H7421-40, Hamamatsu, Hamamatsu City, Japan), respectively. Signals from the two photomultipliers were recorded at a sampling rate of 1 Hz using a data-acquisition card installed in a computer (USB-1901(G)-1020, ADlink, New Taipei, Taiwan).

• Please clarify if the temperature was controlled in motility assays.

Response: All measurements in our work were performed at 23 ℃. It was clarified at line 496.

• L513. It is not clear how theta was selected. Was theta set to be between 0 and pi? If not, P(theta) can be negative?

Response: The θ was set to be between 0 and π. This has now been added at line 581.

• Typo in L442 (and) and L519 (Koff)

Response: Thank you. Corrected.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) From the motor measurements the authors find that the CW bias over-adapts to a level larger than prestimulus, but this is not seen in the FRET measurements. What causes this inconsistency? Fig. 2D seems to rule out any change in CheY binding to the motor.

Response: We thank the reviewer for pointing this out. We have now demonstrated that the imprecise adaptation shown in the FRET assay primarily resulted from the pH-induced intensity change of the fluorescent proteins. As shown in Fig. S5A&C, the FRET signal also shows over-adaptation, similar to the bead assay, when we recalculated the response by correcting the CFP and YFP channels.

We now clarified it at line 315.

(2) It would be useful to compare the response amplitude for potassium (Fig. 3C) to a large concentration of both MeAsp and serine. This is a fairer comparison since your work shows potassium acts on both Tar and Tsr. Alternatively, testing a much larger concentration (~10^6 micromolar) at which MeAsp also binds to Tsr would also be useful.

Response: We thank the reviewer for pointing this out. We have now recalculated the response to potassium by correcting the pH-induced effects on fluorescence intensity of CFP and YFP. The response to 30 mM KCl was 1.060.10 times as large as that to 100 μM MeAsp. The aim of the comparison between the responses to potassium and MeAsp was to provide an idea of the magnitude of the chemotactic response to potassium. The stimulus of 100 μM MeAsp is already a saturating amount of attractant and induces zero-kinase activity, thus using a higher stimulus (adding serine or a larger concentration of MeAsp) is probably not needed. Moreover, a larger concentration (~10^6 micromolar) of MeAsp would also induce an osmotactic response.

(3) The fitted Hill coefficient (~0.5) to the FRET response curve is quite small and the authors suggest this indicates negative cooperativity. Do they have a proposed mechanism for negative cooperativity? Have similar coefficients been measured for other responses?

Response: We thank the reviewer for pointing this out. We have now recalculated the pH-corrected results for the dose-response curve (Fig. S5). The new Hill coefficient is 0.880.14 (meanSD), which is close to the response to MeAsp (1.2) (ref. 46). We suspect that this Hill coefficient of slightly less than 1 results from the differing responses of Tar and Tsr receptors to potassium.

(3a) The authors state a few times that the response to potassium is "very sensitive", but the low Hill coefficient indicates that the response is not very sensitive (at least compared to aspartate and serine responses).

Response: We apologize for the confusion. We described the response to potassium as “very sensitive” due to the small value of K0.5. This has now been clarified at line 236.

(3b) Since the measurements are performed in wild-type cells the response amplitude following the addition of potassium may be biased if the cell has already partially adapted. This seems to be the case since the FRET time series does not plateau after the addition of the stimulus. The accuracy of the response curve and hill coefficient would be more convincing if the experiment was repeated with a cheR cheB deficient mutant.

Response: We thank the reviewer for raising these questions. To observe the low plateau before adaptation, a saturating amount of attractant should be added in a stepwise manner. According to the dose-response curve we measured for potassium, a saturating amount of potassium would be close to 100 mM. In fact, there is a small segment of the low plateau in the step response to 30 mM KCl (Fig. 4C or Fig. S5A). To observe more of this low plateau, we could have used a higher concentration of KCl. However, a stimulation higher than 30 mM KCl will induce substantial physiological changes in the cell, resulting in a significant decrease in fluorescence for both channels (Fig. S7). Therefore, the range of KCl concentration that can be reliably applied in FRET measurements is limited.

The half-time of adaptation at 30 mM KCl was measured to be approximately 80 s, demonstrating a faster adaptation than 0.1 mM MeAsp, which induced a similar magnitude of response. Nevertheless, this is still significantly slower than the time required for medium exchange in the flow chamber, which takes less than 10 s to replace 99% of the medium. Thus, the effect on the measured response magnitude due to adaptation should be small (less than 10%).

We thank the reviewer for the suggestion of measuring the response to potassium in mutants without adaptation enzymes (CheR, CheB) and with the receptors in different methylation levels. However, these mutants are typically less sensitive than the wild-type, exhibiting higher values of K0.5 (ref. 46), and thus require an even higher KCl concentration to see the low plateau. Consistent with this, we attempted to measure the response to potassium in a cheRcheB mutant (HCB1382-pVS88). As shown in Fig. R1, there is no response to up to 30 mM KCl, suggesting that the sensitive region of the mutant is beyond 30 mM KCl.

The relevant text was added at line 413-424.

(4) The authors show that the measured imprecise adaptation can be (at least partially) attributed to pH impacting the FRET signal by changing eCFP and eYFP brightness.

(4a) Comparing Fig. 5C and D, the chemosensing and pH response time scales look similar. Therefore, does the pH effect bias the measured response amplitude (just as it biases the adapted FRET level)?

Response: We agree with the reviewer that the pH effect on CFP and YFP biases the measured response amplitude. We have now performed the measurement of dose-response curve to potassium for the no-receptor mutant (HCB1414-pVS88), as shown in Fig. S4. The pH effects on CFP and YFP were corrected. The dose-response curve and adaptation curve were recalculated and plotted in Fig. S5.

(4b) It would help to measure a full response curve (at many concentrations) for the no-receptor strain as a control. This would help distinguish, as a function of concentration, how much response can be attributed to pH impacting the FRET signal versus the true chemotactic response.

Response: We thank the reviewer for the suggestion. We have now performed the measurements for the no-receptor strain. The impact of pH on CFP and YFP has been corrected. The pH-corrected results, previously in Fig.3-5, are now presented in Fig. 3, Fig. S5 and Fig. 5, respectively.

(5) The biphasic response of Tar is strange and warrants further discussion. Do the authors have any proposed mechanisms that lead to this behavior? For the 10mM and 30mM KCl measurements there is a repellent response followed by an attractant response for both adding and removing the stimuli, why is this?

Response: We thank the reviewer for pointing this out. The Tar-only strain exhibits a repellent response to stepwise addition of low concentrations of potassium less than 10 mM, and a biphasic response above (Fig. 5C). This biphasic response might result from additional pH-effects on the activity of intracellular enzymes such as CheRB and CheA, which may have a different timescale and response from the Tar receptor. We have now added the penultimate paragraph in “Discussion” to talk about the response of the Tar-only strain.

(5a) The fact that Tar and Tsr are both attractant (after the initial repellant response in Tar) appears to be inconsistent with previous work on pH response (Ref 52, Yang and Sourjik Molecular Microbiology (2012) 86(6), 1482-1489). This study also didn't see any biphasic response.

Response: We thank the reviewer for pointing this out. The Tar-only strain shows a repellent response to stepwise addition of low concentrations of potassium, specifically less than 10 mM. This is consistent with previous observations of the response of Tar to changes in intracellular pH (refs. 44,45) and also with the work of Yang and Sourjik (new ref. 53), although the work in ref. 53 dealt with the response to external pH change, and bacteria were known to maintain a relatively stable intracellular pH when external pH changes (Chen & Berg, Biophysical Journal (2000) 78:2280-2284). Interestingly, the Tar-only strain exhibits a biphasic response to high potassium concentrations of 10 mM and above. This biphasic response might result from additional pH-effects on the activity of intracellular enzymes such as CheRB and CheA (ref. 56), which may have a different timescale and response from the Tar receptor. We have now added the penultimate paragraph in “Discussion” to talk about the response of the Tar-only strain.

(5b) The response of Tar to the removal of sodium benzoate (Fig. S2) seems to be triphasic, is there any explanation for this?

Response: We thank the reviewer for pointing this out. We have now acknowledged in the legend of Fig. S2 that this response is interesting and warrants further exploration: “The response to the removal of sodium benzoate seems to be a superposition of an attractant and a repellent response, the reason for which deserves to be further explored.”

(6) Fitting the MWC model leads to N=0.35<1. It is fine to use this as a phenomenological parameter, but can the authors comment on what might be causing such a small effective cluster size for potassium response?

Response: We thank the reviewer for pointing this out. We have now recalculated the pH-corrected results for the dose-response curve (Fig. S5). The new Hill coefficient is 0.880.14 (meanSD), which is close to the response to MeAsp (1.2) (ref. 46). We now refit the MWC model to the pH-corrected dose-response curve, obtaining N of 0.85. We think the small N is due partly to the fact that we are fitting the curve with four parameters: N, Kon, Koff, and fm, while only three features of the sigmoid does-response curve are relevant (the vertical scale, the midpoint concentration, and the slope of the sigmoid). Future experiments may determine these parameters more accurately, but they should not significantly affect the simulation results as long as the wild-type dose-response curve is accurate.

(7) The results of the modeling are closely related to Zhu et. al. Phys. Rev. Lett. 108, 128101. Is the lag time for large T related to the adaptation time?

Response: We thank the reviewer for pointing this out. We used a similar framework of modeling as Zhu et. al. The potassium response was also analogous to the chemotactic response to MeAsp. Thus, the results are closely related to Zhu et al. We have now cited Zhu et al. (Ref. 52) and noted this at line 366.

The lag time for large T is related to the adaptation time. We have now simulated the chemotaxis to potassium for large T with different adaptation time by varying the methylation rate kR. The results are shown in Fig. S8. The simulated lag time decreases with the methylation rate kR, but levels off at high values of kR. Now this has been added at line 603.

Minor issues:

• Fig. 1C: should the axis label be y?

Response: Yes, thank you. Now corrected.

• Line 519: Koff given twice, the second should be Kon.

Response: Thank you. Corrected.

• When fitting the MWC model (Eq. 3 and Fig. 6B) did you fix a particular value for m?

Response: m was treated as a fitting parameter, grouped in the parameter fm.

Reviewer #2 (Recommendations For The Authors):

Minor points: - I suggest explaining the acronyms when they first appear in the text (eg CMC, CW, CCW).

Response: Thank you. Now they have been added.

• L144. L242. "decrease" is ambiguous since membrane potential is negative. I understand the authors meant less negative (which is an increase). I suggest to avoid this expression.

Response: Thank you for the suggestion. Now they have been replaced by “The absolute value of the transmembrane electrical potential will decrease”.

• For Fig 1b - it says the shaded area is SEM in the text, but SD in the legend. Please clarify.

Response: Thank you. The annotation in the legend has now been revised as SEM.

• Fig 1C label of x axis should be "y" instead of "x" to be consistent with Fig 1A.

Response: Thank you. It has now been revised.

• In Figure 2, the number of independent experiments as well as the number of samples should be included.

Response: Thank you. The response in Fig. 2C is the average of 83 motors from 5 samples for wild-type strain (JY26-pKAF131). The response in Fig. 2D is the average of 22 motors from 4 samples for the chemotaxis-defective strain (HCB901-pBES38). They have now been added to the legend.

• Regarding the attractant or repelling action of potassium and sucrose, it would be important to have a move showing the cells' behaviours.

Response: We thank the reviewer for the suggestion. We have now provided Movie S1 to show the cells’ behavior to potassium. As shown in Fig. 3B, the chemotactic response to 60 mM sucrose is very small compared to the response to 30 mM KCl. This implies that a noticeable response to sucrose necessitates higher concentrations of stimulation. However, Jerko et al. [Rosko, J., Martinez, V. A., Poon, W. C. K. & Pilizota, T. Proc. Natl Acad. Sci. USA 114, E7969-E7976 (2017).] have shown that high concentrations of sucrose lead to a significant reduction in the speed of the flagella motor. Thus, in a motility assay for sucrose, the osmolarity-induced motility effect may overwhelm the minor repellent-like response.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Weaknesses:

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

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

Reviewer #2 (Public Review):

Weaknesses:

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

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

Main points:

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

Weaknesses:

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

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

Reviewer #4 (Public Review):

Weaknesses:

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

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

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

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

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

Suggestions:

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

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

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

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

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

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

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

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

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

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

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

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

Figure 2 has been redone.

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

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

Reviewer #2 (Recommendations For The Authors):

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

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

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

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

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

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

It is incorporated.

Reviewer #3 (Recommendations For The Authors):

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

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

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

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

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

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

Reviewer #4 (Recommendations For The Authors):

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

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

Figure 2-Source Data 1 has been corrected.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(5) Minor concerns:

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

They are now corrected.

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

Yes.

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

Corrected

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

This figure has been deleted.

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

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

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

Colors have been added to the legend.

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

This is now added in the legend.

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

This has been updated to Figure 5.

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

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

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

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

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

This sentence is now changed in the discussion.

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

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

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

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Reply to the reviewers

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

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

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

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

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

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

Minor Comments

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

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

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

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

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

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

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

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

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

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

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

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

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

We have now removed this statement as suggested.

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

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

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

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

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

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

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

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

Figure S3b - scale bar

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Significance (Required)):

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

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

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

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

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

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

Major comments:

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

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

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

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

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

Minor comments:

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

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

Reviewer #2 (Significance (Required)):

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

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

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

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

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

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

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

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

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

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

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

Minor comments: -

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Significance (Required)):

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

Author Response

Reviewer #1 (Public Review):

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Author Response

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

(1) BioRxiv version history.

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

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

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

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

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

(2) Statistical analysis.

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

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

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

(3) Carry-over effects

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

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

(4) Sensation and Blinding

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

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

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

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Reply to the reviewers

Manuscript number: RC-2024-02352

Corresponding author(s): Elise, Belaidi

1. General Statements

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

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

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

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

2. Description of the planned revisions

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

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

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

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

Alternatively, why not use IHC for subcellular localization?

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

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

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

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

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

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

Figure to see on the downloaded file.

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

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

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

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

We plan to:

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

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

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

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

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

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

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

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

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

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

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

Please, see the figure on the dowloaded file

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

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

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

Please see the figure on the dowloaded file

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

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

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

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

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

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

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

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

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

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

Please, see the figure on the dowloaded file

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

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

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

Include normoxia data for the a2 KO mice studies.

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

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

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

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

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

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

• *

Please find below the answers or the revisions that have already been incorporated in response to the comments of the reviewer 3. They appear in red in the new manuscript except the modifications performed in the “references” section which appear in black.

1 The authors used H9c2 rat cardiac cells in vitro experiments although they used mouse model in vivo experiments. Using mouse P19.CL6 cardiac cells instead of rat H9c2 cells may much clearer. Why the authors did not use P19.CL6 cells should be explained.

We thank the reviewer for his/her suggestion. P19CL6 cell line has been isolated from pluripotent P19 embryonal carcinoma (EC) cells after long term culture under conditions for mesodermal differentiation (Habara-Ohkubo A, Cell Struc Funct 1996). Therefore, these cells are mostly used to ____study the differentiation of cardiac muscle. Indeed, they were recognized to avoid large variations in the differentiation rates which were extensively reported (Mueller I, J Biomed Biotechnol 2010). To our knowledges no ventricular non-beating mice cell line. In this study, we used H9c2 which are extensively used and recognized as a gold standard cellular model to study the biology of cardiomyocytes including mechanisms involved in cardiac ischemia-reperfusion injury (Paillard M, Circulation, 2013; Zhang G Circulation 2021__), cardiac hypertrophy__ (Zhang N, Cell Death Diff 2020; Hu H, Cardiovasc Res 2020), intra-organites calcium exchanges (Moulin S, Antioxydants, 2022, Paillard M, Circulation, 2013) as drug testing (Beshay NM, J Pharm and Tox Methods, 2007). Recently, H9c2 and P19.CL6 were exposed to intermittent hypoxia (70 cycles of FiO2 1% (5 min) - FiO2 21% (10%)) in order to “mimic OSA” and investigate the transcription level of a pool of genes. The authors show some similiraties and differences of mRNA expression between the two cell lines that, indeed, could be attributed to variations in the cell origin (Takasawa S, IJMS 2022). However, in this study, there are no experiment allowing to assess the state of cardiac cells (apoptosis, life, metabolism, remodeling) questioning the pathophysiologic transposability of the model. Moreover, the number of experiments conducted on H9C2 (pubmed references : 7000 vs 100 for P19.CL6) to understand the mechanism involved in acute and chronic cardiac pathologies makes our choice confident and relevant.

2 The authors described, "The protocol was approved by the French minister (APAFIS#23725-2020012111137561.v2)." in Animals (page 16) without showing approval date, the authors should clearly show the approval date together with their approval numbers.

We added the approvement date in the materials and methods section. It was approved on February 20, 2020.

3 In Figure 2 L, scale bar(s) should be added because figures are magnified and/or reduced by printer.

We agree with the reviewer that scale bars were not visible; we highlighted them.

4 In Figure 3J and 3N, scale bar(s) should be added.

Scale bars have been now added on figures 3J and 3N. All pictures were acquired at the maximal zoom of a camera placed at an equal distance from the slices. Then, analyses were performed, slice per slice, with Image J with the same zoom (x5 to get an image at 100%). In this context, scale was added based on a photo of slice taken close to a ruler.

5 In introduction, "HIF-1" should be changed to "hypoxia-inducible factor 1 (HIF-1)".

Thank you, we replaced HIF-1 by Hypoxia Inducible Factor-1 (HIF-1) in the introduction section.

6 In Results, "Angpt1, Txnip, Nmrk2, Nuak1 or Pfkfb1" should be changed to "Angiopoietin 1 (Angpt1), Thioredoxin-interacting protein (Txnip), Nicotinamide riboside kinase 2 (Nmrk2), NUAK family SNF1-like kinase 1 (Nuak1) or 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkb1)".

We did the modification and let the abbreviation in italic since it concerns genes name.

7 In Chronic intermittent hypoxia, "FiO2" should be changed to "FiO2".

Thank you, we changed it in the materials and methods section.

8 In Western-blot, "Bio-Rad, California, USA" should be changed to "Bio-Rad, Hercules, CA".

Thank you, we did it.

9 In Western-blot, what "tubulin" (α-tubulin or β-tubulin) should be clarified.

We agree with the reviewer that this point should be specified; α-tubulin was stained, we added the “α”.

As mentioned below, we have done all the modifications required by the reviewer in the references section.

10 In Ref. 8, "Antioxidants (Basel) 11 (2022)" should be changed to "Antioxidants (Basel) 11, 2326 (2022)".

Done

11 In Ref.10, "Pharmacol Ther (2016)" should be changed to "Pharmacol Ther 168, 1-11 (2016)".

Done

12 In Ref.13, "Antioxidants (Basel) 11 (2022)" should be changed to "Antioxidants (Basel) 11, 1462 (2022).

Done

13 In Ref. 21, "Diabetes (2017)" should be changed to "Diabetes 66, 2942-2951 (2017)".

Done

14 In Ref. 28, "Adv Biol (Weinh), e2300292 (2023)" should be changed to "Adv Biol (Weinh), 8, 2300292 (2023)".

Done

15 In Ref. 37, "J Am Heart Assoc 6 (2017)" should be changed to "J Am Heart Assoc 6, e006680 (2017)".

Done

16 In Ref. 41, "Eur Respir Rev 32 (2023)" should be changed to "Eur Respir Rev 32, 230083 (2023)".

Done

17 In Ref. 46, "Int J Mol Sci 22 (2020)" should be changed to "Int J Mol Sci 22, 268 (2021)".

Done

18 In Ref. 47, "Int J Mol Sci 21 (2020)" should be changed to "Int J Mol Sci 21, 2428 (2020)". Done

4. Description of analyses that authors prefer not to carry out

__Please find below the answers to the comment of the reviewer 3 that we cannot provide and that is not in the scope of the study. __

• *

Figure 5, multiple phosphorylation sites have been identified on HIF1a. What is the nature of the Thr/SerP-HIF1a antibody? It would be far more preferable (essential?) to identify the site(s) within HIF1a that are phosphorylated by AMPK.

The antibody was provided by Cell Signalling, ref. 9631. Phospho-(Ser/Thr) Phe Antibody detects phospho-serine or threonine in the context of tyrosine, tryptophan or phenylalanine.

The identification of the Phosphorylation sites will require a long-time consuming phosphoproteomic analysis and subsequent functional validation in vivo and in vitro (directed mutagenesis, knock-in mice, …) which are out of the scope of our paper.

The document provides a fascinating overview of the technologies available to monitor personal health and sports performance, showing how the sector is rapidly evolving towards increasingly intelligent and personalized solutions. Exploring a wide range of devices and mobile applications designed to record and analyze physiological and biomechanical parameters, we highlight how we are moving from simple measurement of standard vital signs to sophisticated predictive algorithms based on population information and artificial intelligence.

Personally, I believe it is crucial to highlight the importance of independent validation of these technologies. It is worrying to know that many of them have not yet been adequately tested, raising questions about their effectiveness and usefulness for the average consumer. In a world where we are increasingly surrounded by data and devices, it is essential that the technologies we choose to use are reliable and based on solid scientific evidence.

Analyzing specific devices such as the Zephyr Sensor, E4 Wristband, and Reign Active Recovery Band offers a detailed look at their capabilities and potential benefits. Furthermore, the reference to the Mio SLICE™ bracelet, with its "Personal Activity Score" calculated via an algorithm, is particularly interesting. The clinical study that demonstrated a reduction in the risk of cardiovascular disease for those with a score ≥100 is tangible evidence of the benefits these technologies can offer when tested properly.

Finally, discussion of sleep and cardiorespiratory monitoring technologies, such as the OURA ring and Fitbit Charge2™, highlights current challenges and limitations. Personally, I think sleep tracking is one of the most interesting aspects of these technologies, as rest is critical to overall health and physical performance. However, it is important to be aware of limitations in the precision and interpretation of the data collected, as this may affect our confidence in the results provided.

Overall, the document offers a comprehensive overview of an ever-expanding sector. As technologies for monitoring health and sports performance continue to evolve, it is essential to stay informed about their potential and limitations. Innovation is exciting, but careful research and development is essential to ensure that these technologies are truly useful and reliable for improving our lives and performance.

Author Response

The following is the authors’ response to the original reviews.

Response to Public Reviewer Comments

We again thank the reviewers for the time and effort they clearly put into reviewing our manuscript. We have revised our manuscript to take into account the majority of their suggestions, primary among them being refinements of our model and classification approach, detailed sensitivity analysis of our model, and several new simulations. Their very constructive feedback has resulted in what we feel is a much-improved paper. In what follows, we respond to each of their points.

Reviewer #1:

COMMENT: The reviewer suggested that our control policy classification thresholds should be increased, especially if the behavioral labels are to be subsequently used to guide analyses of neural data which “is messy enough, but having trials being incorrectly labeled will make it even messier when trying to quantify differences in neural processing between strategies.”

REPLY: We appreciate the observation and agree with the suggestion. In the revised manuscript, we simplified the model (as another reviewer suggested), which allowed for better training of the classifier. This enabled an increase in the threshold to 95% to have more confidence in the identified control strategies. Figures 7 and 8 were regenerated based on the new threshold.

COMMENT: The reviewer asked if we could discuss what one might expect to observe neurally under the different control policies, and also suggested that an extension of this work could be to explore perturbation trials, which might further distinguish between the two control policies.

REPLY: It is indeed interesting to speculate what neural activity could underlie these different behavioral signatures. As this task is novel to the field, it is difficult to predict what we might observe once we examine neural activity through the lens of these control regimes. We hope this will be the topic of future studies, and one aspect worthy of investigation is how neural activity prior to the start of the movement may reflect two different control objectives. Previous work has shown that motor cortex is highly active and specific as monkeys prepare for a cued movement and that this preparatory activity can take place without an imposed delay period (Ames et al., 2014; Cisek & Kalaska, 2005; Dekleva et al., 2018; Elsayed et al., 2016; Kaufman et al., 2014; Lara et al., 2018; Perich et al., 2018; Vyas et al., 2018; Zimnik & Churchland, 2021). It seems possible that the control strategies we observed correspond to different preparatory activity in the motor cortex. We added these speculations to the discussion.

The reviewer’s suggestion to introduce perturbations to probe sensory processing is very good and was also suggested by another reviewer. We therefore conducted additional simulations in which we introduced perturbations (Supplementary Material; Figure S10). Indeed, in these model simulations the two control objectives separated more. However, testing these predictions via experiments must await future work.

COMMENT: “It seems like a mix of lambda values are presented in Figure 5 and beyond. There needs to be some sort of analysis to verify that all strategies were equally used across lambda levels. Otherwise, apparent differences between control strategies may simply reflect changes in the difficulty of the task. It would also be useful to know if there were any trends across time?”

REPLY: We appreciate and agree with the reviewer’s suggestion. We have added a complementary analysis of control objectives with respect to task difficulty, presented in the Supplementary Material (Figures S7 and S8). We demonstrate that, overall, the control objectives remain generally consistent throughout trials and difficulty levels. Therefore, it can be concluded that the difference in behavior associated with different control objectives does not depend on the trial sequence or difficulty of the task. A statement to this extent was added to the main text.

COMMENT: “Figure 2 highlights key features of performance as a function of task difficulty. …However, there is a curious difference in hand/cursor Gain for Monkey J. Any insight as to the basis for this difference?”

REPLY: The apparently different behavior of Monkey J in the hand/cursor RMS ratio could be due to subject-to-subject variability. Given that we have data from only two monkey subjects, we examined inter-individual variations between human subjects in the Supplementary Material by presenting individual hand/cursor gain data for all individual human subjects (Figure S1). As can be seen, there was indeed variability, with some subjects not exhibiting the same clear trend with task difficulty. However, on average, the RMS ratio shows a slight decrease as trials grow more difficult, as was earlier shown in Figure 2. We added a sentence about the possibility of inter-individual variations to address the difference in behavior of monkey J with reference to the supplementary material.

Reviewer #2:

(Reviewer #2's original review is with the first version of the Reviewed Preprint. Below is the authors' summary of those comments.)

COMMENT: The reviewer commends the care and effort taken to characterize control policies that may be used to perform the CST, via dual human and monkey experiments and model simulations, noting the importance of doing so as a precursor to future neural recordings or BMI experiments. But the reviewer also wondered if it is all that surprising that different subjects might choose different strategies: “... it makes sense that different subjects might choose to favor different objectives, and also that they can do so when instructed. But has this taught us something about motor control or simply that there is a natural ambiguity built into the task?”

REPLY: The redundancy in the task that allowed different solutions to achieve the task was deliberate, and the motivation for choosing this task for this study. We therefore did not regard the resulting subject-to-subject variability as a finding of our study. Rather, redundancy and inter-individual variability are features ubiquitous in all everyday actions and we explicitly wanted to examine behavior that is closer to such behavior. As commended by the reviewers, CST is a rich task that extends our research beyond the conventional highly-constrained reaching task. The goal of our study was to develop a computational account to identify and classify such differences to better leverage future neural analyses of such more complex behaviors. This choice of task has now been better motivated in the Introduction of the revised manuscript.

COMMENT: The reviewer asks about our premise that subjects may use different control objectives in different trials, and whether instead a single policy may be a more parsimonious account for the different behavioral patterns in the data, given noise and instability in the system. In support of this view, the reviewer implemented a simple fixed controller and shared their own simulations to demonstrate its ability to generate different behavioral patterns simply by changing the gain of the controller. The reviewer concludes that our data “are potentially compatible with any of these interpretations, depending on which control-style model one prefers.”

REPLY: We first address the reviewer’s concern that a simple “fixed” controller can account for the two types of behavioral patterns observed in Experiment 2 (instructed groups) by a small change in the control gain. We note that our controller is also fixed in terms of the plant, the actuator, and the sensory feedback loop; the only change we explore is in the relative weights of position vs. velocity in the Q matrix. This determines whether it is deviations in position or in velocity that predominate in the cost function. This, in turn, generates changes in the gain vector L in our model, since the optimal solution (i.e. the gains L that minimize the cost function) depends on the Q matrix as well as the dynamics of the plant (specifically, the lambda value). Hence, one could interpret the differences arising from changes in the control objective (the Q matrix) as changes in the gains of our “fixed” controller.

More importantly, while the noise and instability in the system may indeed occasionally result in distinct behavioral patterns (and we have observed such cases in our simulations as well), these factors are far from giving an alternative account for the structural differences in the behavior that we attribute to the control objective. To substantiate this point, we performed additional simulations that are provided in the Supplementary Material (Figures S4—6). These simulations show that neither a change in noise nor in the relative cost of effort can account for the two distinct types of behavior. These differences are more consistently attributed to a change in the control objective.

In addition, our approach provides a normative account of the control gains needed to simulate the observed data, as well as the control objectives that underlie those gains. As such, the two control policies in our model (Position and Velocity Control) resulted in control gains that captured the differences in the experimental groups (Experiment 2), both at the single trial and aggregate levels and across different task difficulties. Figure S9 in the Supplementary Material shows how the control gains differ between Position and Velocity Control in our model across different difficulty levels.

We agree,with the reviewer’s overall point, that there are no doubt many models that can exhibit the variability observed in our experimental data, our simulations, or the reviewer’s simulations. Our study aimed to explore in detail not only the model’s ability to generate the variable behavior observed in experimental data, but also to match experimental results in terms of performance levels, gains, lags and correlations across a wide range of lambda values, wherein the only changes in the model were the lambda value and the control objective. Without the details of the reviewer’s model, we are unable to perform a detailed analysis of that model. Even so, we are not claiming that our model is the ‘ground truth,’ only that it is certainly a reasonable model, adopted from the literature, that provides intuitive and normative explanation about the performance of humans and monkeys over a range of metrics, system dynamics, and experimental conditions.

Finally, we understand the reviewer’s concern regarding whether the trial-by-trial identification of control strategy in Figure 8 suggests that (uninstructed) subjects constantly switch control objectives between Position and Velocity. Although it is not unreasonable to imagine that individuals would intuitively try different strategies between ‘keeping the cursor still’ and ‘keeping the cursor at the center’ across trials, we agree that it is generally difficult to determine such trial-to-trial changes, especially when the behavior lies somewhere in between the two control objectives. In such cases, as we originally discussed in the manuscript, an alternative explanation could be a mixed control objective that generates behavior at the intersection of Position and Velocity Control, i.e., between the two slopes in Figure 8. We believe, however, that our modeling approach is still helpful in cases where performance is predominantly based on Position or Velocity Control. After all, the motivation for this study was to parse neural data into two classes associated with each control objective to potentially better identify structure underlying these behaviors.

We clarified these points in the main text by adding further explanation in the Discussion section.

COMMENT: The reviewer suggested additional experiments, such as perturbation trials, that might be useful to further explore the separability of control objectives. They also suggested that we temper our conclusion that our approach can reliably discriminate amongst different control policies on individual trials. Finally, the reviewer suggested that we modify our Introduction and/or Discussion to note past human/monkey research as well as investigations of minimization of velocity-error versus position-error in the smooth pursuit system.

REPLY: We have expanded our simulations to investigate the effects of perturbation on the separability of different control objectives (Figure S10 in Supplementary Materials). We demonstrated that introducing perturbations more clearly differentiated between Position and Velocity Control. These results provide a good basis for further experimental verifications of the control objectives, but we defer these for future work.

We also appreciate the additional past work that bridges human and monkey research that the reviewer highlights, including the related discussions in the eye movement literature on position versus velocity control. We have modified our Introduction and Discussion accordingly.

Reviewer #3:

COMMENT: The reviewer asked whether the observed differences in behavior might be due to some other factors besides the control policy, such as motor noise or effort cost, and suggested that we more systematically ruled out that possibility.

REPLY: We appreciate and have heeded the reviewer’s suggestion. The revised manuscript now includes additional simulations in which the control objective was fixed to either Position or Velocity Control, while other parameters were systematically varied. Specifically, we examined the influence of the relative effort cost, the sensory delay, and motor noise, on performance. The results of these sensitivity analyses are presented in the Supplementary Material, Figures S4—6. In brief, we found that changing the relative effort cost, delay, or noise levels, mainly affected the success rate in performance (as expected), but did not affect the behavioral features originally associated with control objectives. We include a statement about this result in the main text with reference to the details provided in the Supplementary Material.

COMMENT: The reviewer questioned our choice of classification features (RMS position and velocity) and wondered if other features might yield better class separation, such as the hand/cursor gain. In a similar vein, reviewer 2 suggested in their recommendations that we examine the width of the autocorrelation function as a potentially better feature.

REPLY: We note first that our choice of cursor velocity and position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. However, we also explored other features as suggested. We found that they, too, exhibited overlap between the two different control objectives, and did not provide any significant improvement in classification performance (Figures S2 and S3; Supplementary Materials). Of course, that is not to say that a more exhaustive examination of features may not find ones that yield better classification performance than those we investigated, but that is beyond the scope of our study. We refer to this consideration of alternative metrics in the discussion.

COMMENT: The reviewer notes that “It seems that the classification problem cannot be solved perfectly, at least on a single-trial level.” To address this point, the reviewer suggested that we conduct additional simulations under the two different control objectives, and quantify the misclassifications.

REPLY: We appreciate the reviewer’s suggestion, and have conducted the additional simulations as suggested, the results of which are included in the revised manuscript.

COMMENT: “The problem of inferring the control objective is framed as a dichotomy between position control and velocity control. In reality, however, it may be a continuum of possible objectives, based on the relative cost for position and velocity. How would the problem differ if the cost function is framed as estimating a parameter, rather than as a classification problem?”

REPLY: A blended control strategy, formulated as a cost function that is a weighted combination of position and velocity costs, is indeed a possibility that we briefly discussed in the original manuscript. This possibility arises particularly for individuals whose performance metrics lie somewhere between the purely Position or purely Velocity Control. While our model allows for a weighted cost function, which we will explore in future work, we felt in this initial study that it was important to first identify the behavioral features unique to each control objective.

Response to Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

None beyond those stated above.

Reviewer #2 (Recommendations For The Authors):

COMMENT: Line 166 states "According to equation (1), this behavior was equivalent to reducing the sum (𝑝 + 𝑥) when 𝜆 increased, so as to prevent rapid changes in cursor velocity". This doesn't seem right. In equation 1, velocity (not acceleration) depends on p+x. So a large p+x doesn't create a "rapid change in cursor velocity", but rather a rapid change in cursor position.

REPLY: The reviewer is correct and we have corrected this misworded sentence; thank you for catching that.

COMMENT: The reviewer points out the potential confusion readers may have, given our unclear use of ‘control strategy’ vs. ‘control policy’ vs. ‘control objective’. The reviewer suggests that “It would be helpful if this could be spelled out early and explicitly. 'Control strategy' seems perilously close to 'control policy', and it would be good to avoid that confusion. The authors might prefer to use the term 'cost function', which is really what is meant. Or they might prefer 'control objective', a term that they introduce as synonymous with 'control strategy'.”

REPLY: We thank the reviewer for noting this ambiguity. We have clarified the language in the Introduction to explicitly note that by strategy, we mean the objective or cost function that subjects attempt to optimize. We then use ‘control objective’ consistently and removed the term ‘policy’ from the paper to avoid confusion. We also now use Position Control and Velocity Control as the labels for our two control objectives.

COMMENT: The reviewer notes that in Figure 2B and the accompanying text in the manuscript, we need to be clearer about what is being correlated; namely, cursor and hand position.

REPLY: Thank you for pointing out this lack of clarity, which we have corrected as suggested.

COMMENT: The reviewer questions our attribution of decreasing lag with task difficulty as a consequence of subjects becoming more attentive/responsive when the task is harder, and points out that our model doesn’t include this possible influence yet the model reproduces the change in lag. The reviewer suggests that a more likely cause is due to phase lead in velocity compared to position, with velocity likely increasing with task difficulty, resulting in a phase advance in the response.

REPLY: Our attribution of the decrease in lag with task difficulty being due to attention/motivation was a recapitulation of this point made in the paper by Quick et al. [2018]. But as noted by the reviewer, this potential influence on lag is not included in our model. Accordingly, the change in lag is more likely a reflection of the phase response of the closed loop system, which does change with task difficulty since the optimal gains depend upon the plant dynamics (i.e., the value of lambda). We have, therefore, deleted the text in question.

COMMENT: “The Methods tell us rather a lot about the dynamics of the actual system, and the cost functions are also well defined. However, how they got from the cost function to the controller is not described. I was also a bit confused about the controller itself. Is the 50 ms delay assumed when deriving the controller or only when simulating it (the text seems to imply the latter, which might make sense given that it is hard to derive optimal controllers with a hard delay)? How similar (or dissimilar) are the controllers for the two objectives? Is the control policy (the matrix that multiplies state to get u) quite different, or only subtly?”

REPLY: Thanks for pointing this out. For brevity, we had omitted the details and referred readers to the original paper (Todorov, 2005). However, we now revised the manuscript to now include all the details in the Methods section. Hence, the entire section on the model is new. This also necessitated updating all data figures (Figures 3, 4, 5, 6, 7, 8) as they contain modeling results.

COMMENT: “Along similar lines, I had some minor to moderate confusions regarding the OFC model as described in the main text. Fig 3 shows a model with a state estimator, but it isn't explained how this works. …Here it isn't clear whether there is sensory noise, or a delay. The methods say a delay was included in the simulation (but perhaps not when deriving the controller?). Noise appears to have been added to u, but I'm guessing not to x or x'? The figure legend indicates that sensory feedback contains only some state variables, and that state estimation is used to estimate the rest. Presumably this uses a Kalman filter? Does it also use efference copy, as would be typical? My apologies if this was stated somewhere and I missed it. Either way, it would be good to add a bit more detail to the figure and/or figure legend.”

REPLY: As the lack of detail evidently led to some confusion, we now more clearly spell out the details of the model in the Methods, including the state estimation procedure.

COMMENT: The reviewer wondered why we chose to plot mean velocity vs. mean position as in Figure 5, noting that, “ignoring scale, all scatter plots would be identical if the vertical axis were final position (because mean velocity determines final position). So what this plot is really examining is the correlation between final position and average position. Under position control, the autocorrelation of position is short, and thus final position tends to have little to do with average position. Under velocity control, the autocorrelation of position is long, and thus final position tends to agree with average position. Given this, why not just analyze this in terms of the autocorrelation of position? This is expected to be much broader under velocity control (where they are not corrected) than under position control (where they are, and thus disappear or reverse quickly). To me, thinking of the result in terms of autocorrelation is more natural.”

REPLY: The reviewer is correct that the scatter plots in Fig. 5 would be the same (to within a scale factor of the vertical axis) had we plotted final position vs. mean position instead of mean velocity vs. mean position as we did. Our preference for mean velocity vs. mean position stems from a dynamical systems perspective, where position-velocity phase-space analysis is common. We now mention these perspectives in the revised manuscript for the benefit of the reader.

As suggested, we also investigated the width of the (temporal) autocorrelation function (acf) of cursor position for 200 simulated position control trials and 200 simulated velocity control trials, at four different lambda values (50 simulated trials per lambda). Figs. S2A and B (Supplementary Materials) show example trials and histograms of the acf width, respectively. As the reviewer surmised, velocity control trials tend to have wider acfs than position control trials. However, as with the metrics we chose to analyze, there is overlap and there is no visible benefit for the classification.

COMMENT: “I think equation ten is incorrect, but would be correct if the identity matrix were added? Also, why is the last term of B set to 1/(Tau*M). What is M? Is it mass (which above was lowercase m)? If so, mass should also be included in A (it would be needed in two places in the last column). Or if we assume m = 1, then just ignore mass everywhere, including here and equation 5. Or perhaps I'm confused, and M is something else?”

REPLY: Thanks for pointing this out. The Matrix A shown in the paper is for the continuous-time representation of the model. However, as the reviewer correctly mentioned, for the discrete-time implementation of the model, a modification (identity matrix) was added in our simulations. We have now clarified this in the Methods section of the revised manuscript. Also, as correctly pointed out, M is the mass of the hand, which depending on whether the hand acceleration (d^2 p/dt^2) or hand force (F) are taken as the state, it can be included in the A matrix. In our case, the A matrix is modified according to the state vector. Similarly, the B matrix is also modified. This is now clarified in the Methods section of the manuscript.

Reviewer #3 (Recommendations For The Authors):

COMMENT: “Equations 4-8 are written in continuous time, but Equation 9 is written in discrete time. Then Equation 10 is in discrete time. This needs to be tidied up. … I would suggest being more detailed and systematic, perhaps formulating the control problem in continuous time and then converting to discrete time.”

REPLY: Thank you for this helpful suggestion. The model section in the Methods has been expanded to provide further details of the equation of motion, the discretization process, the control law calculation and the state estimation process.

COMMENT: “It seems slightly odd for the observation to include only position and velocity of the cursor. Presumably participants can also observe the state of their own hand through proprioception (even if it were occluded). How would it affect the model predictions if the other states were observable?”

REPLY: Thanks for pointing this out. We initially included only cursor position and velocity since we felt that was the most prominent state feedback, and the system is observable in that case. Nevertheless, we revised the manuscript and repeated all simulations using a full observability matrix. Our findings and conclusions remain unchanged. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

COMMENT: “It seems unnecessary to include the acceleration of the cursor in the formulation of the model. …the acceleration is not even part of the observed state according to line 668… I think the model could therefore be simplified by omitting cursor acceleration from the state vector.”

REPLY: We agree. We have simplified the model, and generated new simulations and figures. Our results and conclusions were unchanged by this modification. With the changes in the modeling, the figures were also updated (Fig.3, 4, 5, 6, 7, 8).

COMMENT: “In the cost function, it's not clear why any states other than position and velocity of the cursor need to have non-zero values. …The choice to have the cost coefficient for these other states be 1 is completely arbitrary… If the point is that the contribution of these other costs should be negligible, then why not just set them to 0?”

REPLY: We agree, and have made this change in the Methods section. Our findings and conclusions were unaffected.

COMMENT: “It seems that the cost matrices were specified after transforming to discrete-time. It is possible however (and perhaps recommended) to formulate in continuous time and convert to discrete time. This can be done cleanly and quite straightforwardly using matrix exponentials. Depending on the discretization timestep, this can also naturally lead to non-zero costs for other states in the discrete-time formulation even if they were zero under continuous time. … A similar comment applies to discretization of the noise.”

REPLY: Thanks for the suggestion. We have expanded on the discretization process in our Methods section, which uses a common approximation of the matrix exponentiation method.

COMMENT: “Most of the parameters of the model seem to be chosen arbitrarily. I think this is okay as the point is to illustrate that the kinds of behaviors observed are within the scope of the model. However, it would be helpful to provide some rationale as to how the parameters were chosen. e.g. Were they taken directly from prior literature, or were they hand-tuned to approximately match observed behavior?”

REPLY: We have revised the manuscript to more clearly note that the noise parameters, as well as parameters of the mechanical system (mass, muscle force, time scale, etc) in our model were taken from previous publications (Todorov, 2005, Cluff et al. 2019). As described in the manuscript, the parameter values of the cost function (Q matrix) were obtained by tuning the parameters to achieve a similar range of success rate with the model as observed in the experimental data. This is now clarified in the Methods section.

COMMENT: “The ‘true’ cost function for this task is actually a 'well' in position space - zero cost within the screen and very high cost elsewhere. In principle, it might be possible to derive the optimal control policy for this more veridical cost function. It would be interesting to consider whether or not this model might reproduce the observed behaviors.”

REPLY: This is indeed a very interesting suggestion, but difficult to implement based on the current optimal feedback control framework. However, this is interesting to consider in future work.

Minor Comments:

COMMENT: “In Figs 4 and 5, the data points are drawn from different conditions with varying values of lambda. How did the structure of this data depend on lambda? Might it be possible to illustrate in the figure (e.g. the shade/color of each dot) what the difficulty was for each trial?”

REPLY: We performed additional analyses to show the effects of task difficulty on the choice of control objective. Overall, we found that the main behavioral characteristics of the control objective remained fairly unchanged across different task difficulties or across time. The results of this analysis are included in Fig. S7 and S8 of the Supplementary Materials.

COMMENT: “Should mention trial duration (6s) in the main narrative of the intro/results.”

REPLY: We now mention this detail when we describe the task for the first time.

COMMENT: “As an alternative to training on synthetic data (which might not match behavior that precisely, and was also presumably fitted to subject data at some level) it might be worth considering to do a cross-validation analysis, i.e. train the classifier on subsets of the data with one participant removed each time, and classify on the held-out participant.”

REPLY: This is indeed a valid point. The main reason to train the classifier based on model simulations was two-fold: first, to have confidence in the training data, as the experimental data was limited and noisy, which would result in less reliable classifications; and second, the model simulations are available for different contexts and conditions, where experimental data is not necessarily available. The latter is a more practical reason to be able to identify control objectives for any subject (who received no instructions), without having to collect training data from matching control subjects who received explicit instructions. Nonetheless, we appreciate the reviewer’s recommendation and will consider that for our future studies.

COMMENT: “line 690 - Presumably the optimal policy was calculated without factoring in any delay (this would be tricky to do), but the 50ms delay was incorporated at the time of simulation?”

REPLY: The discretization of the system equations allowed us to incorporate the delay in the system dynamics and solve for the optimal controller with the delay present. This was done simply by system augmentation (e.g., Crevecoeur et al., 2019), where the states of the system in the current time-step were augmented with the states from the 5 preceding time-steps to form the new state vector x(t)_aug =[x(t) , x(t-1) , … , x(t-d) ]. Similarly, the matrices A, B, and H from the system dynamics could be expanded accordingly to form the new dynamical system:

$$x(t+1){aug} = A{aug} * x(t){aug} + B{aug} * u$$

Then, the optimal control was implemented on the new (augmented) system dynamics.

We have revised the manuscript (Methods) to clarify this issue.

Author Response

The following is the authors’ response to the previous reviews.

eLife assessment

This important study identifies the gene mamo as a new regulator of pigmentation in the silkworm Bombyx mori, a function that was previously unsuspected based on extensive work on Drosophila where the mamo gene is involved in gamete production. The evidence supporting the role of Bm-nano in pigmentation is convincing, including high-resolution linkage mapping of two mutant strains, expression profiling, and reproduction of the mutant phenotypes with state-of-the-art RNAi and CRISPR knock-out assays. While the discussion about genetic changes being guided or accelerated by the environment is extremely speculative and has little relevance for the findings presented, the work will be of interest to evolutionary biologists and geneticists studying color patterns and evolution of gene networks.

Response: Thank you very much for your careful work. In the revised version, we conducted a comparative genomic analysis of the upstream regions of the Bm-mamo gene in 51 wild silkworms and 171 domesticated local silkworms. The analysis of nucleotide diversity (pi) and the fixation index (FSTs) of the Bm-mamo genome sequences in the wild and domesticated silkworm populations were also performed. The results showed that the Bm-mamo genome sequence of local silkworms was relatively conserved, while the upstream sequence of wild silkworms exhibited high nucleotide diversity. This finding suggested a high degree of variability in the regulatory region of the Bm-mamo gene, in wild strains. Additionally, the sequence in this region may have been fixed by domestication selection. We have optimized the description in the discussion section.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

Strengths:

The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

This revision is a much improved manuscript and I command the authors for many of their edits.

Response: Thank you very much for your careful work. With the help of reviewers and editors, we have revised the manuscript to improve its readability.

I find the last part of the discussion, starting at "It is generally believed that changes in gene expression patterns are the result of the evolution of CREs", to be confusing.

In this section, I believe the authors sequentially:

• emphasize the role of CRE in morphological evolution (I agree)

• emphasize that TF, and in particular their own CRE, are themselves important mutational targets of evolution (I agree, but the phrasing need to insist the authors are here talking about the CRE found at the TF locus, not the CRE bound by the TF).

• use the stickleback Pel enhancer as an example, which I think is a good case study, but the authors also then make an argument about DNA fragility sites, which is hard to connect with the present study.

• then continue on "DNA fragility" using the peppered moth and butterfly cortex locus. There is no evidence of DNA fragility at these loci, so the connection does not work. "The cortex gene locus is frequently mutated in Lepidoptera", the authors say. But a more accurate picture would be that the cortex locus is repeatedly involved in the generation of color pattern variants. Unlike for Pel fragile enhancer, we don't know if the causal mutations at this locus are repeatedly the same, and the haplotypes that have been described could be collateral rather than causal. Overall, it is important to clarify the idea that mutation bias is a possible factor explaining "genetic hotspots of evolution" (or genetic parallelism sensu 10.1038/nrg3483), but it is also possible that many genetic hotspots are repeated mutational targets because of their "optimal pleiotropy" (e.g. hub position in GRNs, such as mamo might be), or because of particularly modular CRE region that allow fine-tuning. Thus, I find the "fragility" argument misleading here. In fact the finding that "bd" and "bdf" alleles are different in nature is against the idea of a fragility bias (unless the authors can show increased mutation rates at this locus in a wild silkmoth species?). These alleles are also artificially-selected ie. they increased in frequency by breeding rather than natural selection in the wild, so while interesting for our understand of the genotype-phenotype map, they are not necessarily representative of the mutations that may underlie evolution in the wild.

Response: Thank you very much for your careful work. DNA fragility is an interesting topic, but some explanations for DNA fragility are confusing. One study measured the rate of DNA double-strand breaks (DSBs) in yeast artificial chromosomes (YACs), which are chromosomes containing marine Pel that broke ~25 to 50 times more frequently than did the control. These authors believe that the increase in the mutation rate is caused by DNA sequence characteristics, particularly TG-dinucleotide repeats. Moreover, they found that adding a replication origin on the opposite side of Pel did not cause the fungus to switch fragile, making the forward sequence stable and the reverse complement fragile. Thus, Pel fragility is also dependent on the direction of DNA replication. In summary, they suggested that the special DNA sequence is the cause of DNA fragility. In addition, the sequence features associated with DNA fragility in the Pel region are also found in thousands of other positions in the stickleback and human genomes (Xie KT et al, 2019, science).

In yeast artificial chromosomes (YACs), the characteristics of DNA sequences, such as TG-dinucleotide repeat sequences, may be important reasons for DNA fragility, and these breaks occur during DNA replication. However, the inserted sequence of YAC often undergoes deletion or recombination during cultivation and passage. In addition, yeast is a single-celled organism. Therefore, the results in yeast cannot represent the situation in multicellular organisms. If multicellular organisms are like this, there are several issues as follows:

(1) The DNA replication process occurs separately in different multicellular organisms. Because DNA breakage and repair are independent, they can lead to the presence of different alleles in different cells. This can potentially lead to the occurrence of extensive chimeric organisms. However, we have not found such a situation in the genome sequencing of many multicellular organisms.

(2) If the DNA sequence, TG-dinucleotide repeats, is the determining factor, the mutations near the sequence lose their strong correlation with environmental changes. The researchers conducted yeast artificial chromosome experiments in the same environment and found that the frequency of DNA breaks containing TG dinucleotide repeat sequences was 25 to 50 times greater than that of the control group. This means that, whether in the marine population or the lake population, this part of the sticklebacks’ genome has undergone frequent mutations. However, according to related research, populations of lake sticklebacks, rather than marine populations, often exhibit a decrease in the pelvic phenotype.

(3) Researchers have found thousands of loci in the genome of sticklebacks and humans that contain such sequences (TG-dinucleotide repeats). This means that thousands of sites undergo frequent mutations during DNA replication. Unless these sites do not possess functionality, they will have some impact on the organism, even causing damage. Even if they are not functional sequences, these sequences will gradually be discarded or replaced during frequent mutations rather than being present in large quantities in the genome.

Therefore, the study of DNA fragility in yeast cannot explain the situation in multicellular organisms.

As you noted, we want to express that the frequent variation in the cortex gene should be regulated by targeted regulation involving the GRN in Lepidoptera. In addition, studies on specific epigenetic modifications discovered through the referenced fragile DNA sites suggest that DNA fragility is not determined by the DNA sequence (Ji F, 2020, Cell Res) but rather by other factors, such as epigenetic factors. The sequence features discovered at fragile DNA sites are traces of frequent mutations, not causes.

In this revision, we analyzed the nucleotide diversity of the mamo genome in 51 wild and 171 domestic silkworms. We found high nucleic acid diversity from the third exon to the upstream region of this gene in wild silkworms. We randomly selected 12 wild silkworms and 12 domestic silkworms and compared their upstream sequences to approximately 1 kb. In wild silkworms, there is significant diversity in their upstream sequences. In domestic silkworms, the sequences are highly conserved, but in some silkworms, a long interspersed nuclear element (LINE) is inserted. This finding suggested that there is frequent variation in the sequence of this region in wild silkworms, while fixation occurs in domesticated silkworms. These genomic data are sourced from the pangenome of silkworms (Tong X, 2022, Nat Commun.). In the pangenomic research, 1078 strains (205 local strains, 194 improved strains, 632 mutant strains, and 47 wild silkworms), which included 545 third-generation sequencing genomes, were obtained. An online website was built to utilize these data (http://silkmeta.org.cn/). We warmly welcome you to use these data.

In summary, for clearer expression, we have rewritten this section.

Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC, Schluter D, Bell MA, Vasquez KM, Kingsley DM. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science. 2019 Jan 4;363(6422):81-84. doi: 10.1126/science.aan1425.

Ji F, Liao H, Pan S, Ouyang L, Jia F, Fu Z, Zhang F, Geng X, Wang X, Li T, Liu S, Syeda MZ, Chen H, Li W, Chen Z, Shen H, Ying S. Genome-wide high-resolution mapping of mitotic DNA synthesis sites and common fragile sites by direct sequencing. Cell Res. 2020 Nov;30(11):1009-1023. doi: 10.1038/s41422-020-0357-y.

Tong X, Han MJ, Lu K, Tai S, Liang S, Liu Y, Hu H, Shen J, Long A, Zhan C, Ding X, Liu S, Gao Q, Zhang B, Zhou L, Tan D, Yuan Y, Guo N, Li YH, Wu Z, Liu L, Li C, Lu Y, Gai T, Zhang Y, Yang R, Qian H, Liu Y, Luo J, Zheng L, Lou J, Peng Y, Zuo W, Song J, He S, Wu S, Zou Y, Zhou L, Cheng L, Tang Y, Cheng G, Yuan L, He W, Xu J, Fu T, Xiao Y, Lei T, Xu A, Yin Y, Wang J, Monteiro A, Westhof E, Lu C, Tian Z, Wang W, Xiang Z, Dai F. High-resolution silkworm pan-genome provides genetic insights into artificial selection and ecological adaptation. Nat Commun. 2022 Sep 24;13(1):5619. doi: 10.1038/s41467-022-33366-x.

Lu K, Pan Y, Shen J, Yang L, Zhan C, Liang S, Tai S, Wan L, Li T, Cheng T, Ma B, Pan G, He N, Lu C, Westhof E, Xiang Z, Han MJ, Tong X, Dai F. SilkMeta: a comprehensive platform for sharing and exploiting pan-genomic and multi-omic silkworm data. Nucleic Acids Res. 2024 Jan 5;52(D1):D1024-D1032. doi: 10.1093/nar/gkad956.

Curiously, the last paragraph ("Some research suggests that common fragile sites...") elaborate on the idea that some sites of the genome are prone to mutation. The connection with mamo and the current article are extremely thin. There is here an attempt to connect meiotic and mitotic breaks to Bm-mamo, but this is confusing: it seems to propose Bm-mamo as a recruiter of epigenetic modulators that may drive higher mutation rates elsewhere. Not only I am not convinced by this argument without actual data, but this would not explain how the mutations at the Bm-mamo itself evolved.

Response: Thank you very much for your careful work. This section mainly illustrates that DNA fragility is not determined by sequence but is regulated by other factors in animals. In fruit flies, they found that mamo is an important candidate gene for recombination hotspot setting in meiosis. First, we evaluated PRDM9, which plays an important role in setting recombination hotspots during meiosis. Our purpose in mentioning this information is to illustrate that chromosome recombination is a process of programmed double strand breaks and to answer another reviewer's question about programmed events in the genome. In summary, we suggest that some variations in DNA sequences are procedural results. We have optimized the description of this section in this version.

On a more positive note, I find it fascinating that the authors identified a TF that clearly articulates or orchestrate larval pattern development, and that when it is deleted, can generate healthy individuals. In other words, while it is a TF with many targets, it is not too pleiotropic. This idea, that the genetically causal modulators of developmental evolution are regulatory genes, has been described elsewhere (e.g. Fig 4c in 10.1038/s41576-020-0234-z, and associated refs). To me, the beautiful findings about Bm-mamo make sense in the general, existing framework that developmental processes and regulatory networks "shape" the evolutionary potential and trajectories of organisms. There is a degree of "programmability" in the genomes, because some loci are particularly prone to modulate a given type of trait. Here, Bm-mamo, as a potentially regulator of both CPs and melanin pathway genes, appear to be a potent modulator of epithelial traits. Claiming that there are inherent mutational biases behind this is unwarranted.

Response: Thank you very much for your careful work. I completely agree with your statement that the genome exhibits a certain degree of programmability. On the one hand, some transcription factors can precisely control the spatiotemporal expression levels of some structural genes (such as pigment synthesis genes). On the other hand, these transcription factors are also subject to strict expression regulation. Because the color pattern is complex, changes in single or minority structural genes result in incomplete or imprecise changes in coloring patterns. Nevertheless, several regulatory factors can regulate multiple downstream target genes. Changes in their expression patterns can lead to holistic and significant changes in color patterns. There are long intergenic regions upstream of many important transcription factors, dozens of kilobase pairs (Kb) to hundreds of Kb, which may contain many different regulatory elements for better control of their expression patterns. Therefore, gene regulatory networks can directly regulate transcription factors to modulate a given type of trait. Transcription factors and their downstream target genes can form a functional module, which is similar to a functional module in software or operating systems. This regulation of transcription factors is simpler in terms of steps, which are similar to a single click switch button. The gene regulatory network regulates these modules in response to environmental changes and is widely recognized.

Some people do not agree that genetic variations can also be regulated. They claim that this is completely random. The infinite monkey theorem (Félix-Édouard-Justin-Émile Borel, 1909) states that if an infinite number of monkeys were given typewriters and an infinite amount of time, they would eventually produce the complete works of Shakespeare. Although this theory advocates randomness on the surface, its conclusions are full of inevitability (tail event). In nature, some things we observe do not have obvious regularity because they involve relatively complex factors, and the underlying logic is obscure and difficult to understand. We often name them random. However, as we gradually understand the logic behind this complex event, we can also recognize the procedural nature of this randomness.

Previously, chromosomal recombination during meiosis was believed to be a random event. However, currently, it is believed that the process is procedural. The occurrence of meiotic recombination mentioned earlier indicates that the genome has the ability to self-set the position of double-strand breaks to form new allelic forms. Because meiotic recombination is programmed, transcription factors that recognize DNA sites, enzymes that cleave double strands, and DNA repair systems exist, programming can also introduce genetic variation. A study in plants has provided insights into this programmed mutation (Monroe JG, 2023, nature). Frequent changes in the expression patterns of some transcription factors occur between and/or within species. In this article, we only discuss the possible reasons for variations in the expression patterns of some transcription factors in a general manner and simple reasoning. We have added an analysis of the response of wild silkworms and improved the relevance of the discussion.

Monroe JG, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, Klein M, Hildebrandt J, Neumann M, Kliebenstein D, Weng ML, Imbert E, Ågren J, Rutter MT, Fenster CB, Weigel D. Mutation bias reflects natural selection in Arabidopsis thaliana. Nature. 2022 Feb;602(7895):101-105. doi: 10.1038/s41586-021-04269-6. Epub 2022 Jan 12. Erratum in: Nature. 2023 Aug;620(7973):

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Please structure your Discussion with section headers.

Response: Thank you very much for your careful work. We have added relevant section headers.

• As explained in my public review, I found the two last sections of the Discussion to be dispersed and confusing. I also must say that I carefully read the Response to Reviewers on this, which helped me to better understand the authors' intentions here. Please consider the revision of this Discussion as this feels extremely speculative difficult to connect with Bm-mamo.

Response: Thank you very much for your careful work. We have rewritten this part of the content.

• typo: were found near the TTS of yellow --> TSS

Response: Thank you very much for your careful work. We have made these modifications.

• l. 234 :"expression level of the 18 CP genes in the integument". Consider adding a mention of Figure 7 here, as only Fig. S10 is cited here.

Response: Thank you very much for your careful work. We have made these modifications.

• Editorial comment on the second half of the Abstract:

Wu et al : "We found that Bm-mamo can comprehensively regulate the expression of related pigment synthesis and cuticular protein genes to form color patterns. This indicates that insects have a genetic basis for coordinate regulation of the structure and shape of the cuticle, as well as color patterns. This genetic basis provides the possibility for constructing the complex appearances of some insects. This study provides new insight into the regulation of color patterns."

I respectfully suggest a more accurate rephrasing, where the methods are mentioned, and where the logical argument is more straightforward. For example

"Using RNAi and CRISPR we show that Bm-mamo is a repressor or dark melanin patterns in the larval epithelium. Using in-vitro binding assays and gene expression profiling in wild-type and mutant larvae, we also show that Bm-mamo likely regulate the expression of related pigment synthesis and cuticular protein genes in a coordinated manner to mediate its role in color pattern formation. This mechanism is consistent with a dual role of this transcription factor in regulating both the structure and shape of the cuticle and pigments that are embedded within it. This study provides new insight into the regulation of color patterns as well as in the construction more complex epithelial features in some insects."

I hope this let the ideas of the original version transpire as the authors intended.

Response: Thank you very much for your careful work. We have made these modifications.

Author Response

Public Reviews

We thank both reviewers for taking the time and effort to think critically about our paper and point out areas where it can be improved. In this document, we do our best to clarify any misunderstandings with the hope that further consideration about the strengths and weaknesses of our approach will be possible. Our responses are in bold.

Reviewer #1 (Public Review):

Summary:

In their manuscript, Schmidlin, Apodaca, et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to examine patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation.

We thank the reviewer for this positive assessment of our work. We are happy that the reviewer noted what we feel is a unique strength of our approach: we scaled up experimental evolution by using DNA barcodes and by exploring 12 related selection pressures. Despite this scaling up, we still see phenotypic convergence among the 744 adaptive mutants we study.

The environments we study represent 12 different concentrations or combinations of two drugs, radicicol and fluconazole. Our hope is that this large dataset (774 mutants x 12 environments) will be useful, both to scientists who are generally interested in the genetic and phenotypic underpinnings of adaptation, and to scientists specifically interested in the evolution of drug resistance.

Weaknesses:

(1) One of the objectives of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. This corresponds to a very small fraction of the starting set of ~21 000 lineages that were combined after experimental evolution for fitness measurements.

This is a misunderstanding that we will work to clarify in the revision. Our starting set did not include 21,000 adaptive lineages. The total number of unique adaptive lineages in this starting set is much lower than 21,000 for two reasons.

First, ~21,000 represents the number of single colonies we isolated in total from our evolution experiments. Many of these isolates possess the same barcode, meaning they are duplicates. Second, and more importantly, most evolved lineages do not acquire adaptive mutations, meaning that many of the 21,000 isolates are genetically identical to their ancestor. In our revised manuscript, we will explicitly state that these 21,000 isolated lineages do not all represent unique, adaptive lineages. In figure 2 and all associated text, we will change the word “lineages” to “isolates,” where relevant.

More broadly speaking, several previous studies have demonstrated that diverse genetic mutations converge at the level of phenotype, and have suggested that this convergence makes adaptation more predictable (PMID33263280, PMID37437111, PMID22282810, PMID25806684). Our study captures mutants that are overlooked in previous studies, such as those that emerge across subtly different selection pressures (e.g., 4 𝜇g/ml vs. 8 𝜇g/ml flu) and those that are undetectable in evolutions lacking DNA barcodes. Thus, while our experimental design misses some mutants (see next comment), it captures many others. Note that 774 adaptive lineages is more than most previous studies. Thus, we feel that “our work – showing that 774 mutants fall into a much smaller number of groups” is important because it “contributes to growing literature suggesting that the phenotypic basis of adaptation is not as diverse as the genetic basis (lines 161 - 162).”

As the authors briefly remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance.

The word “briefly” feels a bit unfair because we discuss this bias on 3 separate occasions (on lines 146 - 147, 260 - 264, and in more detail on 706 - 714). We even walk through an example of a class of mutants that our study misses. We say, “our study is underpowered to detect adaptive lineages that have low fitness in any of the 12 environments. This is bound to exclude large numbers of adaptive mutants. For example, previous work has shown some FLU resistant mutants have strong tradeoffs in RAD (Cowen and Lindquist 2005). Perhaps we are unable to detect these mutants because their barcodes are at too low a frequency in RAD environments, thus they are excluded from our collection of 774.”

In our revised version, we will add more text to the first mention of these missing mutants (lines 146 - 147) so that the implications are more immediately made apparent.

While we “miss” some classes of mutants, we “catch” other classes that may have been missed in previous studies of convergence. For example, we observe a unique class of FLU-resistant mutants that primarily emerged in evolution experiments that lack FLU (Figure 3). Thus, we think that the unique design of our study, surveying 12 environments, allows us to make a novel contribution to the study of phenotypic convergence.

One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. This has important implications regarding the general conclusions of the authors regarding the evolution of trade-offs.

We discussed these implications in some detail in the 16 lines mentioned above (146 - 147, 260 - 264, 706 - 714). To add to this discussion, we will also add the following sentence to the end of the paragraph on lines 697 - 714: “This could complicate (or even make impossible) endeavors to design antimicrobial treatment strategies that thwart resistance”.

We will also add a new paragraph that discusses these implications earlier in our manuscript. This paragraph will highlight the strengths of our method (e.g., that we “catch” classes of mutants that are often overlooked) while being transparent about the weaknesses of our approach (e.g., that we “miss” mutants with strong tradeoffs).

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations.

The rate at which new mutations enter a population is driven by various factors such as the mutation rate and population size, so choosing an arbitrary threshold like 25 generations is difficult.

We conducted our fitness competition following previous work using the Levy/Blundell yeast barcode system, in which the number of generations reported varies from 32 to 40 (PMID33263280, PMID27594428, PMID37861305, see PMID27594428 for detailed calculation of the fraction of lineages biased by secondary mutations in this system).

The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay.

We understand how the reviewer came to this misunderstanding and will adjust our revised manuscript accordingly. Previous work has demonstrated that, in this particular evolution platform, most of the mutations actually occur during the transformation that introduces the DNA barcodes (PMID25731169). In other words, these mutations do not accumulate during the 40 generations of evolution, they are already there. So the observation that we collect a genetically diverse pool of adaptive mutants after 40 generations of evolution is not evidence that 40 generations is enough time for secondary mutations to bias abundance values.

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach.

This concern, and all subsequent concerns, seem to be driven by either (a) general concerns about the noisiness of fitness measurements obtained from large-scale barcode fitness assays or (b) general concerns about whether the clusters obtained from our dimensional reduction approach capture this noise as opposed to biologically meaningful differences.

We will respond to each concern point-by-point, but want to start by generally stating that (a) our particular large-scale barcode fitness assay has several features that diminish noise, and (b) we devote 4 figures and 200 lines of text to demonstrating that these clusters capture biologically meaningful differences between mutants (and not noise).

In terms of this specific concern, we performed an analysis of noise in the submitted manuscript: Our noisiest fitness measurements correspond to barcodes that are the least abundant and thus suffer the most from stochastic sampling noise. These are also the barcodes that introduce the nonlinearity the reviewer mentions. We removed these from our dataset by increasing our coverage threshold from 500 reads to 5,000 reads. The clusters did not collapse, which suggests that they were not capturing noise (Figure S7 panel B). But we agree with the reviewer that this analysis alone is not sufficient to conclude that the clusters distinguish groups of mutants with unique fitness tradeoffs.

Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages.

To evaluate the strength of the clustering, we performed numerous analyses including whole genome sequencing, growth experiments, reclustering, and tracing the evolutionary origins of each cluster (Figures 5 - 8). All of these analyses suggested that our clusters capture groups of mutants that have different fitness tradeoffs. We will adjust our revised manuscript to make clear that we do not rely on the results of a clustering algorithm alone to draw conclusions about phenotypic convergence.

We are also grateful to the reviewer for helping us realize that, as written, our manuscript is not clear with regard to how we perform clustering. We are not using UMAP to decide which mutant belongs to which cluster. Recent work highlights the importance of using an independent clustering method (PMID37590228). Although this recent work addresses the challenge of clustering much higher dimensional data than we survey here, we did indeed use an independent clustering method (gaussian mixture model). In other words, we use UMAP for visualization but not clustering. We also confirm our clustering results using a second independent method (hierarchical clustering; Figure S8). And in our revised manuscript, will confirm with a third method (PCA, see below). We will adjust the main text and the methods section to make these choices clearer.

This might paint a misleading picture where clusters appear well separate and well defined but are in fact much fuzzier, which would impact the conclusion that the phenotypic space is constricted.

The salient question is whether the clusters are so “fuzzy” that they are not meaningful. That interpretation seems unreasonable. Our clusters group mutants with similar genotypes, evolutionary histories, and fitness tradeoffs (Figures 5 - 8). Clustering mutants with similar behaviors is important and useful. It improves phenotypic prediction by revealing which mutants are likely to have at least some phenotypic effects in common. And it also suggests that the phenotypic space is constrained, at least to some degree, which previous work suggests is helpful in predicting evolution (PMID33263280, PMID37437111, PMID22282810, PMID25806684).

(4) The authors make the decision to use UMAP and a gaussian mixed model to cluster and represent the different fitness landscapes of their lineages of interest. Their approach has many caveats. First, compared to PCA, the axis does not provide any information about the actual dissimilarities between clusters. Using PCA would have allowed a better understanding of the amount of variance explained by components that separate clusters, as well as more interpretable components.

The components derived from PCA are often not interpretable. It’s not obvious that each one, or even the first one, will represent some intuitive phenotype, like resistance to fluconazole.

Moreover, we see many non-linearities in our data. For example, fitness in a double drug environment is not predicted by adding up fitness in the relevant single drug environments. Also, there are mutants that have high fitness when fluconazole is absent or abundant, but low fitness when mild concentrations are present. These types of nonlinearities can make the axes in PCA very difficult to interpret, plus these nonlinearities can be missed by PCA, thus we prefer other clustering methods.

We will adjust our revised manuscript to explain these reasons why we chose UMAP and GMM over PCA.

Also, we will include PCA in the supplement of our revised manuscript. Please find below PC1 vs PC2, with points colored according to the cluster assignment in figure 4 (i.e. using a gaussian mixture model). It appears the clusters are largely preserved.

Author response image 1.

Second, the advantages of dimensional reduction are not clear. In the competition experiment, 11/12 conditions (all but the no drug, no DMSO conditions) can be mapped to only three dimensions: concentration of fluconazole, concentration of radicicol, and relative fitness. Each lineage would have its own fitness landscape as defined by the plane formed by relative fitness values in this space, which can then be examined and compared between lineages.

We worry that the idea stems from apriori notions of what the important dimensions should be. It also seems like this would miss important nonlinearities such as our observation that low fluconazole behaves more like a novel selection pressure than a dialed down version of high fluconazole.

Also, we believe the reviewer meant “fitness profile” and not “fitness landscape”. A fitness landscape imagines a walk where every “step” is a mutation. Most lineages in barcoded evolution experiments possess only a single adaptive mutation. A single-step walk is not enough to build a landscape, though others are expanding barcoded evolution experiments beyond the first step (PMID34465770, PMID31723263), so maybe one day this will be possible.

Third, the choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered.

We agree. We did not rely on the results of BIC alone to make final decisions about how many clusters to include. We thank the reviewer for pointing out this gap in our writing. We will adjust our revised manuscript to explain that we ultimately chose to describe 6 clusters that we were able to validate with follow-up experiments. In figures 5, 6, 7, and 8, we use external information to validate the clusters that we report in figure 4. And in lines 697 – 714, we explain that there are may be additional clusters beyond those we tease apart in this study.

This directly contradicts the statement in the main text that clusters are robust to noise, as more a stringent inclusion threshold appears to increase and not decrease the optimal number of clusters. Additional criteria to BIC could have been used to help choose the optimal number of clusters or even if mixed Gaussian modeling is appropriate for this dataset.

We are under the following impression: If our clustering method was overfitting, i.e. capturing noise, the optimal number of clusters should decrease when we eliminate noise. It increased. In other words, the observation that our clusters did not collapse (i.e. merge) when we removed noise suggests these clusters were not capturing noise.

More generally, our validation experiments, described below, provide additional evidence that our clusters capture meaningful differences between mutants (and not noise).

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays.

Some types of bar-seq methods, in particular those that look at fold change across two time points, are noisier than others that look at how frequency changes across multiple timepoints (PMID30391162). Here, we use the less noisy method. We also reduce noise by using a stricter coverage threshold than previous work (e.g., PMID33263280), and by excluding batch effects by performing all experiments simultaneously (PMID37237236).

The main assay we use to measure fitness has been previously validated (PMID27594428). No subsequent study using this assay validates using the methods suggested by the reviewer (see PMID37861305, PMID33263280, PMID31611676, PMID29429618, PMID37192196, PMID34465770, PMID33493203).

More to the point, bar-seq has been used, without the reviewer’s suggested validation, to demonstrate that the way some mutant’s fitness changes across environments is different from other mutants (PMID33263280, PMID37861305, PMID31611676, PMID33493203, PMID34596043). This is the same thing that we use bar-seq to demonstrate.

For all of these reasons, we are hesitant to confirm bar-seq itself as a valid way to infer fitness. It seems this is already accepted as a standard in our field.

Having these types of results would help support the accuracy of the main assay in the manuscript and thus better support the claims of the authors.

We don’t agree that fitness measurements obtained from this bar-seq assay generally require validation. But we do agree that it is important to validate whether the mutants in each of our 6 clusters indeed are different from one another in meaningful ways, in particular, in that they have different fitness tradeoffs. We have four figures (5 - 8) and 200 lines of text dedicated to validating whether our clusters capture reproducible and biologically meaningful differences between mutants. Happily, one of these figures (Fig 7) includes growth curves, which are exactly the type of validation experiment asked for by the reviewer.

Below, we walk through the different types of validation experiments that are present in our original manuscript, and additional validation experiments that we plan to include in the revised version. We are hopeful that these validation experiments are sufficient, or at the very least, that this list empowers reviewers to point out where more work is needed.

(1) Mutants from different clusters have different growth curves: In our original manuscript, we measured growth curves corresponding to a fitness tradeoff that we thought was surprising. Mutants in clusters 4 and 5 both have fitness advantages in single drug conditions. While mutants from cluster 4 also are advantageous in the double drug conditions, mutants from cluster 5 are not! We validated these different behaviors by studying growth curves for a mutant from each cluster (Figures 7 and S10).

(2) Mutants from different clusters have different evolutionary origins: In our original manuscript, we came up with a novel way to ask whether the clusters capture different types of adaptive mutants. We asked whether the mutants in each cluster originate from different evolution experiments. Indeed they often do (see pie charts in Figures 6, 7, 8). This method also provides evidence supporting each cluster’s differing fitness tradeoffs.

For example, mutants in cluster 5 appear to have a tradeoff in a double drug condition (described above). They rarely originate from that evolution condition, unlike mutants in nearby cluster 4 (see Figure 7).

(3) Mutants from each cluster often fall into different genes: In our original manuscript, we sequenced many of these mutants and show that mutants in the same gene are often found in the same cluster. For example, all 3 IRA1 mutants are in cluster 6 (Fig 8), both GPB2 mutants are in cluster 4 (Figs 7 & 8), and 35/36 PDR mutants are in either cluster 2 or 3 (Figs 5 & 6).

(4) Mutants from each cluster have behaviors previously observed in the literature: In our original manuscript, we compared our sequencing results to the literature and found congruence. For example, PDR mutants are known to provide a fitness benefit in fluconazole and are found in clusters that have high fitness in fluconazole (lines 457 - 462). Previous work suggests that some mutations to PDR have different tradeoffs than others, which is what we see (lines 540 - 542). IRA1 mutants were previously observed to have high fitness in our “no drug” condition, and are found in the cluster that has the highest fitness in the “no drug” condition (lines 642 - 646). Previous work even confirms the unusual fitness tradeoff we observe where IRA1 and other cluster 6 mutants have low fitness only in low concentrations of fluconazole (lines 652 - 657).

(5) Mutants largely remain in their clusters when we use alternate clustering methods: In our original manuscript, we performed various different reclustering and/or normalization approaches on our data (Fig 6, S5, S7, S8, S9). The clusters of mutants that we observe in figure 4 do not change substantially when we recluster the data. We will add PCA (see above) to these analyses in our revised manuscript.

(6) We will include additional data showing that mutants in different clusters have different evolutionary origins: Cluster 1 is defined by high fitness in low fluconazole that declines with increasing fluconazole (see Fig 4E and Fig 5C). In our revised manuscript, we will show that cluster 1 lineages were overwhelmingly sampled from evolutions conducted in our lowest concentration of fluconazole (see figure panel A below). No other cluster’s evolutionary history shows this pattern (figures 6, 7, and 8).

(7) We will include additional data showing that mutants in different clusters have different growth curves: Cluster 1 lineages are unique in that their fitness advantage is specific to low flu and trades off in higher concentrations of fluconazole. We obtained growth curves for three cluster 1 mutants (2 SUR1 mutants and 1 UPC2 mutant). We compared them to growth curves for three PDR mutants (from clusters 2 and 3). Cluster 1 mutants appear to have the highest growth rates and reach the higher carrying capacity in low fluconazole (see red and green lines in Author response image 2 panel B below). But the cluster 1 mutants are negatively affected by higher concentrations of fluconazole, much more so than the mutants from clusters 2 and 3 (see Author response image 2 panel C below). This is consistent with the different fitness tradeoffs we observe for each cluster (figures 4 and 5). We will include a more detailed version of this analysis and the figures below in our revised manuscript.

Author response image 2.

Validation experiments demonstrate that cluster 1 mutants have uniquely high fitness in only the lowest concentration of fluconazole. (A) The mutant lineages in cluster 1 were largely sampled from evolution experiments performed in low flu. This is not true of other clusters (see pie charts in main manuscript). (B) In low flu (4 𝜇g/ml), Cluster 1 lineages (red/UPC2 and green/SUR1) grow faster and achieve higher density than lineages from clusters 2 and 3 (blue/PDR). This is consistent with barseq measurements demonstrating that cluster 1 mutants have the highest fitness in low flu. (C) Cluster 1 lineages are sensitive to increasing flu concentrations (SUR1 and UPC2 mutants, middle and rightmost graphs). This is apparent in that the gray (8 𝜇g/ml flu) and light blue (32 𝜇g/ml flu) growth curves rise more slowly and reach lower density than the dark blue curves (4 𝜇g/ml flu). But this is not the case for the PDR mutants from clusters 2 and 3 (leftmost graph). These observations are consistent with the bar-seq fitness data presented in the main manuscript (Fig 4E).

With all of these validation efforts combined, we are hopeful that the reviewer is now more convinced that our clusters capture groups of mutants with different fitness tradeoffs (as opposed to noise). We want to conclude by saying that we are grateful to the reviewer for making us think deeply about areas where we can include additional validation efforts as well as areas where we can make our manuscript clearer.

Reviewer #2 (Public Review):

Summary:

Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotypephenotype mapping.

Strengths:

There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory).

We are very grateful for this positive review. This was indeed a lot of work! We are happy that the reviewer noted what we feel is a unique strength of our manuscript: that we survey adaptive isolates across multiple environments, including low drug concentrations.

Weaknesses:

Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one!

We thank the reviewer for these words of encouragement and will work towards catching more low fitness lineages in our next project.

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think:

We think that phrasing the “jump” as a question might help lay readers get from point A to point B. So, in the introduction of our revised manuscript, we will add a paragraph roughly similar to this one: “If two groups of drug-resistant mutants have different fitness tradeoffs, does it mean that they provide resistance through different underlying mechanisms? Alternatively, it could mean that both provide drug resistance via the same mechanism, but some mutations come with a cost that others don’t pay. However, another way to phrase this alternative is to say that both groups of mutants affect fitness through different suites of mechanisms that are only partially overlapping. And so, by identifying groups of mutants with different fitness tradeoffs, we argue that we will be uncovering sets of mutations that impact fitness through different underlying mechanisms. The ability to do so would be useful for genotype-phenotype mapping endeavors.”

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm.

In our revised manuscript, we will carefully review all citations. The issue may stem from our attempt to reach two different groups of scientists. We ourselves are broadly interested in the structure of the genotype-phenotype-fitness map (PMID33263280, PMID32804946). Though the 3 papers the reviewer mentions on lines 132 - 133 all pertain to yeast, we cite them because they are studies about the complexity of this map. Their conclusions, in theory, should apply broadly, beyond yeast. Similarly, the reason we cite papers from yeast, as well as bacteria and cancer, is that we believe general conclusions about the genotype-phenotype-fitness map should apply broadly. For example, the sentence the reviewer highlights, “previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms” is a general observation about the way genotype maps to fitness. So we cited papers from across the tree of life to support this sentence.

On the other hand, because we study drug resistant mutations, we also hope that our work is of use to scientists studying the evolution of resistance. We agree with the reviewer that in this regard, some of our findings may be especially pertinent to the evolution of resistance to antifungal drugs. We will consider this when reviewing the citations in our revised manuscript and add some text to clarify these points.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae).

In the revised manuscript, we will make clear that we study S. cerevisiae.

In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly?

We like this idea and we are working on it, but it is not straightforward. The reviewer is correct in that we can use the sequencing data that we already have. But calling aneuploidy with certainty is tough because its signal can be masked by noise. In other words, some regions of the genome may be sequenced more than others by chance. Given this is not straightforward, at least not for us, this analysis will likely have to wait for a subsequent paper.

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections?

Perhaps because our background lies in general study of the genotype-phenotype map, we did not want to make bold assertions about how our work might apply to pathogenic yeasts. But we see how this could be helpful and will add some discussion points about this. Specifically, we will discuss which of the genes and mutants we observe are also found in Candida. We will also investigate whether our observation that low fluconazole represents a seemingly unique challenge, not just a milder version of high fluconazole, has any corollary in the Candida literature.

Reviewer #2 (Public Review):

Summary:<br /> Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotype-phenotype mapping.

Strengths:<br /> There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory).

Weaknesses:<br /> Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one!

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think:

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae). In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly?

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections?

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Reply to the reviewers

Reviewer #1

• Albeit the link between CSA and NE integrity the work is in my eyes too preliminary. Although the data presented are well done and carefully evaluated they mostly (except Fig 1A) rely on direct comparisons of one patient cell line (CS-A or CS-B) to the same cells expression the wildtype protein. It remains thus open whether the effects seen on LEM2 expression, LEM2-LaimA/C interaction, stress fibre formation, cGAS/STING signaling pathway activation in the CS-A cells are representative for a number of different CS patient derived cells. This is especially important given the small changes observed. Please note that there are alos clear differences between the CSA-wt and CSB-wt cells. Would the HPA CSA KO cells show in addition to NE irregularities (not even quantified) the same phenotypes and can they be reverted by re-expression of the wildtype CSA protein? We would like to thank the reviewer for this comment. Indeed we have previously observed nuclear circularity defects in CSA KO HAP1 cells but we haven’t investigated the other phenotypes in this cell line. One of the main reasons behind this is the technical difficulties associated with performing immunofluorescence with HAP1 cells who are very small and tend to grow in aggregates.

Proposed experimental plan: To address this reviewer’s comment, we will attempt again to use HAP1 cells (WT and CSA KO) and look at nuclear circularity (with quantification), stress fiber formation and cGas foci. If we don’t succeed, we will use an alternative isogenic cell model consisting of fibroblasts in which we will knock out CSA using CRISPR/Cas9. We will then repeat the same experiments as proposed above for the HAP1 cells.

• The link between CSA and SUN1 is not well worked out. What is the effect of SUN2 downregulation and that of nespirins? It remains unclear whether the observed effects are indeed LINC mediated. Proposed experimental plan: To address this point, we will downregulate SUN2 and nesprins using siRNAs in two different cell models (as described above) and assess nuclear shape as well as cGas/STING pathway activation.

Minor comments:

* Fig 1B: Why is HA-Tagged CSA not shown on the CSA western? This would be helpful to compare to the endogenous levels at least in CSB cells. A western showing an housekeeping marker would allow better comparison. Judging from the proteins markers HA-tagged CSA seems much larger as endogenous CSA (first versus second row). Again, less cropped western blots would help.*

We are sorry for the confusion and we have realised that the molecular weights on the western blots were incorrectly labeled on this figure. This will be modified, and the full, uncropped WB will be provided as a supplementary file.

Fig 3A: Is CSA FLAG or HA-tagged? Or both? If both are expressed the question raises of why the CSA-LEM 2 interaction is only seen in an overexpression situation.

• *

CSA is HA tagged. To address this reviewer’s comment we will try performing immunoprecipitation on endogenous proteins.

Fig 5: Inconsistency between figure and figure legend: 20 vs 25 nM Jasplakinolide. I assume Latrunculin A should read Cytochalasin D?

Thank you for pointing this out, and yes this is indeed a mistake on our labeling. We will rectify this on the figure legend.

Fig5B: Not clear why in "CSA-wT cells" Cytochalasin D and Jasplakinolide have the same effect on nuclear envelope shape yet only Jasplakinolide increases the number of blebs.

Cyt D inhibits actin polymerization while jasplakinolide increases polymerization. Likely actin polymerization increases blebs through extra force being put on the nucleus through actin cables/ actin based motility. Both drugs decrease nuclear roundness as they disrupt the normal actin network leading to worsening of nuclear shape through different mechanisms.

Page 10: Method for IF: 4% (v/v) paraformaldehyde and 2% /v/v) should likely read (w/v). Page 19: replace "withl" by "with".

We will rectify both these points.

Reviewer #2

The paper is well-written and for the most part, the data support the conclusions of the authors. Some minor caveats could be addressed to improve the quality of the manuscript.

We would like to thank the reviewer for their positive feedback on our manuscript.

• The phenotype of decreased LEMD2 incorporation into the NE in CS-A cells is minor. Only ~20% and thus, it is not clear whether this is causal of any of the NE abnormalities. It should be better explained how these data add to the story.

To address this point, we will overexpress LEMD2 in CS-A cells and assess whether the NE phenotype can be significantly rescued. This will add value to this part of story.

• Inducing actin polymerization and depolarization impact nuclear morphological abnormalities and nuclear blebbing. Do these treatments impact nuclear fragility and cGAS accumulation at NE break sites?* This is a good point indeed. To address this question, we will include cGas foci staining and quantification upon treatment with these chemicals.

• Depletion of SUN1 in CS-A cells increased nuclear circularity, decreased blebbing, and phosphorylation of TBK1. The impact of SUN1 depletion in cGAS foci formation at NE break sites and phosphorylation of STING is not shown. Such experiments will provide stronger evidence that CS-A activates the cGAS-STING pathway in a SUN1 (mechanical stress)-dependent manner.

* We will address this question by analysing cGas foci and cGas-STING pathway activation upon SUN1 depletion by siRNA.

Reviewer #3

• *

The data are generally clear, well performed and well interpreted with some exceptions:*

1) I appreciate the use of isogenic cell lines (a big plus when dealing with patient-derived cell lines). However, these lines were established 30 years ago and the reported phenotypes might be due to genetic drifts. To exclude this, I suggest to complement the HAP-1 ERCC8 KO cell line with exogenously expressed CSA and assess if this rescues the phenotypes reported. Validation of the KO in these lines, either by western blotting or sequencing is needed.*

This point has also been raised by the first reviewer, and will be addressed as described above (and pasted below):

We would like to thank the reviewer for this comment. Indeed we have previously observed nuclear circularity defects in CSA KO HAP1 cells but we haven’t investigated the other phenotypes in this cell line. One of the main reasons behind this is the technical difficulties associated with performing immunofluorescence with HAP1 cells who are very small and tend to grow in aggregates.

Proposed experimental plan: To address this reviewer’s comment, we will attempt again to use HAP1 cells (WT and CSA KO) and look at nuclear circularity (with quantification), stress fiber formation and cGas foci. If we don’t succeed, we will use an alternative isogenic cell model consisting of fibroblasts in which we will knock out CSA using CRISPR/Cas9. We will then repeat the same experiments as proposed above for the HAP1 cells.

2) Related to the complementation of patient cell lines, the exogenous HA-CSA is not recognised by the anti-CSA in the CSA-null patient cell lines (Fig 1B, second blot). Shouldn't you be able to see this exogenous protein? HA-GFP-CSB in the complemented CSB-null patient cell line runs at the same weight as endogenous CSB (Fig 1B, fourth blot). This is also unexpected. I think you need better characterisation of your cell lines and need to demonstrate the level of exogenous transgenes that have been used to complement the cells and that they localise appropriately, presumably to the nucleus. You should also make sure to cite the paper where they were isolated and describe that they were immortalised (Troelstra et al., 1992) and the paper in which transgenes were stably overexpressed (Qiang et al., 2021).*

*

As mentioned above, we have realised that we made some mistakes with the labeling of the molecular weight on this western blot. This will be corrected and the full uncropped western blot will be provided as a supplementary figure.

We will also cite the suggested papers accordingly.

3) Immunolocalisation of INM proteins is notoriously tricky and the permeabilisation steps include only 0.2% Tx100, which can be insufficient to permeabilise the INM. I appreciate the Emerin and Lamin immunostaining seems to have worked, but in many cases successful immunostaining can be antibody-specific. Can you try harsher permeabilisation to expose LEM2 epitopes? I'm somewhat uncomfortable with the suggestion that there is a cytosolic (ER?) pool of endogenous LEM2 as this runs counter to the literature and feel that your antibody or fixation conditions are illuminating a non-specific protein. The WB in Fig 2E shows that there is virtually no LEM2 in the "soluble" fraction. I would be more cautious on this cytoplasmic/nuclear pool interpretation. Biochemical nuclear and cytoplasmic fractionation would help clarify the signal in a NE vs a non-NE pool.*

*

As suggested by the reviewer, we will try harsher permeabilisation conditions to test the LEMD2 antibody. As we suggest in the manuscript however, we think that the “cytoplasmic” LEMD2 pool we observed by IF in the absence of pre-extraction is indeed unspecific. This is why we have performed the rest of the experiments with a pre-extraction step, that we have shown to give a specific LEMD2 signal that disappear upon depleting LEMD2 by siRNA.

4) Page 15: "Using a Proximity Ligation Assay (PLA), we showed a significant reduction in the number of PLA foci in CS-A cells compared to the WT(HA-CSA) cells, reflecting a reduced number of LEMD2-lamin A/C complexes (Figure 2G, 2H). This data suggests defects in the incorporation of LEMD2 into the NE and lamin protein complexes in CS-A cells". If you have less LEM2 in the NE, it is quite expected that you will have less "LEM2-laminA/C" complexes. To me the logic doesn't hold and this data does not suggest that there is an underlying defect in LEM2-lamin interaction. To ascertain whether there is such a defect one could perform an IP against LEM2 and quantify laminA/C, normalizing by the amount of LEM2 in the input.

We feel we may not have been clear in how we interpreted this data. What we mean is that in each individual cell, the number of Lamin-LEMD2 complexes is decreased, probably indeed due to the fact that there is less LEMD2 altogether within the nucleus in the absence of CSA. We will clarify this in the text.

5) "We overexpressed LEMD2-GFP and Flag-CSA constructs, followed by GFP pulldown in WT(HA-CSA) cells". Since the co-IP data are obtained in overexpression conditions (of both HA-CSA and Flag-CSA?), the authors should validate the interaction between LEM2 and CSA using an orthogonal approach. Perhaps anti-HA capture of the WT(HA-CSA) cells would allow you to immunoblot for endogenous LEM2?*

*

To address this point, we will try to immunoprecipitate HA-CSA and look at endogenous LEMD2.

6) Related to the CSA-LEM2 binding in the above experiment, the procedure involves combining a native detergent-extracted cytoplasmic pool with a denatured (RIPA-extracted) nuclear pool for performing the GFP-trap. From which pool was the tagged CSA bound to LEM2 in?*

*

We are sorry about the confusion. We didn’t try to run the IP from the different pools but instead from the combined pools, to ensure we were looking in the whole cell extract. We would expect however that the interaction occurs in the nuclear pool as both CSA and LEMD2 are nuclear proteins.

7) "The absence of CSA in CS-A patient cells does not affect the mobility of LEMD2 at the NE but instead decreases its interaction with A-type lamins". To me the fact that loss of CSA decreases LEM2-lamin interaction is not well supported (see point 3).

• *

See our response to point 4

8) "Here, we showed by immunoprecipitation that LEMD2 also interacts with CSA. This suggests that the recruitment and stabilization of LEMD2 to the NE is mediated by an interaction with CSA, although the mechanism remains unclear". I think this is an overstatement: there are no data suggesting that CSA recruits or stabilises LEM2 at the NE.

* *We will tone down this statement in the text

9) As the authors suggest in the discussion, it would be worth checking whether LEM2 overexpression is able to rescue some of the NE defects reported, strengthening the hypothesis that LEM2 levels are at least in part responsible for the phenotypes reported.

To address this point, we will perform LEMD2 overexpression in the CSA cells, and analyse the nuclear envelope defects and ruptures (shape and cGas foci quantification)

10) To me it is not clear how the reported phenotypes are interrelated. The first part of the manuscript shows that CSA interacts with LEM2, and that loss-of-function CSA impacts on LEM2 levels and LEM2-lamin interaction, suggesting a direct role for CSA at the nuclear envelope. The second part of the manuscript shows that cells with defective CSA have more actin stress fibres and releasing the cytoskeleton-nuclear tethering is able per se to rescue the nuclear membrane and cGAS phenotypes. How do the authors reconciliate these two parts? Is CSA directly involved in both inner nuclear membrane homeostasis and actin cytoskeleton modulation or is this latter role upstream and the NE defects a mere consequence of increased cytoskeleton rigidity?

At this point indeed we cannot draw definitive conclusions as to whether the two described phenotypes are inter-related. However, by addressing the other points raised by the reviewers, we hope this will help clarifying the mechanism.

11) It is not clear how or why actin stress fibres are elevated in the CS-A cells. Can the authors provide any insight based on their RNAseq analysis? Demonstrating a link to ROCK, LIMK or Rho signalling would be interesting and verifying ppMLC2 levels would help explain why contractility is enhanced. Additionally, is the increase in contractility dependent upon any of the genes identified as up- or downregulated in RNAseq? Presently, the manuscript is missing a link between its two halves.*

*

We would like to reiterate that the RNASeq analysis we performed was done on previously published data from another group (as described in the text). To address the point raised by the reviewer, we will look more specifically into our analysis to look at ROCK, LIMK or Rho signalling to see if any of these pathways appear to be modulated by the absence of CSA.

12) Related to point 1, the RNAseq comparison was performed on patient cells lacking CS-A and patient cells lacking CS-A and later over-expressing HA-CSA, and this comparison is used extensively for phenotype description in the manuscript. In isn't clear to me that this is the most insightful comparison to make; the rescue by overexpression is not as elegant as CRISPR reversion and the ko fibroblasts have presumably been surviving well in culture without CS-A before this protein was overexpressed. Can you validate the differential expression of any identified proteins in the acute HAP1 ko? Can you validate any of the differentially expressed proteins in comparison to normal fibroblasts (e.g., 13O6, as per Qiang et al., 2021)?

As we will validate our experiments in an additional cell model (as described above), we will also indeed validate the level of expression of cytoskeletal proteins upon CSA KO/rescue.

Minor comments

* - Page 14: "To characterize the NE phenotypes further, we obtained CS patient-derived cell lines carrying loss-of-function mutations in CSA (CS-A cells) or CSB (CS-B cells), and their respective isogenic control cell lines (WT(HACSA) and WT(HA-GFP-CSB))." What type of loss-of-function? Is the mutant protein still produced? In Fig 6A there seem to be a band in the CS-A blot (second lane), but in Fig 1B, there isn't. I think this is important to know to interpret the phenotype related to LEM2 interaction.*

We can clarify that in the text. Indeed, the loss of function mutation leads to the absence of CSA protein.

- Figure 1B is poorly annotated. What do - and + stand for? In general, I find a bit confusing how the WB are presented throughout the manuscript, specifically how the antibodies are reported (e.g., HA-CSA instead of HA). Please mark up all western blots with antisera used. Please make sure all expected bands are within the crops - e.g., Fig 3B, the anti-LEM2 blot should be expanded vertically to show the LEM2-GFP relative to endogenous LEM2.

We will correct these on the figures

- From the methods, it appears that you obtained a Please provide clarity on which construct was used in which figure, and verify that an N-terminally tagged LEM2 still localises to the NE.

We actually cloned LEMD2 into an empty pEGFP vector but still maintained LEM2-GFP. We will remove the C1plasmid from the methods to avoid confusion as we removed the MCS and GFP and just used the blank vector and inserted lem2-gfp as we obtained it.

- Fig 1I: there is some text on top of the upper panels (DAPI, cGAS, Merge).

• *

* - "Through gene ontology analysis, we found that genes involved in endoplasmic reticulum (ER) stress were differentially expressed (Figure 4B)". I don't think that the way data are shown in Fig 4B is effective. Since GO has been performed, I would replace the table with a GO enrichment analysis graph. Ensure to report all the data in a supplementary .xls so that others can see and reuse it. Is there a mandated repository that accepts RNAseq data?*

The RNAseq experiment and data was performed by another group and reported in a previous study, as referenced in the main text of the manuscript (Epanchintsev A, Costanzo F, Rauschendorf MA, Caputo M, Ye T, Donnio LM, et al. Cockayne’s Syndrome A and B Proteins Regulate Transcription Arrest after Genotoxic Stress by Promoting ATF3 Degradation. Mol Cell. 2017 Dec;68(6):1054-1066.e6.). Here, we only re-analysed their data using STRING pathway analysis, as detailed in the Material and Methods. However, as suggested by the reviewer, we will replace the table by a GO enrichment graph.

- The volcano plot looks weird with many values at the maximum log10 (P-value) - is the data processed appropriately?

As mentioned above, the RNA Seq analysis was performed and published in a different study. We think this is because the Y axis shows adjusted P values.

- Figure 5B: the legend says "Latrunculin A". Please correct.

We will correct this

- For a Wellcome funded researcher, I'm surprised that the mandated OA statement and RRS is absent from the acknowledgements.

We will of course comply with the open access policy of the Wellcome Trust. However, and based on the WT requirements detailed on their website, we believe the acknowledgement section complies with the funder’s policy: “All research publications must acknowledge Wellcome's support and list the grant reference number which funded the research reported.”

Maybe mention the changes in nuclear shape is not a causative of nuclear blebbing. But maybe not say that they are completely mutually exclusive phenotype to each other.

suggestion

Maybe say that we will overexpress LEMD2 in CS-A cells and show that the NE phenotype can be significantly rescued. This will add value to this part of story. I remember when I did the FRAP experiment, CS-A cells with expression of LEMD2-GFP (that doesn’t form aggregates) looks better in term of shape.

I think Anne, please check the plasmid map? According to the lab inventory (Plasmids Anne), it is LEMD2-GFP. So probably GFP is at C-terminus.

I think there was a part in discussion was LEMD2-GFP was mistakenly written as GFP-LEMD… But I am sure I used LEMD2-GFP throughout the work

We cloned it into an empty pEGFP vector but still maintained LEM2-GFP. Maybe remove the C1 in the methods to avoid confusion as we removed the MCS and GFP and just used the blank vector and inserted lem2-gfp as we obtained it.

Same construct was used for GFP pulldown and for FRAP. And we can see in FRAp that they localise to the NE. SO it should localise to the NE. Maybe mention that we will do a LEMD2-GFP over expression experiment in CS-A cells and show that they do localise to the NE.

I don’t remember fully if Denny did this and what came out. I thought he did and ER stress and cytoskeleton regulation came out as enriched terms?

Denny will have to check this but I think this is because the Y axis shows adjusted P values?? I have the same in my data and Jack told me this is an artefact of the analysis if you adjust for multiple comparisons and is something more often seen in mass spec data

Author Response

Reviewer #1 (Public Review):

Summary:

The study investigates the role of cylicin-1 (CYLC1) in sperm acrosome-nucleus connections and its clinical relevance to male infertility. Using mouse models, the researchers demonstrate that cylicin-1 is specifically expressed in the post acrosomal sheath-like region in spermatids and plays a crucial role in mediating acrosome-nucleus connections. Loss of CYLC1 results in severe male subfertility, characterized by acrosome detachment and aberrant head morphology in sperm. Further analysis of a large cohort of infertile men reveals CYLC1 variants in patients with sperm head deformities. The study provides valuable insights into the role of CYLC1 in male fertility and proposes CYLC1 variants as potential risk factors for human male infertility, emphasizing the importance of mouse models in understanding the pathogenicity of such variants.

We appreciate the comprehensive summary of reviewer 1.

Strengths:

This article demonstrates notable strengths in various aspects. Firstly, the clarity and excellent writing style contribute to the accessibility of the content. Secondly, the employed techniques are not only relevant but also complementary, enhancing the robustness of the study. The precision in their experimental design and the meticulous interpretation of results reflect the scientific rigor maintained throughout the study. Furthermore, the decision to create a second mouse model with the exact CYLC1 mutation found in humans adds significant qualitative value to the research. This approach not only validates the clinical relevance of the identified variant but also strengthens the translational impact of the findings.

We appreciate the positive comment of reviewer 1.

Weaknesses:

There are no obvious weaknesses. While a few minor refinements, as suggested in the recommendations to authors, could enhance the overall support for the data and the authors' messages, these suggested improvements in no way diminish the robustness of the already presented data.

In the recommendation for the authors, reviewer 1 mentioned a recent study (Schneider et al., eLife, 2023) showing that Cylc1-KO mice exhibits a reduced sperm count, an observation not noted in our current study. We would like to comment that that main and most important phenotype of Cylc1-KO mice in both studies is quite similar, including male subfertility and abnormal head morphology. We think the different targeting strategy and mouse strain may cause this discrepancy. In Schneider’s and our current studies, the total motility abnormality of Cylc1-KO mice are not observed. We appreciate the suggestion of reviewer 1 to further examine the detailed parameters of motility such as VCL, VSL, and ALH. Given that the head deformation is the most obvious phenotype of Cylc1-KO mice and the focus of our study, we feel sorry that this detailed analysis of sperm motility was not performed in the current stage. Reviewer 1 also asked whether Cylc1-KO female mice are fertile or not. Given that Cylc1 is an X chromosome gene and Cylc1-KO (Cylc1-/Y) mice are severely subfertile, we do not obtain enough Cylc1-KO female mice to examine their fecundity. We also would like to thank reviewer 1 to point out several inaccurate descriptions.

Reviewer #2 (Public Review):

Summary:

To verify the function of PT-associated protein CYLC1, the authors generated a Cylc1-KO mouse model and revealed that loss of cylicin-1 leads to severe male subfertility as a result of sperm head deformities and acrosome detachment. Then they also identified a CYLC1 variant by WES analysis from 19 infertile males with sperm head deformities. To prove the pathogenicity of the identified mutation site, they further generated Cylc1-mutant mice that carried a single amino acid change equivalent to the variant in human CYLC1. The Cylc1-mutant mice also exhibited male subfertility with detached acrosomes of sperm cells.

We appreciate the comprehensive summary of reviewer 2.

Strengths:

The phenotypes observed in the Cylc1-KO mice provide strong evidence for the function of CYLC1 as a PT-associated protein in spermatogenesis and male infertility. Further mechanistic studies indicate that loss of cylicin-1 in mice may disrupt the connections between the inner acrosomal membrane and acroplaxome, leading to detached acrosomes of sperm cells.

We appreciate the positive comment of reviewer 2.

Weaknesses:

The authors identified a missense mutation (c.1377G>T/p. K459N) from 19 infertile males with sperm head deformities. The information for the variant in Table 1 is insufficient to determine the pathogenicity and reliability of the mutation site. More information should be added, including all individuals in gnomAD, East Asians in gnomAD, 1000 Genomes Project for allele frequency in the human population; MutationTaster, M-CAP, FATHMM, and more other tools for function prediction. Then, the expression of CYLC1 in the spermatozoa from men with CYLC1 mutation should be explored by qPCR, Western blot, or IF staining analyses. Although 19 infertile males were found carrying the same missense mutation (c.1377G>T/p. K459N), their phenotypes are somewhat different. For example, sperm concentrations for individuals AAX765, BBA344, and 3086 are extremely low but this is not observed in other infertile males. Then, progressive motility for individuals AAT812, 3165, 3172, 3203, and 3209 are extremely low but this is also not observed in other infertile males. It is worth considering why different phenotypes are observed in probands carrying the same mutation.

We appreciate the suggestion of reviewer 2. First, Table 1 shows the information of the variant identified in CYLC1 gene, including allele frequency in gnomAD and functional prediction by SIFT, PolyPhen-2, and CADD. Given that mutant mice is a gold standard to confirm the pathogenicity of a variant, we generate Cylc1-mutant mice and Cylc1-mutant mice exhibit male subfertility with sperm acrosome detachment. The animal evidence is much more solid than bioinformatics prediction to confirm the pathogenicity of the identified variant in the CYLC1 gene. Second, the expression of CYLC1 in the spermatozoa from patients have been examined by IF staining (Fig. 5B). Unfortunately, the patients declined to continue in the project to donate more semen for qPCR and Western blot analyses. Third, the reviewer 2 asks why not all patients with CYLC1 gene mutation show the identical phenotype. Although some patients exhibit low sperm count or reduced motility, sperm head deformities are the shared phenotype of 19 patients. Many factors, such as way of life, may affect sperm quality. Perfectly identical phenotype of all 19 patients carrying the CYLC1 mutation is idealistic and will not always happen in clinical diagnosis. We also appreciate other suggestions from reviewer 2.

Author Response

The following is the authors’ response to the original reviews.

Reviewer 1

Major points:

R1C1: I appreciate that the data are aligned, in some points, with related studies of this niche. However, it would help the reader to have this alignment explored more extensively in the Discussion as well.

Answer: We acknowledge that the discussion would benefit from additional comparisons to the available datasets. We thus add the following comment after the first paragraph of the discussion: “Previous studies of the different sub-populations of SVZ progenitors were carried out using transcriptomic approaches based on the expression of various more or less specific markers. These approaches have made it possible to identify quiescent and activated neural stem cells as well as mature neuroblasts, but have been faced with the strong influence of the cell cycle on cell clustering. Indeed, neural progenitors in these studies cycling have been gathered in either “mitotic” clusters (Llorens et al. 2015, Zywitza et al. 2018, Cebrian et al. 2021) or “neural progenitor cells” clusters (Dulken et al. 2017) that had no clear biological significance and hindering identification of subtypes of SVZ cycling progenitors. Our study, combining, for the first time, characterization of Facs-isolated cells and an irradiation-based model of sequential regeneration, allowed to clearly distinguish the molecular profiles of TAP and iNB among cycling progenitors reflecting differences in their in vitro and in vivo respective potentials”.

R1C2: The data on multilineage differentiation, both in culture and upon engraftment, would be greatly strengthened by quantification. What is the relative yield of TUJ1/DCX-positive cells versus the other marker combinations? Specifically regarding the multilineage differentiation in vitro - because different media conditions are used to generate each lineage, it may be difficult to determine relative yield. Could a differentiation system that allows production of all 3 lineages be used instead?

If the fraction of non-DCX/TUJ1-labeled progeny is low, particularly in vivo, this might suggest that while multilineage differentiation is possible, it is a much less likely cellular state outcome than production of mature neuroblasts. Some suggested references with examples of the culture conditions, experimental conditions, and discussions highlighted in the public review: Culture conditions that allow simultaneous trilineage differentiation. PMID: 17615304 Influence of culture conditions on potency: similar to issues covered in PMID: 21549325.

Answer: We agree with the reviewer that quantification of a multilineage differentiation in vitro would improve the characterization of the relative potencies of the different SVZ progenitor.

According to PMID: 17615304 and PMID: 21549325, and in agreement with our own experience, the only culture condition that allows neurosphere-derived neural progenitors to differentiate in vitro into the three lineages is the removal of mitogens from the culture medium. However, this does not work on freshly isolated SVZ cells, which remain in an undifferentiated state in this condition.

This is why we chose to use specific differentiation media for each of the 3 lineages as in Figure 1C. It is also for this reason that we performed as many experiments as possible in vivo rather than in vitro as in Figure S2. In the new version, we have added a quantitative analysis of stainings by antibodies against GFAP, CNPase or DCX of GFP-positive cells persisting at IS, where high number of grafted cells were found in Figure S2B. This was performed by using the NIS software measuring eGFP-, GFAP-, CNPase- and DCX-positive areas. The intersection between each marker and eGFP areas was then determined as a percentage of staining (Figure S2C). The results showed that approximately one third of GFP+ cells expressed GFAP or DCX. The quantitative analysis of CNPase expression was complicated by CNPase-positive host cells, but the stronger CNPase staining in eGFP-positive areas clearly revealed the expression of CNPase by a significant proportion of eGFP-positive cells.

R1C3: Additionally, for claims similar to what is currently made in the text, it would be extremely valuable to confirm the purity of the sort for each population - for example by fixing and staining the sorted fraction with additional antibodies that confirm cell identity.

Answer: We have previously shown in Daynac et al. 2013 that s-iNB expressed the neuroblast markers CD24 and DCX, but also markers of neural progenitors such as Mash1, a basic helix-loop-helix transcription factor. As suggested by the reviewer, we have further investigated the expression of other markers of neural progenitors by sorted cells. The results showed that the proportion of DLX2+ cells a marker of proliferating progenitors (Doetsch et al. 2002) was very high in aNSC/TAP (98%) and progressively decreased in iNB (82%) and mNB (25%). Similarly, the expression of the transcription factor SOX2 that plays an essential role in the maintenance of neural progenitors (PMID: 25126380) accounted for 78% of aNSC/TAP, 70% of iNB and 17% of mNB.

Altogether, these new data confirmed the identity of the different cell populations and particularly that of iNB. They are commented at the beginning of the Results and shown in Figure S1.

R1C4: Line 125: GFAP alone doesn't necessarily indicate a "conversion to NSCs" - this conclusion could be greatly strengthened by inclusion of more markers, particularly at the protein level, or cyto-architectural studies.

Answer: We agree with the reviewer that GFAP expression alone is not sufficient to evidence the presence of NSC in the SVZ. We have thus modified the text accordingly: “Importantly, eGFP+ cells were present in the SVZ of all the animals transplanted with eGFP+s-iNB and eGFP+s-NSC/TAP (Fig. 1Db, Fig. 1Dc), some of them expressing GFAP indicating the generation of astrocytes, and therefore possibly NSC”.

R1C5: Could these cellular states be reflective of preferential translation of DCX? It would be very helpful to see the flow cytometry sort data for iNBs / mNBs used in Figure 6, particularly if these cells were also fixed and stained directly for DCX protein.

Answer: As suggested by the reviewer, freshly FAC-sorted iNB and mNB were fixed and labelled with an anti-DCX monoclonal antibody after permeabilization. As shown in the figure below, we found a higher level of DCX expression in mNB than in iNB. Therefore, this result tends to indicate that the proliferation capacity is somehow related to the level of DCX expression. However, because of the relatively low importance of this result, we decided not to include them in the manuscript.

Author response image 1.

Modal histogram representation of DCX expression level in unstained, iNB and mNB cells determined by flow cytometry (FlowJo).

<R1C6: Figure S8 is all zeroes, showing the GFP+Dcxhigh NBs do not retain proliferative capacity. But we don't get a direct experimental comparison to EGFPnegative/lowDcxlow iNB engraftment, which would strengthen the conclusions of the paper.

Answer: Unfortunately, there is no method available to analyse the eGFPnegative/lowDcxlow iNB engraftment: by definition, these cells do not express eGFP and the use of a tracker is not appropriate for long periods of time — and thus a high number of cell divisions — after engraftment. However, to us, this control is not needed to conclude that GFP+Dcxhigh iNB have no (or at least a lower) stem cell potential in vivo considering that we have shown in Figure 1 and Table 1 that the whole iNB population is able to generate the different types of neural cells.

R1C7: Transplant data in Table 1 - a relatively small proportion of transplant derived cells are in OB, etc. Given that A cells are thought to cycle at least once in vivo, is this expected?

Answer: The reviewer is right considering that a relatively small proportion of transplant derived cells were found in the OB. However, we should consider that we used immunocompetent mice as receivers, which could have significantly reduced the engraftment efficiency, and the migration of engrafted cells outside the injection site.

R1C8: A caveat is that there is not much functional testing of the proposed model, especially for the interconversion of iNB states suggested by the diagram in Figure 7. The text is relatively restrained in proposing this model, so it is reasonable to keep - but perhaps should be noted that this part of the model will need additional testing.

Answer: Data presented in Figure 6 clearly suggest that Dcxhigh iNB have similar in vitro potential than Dcxlow iNB, whereas they don’t have such potential in vivo (Figure S10). This suggests that, providing they are in appropriate conditions, Dcxhigh iNB could reacquire stem/progenitor properties. However, we agree that this hypothesis requires further investigation. Therefore, as suggested by the reviewer, we have added in the Figure 7 legend: “Possible interconversion of iNB states would require further experimental confirmation.”

Additional minor points:

R1C9: Introduction: the SVZ is described as "the lateral wall" - however, several works in the mouse have also examined the medial wall and callosal roof, as cited later in the intro. Suggest rephrasing the second sentence (line 48) and later sentence (line 66) to clarify that "the SVZ" encompasses all of these subregions, they are not necessarily separate niches. Answer: As indicated by the reviewer, the SVZ encompasses distinct subdomains, with NSCs having a regional identity based on their location in the lateral or septal wall of the ventricle and generating different types of neuronal and glial progeny (PMID:34259628.). To address the reviewer concern about possible confusion and clearly indicate that SVZ encompass several subdomains, we have modified the sentence line 66 as follows: “Since then, the single cell RNA-sequencing has revolutionized the field and has made it possible to precisely elucidate the transcriptome of SVZ cells present in the LW and in the septal wall which also harbors NSC niches”.

However, we did not modify the line 48, since in this sentence we just indicate that the largest neurogenic niche in the adult brain reside in the LW of the SVZ.

R1C10: Line 77: "exposure" not "exposition"

Answer: The error has been corrected in the revised manuscript.

R1C11: As noted in the Public Review - the use of the term "D1/D2" cells seems likely to confuse readers who are also versed in dentate gyrus neurogenesis. Recommend removing this term from the manuscript.

Answer: We agree that the D1/D2 terminology could bring confusion, D cells referring to Tanycytes in the hypothalamus. We now refer to iNB1 for DcxLow iNB and iNB2 for DcxHigh iNB in the revised manuscript.

Reviewer 2

Major comments:

Lack of rigor

R2C1: There is a lack of appropriate normalization controls for the microarray data. As there is a decreased level of transcription in quiescent NSCs, there needs to be a cell number control (spike-ins based on cell numbers). Without this normalization, the readout can be greatly skewed.

Answer: We agree that qNSC are marked by a decreased level of transcription due to quiescence. To overcome this problem in the Clariom assays, we thus chose to calibrate each population, with a fixed amount of cRNA and cDNA using Hela cells as internal control. We totally agree that this method is not optimal but it appears to be efficient in the end. Indeed, it should be noticed that it has been adopted, thus with the same rigor, in other microarray studies published in the field (PMID: 24811379) and also on skeletal muscle cells (PMID: 29273087). Moreover, interestingly the transcriptomic signature of qNSC matches perfectly with those from other studies and particularly to those of related clusters in single cell experiments (including ours, Figure S5). This is probably linked to the fact that more importantly that the number of cells, the main characteristic of these cells is the lack of expression of genes involved in cell proliferation and metabolism. Whatever so, these data confirming previously published are not the main information of our manuscript, which is mainly dedicated to the characterization of proliferating cells, which is not impaired by our choices of normalization.

R2C2: The absolute segregation of clusters in the single-cell analysis is currently entirely in agreement with the cell cycle stage. This suggests that in the author's analysis, the clustering in 3F is entirely shaped by the cell cycle, making that the defining characteristic of the author's definitions for their cell types. Has an analysis been done that regresses out cell cycle-associated genes to see if there are clusters for different cell states/types that are identified in the absence of cell cycle stage being the defining factor? (Barron and Li, 2016). For example, just as you would see a difference in cluster if you are a quiescent or activated NSC as compared to a neuroblast for example, even without the contribution of cell cycle. These are different cell types.

Answer: We agree that cell cycle regression would theoretically allow for further discrimination between cycling cells along successive neurogenic stages. We have already performed regression using several methods, including regressing using S- and G2/M-score regression as indicated in the Seurat workflow, removing cell cycle-related PCs from UMAP calculation as used in the Cebrian-Sylla study, and using alternative gene sets such as the ones provided by the tricycle method (PMID: 35101061). These regression methods have all been used on our datasets, the original Cebrian-Sylla datasets and a combination of our datasets with the Cebrian-Sylla original datasets to increase cell number and clustering resolution. However, none of these methods modified the clustering of cycling cells.

In fact, the strong influence of the cell cycle over clustering highlights the relevance of our depletion/replenishment approaches to decipher the molecular changes masked by the cell cycle, as discussed below.

R2C3: The use of the DCX-CreERT2 line is a lineage tracing line. Once DCX is expressed, Cre recombines the DNA to allow for fluorescence. It is binary, on or off associated with DCX expression. And once on, it is always on, whether the cell is currently expressing DCX or not. As the authors had previously described a DCXlow condition, the eGFP- cells would not reflect DCXlow, but no DCX at all. And the eGFP+ cells may not be currently expressing DCX anymore. The authors should have used a system where the DCX promoter itself drives fluorescence.

Answer: We took advantage of the DCX-CreERT2 line to demonstrate that some neural cells that have recently acquired DCX expression (i.e. eGFP+ iNB) could keep (or recover) the potential of neural progenitors in vitro. Of course, some of these GFP+ cells could have stopped to express DCX. This is probably the case when they differentiate into astrocytes and oligodendrocytes in vitro as shown in Figure 6.

Whatever so, the use of the Dcx promoter as a direct driver of eGFP fluorescence would have totally impeded our capacity to demonstrate such changes in cell fate in vivo because of the impossibility to track oligodendrocytes or astrocytes derived from iNB because of the loss of Dcx expression.

R2C4: The lack of analysis of images (differentiation, for example) limits the conclusions of the in-vitro data, and the images with unclear staining, limit the conclusions of the in-vivo experiments.

Answer: This comment is similar to that of R1C2. We have now added a quantification in Figure S2.

R2C5: The cited difference in splicing differences in cell types was interesting (though did not show up in the transcriptome enrichment analyses Fig S2) and would be something to further pursue, however, this was a very limited analysis. There was no further study of these splicing mediators beyond single-cell data.

Answer: We now show enrichments of GO terms corresponding to mRNA splicing isoforms in the different types of sorted SVZ cells (Figure S4). This analysis clearly revealed that spliced genes in SVZ cells are mainly involved in neuron development and neurogenesis. Interestingly this also showed that qNSC logically differed from the other cell types by splicing concerning genes involved in mitosis and cell cycle, consistently with their quiescent state. More importantly, GO annotations of differentially spliced isoforms further confirmed that s-TAP and s-iNB have distinct features. We agree with the reviewer that further analysis of splicing mediators would be very important for understanding molecular changes involved in neurogenesis. However, we think that it is largely beyond the scope of this study.

R2C6: Fig 1C - Show values, not just pictures. You may need to shift your current differentiation paradigm to do so by removing growth factors instead of unique differentiation conditions.

Answer: See the answer to R1C2.

R2C7: Fig S1A - Stainings for GFAP and DCX are not clear. It is very hard to distinguish which cells are associated with these signals.

Answer: This figure (now Figure S2A) shows an eGFP+iNB cell (white arrow) that has reached the rostral migratory stream and expressed DCX (inset a3), but not GFAP (inset a2). This is now indicated in the figure legend. We have also moved the arrow for more clarity.

R2C8: Fig S1B2 - There is red staining everywhere, so it is very hard to see a specific CNPase signal.

Answer: We have added a new figure (Fig S2B) distinguishing eGFP+CNPase+ cells (yellow arrows) from eGFP+CNPase- cells (white arrow).

R2C9: Line 174 - It's the mRNA that you are detecting is being downregulated - be more specific as you are not showing protein downregulation.

Answer: We specified, "encoding" a major splicing repressor in the Line 174 text to refer to the mRNA: “Interestingly, Ptbp1, encoding a major splicing repressor”.

R2C10: Line 189 - text in this line have some clusters not shown in the figure - (clusters 6 and 15, DCX+ Ki67+ neuroblasts) - which would be an important thing to visualize. As is shown now, the authors are only showing that iNBs are similar to mitotic TAPs.

Answer: Clusters 6 and 15 have been added to Figure S5.

R2C11: Fig 3D-E - Why is cluster 17 called aNSCs (3E) when it has the highest GFAP (Fig 3D). Typically, the highest GFAP cells are qNSCs or astrocytes, not aNSCs.

Answer: We previously reported that the level of gfap mRNA expression in neural stem cells (quiescent and activated) did not exactly reflect the amount of protein in these cells. This is the reason why we also used the Slc1a3 marker (Glast), which is highly expressed both at the RNA and protein levels in quiescent NSCs (Daynac et al. 2013).

R2C12: Line 216 - You said in line 216 cluster 13 were astrocytes, then you said in line 227 that cluster 13 was s-qNSC. Which is it?

Answer: This is due to the fact that we performed two distinct analyses.

In the first one (line 216), cells were scored based on datasets provided by Cebrian et al. with one dataset containing genes enriched in astrocytes, and another one, genes enriched in quiescent B-cells. Therefore, cluster 13 was shown to contain 73% cells expressing astrocyte markers, whereas cluster 4 gathered cells expressing both qNSC (B-cells, 48%) and astrocyte (52%) genes.

In the second one (line 227), cells were scored using our transcriptomic signatures of FAC-sorted SVZ cells, which do not include differentiated astrocytes. We demonstrated that the cluster 13 cells only expressed s-qNSC genes.

R2C13: Line 214 - While other clusters were all named in lines 214-221 that were then further discussed in lines 227-230, clusters 15 and 19 were not. You associate both of those clusters with s-iNB - what was it associated with in the above section?

Answer: Lines 219-221 have been reworded as follows: Clusters 10, 5, 15, 12, and 8 were defined as cycling progenitors based on the expression of proliferative markers such as Top2a, Mki67, Ascl1. Clusters 1, 3, 7 and 9 were identified as mNB due to the loss of Mki67, Top2 a and Ascl1 expressions and the expression of Robo2 and Dcx. Cluster 19 that have lost Ascl1 but still expressing Top2a and Mki67 together with Robo2 and Dcx appears at the transition between iNB and mNB.

R2C14: Fig 3I-J - 5 days after irradiation, I would like to see from tissue slices how many cells are dividing compared to 1day post-irradiation and controls. In other paradigms, such as temozolomide experiments (Kalamakis et al), by 5 days we should see less cells in quiescence and more of those quiescent cells exiting quiescence into the cell cycle. Why would there be more cells in quiescence in the irradiated brain? Even if they are radiation resistant, the base number should be comparative between controls and irradiated, which is not what you show in Fig 3I-J. And R2C14)

Line 234-235 - the text says normalized to numbers of qNSCs which is supposed to be the same (which I agree should be the same). However, your graph in 3I and J shows more qNSCs in irradiated conditions, which would influence greatly and is currently hard to interpret.

Answer: As stated by the reviewer, there is no increase in the absolute number of quiescent cells in the irradiated SVZ. The reconstitution of SVZ cell populations after 4Gy irradiation has already been studied by our group (Daynac et al. 2013, see Fig. 3F), showing that s-iNB and s-mNB are still under-represented after 5 days, while qNSC are in similar numbers as in unirradiated SVZ. Therefore, this led to an over-representation of quiescent cells and early SVZ progenitors in Figure 3J as compared in Figure 3I.

R2C15: Fig 6A - the authors show a significant difference in neurospheres between eGFP- (DCX-) and eGFP+ (DCX+) iNBs - as would be expected as DCX suggests a further commitment towards neurogenic fates, yet your population doubling is the same.

Answer: To determine the population doublings, the medium was changed and cells numbered every 7 days. This condition masked the differences between two cell populations reaching the plateau phase at different time, explaining why eGFP-iNB and eGFP+iNB could not be clearly distinguished by this technique.

R2C16: Fig 6C - Differentiation data (in-vitro) should be quantified in 6C, just as was mentioned for 1C. These values should be done for both of the populations (eGFP-iNB, and eGFP+iNB) and not just compared to the previous pictures which were on total iNB. Again, numbers are required, not just picture examples.

Answer: Quantitative data have been given in Figure 6D showing that approximately 60-80% of cells eGFP+iNB are able to differentiate in either neurons, oligodendrocytes or astrocytes. We did not analyze the differentiation of eGFP-iNB since it would not add any supplementary information.

R2C17: Fig S8 - The authors did not show if the lack of engraftment of eGFP+ cells is due to the transplant (previously you showed only 2/3 worked in a similar paradigm). It would be helpful if the authors would have some means to visualize the DCX low cells to confirm they worked as before in the transplantation (another color? Another type of mouse (Thy1 antigen differences)?) Answer: Unfortunately, the Thy1 antigen has not been documented in mouse subventricular zone progenitors, but only in neurons (PMID: 10813783). Thy1 antigen has also been described in bipotent glial progenitor cell (GCP) from the developing human brain giving rise to oligodendrocytes (PMID: 36931245).

As shown, in Figure S10 we have performed 5 grafts with s-iNB eGFP+ cells, 2 alone and 3 mixed with eGFP- cells and never found any eGFP+ cells 5 weeks after grafting. Moreover, we did not find any eGFP+ cells in the brains of 3 other animals 2 weeks after grafting with s-iNB eGFP+ cells (These data have been added to Figure S10). As compared to the results described in Figure 1 this clearly shows that iNB DCXhigh are not able to generate persistent cells in the grafted brains similarly as mNB.

R2C18: Fig S8 - Why were there no eGFP cells even at the injection site? DCX expression promotes migration, indeed DCX expression becomes very high in cells in the SVZ as they begin to exit to go to the migratory stream. If one didn't see migration, one would expect you would still have survival. Currently, the authors show no cells at 5 weeks, however, they would need to show earlier timepoints as well to determine what is happening with these cells. It is possible these GFP+ cells are not even expressing DCX anymore (see above).

Answer: As stated above, we did not find any GFP+ cells in the brains of 3 other animals 2 weeks after grafting with s-iNB eGFP+ cells (see Figure S10).

R2C19: Line 320 - the authors suggest a subpopulation of NEURONS continues to divide and cite 2 works from the 1990s showing proliferating SVZ cells can differentiate. Our knowledge of this system has come dramatically forward since the 1990s as well as technologically, and to date, neurons have not been shown to divide.

Answer: We apologize for this lack of clarity, as we agree that neurons correspond to differentiated non-cycling cells, but we used the terminology used in these articles. The incorrect part of the sentence Line 320 has thus been deleted from the text.

R2C20: Fig 7 - The whole figure is based on changing levels of RSR genes which were not confirmed in any way to be involved in any of these stages, only descriptively in single-cell analyses.

Answer: As stated above, in our opinion, further characterization of the involvement of RSR genes in neurogenesis is largely beyond the scope of our manuscript. Nevertheless, we think that the role of RSR genes in neurogenesis is an important question that should be addressed in further studies.

Overstatement of findings

R2C21: Fig 1 - Authors did not compare all cell types in each condition but made overstatements about their relationships to each other between graphs. There should also be separate graphs showing all cell types at 4% and a separate one at 20%.

Answer: In the revised version, Figure 1 shows the graph comparing all cell types at 4%O2 and a separate one at 20% as requested by the reviewer. The graphs clearly shows that 4%O2 promotes iNB proliferation compared to the 20% condition.

R2C22: Fig 1D-b2 - Why does DCX look nuclear? One can't say they are only NSCs if they are GFAP as astrocytes also express GFAP. The authors would need another marker to separate those populations. In the text, the authors say expressing GFAP (line 124) which means NSC, but then in line 127 expressing GFAP means astrocytes - which further shows you need additional markers to validate those 2 different cell types. Answer: DCX nuclear translocation has been shown to improve cellular proliferation (PMID:32050972).

As indicated in R1C4. The text has been modified as follows: “Importantly, eGFP+ cells were present in the SVZ of all the animals transplanted with s-iNB eGFP+ and s-NSC/TAP eGFP+ (Fig. 1Db, 1Dc), some of them expressing GFAP indicating the generation of astrocytes, and therefore possibly NSC”.

R2C23: Fig S2 - The transcriptome signature for s-iNBs is very similar to s-TAP, basically suggesting the iNBs are further along in cell cycle.

Answer: This is now the Figure S3. Functional enrichment analysis of individual transcriptome signatures revealed that both s-TAP and s-iNB are enriched in genes related to the cell cycle although with different GO terms enrichments. Indeed, s-TAP are enriched in genes related to G1, G1/S and S phase (but with low -log10 adjusted p-values) and s-iNB with genes related to cell cycle mitosis and M phase (with high -log10 adjusted p-values).

We have previously shown that around 33 % s-iNB have DNA content>2N, versus around 26% of s-TAP and s- aNSC (Daynac et al. 2013), which is in accordance with GO terms enrichments. However, these data have also shown that most s-iNB and s-TAP are in G1, indicating that siNB are not just further along mitosis than TAP.

Moreover, our transcriptomic data clearly show that s-iNB are distinct from s-TAP: 1) according to principal component analyses (Figure 2B et C), the whole transcriptome of s-TAP is closer to that of s-aNSCs than to that of s-iNB (10% variations in PCA2), 2) the heatmap in Figure 2D shows that they have different RSR genes expression profiles, 3) the new Figure S4 shows that GO annotations of differentially spliced isoforms further confirmed that s-TAP and s-iNB have distinct features, and 5) Figure S5 shows that s-iNB expressed genes associated to either TAP or NB that have been described in previous studies, whereas s-TAP did not express genes associated to NB, but look closer to aNSC. Finally, scRNAsq cell clusters related to s-iNB are distinct from the cluster related to s-TAP as shown 1) in Figure 3D and 2) in Figure 4.

R2C24: Fig 3 - The lack of information about timepoint 0 after irradiation, and when proliferation and cell cycle entry begins again following irradiation, limits our interpretation of the single-cell irradiated data.

Answer: We have previously reported the relative abundance of each SVZ neural progenitors in the young adult mouse brain in several papers. Particularly, we based our interpretation on our SVZ irradiation model reported in Daynac et al. 2013 demonstrating a radio resistance of qNSC re-entering into the cell cycle as early as 2 days after 4Gy irradiation successively regenerating aNSC, TAP then iNB and mNB.

R2C25: Fig S3 - These results effectively show that the s-aNSCs and s-TAPs are actually less specific when compared to that same identity in other studies, and that the iNBs are most similar to mitotic TAPs. This supports what was mentioned above, which is that the transcriptional signatures are very similar between the s-TAPs and i-NBs, showing these are not a unique cell state, but just a bit further along mitosis within the TAP cell state.

Answer: This is now the Figure S5. In this figure, we show that s-iNB expressed genes associated to either TAP or NB that have been described in previous studies, whereas s-TAP did not express genes associated to NB, but look like closer to aNSC. As indicated above in R2C23, s-iNB are not just a bit further along mitosis within the TAP cell state. Indeed, we give several data showing that s-iNB and s-TAP have different transcriptomic profiles.

R2C26: Fig 4B - The focus on Ptbp1 as being associated with the iNB cluster border to mNB is expected as all previous studies of Ptbp1 have focused on its role in the progression of other cell types through the cell cycle, its control of cell cycle regulators, and a cell cycle mRNA regulon (Monzon-Casanova et al, 2018, 2019, 2020). This further supports these analyses are specifically defined by cell cycle stages.

Answer: We totally agree that Ptbp1 expression distinguishes cycling cells from postmitotic neuroblasts in accordance with previously published paper, and that based on this unique gene we cannot find any differences between cycling cells ie. aNSC, TAP and iNB. However, as shown in the manuscript and stated above (R2C23 and 25), these cells can be distinguished by their respective expression of many other genes, including other RSR genes.

R2C27: Line 281-282 is an overstatement - the authors suggest that this is a new type of cycling neural progenitor - when all studies point to it being the end of mitosis TAPs as they go on their way to mNBs. This clearly shows a trajectory and not a defined, binary cell type.

Answer: We agree with this statement that the use of the word "type" was misleading, and changed it to "stage" to better reflect that s-iNB are a distinct stage along the differentiation process according to our pseudotime cell-trajectory analysis.

Author response image 2.

Pseudotime analysis using Monocle 3 (excluding the cluster 13 corresponding to astrocytes and starting from s-qNSC) revealed two branches starting from s-TAP, one towards cell cycle the other towards neuronal differentiation.

minor comments:

R2C28: Fig 3D - For ease, please define what you called the clusters in 3D - not just cluster numbers

Answer: We chose not to call the clusters in 3D because their identification (Group names) is based on data presented after in Figures 3E, F and G.

R2C29: Fig 3E-F - Show astrocytes by text in 3E and F

Answer: As discussed above, astrocytes cannot be shown in these figures because they are based on our signatures which did not include astrocyte signature.

Author Response

The following is the authors’ response to the original reviews.

eLife assessment

This manuscript presents a valuable approach to exploring CD4+ T-cell response in mice across stimuli and tissues through the analysis of their T-cell receptor repertoires. The authors use a transgenic mouse model, in which the possible diversity of the T-cell receptor repertoire is reduced, such that each of a diverse set of immune exposures elicits more detectably consistent T-cell responses across different individuals. However, whereas the proposed experimental system could be utilized to study convergent T-cell responses, the analyses done in this manuscript are incomplete and do not support the claims due to limitations in the statistical analyses and lack of data/code access.

We worked to address the reviewers' concerns below, point-by-point.

All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

We added the Data availability statement to the manuscript.

Public Reviews:

Reviewer #1 (Public Review):

The authors investigate the alpha chain TCR landscape in conventional vs regulatory CD4 T cells. Overall I think it is a very well thought out and executed study with interesting conclusions. The authors have investigated CDR3 alpha repertoires coupled with a transgenic fixed CDR3beta in a mouse system.

Strengths:

• One of a kind evidence and dataset.

• State-of-the-art analyses using tools that are well-accepted in the literature.

• Interesting conclusions on the breadth of immune response to challenges across different types of challenges (tumor, viral and parasitic).

Thank you for the positive view.

Weaknesses:

• Some conclusions regarding the eCD4->eTreg transition are not so strong using only the data.

The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• Some formatting issues.

We are working on the manuscript to correct minor errors and formatting.

Reviewer #2 (Public Review):

This study investigates T-cell repertoire responses in a mouse model with a transgenic beta chain, such that all T-cells in all mice share a fixed beta chain, and repertoire diversity is determined solely by alpha chain rearrangements. Each mouse is exposed to one of a few distinct immune challenges, sacrificed, and T-cells are sampled from multiple tissues. FACS is used to sort CD4 and Treg cell populations from each sample, and TCR repertoire sequencing from UMI-tagged cDNA is done.

Various analyses using repertoire diversity, overlap, and clustering are presented to support several principal findings: 1) TCR repertoires in this fixed beta system have highly distinct clonal compositions for each immune challenge and each cell type, 2) these are highly consistent across mice, so that mice with shared challenges have shared clones, and 3) induction of CD4-to-Treg cell type transitions is challenge-specific.

The beta chain used for this mouse model was previously isolated based on specificity for Ovalbumin. Because the beta chain is essential for determining TCR antigen specificity, and is highly diverse in wildtype mice, I found it surprising that these mice are reported to have robust and consistently focused clonal responses to very diverse immune challenges, for which a fixed OVA-specific beta chain is unlikely to be useful. The authors don't comment on this aspect of their findings, but I would think it is not expected a priori that this would work. If this does work as reported, it is a valuable model system: due to massively reduced diversity, the TCR repertoire response is much more stereotyped across individual samples, and it is much easier to detect challenge-specific TCRs via the statistics of convergent responses.

This was to some extent expected, since these mice live almost normally and have productive adaptive immune responses and protection. In real life, there are frequent TCR-pMHC interactions where the TCR-alpha chain dominates (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701794/; https://pubmed.ncbi.nlm.nih.gov/37047500/). On the fixed TCR-beta background this mechanics starts working full-fledged, essentially substituting TCR-beta diversity, at the extent of relatively simplified TCRab repertoire and probably higher cross-reactivity.

We agree that this is a valuable model, for sure, and indicated this in the last sentence of our Discussion. Now we are also adding this point to the abstract.

While the data and analyses present interesting signals, they are flawed in several ways that undermine the reported findings. I summarize below what I think are the most substantive data and analysis issues.

(1) There may be systematic inconsistencies in repertoire sampling depth that are not described in the manuscript. Looking at the supplementary tables (and making some plots), I found that the control samples (mice with mock challenge) have consistently much shallower sampling-in terms of both read count and UMI count-compared with the other challenge samples. There is also a strong pattern of lower counts for Treg vs CD4 cell samples within each challenge.

The immune response of control mice is less extensive, as it should be. Just like the fact that the number of Tregs in tissues is lower than CD4, this is normal. So this all follows the expectations. But please note that we were very accurate everywhere with appropriate data normalisation, using all our previous extensive experience (https://pubmed.ncbi.nlm.nih.gov/29080364/).

In particular (now adding more relevant details to Methods):

For diversity metrics calculations, we randomly sampled an equal number of 1000 UMI from each cloneset. Samples with UMI < 700 were excluded from analysis.

For amino acid overlap metrics calculations, we selected top-1000 largest clonotypes from each cloneset. Samples with clonotype counts < 700 were excluded from analysis.

For nucleotide overlaps metrics calculations (eCD4-eTreg), we selected top-100 clonotypes from each cloneset. Samples with clonotypes < 100 were excluded from analysis.

The top N clonotypes were selected as the top N clonotypes after randomly shuffling the sequences and aligning them in descending order. This was done in order to get rid of the alphabetical order for clonotypes with equal counts (e.g. count = 1 or 2).

Downsampling was carried out using software vdjtools v.1.2.1.

(2) FACS data are not reported. Although the graphical abstract shows a schematic FACS plot, there are no such plots in the manuscript. Related to the issue above, it would be important to know the FACS cell counts for each sample.

Yes, we agree that this is valuable information that should be provided. Unfortunately, this data has not been preserved.

(3) For diversity estimation, UMI-wise downsampling was performed to normalize samples to 1000 random UMIs, but this procedure is not validated (the optimal normalization would require downsampling cells). What is the influence of possible sampling depth discrepancies mentioned above on diversity estimation? All of the Treg control samples have fewer than 1000 total UMIs-doesn't that pose a problem for sampling 1000 random UMIs?

Indeed, I simulated this procedure and found systematic effects on diversity estimates when taking samples of different numbers of cells (each with a simulated UMI count) from the same underlying repertoire, even after normalizing to 1000 random UMIs. I don't think UMI downsampling corrects for cell sampling depth differences in diversity estimation, so it's not clear that the trends in Fig 1A are not artifactual-they would seem to show higher diversity for control samples, but these are the very same samples with an apparent systematic sampling depth bias.

We evaluated this approach through all our work, and summarised in the ref: https://pubmed.ncbi.nlm.nih.gov/29080364/. Altogether, normalising to the same count of randomly sampled UMI seems to be the best approach (although, preferably, the initial sequencing depth should be essentially higher for all samples than the sampling threshold used). Initial sorting of identical numbers of cells and ideally uniform library preparation and sequencing is generally not realistic and does not work in the real world, while UMI downsampling does the same work much better.

(4) The Figures may be inconsistent with the data. I downloaded the Supplementary Table corresponding to Fig 1 and made my own version of panels A-C. This looked quite different from the diversity estimations depicted in the manuscript. The data does not match the scale or trends shown in the manuscript figure.

There was a wrong column for Chao1, now correcting. Also, please note that we only used samples with > 700 UMI. Supplementary Table now corrected accordingly. Also, please note that Figure 1 shows the results for lung samples only.

(5) For the overlap analysis, a different kind of normalization was performed, but also not validated. Instead of sampling 1000 UMIs, the repertoires were reduced to their top 1000 most frequent clones. It is not made clear why a different normalization would be needed here. There are several samples (including all Treg control samples) with only a couple hundred clones. It's also likely that the noted systematic sampling depth differences may drive the separation seen in MDS1 between Treg and CD4 cell types. I also simulated this alternative downsampling procedure and found strong effects on MDS clustering due to sampling effects alone.

That’s right, for the overlap analysis (which values are mathematically proportional to the clonotype counts in both compared repertoires, so the difference in the counts causes major biases) the right way to do it is to choose the same number of clonotypes. See Ref. https://pubmed.ncbi.nlm.nih.gov/29080364/.

We kept only samples with > 700 for the overlap analyses. Some relatively poor samples are present in all challenges, while MDS1 localization has clear reproducible logic, so we are confident in these results.

It is not made clear how the overlap scores were converted to distances for MDS. It's hard to interpret this without seeing the overlap matrix.

This is a built-in feature in VDJtools software (https://pubmed.ncbi.nlm.nih.gov/26606115/). See also here: https://vdjtools-doc.readthedocs.io/en/master/overlap.html.

(6) The cluster analysis is superficial, and appears to have been cherry-picked. The clusters reported in the main text have illegibly small logo plots, and no information about V/J gene enrichments. More importantly, as the caption states they were chosen from the columns of a large (and messier-looking) cluster matrix in the supplementary figure based on association with each specific challenge. There's no detail about how this association was calculated, or how it controlled for multiple tests. I don't think it is legitimate to simply display a set of clusters that visually correlate; in a sufficiently wide random matrix you will find columns that seem to correlate with any given pattern across rows.

Particular CDR3 sequences and VJ segments do not mean much for the results of this manuscript. Logos are given just for visual explanation of how the consensus motifs of the clusters look like.

We now add two more Supplementary Tables and a Supplementary Figure with full information about clusters.

We disagree that the Supplementary Figure 1 (representing all the clusters) looks “messy”. Vice versa, it is surprisingly “digital”, showing the clear patterns of responses and homings. This becomes clear if you visually study it for a while. But yes, it is too big to let the reader focus on this or that aspect. That is why we need to select TCR clusters to illustrate this or that aspect discussed in the work, but they were selected from the overall already structured picture.

(7) The findings on differential plasticity and CD4 to Treg conversion are not supported. If CD4 cells are converting to Tregs, we expect more nucleotide-level overlap of clones. This intuition makes sense. But it seems that this section affirms the consequent: variation in nucleotide-level clone overlap is a readout of variation in CD4 to Treg conversion. It is claimed, based on elevated nucleotide-level overlap, that the LLC and PYMT challenges induce conversion more readily than the other challenges. It is not noted in the textual interpretations, but Fig 4 also shows that the control samples had a substantially elevated nucleotide-level overlap. There is no mention of a null hypothesis for what we'd expect if there was no induced conversion going on at all. This is a reduced-diversity mouse model, so convergent recombination is more likely than usual, and the challenges could be expected to differ in the parts of TCR sequence space they induce focus on. They use the top 100 clones for normalization in this case, but don't say why (this is the 3rd distinct normalization procedure).

Your point is absolutely correct: “This is a reduced-diversity mouse model, so convergent recombination is more likely than usual”. Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

Although interpretations of the reported findings are limited due to the issues above, this is an interesting model system in which to explore convergent responses. Follow-up experimental work could validate some of the reported signals, and the data set may also be useful for other specific questions.

Yes, thank you for your really thorough analysis. We fully agree with your conclusion.

Reviewer #3 (Public Review):

Nakonechnaya et al present a valuable and comprehensive exploration of CD4+ T cell response in mice across stimuli and tissues through the analysis of their TCR-alpha repertoires.

The authors compare repertoires by looking at the relative overlap of shared clonotypes and observe that they sometimes cluster by tissue and sometimes by stimulus. They also compare different CD4+ subsets (conventional and Tregs) and find distinct yet convergent responses with occasional plasticity across subsets for some stimuli.

The observed lack of a general behaviour highlights the need for careful comparison of immune repertoires across cell subsets and tissues in order to better understand their role in the adaptive immune response.

In conclusion, this is an important paper to the community as it suggests several future directions of exploration.

Unfortunately, the lack of code and data availability does not allow the reproducibility of the results.

Thank you for your positive view.

All data on immune repertoires are deposited here: https://figshare.com/articles/dataset/Convergence_plasticity_and_tissue_residence_of_regulatory_and_effector_T_cell_response/22226155

We added the Data availability statement to the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• In the manuscript at "yielding 13,369 {plus minus} 1,255 UMI-labeled TCRα cDNA molecules and 3233 {plus minus} 310 TCRα CDR3 clonotypes per sample" I'm not sure how can there be fewer unique DNA molecules than clonotypes in each sample.

That was our mistake for sure, now corrected.

• In the manuscript at "This indicates that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

I'm not sure it's possible to conclude that a drop in diversity in all conditions necessarily signals a focused nature. Since at this stage, the nature of the colotypes was not compared between conditions, it is not possible to claim a focused nature of the response.

We have softened the wording:

"This could indicate that the amplitude and focused nature of the effector and regulatory T cell response in lungs is generally comparable."

• What are your thoughts on why there is such a large overlap between Treg and Teff in the Lung in control? For some replicates it is almost as much as a post-LLC challenge!

There is some natural dispersion in the data, which is generally expectable. The overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• In the manuscript at "These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches" I'm not sure we can conclude this from the Figure. Wouldn't you expect the samples to be grouped by color (the different challenges)? Maybe I'm not understanding the sentence!

This is a different story, about resident Tregs, irrespective of the challenge.

The whole explanation is here in the text:

“Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

And in the abstract:

“Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

• In the manuscript at " Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells ":

5b and 5d seem to have the same pattern: Spleen and MLN group together, AxLN and IgLN together and thymus is separate. Do you mean to say that the groups are more diffuse? I feel like the pattern really is the same and it's likely due to some noise in the data…

Yes, we just mean here that eTreg groups are less diffuse - means more convergent.

• I'm not sold on the eCD4 to eTreg conversion evidence. Why only limit to the top 100 clones? The top 1000 clones were used in previous analyses! Moreover, the authors claim that calculating relative overlap (via F2) of matching CDR3+V+J genes is evidence of a conversion between eCD4 and eTreg. I think to convince myself of a real conversion, I would track the cells between groups, unfortunately, I'm not sure how to track this.. Maybe looking at the thymus population? For example, what is the overlap in the thymus vs. after the challenge? I don't have an answer on how to verify but I feel that this conclusion is a bit on the weaker end.

Distinct normalisation procedure was required to focus on the most expanded clonotypes to avoid the tail of (presumably cross-reactive) and identical TCRs present in all repertoires in these limited-repertoire mice. So we downsampled as strictly as possible to minimise this background signal of nucleotide overlap, and only this strict downsampling to the top-100 clonotypes allowed us to visualise the difference between the challenges. This is a sort of too complicated explanation that would overload the manuscript. But your comments and our answers will be available to the reader who wants to go into all the details.

The observed (at this strict downsampling) overlaps between the top-nucleotide clones in both LLC and PYMT challenges are prominently above the average, and this result is reproducible in lungs and skin, so we have no doubts in interpretations based on these data. Further experiments with different methods, including tracking the clonal fates, should clarify and confirm/correct/disprove our findings.

• There is a nuance in the analysis between Figure 3 and Figure 5 which I think I am not grasping. Both Figures use the same method and the same data but what is different? I think the manuscript would benefit from making this crystal clear. The conclusions will likely be more evident as well!

As explained in the text and above, on Figure 5 “we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge.”

The idea of this mini-chapter of the manuscript is to reveal tissue-resident Tregs, distinct for distinct tissues, resident there in all these mice, irrespectively of the challenge we applied. And they are really there (!).

• Do the authors plan to share their R scripts?

All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

Minor typos and formatting issues to address:

• Typo in Figure 2a the category should read "worm" instead of "warm"

Corrected.

• Figure 2a heatmap is missing a color bar indicating the value ranges

The detailed information can be found in additional Supplementary materials.

• Figure 2f is never mentioned in the manuscript!

Corrected.

• "eTreg repertoire upon lung challenge is reflected in the draining lymph node" - the word upon is of a lower size

Corrected.

• The authors should make the spelling of eTreg uniform across the manuscript (reg in subscript vs just lower case letters. Same goes for CDR3a vs CDR3\alpha

Corrected.

• Figure 4a-d p-values annotations are not shown. Is it because they are not significant?

Corrected.

• The spelling of FACS buffer should be uniform (FACs vs FACS, see methods)

Corrected.

• In the gating strategy, I would make a uniform annotation for the cluster of differentiation, for example, "CD44 high" vs "CD44^{hi}", pos vs + etc.

Corrected.

• Citation for MIGEC software (if available) is missing from methods

Present in the text so probably sufficient.

Reviewer #2 (Recommendations For The Authors):

I noticed the data was made available via Figshare in the preprint, but there is no data availability statement in the current ms.

We provided Data availability statement.

The methods state that custom scripts were written to perform the various analyses. Those should be made available in a code repository, and linked in the ms.

All calculations were performed in VDJtools. R was only used to build figures. Corrected this in Methods.

The title mentioned "TCR repertoire prism", so I thought "prism" was the name of a new method or software. But then the word "prism" didn't appear anywhere in the ms.

We just mean viewing or understanding something from a different perspective or through a lens that reveals different aspects or nuances.

Figure 1D lacks an x-axis label.

Worked on the figures in general.

Reviewer #3 (Recommendations For The Authors):

• The paper is very concise, possibly a bit too much. It could use additional explanations to properly affirm its relevance, for example:

why the choice of fixing the CDR3beta background?

To make repertoire more similar across the mice, and to track all the features of repertoire using only one chain.

to what it is fixed?

As explained in Methods:

“C57BL/6J DO11.10 TCRβ transgenic mice (kindly provided by Philippa Marrack) and crossed to C57BL/6J Foxp3eGFP TCRa-/- mice.”

What do you expect to see and not to see in this specific system and why it is important?

As stated above: we expected repertoire to be more similar across the mice, and it is important to find antigen-specific TCR clusters across mice, and to be able to track all the features of the TCR repertoire using only one chain.

Does this system induce more convergent responses? If so, can we extrapolate the results from this system to the full alpha-beta response?

Such a model, compared to conventional mice, is much more powerful in terms of the ability of monitoring convergent TCR responses. At the same time, it behaves natural, mice live almost normally, so we believe it reflects natural behaviour of the full fledged alpha-beta T cell repertoire.

• Is the lack of similarity of other tissues to Lung/MLN due to a lack of a response?

As indicated in the title of the corresponding mini-chapter: “eTreg repertoire upon lung challenge is reflected in the draining lymph node”. And conclusion of this mini-chapter is that “these results demonstrate the selective tissue localization of the antigen-focused Treg response. ”

Can you do a dendrogram like 2a for the other tissues to better clarify what is going on there? There is space in the supplementary material.

We built lots of those, but in such single dimension mostly they are less informative compared to 2D MDS plots.

• Figure 5 seems a bit out of place as it looks more related to Figure 2. It could maybe be integrated there, sent to supplementary or become Figure 3?

This is a different story, about resident Tregs, irrespective of the challenge.

The whole explanation is here in the text:

“Global CDR3α cluster analysis revealed that characteristic eTreg TCR motifs were present in distinct lymphatic tissues, including spleen and thymus, irrespective of the applied challenge (Supplementary Fig. 1). To better illustrate this phenomenon, we performed MDS analysis of CDR3α repertoires for distinct lymphatic tissues, excluding the lungs due to their otherwise dominant response to the current challenge. This analysis demonstrated close proximity of eTreg repertoires obtained from the same lymphatic tissues upon all lung challenges and across all animals (Fig. 5a, b). These results indicate that distinct antigenic specificities are generally characteristic for eTreg cells that preferentially reside in particular lymphatic niches. Notably, the convergence of lymphatic tissue-resident TCR repertoires was less prominent for the eCD4 T cells (Fig. 5c, d).”

And in the abstract:

“Additionally, our TCRα repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. ”

• Have you explored more systematically the role of individual variability? If you stratify by individual, do you observe any trend? If not this is also an interesting observation to highlight and discuss.

This is inside the calculations and figures/ one dot = 1 mice, so this natural variation is there inside.

• Regarding the MDS plots: why are 2 dimensions the right amount? Maybe with 3, you can see both tissue specificity and stimuli contributions. Can you do a stress vs # dimensions plot to check what should be the right amount of dimensions to more accurately reproduce the distance matrix?

Tissue specificity and stimuli contribution is hard to distinguish without focussing on appropriate samples, as we did on Fig. 3 and 5. The work is already not that simple as is, and attempting to analyse this in multidimensional space is far beyond our current abilities. But this is an interesting point for future work, thank you.

• Figure 2: A better resolution is needed in order to properly resolve the logo plots at the bottom.

Yes, we worked on Figures, and also provide new Supplementary Figure with all the logos.

• No code or data are made available. There is also a lack of supplementary figures that complement and expand the results presented in the main text.

We believe that the main text, although succinct, contains lots of information to analyse and conclusions (preliminary) to make. So we do not see it rational to overload it further.

Author Response

The following is the authors’ response to the current reviews.

Overall Response

We thank the reviewers for reviewing our manuscript, recognizing the significance of our study, and offering valuable suggestions. Based on the reviewer’s comments and the updated eLife assessment, we would like to chose the current version of our manuscript as the Version of Record of our manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model which takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input, than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter.

The authors control for some degree of redundancy between their training and test sets, both using sequence and structural similarity criteria. This is more careful than can be said of most works in the field of PPI prediction.

As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

We thank the reviewer for recognizing the strengths of our work!

Weaknesses:

The authors check for performance drops when the test set is restricted to pairs of interacting proteins such that the chain pair is not similar as a pair (in sequence or structure) to a pair present in the training set. A more challenging test would be to restrict the test set to pairs of interacting proteins such that none of the chains are separately similar to monomers present in the training set. In the case of structural similarity (TM-scores), this would amount to replacing the two "min"s with "max"s in Eq. (4). In the case of sequence similarity, one would simply require that no monomer in the test set is in any MMSeqs2 cluster observed in the training set. This may be an important check to make, because a protein may interact with several partners, and/or may use the same sites for several distinct interactions, contributing to residual data leakage in the test set.

We thank the reviewer for the suggestion! In the case of protein-protein prediction (“0D prediction”) or protein-protein interfacial residue prediction(“1D prediction”), we think making none of the chains in the test set separately similar to monomers in the training set is necessary, as the reviewer pointed out that a protein may interact with several partners, and may even use the same sites for the interactions. Since the task of this study is predicting the inter-protein residue-residue contacts (“2D prediction”), even though a protein uses the same site to interact with different partners, as long as the interacting partners are different, the inter-protein contact maps would be different. Therefore, we don’t think that in our task, making this restriction to the test set is necessary.

The training set of AFM with v2 weights has a global cutoff of 30 April 2018, while that of PLMGraph-Inter has a cutoff of March 7 2022. So there may be structures in the test set for PLMGraph-Inter that are not in the training set of AFM with v2 weights (released between May 2018 and March 2022). The "Benchmark 2" dataset from the AFM paper may have a few additional structures not in the training or test set for PLMGraph-Inter. I realize there may be only few structures that are in neither training set, but still think that showing the comparison between PLMGraph-Inter and AFM there would be important, even if no statistically significant conclusions can be drawn.

We thank the reviewer for the suggestion! It is not enough to only use the date cutoff to remove the redundancy, since similar structures can be deposited in the PDB in different dates. Because AFM does not release the PDB codes of its training set, it is difficult for us to totally remove the redundancy. Therefore, we think no rigorous conclusion can be drawn by including these comparisons in the manuscript. Besides, the main point of this study is to demonstrate that the integration of multiple protein language models using protein geometric graphs can dramatically improve the model performance for inter-protein contact prediction, which can provide some important enlightenments for the future development of more powerful protein complex structure prediction methods beyond AFM, rather than providing a tool which can beat AFM at this moment. We think including too many stuffs in the comparison with AFM may distract the readers. Therefore, we choose to not include these comparisons in the manuscript.

Finally, the inclusion of AFM confidence scores is very good. A user would likely trust AFM predictions when the confidence score is high, but look for alternative predictions when it is low. The authors' analysis (Figure 6, panels c and d) seems to suggest that, in the case of heterodimers, when AFM has low confidence, PLMGraph-Inter improves precision by (only) about 3% on average. By comparison, the reported gains in the "DockQ-failed" and "precision-failed" bins are based on knowledge of the ground truth final structure, and thus are not actionable in a real use-case.

We agree with the reviewer that more studies are needed for providing a model which can well complement or even beat AFM. The main point of this study is to demonstrate that the integration of multiple protein language models using protein geometric graphs can dramatically improve the model performance for inter-protein contact prediction, which can provide some important enlightenments for the future development of more powerful protein complex structure prediction methods beyond AFM.

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep learning approach for predicting inter-protein contacts, which is crucial for understanding proteinprotein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

We thank the reviewer for recognizing the strengths of our work!

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• I recommend renaming the section "Further potential redundancies removal between the training and the test" to "Further potential redundancies removal between the training and the test sets"

Changed.

• In lines 768-769, the sentence seems to end prematurely in "to use more stringent threshold in the redundancy removal"

Corrected.

• In Eq. (4), line 789, there are many instances of dashes that look like minus signs, creating some confusion.

Corrected.

• I think I may have mixed up figure references in my first review. When I said (Recommendations to the authors): "p. 22, line 2: from the figure, I would have guessed "greater than or equal to 0.7", not 0.8", I think I was referring to what is now lines 423-424, referring to what is now Figure 5c. The point stands there, I think.

Corrected.

• A couple of new grammatical mishaps have been introduced in the revision. These could be rectified.

We carefully rechecked our revisions, and corrected the grammatical issues we found.

Reviewer #2 (Recommendations For The Authors):

Most of my concerns were resolved through the revision. I have only one suggestion for the main figure.

The current scatter plots in Figure 2 are hard to understand as too many different methods are abstracted into a single plot with multiple colors. I would suggest comparing their performances using box plot or violin plot for the figure 2.

We thank the reviewer for the suggestion! In the revision, we tried violin plot, but it does not look good since too many different methods are included in the plot. Besides, we chose the scatter plot as it can provide much more details. We also provided the individual head-to-head scatter plots as supplementary figures, we think which can also be helpful for the readers to capture the information of the figures.

---

The following is the authors’ response to the original reviews.

Overall Response

We would like to thank the reviewers for reviewing our manuscript, recognizing the significance of our study, and offering valuable suggestions. We have carefully revised the manuscript to address all the concerns and suggestions raised by the reviewers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model that takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter. As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

We thank the reviewer for recognizing the strengths of our work!

Weaknesses:

My biggest issue with this work is the evaluations made using bound monomer structures as inputs, coming from the very complexes to be predicted. Conformational changes in protein-protein association are the key element of the binding mechanism and are challenging to predict. While the GLINTER paper (Xie & Xu, 2022) is guilty of the same sin, the authors of CDPred (Guo et al., 2022) correctly only report test results obtained using predicted unbound tertiary structures as inputs to their model. Test results using experimental monomer structures in bound states can hide important limitations in the model, and thus say very little about the realistic use cases in which only the unbound structures (experimental or predicted) are available. I therefore strongly suggest reducing the importance given to the results obtained using bound structures and emphasizing instead those obtained using predicted monomer structures as inputs.

We thank the reviewer for the suggestion! In the revision, to emphasize the performance of PLMGraph-Inter using the predicted monomer structures, we moved the evaluation results based on the predicted monomer from the supplementary to the main text (see the new Table 1 and Figure 2 in the revised manuscript) and re-organized the two subsections “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets” and “Impact of the monomeric structure quality on contact prediction” in the main text.

In particular, the most relevant comparison with AlphaFold-Multimer (AFM) is given in Figure S2, not Figure 6. Unfortunately, it substantially shrinks the proportion of structures for which AFM fails while PLMGraph-Inter performs decently. Still, it would be interesting to investigate why this occurs. One possibility would be that the predicted monomer structures are of bad quality there, and PLMGraph-Inter may be able to rely on a signal from its language model features instead. Finally, AFM multimer confidence values ("iptm + ptm") should be provided, especially in the cases in which AFM struggles.

We thank the reviewer for the suggestion! It is worth noting that AFM automatically searches monomer templates in the prediction, and when we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) at least 20 templates were identified (AFM employed the top 20 templates in the prediction), and 87.8% of the targets employed the native templates (line 455-462 in page 25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”). Therefore, we think Figure 6 not Figure S5 (the original Figure S2) shows a fairer comparison. Besides, it is also worth noting the targets used in this study would have a large overlap with the training set of AlphaFold-Multimer, since AFM used all protein complex structures in PDB deposited before 2018-04-30 in the model training, which would further cause the overestimation of the performance of AFM (line 450-455 in page 24-25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

To mimic the performance of AlphaFold2 in real practice and produce predicted monomeric structures with more diverse qualities, we only used the MSA searched from Uniref100 protein sequence database as the input to AlphaFold2 and set to not use the template (line 203~210 in page 12 in the subsection of “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets”). Since some of the predicted monomer structures are of bad quality, it is reasonable that the performance of PLMGraph-Inter drops when the predicted monomeric structures are used in the prediction. We provided a detailed analysis of the impact of the monomeric structure quality on the prediction performance in the subsection “Impact of the monomeric structure quality on contact prediction” in the main text.

We provided the analysis of the AFM multimer confidence values (“iptm + ptm”) in the revision (Figure 6, Figure S5 and line 495-501 in page 27 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

Besides, in cases where any experimental structures - bound or unbound - are available and given to PLMGraph-Inter as inputs, they should also be provided to AlphaFold-Multimer (AFM) as templates. Withholding these from AFM only makes the comparison artificially unfair. Hence, a new test should be run using AFM templates, and a new version of Figure 6 should be produced. Additionally, AFM's mean precision, at least for top-50 contact prediction, should be reported so it can be compared with PLMGraph-Inter's.

We thank the reviewers for the suggestion, and we are sorry for the confusion! In the AFM runs to predict protein complex structures, we used the default setting of AFM which automatically searches monomer templates in the prediction. When we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) employed at least 20 templates in their predictions (AFM only used the top 20 templates), and 87.8% of the targets employed the native template. We further clarified this in the revision (line 455462 in page 25 in the subsection of “Comparison of PLMGraph-Inter with AlphaFoldMultimer”). We also included the mean precisions of AFM (top-50 contact prediction) in the revision (Table S5 and line 483-484 in page 26 in the subsection of “Comparison of PLMGraph-Inter with AlphaFold-Multimer”).

It's a shame that many of the structures used in the comparison with AFM are actually in the AFM v2 training set. If there are any outside the AFM v2 training set and, ideally, not sequence- or structure-homologous to anything in the AFM v2 training set, they should be discussed and reported on separately. In addition, why not test on structures from the "Benchmark 2" or "Recent-PDB-Multimers" datasets used in the AFM paper?

We thank the reviewer for the suggestion! The biggest challenge to objectively evaluate AFM is that as far as we known, AFM does not release the PDB ids of its training set and the “Recent-PDB-Multimers” dataset. “Benchmark 2” only includes 17 heterodimer proteins, and the number would be further decreased after removing targets redundant to our training set. We think it is difficult to draw conclusions from such a small number of targets.

It is also worth noting that the AFM v2 weights have now been outdated for a while, and better v3 weights now exist, with a training cutoff of 2021-09-30.

Author response image 1.

The head-to-head comparison of qualities of complex predicted by AlphaFold-Multimer (2.2.0) and AlphaFold-Multimer (2.3.2) for each target PPI.

We thank the reviewer for reminding the new version of AFM. The only difference between AFM V3 and V2 is the cutoff date of the training set. During the revision, we also tested the new version of AFM on the datasets of HomoPDB and HeteroPDB, but we found the performance difference between the two versions of AFM is actually very little (see the figure above, not shown in the main text). One reason might be that some targets in HomoPDB and HeteroPDB are redundant with the training sets of the two version of AFM. Since our test sets would have more overlaps with the training set of AFM V3, we keep using the AFM V2 weights in this study.

Another weakness in the evaluation framework: because PLMGraph-Inter uses structural inputs, it is not sufficient to make its test set non-redundant in sequence to its training set. It must also be non-redundant in structure. The Benchmark 2 dataset mentioned above is an example of a test set constructed by removing structures with homologous templates in the AF2 training set. Something similar should be done here.

We thank the reviewer for the suggestion! In the revision, we explored the performance of PLMGraph-Inter when using different thresholds of fold similarity scores of interacting monomers to further remove potential redundancies between the training and test sets (i.e. redundancy in structure ) (line 353-386 in page 19-21 in the subsection “Ablation study”; line 762-797 in page 41-43 in the subsection “Further potential redundancies removal between the training and the test”). We found that for heteromeric PPIs (targets in HeteroPDB), the further removal of potential redundancy in structure has little impact on the model performance (~3%, when TM-score 0.5 is used as the threshold). However, for homomeric PPIs (targets in HomoPDB), the further removal of potential redundancy in structure significantly reduce the model performance (~18%, when TM-score 0.5 is used as the threshold) (see Table 2). One possible reason for this phenomenon is that the binding mode of the homomeric PPI is largely determined by the fold of its monomer, thus the does not generalize well on targets whose folds have never been seen during the training.

Whether the deep learning model can generalize well on targets with novel folds is a very interesting and important question. We thank the reviewer for pointing out this! However, to the best of our knowledge, this question has rarely been addressed by previous studies including AFM. For example, the Benchmark 2 dataset is prepared by ClusPro TBM (bioRxiv 2021.09.07.459290; Proteins 2020, 88:1082-1090) which uses a sequence-based approach (HHsearch) to identify templates not structure-based. Therefore, we don’t think this dataset is non-redundant in structure.

Finally, the performance of DRN-1D2D for top-50 precision reported in Table 1 suggests to me that, in an ablation study, language model features alone would yield better performance than geometric features alone. So, I am puzzled why model "a" in the ablation is a "geometry-only" model and not a "LM-only" one.

Using the protein geometric graph to integrate multiple protein language models is the main idea of PLMGraph-Inter. Comparing with our previous work (DRN-1D2D_Inter), we consider the building of the geometric graph as one major contribution of this work. To emphasize the efficacy of this geometric graph, we chose to use the “geometry-only” model as the base model.

Reviewer #1 (Recommendations For The Authors):

Some sections of the paper use technical terminology which limits accessibility to a broad audience. An obvious example is in the section "Results > Overview of PLMGraph-Inter > The residual network module": the average eLife reader is not a machine learning expert and might not be familiar with a "convolution with kernel size of 1 * 1". In general, the "Overview of PLMGraph-Inter" is a bit heavy with technical details, and I suggest moving many of these to Methods. This overview section can still be there but it should be shorter and written using less technical language.

We thank the reviewer for the suggestion! We moved some technical details to the Methods section in the revision (line 184-185 in page 11; line 729-735 in page 39).

List of typos and minor issues (page number according to merged PDF):

• p. 3. line -3: remove "to"

Corrected (line 36, page 3)

• p. 5, line 7: "GINTER" should be "GLINTER"

Corrected (line 64, page 5)

• p. 6, line -4: "Given structures" -> "Given the structures"

Corrected (line 95, page 6)

• p. 6, line -2: "with which encoded"... ?

We rephrased this sentence in revision. (line 97, page 6)

• p. 9, line 1: "principal" -> "principle"

Corrected (line 142, page 9)

• p. 13, line 1: "has" -> "but have"

Corrected (line 231, page 13)

• p. 14, lines 6-7: "As can be seen from the figure that the predicted" -> "As can be seen from the figure, the predicted"

We rephrased this paragraph, and the sentence was deleted in the revision (line 257-259 in page 15).

• p. 18, line 1: the "five models" are presumably models a-e? If so, say "of models a-e"

Corrected (line 310, page 17)

• p. 22, line 2: from the figure, I would have guessed "greater than or equal to 0.7", not 0.8

Based the Figure 3C, we think 0.8 is a more appropriate cutoff, since the precision drops significantly when the DTM-score is within 0.7~0.8.

• p. 23, lines 2-3: "worth to making" -> "worth making"

Corrected (line 443, page 24)

• p. 24, line -5: "predict" -> "predicted"

Corrected (line 484, page 26)

• p 28, line -5: Please clarify what you mean by "We doubt": are you saying that you don't think these rearrangements exist in nature? If not, then reword.

Corrected (line 566, page 30)

• Figure 2, panel c, "DCPred" in the legend should be "CDPred"

Corrected

• Figures 3 and 5: Please improve the y-axis title in panel C. "Percent" of what?

We changed the “Percent” to “% of targets” in the revision.

We thank the reviewer for carefully reading our manuscript!

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep-learning approach for predicting inter-protein contacts, which is crucial for understanding proteinprotein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost) still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

The conclusions of this paper are mostly well supported by data, but test examples should be revisited with a more strict sequence identity cutoff to avoid any potential information leakage from the training data. The main figures should be improved to make them easier to understand.

We thank the reviewer for recognizing the significance of our work! We have carefully revised the manuscript to address the reviewer’s concerns.

(1) The sequence identity cutoff to remove redundancies between training and test set was set to 40%, which is a bit high to remove test examples having homology to training examples. For example, CDPred uses a sequence identity cutoff of 30% to strictly remove redundancies between training and test set examples. To make their results more solid, the authors should have curated test examples with lower sequence identity cutoffs, or have provided the performance changes against sequence identities to the closest training examples.

We thank the reviewer for the valuable suggestion! The “40 sequence identity” is a widely used threshold to remove redundancy when evaluating deep-learning based protein-protein interaction and protein complex structure prediction methods, thus we also chose this threshold in our study (bioRxiv 2021.10.04.463034, Cell Syst. 2021 Oct 20;12(10):969-982.e6). In the revision, we explored whether PLMGraph-inter can keep its performance when more stringent thresholds (30%,20%,10%) is applied (line 353386 in page 20-21 in the subsection of “Ablation study” and line 762-780 in page 40 in the subsection of “Further potential redundancies removal between the training and the test”). The result shows that even when using “10% sequence identity” as the threshold, mean precisions of the predicted contacts only decreases by ~3% (Table 2).

(2) Figures with head-to-head comparison scatter plots are hard to understand as scatter plots because too many different methods are abstracted into a single plot with multiple colors. It would be better to provide individual head-tohead scatter plots as supplementary figures, not in the main figure.

We thank the reviewer for the suggestion! We will include the individual head-to-head scatter plots as supplementary figures in the revision (Figure S1 and Figure S2 in the supplementary).

(3) The authors claim that PLMGraph-Inter is complementary to AlphaFoldmultimer as it shows better precision for the cases where AlphaFold-multimer fails. To strengthen the point, the qualities of predicted complex structures via protein-protein docking with predicted contacts as restraints should have been compared to those of AlphaFold-multimer structures.

We thank the reviewer for the suggestion! We included this comparison in the revision (Figure S7).

(4) It would be interesting to further analyze whether there is a difference in prediction performance depending on the depth of multiple sequence alignment or the type of complex (antigen-antibody, enzyme-substrates, single species PPI, multiple species PPI, etc).

We thank the reviewer for the suggestion! We analyzed the relationship between the prediction performance and the depth of MSA in the revision (Figure S4 and Line 253264 in page 15 in the subsection of “Evaluation of PLMGraph-Inter on HomoPDB and HeteroPDB test sets” and line 798-806 in page 42 in the subsection of “Calculating the normalized number of the effective sequences of paired MSA”).

Reviewer #2 (Recommendations For The Authors):

I have the following suggestions in addition to the public review.

(1) Overall, the manuscript is well-written; however, I recommend a careful review for minor grammar corrections to polish the final text.

We carefully checked the manuscript and corrected all the grammar issues and typos we found in the revision.

(2) It would be better to indicate that single sequence embeddings, MSA embeddings, and structure embeddings are ESM-1b, ESM-MSA & PSSM, and ESM-IF when they are first mentioned in the manuscript e.g. single sequence embeddings from ESM-1b, MSA embeddings from ESM-MSA and PSSM, and structural embeddings from ESM-IF.

We revised the manuscript according to the reviewer’s suggestion (line 86-88 in page 6; line 99-101 in page 7).

(3) I don't think "outer concatenation" is commonly used. Please specify whether it's outer sum, outer product, or horizontal & vertical tiling followed by concatenation.

It is horizontal & vertical tiling followed by concatenation. We clarified this in the revision (line 129-130 in page 8).

(4) 10th sentence on the page where the Results section starts, please briefly mention what are the other 2D pairwise features.

We clarified this in the revision (line 131-132 in page 8).

(5) In the result section, it states edges are defined based on Ca distances, but in the method section, it says edges are determined based on heavy atom distances. Please correct one of them.

It should be Ca distances. We are sorry for the carelessness, and we corrected this in the revision (line 646 in page 35).

(6) For the sentence, "Where ESM-1b and ESM-MSA-1b are pretrained PLMs learned from large datasets of sequences and MSAs respectively without label supervision,", I'd suggest replacing "without label supervision" with "with masked language modeling tasks" for clarity.

We revised the manuscript according to the reviewer’s suggestion (line 150-151 in page 9).

(7) It would be better to briefly explain what is the dimensional hybrid residual block when it first mentioned.

We explained the dimensional hybrid residue block when it first mentioned in the revision (line 107 in page 7).

(8) Please include error bars for the bar plots and standard deviations for the tables.

We thank the reviewer for the suggestion! Our understanding is the error bars and standard deviations are very informative for data which follow gaussian-like distributions, but our data (precisions of the predicted contacts) are obviously not this type. Most previous studies in protein contact prediction and inter-protein contact prediction also did not include these in their plots or tables. In our case, including these elements requires a dramatic change of the styles of our figures and tables, but we would like to not change our figures and tables too much in the revision.

(9) Please indicate whether the chain break is considered to generate attention map features from ESM-MSA-1b. If it's considered, please specify how.

The paired sequences were directly concatenated without using any letter to connect them, which means we did not consider chain break in generating the attention maps from ESM-MSA-1b.

Author Response

Reviewer #1 (Public Review):

Response to reviewer 1 comments on “weaknesses”:

“A weakness in the approach is the use of genetic models that do not offer complete deletion of the prolactin receptor from targeted neuronal populations...”

We acknowledge that neither model used provided a complete deletion of the prolactin receptor (Prlr) from the targeted neuronal populations. We suspect that incomplete deletion of targeted genes is not uncommon in these sort of studies, but this remains the best approach to addressing our question, and we believe we have been thorough and transparent in reporting the degree of deletion observed. We thought we had appropriately discussed the implications of the low proportion of Kiss1 cells still expressing Prlr, but will certainly revisit to ensure it is discussed thoroughly. This does not detract, however, from the key conclusion that prolactin action is necessary for full suppression of fertility in lactation in the mouse.

“Results showing no impact of progesterone on LH secretion during lactation are surprising, given the effectiveness of progesterone-containing birth control in lactating women...”

We think that this comment misrepresents what has been done in our study. We did not report a lack of impact of progesterone, as exogenous progesterone was never administered to mice. We did, however, give mifepristone as a progesterone receptor antagonist to determine whether endogenous progesterone contributed to the suppression of kisspeptin neuronal activity. We found that mifepristone, at levels sufficient to terminate pregnancy, had no effect on pulsatile LH secretion in lactating mice. This is consistent with our prior observation that progesterone levels are low in mouse lactation, suggesting that progesterone does not contribute significantly to the suppression of kisspeptin neuronal activity during lactation in the mouse. We agree with the reviewer that if we had given exogenous progesterone, it likely would result in suppression of pulsatile LH secretion (as it does in women). Indeed, in other work, we have found that progesterone administration profoundly suppresses activity of the kisspeptin neurons in mice (https://doi.org/10.1210/en.2019-00193). But this was not the point of the present experiment. We will review how we have described this experiment to ensure that this is absolutely clear.

“While the authors assert their findings may reflect an important role for prolactin in lactational infertility in other mammalian species, that remains to be seen….”

We acknowledge that our study cannot address whether prolactin is necessary for the suppression of lactation in other mammalian species. We hope our data may stimulate a re-examination of this question in other species, however, as some of the prior methodology (such as using pharmacological suppression of prolactin) may have had off target effects that confound interpretation. We thought that this point was discussed appropriately in the manuscript but we will certainly check and make sure this is addressed suitably.

Author Response

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

The authors present a number of deep-learning models to analyse the dynamics of epithelia. In this way, they want to overcome the time-consuming manual analysis of such data and also remove a potential operator bias. Specifically, they set up models for identifying cell division events and cell division orientation. They apply these tools to the epithelium of the developing Drosophila pupal wing. They confirm a linear decrease of the division density with time and identify a burst of cell division after the healing of a wound that they had induced earlier. These division events happen a characteristic time after and a characteristic distance away from the wound. These characteristic quantities depend on the size of the wound.

Strength:

The methods developed in this work achieve the goals set by the authors and are a very helpful addition to the toolbox of developmental biologists. They could potentially be used on various developing epithelia. The evidence for the impact of wounds on cell division is solid.

Weakness:

Some aspects of the deep-learning models remained unclear, and the authors might want to think about adding details. First of all, for readers not being familiar with deep-learning models, I would like to see more information about ResNet and U-Net, which are at the base of the new deep-learning models developed here. What is the structure of these networks?

We agree with the Reviewer and have included additional information on page 8 of the manuscript, outlining some background information about the architecture of ResNet and U-Net models.

How many parameters do you use?

We apologise for this omission and have now included the number of parameters and layers in each model in the methods section on page 25.

What is the difference between validating and testing the model? Do the corresponding data sets differ fundamentally?

The difference between ‘validating’ and ‘testing’ the model is validating data is used during training to determine whether the model is overfitting. If the model is performing well on the training data but not on the validating data, this a key signal the model is overfitting and changes will need to be made to the network/training method to prevent this. The testing data is used after all the training has been completed and is used to test the performance of the model on fresh data it has not been trained on. We have removed refence to the validating data in the main text to make it simpler and add this explanation to the methods. There is no fundamental (or experimental) difference between each of the labelled data sets; rather, they are collected from different biological samples. We have now included this information in the Methods text on page 24.

How did you assess the quality of the training data classification?

These data were generated and hand-labelled by an expert with many years of experience in identifying cell divisions in imaging data, to give the ground truth for the deep learning model.

Reviewer #1 (Recommendations For The Authors):

You repeatedly use 'new', 'novel' as well as 'surprising' and 'unexpected'. The latter are rather subjective and it is not clear based on what prior knowledge you make these statements. Unless indicated otherwise, it is understood that the results and methods are new, so you can delete these terms.

We have deleted these words, as suggested, for almost all cases.

p.4 "as expected" add a reference or explain why it is expected.

A reference has now been included in this section, as suggested.

p.4 "cell divisions decrease linearly with time" Only later (p.10) it turns out that you think about the density of cell divisions.

This has been changed to "cell division density decreases linearly with time".

p.5 "imagine is largely in one plane" while below "we generated a 3D z-stack" and above "our in vivo 3D image data" (p.4). Although these statements are not strictly contradictory, I still find them confusing. Eventually, you analyse a 2D image, so I would suggest that you refer to your in vivo data as being 2D.

We apologise for the confusion here; the imaging data was initially generated using 3D z-stacks but this 3D data is later converted to a 2D focused image, on which the deep learning analysis is performed. We are now more careful with the language in the text.

p.7 "We have overcome (...) the standard U-Net model" This paragraph remains rather cryptic to me. Maybe you can explain in two sentences what a U-Net is or state its main characteristics. Is it important to state which class you have used at this point? Similarly, what is the exact role of the ResNet model? What are its characteristics?

We have included more details on both the ResNet and U-Net models and how our model incorporates properties from them on Page 8.

p.8 Table 1 Where do I find it? Similarly, I could not find Table 2.

These were originally located in the supplemental information document, but have been moved to the main manuscript.

p.9 "developing tissue in normal homeostatic conditions" Aren't homeostatic and developing contradictory? In one case you maintain a state, in the other, it changes.

We agree with the Reviewer and have removed the word ‘homeostatic’.

p.9 "Develop additional models" I think 'models' refers to deep learning models, not to physical models of epithelial tissue development. Maybe you can clarify this?

Yes, this is correct; we have phrased this better in the text.

p.12 "median error" median difference to the manually acquired data?

Yes, and we have made this clearer in the text, too.

p.12 "we expected to observe a bias of division orientation along this axis" Can you justify the expectation? Elongated cells are not necessarily aligned with the direction of a uniaxially applied stress.

Although this is not always the case, we have now included additional references to previous work from other groups which demonstrated that wing epithelial cells do become elongated along the P/D axis in response to tension.

p.14 "a rather random orientation" Please, quantify.

The division orientations are quantified in Fig. 4F,G; we have now changed our description from ‘random’ to ‘unbiased’.

p.17 "The theories that must be developed will be statistical mechanical (stochastic) in nature" I do not understand. Statistical mechanics refers to systems at thermodynamic equilibrium, stochastic to processes that depend on, well, stochastic input.

We have clarified that we are referring to non-equilibrium statistical mechanics (the study of macroscopic systems far from equilibrium, a rich field of research with many open problems and applications in biology).

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

In general, novelty over previous work does not seem particularly important. From a methodological point of view, the models are based on generic architectures of convolutional neural networks, with minimal changes, and on ideas already explored in general. The authors seem to have missed much (most?) of the literature on the specific topic of detecting mitotic events in 2D timelapse images, which has been published in more specialized journals or Proceedings. (TPMAI, CCVPR etc., see references below). Even though the image modality or biological structure may be different (non-fluorescent images sometimes), I don't believe it makes a big difference. How the authors' approach compares to this previously published work is not discussed, which prevents me from objectively assessing the true contribution of this article from a methodological perspective.

On the contrary, some competing works have proposed methods based on newer - and generally more efficient - architectures specifically designed to model temporal sequences (Phan 2018, Kitrungrotsakul 2019, 2021, Mao 2019, Shi 2020). These natural candidates (recurrent networks, long-short-term memory (LSTM) gated recurrent units (GRU), or even more recently transformers), coupled to CNNs are not even mentioned in the manuscript, although they have proved their generic superiority for inference tasks involving time series (Major point 2). Even though the original idea/trick of exploiting the different channels of RGB images to address the temporal aspect might seem smart in the first place - as it reduces the task of changing/testing a new architecture to a minimum - I guess that CNNs trained this way may not generalize very well to videos where the temporal resolution is changed slightly (Major point 1). This could be quite problematic as each new dataset acquired with a different temporal resolution or temperature may require manual relabeling and retraining of the network. In this perspective, recent alternatives (Phan 2018, Gilad 2019) have proposed unsupervised approaches, which could largely reduce the need for manual labeling of datasets.

We thank the reviewer for their constructive comments. Our goal is to develop a cell detection method that has a very high accuracy, which is critical for practical and effective application to biological problems. The algorithms need to be robust enough to cope with the difficult experimental systems we are interested in studying, which involve densely packed epithelial cells within in vivo tissues that are continuously developing, as well as repairing. In response to the above comments of the reviewer, we apologise for not including these important papers from the division detection and deep learning literature, which are now discussed in the Introduction (on page 4).

A key novelty of our approach is the use of multiple fluorescent channels to increase information for the model. As the referee points out, our method benefits from using and adapting existing highly effective architectures. Hence, we have been able to incorporate deeper models than some others have previously used. An additional novelty is using this same model architecture (retrained) to detect cell division orientation. For future practical use by us and other biologists, the models can easily be adapted and retrained to suit experimental conditions, including different multiple fluorescent channels or number of time points. Unsupervised approaches are very appealing due to the potential time saved compared to manual hand labelling of data. However, the accuracy of unsupervised models are currently much lower than that of supervised (as shown in Phan 2018) and most importantly well below the levels needed for practical use analysing inherently variable (and challenging) in vivo experimental data.

Regarding the other convolutional neural networks described in the manuscript:

(1) The one proposed to predict the orientation of mitosis performs a regression task, predicting a probability for the division angle. The architecture, which must be different from a simple Unet, is not detailed anywhere, so the way it was designed is difficult to assess. It is unclear if it also performs mitosis detection, or if it is instead used to infer orientation once the timing and location of the division have been inferred by the previous network.

The neural network used for U-NetOrientation has the same architecture as U-NetCellDivision10 but has been retrained to complete a different task: finding division orientation. Our workflow is as follows: firstly, U-NetCellDivision10 is used to find cell divisions; secondly, U-NetOrientation is applied locally to determine the division orientation. These points have now been clarified in the main text on Page 14.

(2) The one proposed to improve the quality of cell boundary images before segmentation is nothing new, it has now become a classic step in segmentation, see for example Wolny et al. eLife 2020.

We have cited similar segmentation models in our paper and thank the referee for this additional one. We had made an improvement to the segmentation models, using GFP-tagged E-cadherin, a protein localised in a thin layer at the apical boundary of cells. So, while this is primarily a 2D segmentation problem, some additional information is available in the z-axis as the protein is visible in 2-3 separate z-slices. Hence, we supplied this 3-focal plane input to take advantage of the 3D nature of this signal. This approach has been made more explicit in the text (Pages 14, 15) and Figure (Fig. 2D).

As a side note, I found it a bit frustrating to realise that all the analysis was done in 2D while the original images are 3D z-stacks, so a lot of the 3D information had to be compressed and has not been used. A novelty, in my opinion, could have resided in the generalisation to 3D of the deep-learning approaches previously proposed in that context, which are exclusively 2D, in particular, to predict the orientation of the division.

Our experimental system is a relatively flat 2D tissue with the orientation of the cell divisions consistently in the xy-plane. Hence, a 2D analysis is most appropriate for this system. With the successful application of the 2D methods already achieving high accuracy, we envision that extension to 3D would only offer a slight increase in effectiveness as these measurements have little room for improvement. Therefore, we did not extend the method to 3D here. However, of course, this is the next natural step in our research as 3D models would be essential for studying 3D tissues; such 3D models will be computationally more expensive to analyse and more challenging to hand label.

Concerning the biological application of the proposed methods, I found the results interesting, showing the potential of such a method to automatise mitosis quantification for a particular biological question of interest, here wound healing. However, the deep learning methods/applications that are put forward as the central point of the manuscript are not particularly original.

We thank the referee for their constructive comments. Our aim was not only to show the accuracy of our models but also to show how they might be useful to biologists for automated analysis of large datasets, which is a—if not the—bottleneck for many imaging experiments. The ability to process large datasets will improve robustness of results, as well as allow additional hypotheses to be tested. Our study also demonstrated that these models can cope with real in vivo experiments where additional complications such as progressive development, tissue wounding and inflammation must be accounted for.

Major point 1: generalisation potential of the proposed method.

The neural network model proposed for mitosis detection relies on a 2D convolutional neural network (CNN), more specifically on the Unet architecture, which has become widespread for the analysis of biology and medical images. The strategy proposed here exploits the fact that the input of such an architecture is natively composed of several channels (originally 3 to handle the 3 RGB channels, which is actually a holdover from computer vision, since most medical/biological images are gray images with a single channel), to directly feed the network with 3 successive images of a timelapse at a time. This idea is, in itself, interesting because no modification of the original architecture had to be carried out. The latest 10-channel model (U-NetCellDivision10), which includes more channels for better performance, required minimal modification to the original U-Net architecture but also simultaneous imaging of cadherin in addition to histone markers, which may not be a generic solution.

We believe we have provided a general approach for practical use by biologists that can be applied to a range of experimental data, whether that is based on varying numbers of fluorescent channels and/or timepoints. We envisioned that experimental biologists are likely to have several different parameters permissible for measurement based on their specific experimental conditions e.g., different fluorescently labelled proteins (e.g. tubulin) and/or time frames. To accommodate this, we have made it easy and clear in the code on GitHub how these changes can be made. While the model may need some alterations and retraining, the method itself is a generic solution as the same principles apply to very widely used fluorescent imaging techniques.

Since CNN-based methods accept only fixed-size vectors (fixed image size and fixed channel number) as input (and output), the length or time resolution of the extracted sequences should not vary from one experience to another. As such, the method proposed here may lack generalization capabilities, as it would have to be retrained for each experiment with a slightly different temporal resolution. The paper should have compared results with slightly different temporal resolutions to assess its inference robustness toward fluctuations in division speed.

If multiple temporal resolutions are required for a set of experiments, we envision that the model could be trained over a range of these different temporal resolutions. Of course, the temporal resolution, which requires the largest vector would be chosen as the model's fixed number of input channels. Given the depth of the models used and the potential to easily increase this by replacing resnet34 with resnet50 or resnet101 the model would likely be able to cope with this, although we have not specifically tested this. (page 27)

Another approach (not discussed) consists in directly convolving several temporal frames using a 3D CNN (2D+time) instead of a 2D, in order to detect a temporal event. Such an idea shares some similarities with the proposed approach, although in this previous work (Ji et al. TPAMI 2012 and for split detection Nie et al. CCVPR 2016) convolution is performed spatio-temporally, which may present advantages. How does the authors' method compare to such an (also very simple) approach?

We thank the Reviewer for this insightful comment. The text now discusses this (on Pages 8 and 17). Key differences between the models include our incorporation of multiple light channels and the use of much deeper models. We suggest that our method allows for an easy and natural extension to use deeper models for even more demanding tasks e.g. distinguishing between healthy and defective divisions. We also tested our method with ‘difficult conditions’ such as when a wound is present; despite the challenges imposed by the wound (including the discussed reduction in fluorescent intensities near the wound edge), we achieved higher accuracy compared to Nie et al. (accuracy of 78.5% compared to our F1 score of 0.964) using a low-density in vitro system.

Major point 2: innovatory nature of the proposed method.

The authors' idea of exploiting existing channels in the input vector to feed successive frames is interesting, but the natural choice in deep learning for manipulating time series is to use recurrent networks or their newer and more stable variants (LSTM, GRU, attention networks, or transformers). Several papers exploiting such approaches have been proposed for the mitotic division detection task, but they are not mentioned or discussed in this manuscript: Phan et al. 2018, Mao et al. 2019, Kitrungrotaskul et al. 2019, She et al 2020.

An obvious advantage of an LSTM architecture combined with CNN is that it is able to address variable length inputs, therefore time sequences of different lengths, whereas a CNN alone can only be fed with an input of fixed size.

LSTM architectures may produce similar accuracy to the models we employ in our study, however due to the high degree of accuracy we already achieve with our methods, it is hard to see how they would improve the understanding of the biology of wound healing that we have uncovered. Hence, they may provide an alternative way to achieve similar results from analyses of our data. It would also be interesting to see how LTSM architectures would cope with the noisy and difficult wounded data that we have analysed. We agree with the referee that these alternate models could allow an easier inclusion of difference temporal differences in division time (see discussion on Page 20). Nevertheless, we imagine that after selecting a sufficiently large input time/ fluorescent channel input, biologists could likely train our model to cope with a range of division lengths.

Another advantage of some of these approaches is that they rely on unsupervised learning, which can avoid the tedious relabeling of data (Phan et al. 2018, Gilad et al. 2019).

While these are very interesting ideas, we believe these unsupervised methods would struggle under the challenging conditions within ours and others experimental imaging data. The epithelial tissue examined in the present study possesses a particularly high density of cells with overlapping nuclei compared to the other experimental systems these unsupervised methods have been tested on. Another potential problem with these unsupervised methods is the difficulty in distinguishing dynamic debris and immune cells from mitotic cells. Once again despite our experimental data being more complex and difficult, our methods perform better than other methods designed for simpler systems as in Phan et al. 2018 and Gilad et al. 2019; for example, analysis performed on lower density in vitro and unwounded tissues gave best F1 scores for a single video was 0.768 and 0.829 for unsupervised and supervised respectively (Phan et al. 2018). We envision that having an F1 score above 0.9 (and preferably above 0.95), would be crucial for practical use by biologists, hence we believe supervision is currently still required. We expect that retraining our models for use in other experimental contexts will require smaller hand labelled datasets, as they will be able to take advantage of transfer learning (see discussion on Page 4).

References :

We have included these additional references in the revised version of our Manuscript.

Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231. >6000 citations

Nie, W. Z., Li, W. H., Liu, A. A., Hao, T., & Su, Y. T. (2016). 3D convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 55-62).

Phan, H. T. H., Kumar, A., Feng, D., Fulham, M., & Kim, J. (2018). Unsupervised two-path neural network for cell event detection and classification using spatiotemporal patterns. IEEE Transactions on Medical Imaging, 38(6), 1477-1487.

Gilad, T., Reyes, J., Chen, J. Y., Lahav, G., & Riklin Raviv, T. (2019). Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics, 35(15), 2644-2653.

Mao, Y., Han, L., & Yin, Z. (2019). Cell mitosis event analysis in phase contrast microscopy images using deep learning. Medical image analysis, 57, 32-43.

Kitrungrotsakul, T., Han, X. H., Iwamoto, Y., Takemoto, S., Yokota, H., Ipponjima, S., ... & Chen, Y. W. (2019). A cascade of 2.5 D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image. IEEE/ACM transactions on computational biology and bioinformatics, 18(2), 396-404.

Shi, J., Xin, Y., Xu, B., Lu, M., & Cong, J. (2020, November). A Deep Framework for Cell Mitosis Detection in Microscopy Images. In 2020 16th International Conference on Computational Intelligence and Security (CIS) (pp. 100-103). IEEE.

Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., ... & Kreshuk, A. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife, 9, e57613.

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

Comments on revised version:

Regarding the Reviewer's 1 comment on the architecture details, I have now understood that the precise architecture (number/type of layers, activation functions, pooling operations, skip connections, upsampling choice...) might have remained relatively hidden to the authors themselves, as the U-net is built automatically by the fast.ai library from a given classical choice of encoder architecture (ResNet34 and ResNet101 here) to generate the decoder part and skip connections.

Regarding the Major point 1, I raised the question of the generalisation potential of the method. I do not think, for instance, that the optimal number of frames to use, nor the optimal choice of their time-shift with respect to the division time (t-n, t+m) (not systematically studied here) may be generic hyperparameters that can be directly transferred to another setting. This implies that the method proposed will necessarily require re-labeling, re-training and re-optimizing the hyperparameters which directly influence the network architecture for each new dataset imaged differently. This limits the generalisation of the method to other datasets, and this may be seen as in contrast to other tools developed in the field for other tasks such as cellpose for segmentation, which has proven a true potential for generalisation on various data modalities. I was hoping that the authors would try themselves testing the robustness of their method by re-imaging the same tissue with slightly different acquisition rate for instance, to give more weight to their work.

In this regard, and because the authors claimed to provide clear instructions on how to reuse their method or adapt it to a different context, I delved deeper into the code and, to my surprise, felt that we are far from the coding practice of what a well-documented and accessible tool should be.

To start with, one has to be relatively accustomed with Napari to understand how the plugin must be installed, as the only thing given is a pip install command (that could be typed in any terminal without installing the plugin for Napari, but has to be typed inside the Napari terminal, which is mentioned nowhere). Surprisingly, the plugin was not uploaded on Napari hub, nor on PyPI by the authors, so it is not searchable/findable directly, one has to go to the Github repository and install it manually. In that regard, no description was provided in the copy-pasted templated files associated to the napari hub, so exporting it to the hub would actually leave it undocumented.

Regarding now the python notebooks, one can fairly say that the "clear instructions" that were supposed to enlighten the code are really minimal. Only one notebook "trainingUNetCellDivision10.ipynb" has actually some comments, the other have (almost) none nor title to help the unskilled programmer delving into the script to guess what it should do. I doubt that a biologist who does not have a strong computational background will manage adapting the method to its own dataset (which seems to me unavoidable for the reasons mentioned above).

Finally regarding the data, none is shared publicly along with this manuscript/code, such that if one doesn't have a similar type of dataset - that must be first annotated in a similar manner - one cannot even test the networks/plugin for its own information. A common and necessary practice in the field - and possibly a longer lasting contribution of this work - could have been to provide the complete and annotated dataset that was used to train and test the artificial neural network. The basic reason is that a more performant, or more generalisable deep-learning model may be developed very soon after this one and for its performance to be fairly compared, it requires to be compared on the same dataset. Benchmarking and comparison of methods performance is at the core of computer vision and deep-learning.

Author Response

The following is the authors’ response to the current reviews.

eLife assessment

This important study used Voltage Sensitive Dye Imaging (VSDI) to measure neural activity in the primary visual cortex of monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. The authors show convincingly that the initial effect of the mask ran counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. They interpret these results in terms of influences from the receptive field center, and although an alternative view that emphasizes the role of the receptive field surround also seems reasonable, this study stands as an interesting and important contribution to our understanding of mechanisms of visual perception.

Public Reviews:

Reviewer #1 (Public Review):

This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings.

The authors have done a good job responding to the main points of my previous review. One important question remains, as stated in that review:

"My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry at al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here."

Their rebuttal of my first review is not convincing -- I still believe that surround influences are important and perhaps predominant in determining the outcome of the experiments. This is particularly clear for the "paradoxical" dynamics that they observe, which seem exactly to reflect the behavior of the surround.

The authors' arguments to the contrary are based on three main points. First, their stimuli cover the center and surround, unlike those of many previous experiments, so they argue that this somehow diminishes the impact of the surround. But the argument is not accompanied by data showing the effects of center stimuli alone or surround stimuli alone. Second, their model -- a normalization model -- does not need surround influences to account for the masking effect. Third, they cite human psychophysical masking results from their collaborators (Sebastian et al 2017), but do not cite an equally convincing demonstration that surround contrast creates potent orientation selective masking when presented alone (Petrov et al 2005, https://doi.org/10.1523/JNEUROSCI.2871-05.2005).

At the end of the day, these issues will be resolved by further experiments, not argumentation. The paper stands as an excellent contribution, but it might be wise for the authors to be less doctrinaire in their interpretations.

We thank the reviewer for their positive comments and constructive criticism. In general, we agree with the reviewer’s comments. Importantly, we do not claim that there is no effect from the surround. What we say in the discussion is:

“Because our targets are added to the background rather than occluding it, it is likely that a significant portion of the behavioral and neural masking effects that we observe come from target-mask interactions at the target location rather than from the effect of the mask in the surround.”

We still stand by this assessment. We also make the point that, at least within the framework of our delayed normalization model, there is no need for the normalization mechanism to extend beyond the center mechanism to account for our results, and even if the normalization mechanism is somewhat larger than the center, the overlap region at the center would still have a large contribution to the modulations. Overall, we agree that these issues will be need to be resolved by future experiments.

For the reasons discussed in our previous reply, we disagree with the reviewers’ statement “…this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature”. For similar reasons we disagree with the statement “It is nice to see that VSDI results square well with those from prior extracellular recordings”.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

We thank the reviewer for their positive comments.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

None, except perhaps for a more balanced representation of the "surround" possibility in the Discussion. The Petrov et al paper (https://doi.org/10.1523/JNEUROSCI.2871-05.2005) should be considered and cited.

As discussed above, we believe that our discussion of possible contribution from the surround is balanced. While the paper by Petrov et al is interesting, the stimuli used to study the surround effects are quite different (e.g., gap between center and surround, and the sharp edge of the surround inner boundary) so direct comparison with our results is not possible.

Reviewer #2 (Recommendations For The Authors):

The authors have addressed the questions/suggestions I raised in my review.

---

The following is the authors’ response to the original reviews.

We thank the reviewers for their helpful comments and suggestions.

eLife assessment

This is an important contribution that extends earlier single-unit work on orientation-specific center-surround interactions to the domain of population responses measured with Voltage Sensitive Dye (VSD) imaging and the first to relate these interactions to orientation-specific perceptual effects of masking. The authors provide convincing evidence of a pattern of results in which the initial effect of the mask seems to run counter to the behavioral effects of the mask, a pattern that reversed in the latter phase of the response. It seems likely that the physiological effects of masking reported here can be attributed to previously described signals from the receptive field surround.

We thank the reviewers for bringing up the relation of our results to findings from previous orientation-specific center-surround interactions studies. In our final manuscript, we added a paragraph discussing this important issue. Briefly, for multiple reasons, we believe that the orientation-dependent behavioral and neural masking effects that we observe are unlikely to depend on previously described center-surround interactions in V1. First, in human subjects, perceptual similarity masking effects are almost entirely accounted for by target-mask interactions at the target location and are recapitulated when the mask has the same size and location as the target (Sebastian et al 2017). Second, in our computational model, the effect of mask orientation on the dynamics of the response are qualitatively the same if the mask is restricted to the size and location of the target while mask contrast is increased (Fig. 8 – figure supplement 3). Third, in our model, the results are qualitatively the same when the spatial pooling region for the normalization signal is the same as that for the excitation signal (Fig. 8 – figure supplement figure 1). These considerations suggest that center-surround interactions may not be necessary for neural and behavioral similarity masking effects with additive targets.

We would also like to point out some key differences between the stimuli that we use and the ones used in most previous center-surround studies. First, in our experiments, the target and the mask were additive, while in most previous center-surround studies the target occludes the background. Such studies therefore restrict the mask effect to the surround, while in our study we allow target-mask interactions at the center. Second, most center-surround studies have a sharp-edged target/surround, while in our experiments no sharp edges were present. Unpublished results from our lab suggest that such sharp edges have a large impact on V1 population responses. A third key difference is that our stimuli were flashed for a short interval of 250 ms corresponding to a typical duration of a fixation in natural vision, while most previous center-surround studies used either longer-duration drifting stimuli or very short-duration random-order stimuli for reverse-correlation analysis.

In addition, we would like to emphasize that our results go beyond previous studies in two important ways. First, we study the effect of similarity masking in behaving animals and quantitatively compare the effect of similarity masking on behavior and physiology in the same subjects and at the same time. Second, VSD imaging allows us to capture the dynamics of superficial V1 population responses over the entire population of millions of neurons activated by the target at two important spatial scales. Such results therefore complement electrophysiological studies that examine the activity of a very small subset of the active neurons.

Public Reviews:

Reviewer #1 (Public Review):

This is a clear account of some interesting work. The experiments and analyses seem well done and the data are useful. It is nice to see that VSDI results square well with those from prior extracellular recordings. But the work may be less original than the authors propose, and their overall framing strikes me as odd. Some additional clarifications could make the contribution more clear.

Please see our reply above regarding the agreement with previous studies and framing.

My reading is that this is primarily a study of surround suppression with results that follow pretty directly from what we already know from that literature, and although they engage with some of the literature they do not directly mention surround suppression in the text. Their major effect - what they repeatedly describe as a "paradoxical" result in which the responses initially show a stronger response to matched targets and backgrounds and then reverse - seems to pretty clearly match the expected outcome of a stimulus that initially evokes additional excitation due to increased center contrast followed by slightly delayed surround suppression tuned to the same peak orientation. Their dynamics result seems entirely consistent with previous work, e.g. Henry et al 2020, particularly their Fig. 3 https://elifesciences.org/articles/54264, so it seems like a major oversight to not engage with that work at all, and to explain what exactly is new here.

We thank the reviewer for the pointing out this previous work which we now cite in the final version of the manuscript. For the reasons discussed above, while this study is interesting and related to our work, we believe that our results are quite distinct.

• In the discussion (lines 315-316), they state "in order to account for the reduced neural sensitivity with target-background similarity in the second phase of the response, the divisive normalization signal has to be orientation selective." I wonder whether they observed this in their modeling. That is, how robust were the normalization model results to the values of sigma_e and sigma_n? It would be useful to know how critical their various model parameters were for replicating the experimental effects, rather than just showing that a good account is possible.

Thank you for this suggestion. In the final manuscript we include a supplementary figure that shows how the model’s predictions are affected by the orientation tuning and spatial extent of the normalization signal, and by the size and contrast of the mask (Fig. 8 – figure supplement 1-4).

• The majority of their target/background contrast conditions were collected only in one animal. This is a minor limitation for work of this kind, but it might be an issue for some.

We agree that this is a limitation of the current study. These are challenging experiments and we were unable to collect all target/background contrast combinations from both monkeys. However, in the common conditions, the results appear similar in the two animals, and the key results seem to be robust to the contrast combination in the animal in which a wider range of contrast combinations was tested. We added these points to the discussion in the final manuscript.

• The authors point out (line 193-195) that "Because the first phase of the response is shorter than the second phase, when V1 response is integrated over both phases, the overall response is positively correlated with the behavioral masking effect." I wonder if this could be explored a bit more at the behavioral level - i.e. does the "similarity masking" they are trying to explain show sensitivity to presentation time?

We agree that testing the effect of stimulus duration on similarity masking is interesting, but unfortunately, it is beyond the scope of the current study. We would also like to point out that the duration of the presentation was selected to match the typical time of fixation during natural behaviors, so much shorter or much longer stimulus durations would be less relevant for natural vision.

• From Fig. 3 it looks like the imaging ROI may include some opercular V2. If so, it's plausible that something about the retinotopic or columnar windowing they used in analysis may remove V2 signals, but they don't comment. Maybe they could tell us how they ensured they only included V1?

We thank the reviewer for this comment. As part of our experiments, we extract a detailed retinotopic map for each chamber, so we were able to ensure that the area used for the decoding analysis lays entirely within V1. We now incorporate this information in the final manuscript (Fig. 3 – figure supplement 1).

• In the discussion (lines 278-283) they say "The positive correlation between the neural and behavioral masking effects occurred earlier and was more robust at the columnar scale than at the retinotopic scale, suggesting that behavioral performance in our task is dominated by columnar scale signals in the second phase of the response. To the best of our knowledge, this is the first demonstration of such decoupling between V1 responses at the retinotopic and columnar scales, and the first demonstration that columnar scale signals are a better predictor of behavioral performance in a detection task." I am having trouble finding where exactly they demonstrate this in the results. Is this just by comparison of Figs. 4E,K and 5E,K? I may just be missing something here, but the argument needs to be made more clearly since much of their claim to originality rests on it.

We thank the reviewer for this comment. In the final manuscript we are more explicit when we discuss this point and refer to the relevant panels in Figs. 4, 5 and their figure supplements. To substantiate this key claim, we also report the timing of the transition between the two phases in all temporal correlation panels and report the neural-behavioral correlation for the integration period.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

Points to Consider / Possible Improvements

The biphasic nature of the relationship between neural and behavioral modulation by the mask and the surprising finding that the two are anticorrelated in the initial phase are left as a mystery. The paper would be more impactful if this mystery could be resolved.

We thank the reviewer for the positive comments. In our view, while our results are surprising, there may not be a remaining mystery that needs to be resolved. As our model shows, the biphasic nature of V1’s response can be explained by a delayed orientation-tuned gain control. Our results are consistent with the hypothesis that perception is based on columnar-scale V1 signals that are integrated over an approximately 200 ms long period that incorporates both the early and the late phase of the response, since such decoded V1 signals are positively correlated with the behavioral similarity masking effect (Fig. 5D, J; Fig. 5 – figure supplement 1). We now explain this more clearly in the discussion of our final manuscript.

The finding is based on analyses of the correlation between behavior and neural responses. This appears in the main body of the manuscript and is detailed in Figures S1 and S2, which show the correlation over time between behavior and target response for the retinotopic and columnar scale.

One possible way of thinking of this transition from anti- to positive correlation with behavior is that it might reflect the dynamics of a competitive interaction between mask and target, with the initial phase reflecting predominantly the mask response, with the target emerging, on some trials, in the latter phase. On trials when the mask response is stronger, the probability of the target emerging in the latter phase, and triggering a hit, might be lower, potentially explaining the anticorrelation in the initial phase. The sustained response may be a mixture of trials on which the target response is or is not strong enough to overcome the effect of the mask sufficiently to trigger target detection.

It would, I think, be worth examining this by testing whether target dynamics may vary, depending on whether the monkey detected the target (hit trials) or failed to detect the target (miss trials). Unless I missed it I do not think this analysis was done. Consistent with this possibility, the authors do note (lines 226-229) that "The trajectories in the target plus mask conditions are more complex. For example, when mask orientation is at +/- 45 deg to the target, the population response is initially dominated by the mask, but then in mid-flight, the population response changes direction and turns toward the direction of the target orientation." This suggests (to this reviewer, at least) that the emergence of a positive correlation between behavioral and neural effects in the latter phase of the response could reflect either a perceptual decision that the target is present or perhaps deployment of attention to the location of the target.

It may be that this transition reflected detection, in which it might be more likely on hit trials than miss trials. Given the SNR it would presumably be difficult to do this analysis on a trial-by-trial basis, but the hit and miss trials (which make each make up about 1/2 of all trials) could be averaged separately to see if the mid-flight transition is more prominent on hit trials. If this is so for the +/- 45 degree case it would be good to see the same analysis for other combinations of target and mask. It would also be interesting to separate correct reject trials from false alarms, to determine whether the mid-flight transition tends to occur on false alarm trials.

If these analyses do not reveal the predicted pattern, they might still merit a supplemental figure, for the sake of completeness.

We thank the reviewer for suggesting this interesting possibility. The original analysis in the manuscript was based on both correct and incorrect trials, raising the possibility that our results reflect some contribution from decision- and/or attention-related signals rather than from low-level nonlinear encoding mechanisms in V1 that we postulate in our model (Fig. 8). To explore this possibility, we re-examined our results while excluding error trials. We found that our key results from Figs 4 and 5 – namely that there is an early transient phase in which the neural and behavioral similarity effects are anti-correlated, and a later sustained phase in which they are positively correlated – hold even for the subset of correct trials, reducing the possibility that decision/attention-related signals play a major role in explaning our results. We now include the results of this analysis as a supplementary figure in the final manuscript (Fig. 4 – figure supplement 2). While there may be some interesting differences in the response dynamics between correct and incorrect trials, the current study was not designed to address this question and the large number of conditions and small number of repeats that it necessitated make this data set suboptimal for examining these phenomena.

References

Sebastian S, Abrams J, Geisler WS. 2017. Constrained sampling experiments reveal principles of detection in natural scenes. Proc Natl Acad Sci U S A 114: E5731-e40