Author Response

Reviewer #3 (Public Review):

Gomolka et al. are trying to establish how aquaporin-4 (AQP4) water channels, a key component of the glymphatic system, facilitate brain-wide movement of interstitial fluid (ISF) into and through the interstitial space of the brain parenchyma. Authors employ a number of advanced non-invasive techniques (diffusion-weighted MRI and high-resolution 3D non-contrast cisternography), invasive dynamic-contrast enhanced (DCE-) MRI along with ex-vivo histology to build a robust picture of the effects of the removal of AQP4 on the structure and the fluid dynamics in the mouse brain. This work is a further step for the implementation of non-invasive tools for studying the glymphatic system.

The main strengths of the manuscript are in the extensive brain-wide and regional analysis, interrogating potential changes in the structural composition, tissue architecture, and interstitial fluid dynamics due to the removal of AQP4. The authors demonstrate an increase in the interstitial fluid volume space, an increase in total brain volume, and a higher brain water content in AQP4 knockout mice. Importantly, an increase in apparent diffusion coefficient (ADC) was reported in most brain regions in the AQP4-KO animals which would suggest an increase in the movement of the fluid, which is supported by an increase in interstitial fluid space measures by real-time iontophoresis with tetramethylammonium (TMA). There is a reduction in the ventricular CSF space compartment while the perivascular space remains consistent. A reduction in gadolinium-based MRI tracer influx into many regions of the AQP4 KO mouse brain parenchyma is found, which supports conclusions of slowing down of fluid transfer while noting that the tracer dynamics in the main CSF compartments show no significant differences.

The interpretation of non-invasive measures of the interstitial fluid dynamics in relationship to regional AQP4 expression is less well supported. The regional AQP4 channel expression in WT mice positively correlates with the ADC and extravascular diffusivity (D) measures. However, their finding that regional ADC also increases when AQP4 is removed weakens the conclusion that the removal of AQP4 leads to interstitial fluid stagnation.

We are thankful to the reviewer for the positive feedback. Indeed, we aimed to provide the scientific field with the most detailed and objective assessment on effect of congenital loss of AQP4 channel on the brain water homeostasis and glymphatic transport. Therefore, we predominantly employed MRI techniques enabling non-invasive assessment, while superimposing obtained findings to standard DCE-MRI and physiological evaluation in-vivo and ex-vivo.

In response to the remark, it is indeed difficult to discuss this phenomena other than relating the regional AQP4 expression to a specific metabolic or morphological structure in WT mice brain, thus associating AQP4 channel expression with regional water distribution. This would have a background not only in to date report highlighting upregulation of AQP4 in response to fluid stagnation, but also in possibility of rapid AQP4 relocalization after acute water intoxication (as comprehensively reviewed by Salman et al. 2022). This would also not reject the possibility that AQP4 is by default expressed more in the regions of functionally higher water content, reflected by higher ADC measures.

In KO mice, we found deletion of AQP4 channel affecting mainly the brain water homeostasis (Figure 1), and thus increased slow MR diffusion metrics would be related to increased brain swelling and increased ISF space compared to WT littermates (Figure 2). However, it is not excluded that this might be rather a superposition of two opposing effects: decrease in measured ADC due to decrease water exchange, and even larger increase in ADC as a manifestation of increased ISF space volume resulting from prior phenomenon. Such explanation was previously presented based on estimation using Latour’s model of long-time diffusion behavior (Pavlin et al. 2017, https://pubmed.ncbi.nlm.nih.gov/28039592/) and connected to rather to enlarged interstitial space Urushihata et al. 2021, https://pubmed.ncbi.nlm.nih.gov/34617156/) that are not paralleled by changes in blood perfusion between genotypes (Zhang et al. 2019, https://pubmed.ncbi.nlm.nih.gov/31220136/).

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors investigate the genes involved in the retention of eggs in Aedes aegypti females. They do so by identifying two candidate genes that are differentially expressed across the different reproductive phases and also show that the transcripts of those two genes are present in ovaries and in the proteome. Overall, I think this is interesting and impressive work that characterizes the function of those two specific protein-coding genes thoroughly. I also really enjoyed the figures. Although they were a bit packed, the visuals made it easy to follow the authors' arguments. I have a few concerns and suggested changes, listed below.

1) These two genes/loci are definitely rapidly evolving. However, that does not automatically imply that positive selection has occurred in these genes. Clearly, you have demonstrated that these gene sequences might be important for fitness in Aedes aegypti. However, if these happen to be disordered proteins, then they would evolve rapidly, i.e., under fewer sequence constraints. In such a scenario, dN/dS values are likely to be high. Another possibility is that as these are expressed only in one tissue and most likely not expressed constitutively, they could be under relaxed constraints relative to all other genes in the genome. For instance, we know that average expression levels of protein-coding genes are highly correlated with their rate of molecular evolution (Drummond et al., 2005). Moreover, there have clearly been genome rearrangements and/or insertion/deletions in the studied gene sequences between closely- related species (as you have nicely shown), thus again dN/dS values will naturally be high. Thus, high values of dN/dS are neither surprising nor do they directly imply positive selection in this case. If the authors really want to investigate this further, they can use the McDonald Kreitman test (McDonald and Kreitman 1991) to ask if non- synonymous divergence is higher than expected. However, this test would require population-level data. Alternatively, the authors can simply discuss adaptation as a possibility along with the others suggested above. A discussion of alternative hypotheses is extremely important and must be clearly laid out.

We agree with the reviewer’s point that rapid evolution is not the same as positive selection. We also agree with the reviewer’s point that McDonald-Kreitman test (MK test) is more powerful than dN/dS analysis. We took advantage of a large population dataset from Rose et al. 2020. After filtering the data, we kept 454 genomes for MK tests. We found both genes are marginally significant or insignificant (tweedledee p = 0.068; tweedledum p = 0.048), despite that these are small genes and have low Pn values. This suggests that it is likely the genes evolve under positive selection.

In line with the reviewer’s suggestion, we performed another analysis using a large amount of population data. We asked if the SNP frequencies of tweedledee and tweedledum are correlated with environmental variables. We found that when compared to a distribution of 10,000 simulated genes with randomly-sampled genetic variants, both tweedledee and tweedledum showed significant correlation to multiple ecological variables reflecting climate variability, such as mean diurnal range, temperature seasonality, and precipitation seasonality (p<0.05). These results are now incorporated into the manuscript in Figure 5 and Figure 5 – Figure supplement 1.

2) The authors show that the two genes under study are important for the retention of viable eggs. However, as these genes are close to two other conserved genes (scratch and peritrophin-like gene), it is unclear to me how it is possible to rule out the contribution of the conserved genes to the same phenotype. Is it possible that the CRISPR deletion leads to the disruption of expression of one of the other important genes nearby (i.e., in a scratch or peritrophin-like gene) as the deleted region could have included a promoter region for instance, which is causing the phenotype you observe? Since all of these genes are so close to each other, it is possible that they are co-regulated and that tweedledee and tweedledum and expressed and translated along with the scratch and peritrophin-like gene. Do we know whether their expression patterns diverge and that scratch and peritrophin-like genes do not play a role in the retention of viable eggs?

This is a fair criticism; however, we think the chance that the phenotypes are caused by interrupting nearby genes is very low. First, peritrophin-like acts in the immune response, and scratch is a brain-biased transcription factor. Neither of the genes show expression in the ovary before or after blood feeding (TPM <1 or 2 are generally considered unexpressed, while scratch and peritrophin-like expression levels are overall lower than 0.1 TPM).

This suggests that peritrophin-like and scratch are not likely to function in the ovary. Thus, although we cannot completely rule out the gene knockout impacts regulation of very distant genes, it is unlikely. Since the mounting evidence we show in this manuscript that tweedledee and tweedledum are highly translated in the ovary after blooding feeding, under the principle of parsimony, we expect the phenotypes came from knocking out the highly expressed and translated genes.

Reviewer #2 (Public Review):

This manuscript is overall quite convincing, presenting a well- thought-out approach to candidate gene detection and systemic follow- ups on two genes that meet their candidate gene criteria. There are several major claims made by the authors, and some have more compelling evidence than others, but in general, the conclusions are quite sound. My main issues stem from how the strategy to identify genes playing a role in egg retention success has led to very particular genes being examined, and so I question some of the elements of the discussion focusing on the rapid evolution and taxon- uniqueness of the identified genes. In short, while I believe the authors have demonstrated that tweedledee and tweedledum play an important role in egg retention, I'm not sure whether this study should be taken as evidence that taxon-specific or rapidly evolving genes, in general, are responsible for this adaptation, or simply play an important role in it.

We have revised the paper to make it clearer that the focus is indeed on these two genes on not on the greater question of taxon-specific or rapidly-evolving genes.

First, the authors present evidence that Aedes aegypti females can retain eggs when a source of fresh water is lacking, confirming that females are not attracted to human forearms while retaining eggs and that up to 70% of the retained eggs hatch after retaining them for nearly a week. This ability is likely an important adaptation that allows Aedes aegypti to thrive in a broad range of conditions. The data here seem fairly compelling.

Based on this observation, the authors reason that genes responsible for the ability to retain eggs must: 1) be highly expressed in ovaries during retention, but not before or after. 2) be taxon-specific (as this behavior seems limited to Aedes aegypti). While this approach to enriching candidate genes has proven fruitful in this particular case, I'm not sure I agree with the authors' rationale. First, even genes at a low expression in the ovaries may be crucial to egg retention. Second, while egg-laying behavior is vastly varied in insects, I'm not sure focusing on taxon-restricted genes is necessary. It is entirely possible that many of the genes identified in Figure 2E play a crucial role in egg retention evolution. These are minor issues, but they are relevant to some later points made by the authors.

We regret framing the discovery of tweedledee and tweedledum in the original submission using this somewhat artificial set of filtering criteria. The reality is that the genes caught our attention for their novel sequence, tight genetic linkage, and interesting expression profile. That really is the focus of the paper, not these other peripheral questions that have been the focus of attention of the reviews. We really do apologize for all of the confusion about what this paper is about.

Nonetheless, the authors provide very compelling evidence that the two genes meeting their criteria - tweedledee and tweedledum, play an important role in egg retention. The genes seem to be expressed primarily in ovaries during egg retention (some observed expression in brain/testes is expected for any gene), and the proteins they code seem to be found in elevated quantities in both ovaries and hemolymph during and immediately after egg retention. RNA for the genes is detected in follicles within the ovary, and CRISPR knockouts of both the genes lead to a large decrease in egg viability post retention.

My earlier qualms about their search strategy relay into some issues with Figure 4, which describes how the two genes are 1) taxon- restricted and 2) have evolved very rapidly. Neither of the two statements is unexpected given the authors' search strategy. Of course, the genes examined precisely for their lack of homologs do not have any homologs. Similarly, by limiting themselves to genes that show a lack of homology (i.e. low sequence similarity) to other genes as well as genes with high expression levels in the ovaries, a higher rate of evolution is almost inevitable to infer (as ovary expressed genes tend to evolve more rapidly in mosquitoes). I agree with the authors that inferences of the evolutionary history of these genes are quite difficult because of their uniqueness, and I especially appreciate their attempts to identify homologs (although I really dislike the term "conceptualog").

We have removed our term “conceptualog” and replaced with the mor conventional “putative ortholog”

This leads to my main (fairly minor) issue of the paper - the discussion on the evolutionary history of these genes and its implications (sections "Taxon-restricted genes underlie tailored adaptations in a diverse world" and "Evolutionary histories and catering to different natural histories"). As noted, inferring this history is very difficult because the authors have focused on two rapidly evolving, taxon-restricted genes. The analyses they have performed here definitely demonstrate that the genes play an important role in egg retention, however, they do not show that taxon-restricted genes play a disproportionate role in egg retention evolution. Indeed, the only data relevant to this point would be the proportion of genes in Figure 2E that are taxon-restricted (3/9), but I'm not sure what the null expectation for this proportion for highly expressed ovary genes is to begin with. Furthermore, the extremely rapid evolution of this gene makes it hard to judge how truly taxon-restricted it is. My own search of tweedle homologs identified multiple as previously having been predicted to be "Knr4/Smi1-like", and while no similar genes are located in a similar location in melanogaster, there is generally little synteny conservation in Drosophila (for instance Bhutkar et al 2008), so I'm unsure what can really be said about their evolutionary origins/lack of homologs in Drosophila.

In short - the manuscript makes clear that tweedledee and tweedledum play an important role in egg retention in A. aegypti, nonetheless, it is not clear that this is a demonstration of how important taxon- restricted genes are to understanding the evolution of life-history strategies.

Again, we should have never framed the paper the way we did in the original version. We make no claims whatsoever that taxon-restricted genes in general should play a role in this biology, only that the two candidate genes under study influence egg viability after extended retention. We hope that the framing is clearer in this revision.

Author Response

Reviewer #1 (Public Review):

Understanding the evolution of broadly neutralizing influenza antibodies is key to developing a more universal vaccine. In this study, Phillips et al. performed a comprehensive analysis of the evolutionary pathway of CH65, which is an H1-specific broadly neutralizing antibody. The authors generated a combinatorial mutant library with 2^16 members that contained all possible evolutionary intermediates between the unmutated common ancestor (UCA) and CH65, less two mutations that did not affect binding. The binding affinity of each member in the library was measured against HAs from MA90 and SI06, which were isolated 16 years apart, as well as MA90 with a UCA escape mutation G189E. The binding affinity was measured using a high-throughput approach that combined yeast display and Tite-Seq, with careful experimental validation. The results showed that epistasis between mutations within the heavy chain and also across heavy and light chains plays an important role in CH65 to evolve breadth. Although this study highly resembles a previous study by the authors that focused on another broadly neutralizing influenza antibody called CR9114 (Phillips et al., eLife 2021), there are several key differences. Firstly, CR9114 is a HA stem-directed antibody, whereas CH65 binds to the receptor-binding site of HA. Secondly, their previous study only studied the mutations in the heavy chain, whereas the present study looked at mutations in both heavy and light chains. Lastly, the present study provided a structural mechanism of epistasis by solving crystal structures. Such investigation of structural mechanisms was absent in their previous study. Overall, the data quality in this study is very high. In addition, the results have important implications for vaccine development.

We thank Reviewer #1 for their review of our work and have implemented each of their suggestions to improve the clarity of our manuscript.

Reviewer #2 (Public Review):

Although many broadly-neutralizing antibodies were discovered against virus accumulating mutations such as HIV, Influenza, and Sars-CoV-2, the methodology to induce such antibodies or design to generate them is highly demanded. The authors take the broadly-neutralizing antibody, CH65 as a model antibody and try to recapitulate the generation of the broadly-neutralizing antibody from an unmutated common ancestor over time. By performing Tite-Seq assays, Epistasis analysis, Pathway analysis, and Affinity measurement, and structural study, the authors proposed a scenario of the evolution of CH65.

Strengths

Combining the models and affinity/structure data, the authors enable us to show the possible track of gaining the breadth of the CH65 antibody from the unmutated repertoire. Using the Tite-Seq assay, the authors took a forward genetics approach which is high-throughput and non-bias and mimics the situation of the evolution of a B cell repertoire in an individual over time. The data is robust, and its outcome will provide an opportunity to build a prediction model to design the antibody in silico. Especially their identification of amino acid positions important for epistasis mode in antibody evolution is valuable. Antigen selection scenarios are decisive in this study.

Weakness

The proposed scenarios cannot be tested using human CH65. The readers would have great interest in how these hypothetical scenarios are fitting to the evolution occurring in vivo situation, especially in a quantitative way. The broadly neutralizing antibodies often react with self-antigens as the authors cite previous work(ref 19). How do these environmental factors affect the evolution of the antibody? These already-known facts could be mentioned and discussed in detail.

We thank Reviewer #2 for these comments and agree that applying these insights to understand in vivo antibody affinity maturation would be fascinating. As the Reviewer points out, our study is limited to examining antigen affinity and neglects other properties that are known to impact antibody affinity maturation (e.g., autoreactivity). As we mention in the Discussion, our work shows how the acquisition of breadth is shaped by mutations that interact epistatically to determine binding affinity, and future work is required to understand how these mutations and interactions may also impact the myriad other properties relevant to antibody maturation.

Author Response

Reviewer #2 (Public Review):

The paper has two key messages: the discovery and the function of LncSox17. Claims of gene discovery are today untrivial, given the large number of genome-wide datasets. Of course, I understand the authors cannot check everything but I feel some more clear and deep analysis of current databases is lacking.

The reviewer is right when stating that there is an extremely high number of publicly available datasets and resources. In the current manuscript, we used Ensembl genes, Genecode V36 and Genecode V36 lncRNAs (commonly used datasets for gene and transcript annotation) and could not find reports of long non-coding RNAs with similar location, length and strand of T-REX17 (see Fig. 1). To further ensure that we did not overlook it, during the revision we inspected these datasets again, coming to the same conclusion that T-REX17 has not been previously reported at this locus.

As we show, T-REX17 is only very transiently expressed in definitive endoderm and given that there are few available RNA-seq datasets covering this developmental transition from hiPSCs it is not entirely surprising that it has been missed in the past.

Also, the exact coordinates of the lncRNA are not easy to find in the manuscript.

This is certainly an important annotation we missed in the manuscript. We now updated the legend of Figure 1A to include the exact genomic location of T-REX17.

Many statistical analyses are rather lacking. In particular I did not find details of how the DEGs were identified during differentiation (FDR? How many replicates?).

We thank the reviewer for pointing this out. We now specify in the Methods section (page 42, lines 1037-1039) and in the figure legends (page 54, lines 1269-1271) how the DEGs have been identified, which thresholds have been used, and number of replicates performed.

The results of the smFISH are surprising, since the level of expression seems rather low in comparison to the qPCR (only 4 times less expressed than Sox17) or the RNA-seq.

Direct quantitative comparisons between smFISH and qPCR (or RNA-seq) assays are in general quite hard since the two technologies rely on different biochemical principles. qPCR and RNA-seq include an amplification step, and therefore their interpretation should be considered as relative rather than absolute. On the other hand, smFISH offers a more absolute quantitative information and provides clues about the subcellular localization of the investigated target. At the same time, in smFISH experiments, individual foci could represent the accumulation of more than one molecule, making it hard to accurately infer gene expression levels from images. Throughout the manuscript we combine the two assays in an attempt to provide more robust information about T-REX17 expression dynamics.

We would also like to note the high specificity of our smFISH signal, given that we do not observe any detectable foci for T-REX17 in undifferentiated cells (Fig. 2C) or T-REX17 depleted endoderm cells (Fig. 3C).

Author Response

1) Response to the Editor

We thank the Editor and the Reviewers for the kind words, the helpful suggestions, and the points of critique, which have all helped us substantially strengthen the manuscript in this revised version. Regarding the 3 general critiques highlighted by the Editor:

Essential Revisions:

1) Some hypothesis, and in particular the one that all individuals have the same inter-burst interval distribution should be tested/justified/discussed.

(a) We have generalized the theory to directly address this point by relaxing the assumption of an identical inter-burst interval for all individuals. In short: the main insights continue to hold and we discuss the nuances in the text.

(b) Experimentally, the hypothesis that all single fireflies isolated from the group exhibit the same interburst interval (IBI) distribution could not be rigorously tested. The main reason is practical: in order to compare IBI distributions across individuals, we would need to collect a large number of fireflies and track them for long durations, which was not realistic given our experimental setup and the short window of firefly emergence. In addition, external environmental factors might slightly alter behaviors as well, making comparisons even more complex. Thus, due to paucity of field data, we eventually use the assumption that all individual fireflies follow the same IBI distribution.

2) Comparison between the models and the data must be improved, in particular through a quantification of the differences between distributions and sensitivity analysis of the numerical results.

(a) Regarding the comparison of the agent-based simulations with experimental data, in Fig. 7, we compare the underlying distributions using the two-sided Kolgomorov-Smirnov statistical test for goodness-of-fit. These appear to us the most straightforward and informative approaches, without over-fitting.

(b) Regarding sensitivity analysis for the agent-based simulations, for each β value from 0 to 1 we statistically compared simulations to the experimental distributions to find the most well-fitted β.

(c) Finally, owing to experimental constraints leading to sparsity of available data in characterizing the interburst distribution, we strive to strike a delicate balance between sophisticated statistical tools to compare theoretical and simulation distributions (with unrestricted access to large sample sizes) to the finite samples in the empirical distributions. As such, we think it is the apposite to use the first two moments of respective distributions In Fig. 3 to show the striking similarity of trends.

3) More discussion of the modeling in connection to past theoretical results and existing literature is necessary to better contextualize the present work and assess its originality.

We have done this closely following the specific suggestions from reviewers.

2) Revised terminology: removing usage of “model”

Since unintended ambiguity may be caused by use of the word “model”, which could refer to either (1) the theoretical framework, principle of emergent periodicity, and attendant analytic calculation , or (2) the agent-based simulation in the computational realization, we have removed all instances of the word “model” from the results presented in the paper, and replaced by the specific meaning (theory or simulation) in each context.

Similarly, in responding to Reviewers’ comments, we clarify what we understand by their use of the word “model” in each case.

3) Addressing an error in the agent-based simulation code

We (OM and OP) have now addressed an inadvertent unit typo in the agent-based simulation code. The discharging time (Td) before the typo was fixed was set to 10000ms. After the fix, the Td value was correctly set to 100ms. This caused very slow discharges, keeping the voltage high until any beta addition was received, resulting in more frequent bursts than we’d actually expect from the model dynamics. This has been fixed, and in our responses to the reviewers, we address the results of this fix by referring to the “unit typo”. We corrected the panels corresponding to agent-based simulation in Figs. 3 and 5 to reflect the new numerical simulation results, as well as the corresponding sections in the text of the paper.

4) Addressing changes to experimental dataset

We increased the size of our N=1 dataset (N is number of fireflies) to correctly match what was reported in the original text of 10 samples. Additionally, we have added characterization of the size of the datasets for N=5, 10, 15, and 20 fireflies.

5) Response to Reviewer 1

We thank the Reviewer for kind remarks, and the highlights of the strengths of the paper.

Regarding concerns raised, point by point:

Reviewer #1 (Public Review):

Weaknesses:

The work presented here is an excellent start at understanding the collective behavior of this particular species of firefly. However, the model does not apply to other species in which individual males are intrinsically rhythmic. So the model is less general than it may appear at first.

We take the Reviewer’s point well. We have added text to the paper to clearly highlight this point.

The modeling framework is also developed under the very stylized conditions of experiments conducted in a small tent. While that is a natural place to begin, future work should consider the conditions that fireflies encounter in the wild. Swarms that are spread out in space would require a model with a more complicated structure, perhaps with network connectivity and coupling strengths that both change in time as fireflies move around. This is not so much a weakness of the present work as a call to arms for future research.

We agree with the Reviewer that this is an exciting call to arms for future research!

Other comments:

This assumption that all individuals have the same IBI distribution could be directly tested. Has this been done? If not, why not? e.g. Are there difficulties with letting one firefly flash long enough to collect sufficient data to fill out the distribution?

1. We have generalized the theory to directly address this point by relaxing the assumption that all individuals exhibit the same inter-burst interval distribution. In short: the main insights continue to hold and we discuss the nuances in the text.

2. Experimentally, hypothesis that all single fireflies isolated from the group exhibit the same interburst interval (IBI) distribution could not be rigorously tested. The main reason is practical: in order to compare IBI distributions across individuals, we would need to collect a large number of fireflies and track them for long durations, which was not realistic given our experimental setup and the short window of firefly emergence. In addition, external environmental factors might slightly alter behaviors as well, making comparisons even more complex. Thus, due to paucity of field data, we eventually use the assumption that all individual fireflies follow the same IBI distribution.

The derivation given in 6.2.1 is clearer than the approach taken here, which unnecessarily introduces Q, q, and c and then never uses them again.

We agree with the Reviewer and have accordingly revised the manuscript.

We have also implemented the suggested edits in the marked up manuscript. We are grateful for the detailed feedback, which helped us substantially extend results, and improve presentation and clarity.

6) Response to Reviewer 2

We thank the Reviewer for their thorough feedback. We provide point by point responses below.

Reviewer #2 (Public Review):

1) The biological relevance of certain hypotheses is insufficiently discussed. This is important because if the observed behaviour is a universal one, alternative models may explain it as well.

We thank the reviewer for raising this point. The main hypotheses underlying our models are: 1) individual fireflies in isolation flash at random intervals; 2) these random intervals are drawn from the empirical distribution reported (implicitly: all fireflies follow the same distribution); 3) once a firefly flashes, it triggers all others. Hypothesis 1) is directly supported by the data presented. Hypothesis 2) is comprehensively addressed in the revised manuscript, as discussed previously. Hypothesis 3) is central to the proposed principle, and enables intrinsically non-oscillating individuals to oscillate periodically when in a group. The resulting phenomenon has been compared to experimental data and extensively discussed in the manuscript. Further, we have also simulated the effect of changing the strength of coupling between fireflies based on this hypothesis in the revised section on agent-based simulation.

2) Comparison between the models and the data could be improved, in particular through quantification of the differences between distributions and sensitivity analysis of the numerical results.

1. Regarding the comparison of the agent-based simulations with experimental data, in Fig. 7, we compare the underlying distributions using the two-sided Kolgomorov-Smirnov statistical test for goodness-of fit. These appear to us the most straightforward and informative approaches, without over-fitting.

2. Regarding sensitivity analysis for the agent-based simulations, for each β value from 0 to 1 we statistically compared simulations to the experimental distributions to find the most well-fitted β.

3. Finally, owing to experimental constraints leading to sparsity of available data in characterizing the interburst distribution, we strive to strike a delicate balance between sophisticated statistical tools to compare theoretical and simulation distributions (with unrestricted access to large sample sizes) to the finite samples in the empirical distributions. As such, we think it is the apposite to use the first two moments of respective distributions In Fig. 3 to show the striking similarity of trends.

Reviewer #2 (Recommendations for the authors):

A. The assumption that single-firefly spikes obey the same distribution (there is no individual variation in the frequency, or even of the composing number of bursts, of the flash) does not seem to have been verified on the data, that are instead pulled together in one single distribution (Fig. 1D). Moreover, the main feature of such distribution is that it has a minimum at 12 secs (discarding the faster bursts that are not considered in the model) and that it is sufficiently skewed so that it takes a minimal coupling for collective synchrony to emerge. I think that the agreement between the distributions for different N would be more meaningfully discussed having previous work as a reference, whereas now this is relegated to the discussion, so that it is unclear how much of the theoretical results are novel and/or unexpected. Quantification of the distance between distributions would also be interesting: it looks like the two models (analytical and simulations) disagree more among themselves than with the data.

Regarding the hypothesis that all individual fireflies exhibit the same interflash interval, please see our response to Main Point 1. Regarding comparing the analytical theory and numerical simulation analysis, Figs. 3 and 5 have been revised after a unit typo was found in the code (see Section 2). Following the update, the analytical and numerical models agree in (1) the location of the peak in Fig. 3 for all N values, and (2) the peak approaches the minimum of the input distribution as N increases.

B. If I understand correctly, simulations are introduced as a way to get a dependence on the intensity of the coupling (\beta). There are several issues here. First, I do not see how the coupling constant could change in the present experimental setup, where all fireflies presumably see each other (different from when there is vegetation). Second, looking at Fig. 3, the critical coupling strength appears to depend very weakly from N, and it is not clear how the 'detailed comparison' that leads to the fit is realized (in fact, the fitted \betas look larger that those at which the transition occurs in Fig. 3A). I think a sensitivity analysis is needed in order to understand how do results change when \beta is changed, and also what is the effect of the natural Tb distribution (Fig. 2 F). Results of the simulations might be clearer if instead of using the envelope of the experimental results, the authors tried to fit it to a standard distribution (ex. Poisson) so that it can be regularized. This should allow to trace with higher resolution the boundary between asynchronous and synchronous firing.

We have included agent-based numerical simulations as a way to provide a concrete instantiation of the theory principle and analytical results in the preceding section. While the analytic theory results are fitting parameters free, in the agent-based simulations, we introduce an additional fitting parameter, to see what happens when we relax one hypothesis of the analytical theory: the instantaneous triggering of all fireflies upon an initial flasher. Additionally, the agent-based simulations pave the way for future work, allowing for convenient exploration of the connectivity between individuals and analysis of the behavior of individual fireflies. in this context, please note that Fig. 5 has been corrected (see above), leading to a stronger co-dependence of β and N. In addition to the envelopes, we also report the trends in the first empirical moments (mean and STD) for comparison and tracking of the transition to synchrony.

C. More care should be put in explaining what are the initial conditions hypothesized for the different models. For instance, the results of paragraph 3 are understandable if all fireflies are initialized just after firing, something that is only learnt at the end of the paragraph. I also wonder whether initial conditions may be involved with T_bs in the low-coupling region of Fig. 3A not being uniformly distributed, as I would have expected for a desynchronized population.

We have clarified that, indeed, all fireflies are re-initialized after firing. The initial conditions then become a new random vector of interflash intervals. Importantly, we found after receiving the reviews that, due to inconsistent units in our numerical simulation code, Fig. 5 was incorrect. With proper units, the new results show a much more widespread distribution at low coupling, as expected by the Reviewer.

D. I found that equations were hard to understand either because one of the variables was not precisely (or at all) defined, or because some information was missing: Eq. 1: q is not defined Eq. 2: explain what it means: the prob. that others have not flashed times that that one flashes. Also, say explicitly what is the 'corresponding PDF. Eq. 3: the equation for \epsilon(t) to which this is coupled is missing Why introduce \beta_{i,j} and T_bi if they are then taken independent of the indexes? Definitions of collective and group burst interval should be provided. It would be clearer if t_b0 was defined in the first paragraph of the results, so as to clarify as well its relation with T_b. Define T^i_b in the caption of Fig. 3 (they are defined later than the figure is first discussed). The definition of 'the vertical axis label' (maybe find a word for that...) is pretty cumbersome. I could imagine that other definitions would allow the lines in Fig. 3 E to converge to the same line for large betas, which would make more sense, considering that in the strong coupling limit I see no reason why the collective spiking should not be the same for different N (the analytical model could help here).

Thank you for these comments; we have incorporated these and related changes.

E. I think that the author's reading of the two 'dynamical quorum sensing' papers they cite is incorrect: De Monte et al. was not about the Kuramoto model, but the same limit cycle oscillators as in Strogatz; Taylor et al. considers excitable systems, potentially closer to noisy integrate-and-fire, at least in that they do not have self-sustained oscillations. Both papers show that oscillations appear above a certain density threshold, and that the frequency of oscillations increases with density, as found in this work. A more accurate link to previous publications in the field of synchronization theory, including the models by Kurths and colleagues for fireflies, would be useful both in the introduction and in the discussion, and would help the reader to position this work and appreciate its original contributions.

1. Thank you for pointing out an inaccuracy in our literature citations regarding synchronization. We have now made corrections to address this point.

2. While we take the Reviewer’s points well, our theory framework (“model”), building off of the principle of emergent periodicity we propose here, is fundamentally different in the nature of individuals from extant “models”. The reference in question has individuals as oscillators, and the fastest frequency is the frequency of the fastest individual oscillator. In contrast, in our work there is no fastest individual oscillator and the “fastest frequency” has a completely different meaning, since individuals do not have a particular frequency associated with them. In this sense, our work is not inspired by theirs. That said, we have included citations as suggested by the Reviewer.

F. The authors say that part of the data is unpublished. I guess they mean that the whole data set will be published with this manuscript. I think the formulation is ambiguous.

Thank you for this comment. We have now clarified that the data will indeed be published with the manuscript.

Author Response

Reviewer #1 (Public Review):

This paper tests whether people vary their reliance on episodic memory vs. incremental learning as a function of the uncertainty of the environment. The authors posit that higher uncertainty environments should lead to more reliance on episodic memory, and they find evidence for this effect across several kinds of analyses and across two independent samples.

The paper is beautifully written and motivated, and the results and figures are clear and compelling. The replication in an independent sample is especially useful. I think this will be an important paper of interest to a broad group of learning, memory, and decisionmaking researchers. I have only two points of concern about the interpretation of the results:

1) My main concern regards the indirect indicator of participants' use of episodic memory on a given trial. The authors assume that episodic memory is used if the value of the chosen object (as determined by its value the last time it was presented) does not match the current value of the deck it is presented in. They find that these mismatch choices happen more often in the high-volatility environment. But if participants simply choose in a more noisy/exploratory way in the high volatility environment, I believe that would also result in more mismatched judgments. What proportion of the trials labeled as episodic should we expect to be a result of noise or exploration? It seems conceivable that a judgment to explore could take longer, and result in the observed RT effects. Perhaps it could be useful to match up putative episodic trials with later recognition memory for those particular items. The across-subjects correlations are an indirect version of this, but could potentially be subject to a related concern if participants who explore more (and are then judged as more episodic) also simply have a better memory.

Thank you for this important suggestion. We agree that noisy/exploratory choices could potentially masquerade as episodic on the episodic-based choice index used as one of our behavioral measures. As pointed out, this is because participants may be more likely to make noisier incremental value-based decisions in the high volatility compared to the low volatility environment. In our revision, we provided a new analysis that shows that, as the reviewer predicted, choices are indeed more noisy in the high volatility environment. We answer this concern in two ways. First, we took this noise into account in our analysis of the episodic/incremental tradeoff and show that it does not account for the main findings. And second, we provided a new analysis of subsequent memory that shows that choices that are defined as episodic during the decision-task are also associated with better recognition memory later on. These new analyses are described below as well.

We used a mixed-effects logistic regression model to test for an interaction effect of environment and model-estimated deck value on whether the orange deck was chosen. We fit this model only to trials without the presence of a previously seen object in order to achieve a more accurate measure of noise specific to incremental learning. In both the main and replication samples, participants did indeed make noisier incremental decisions in the high compared to the low volatility environment (Main: 𝛽 = −1.589, 95% 𝐶𝐼 = [−2.091, −1.096], Replication: 𝛽 = −1.255, 95% 𝐶𝐼 = [−1.824, −0.675]). To account for the possibility that the measured difference between environments in our episodic-based choice index may be related to this difference in incremental noise between the environments, we included each participant’s random effect of the environment by deck value interaction from this model as a covariate in our analysis of the effect of environment on the episodic-based choice index. While each participants’ propensity to choose with greater noise in the high volatility environment did have an effect on the episodic-based choice index (Main: 𝛽 = 0.042, 95% 𝐶𝐼 = [0.012, 0.072], Replication: 𝛽 = 0.055, 95% 𝐶𝐼 = [0.027, 0.082]), the effect of environment was similar to that originally reported in the manuscript for both samples following this adjustment. The reported effects (lines 178 and Appendix 1) and methods (lines 643-655) have been updated to reflect these changes.

We applied a similar logic to the reaction time analysis, to address the possibility that decisions based on exploration may take longer compared to decisions based on exploitation of learned deck value. We included a covariate in the analysis of the effect of episodic-based choices on reaction time that captured possible slowing due to switching from choosing one deck to the other (lines 656-662) and found that the slower reaction times on episodic choices are not fully explained by exploration. Because in this task a decision to explore is captured by switching from one deck to another, the effect of episodic-based choices on reaction time reported in the manuscript should account for this behavior. We have clarified this reasoning in the methods (lines 661-662).

Finally, thank you for the idea to sort objects in the recognition memory test by whether they were from episodic- or incremental-based choice trials to provide a further test of whether our approach for sorting episodic decisions withstands an independent test. We performed this analysis and found that, in both samples, participants had better memory for objects from episodic-based choice trials. This result provides further support for the putative episodic nature of these trials and is now reported in the Results (lines 300-304 and Appendix 1), Methods (lines 737-742) and appears as a new panel in Figure 5 (Figure 5A).

2) The paper is framed as tapping into a trade-off between the use of episodic memory vs. incremental learning, but it is not clear why participants would not use episodic memory in this particular task setup whenever it is available to them. The authors mention that there is "computational expense" to episodic memory, but retrieval of an already-established strong episodic memory could be quite effortless and even automatic. Why not always use it, since it is guaranteed in this task to be a better source of information for the decision? If it is true that RT is higher when using episodic memory, that is helpful toward establishing the trade-off, so this links to the concern above about how confident we can be about the use of episodic memory in particular trials.

Thank you for raising this important point and for giving us the opportunity to clarify. We now address this point in two ways: first, we provide a new analysis of episodic memory and choice behavior and we address this point explicitly in the discussion.

As now emphasized in the paper (lines 118-122 and lines 384-388), in this task, it is true that an observer with perfect episodic memory should always make use of it whenever available (i.e. on trials featuring previously seen objects). However, human memory is fallible and resourcelimited, and we find that participants with less reliable episodic memory overall actually relied less on this strategy and more on incremental learning throughout the task (Figure 5C and 5D). In other words, there is noise and uncertainty also in the episodic memory trace. While it is not the main focus of our study, the noise in episodic memory is indeed another reason why trading off between episodic memory and incremental learning is advantageous for behavior. We further agree that while the RT effects show that, relative to using incremental value, episodic memory retrieval takes longer, we cannot make strong statements about effort or “computational expense” per se from our data. Accordingly, we have removed the “computational expense” phrase (line 491), as well as our suggestion that episodic retrieval is “perhaps more effortful overall” (line 181), from the paper.

Reviewer #2 (Public Review):

This manuscript addresses the broad question of when humans use different learning and memory systems in the service of decision-making. Previous studies have shown that, even in tasks that can be performed well using incremental trial-and-error learning, choices can sometimes be based on memories of individual past episodes. This manuscript asks what determines the balance between incremental learning and episodic memory, and specifically tests the idea that the uncertainty associated with each alters the balance between them in a rational way. Using a task that can separate the influence of incremental learning and episodic memory on choice in two large online samples, several lines of evidence supporting this hypothesis are reported. People are more likely to rely on episodic memory in more volatile environments when incremental learning is more uncertain and during periods of increased uncertainty within a given environment. Individuals with more accurate episodic memories are also more likely to rely on episodic memory and less likely to rely on incremental learning. These data are compelling, even more so because all of the main findings are directly replicated in a second sample. These data extend the notion of uncertainty-based arbitration between different forms of learning/memory, which has been proposed and evaluated in other contexts, to the case of episodic memory versus incremental learning.

The weaknesses in the paper are mostly minor. One potential weakness is the nature of the online sample. Many participants apparently did not respond to the volatility manipulation, making it impossible to test whether this altered their choices. It is unclear whether this is a feature of online samples (where people can be distracted, unmotivated, etc.) or of human performance more generally.

Thank you for your comments. Indeed, we also found it interesting that many participants were insensitive to the manipulation of volatility in our study, as assessed and filtered based on the initial deck learning task. As you note, our study is not positioned to determine the cause and whether this is due to the online population or human performance more generally, and we added a discussion of this point to the paper (lines 477-485). Also, fractions exceeding 1/3 apparently inattentive participants are very much the norm in our experience with other online studies across many tasks. While there is much to say about the implications of this (see e.g. Zorowitz, Niv & Bennett PsyArXiv 2021), our basic philosophy (which we follow here) is that it is best practice, and conservative, to exclude aggressively so as to focus analyses on those participants for whom the experimental questions can meaningfully be asked.

Reviewer #3 (Public Review):

The purpose of this work is to test the hypothesis that uncertainty modulates the relative contributions of episodic and incremental learning to decisions. The authors test this using a "deck learning and card memory task" featuring a 2-alternative forced choice between two cards, each showing a color and an object. The cards are drawn from different colored decks with different average values that stochastically reverse with fixed volatility, and also feature objects that can be unfamiliar or familiar. Objects are not shown more than twice, and familiar objects have the same value as they did when shown previously. This allows the authors to construct an index of episodic contributions to decision-making: in cases where the previous value of the object is incongruous with the incrementally observed value, the subject's choice reveals which strategy they are relying on.

The key manipulation is to introduce high- and low- volatility conditions, as high volatility has been shown to induce uncertainty in incremental learning by causing subjects to adopt an optimal low learning rate. The authors find that the subjects show a higher episodic choice index in the high-volatility condition, and in particular immediately after reversals when the model predicts uncertainty is at a maximum. The authors also construct a trial-wise index of uncertainty and show that episodic index correlates with this measure. The authors also find that at the subject level, the overall episodic choice index correlates with the ability to accurately identify familiar objects, and the reason that this indicates higher certainty in episodic memory is predicting the usage of episodic strategies. The authors replicate all of their findings in a second subject population.

This is a very interesting study with compelling results on an important topic. The task design was a clever way to disentangle and measure different learning strategies, which could be adopted by others seeking to further understand the contributions of different strategies to decision-making and its neural underpinnings. The article is also very clearly written and the results clearly communicated.

A number of questions remain regarding the interpretation of the results that I think would be addressed with further analysis and modeling.

At a conceptual level, I was unsure about the equivalence drawn between volatility and uncertainty: the main experiments and analyses all regard reversals and comparisons of volatility conditions, but the conclusions are more broadly about uncertainty. Volatility, as the authors note, is only one way to induce uncertainty. It also doesn't seem like the most obvious way to intervene on uncertainty (eg manipulated trial-wise variance seems more obvious). The trial-wise relative uncertainty measurements in Fig 4 speak a bit more to the question of uncertainty more generally, but these were not the main focus and also do not disambiguate between trial-wise uncertainty derived from reversals versus within block variation.

Thank you for your comments. We agree that this distinction was unclear and appreciate the opportunity to clarify. We hope the manuscript is now clear about the conceptual distinction between uncertainty as the construct of theoretical interest vs. volatility as the operational manipulation being used to access it. We have adjusted the presentation and added discussion to clarify this, and also enhanced the trial-wise analyses to strengthen the interpretation of results in terms of uncertainty more generally. Regarding obviousness, we think perhaps there is a difference between areas of study on this point. While trial-wise outcome variance (which we call stochasticity) has been widely used to manipulate uncertainty in perceptual and sensorimotor studies, it has been more rarely manipulated in reward learning studies, where instead the volatility manipulation we use has predominated. We have a recent paper reviewing examples of both and arguing that the field has underemphasized the importance of stochasticity, so we are sympathetic here (Piray and Daw, Nature Communications 2021).

In any case, to address these points on revision, we have reframed the first section of the results, where we look at effects of environment on episodic-based choice, to focus primarily on volatility. Specifically, we have expanded on our explanation of how volatility induces uncertainty, changed the subtitle of the section from ‘uncertainty’ to ‘volatility’, and have specified that the prediction in this section is primarily about volatility (lines 97 and 116-123). We also reframed the second section of the results to be primarily about the uncertainty induced by volatility: while differences between the environments capture coarse effects of volatility, trialwise uncertainty should be present following reversals across both environments. We have now focused our explanation in this section on trial-wise uncertainty within the environments rather than volatility between the environments (lines 184-192). Further, we agree that there are other sources of uncertainty besides volatility that we did not manipulate in the paper, and that it remains for future work whether their manipulation would produce similar results. To amend this, we have added a new paragraph to the discussion covering these alternative sources and further qualifying the scope of our conclusions (lines 434-446).

We also agree that our analyses in Figure 4 did not yet speak to differences in episodic-based choice that may arise due to blockwise volatility (as captured by the categorical effect of environment) vs. trial-to-trial fluctuations in uncertainty (as captured by relative uncertainty, over and above the blockwise effect). We have addressed this by adding an additional, separate effect of the interaction between environment and episodic value to our combined choice models which is explained in more detail in the recommendations for the authors portion of our response. These changes and results are described in the Methods (lines 686-694) and Results (lines 276-277; Figure 4C).

Another key question I had about design choice was the decision to use binary rather than drifting values. Because of this, the subjects could be inferring context rather than continuously incrementing value estimates (eg Gershman et al 2012, Akam et al 2015): the subjects could be inferring which context they are in rather than tracking the instantaneous value + uncertainty. I am not sure this would qualitatively affect the results, as volatility would also affect context confidence, but it is a rather different interpretation and could invoke different quantitative predictions. And it might also have some qualitative bearing on results: the subjects have expectations about how long they will stay in a particular environment, and they might start anticipating a context change after a certain amount of time which would lead to an increase in uncertainty not just immediately after switches, but also after having stayed in the environment for a long period of time. Moreover, depending on the variance within context, there may be little uncertainty following context shifts.

Thank you for raising this important point. To address the possibility that the task structure could have encouraged participants to infer context rather than engage in incremental learning, we added an alternative contextual inference (CI) model, based on a hidden Markov model with two hidden states (e.g. that either the red deck is lucky and the blue deck unlucky or vice versa). This model is now described in the Results of the main text (lines 226-228), listed in the Methods (line 674), and explained in detail in Appendix 3 alongside the computational models of incremental learning. Following model comparison, we found that this model provided a worse fit than the incremental learning models we previously presented in both samples, suggesting that incremental learning is a better descriptor of participants’ choices in this task than contextual inference. The results of this comparison are reflected in an updated Figure 3A.

Author Response

Reviewer #1 (Public Review):

Slusarczyk et al present a very well written manuscript focused on understanding the mechanisms underlying aging of erythrophagocytic macrophages in the spleen (RPM) and its relationship to iron loading with age. The manuscript is diffuse with a broad swath of data elements. Importantly, the manuscript demonstrates that RPM erythrophagocytic capacity is diminished with age, restored in iron restricted diet fed aged mice. In addition, the mechanism for declining RPM erythrophagocytic capacity appears to be ferroptosis-mediated, insensitive to heme as it is to iron, and occur independently of ROS generation. These are compelling findings. However, some of the data relies on conjecture for conclusion and a clear causal association is not clear. The main conclusion of the manuscript points to the accumulation of unavailable insoluble forms of iron as both causing and resulting from decreased RPM erythrophagocytic capacity.

We are proposing that intracellular iron accumulation progresses first and leads to global proteotoxic damage and increased lipid peroxidation. This eventually triggers the death of a fraction of aging RPMs, thus promoting the formation of extracellular iron-rich protein aggregates. More explanation can be found below. Besides, iron loading suppresses the erythrophagocytic activity of RPMs, hence further contributing to their functional impairment during aging.

In addition, the finding that IR diet leads to increased TF saturation in aged mice is surprising.

We believe that this observation implies better mobilization of splenic iron stores, and corroborates our conclusion that mice that age on an iron-reduced diet benefit from higher iron bioavailability, although these differences are relatively mild. More explanation can be found in our replies to Reviewer #2.

Furthermore, whether the finding in RPMs is intrinsic or related to RBC-related changes with aging is not addressed.

We now addressed this issue and we characterized in more detail both iron and ROS levels in RBCs.

Finally, these findings in a single strain and only female mice is intriguing but warrants tempered conclusions.

We tempered the conclusions and provided a basic characterization of the RPM aging phenotype in Balb/c female mice.

Major points:

1) The main concern is that there is no clear explanation of why iron increases during aging although the authors appear to be saying that iron accumulation is both the cause of and a consequence of decreased RPM erythrophagocytic capacity. This requires more clarification of the main hypothesis on Page 4, line 17-18.

We thank the reviewer for this comment. It was previously reported that iron accumulates substantially in the spleen during aging, especially in female mice (Altamura et al., 2014). Since RPMs are those cells that process most of the iron in the spleen, we aimed to explore what is the relationship between iron accumulation and RPM functions during aging. This investigation led us to uncover that indeed iron accumulation is both the cause and the consequence of RPM dysfunction. Specifically, we propose that intracellular iron loading of RPMs precedes extracellular deposition of iron in a form of protein-rich aggregates, driven by RPMs damage. To support this, we now show that the proteome of RPMs overlaps with those proteins that are present in the age-triggered aggregates (Fig. 3F). Furthermore, corroborating our model, we now demonstrate that transient iron loading of RPMs via iron-dextran injection (new Fig. 3G) leads to the formation of protein-rich aggregates, closely resembling those present in aged spleens (new Fig. 3H). This implies that high iron content in RPMs is indeed a major driving factor that leads to aggregation of their proteome and cell damage. Importantly, we now supported this model with studies using iRPMs. We demonstrated that iron loading and blockage of ferroportin by synthetic mini-hepcidin (PR73)(Stefanova et al., 2018) cause protein aggregation in iRPMs and lead to their decreased viability only in cells that were exposed to heat shock, a well-established trigger of proteotoxicity (new Fig. 5K and L). We propose that these two factors, namely age-triggered decrease in protein homeostasis and exposure to excessive iron levels, act in concert and render RPMs particularly sensitive to damage during aging (see also Discussion, p. 16).

In parallel, our data imply that the increased iron content in aged RPMs drives their decreased erythrophagocytic activity, as we now better documented by more extensive in vitro experiments in iRPMs (new Fig 6E-H). We cannot exclude that some of the senescent splenic RBCs that are retained in the red pulp and evade erythrophagocytosis due to RPM defects in aging, may also contribute to the formation of the aggregates. This is supported by the fact that mice that lack RPMs as well exhibit iron loading in the spleen (Kohyama et al., 2009; Okreglicka et al., 2021), and that the proteome of aggregates overlaps to some extent with the proteome of erythrocytes (new Fig. 3F).

We believe that during aging intracellular iron accumulation is chiefly driven by ferroportin downregulation, as also suggested by Reviewer#3. We now show that ferroportin drops significantly already in mice aged 4 and 5 months (new Fig. 4H), preceding most of the other impairments. This drop coincides with the increase in hepcidin expression, but if this is the sole reason for ferroportin suppression during early aging would require further investigation outside the scope of the present manuscript.

In sum, to address this comment, we now modified the fragment of the introduction that refers to our hypothesis and major findings to be more clear (p. 4), we improved our manuscript by providing new data mentioned above and we added more explanation in the corresponding sections of the Results and Discussion.

2) It is unclear if RPMs are in limited supply. Based on the introduction (page 4, line 13-15), they have limited self-renewal capacity and blood monocytes only partially replenished. Fig 4D suggests that there is a decrease in RPMs from aged mice. The %RPM from CD45+ compartment suggests that there may just be relatively more neutrophils or fewer monocytes recruited. There is not enough clarity on the meaning of this data point.

Thank you for this comment. We fully agree that %RPMs of CD45+ splenocytes, although well-accepted in literature (Kohyama et al., 2009; Okreglicka et al., 2021), is only a relative number. Hence, we now included additional data and explanations regarding the loss of RPMs during aging.

It was reported that the proportion of RPMs derived from bone marrow monocytes increases mildly but progressively during aging (Liu et al., 2019). This implies that due to the loss of the total RPM population, as illustrated by our data, the cells of embryonic origin are likely even more affected. We could confirm this assumption by re-analysis of the data from Liu et al. that we now included in the manuscript as Fig. 5E. These data clearly show that the representation of embryonically-derived RPMs drops more drastically than the percent of total RPMs, whereas the replenishment rate from monocytes is not affected significantly during aging. Consistent with this, we have not observed any robust change in the population of monocytes (F4/80-low, CD11b-high) or pre-RPMs (F4/80-high, CD11b-high) in the spleen at the age of 10 months (Figure 5-figure supplement 2A and B). We also have detected a mild decrease, not an increase, in the number of granulocytes (new Figure 5-figure supplement 2C). Furthermore, we measured in situ apoptosis marker and found a clear sign of apoptosis in the aged spleen (especially in the red pulp area), a phenotype that is less pronounced in mice on an IR diet (new Fig. 5O). This is consistent with the observation that apoptosis markers can be elevated in tissues upon ferroptosis induction (Friedmann Angeli et al., 2014) and that the proteotoxic stress in aged RPMs, which we now emphasized better in our manuscript, may also lead to apoptosis (Brancolini & Iuliano, 2020). Taken together, we strongly believe that the functional defect of embryonically-derived RPMs chiefly contributes to their shortage during aging.

3) Anemia of aging is a complex and poorly understood mechanistically. In general, it is considered similar to anemia of chronic inflammation with increased Epo, mild drop in Hb, and erythroid expansion, similar to ineffective erythropoiesis / low Epo responsiveness. It is not surprising that IR diet did not impact this mild anemia. However, was the MCV or MCH altered in aged and IR aged mice?

We now included the data for hematocrit, RBC counts, MCV, and MCH in Figure 1-figure supplement 5. Hematocrit shows a similar tendency as hemoglobin levels, but the values for RBC counts, MCV, and MCH seem not to be altered. We also show now that the erythropoietic activity in the bone marrow is not affected in aged versus young mice. Taken together, the anemic phenotype in female C57BL/6J mice at this age is very mild, which we emphasized in the main text, and is likely affected by other factors than serum iron levels (p. 6).

4) Page 6, line 23 onward: the conclusion is that KC compensate for the decreased function of RPM in the spleen, based on the expansion of KC fraction in the liver. Is there evidence that KCs are engaged in more erythrophagocytosis in aged mice? Furthermore, iron accumulation in the liver with age does not demonstrate specifically enhanced erythrophagocytosis of KC. Please clarify why liver iron accumulation would not be simply a consequence of increased parenchymal iron similar to increased splenic iron with age, independent of erythrophagocytic activity in resident macrophages in either organ.

Thanks for these questions. For the quantification of the erythrophagocytosis rate in KC, we show, as for the RPMs (Fig. 1K), the % of PKH67-positive macrophages, following transfusion of PKH67-stained stressed RBCs (Fig. 1M). The data implies a mild (not statistically significant) drop (of approx. 30%) in EP activity. We believe that it is overridden by a more pronounced (on average, 2-fold) increase in the representation of KCs (Fig. 1N). The mechanisms of iron accumulation between the spleen and the liver are very different. In the liver, we observed iron deposition in the parenchymal cells (not non-parenchymal, new Fig. 1P) that we currently characterizing in more detail in a parallel manuscript. Our data demonstrate a drop in transferrin saturation in aged mice. Hence, it is highly unlikely that aging would be hallmarked by the presence of circulating non-transferrin-bound iron that would be sequestered by hepatocytes, as shown previously (Jenkitkasemwong et al., 2015). Thus, the iron released locally by KCs is the most likely contributor to progressive hepatocytic iron loading during aging. The mechanism of iron delivery to hepatocytes from erythrophagocytosing KCs was demonstrated by Theurl et al.(Theurl et al., 2016), and we propose that it may be operational, although in a much more prolonged time scale, during aging. We now discussed this part better in our Results sections (p. 7).

5) Unclear whether the effect on RPMs is intrinsic or extrinsic. Would be helpful to evaluate aged iRPMs using young RBC vs. young iRPMs using old RBCs.

We are skeptical if the generation of iRPMs cells from aged mice would be helpful – these cells are a specific type of primary macrophage culture, derived from bone marrow monocytes with MCSF1, and exposed additionally to heme and IL-33 for 4 days. We do not expect that bone marrow monocytes are heavily affected by aging, and would thus recapitulate some aspects of aged RPMs from the spleen, especially after 8-day in vitro culture. However, to address the concerns of the reviewer, we now provide additional data regarding RBC fitness. Consistent with the time life-span experiment (Fig, 2A), we show that oxidative stress in RBCs is only increased in splenic, but not circulating RBCs (new Fig. 2C, replacing the old Fig. 2B and C). In addition, we show no signs of age-triggered iron loading in RBCs, either in the spleen (new Fig. 2F) or in the circulation (new Fig. 2B). Hence, we do not envision a possibility that RPMs become iron-loaded during aging as a result of erythrophagocytosis of iron-loaded RBCs. In support of this, we also have observed that during aging first RPMs’ FPN levels drop, afterward erythrophagocytosis rate decreases, and lastly, RBCs start to exhibit significantly increased oxidative stress (presented now in new Fig. 4H, J and K).

6) Discussion of aggregates in the spleen of aged mice (Fig 2G-2K and Fig 3) is very descriptive and non-specific. For example, if the iron-rich aggregates are hemosiderin, a hemosiderin-specific stain would be helpful. This data specifically is correlatory and difficult to extract value from.

Thanks for these comments. To the best of our knowledge Prussian blue Perls’ staining (Fig. 2J) is considered a hemosiderin staining. Our investigations aimed to better understand the nature and the origin of splenic iron deposits that to some extent are referred to as hemosiderin. Most importantly, as mentioned in our reply R1 Ad. 1. to assign causality to our data, we now demonstrated that iron accumulation in RPMs in response to iron-dextran (Fig. 3G) increases lipid peroxidation (Fig. 5F), tends to provoke RPMs depletion (Fig. 5G) and triggers the formation of protein-rich aggregates (new Fig. 3H). Of note, we assume that the loss of embryonically-derived RPMs in this model may be masked by simultaneous replenishment of the niche from monocytes, a phenomenon that may be addressed by future studies using Ms4a3-driven reporter mice (as shown for aged mice in our new Fig. 5E).

7) The aging phenotype in RPMs appears to be initiated sometime after 2 months of age. However, there is some reversal of the phenotype with increasing age, e.g. Fig 4B with decreased lipid peroxidation in 9 month old relative to 6 month old RPMs. What does this mean? Why is there a partial spontaneous normalization?

Thanks for this comment and questions. Indeed, the degree of lipid peroxidation exhibits some kinetics, suggestive of partial normalization. Of note, such a tendency is not evident for other aging phenotypes of RPMs, hence, we did not emphasize this in the original manuscript. However, in a revised version of the manuscript, we now present the re-analysis of the published data which implies that the number of embryonically-derived RPMs drops substantially between mice at 20 weeks and 36 weeks (new Fig. 5E). We think that the higher proportion of monocyte-derived RPMs in total RPM population later in aging (9 months) might be responsible for the partial alleviation of lipid peroxidation. We now discussed this possibility in the Results sections (p. 12).

8) Does the aging phenotype in RPMs respond to ferristatin? It appears that NAC, which is a glutathione generator and can reverse ferroptosis, does not reverse the decreased RPM erythrophagocytic capacity observed with age yet the authors still propose that ferroptosis is involved. A response to ferristatin is a standard and acceptable approach to evaluating ferroptosis.

We fully agree with the Reviewer that using ferristatin or Liproxstatin-1 would be very helpful to fully characterize a mechanism of RPMs depletion in mice. However, previous in vivo studies involving Liproxstatin-1 administration required daily injections of this ferroptosis inhibitor (Friedmann Angeli et al., 2014). This would be hardly feasible during aging. Regarding the experiments involving iron-dextran injection, using Liproxstatin-1 would require additional permission from the ethical committee which takes time to be processed and received. However, to address this question we now provide data from iRPMs cell cultures (new Fig.5 K-L). In essence, our results imply that both proteotoxic stress and iron overload act in concert to trigger cytotoxicity in RPM in vitro model. Interestingly, this phenomenon does not depend solely on the increased lipid peroxidation, but when we neutralize the latter with Liproxstatin-1, the cytotoxic effect is diminished (please, see also Results on p. 13 and Discussion p. 15/16).

9) The possible central role for HO-1 in the pathophysiology of decreased RPM erythrophagocytic capacity with age is interesting. However, it is not clear how the authors arrived at this hypothesis and would be useful to evaluate in the least whether RBCs in young vs. aged mice have more hemoglobin as these changes may be primary drivers of how much HO-1 is needed during erythrophagocytosis.

Thanks for this comment. We got interested in HO-1 levels based on the RNA sequencing data, which detected lower Hmox-1 expression in aged RPMs (Figure 3-figure supplement 1). We now show that the content of hemoglobin is not significantly altered in aged RBCs (MCH parameter, Figure 1-figure supplement 5E), hence we do not think that this is the major driver for Hmox-1 downregulation. Likewise, the levels of the Bach1 message, a gene encoding Hmox-1 transcriptional repressor, are not significantly altered according to RNAseq data. Hence, the reason for the transcriptional downregulation of Hmox-1 is not clear. Of note, HO-1 protein levels in the total spleen are higher in aged versus young mice, and we also detected a clear appearance of its nuclear truncated and enzymatically-inactive form (see a figure below, we opt not to include this in the manuscript for better clarity). The appearance of truncated HO-1 seems to be partially rescued by the IR diet. It is well established that the nuclear form of HO-1 emerges via proteolytic cleavage and migrates to the nucleus under conditions of oxidative stress (Mascaro et al., 2021). This additionally confirms that the aging spleen is hallmarked by an increased burden of ROS. Moreover, we also detected HO-1 as one of the components of the protein iron-rich aggregates. Thus, we propose that the low levels of the cytoplasmic enzymatically active form of HO-1 in RPMs (that we preferentially detect with our intracellular staining and flow cytometry) may be underlain by its nuclear translocation and sequestration in protein aggregates that evade antibody binding [this is also supported by our observation that the protein aggregates, despite the high content of ferritin (as indicated by MS analysis) are negative for L-ferritin staining. Of note, we also cannot exclude that other cell types in the aging spleen (eg. lymphocytes) express higher levels of HO-1 in response to splenic oxidative stress.

Fig. Total splenic levels of HO-1 in young, aged IR and aged mice.

Reviewer #2 (Public Review):

Slusarczyk et al. investigate the functional impairment of red pulp macrophages (RPMs) during aging. When red blood cells (RBCs) become senescent, they are recycled by RPMs via erythrophagocytosis (EP). This leads to an increase in intracellular heme and iron both of which are cytotoxic. The authors hypothesize that the continuous processing of iron by RPMs could alter their functions in an age-dependent manner. The authors used a wide variety of models: in vivo model using female mice with standard (200ppm) and restricted (25ppm) iron diet, ex vivo model using EP with splenocytes, and in vitro model with EP using iRPMs. The authors found iron accumulation in organs but markers for serum iron deficiency. They show that during aging, RPMs have a higher labile iron pool (LIP), decreased lysosomal activity with a concomitant reduction in EP. Furthermore, aging RPMs undergo ferroptosis resulting in a non-bioavailable iron deposition as intra and extracellular aggregates. Aged mice fed with an iron restricted diet restore most of the iron-recycling capacity of RPMs even though the mild-anemia remains unchanged.

Overall, I find the manuscript to be of significant potential interest. But there are important discrepancies that need to be first resolved. The proposed model is that during aging both EP and HO-1 expression decreases in RPMs but iron and ferroportin levels are elevated. In their model, the authors show intracellular iron-rich proteinaceous aggregates. But if HO-1 levels decrease, intracellular heme levels should increase. If Fpn levels increase, intracellular iron levels should decrease. How does LIP stay high in RPMs under these conditions? I find these to be major conflicting questions in the model.

We thank the Reviewer for her/his valuable feedback. As we mentioned in our replies we can only assume that a small misunderstanding in the interpretation of the presented data underlies this comment. We show that ferroportin levels in RPMs (Fig. 1F) are modulated in a manner that fully reflects the iron status of these cells (both labile and total iron levels, Figs. 1H and I). FPN levels drop in aged RPMs and are rescued when mice are maintained on a reduced iron diet. As pointed out by Reviewer#3, and explained in our replies we believe that ferroportin levels are critical for the observed phenotypes in aging. We now described our data in a more clear way to avoid any potential misinterpretation (p.6).

Reviewer #3 (Public Review):

This is a comprehensive study of the effects of aging of the function of red pulp macrophages (RPM) involved in iron recycling from erythrocytes. The authors document that insoluble iron accumulates in the spleen, that RPM become functionally impaired, and that these effects can be ameliorated by an iron-restricted diet. The study is well written, carefully done, extensively documented, and its conclusions are well supported. It is a useful and important addition for at least three distinct fields: aging, iron and macrophage biology.

The authors do not explain why an iron-restricted diet has such a strong beneficial effect on RPM aging. This is not at all obvious. I assume that the number of erythrocytes that are recycled in the spleen, and are by far the largest source of splenic iron, is not changed much by iron restriction. Is the iron retention time in macrophages changed by the diet, i.e. the recycled iron is retained for a short time when diet is iron-restricted (making hepcidin low and ferroportin high), and long time when iron is sufficient (making hepcidin high and ferroportin low)? Longer iron retention could increase damage and account for the effect. Possibly, macrophages may not empty completely of iron before having to ingest another senescent erythrocyte, and so gradually accumulate iron.

We are very grateful to this Reviewer for emphasizing the importance of the iron export capacity of RPMs as a possible driver of the observed phenotypes. Indeed, as mentioned above, we now show in the revised version of the manuscript that ferroportin drops early during aging (revised Fig. 4). Importantly, we now also observed that iron loading and limitation of iron export from iRPMs via ferroportin aggravate the impact of heat shock (a well-accepted trigger of proteotoxicity) on both protein aggregation and cell viability (new Fig. 5K and L). Physiologically, recent findings show that aging promotes a global decrease in protein solubility [BioRxiv manuscript (Sui X. et al., 2022)], and it is very likely that the constant exposure of RPMs to high iron fluxes renders these specialized cells particularly sensitive to proteome instability. This could be further aggravated by a build-up of iron due to the drop of ferroportin early during aging, ultimately leading to the appearance of the protein aggregates as early as at 5 months of age in C57BL/6J females. Based on the new data, we emphasized this model in the revised version of the manuscript (please, see Discussion on p. 16)

Author Response

Reviewer #1 (Public Review):

This manuscript clearly demonstrates that murine malaria infection with Plasmodium chabaudi impairs B cells' interaction with T cells, rather than DCs interaction with T cells. The authors elegantly showed that DCs were activated, capable of acquiring antigens and priming T cells during P. chabaudi infection. B cells are the main APC to capture particulate antigens such as infected RBC (iRBC), while DCs preferentially take up soluble antigens. This study is important to understand how ongoing infections such as malaria may negatively affect heterologous immunizations.

Overall, the experimental designs are straightforward, and the manuscript is well-written. However, there were several limitations in this study.

Specific comments:

1) The mechanism of how the prior capture of iRBC by B cells lead to the impairment of B-T interaction was not understood. It is unclear whether the impairment of B-T cell interaction is due to direct BCR interaction with iRBC, or an indirect response to extrinsic factors induced by malaria infection.

We believe we have carefully demonstrated that impairment of B-T interactions does not require specific BCR-antigen interactions between B cells and iRBCs (for a complete explanation of this point, please see the response to the next comment). However, the question remains whether direct, antigen-nonspecific iRBC-B cell interactions (i.e., not mediated by the BCR) or additional extrinsic factors, or a combination, are responsible for the observed defects in Tfh and GC B cell populations.

Existing studies from other infection models are informative in answering this question. Daugan et al (Front Immunol 2016; PMID 27994594) previously published experiments similar to ours, but used LCMV instead of Plasmodium. That is, they immunized uninfected or LCMV-infected mice with the well-studied immunogen NPP-CGG and measured NP-specific antibody production and other parameters. They found that LCMV infection concurrent with immunization (or 4-8 days before) significantly decreased the numbers of NP-specific splenic antibody-secreting cells and IgG1 titers, and caused major disruptions to splenic architecture. These defects were shown to require type I interferon (T1IFN) signaling in B cells. However, T1IFN is unlikely to be solely responsible for the observed phenotypes, because simultaneous infection with VSV, another virus that also induces T1IFN, did not cause any defects in NP-specific antibody production. Contrasting with the work of Daugan et al, Banga et al (PloS One 2015; PMID 25919588) found that infecting with LCMV (or with Listeria monocytogenes) two days after heterologous immunization did not disrupt immunogen-specific responses, whereas P. yoelii did. Examining both these studies, we hypothesize that both LCMV and Plasmodium infections can disrupt humoral responses, but that LCMV does so within a narrower time frame, thereby yielding different results depending on whether infection comes a few days before or a few days after immunization.

Complementing these studies of heterologous immunization, additional publications have reported that cytokines induced by several different pathogenic infections drive disruption of germinal centers and decreases in antibody titers specific for the pathogen itself, often correlated with disordered splenic architecture. Glatman Zaretsky et al. (Infect Immun 2012; PMID 22851754) showed that Toxoplasma gondii infection causes transient disruption of splenic architecture and loss of defined GCs by microscopy. These defects were partially due to decreased lymphotoxin expression by B cells, and were rescued by a lymphotoxin receptor agonist. Similarly, we previously reported that blood-stage Plasmodium infection disrupted germinal center responses to a Plasmodium liver-stage antigen (Keitany et al. Cell Rep 2016; PMID 28009289). In this context, however, the same lymphotoxin receptor agonist had no effect on GCs; instead, blockade of the pro-inflammatory cytokine interferon gamma partially restored antibody responses to the liver-stage antigen. Overall, we favor the hypothesis that several different pathogens can disrupt GCs and antibody responses indirectly by inducing inflammation and a disordered splenic environment; however, the precise mechanisms of disruption likely differ from infection to infection, with different cytokines or other effectors playing key roles in some but not other settings. Importantly, not all pathogens disrupt antibody production, since again, infection with VSV or L. monocytogenes did not affect immunogen-specific titers in immunized mice (Daugan Front Immunol 2016; Banga et al. 2015). We have now addressed this topic at length in the Discussion (lines 399-418).

The existence of indirect, inflammation- or cytokine-related mechanisms that may interfere with germinal center formation and antibody production does not preclude additional direct interactions between B cells and iRBCs that might also affect B cell function. We address this possibility more fully in the response to the next comment.

2) Would malaria infection in MD4 mouse that carries transgenic BCR that does not recognize malaria parasite impair subsequent B cell response to HEL immunization? This may clarify whether the impairment of subsequent B cell response is BCR-specific. If malaria impairs subsequent B cell response to HEL in MD4 mouse, it might suggest that other cell types and B cell-extrinsic factors might be involved in causing the impaired B cell responses, instead of malaria affecting B cells directly.

The question of whether the impairments we observe require BCR-specific interactions with iRBCs is an important one. However, we believe that the experiment the reviewer proposes to address this question has technical limitations; further, we assert that we have already provided data to address a requirement for BCR specificity.

With regard to the proposed experiment of immunizing MD4 mice with HEL in the presence or absence of malaria infection: MD4 mice, in which B cells express a transgenic receptor specific for HEL, can be expected to mount a massive, monoclonal response to direct immunization with HEL that would be very different from the physiological context of a polyclonal B cell population. We are doubtful that this experimental setup would be informative for the question at hand, especially because we are studying the effects of B-Tfh interactions, which are already limiting in the physiological setting of a polyclonal B cell response, but would be massively unbalanced in an MD4 mouse where all B cells express the receptor for HEL.

Usually, investigators studying MD4 B cell responses generate a more physiological setting by adoptively transferring a small but detectable number of MD4 transgenic B cells into a mouse with a normal polyclonal B cell population, and immunizing that mouse. We maintain that this approach is essentially what we have done in our study, except that instead of using transferred. transgenic cells to identify a B cell population of known specificity, we have used tetramers to detect a specific population of endogenous B cells in a polyclonal setting. By examining GP-specific B cells in our immunization experiments, we restricted our analysis to B cells that could not have had any BCR-mediated, antigen-specific interactions with iRBCs (because the GP antigen is not present in the iRBCs; it is delivered as a soluble protein antigen, 5 days after initiation of infection). Because we see dysfunction in the GP-specific T and B cell populations despite the absence of this antigen within iRBCs, we can conclude that the disruptions to these populations are not due to antigen-specific iRBC-BCR interactions.

We do also show (using MD4 B cells in Fig. S1B) that selective interactions between iRBCs and B cells do not require an antigen-specific BCR. Thus, it is still possible that direct interactions between iRBCs and B cells (that are independent of antigen binding to the BCR) are responsible for disrupting subsequent adaptive responses, perhaps in addition to the more indirect factors that we discuss in the response to Comment #1 above. We are very interested in this possibility, which is discussed in lines 428-436 of the manuscript. But the use of MD4 B cells would not address this specific question. Instead, we would need to identify an alternative pathway or receptor that mediates the iRBC-B cell interaction, and study the effects of blocking that pathway on downstream adaptive responses. We have spent considerable time and energy on this question, but have not yet been able to identify such a pathway; this remains a matter for further study.

3) MD4 mice were mentioned in the Methods in vitro RBC binding, although none of the figures described the usage of MD4 mice. This experiment data might be important to show whether RBC binding to B cells is mediated through BCR.

Cells from MD4 mice were used in Figure S1B to show that in vitro binding of iRBCs to B cells did not require interaction with an antigen-specific BCR. We agree that this is an important point and have revised the text (lines 152-156) to outline it more clearly.

4) Does P. chabaudi infection have any effects on B cell uptake of subsequent antigens, such as soluble antigen PE or particulate antigen CFSE-labeled P. yoelii iRBC?

We examined uptake of PE by B cells in P. chabaudi-infected mice (5 days post-infection) compared to naïve mice. There was a trend towards increased uptake in the infected mice, but this difference was not significant. These data are taken from the same samples that did reveal a significant increase in PE uptake by DCs in infected mice (Fig. 3C). We have now included the B cell data in the paper as Figure 3D, and discussed them in lines 231-232.

5) Is this phenomenon specific to malaria infection? Does malaria-irrelevant particulate immunization affect T-B interaction of subsequent heterologous immunization?

We do not believe this phenomenon is specific to malaria infection; please see the extensive discussion of this point in the response to Comment #1 above. We would hypothesize that malaria-irrelevant particle immunization (as with nanoparticles) would not affect T-B interactions for subsequent heterologous immunizations, however, since the disruption seems to be associated with the massive inflammation and splenic disorganization that occurs following certain infections.

6) Despite the impaired Tfh and GC 8 days after immunization following malaria infection, Fig. 5F showed GP-specific IgG eventually increased to the same level as the uninfected immunized mice on day 23. Did the authors check whether these mice had a delayed Tfh and GC response that eventually increase on day 23? Are these antibody responses derived from GC, or GC-independent response?

We have now examined GP-specific T cell numbers and polarization between days 23 and 35 post-immunization. We found that although a defect persists in the percentage of GP66-specific T cells that exhibit a GC Tfh phenotype at later timepoints, the absolute number of GC Tfh cells is not significantly defective in infected mice at these times. Concurrently there is a slight (though nonsignificant) increase in the total numbers of GP66+ T cells in the infected mice; we believe that this modest overall expansion permits recovery of the GC Tfh population numbers despite the continued defect in their frequency. These findings are consistent with our observation that antibody levels recover in infected mice by 3 weeks post-infection. We have added these data to Figure 4 (E-G) and discuss them in lines 283-293.

7) Does recovery from malaria infection by antimalarial treatment rescue the B cell response to subsequent heterologous immunization?

We have shown previously that drug-mediated clearance of blood-stage Plasmodium infection restores GC and antibody responses to a liver-stage-specific antigen, which normally are disrupted by emergence of the blood-stage (Keitany et al. Cell Rep 2016). We have also shown that antimalarial drug treatment restores GC responses in mice lacking the innate immune sensor CGAS, which have higher parasitemia, exacerbated splenic disruption, and diminished GC responses following P. yoelii infection (Hahn et al., JCI Insight 2018). Based on these results we hypothesize that drug-mediated clearance of blood-stage infection would also rescue B cell responses to heterologous immunization.

8) Fig. 1C shows more nRBC was taken up than iRBC in B cells, but Line 142 states that "B cells bound significantly more iRBC than nRBC. Is there a mistake in the figure arrangement? Why do B cells take up for naïve RBC than iRBC?

The symbols in the figure legend were switched in error; the filled circles are actually iRBC+ and the outlined circles are nRBC+. We regret the error and appreciate the reviewer bringing it to our attention. We have corrected the figure.

9) Fig. S1 C and D are confusing. CD45.1+ CD45.2+ mouse did not receive labeled iRBC, but why iRBC was detected as much as 40% in the spleen of this naïve mouse?

The experiment depicted in Figs. S1 C and D was designed to test whether B cells actually bound injected iRBCs in vivo, or whether the binding occurred during processing of the tissue. With this experimental setup (injecting labeled iRBCs into CD45.2+ mice, then excising and disrupting the spleen together with an untreated CD45.1+ CD45.2+ spleen), iRBC signal from in vivo uptake should be observed only in CD45.2+ splenocytes, whereas iRBC binding that occurs during tissue processing will be distributed between the two genotypes. Thus, the ~40% of iRBC signal observed in CD45.1+ CD45.2+ B cells leads us to conclude that much of the observed B cell binding from our in vivo experiments occurs during processing, as we state in the text (lines 151-152). Even so, in vitro experiments clearly show that B cells selectively bind iRBCs over naïve RBCs in a setting where processing is not a confounder (Fig. S1B). To clear up any confusion, we have expanded the description of the experiment and its interpretation in the Supplemental Figure Legend.

Reviewer #2 (Public Review):

The data presented support the conclusions of the paper, and my concerns are largely conceptual in how we understand this data in the context of malaria infection in vaccination in endemic areas

1) The data is presented based on the idea that antigen uptake and presentation differ between particle and soluble antigens, and that during malaria infection particle uptake is more important due to circulating iRBCs. However, during parasite invasion of RBCs, the parasite sheds large amounts of antigen into the circulation, at least some of which would then be found in a soluble form in the circulation. Can the authors comment on this aspect of infection and if/how this may impact the interpretation of results? Do authors assume that any soluble antigen taken up and presented (via DCs?) during infection would be impacted as for GP66 soluble antigen? Or could an interaction on immune responses where the antigen is presented via both particle and soluble pathways?

This is an important point and we have now discussed it further in the text (lines 111-115, 204-210, and 356-357). In our previously published study, where we extensively characterized CD4 T cell responses to the GP66 epitope expressed by P. yoelii, the epitope was fused to a parasite protein (Hep17) that localizes to the parasitophorous vacuole membrane, and so we do assume that the majority of this antigen is encountered by APCs in the context of an iRBC, rather than shed in soluble form. In contrast, some merozoite surface antigens such as cleaved MSP1 are shed copiously from the parasite coat upon RBC invasion, and therefore would be expected to exist in soluble as well as parasite-associated form.

Unfortunately, our laboratory does not currently have tetramer reagents or access to transgenic mice that would allow us to assess T cell responses specific for shed or soluble parasite antigens. But a previous study from Stephens et al. (Blood 2005; PMID 15890689) reported that T cells with a transgenic TCR specific for an epitope in the shed portion of MSP1 could boost antibody production when transferred into T cell-deficient mice infected with P. chabaudi, suggesting that at least some fraction of the MSP1-specific T cells differentiate into T helper cells capable of supporting B cell activity. However, antibody production was significantly delayed in this setting compared to its usual kinetics in wild-type mice. Further side-by-side characterization would be needed to assess differentiation of these MSP1-specific transgenic T cells during infection, and determine whether they are primed from B cells or from DCs (or a combination).

We will note that we have extensively characterized B cell responses to MSP1 during both infection and immunization. While robust and T-dependent, MSP1-specific B cell responses in infected mice are delayed relative to their kinetics in mice immunized with recombinant MSP1 or other protein antigens. This may indicate that MSP1-specific T cell activation or cognate B-T interactions are defective in infected mice relative to immunized mice, despite the presumed presence of soluble (shed) MSP1 during infection. If this is the case, it suggests that the defects we describe in the current manuscript exist for both particle-associated and soluble parasite antigens. However, as we mentioned above, a careful characterization of MSP-1-specific T cell differentiation is needed to really understand this, and that requires additional tools that we can’t easily access at this time.

2) Impact of particle antigen opsonisation on antigen uptake and presentation. The authors use parasites isolated from mice who have been infected for 6-7 days to investigate the ability of different subsets to update particle antigens. At this time point, have mice developed antibody responses that opsonise these parasites, or are antibody levels low and parasites opsonised? Would opsonised parasites, such as those coated with sera from children in a setting of chronic infection, have a different pattern/ability to be opsonised by different immune cell subsets? And/or would opsonisation change how the DC and other cell types are processing/presenting antigens? While these issues could be addressed experimentally either now or in the future, the manuscript should at least consider this issue because, during a human infection in areas of high exposure, individuals are likely to have reasonable levels of antibodies with opsonised parasites circulating.

We ourselves have been very interested in the question of whether host antibodies (or other host factors such as complement) might affect uptake of iRBCs. As the reviewer notes, the iRBCs we use in our experiments are taken from mice 6-7 days post-infection, at which time some amount of anti-parasite antibody has developed. We spent a considerable amount of time trying to measure effects of opsonizing antibody, or even deposited complement, on uptake of iRBCs. However, we did not see any change in B cell binding of iRBCs in vitro when we blocked complement receptor with anti-CD21; blocked antibody receptors (Fc receptors) with anti-CD16/CD32 or excess normal mouse serum; or used iRBCs taken from complement-depleted mice (treated with cobra venom factor) or from uMT mice (which entirely lack B cells and antibody). Thus, we have not been able to find any role for opsonizing antibody (or complement) in iRBC uptake. We have not included these experiments in the manuscript because they yielded only negative data, and we were not able to design positive controls robust enough to give us confidence that the experiments were technically sound (and therefore that the negative results were due to the underlying biology and not to experiment failure). We have added a discussion point about this issue (lines 438-442).

3) While authors show that malaria infection disrupts the response to soluble antigens, the relevance directly to vaccination should be considered carefully, specifically because vaccines of soluble antigens are largely given alongside adjuvants which also will modulate DC function. Again, this could be addressed experimentally now or in the future, but definitely should be mentioned and considered when interpreting the results.

Whenever we performed soluble protein immunizations to examine adaptive immune responses in this study, the immunogen was delivered in adjuvant, specifically Sigma Adjuvant System (SAS), as described in the Methods. This adjuvant contains the Monophosphoryl Lipid A component from Salmonella in an oil-water emulsion, and as such, its formulation is at least roughly similar to the AS01 adjuvant used in Mosquirix (RTS,S), the only licensed anti-malaria vaccine, as well as other vaccines currently used in humans. SAS has been shown to elicit very high titers of neutralizing antibodies in mice (Sastry et al., PloS One 2017, PMID 29073183). Therefore our results should have relevance for vaccination in humans. We have modified the manuscript text (lines 454-455 to highlight that in this study, protein immunogens were administered with adjuvant.

Author Response

Reviewer #1 (Public Review):

The study by Xie et al., investigates whether the entorhinal-DG/CA3 pathway is involved in working memory maintenance. The main findings include a correlation between stimulus and neural similarities that was specific for cued stimulus and entorhinal-DG/CA3 locations. The authors observed similar results (cuing and region specificity) using inverted encoding modeling approach. Finally, they also showed that trials in which participants made a smaller error showed a better reconstruction fidelity on the cued side (compared to un-cued). This effect was absent for larger-error trials.

The study challenges a widely held traditional view that working memory and episodic memory have largely independent neural implementations with the MTL being critical for episodic memory but not for working memory. The study adds to a large body of evidence showing involvement of the hippocampus across a range of different working memory tasks and stimuli. Nevertheless, it still remains unclear what functions may hippocampus play in working memory.

We thank the reviewer’s positive appraisal of the current research, which adds to the growing research interest in the MTL’s contribution to WM.

Reviewer #2 (Public Review):

Xie et al. investigated the medial temporal lobe (MTL) circuitry contributions to pattern separation, a neurocomputational operation to distinguish neutral representations of similar information. This presumably engages both long-term memory (LTM) and working memory (WM), bridging the gap between the working memory (WM) and long-term memory (LTM) distinction. Specifically, the authors combined an established retro-cue orientation WM task with high-resolution fMRI to test the hypothesis that the entorhinal-DG/CA3 pathway retains visual WM for a simple surface feature. They found that the anterior-lateral entorhinal cortex (aLEC) and the hippocampal DG/CA3 subfield both retained item-specific WM information that is associated with fidelity of subsequent recall. These findings highlight the contribution of MTL circuitry to item-specific WM representation, against the classic memory models.

I am a long-term memory researcher with expertise in representational similarity analysis, but not in inverted encoding modeling (IEM). Therefore, I cannot verify the correctness of these models and I will leave it to the other reviewers and editors. However, after an in-depth reading of the manuscript, I could evaluate the significance of the present findings and the strength of evidence supporting these findings. The conclusions of this paper are mostly well supported by data, but some aspects of image acquisition and data analysis need to be clarified.

We thank the reviewer for positive appraisal of the current study.

I would like to list several strengths and weaknesses of this manuscript:

Strengths:

• Methodologically, the authors addressed uncertainty in previous research resulting from several challenges. Namely, they used a high-resolution fMRI protocol to infer signals from the MTL substructures and an established retro-cue orientation WM task to minimize the task load.

• The authors selected a control ROI - amygdala - irrelevant for the experimental task, and at the same time adjacent to the other MTL ROIs, thus possibly having a similar signal-to-noise ratio. The reported effects were observed in the aLEC and DG/CA3, but not in the amygdala.

• Memory performance, quantified as recall errors, was at ceiling - an average recall error of 12 degrees was only marginally away from the correct grating towards the closest incorrect grating (predefined with min. 20 degrees increments). However, the authors controlled for the effects of recall fidelity on MTL representations by comparing the IEM reconstructions between precise recall trials and imprecise recall trails (resampled to an equal number of trials). The authors found that precise recall trails have yielded better IEM reconstruction quality.

• The author performed a control analysis of time-varying IEM to exclude a possibility that the mid-delay period activity in the aLEC-DG/CA3 contains item-specific information that could be attributed to perceptual processing. This analysis showed that the earlier TR in the delay period contains information for both cued and uncued items, whereas the mid-delay period activity contains the most information related to the cued, compared to uncued, item.

We thank the reviewer for highlighting the multiple strengths of the current study.

Weaknesses:

• The authors formulate their main hypothesis building on an assumption related to the experimental task. This task requires correctly selecting the cued grating orientation while resisting the interference from internal representations of the other orientation gratings. The authors hypothesize that if this post-encoding information selection function is supported by the MTL-s entorhinal-DG/CA3 pathway, the recorded delay-period activity should contain more information about the cued item that the uncued item (even if both are similarly remembered). Thus, the assumption here is that resolving the interference would be reflected by a more distinct representation in MTL for the cued item. Could it be the opposite, namely the MTL could better represent the unresolved interference, for example by the mechanism of hippocampal repulsion (Chanales et al., 2017). It could strengthen the findings if the authors comment on the contrary hypothesis as well.

We thank the reviewer for pointing out this interesting alternative hypothesis. Because of the different task design (e.g., over the course of learning vs. WM) and stimuli (e.g., spatial memory vs. orientation grating), it is hard to directly compare Chanales et al.’s findings with the current results. That said, we think the idea that the representation of similar information would lead to greater task demand on the MTL is consistent with our intuition regarding the role of the MTL in supporting the qualitative aspect of WM representation. We have now further discussed this issue in our revised manuscript to invite further consideration of the suggested alternative hypothesis,

“Our data suggest that this process would result in more similar and stable representations for the same remembered item across trials, as detected by multivariate correlational and decoding analyses in the current study. However, under certain task conditions (e.g., learning spatial routes in a naturalistic task over many repetitions), the MTL may maximally orthogonalize overlapping information to opposite representational patterns (hence “repulsion”) to minimize mnemonic interference (Chanales et al., 2017). It remains to be determined how these learning-related mechanisms in a more complex setting are related to MTL’s contributions to WM of simple stimulus features.”

• It is not clear for me why the authors chose the inverted encoding modelling approach and what is its advantage over the others multivoxel pattern analysis approaches, for example representational similarity analysis also used in this study. How are these two complementary? Since the IEM is still a relatively new approach, maybe a little comment in the manuscript could help emphasizing the strength of the paper? Especially that this paper is of interest to researchers in the fields of both working memory and long-term memory, the latter being possibly not familiar with the IEM.

We thank the reviewer for this suggestion. In principle, the IEM is a multivariate pattern classification analysis based on an encoding model. There is no fundamental difference between this approach and other machine-learning or classification approaches, except that the IEM is a more model-based approach and therefore can be more computationally efficient (see Xie et al., 2023 for a conceptual overview for multivariate analysis of high-dimensional neural data). The relationship between IEM and representational similarity is grounded in item-specific information that could lead to shared neural variance. How these two analyses are complimented each other is well characterized by a recent theoretical review (Kriegeskorte & Wei, 2021). The rationale is that trial-wise RSA reveals shared neural variance between items, implying the presence of item-specific information in the recorded neural data. And the IEM approach or other classification algorithms can more directly test this item-specific information under a prediction-based framework (e.g., train the data and test on a hold-out set). As a result, the findings of these two methods are correlated at the subject-level (Figure S4), which is important to note for the purpose of analytical reliability. Furthermore, using the IEM also allows us to compare our current findings with that from the previous research (Figure S3), addressing some replicability questions in the field (e.g., Ester et al., 2015).

We have clarified more on this issue in the paragraph when we first introduce IEM,

“To directly reveal the item-specific WM content, we next modeled the multivoxel patterns in subject-specific ROIs using an established inverted encoding modeling (IEM) method (Ester et al., 2015). This method assumes that the multivoxel pattern in each ROI can be considered as a weighted summation of a set of orientation information channels (Figure 3A). By using partial data to train the weights of the orientation information channels and applying these weights to an independent hold-out test set, we reconstruct the assumed orientation information channels to infer item-specific information for the remembered item – operationalized the resultant vector length of the reconstructed orientation information channel normalized at 0° reconstruction error (Figure S2). As this approach verifies the assumed information content based on observed neural data, its results can be efficiently computed and interpreted within the assumed model even when the underlying neuronal tuning properties are unknown (Ester et al., 2015; Sprague et al., 2018). This approach, therefore, complements the model-free similarity-based analysis by linking representational geometry embedded in the neural data with item-specific information under a prediction-based framework (Kriegeskorte and Wei, 2021; Xie et al., 2023). Based on this method, previous research has revealed item-specific WM information in distributed neocortical areas, including the parietal, frontal, and occipital-temporal areas (Bettencourt and Xu, 2015; Ester et al., 2015; Rademaker et al., 2019; Sprague et al., 2016), which are similar to those revealed by other multivariate classification methods (e.g., support vector machine, SVM, Ester et al., 2015). We have also replicated these IEM effects in the current dataset (Figure S3).”

Overall, this work can have a substantial impact of the field due to its theoretical and conceptual novelty. Namely, the authors leveraged an established retro-cue task to demonstrate that a neurocomputational operation of pattern separation engages both working-memory and long-term memory, both mediated by the MTL circuitry, beyond the distinction in classic memory models. Moreover, on the methodological side, using the multivariate pattern analyses (especially the IEM) to study neural computations engaged in WM and LTM seems to be a novel and promising direction for the field.

Thanks for the reviewer for this positive appraisal of the current study.

Reviewer #3 (Public Review):

This work addresses a long-standing gap in the literature, showing that the medial temporal lobe (MTL) is involved in representing simple feature information during a low-load working memory (WM) delay period. Previously, this area was suggested to be relevant for episodic long-term memory, and only implicated in working memory under conditions of high memory load or conjunction features. Using well-rounded analyses of task-dependent fMRI data in connection with a straightforward behavioural experiment, this paper suggests a more general role of the medial temporal lobe in working memory delay activity. It also provides a replication of previous findings on item-specific information during working memory delay in neocortical areas.

We thank the reviewer for highlighting the contribution of the current study to fill a gap in the literature.

Strengths:

The study has strengths in its methods and analyses. Firstly, choosing a well-established cueing paradigm allows for straightforward comparison with past and future studies using similar paradigms. The authors themselves show this by replicating previous findings on delay-period activity in parietal, frontal, and occipito-temporal areas, strengthening their own and previous findings. Secondly, they use a template with relatively fine-grained MTL-subregions and choose the amygdala as a control area within the MTL. This increases confidence in the finding that the hippocampus in particular is involved in WM delay-period activity. Thirdly, their combined use stimulus-based representational similarity analysis as well as Inverted Encoding Modeling and the convergence on the same result is encouraging. Finally, despite focusing on the delay period in their main findings, extensive supplementary materials give insight into the time-course of processing (encoding) which will be helpful for future studies.

We thank the reviewer for highlighting multiple strengths of this current study.

Weaknesses:

While the evidence generally supports the conclusions, there are some weaknesses in behavioural data analysis. The authors demonstrated fine stimulus discrimination in the neural data using Inverted Encoding Modeling (IEM), however the same standard is not applied in the behavioural data analysis. In this analysis, trials below 20 degrees and trials above 20 degrees of memory error are collapsed to compare IEM decoding error between them. As a result, the "small recall error" group encompasses a total range of 40 degrees and includes neighbouring stimuli. While this is enough to demonstrate that there was information about the remembered stimulus, it does not clarify whether aLEC/CA3 activity is associated with target selection only or also with reproduction fidelity. It leaves open whether fine-grained neural information in MTL is related to memory fidelity.

We thank the reviewer for this cautious note. As the current task is optimized to reveal the neural representation during visual WM and as our participants are cognitively normal college students, participants’ behavioral performance in the current experiment tends to be very good (Figure 1). This leaves us relatively small variation to further probe the behavioral outcomes of the task. We have recently generalized our findings using intracranial EEG and confirmed that trial-by-trial mnemonic discrimination during a short delay is indeed associated with the fidelity of item-specific WM representation (Xie, Chapeton, et al., in press).

We have further discussed this issue in the revised Discussion,

“… These two approaches are therefore complementary to each other. Nevertheless, these analyses are correlational in nature. Hence, although fine-grained neural representations revealed by these analyses are associated with participants’ behavioral outcomes (Figure 4), it remains to be determined whether the entorhinal-DG/CA3 pathway contributes to the fidelity of the selected WM representation or also to the selection of task-relevant information. Strategies for resolving this issue can involve generalizing the current findings to other WM tasks without an explicit requirement of information selection (e.g., intracranial stimulation of the MTL in a regular WM task without a retro-cue manipulation, Xie et al., in press) and/or further exploring how the frontal-parietal mechanisms related to visual selection and attention interact with the MTL system (Panichello and Buschman, 2021).”

Moreover, the authors could be more precise about the limitations of the study and their conclusions. In particular, the paper at times suggests that the results contribute to elucidating common roles of the MTL in long-term memory and WM, potentially implementing a process called pattern separation. However, while the paper convincingly shows MTL-involvement in WM, there is no comparison to an episodic memory condition. It therefore remains an open question whether it fulfils the same role in both scenarios. Moreover, the paradigm might not place adequate pattern separation demands on the system since information about the un-cued item may be discarded after the cue.

We thank the reviewer for this cautious note. We have now included a more detailed discussion on this issue.

In the Discussion,

“To more precisely reveal the MTL mechanisms that are shared across WM and long-term memory, future research should examine the extent to which MTL voxels evoked by a long-term memory task (e.g., mnemonic similarity task, Bakker et al., 2008) can be directly used to directly decode mnemonic content in visual WM tasks using different simple stimulus features.”

Author Response

Reviewer #2 (Public Review):

In regions that implement an elimination strategy prolonged periods of no local transmission mean that there is no data available to estimate Reff using the currently available methods. Transmission rates from travellers to community members, and between community members, are different when border restrictions occur, as is frequently the case when implementing an elimination strategy. When cases are low and importation risk is high, a reasonable estimation method must acknowledge this transmission heterogeneity, for example, as shown in equations 5-8 and 10-11 of this paper.

The calculation of transmission potential adds significant data requirements (summarized in Figure 1), such that some regions where the methodology would be valuable may lack the data to estimate the macro- and micro-distancing parameters. In the paper, such parameters are estimated from weekly surveys performed by market research groups and the University of Melbourne. In contrast, using existing methods in regions where local spread does occur, Reff can be calculated and generate reasonable insight with relatively little data. Due to the additional data requirements, the calculation of transmission potential is less accessible than some current approaches to calculate Reff in regions with local spread.

We agree with these comments about the need for behavioural data. We believe this point is made clearly in our existing discussion text, copied below:

Despite its demonstrated impact, there are limitations to our approach. Firstly, it relies on data from frequent, population-wide surveys. In Australia, these data are collected for government and made available to our analysis team by a market research company which has access to an established “panel” of individuals who have agreed to take part in surveys of public opinion. Researchers and governments in many other countries have used such companies for rapid data collection to support pandemic response [23, 25]. However, these survey platforms are not readily available in all settings.

We also believe it is clear throughout the manuscript that transmission potential provides complementary information to Reff, and unlike Reff can be calculated in the absence of transmission.

The authors describe "macro-distancing": the rate of non-household contacts; and "micro-distancing": the transmission probability per non-household contact. This terminology "micro-distancing" gives the false impression that transmission probability depends solely on distance. In the paper, transmission probability is estimated from survey responses to the question 'are you staying 1.5m away from people who are not members of your household?'. This data is limited to estimate the transmission probability and overlooks the impact of mask use, improved ventilation, and meeting outdoors (all non-distance-based approaches). The paper mentions that self-reported hand hygiene could be used to estimate micro-distancing. COVID-19 spreads through airborne transmission, but the paper gives no mention of ventilation or mask-wearing.

We agree with these important points and have adjusted the terminology for micro-distancing behaviour to improve clarity. We now refer to it as “precautionary micro-behaviour” since adherence to the 1.5 metre rule is used as a proxy/indicator for change over time in all behaviours that influence transmission (other than those reducing the number of contacts). This includes behaviours such as mask-wearing, preference for outdoor gatherings, hand hygiene etc .

In addition to changing the terminology for this metric throughout the manuscript, we have added the following explanation to the “Model” section of the manuscript (lines 100-105):

The modelling framework uses adherence to the 1.5 metre rule as a proxy for all behaviours (other than those reducing the number of contacts) that may influence transmission, and so is intended to capture the use of masks, preference for outdoor gatherings, and hand hygiene, among other factors. The 1.5 metre rule was a suitable proxy because it was consistent public health advice throughout the analysis period and time-series data were available to track adherence to this metric over time.

Some of the writing lacks precision around the descriptions of Reff. Notably, Reff is not a rate because it does not have units 'per time'. There is a lack of clarity that Reff is infections generated over an individual's entire infectious period. Other metrics of outbreak growth are rates, for example, an exponential growth rate parameter. This lack of clarity in the writing does not impact the methodology.

Thank you for pointing out this lack of clarity, we have removed references to Reff as a ‘rate’ throughout. We have added to our initial definition of Reff (lines 29-32) that the infections cover the entire infectious period:

A key element of epidemic response is the close monitoring of the speed of disease spread, via estimation of the effective reproduction number (Reff) — the average number of new infections caused by an infected individual over their entire infectious period, in the presence of public health interventions and where no assumption of 100% susceptibility is made.

In the paper, model parameters are estimated from multiple independent data sources using carefully derived inference models that include complex considerations such as right-censoring of reported cases. While data availability may be a limitation to calculating the transmission potential, the modelling and statistics in the paper are rigorous, and calculation of the transmission potential fills a gap by allowing regions that implement elimination strategies to estimate a quantity similar to Reff.

We thank the reviewer for their positive feedback.

Author Response

Reviewer #2 (Public Review):

In the current manuscript, Feng et al. investigate the mechanisms used by acute leukemia to get an advantage for the access to the hematopoietic niches at the expense of normal hematopoietic cells. They propose that B-ALLs hijack the niche by inducing the downmodulation of IL7 and CXCL12 by stimulating LepR+ MSCs through LTab/LTbR signaling. In order to prove the importance of LTab expression in B-ALL growth, they block LTab/LTbR signaling either through ligand/receptor inactivation or by using a LTbR-Ig decoy. They also show that CXCL12 and the DNA damage response induce LTab expression by B-ALL. They finally propose that similar mechanisms also favor the growth of acute myeloid leukemia.

Although the proposed mechanism is of particular interest, further experiments and controls are needed to strongly support the conclusions.

1/ Globally, statistics have to be revised. The authors have to include a "statistical analysis" section in the Material and Methods to explain how they proceeded and specify for each panel in the figure legend which tests they used according to the general rules of statistics.

We apologize for the lack of details. This has been corrected in the revised manuscript.

2/ The setup of each experiment is confusing and needs to be detailed. Cell numbers are not coherent from one experiment to the other. As an example, there are discrepancies between Fig1 and Fig2. Based on the setup of the experiment in Fig.2 (Injection of B-ALL to mice followed by 2 injections of treatment every 5 days), mice have probably been sacrificed 12-14 days post leukemic cell injection. However, according to Fig.1, B cells and erythroid cells at this time point should be decreased >10 times while they are only decreased 2-4 times in Fig.2. This is also the case in Fig.4B-J or Fig.5D with even a lower decrease in B cells and erythroid cells despite a high number of leukemic cells. Please explain and give the end point for each experiment in each figure (main and supplemental).

We understand the reviewer concern but we’d like point out the following: kinetic experiments such as these were reproduced multiple times in the laboratory. However, when comparing side-by-side experiments performed over the course of several months discrepancies in the exact days when leukemia shuts-down hematopoiesis are bound to happen. This is because there are numerous variables at play that we can minimize to the extent possible, but we cannot completely eliminate. For example, we took all possible steps to work with stable batches of preB-ALL cells. However, it is impossible to be absolutely certain that the batch in one experiment is identical to another experiment. Cells have to be expanded for adoptive transfer, which inevitably carries some variability (all biological systems undergo random mutations, including purchased C57Bl6/J from reputable vendors); slight differences in ALL engraftment (i.e. injection variability) can occur such that kinetics may change by a couple of days, etc. The findings we reported here are highly reproducible: ALL shuts down lymphopoiesis and erythropoiesis acutely, less so myelopoiesis; that LTbR signaling is the major mechanism shutting down lymphopoiesis but not erythropoiesis; that ALLs up-regulate LTbR ligands when compared to non-leukemic cells of the same lineage and at a similar developmental stage; that CXCR4 and DSB pathways both promote lymphotoxin a1b2 expression. The exact kinetics of these experiments will vary, or at least carry a margin of error that is to the best of our capability impossible to eliminate.

3/ To formally prove that the observed effect is really due to LTab/LTbR signaling, the authors must perform further control experiments. LTbR signaling is better known for its positive role on lymphocyte migration. They cannot rule out by blocking LTbR signaling, that they inhibit homing of leukemic cells into the bone marrow through a systemic/peripheral effect, more than through an impaired crosstalk with BM LepR+ cells. They must confirm for inhibited/deficient LTbR signaling conditions, as compared to control, that similar B-ALL numbers home to the BM parenchyma at an early time point after injection. Furthermore, they cannot exclude that the effect on the expression of IL7 (and other genes), and consequently the effect on B cell numbers, is not simply due to the tumor burden. Indeed, B-ALL numbers/frequencies are different between control and inhibited/deficient signaling conditions at the time of analysis. The analyses should thus be performed at similar low and high tumor burden in the BM for both control and inhibited/deficient LTbR signaling conditions.

We performed ALL homing experiments into control and LTbR∆ and found no significant differences in ALL frequency or number in BM 24h after transplantation. These data have been included in Figure 4A.

We also performed experiments to control for the number of ALL cells in the bone marrow. Briefly, we compared the impact of 3 million WT ALLs with that of 3 and 9 million Ltb-deficient ALLs on Il7-GFP expression in BM MSCs. The number of Ltb-deficient ALLs in the BM of mice recipient of 9 million ALLs was equivalent to that of mice that received 3 million WT ALLs 7 days after transplantation. Importantly, Il7 was only downregulated in mice transplanted with WT ALLs. These data have been included in Figure 4R and 4S.

4/ LT/LTbR signaling is particularly known for its capacity to stimulate Cxcl12 expression. How do the authors explain that they see the opposite?

The reviewer is alluding to a well-known role of LTbR signaling as an organizer of immune cells in secondary lymphoid organs such as spleen and lymph nodes, and particularly its role in promoting CXCL13, CCL19, CCL21 production by fibroblastic reticular cells of these organs. Both the B cell follicle and the T-zone do not express CXCL12 abundantly. Furthermore, in the B cell follicle niche, LTbR signaling is critical for the maturation of Follicular Dendritic Cells, yet FDCs hardly produce CXCL12 as well. So, while LTbR is a well-known regulator of cell organization through the production of homeostatic chemokines and lipid chemoattractants, CXCL12 itself is not one of the major chemokines controlled by this pathway. In summary, we do not think our data is in any way incompatible with prior studies on the LTbR pathway, and even if it was, to our knowledge this is the first study on cell-intrinsic effects of LTbR signaling in BM MSCs.

5/ The authors show that CXCL12 stimulates LTa expression in their cell line. They then propose that CXCR4 signaling in leukemic cells potentiates ALL lethality by showing that a CXCR4 antagonist reverses the decrease in IL7 and improves survival of the mice. This experiment is difficult to interpret. CXCL12 has been shown to be important for migration/retention of B-ALL in the BM and the decreased tumor burden is probably linked to a decreased migration more than an impaired crosstalk with LepR+ cells (see also point 3). If CXCL12 increases LTab expression, CXCR4 blockade should do the opposite. This result should be presented. The contradiction is that if B-ALLs induce a decrease in CXCL12 in the BM (in addition to IL7) and that CXCL12 regulates LTab levels, leukemic cells should be exhausted. Similarly, IL7 has been previously shown to stimulate LTab expression and B-ALL cells express the IL7R. Again, a decrease in IL7 should be unfavorable to B-ALL. How do they explain these discrepancies?

We thank the reviewer suggestion of testing the impact of CXCR4 blocking in vivo on LTa1b2 expression. We performed these experiments which have now been included in the revised manuscript (Fig. 5C and 5D). In summary, we observed reduced LTa1b2 on ALLs transplanted into mice treated with AMD3100, a well-known CXCR4 antagonist. These data also show that CXCR4 signaling is not the only mechanism driving LTa1b2. These results further strengthen the main conclusions of the manuscript. Finally, to our knowledge no study has reported Lymphotoxin a1b2 upregulation in B-ALLs by IL-7.

6/ In Supp 4A, RAG-/- mice are blocked at the pro-B cell stage and do not have pre-B cells. Please compare LTa and LTb expression by Artemis deficient pre-B cell to wt pre-B cells. In this experiment, the authors show that similarly to B-ALL artemis-/- pre-leukemic pre-B cells express high levels of LTab and induce IL7 downmodulation. Using mice deficient for LTbR in LepR+ cells, they show that IL7 expression is increased. However, in opposition to leukemic cells (see Figure 4F), pre-leukemic cells are increased in absence of LTab/LTbR signaling. Please explain this discrepancy. The authors use only one B-ALL model cell line for their demonstration (BCR-ABL expressing B-ALL). Another model should be used to confirm whether LTab/LTbR signaling does favor leukemic/pre-leukemic B cell growth.

We apologize for the confusion. The mice that were used in this study were initially described by Barry Sleckman and colleagues (Bredemeyer et al. Nature 2008). Briefly, they crossed Artemis-deficient mice with VH147 IgH transgenic and EμBcl-2 transgenic mice to generate mice in which B cell development is arrested at the preB cell stage. The Vh147 heavy chain allows their development to the pre-BCR+ preB cell stage but Artemis deficiency prevents Rag protein re-expression and hence B cell can’t recombine light chain genes. The EμBcl-2 transgene allows preB cells to survive despite carrying unrepaired double-strand DNA breaks (DSB).

Regarding the discrepancy noted by the reviewer we argue that this is not a discrepancy. While ALLs can grow in vitro and in vivo in the absence of IL7, non-leukemic developing B cells are strictly IL7 dependent. PreB cells carrying unrepaired DSBs still express IL7 receptor and although no data is currently available on whether these cells are also IL7-dependent, we speculate that they are. Because up-regulation of Lymphotoxin a1b2 in preB cells carrying unrepaired DSBs promotes IL7 downregulation we speculate that this mechanism may contribute to the efficient elimination of pre-leukemic preB cells in vivo. We revised the manuscript to include this explanation of the mouse model and discussion on how we think the LTbR pathway may play a role in pre-leukemic states.

Finally, the data presented in this study includes two distinct leukemia mouse models. It also includes data from human B-ALL and AML samples that is in agreement with the mouse data presented here. We respectfully disagree with the reviewer that a third model is needed to confirm a role for the LTa1b2/LTbR pathway in leukemia.

7/ Pre-B cells are composed of large pre-B cells (pre-BCR+) and small pre-B cells (pre-BCR-). BCR-ABL B-ALL cells express the pre-BCR. What is the level of expression of LTa and LTb by each of these 2 subsets as compared to BCR-ABL B-ALL?

This is a misconception. The difference between large and small preB cells is simply that large preB cells are in S/G2 phase of the cell cycle. Their increased size is a mere consequence of doubling DNA, protein, membrane content, etc.

Author Response

Reviewer #1 (Public Review):

In this study, they demonstrate that neonatal mice produce more CD43- B cellderived IL-10 following anti-BCR stimulation than adult mice. This is due to autocrine mechanisms whereby anti-BCR stimulation leads to pSTAT5 upregulation and production of IL-6 which then enhances IL-10 production via pSTAT3. These are interesting results for the regulatory B cell field, demonstrating that signaling is different in adult vs neonatal B cells and in particular for researchers studying the mechanisms underpinning the enhanced susceptibility to infection. The authors in the main achieved their aim and the results support their conclusions. However, considering that other studies have previously addressed the mechanisms contributing to enhanced IL-10 production in neonates, in the manuscript, there are some experimental decisions and data presentation decisions that at times need more explanation. An important additional comment is that the introduction/discussion is at times insufficiently referenced to put the data in context for non-experts in this field and that numbers in general are low for an in vitro study.

We have now updated the introduction and discussion to provide more insight into our study. We hope that our study is now more understandable for non-experts.

Reviewer #2 (Public Review):

This paper reports that neonatal CD43- B cells produce IL-10 upon BCR stimulation, which inhibits TNF-alpha secretion from the peritoneal macrophage. In the neonatal CD43- B cells, the BCR-mediated signal transmitted Stat5 activation and induced IL-6 production, and subsequently, the secreted IL-6 activated Stat3 finally leading to IL-10 production. The authors identified a unique signaling pathway leading to IL-10 production and revealed the different responses between CD43+ and CD43- B cells against BCR crosslinking. A weakness of this study is that the neonatal CD43- B cell subset secreting IL-10 has not been characterized and discussed as well. BCR expression levels between adult CD43- B cells and neonatal CD43- B cells have been overlooked to explain the different reactivity. Clarity on these points would substantially enhance the impact of the manuscript.

We thank the reviewer for the suggestion to measure BCR levels. We now measured the IgM and IgD levels on neonatal and adult B cell C43+ and CD43- subsets (Figure 1figure supplement 5).

Author Response

Reviewer #1 (Public Review):

This is an exciting paper describing the development of a robust differentiation of the common marmoset induced pluripotent stem cells (iPSCs) into primordial germ cell-like cells and subsequently into spermatogonia-like cells when combined with testis somatic cells. The work is of high quality, but some experimental details and protocols are missing which are necessary for a new protocol development - for example, reconstitution methods and protocols are missing completely in the manuscript and additional details in various aspects of the differentiation and cell maintenance are missing. Despite this, the work is valuable and would be of interest to the germ cell and in vitro gametogenesis communities. The data suggest that marmosets are very similar to humans and macaques, and indeed previously established protocols for PGCLC induction and likely previously published testis reconstitution methods/differentiation were employed here to generate the spermatogonia-like cells.

We greatly appreciate the positive comments of the reviewer on our manuscript. We have added experimental details of our germ cell differentiation schemes in Materials and Methods.

Reviewer #2 (Public Review):

This paper identifies the need for improved pre-clinical models for the study of human primordial germ cells (PGCs) and suggests the common marmoset (Callithrix jacchus) as a suitable primate model. In vitro gametogenesis offers an alternative method to generate germ cells from pluripotent stem cells for study and potential pre-clinical applications. Therefore, the authors aimed to take the first steps toward developing this technology for the marmoset. Here, iPSCs have been derived from the marmoset and differentiated to PGC like-cells (PGCLCs) in vitro that have similarities in gene expression with PGCs identified from single-cell studies of marmoset embryos, as demonstrated through immunofluorescence and RT-qPCR approaches, as well as RNA-sequencing.

The authors have successfully developed a protocol that produces PGCLCs from marmoset iPSCs. These are shown to express key germline gene markers and are further shown to correlate in gene expression with PGCs from the marmoset. This study uses a 2D culture system for further expansion of the PGCLCs. When cultured with mouse testicular cells in a xenogeneic reconstituted testis culture, evidence is provided that cjPGCLCs have the capacity to develop further, expressing marker genes for later germline differentiation. However, the efficiency of generating these prospermatogonia-like cells in culture is unclear. Nonetheless, with the importance of developing protocols across species for in vitro gametogenesis, this paper takes a key step towards generating a robust preclinical system for the study of germ cells in the marmoset.

We thank the reviewer for the encouraging comment. By IF analyses, we identified 0.89 and 3.3% of DAZL or DDX4 positive cells, respectively (DDX4+TFAP2C+ cells [4/123, 3.3% among all TFAP2C+ cells] and DAZL+TFAP2C+ cells [2/232, 0.86% among all TFAP2C+ cells]). Overall scarcity of cells and lack of fluorescence reporters (DAZL and DDX4 are cytoplasmic proteins necessitating technically challenging intracellular staining procedure to be assessed by flow cytometry), we were not able to provide the flow cytometric plots in this study. This has been described in the revised manuscript (page 11, Results, “Maturation of cjPGCLCs into early prospermatogonia-like state”).

The claims of the authors are generally justified by the data provided; however, some conclusions should be clarified. In particular, the authors have failed to show convincingly that cjPGCLCs are a distinct cell type to the iPSCs that generated them. cjiPSCs cultured in feeder conditions (OF) with IWR1 are reported to cluster closely with the derived cjPGCLCs using principal component analysis of RNA-Seq data. This contrasts with the cjiPSCs cultured in feeder-free (FF) conditions which maintain a more undifferentiated/less primed state, and are not capable of differentiating to the germline lineage. Therefore, the OF/IWR1 cjiPSCs could rather be an intermediate cell-state between iPSCs and cjPGCLCs.

Although OF/IWR1 cjiPSCs are closer to cjPGCLCs than cjiPSCs cultured in other conditions, they are pluripotent (as evidenced by trilineage differentiation assay, morphological assessment, and expression of pluripotency markers, Figure 3–figure supplement 2) and do not express most of key germ cell markers (Figure 6–figure supplement 1C). Our newly added scRNA-seq analyses also highlighted the differences between OF/IWR1 and cjPGCLCs and the molecular dynamics associated with the transition.

The reasons behind improved germline competence of iPSCs in the different media conditions are unclear. The authors reject the idea that this is due to the presence of IWR1, since this condition has not affected FF iPSCs. However, the efficiency of differentiation was greatly increased in OF conditions when IWR1 was used, indicating inhibition of WNT does indeed have a positive effect on induction to the germline lineage. This area requires further clarification.

As the reviewer pointed out, inclusion of IWR1 in cultures of OF cjiPSCs upregulates some pluripotency markers (SSEA3, SSEA4) and reduces meso/endodermal differentiation. Thus, the undifferentiated/less primed state under the Wnt inhibition might positively affect germ cell differentiation of OF cjiPSCs. However, FF cjiPSCs are pluripotent and are not germline competent, even in the presence of IWR1, suggesting that there are factors in FF culture conditions that make them incompetent for germline differentiation. Because FF cultures utilize PluriStem™ medium, a proprietary product of MilliporeSigma, we were unable to define the factor that confers such germline incompetence.

Another area requiring clarification is the reporting of RNA sequencing data as representative of a developmental trajectory, without defining which cell lines produced clusters, or defining the stages of this trajectory. The authors refer to the identification of four clusters representative of a developmental trajectory, however, they provide unclear information as to what this refers to. Importantly, detailed transcriptomic comparisons between in vivo-derived PGCs and in vitro PGCLCs are not provided.

Our original analysis revealed which cell lines produced clusters (Figure 6A) and defined the stages of the trajectory (iPSCs feeder free, iPSCs on feeder, PGCLCs, expansion, Figure 6C). The four clusters to which the reviewer refers are gene clusters that are defined by unsupervised clustering analysis of variably expressed genes across the samples (Figure 6D). As it is defined computationally, it is not possible to unequivocally define gene clusters by particular cell types. However, we found that these gene clusters revealed insightful patterns (1, genes higher in cjiPSCs; 2, genes higher in cjPGCLCs; 3, genes higher in expansion culture cjPGCLCs; 4, genes higher in d2 cjPGCLCs). We have added sample information to the Figure 6D to further clarify the meaning of the data and a brief explanation of gene clusters in the figure legend. To define the trajectory in a more unbiased manner, we performed scRNA-seq and have added additional trajectory analyses (Figure 7A-K in the revised manuscript). Moreover, we also added the transcriptomic comparison as the reviewer suggested (Figure 7L, M in the revised manuscript).

Functional validation of iPSC lines generated in the study is not provided besides confirming that the cells express pluripotency markers OCT3/4, SOX2, and NANOG. It is important to confirm tri-lineage differentiation of iPSCs, e.g., through an embryoid body assay. Since FF cjiPSCs were unable to differentiate into cgPGCLCs, it is even more important to confirm cells are genuine iPSCs.

We performed a trilineage differentiation assay and confirmed that they can generate three germ layers.

In summary, although there are issues surrounding clarity, this paper is generally justified in its conclusions. The authors present an optimised protocol for the derivation of PGCLCs from marmoset iPSC-like cells, with defined expansion conditions and evidence of further differentiation to prospermatogonia-like cells.

We thank the reviewer for the encouraging comment.

Author Response

Reviewer #1 (Public Review):

Sayin et al. sought to determine if bacterial drug resistance has impact on drug efficacy. They focused on gemcitabine, a drug used for pancreatic cancer that is metabolized by E. coli. Using an innovative combination of genetic screens, experimental evolution, and cancer cell co-cultures to reveal that E. coli can evolve resistance to gemcitabine through loss-of-function mutations in nupC, with potential downstream consequences for drug efficacy.

Major strengths include:

• Paired use of genetic screens and experimental evolution

• The spheroid model is a creative approach to modeling the tumor microbiome that I hadn't seen before

• Rigorous microbiology, including accounting for mutation rate in both selective and non-selective conditions

• Timely research question

Major weaknesses of the methods and results include the following:

1) Limited scope of the current work. Just a single drug-bacterial pair is evaluated and there are no experiments with microbial communities, animal models, or attempts to test the translational relevance of these findings using human microbiome datasets.

We agree with the reviewer that uncovering evidence from human microbiome datasets will be very exciting and complementary to our study. However, since gemcitabine is administered intravenously it’s unclear whether it will impose a considerable selective pressure on the gut microbiome. Therefore, it also remains unclear if adaptive mutations, as those we identified, are expected to be found in datasets for the gut microbiome. While metagenomics datasets that are bacterial-centric of infected pancreatic tumors will be ideal for addressing the reviewer’s suggestion, they do not exist to the best of our knowledge. It should be noted however, that our work generated hypotheses that can be tested in pancreatic tumor tissues infected with gammaproteobacteria and can be tested in the future by targeted sequencing for the specific genes of interest (e.g, nupC and cytR).

2) No direct validation of the primary genetic screen. The authors use a very strict cutoff (16-fold-change) without any rationale for why this was necessary. More importantly, a secondary screen is necessary to evaluate the reproducibility of the results, either by testing each KO in isolation or by testing a subset of the library again.

We used a strict cutoff to allow the reader to focus on a manageable list of gene names in the main figure (2E). To partly address this limitation in scope, we also included results from pathway enrichment analysis in the same figure (2F). This analysis utilizes all enrichment values and is therefore independent from any choice of cutoff value. We also now refer the reader to explore more of the hit genes in the supplementary information (line 152).

As the reviewer suggested we evaluated the reproducibility of the results by performing two validation screens. The first validation screen was performed as a biological replicate of the original screen and relied on the original collection of knockouts strains. The second validation screen was performed with a knockout strain collection that was cloned independently from the strains used in our original screen. The results from these two completely independent biological replicates are presented on supp. figure 1D. The results (resistance/sensitivity) from the two screens are highly correlated. We refer to this comparison in the main text (lines 142-147).

3) Some methodological concerns about the spheroid system. As I understood it, these cells are growing aerobically, which may not be the best model for the microbiome. Furthermore, bacterial auxotrophs are used and only added for 4 hours, which will really limit their impact. It also was unclear if the spheroids are truly sterile. Finally, the data lacks statistical analysis, making it unclear which KOs are meaningful. Delta-cdd looks clearly distinct by eye, but the other two genes are more subtle.

The 4 hour time interval chosen to address two opposing requirements of the co-culture system – mitigate overgrowth of the bacterial cultures (which hinders spheroid growth irrespective of the drug) while still allowing enough incubation time to allow for drug degradation. As the reviewer notes, removal after 4 hours may limit the bacteria impact. However, such a limitation will only result in underestimation the bacterial impact (but will have no impact on how we evaluate how strains compare to one-another). We now comment on this in the methods section (lines 699-705).

We do not expect the spheroid to remain infected after bacterial removal since we treat spheroids with antibiotics. We didn’t not detect any bacterial growth in the 7 days post infection in the microscope and we did not observe influence on spheroid growth when compared to spheroid that were not infected. Growth of spheroid before infection was performed w/o antibiotics and we did not detect any evidence of bacterial growth prior to introducing the bacteria intentionally (the cell-line itself was also tested for animal pathogens and bacterial contamination prior to the experiments).

We repeated the spheroid experiments and observed similar shifts in the EC50 fronts. We now include these replicates as supplementary figure 7. We comment on these replicates in the main text (lines 273-274).

Author Response

Reviewer #1 (Public Review):

This is an elegant and fascinating paper on individuality of structural covariance networks in the mouse. The core precepts are based on a series of landmark papers by the same authors that have found that individuality exists in inbred mice, and becomes entrenched when richer environments are available. Here they used structural MRI to provide whole brain analyses of differences in brain structure. They first replicated brain (mostly hippocampal) effects of enrichment. Next, they used their roaming entropy measurements to show that, after dividing their mice into two groups based on their roaming entropy, that there were no differences in brain structure between the two groups yet significant differences in brain networks as measured by structural covariance. Overall I enjoyed this paper, though am confused (and possibly concerned) about how they arrived at their two groups and have some less important methods questions.

The division of mice into two groups (down and flat) is confusing. The methods appear to suggest that k-means clustering combined with the silhouette method was used (line 380). The actual analyses used involves 2 groups of 15 mice each. The body of the manuscript suggests that 10 intermediate mice were excluded (line 100), but the methods (line 390) suggest that 8 mice were excluded, 2 for having intermediate results and 6 for having high RE slope values.

This leads to a series of questions:

• How many mice were excluded and for what reasons, given the discrepancy between body and methods?

The discrepancy was an oversight that has been corrected. The statement with the exclusion of six upward sloping and two intermediates is correct. For the rationale see above and the inserted text in the discussion.

• Was the k-means clustering actually used? It appears that the main division of mice was based on visual assessments.

The superfluous paragraph in the method section was removed.

• If the clustering was used, did it result in 2 or 3 groups?

Slope distribution did not reveal clear groups, so it did not offer an advantage over the arbitrary decision based on slope values and described above. We have now added a graphic depiction of the slope values next to the ‘flat’ or ‘down’ matrices for greater clarity (Fig. 3B).

• The intermediate group bothers me (if it was indeed 10 intermediate mice as indicated by the body rather than 2 as indicated in the methods): if these are indeed intermediate shouldn't they be analyzed and shown to be intermediate on the graph or other measures?

These were only 2 mice, for which the matrix cannot be calculated.

• Please explain the reasoning for excluding mice for having too high of a slope (if there were indeed mice excluded for having too high of a slope).

We went to long discussions among the authors and finally decided in favor of two equally-sized groups with homogenous patterns. The effect that we observed is so large and obvious that it survives all sorts of regrouping. We have also followed the suggestion to present the continuous correlation across the whole range of animals (Fig. 2)

I'd also appreciate more discussion about the structural covariance differences between flat and down mice. It is not clear what the direction of effects are - it appears that flats show mostly increases in covariance?

Yes, covariance is greater in the top (flat) than bottom (down) group.

The structural covariance matrix for those mice with a ‘flat’ RE suggests a much higher degree of inter-regional correlation in comparison to ‘down’ or STD mice, findings confirmed and extended by the NBS analysis.

Reviewer #2 (Public Review):

Lopes et al. use genetically identical mice to address a topic of broad interest: how does variation in roaming behaviour across individuals (here, quantified via the roaming entropy) arise over time when exposed to an enriched environment, and how does this variation in behaviour relate to brain structure and networks. Specifically, by examining the roaming entropy of mice and the sizes of brain structures, the authors convincingly show 1) an increase in variability in roaming behaviour over a period of 12 weeks, 2) that mice that roam more contain an increased number of doublecortin positive cells in the dentate gyrus (indicating higher levels of neurogenesis), and 3) that roaming is associated with widespread differences in neuroanatomy. The authors additionally partition mice into two groups characterized by roaming trajectories (continuous "flat" roamers and habituating "down" roamers), construct structural covariance networks for these groups, and show that the structural covariance network for "down" roamers is similar to mice housed in standard conditions and contrasts that of "flat" roamers.

A major strength of this study is the wealth of roaming data generated by the RFID setup; the high temporal resolution, fair spatial resolution, and long period of observation (3 months) allow for measures such as roaming entropy to be precisely quantified and tracked over time. Coupled with high-resolution whole brain structural MRI and histological measurements of neurogenesis in the dentate gyrus, the dataset generated is an incredibly valuable one to probe brain-behaviour relationships. Importantly, this study showcases the power of animal studies--because the subject mice are inbred, they are virtually identical in their genetics, and therefore any variation in the data collected should arise from the non-shared environment.

An area of improvement for this study follows from its strength: the dataset collected here contains far more information on mouse behaviours than is analyzed. For instance, the sizes of a broad set of regions were found to be statistically associated with roaming behaviour, but determining how much of this anatomical variation is specifically related to differential exploration of the static environment as opposed to social contact with other animals (which could presumably be determined from the RFID data) would make this study much more impactful and interesting to the community.

An important limitation in the network analyses performed is the small number of mice. Due to sampling variation, a large number of individuals are required to estimate correlation coefficients with reasonable precision. While large-scale similarities and differences between the structural covariance (correlation) matrices are visually apparent and quite striking, confidence in these results would be increased with the inclusion of more subjects, and/or a replication cohort.

We fully agree to this judgement. It is not straightforward, however, to further increase N in these studies, both for cost and logistic reasons. Rather than investing into further improving this current study, we decided to learn from our findings and design follow-up studies that take the next steps.

Finally, while both roaming behaviour and brain structure are assessed, relationships between these measures are associative. Since brain structure was only examined at one timepoint (post-enrichment), the direction of causation cannot be assessed. It remains to be seen if behavioural variation drives anatomical variation through plasticity, or whether anatomical variation present before enrichment is predictive of future behaviours. To their credit, the authors are careful not to make causal inferences. In the context of this brain-behaviour studies, this is an important limitation to recognize, but this does not detract from the important relationships between roaming behaviour and brain structure found by the authors in this study.

In summary, while there is much more to do in studying relationships between the environment, brain structure, and behaviour, Lopes et al. take an important step ahead in describing relationships between individual roaming behavioural trajectories, brain structure, and structural covariance networks.

Author Response

Reviewer #1 (Public Review):

This study elucidates a role of EHD2 as a tumor/metastasis promoting protein. Prior work has found varying results indicating that high expression of EHD2 is either associated with good or poor outcomes. In this work the authors find that EHD2 is expressed in both the nucleus and cytoplasm, and that high cytoplasmic to nuclear expression is associated with a poor prognosis. Using WT and either shRNA knockdown or CRISPR KO cells, they show that EHD2 promotes 3D growth, migration and invasion in vitro, and tumor growth and metastasis in vivo. Importantly, re-expression of EHD2 in KO cells rescues the loss of function phenotype. Mechanistically, the investigators show that the loss of EHD2 decreases the calveoli and that this decreases the Orai1/Stim induced calcium influx. Finally, they show that inhibitors of store operated calcium entry (SOCE) phenocopies the loss of EHD2. Together the data support a protumorigenic role for EHD2 via store-operated calcium entry and reinforce the utility of targeting calveoli and SOCE in tumors with high cytosolic EHD2. This study provides a rationale for using SOCE inhibitors in a subset of breast cancers, and a potential predictive biomarker for using SOCE inhibitors based on high expression of EHD2.

We are grateful for the positive comments. Since this paragraph is to be published in the event of our manuscript being accepted, we request the correction of one typo in the paragraph: “calveoli” should be “caveolae”.

Reviewer #2 (Public Review):

The manuscript by Luan et. al. describes the role of EHD2 in promoting breast tumor growth. They showed that EHD2 cytoplasmic staining predicts poor patient outcome. Both EHD2 KO or knockdown cells showed decreased cell migration/invasion abilities and significant reduction of tumor growth and metastasis in mice. The authors further showed that the levels of EHD2 and Cav1/2 correlate with each other. EHD2 KO cells showed defects on Ca2+ trafficking. Overexpressing the SOCE factor STIM1 partially rescued SOCE defects in EHD2 KO cells. Treatment of the SOCE inhibitor SKF96365 inhibited tumor cell migration in vitro and tumor growth in vivo.

Major strengths: The authors showed that EHD2 cytoplasmic levels predict patient survival and provided strong evidence that EHD2 knockout or knockdown inhibits tumor cell migration in vitro and tumor growth in vivo. The authors also showed that SKF96365, which inhibits SOCE, suppresses tumor growth in vivo.

Major weaknesses: The connection between EHD2 and SOCE is weak.

We are thankful to the reviewer for her/his assessment of the strengths in our manuscript and appreciate her/his pointing to its weaknesses. We agree that more studies will be needed to fully establish the connection of EHD2 to SOCE and have appropriately moderated our statements in the results and discussion sections of the manuscript. We have also added statements about the need for such future studies.

Author Response

Reviewer #1 (Public Review):

In this manuscript by Ramaprasad et al., the authors report on the functional characterization of the P. falciparum glycerophosphodiesterase to assess its role in phospholipid biosynthesis and development of asexual stages of the parasite. The authors utilized loxP strategy to conditionally knock-out the target gene, they also carried out complementation assays to show recovery of the knock-out parasites. They further show that Choline supplementation is also able to rescue the knock-out phenotype. Quantitative lipidomic analyses show effect on majority of membrane phospholipids. In vitro activity assays and metabolic labelling assays shows role of GDPD in metabolism of exogenous lysoPC for PC synthesis. The manuscript deciphers the functional role of an important component of lipid metabolism and phospholipid synthesis in the parasite, which are crucial metabolic pathways required for replication of the parasite in the host cell.

We thank the Reviewer for assessing our work and for the following helpful suggestions.

Reviewer #2 (Public Review):

The authors use a conditional Lox/Cre knock-out system to test and confirm the essentiality of glycerophosphodiester phosphodiesterase (GDPD) for blood-stage parasites and a key role in mobilizing choline from precursor lysophosphocholine (LPC) for parasite phospholipid synthesis. Prior works had identified serum LPC as the key choline source for parasites, localized this enzyme in parasites, and suggested an essential function in releasing choline, but this key function had remained untested in parasites. This manuscript critically advances mechanistic understanding of parasite phospholipid metabolism and its essentiality for blood-stage Plasmodium and identifies a potential new drug target.

Overall, this study is well constructed and rigorously performed, and the data provide strong support for the central conclusions about GDPD essentiality and functional contribution to parasite phosphocholine metabolism. The observation that exogenous choline largely rescues parasites from lethal deletion of GDPD is especially compelling evidence for a critical and dominant role in choline mobilization. A few aspects of the paper, however, are not fully supported by the current data and/or need clarification.

We thank the reviewer for this very positive assessment and the helpful suggestions below.

1) GDPD localization

a) The authors conclude that GDPD is localized to the parasitophorous vacuole (PV) and parasite cytoplasm (lines 114-115), which is consistent with the prior 2012 Klemba paper. However, the data in the present paper (Figures 2A and 2E) only seem to support cytoplasmic localization but don’t obviously suggest a population in the PV, in part because no co-staining with a PV marker is shown. The legend for Fig. 2E indicates staining with the PV marker, SERA5, but such co-stain is not shown in the figures or figure supplements. This data should ideally be included and described.

We apologise for this error and omission in our original submission. In response to this suggestion, we have now generated new data that demonstrate co-localisation of the PV marker SP-mScarlet (Mesen-Ramirez et al., 2019) with GDPD in our GDPD-GFP line. In the revised manuscript we now include those new data in Fig 2A and we have also corrected the legend of the revised Fig 2E to reflect what is being shown.

b) How do the authors explain cytoplasmic localization for GDPD? This protein contains an N-terminal signal peptide, which can account for secretion to the PV but would contradict a cytoplasmic population. The 2012 Klemba paper suggested that internal Met19 might provide an alternate site for translation initiation without a signal peptide and thus result in cytoplasmic localization. Some discussion of this ambiguity, its relation to understanding GDPD function, and a possible path to resolve experimentally seem necessary, especially as the authors suggest from data in Fig. 7 that this enzyme may have functions beyond choline mobilization, which may relate to distinct forms in different sub-cellular compartments.

The Reviewer raises an excellent point here. We agree that the apparent dual localization of GDPD and the question of its potential function in both compartments is intriguing. Since lysoPC is efficiently internalised into the parasite, one simple possible explanation (which we failed to state earlier) is that GDPD performs a similar enzymatic function in both compartments. Given the importance of choline for parasite membrane biogenesis, it would not be surprising for GDPD activity to be required at high abundance in order to maintain sufficient choline levels in the parasite. We have now modified lines 403 onwards in the revised Discussion to provide more perspective on this point, as follows: “Based on protein localisation, ligand docking and sequence homology analyses, we can further speculate regarding aspects of PfGDPD function not explored in this study. It has been previously suggested that the gene could use alternative start codons via ribosomal skipping to produce distinct PV-located and cytosolic variants of the protein (Denloye et al., 2012). PfGDPD could potentially perform similar functions in both compartments by facilitating the breakdown of exogenous lysoPC both within the PV and within the parasite cytosol (Brancucci et al., 2017). This scale of enzyme activity may be essential for the parasite to meet its choline needs, given the high levels of PC synthesis during parasite development and its crucial importance for intraerythrocytic membrane biogenesis. PfGDPD may also have other roles during asexual stages such as temporal and localised recycling of intracellular PC or GPC by the PfGDPD fraction expressed in the cytosol. Finally, our ligand docking simulations also do not rule out catalytic activity towards additional glycerophosphodiester substrates such as glycerophosphoethanolamine and glycerophosphoserine (Figure 6-figure supplement 1A and B). Further investigation that involves variant-specific conditional knockout of the gdpd gene could help us further dissect the role of PfGDPD in the parasite.”

2) The phenotypes depicted by representative microscopy images in panel 4E (especially for choline rescue) should be supported by population-level analysis by flow cytometry or microscopy of many parasites to establish generality.

We agree that this would be informative, and in the revised manuscript we have now added a representative microscopy image as source data (Figure 4E_G1+Cho48h-sourcedata.png). It is also worth pointing out that G1 is a clonal line generated from the RAP+ Choline+ parasite population. Both population-level analysis by flow cytometry (Fig 4A) and microscopic images (Fig 4D) are therefore also applicable to the G1 line.

3) The analysis in the last results section (starting on line 296) seems preliminary.

a) For panel 7B, a population analysis of many parasites, with appropriate statistics, is important to establish a generalizable defect beyond the single image currently provided.

b) The data here would seem to be equally explained by an alternative model that GDPD∆ parasites are competent to form gametocytes but their developmental stall (due to choline deficiency) prevents progression to gametocytogenesis. The authors speculate that GDPD may play other roles in phospholipid metabolism beyond choline mobilization that are essential for gametocytogenesis. Their model, if correct, predicts that a GDPD deletion clone from +RAP treatment that is rescued by exogenous choline should not form gametocytes. Testing this prediction would be important to strongly support the conclusion of broader roles for GDPD in sexual development beyond choline mobilization.

We interpreted our results precisely as the reviewer suggests here – that the developmental stall during trophozoite stages is severe enough to prevent sexual differentiation. A priori, we have no reason to suspect that GDPD plays other roles that are selectively essential for gametocyte development. We speculated that GDPD might have other roles in asexual stages but not necessarily based on this experiment. In the revised manuscript we have modified line 313 accordingly to remove ambiguity: “This result implies that the loss of PfGDPD causes a severe block in PC synthesis resulting in the death of asexual parasites before they get to form gametocytes.”

We have also altered line 411 in the Discussion to: “PfGDPD may also have other roles during asexual stages such as temporal and localised recycling of intracellular PC or GPC by the PfGDPD fraction expressed in the cytosol.”

We agree with the reviewer that the analysis is preliminary. Since we lose RAP-treated GDPD:HA:loxPintNF54 populations after cycle 1, we were unable to do more detailed analysis with the line. We also wished to carry out the experiment that the reviewer suggests here to analyze choline-rescued mutants. However, we would be unable to test for this as choline supply alone would suppress sexual differentiation in these parasites (as shown in Brancucci et al., 2017).

Reviewer #3 (Public Review):

In this work, Ramaprasad et al. aimed to investigate the roles of a glycerophosphodiesterase (PfGDPD) in blood stage malaria parasites. to determine its role, they generated a conditional disruption parasites line of PfGDPD using the DiCre system. RAP-induced DiCre-mediated excision results in removal of the catalytic domain of this protein. Loss of this domain leads to a significant reduction of parasite survival, specifically affecting trophozoite stages. They suggest that there is an invasion defect when this protein domain is deleted. They additionally show the introduction of an episomal expression of PfGDPD can rescue the activity of the protein and restore parasite development. Interestingly, exogenous choline can rescue the effects of the loss of PfGDPD. Lipidomic analyses with labelled LPC show that choline release from LPC is severely reduced upon protein ablation and in turn prevents de novo PC synthesis. These experiments also show increase in DAG levels suggesting a defect in the Kennedy pathway. The authors purified PfGDPD and enzymatically show that this protein facilitates the release of choline from GPC. Additionally, the paper also briefly looks at the effects of the protein during sexual blood stages and show this is unlikely to be involved in sexual differentiation.

This paper is of interest to the community since the breakthrough paper of Brancucci et al. (2017), which showed us that decreased LPC levels induce sexual differentiation. This work brings novel insight into a GDPD responsible for the release of choline from GPC which actual seems more relevant to asexual stages and not sexual stage parasites. The authors have been extremely thorough in their experimentations on parasite viability and the exact role of this protein.

We thank the reviewer for this positive assessment and the helpful comments.

Author Response

Reviewer #1 (Public Review):

It is a strength of the current manuscript that it provides a near-complete picture of how the metamorphosis of a higher brain centre comes about at the cellular level. The visualization of the data and analyses is a weakness.

I do not see any point where the conclusions of the authors need to be doubted, in particular as speculations are expressly defined as such whenever they are presented.

The fact that molecular or genetic analyses of how the described metamorphic processes are organized are not presented should, I think, not compromise enthusiasm about what is provided at the cellular level.

We appreciate the comments and guidance that Reviewer #1 has given us on data presentation. We have tried to simplify figures and make the images larger. For the developmental figures, a couple of illustrative examples are provided in the main figure with the remainder given in “figure supplements”

Reviewer #2 (Public Review):

This very nice piece of work describes and discusses the developmental progression of larval neurons of the mushroom body into those in the adult Drosophila brain. There are many surprising findings that reveal a number of strategies for how brain development has evolved to serve both the early functions specific to the larval brain and then their eventual roles in the adult brain. I think it is fascinating biology and I was educated while reviewing the paper.

Line 115-116. 'Output from PPL1 compartments direct avoidance behavior, while that from PAM compartments results in attraction'. This is not correct and is actually reversed. The learning rule is depression so that aversive learning reduces the drive to approach pathways whereas appetitive learning reduces the drive to avoidance pathways. This should be corrected and reference made to studies demonstrating learning-directed depression.

Line 222. It provides feed-forward inhibition from y4>2>1. I could be wrong but I'm not aware that there is functional evidence for this glutamatergic neuron being inhibitory. It's currently speculation.

We have noted that this function was proposed by Aso et al.

Line 242. I think it would be nice if the authors focused on extreme changes and showed larger and nicer images. The rest can be summarized but why not pick a few of the best examples to illustrate the strategies they consider in the discussion?

We have reduced the number of neurons shown in the new Figs 5 and 6. Hopefully, the images are now large enough to appreciate. Data for the remaining neurons are now in Figure Supplements for Figs 5 and 6.

Line 249 'became sexually dimorphic'. I may have missed it somewhere but this immediately made me think about the sex of all the images that are shown. Is this explicitly stated somewhere? Was it tracked in all larvae, pupae, and adults?

We now begin the Methods addressing this point. We did an initial screen and found sex-specific differences only in MBIN-b1 and -b2. After this time, we kept no records as to the sex of the fly that was used except for the latter cells.

Reviewer #3 (Public Review):

Truman et al. investigated the contribution and remodeling of individual larval neurons that provide input and output to the Drosophila mushroom body through metamorphosis. Hereto, they used a collection of split-GAL4 lines targeting specific larval mushroom body input and output neurons, in combination with a conditional flip-switch and imaging, to follow the fates of these cells.

Interestingly, most of these larval neurons survive metamorphosis and persist in the adult brain and only a small percentage of neurons die. The authors also elegantly show that a substantial number of neurons actually trans-differentiate and exert a different role in the larval brain, compared to their final adult functionality (similar to their role in hemimetabolous insects). This process is relatively understudied in neuroscience and of great interest.

Using the ventral nerve cord as a proxy, the authors claim that the larval state of the neuron would be their derived state, while their adult identity is ancestral. While the authors did not show this directly for the mushroom body neurons under study, it is a very compelling hypothesis. However, writing the manuscript from this perspective and not from the perspective of the neuron (which first goes through a larval state, metamorphosis, and finally adult state), results in confusing language and I would suggest the authors adjust the manuscript to the 'lifeline' of the neuron.

We have tried to be more “linear” in our presentation. This should make the text less confusing.

In general, this manuscript does not explain how the larval brain has evolved as the title suggests but instead describes how the larval brain is remodeled during metamorphosis. It thus generates perspectives on the evolution of metamorphosis, rather than the larval state. Additionally, this manuscript would benefit from major rearrangements in both text and figures for the story to be better comprehended.

We think that the end of the Discussion does relate to how a larval brain evolves. The evolution of the larval brain is faced with constraints related to the shortened period of embryonic development and the highly conserved temporal and spatial mechanisms that insects use to generate their neuronal phenotypes. These constraints result in a potential mismatch between the neurons that are needed and those that are actually made (revealed by the adult phenotypes of these neurons). The larva then turns to trans-differentiation to temporarily transform unneeded (or dead) neurons into the missing cell types to build its larval circuits.

We think that these ideas provide some new insights into how a larval brain may have evolved and that our title is appropriate.

The introduction is very focused on the temporal patterning of the insect nervous system, while none of the data collected incorporate this temporal code. Temporal patterning comes back in the discussion but is purely speculative.

The Speculation about the importance of temporal patterning is now brought in late in the Discussion in reference to Figure 12

Furthermore, the second part of the introduction describes one strategy for remodeling and why that strategy is not likely but does not present an alternative hypothesis. The first section of the results might serve as a better introduction to the paper instead, as it places the results of the paper better and concludes with the main findings. The accompanying Figure 1 would also benefit from a schematic overview of the larval and adult mushroom bodies as presented in Fig. 2A (left).

This has been revised in the spirit of these comments

In the second results section, the authors show the post-metamorphic fates of mushroom body input and output neurons and introduce the concept of trans-differentiation. Readers might benefit from a short explanation of this process. I also encourage the authors to revisit this part of the text since it gives the impression that the neurons themselves undergo active migration (instead of axon remodeling).

We have tried to make it clear that there is no cell migration. Rather there is retraction/fragmentation of larval arbors followed by outgrowth to new, adult targets

The discussion starts with a very comprehensive overview of the different strategies that neurons could use during metamorphosis (here too, re-writing the text from the neurons' perspective would increase the reflection of what actually happens to them).

The Discussion now begins by dealing with gross changes in the MB, with reference to the compartments and eventually moves to changes in individual cells. We have reduced our discussion of the metamorphic strategies of cells and no longer have Fig 8A

The discussion covers multiple topics concerning trans-differentiation, metamorphosis, memory, and evolution and is often disconnected from the results. It could be significantly shortened to discuss the results of the paper and place them in current literature. Generally, the figures supporting the discussion are hard to comprehend and often do not reflect what the text is saying they are showing.

The Discussion is still long, but, hopefully, our organization now makes it much easier to read and comprehend.

Author Response:

Reviewer #1 (Public Review):

Monfared et al. construct a three-dimensional phase-field model of cell layers and use it to examine cellular extrusion by independently tuning cell-substrate and cell-cell adhesion. In line with earlier studies (in some of which some of the authors were involved), they find that extrusion is linked to topological defects in cellular arrangement and relieving stress.<br /> The authors claim that their development of the three-dimensional phase field model is crucial for understanding cell extrusion (which I agree with the authors is inherently three-dimensional). However, I don't think the data they currently present clearly demonstrate that the three-dimensional model adds significantly more to our understanding of extrusion events than earlier two-dimensional models.

In the end, I think that the more important achievement of this work -- and one that is likely to be more influential -- is developing a three-dimensional phase field model for cell monolayers rather than any specific result regarding extrusion.

We sincerely thank the reviewer for their time examining our manuscript and providing critical feedback. We are confident that our detailed response provided below and additional analyses have further highlighted the importance of three-dimensional stresses.

Reviewer #2 (Public Review):

The paper provides a natural extension of 2D multiphase field models for cell monolayers to 3D, addressing cell deformations, cell-cell interaction, cell-substrate interactions and active components for the cells. As known from 2D, the cell arrangement leads to positional (hexatic) defects and if the elongation of the cells is coarse-grained to define a global nematic order also to orientational (nematic) defects. These defects are characterized, see Figure 2. However, this is done in 2D and it remains unclear if the projected basal or apical side is considered in this figure and the following statistics. The authors identify correlations between orientational defects and extrusion events. In terms of positional defects such statistics seem not to be considered and the relation between positional defects and cell extrusion events remains vague. Also in-plane and out-of-plane stresses are computed. These results confirm a mechanical origin for cell extrusions. However, these are the only 3D information provided. The final claim that the results clearly demonstrate the existence of a mechanical route related with hexatic and nematic disclinations is not clear to me. 3D vertex models for such systems e.g. showed the importance of different mechanical behavior of the apical and basal side and identified scutoids as an essential geometric 3D feature in cell monolayers. These results are not discussed at all. A comparison of the 3D multiphase field model with such results would have been nice.

We thank the reviewer for bringing to our attention the work on scutoids, which we now discuss in the manuscript as an important geometric feature of 3D layers on curved surfaces. We shall, however, emphasize that scutoids are specific to monolayers on curved surfaces, while we focus on a cell monolayer on flat substrates here. Moreover, we shall argue that the difference between apical and basal sides is just one element of the 3D complexity of cell layers. Here, we focus on another aspect of 3D complexity that is not accessible in 2D: the development of 3D mechanical stress and its role in an inherently 3D problem of cell extrusion. Nevertheless, as discussed in detail responses below we have now added additional analyses varying the monolayer interaction with the substrate on the basal side.

Reviewer #3 (Public Review):

In this paper, the authors studied the influence of topological defects on extrusion events using 3D multi-phase field simulations. By varying cell-cell and cell-substrate parameters, this study helps to better understand the influence of mechanical and geometrical parameters on cell extrusion and their linkage to topological defects.

First the authors show that extrusion events and topological defects of nematic and hexatic order are typically found in their system, and then that extrusions occur, on average, at a distance of a few cell sizes from a + and - 1/2 defects. Next, the author analyse at extrusion events the temporal evolution of the local isotropic stress and the local out-of-plane shear stress, showing that near the instant of extrusion, the isotropic stresses relax and the shear stresses fluctuate around a vanishing value. Finally, the authors analyse both the distribution of isotropic stress and the average isotropic stress pattern near +1/2 defects.

We are grateful to the reviewer for their time examining our manuscript and providing critical feedback that has certainly improved our manuscript. In what follows, we provide detailed responses to each comment, including additional statistics that we have computed and now include in the manuscript for completion.

Author Response

Reviewer #1 (Public Review):

Junctophilin is mostly known as a structural anchor to keep excitation-contraction (E-C) proteins in place for healthy contractile function of skeletal muscle. Here the authors provide a new interesting role in skeletal muscle for Junctophilin (44 kD segment, JPh44), where it translocates to the nuclei and influences gene transcription. Also, the authors have shown that Calpain 1 can digest junctophilin to generate the 44 kDa segment. The field of skeletal muscle generally knows little about how E-C coupling proteins have dual role and influence gene regulation that subsequently may alter the muscle function and metabolism. This part of the manuscript is solid, informative, and novel. The authors use advanced imaging and genetic manipulations of junctophilin etc to support their hypothesis. The authors then also aim to link this mechanism to hyperglycemia in individuals susceptible for malignant hyperthermia as they have elevated levels of the 44kDa segment. However, the power of the analyses are low and the included data comparisons complicates the possibility to interpret the results and its relevance. Nevertheless, the data supporting the novel dual role of junctophilin would likely be appreciated and gain attention to the muscle field.

Thanks for your constructive reading. We agreed (in our answer to Item 1) to your concern regarding power of the tests. To improve it we would need many more individual patients (which, after the pandemic peaks, are starting to be recruited again, although at a pace of no more than 2 per month). We are committed to updating the present report as soon as we obtain, say, 20 more MHS and MHN patients –a minimum to impact power of the tests. In any case, we claim that power is not an acute concern, as this communication deals mainly with positive results, where significance is of the essence.

We have established significance in most of the observations communicated here; in the few cases where p is marginal, significance is inferred by correlations.

Reviewer #2 (Public Review):

Skeletal muscle is the main regulator of glycemia in mammals and a major puzzle in the field of diabetes is the mechanism by which skeletal muscle (as well as other tissues) become insensitive to insulin or decrease glucose intake. the authors had proposed in a previous publication that high intracellular calcium, by means of calpain activation, could cleave and decrease the availability of GLUT4 glucose transporters. In this manuscript, the authors identify two additional targets of calpain activation. One of them is GSK3β, a specialized kinase that when cleaved, inhibits glycogen synthase and impairs glucose utilization. The second target is junctophilin 1, a protein involved in the structure of the complex responsible for E-C coupling in skeletal muscle. The authors succeeded in showing that a fragment of junctophilin1 (JPh44) moves from the triad to other cytosolic regions including the nuclei and they show changes in gene expression under these conditions, some of them linked to glucose metabolism.

Overall, the manuscript shows a novel and audacious approach with a careful treatment of the data (that was not always easy nor obvious) that allow sensible conclusions and definitively constitutes a step forward in this field.

Thanks for the generous report.

Reviewer #3 (Public Review):

First, we express utmost gratitude for your critical work on our manuscript. Your concerns made us perform additional experiments and validations, eventually forcing us to abandon a couple of erroneous notions and therefore improving our understanding and interpretations. Because your concerns were already in the “Essentials” list assembled by the Editor, our responses here will mostly refer to our earlier answers to the items in that list.

1) Figure 1 A and B show a western blot of proteins isolated from muscles of MHN and MHS individuals decorated with two different antibodies directed against JPH1. According to the manufacturer, antibody A is directed against the JPH1 protein sequence encompassing amino acids 387 to 512 while antibody B is directed against a no better specified C-terminal region of JPH1. Surprisingly, antibody B appears not to detect the full-length protein in lysates from human muscles, but recognizes only the 44 kDa fragment of JPH1. However, to the best of the reviewer's knowledge, antibody B has been reported by other laboratories to recognize the full-length JPH1 protein.

The reason for the failure of ab B to recognize the full human protein may be that it was raised against a murine immunogen (this interpretation was communicated to us by G.D. Lamb, who co-authored the 2013 paper by Murphy et al. where the failure was noted). It recognizes both JPh1 and JPh44 of murine muscle in our hands.

Thus, is not obvious why here this antibody should recognize only the shorter fragment.

We agree entirely. In spite of the difficulties in interpretation, the recognition of human JPh44 by the ab is, however, a fact, repeatedly demonstrated in the present study, which can be used to advantage.

In addition, in MHS individuals there is no direct correlation between reduction in the content of the full-length JPH1 protein and appearance of the 44 kDa JPH1fragment, since, as also reported by the authors, no significant difference between MHN and MHS can be observed concerning the amount of the 44 kDa JPH1.

Tentative interpretations of the lack of correlation have been presented in the response to Item 14, above.

Based on the data presented, it is very difficult to accept that antibody A and B have specific selectivity for JPH1 and the 44 kDa fragment of JPH1.

Indeed, we now acknowledge that Ab A reacts equally with JPh1 and the 44 kDa fragment (and provide quantitative evidence for it in Supplement 1 to Fig. 8). We also provide conclusive evidence of the specificity of ab B (e.g., Supplement 2 to Fig. 1).

2) In Figure 2B staining of a nucleus is shown only with antibody B against the 44 kDa JPH1 fragment, while no nucleus stained with antibody A is shown in Fig 2A. Images should all be at the same level of magnification and nuclear staining of nuclei with antibody A should be reported. In Figure 2Db labeling of JPH1 covers both the nucleus and the cytoplasm, does it mean that JPH1 also goes to the nucleus? One would rather think that background immunofluorescence may provide a confounding staining and authors should be more cautious in interpreting these data.

These items are fully covered in our response to Item 16.

Images in 2D and 2E refer to primary myotubes derived from patients. The authors show that RyR1 signals co-localizes with full-length JPH1, but not with the 44 kDa fragment, recognized by antibody B. How do the authors establish myotube differentiation?

Myotubes are studied 5-10 days after switching cells to differentiation medium, which is DMEM-F12 supplemented with 2.5% horse serum, as explained in Figueroa et al 2019. Cells with more than 3 nuclei were considered myotubes. Myotubes with similar degree of maturation (number of nuclei) were selected for experimental comparisons.

3) Figure 3 A-C. The authors show images of a full-length JPH1 tagged with GFP at the N-terminus and FLAG at the C- terminus. In Figure 3Ad and Cd the Flag signal is all over the cytoplasm and the nuclei: since these are normal mouse cells and fibers, it is surprising that the FLAG signal is in the nuclei with an intensity of signal higher than in patient's muscle.

Can the authors supply images of entire myotubes, possibly captured in different Z planes? How can they distinguish between the cleaved and uncleaved JPH1 signals, especially in mouse myofibers, where calpain is supposed not to be so active as in MHS muscle fibers?

Answer fully provided to Items 16b and 17 in Essentials list.

4) If the 44 kDa JPH1 fragment contains a transmembrane domain, it is difficult to understand the dual sarcoplasmic reticulum and nuclear localization. To justify this the authors, in the Discussion session, mention a hypothetical vesicular transport of the 44 kDa JPH1 fragment by vesicles. Traffic of proteins to the nucleus usually occurs through the nuclear pores and does not require vesicles. Even if diffusion from the SR membrane to the nuclear envelope occurs, the protein should remain in the compartment of the membrane envelope. There is no established evidence to support such an unusual movement inside the cells.

In agreement with the criticism, we have removed the speculation from the Discussion.

5) In Figure 5, the authors show the effect of Calpain1 on the full-length and 44 kDa JPH1 fragment in muscles from MHS patients. Can the authors repeat the same analysis on recombinant JPH1 tagged with GFP and FLAG?

We agree that confirmatory evidence of the calpain effect on dual-tagged recombinant JPh1 would be desirable. However, we think an in-depth study is required to follow up on the number of JPh1 fragments generated by calpain (or by different calpain isoforms) and their positions, similar to the detailed study of JPh2 fragmentation Wang et al. in 2021 (5).

Can the authors provide images from MHN muscle fibers stained with JPH1 and Calpain1.

We complied with the request.

6) In Figure 6, the authors show images of MHS derived myotubes transfected with FLAG Calpain1 and compare the distribution of endogenous JPH1 and RYR1 in two cells, one expressing FLAG Calpain1 (cell1) and one not expressing the recombinant protein. They state that cell1 shows a strong signal of JPH1 in the nucleus, while this is not observed in cell2. Nevertheless, it is not clear where the nucleus is located within cell2 since the distribution of JPH1 is homogeneous across the cell. Can the authors show a different cell?

In agreement, we now show a comparison between cultures with and without transfection in Supplement 1 to Fig. 6.

7) In Figure 7, panels Bb and Db: nuclei appear to stain positive for JPH1. It is not clear why in panels Ac, Bc they show a RYR1 staining while in panels Cc and Dc they show N-myc staining. The differential localization to nuclei appears rather poor also in these panels.

We have entirely removed from the manuscript the description of experiments of exposure to extracellular calpain, including Fig. 7 and three associated tables.

8) The strong nuclear staining in Figure 8, panels C and D is very different from the staining observed in Fig. 2 and Fig. 3. Transfection should not change the ratio between nuclear and cytoplasmic distribution.

Transfection is an intrusive procedure, which requires production and trafficking of an exogenous protein. This protein, furthermore, is an artificial construct (in this case, a “stand-in”, which adds to the native protein and therefore is akin to overexpression). For the above reasons, we believe that differences in intensity of nuclear staining may obey to multiple causes and should not be especially concerning.

Author Response

Reviewer #1 (Public Review):

1) This study performs an interesting analysis of evolutionary variation and integration in forelimb/hand bone shapes in relation to functional and developmental variation along the proximo-distal axis. They found expected patterns of evolutionary shape variation along the proximo-distal axis but less expected patterns of shape integration. This study provides a strong follow-up to previous studies on mammal forelimb variation, adding and testing interesting hypotheses with an impressive dataset. However, this study could better highlight the relevance of this work beyond mammalian forelimbs. The study primarily cites and discusses mammalian limb studies, despite the relevance of the suggested findings beyond mammals and forelimbs. Furthermore, relevant work exists in other tetrapod clades and structures related to later-developing traits and proximo-distal variation. Finally, variations in bone size and shape along the proximo-distal axis could be affecting evolutionary patterns found here and it would be great to make sure they are not influencing the analysis/results.

We appreciate the reviewer’s comments, and we acknowledge the importance of including examples of non-mammalian lineages in our study. We attended to the recommendation and included more examples of other tetrapod taxa in our text and in our references, providing a more inclusive discussion of limb bone diversity beyond mammals. We also explain below why the results obtained are not inflated by variation of bigger versus smaller sizes of bones.

Reviewer #2 (Public Review):

10) Congratulations on producing a very nice study. Your study aims to examine the morphological diversity of different mammalian limb elements, with the ultimate goal seemingly to test expectations based on the different timing of development of the limb bones. There's a lot to like: the sample size is impressive, the methods seem appropriate and sound, the results are interesting, the figures are clear, and the paper is very well written. You find greater diversity and integration in distal limb segments compared to proximal elements, and this may be due to the developmental timing and/or functional specialization of the limb segments. These are interesting results and conclusions that will be of interest to a broad readership. And the large dataset will likely be valuable to future researchers who are interested in mammalian limb morphology and evolution. I have one major concern with how you frame your discussion and conclusions, which I explain below. But I think you can address this issue with some text edits.

We sincerely thank the reviewer for his constructive recommendations and for his appreciation of our work. We addressed the issue raised as detailed below.

11) Major concern - is developmental timing the best hypothesis?

You discuss two potential drivers for the relatively greater diversity in distal elements: 1) later development and 2) greater functional specialization. Your data doesn't allow you to fully test these two hypotheses (e.g. you don't have detailed evo-devo data to infer developmental constraints), and I think you realize this - you use phrases like "consistent with the hypothesis that ...". You seem to compromise and conclude that both factors (development + function) are likely driving greater autopod diversity (e.g. Lines 302-306). Being unable to fully test these hypotheses weakens the impact of your conclusions, making them a bit more speculative, but otherwise, it isn't a critical issue.

But my concern is that you seem to favor developmental factors over functional factors as the primary drivers of your results, and that seems backwards to me. For instance, early in the Abstract (Line 32) and early in the Discussion (Line 201) you mention that your results are consistent with the developmental timing hypothesis, but it's not until later in the Abstract or Discussion that you mention the role of functional diversity/specialization/selection. The problem with favoring the development hypothesis is that your integration results seem to contradict that hypothesis, at least based on your prediction in the Introduction (Line 126; although you spend some of the Discussion trying to make them compatible). Later in the paper, you acknowledge that functional specialization (rather than developmental factors) might be a better explanation for the integration results (Lines 282-284, 345-347), but, again, this is only after discussions about developmental factors.

When you first start discussing functional diversity, you say, "high integration in the phalanx and metacarpus, possibly favoured the evolution of functionally specialized autopod structures, contributing to the high variation observed in mammalian hand bones." (Line 282). This implies that integration led to functional diversity in the autopod. But I'd flip that: I think the functional specialization of the hand led to greater integration. Integration does not result solely from genetic/developmental factors. It can also result from traits evolving together because they are linked to the same function. From Zelditch & Goswami (2021, Evol. & Dev.): "Within individuals, integration is customarily ascribed to developmental and/or functional interdependencies among traits (Bissell & Diggle, 2010; Cheverud, 1982; Wagner, 1996) and modularity is thus due to their developmental and/or functional independence."

In sum, I think your results capture evidence of greater functional specialization in hands relative to other segments. You're seeing greater 1) disparity and 2) integration in hands, and both of those are expected outcomes of greater functional specialization. In contrast, I think it's harder to fit your results to the developmental timing hypothesis. Thus, I recommend that throughout the paper (Abstract, Intro, Discussion) you flip your discussion of the two hypotheses and start with a discussion on how functional specialization is likely driving your results, and then you can also note that some results are consistent with the development hypothesis. You could maintain most of your current text, but I'd simply rearrange it, and maybe add more discussion on functional diversity to the Intro.

Or, if you disagree and think that there's more support for the development hypothesis, then you need to make a better case for it in the paper. Right now, it feels like you're trying to force a conclusion about development without much evidence to back it up.

We thank the reviewer for his thoughtful and thorough comment. We agree that the results provided, particularly those of integration, support the hypothesis that functional specialization contributes to the uneven diversity of limb bones. We addressed the concerns by substantially changing our discussion, particularly moderating (and removing) sections on the developmental constraints and adding new arguments for other possible drivers for the diversity of limb bones, such as function. However, the goal of the paper was to test whether the data corroborate - or not - the predictions derived from the developmental hypothesis, and they largely do. Therefore, we decided to keep the developmental hypothesis presented first in the introduction and in the discussion section, as we believe this sequence provides more coherence considering the hypothesis tested (we believe that detailing the role of functional specialization particularly in the introduction would mislead the reader to think that we directly tested for these parameters). Following the discussion of the integration results, we then go on to discuss the possible role of functional specialization on the results obtained (lines 262-285, see also lines 216-234). Yet, these are not tested in this paper and remain to be tested in a future analysis focusing specifically on the role of ecology and function in driving variation in the mammalian limb.

12) Limitations of the dataset

Using linear measurements is fine, but they mainly just capture simple aspects of the elements (lengths and widths). You should acknowledge in your paper the limitations of that type of data. For example, the deltoid tuberosity of the humerus can vary considerably in size and shape among mammals, but you don’t measure that structure. The autopod elements don’t have a comparable process, meaning that if you were to measure the deltoid tuberosity then you’d likely see a relative increase in humerus disparity (although my guess is that it’d still be well below that of the autopod). And you omit the ulna from your study, and its olecranon process varies considerably among taxa and its length is a very strong correlate of locomotor mode. In other words, your finding of the greatest disparity in the hand might be due in part to your choice of measurements and the omission of measurements of specific processes/elements. I recommend that you add to your paper a brief discussion of the limitations of using linear measurements and how you might expect the results to change if you were to include more detailed measurements and/or more elements.

We followed the recommendation and included a discussion about the dataset limitations, acknowledging for the possible impact of the measurements and the bones chosen in the results obtained (Lines 235-260).

Reviewer #3 (Public Review):

32) This paper uses a large (638 species representing 598 genera in 138 families) extant sample of osteologically adult mammals to address the question of proximodistal patterns of cross-taxonomic diversity in forelimb bony elements. The paper concludes, based on a solid phylogenetically controlled multivariate analysis of liner measurements, that proximal forelimb elements are less morphologically diverse and evolutionarily flexible than distal forelimb elements, which the paper concludes is consistent with a developmental constraint axis tied to limb bud growth and development. This paper is of interest to researchers working on macroevolutionary patterns and sources of morphological diversity.

Methodological review Strengths:

The taxonomic dataset is very comprehensive for this sort of study and the authors have given consideration to how to identify bony elements present in all mammalian taxa (no small task with this level of taxonomic breadth). Multivariate approaches as used in this study are the gold standard for addressing questions of morphological variations.

The authors give consideration to two significant confounders of analyses operating at this scale: phylogeny and body size. The methods they use to address these are appropriate, although as I note below body size itself may merit more consideration.

We sincerely thank the reviewer for his appreciation of our study. We addressed the main concerns pointed out below.

Weaknesses:

33) The authors assume a lot of knowledge on the part of the reader regarding their methods. Given that one of their key metrics (stationary variance) is largely a property as I understand it of OU models, more explanation on the authors' biological interpretation of stationary variance would help assess the strength of their conclusions, especially as OU models are not as straightforward as they first appear in their biological interpretation (Cooper et al., 2016).

We acknowledge that this may not be straightforward and now include a more extensive explanation of the approach and the metrics used. We detailed the explanation about the stationary variances in the methods, contextualizing the biological meaning (lines 456-469).

34) It is unclear what the authors mean when they say they "simulated the trait evolution under OU processes on 100 datasets". Are the 100 datasets 100 different tree topologies (as seems to be the case later "we replicated the body mass linear regressions with 100 trees from Upham et al (2019)." If that is so, what is the rationale for choosing 100 topologies and what criteria were used to select the 100 topologies?

We understand the explanation may have been confusing. Globally, we used a parametric bootstrap approach to assess the uncertainty around point estimates for morphological diversity and integration. That is, we first simulated 100 datasets on the maximum clade credibility tree (MCC tree, that summarizes 10,000 trees from Upham et al. 2019) – using the best fit model on our original data (i.e., an OU process) with parameters estimates from this model fit. The model (an OU process) was then fit to these 100 simulated traits, and the distribution of parameters estimates obtained was used to assess the variability around the point estimate (for the determinant, the trace, and the measure of integration) obtained on empirical data. We did not used the simulated dataset to estimate the significance of the stationary variances. We fitted the empirical datasets with 100 trees randomly sampled from the credible set of 10,00 trees of Upham et al (2019) – instead of using the MCC – to further assess the variability due to the tree topology and branching times uncertainties. We included this expanded explanation in the methods in lines 421-428 and 471.

35) The way the authors approach body mass and allometry, while mathematically correct, ignores the potential contribution of body mass to the questions the authors are interested in. Jenkins (1974) for example argued that small mammals would converge on similar body posture and functional morphology because, at small sizes, all mammals are scansorial if they are not volant. Similarly, Biewener (1989) argued that many traits we view as cursorial adaptations are actually necessary for stability at large body sizes. Thus size may actually be important in determining patterns of variation in limb bone morphology.

We agree with the observation. We believe that categorizing the groups according to size would provide a meaningful overview on the effect of size on the diversity and evolution of limb bones. Although insightful and worthy of investigation, we were particularly interested in understanding whether developmental timing corresponds to bone diversification more broadly across Mammalia and thus considered only the size residual values. This issue will be addressed in our future works. We discussed in the lines 329-341 the potential contribution of body size to limb segment diversification and the importance of considering this aspect in future studies.

36) Review of interpretation.

The authors conclude that their result, in showing a proximo-distal gradient of increasing disparity and stationary variance in forelimb bone morphology, supports the idea that proximo-distal patterning of limb bone development constrains the range of morphological diversity of the proximal limb elements. However, this correlation ignores two important considerations. The first is that the stylopod connects to the pectoral girdle and the axial skeleton, and so is feasibly more constrained functionally, not developmentally in its morphological evolution. The second, related, issue arises from the authors' study itself, which shows that the lowest morphological integration is found in the stylopod and zeugopod, whereas the autopod elements are highly integrated. This suggests a greater tendency towards modularity in the stylopod and zeugopod, which is itself a measure of evolutionary lability (Klingenberg, 2008). And indeed the mammalian stylopod is developmentally comprised of multiple elements (the epiphyses and diaphysis) that are responding to very different developmental and biomechanical signals. Thus, for example, the functional signal in stylopod (Gould, 2016) and zeugopod (MacLeod and Rose, 1993) articular surface specifically is very high. What is missing to fully resolve the question posed by the authors is developmental data indicating whether or not the degree of morphological disparity in the hard tissues of the forelimb change over the course of ontogeny throughout the mammalian tree, and whether changing functional constraints over ontogeny (as is the case in marsupials) affect these patterns.

We thank the reviewer for sharing such an interesting reinterpretation of the results. Combined to the recommendations from the other two reviewers, we substantially changed our discussion, specially modifying the interpretation of results concerning trait integration. We discussed the possible role of the functional variation at the articulations on element integration in lines 263-285.

Author Response

Reviewer #2 (Public Review):

This paper investigates the maintenance and function of memory follicular helper T (Tfh) cell subsets using in vitro approaches, murine immunization models and vaccine-challenged humans. Murine Tfh cell subsets (Tfh1, Tfh2, Tfh17) were generated using in vitro polarization (iTfh1, iTfh2, iTfh17), and then tested for support of humoral response following adoptive transfer or adoptive transfer with resting in vivo for 35 days. iTfh17 cells were statistically better than iTfh1 and iTfh2 cells in promoting GC B cell and plasma cell maturation after resting in vivo, although all 3 populations were capable of B cell help. Tfh17 cells were comparatively enriched among blood borne Tfh central memory cells in humans, and were enriched at the memory phase of vaccination with hepatitis B and influenza vaccines, compared to effector phase, suggesting the possibility they are comparatively superior in Tfh cell memory formation, with greater persistence in aged individuals.

Significance

The enrichment of Tfh17 cells in Tfh cell central memory compartment and the dominance of Tfh17 cell population and the Tfh17 transcriptional signature in circulating Tfh cells at the memory phase are nicely demonstrated, and may well be helpful for understanding the heterogeneity of memory Tfh cells and potentially providing clues for vaccine design. The in vitro differentiation system for mouse Tfh cells also provides a strategy for others to build upon in dissection of Tfh cell development and function.

Points to consider

1) Even though Tfh17 cells are more likely to persist at memory timepoints in mice and in humans, or produce more GC B cells or plasma cells following transfer, all subsets can do this. Is GC output otherwise distinguishable following transfer of the individual subsets, or is their effect (cytokine related perhaps) pre-GC with differential CSR? It is also not clear if the individual subsets populate the GC and assuming they do so, if their respective phenotypes persist when they become GC Tfh cells.

We have conducted new experiments and showed that iTfh17 preferentially generate more GC-Tfh cells in the delay immunization (after 35 day’s resting in vivo). Furthermore, different iTfh subsets maintained polarized cytokine profiles after antigen re-exposure and prompt specific CSR as their Th1 or Th2 counterparts. Please refer to the response (2) to Essential Revisions for details.

2) iTfh17 cells induce more GC B cells and antibodies after resting and antigen challenge (Figures 1, 2). However, it's not clear whether this effect is a consequence of comparatively enhanced iTfh17 survival during resting (as suggested by latter figures), or better expansion or differential skewing to Tfh differentiation during challenge (as suggested by Figure 1 J,K). The total number of remaining adoptively-transferred cells right before challenge and 7 days post challenge will be helpful to understand that.

We have conducted new experiments and our results suggested that the superior immunological memory maintenance of iTfh17 cells was attributed to their better survival capacity and better maintenance of the potential to differentiate into GC-Tfh cells. Please refer to the response (2) to Essential Revisions for details.

3) The authors tried to address whether Tfh17 cells have better ability to survive till memory phase or Tfh17 cells with memory potential are generated at higher frequency at the effector phase of vaccination (Figure 5); however, the experiment is not conclusive. The cTfh population 7 days post vaccination is a mixed population with effector Tph cells and Tfh memory precursors. The increased frequency of Th17 cells at day 28 compared to day 7 could be a consequence of superior survival ability, or Tfh memory precursors with Tfh17 signature are better generated.

As indicated in our gating strategy and the widely accepted definition of cTfh cells - CD4+ CD45RA- CXCR5+ (line 69), we respectively disagree with the reviewer’s comment ‘The cTfh population 7 days post vaccination is a mixed population with effector Tph cells and Tfh memory precursors’. The effector Tph population is defined as PD-1hiCXCR5-CD4+ T cells (Rao DA et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis, Nature 2017)

4) Experiments to confirm expansion ability of the human subsets or their B cell helper ability were not performed.

In our new experiments, we demonstrated that iTfh1/2/17 cells showed comparable expansion ability.

Human cTfh1/2/17 cells’ expansion ability and B helper ability were reported previously by Morita et al. (Human blood CXCR5(+)CD4(+) T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion, Immunity 2011, Figure 4C-D). Human cTfh1/2/17 cells showed comparable expansion ability when co-culturing with SEB-pulsed naive B cells, and cTfh17 cells had superior B cell helper function over cTfh1 but not cTfh2 cells in promoting the B cell expansion and plasma cell formation.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Goering et al. investigate subcellular RNA localization across different cell types focusing on epithelial cells (mouse C2bbe1 and human HCA-7 enterocyte monolayers, canine MDCK epithelial cells) as well as neuronal cultures (mouse CAD cells). They use their recently established Halo-seq method to investigate transcriptome-wide RNA localization biases in C2bbe1 enterocyte monolayers and find that 5'TOP-motif containing mRNAs, which encode ribosomal proteins (RPs), are enriched on the basal side of these cells. These results are supported by smFISH against endogenous RP-encoding mRNAs (RPL7 and RPS28) as well as Firefly luciferase reporter transcripts with and without mutated 5'TOP sequences. Furthermore, they find that 5'TOP-motifs are not only driving localization to the basal side of epithelial cells but also to neuronal processes. To investigate the molecular mechanism behind the observed RNA localization biases, they reduce expression of several Larp proteins and find that RNA localization is consistently Larp1-dependent. Additionally, the localization depends on the placement of the TOP sequence in the 5'UTR and not the 3'UTR. To confirm that similar RNA localization biases can be conserved across cell types for other classes of transcripts, they perform similar experiments with a GA-rich element containing Net1 3'UTR transcript, which has previously been shown to exhibit a strong localization bias in several cell types. In order to determine if motor proteins contribute to these RNA distributions, they use motor protein inhibitors to confirm that the localization of individual members of both classes of transcripts, 5'TOP and GA-rich, is kinesin-dependent and that RNA localization to specific subcellular regions is likely to coincide with RNA localization to microtubule plus ends that concentrate in the basal side of epithelial cells as well as in neuronal processes.

In summary, Goering et al. present an interesting study that contributes to our understanding of RNA localization. While RNA localization has predominantly been studied in a single cell type or experimental system, this work looks for commonalities to explain general principles. I believe that this is an important advance, but there are several points that should be addressed.

Comments:

1) The Mili lab has previously characterized the localization of ribosomal proteins and NET1 to protrusions (Wang et al, 2017, Moissoglu et al 2019, Crisafis et al., 2020) and the role of kinesins in this localization (Pichon et al, 2021). These papers should be cited and their work discussed. I do not believe this reduces the novelty of this study and supports the generality of the RNA localization patterns to additional cellular locations in other cell types.

This was an unintentional oversight on our part, and we apologize. We have added citations for the mentioned publications and discussed our work in the context of theirs.

2) The 5'TOP motif begins with an invariant C nucleotide and mutation of this first nucleotide next to the cap has been shown to reduce translation regulation during mTOR inhibition (Avni et al, 1994 and Biberman et al 1997) and also Lapr1 binding (Lahr et al, 2017). Consequently, it is not clear to me if RPS28 initiates transcription with an A as indicated in Figure 3B. There also seems to be some differences in published CAGE datasets, but this point needs to be clarified. Additionally, it is not clear to me how the 5'TOP Firefly luciferase reporters were generated and if the transcription start site and exact 5'-ends of these constructs were determined. This is again essential to determine if it is a pyrimidine sequence in the 5'UTR that is important for localization or the 5'TOP motif and if Larp1 is directly regulating the localization by binding to the 5'TOP motif or if the effect they observe is indirect (e.g. is Larp1 also basally localized?). It should also be noted that Larp1 has been suggested to bind pyrimidine-rich sequences in the 5'UTR that are not next to the cap, but the details of this interaction are less clear (Al-Ashtal et al, 2021)

We did not fully appreciate the subtleties related to TOP motif location when we submitted this manuscript, so we thank the reviewer for pointing them out.

We also analyzed public CAGE datasets (Andersson et al, 2014 Nat Comm) and found that the start sites for both RPL7 and RPS28 were quite variable within a window of several nucleotides (as is the case for the vast majority of genes), suggesting that a substantial fraction of both do not begin with pyrimidines (Reviewer Figure 1). Yet, by smFISH, endogenous RPL7 and RPS28 are clearly basally/neurite localized (see new figure 3C).

Reviewer Figure 1. Analysis of transcription start sites for RPL7 (A) and RPS28 (B) using CAGE data (Andersson et al, 2014 Nat Comm). Both genes show a window of transcription start sites upstream of current gene models (blue bars at bottom).

A more detailed analysis of our PRRE-containing reporter transcripts led us to find that in these reporters, the pyrimidine-rich element was approximately 90 nucleotides into the body of the 5’ UTR. Yet these reporters are also basally/neurite localized. The organization of the PRRE-containing reporters is now more clearly shown in an updated figure 3D.

From these results, it would seem that the pyrimidine-rich element need not be next to the 5’ cap in order to regulate RNA localization. To generalize this result, we first used previously identified 5’ UTR pyrimidine-rich elements that had been found to regulate translation in an mTOR-dependent manner (Hsieh et al 2012). We found that, as a class, RNAs containing these motifs were similarly basally/neurite localized as RP mRNAs. These results are presented in figures 3A and 3I.

We then asked if the position of the pyrimidine-rich element within the 5’ UTR of these RNAs was related to their localization. We found no relationship between element position and transcript localization as elements within the bodies of 5’ UTRs were seemingly just as able to promote basal/neurite localization as elements immediately next to the 5’ cap. These results are presented in figures 3B and 3J.

To further confirm that pyrimidine-rich elements need not be immediately next to the 5’ cap, we redesigned our RPL7-derived reporter transcripts such that the pyrimidine-rich motif was immediately adjacent to the 5’ cap. This was possible because the reporter uses a CMV promoter that reliably starts transcription at a known nucleotide. We then compared the localization of this reporter (called “RPL7 True TOP”) to our previous reporter in which the pyrimidine-rich element was ~90 nt into the 5’ UTR (called “RPL7 PRRE”) (Reviewer Figure 2). As with the PRRE reporter, the True TOP reporter drove RNA localization in both epithelial and neuronal cells while purine-containing mutant versions of the True TOP reporter did not (Reviewer Figure 2A-D). In the epithelial cells, the True TOP was modestly but significantly better at driving basal RNA localization than the PRRE (Reviewer Figure 2E) while in neuronal cells the True TOPs were modestly but insignificantly better. Again, this suggests that pyrimidine-rich motifs need not be immediately cap-adjacent in order to regulate RNA localization.

Reviewer Figure 2. Experimental confirmation that pyrimidine-rich motif location within 5’ UTRs is not critical for RNA localization. (A) RPL7 True TOP smFISH in epithelial cells. (B) RPL7 True TOP smFISH in neuronal cells. (C) Quantification of epithelial cell smFISH in A. (D) Quantification of neuronal cell smFISH in D. (E) Comparison of the location in epithelial cells of endogenous RPL7 transcripts, RPL7 PRRE reporter transcripts, and PRL7 True TOP reporter transcripts. (F) Comparison of the neurite-enrichment of RPL7 PRRE reporters and RPL7 True TOP reporters. In C-F, the number of cells included in each analysis is shown.

In response to the point about whether the localization results are direct effects of LARP1, we did not assay the binding of LARP1 to our PRRE-containing reporters, so we cannot say for sure. However, given that PRRE-dependent localization required LARP1 and there is much evidence about LARP1 binding pyrimidine-rich elements (including those that are not cap-proximal as the reviewer notes), we believe this to be the most likely explanation.

It should also be noted here that while pyrimidine-rich motif position within the 5’ UTR may not matter, its location within the transcript does. PRREs located within 3’ UTRs were unable to direct RNA localization (Figure 5).

3) In figure 1A, they indicate that mRNA stability can contribute to RNA localization, but this point is never discussed. This may be important to their work since Larp1 has also been found to impact mRNA half-lives (Aoki et al, 2013 and Mattijssen et al 2020, Al-Ashtal et al 2021). Is it possible the effect they see when Larp1 is depleted comes from decreased stability?

We found that PRRE-containing reporter transcripts were generally less abundant than their mutant counterparts in C2bbe1, HCA7, and MDCK cells (figure 3 – figure supplements 5, 6, and 8) although the effect was not consistent in mouse neuronal cells (figure 3 – figure supplement 13).

However, we don’t think it is likely that the changes in localization are due to stability changes. This abundance effect did not seem to be LARP1-dependent as both PRRE-containing and PRRE-mutant reporters were generally more expressed in LARP1-rescue epithelial cells than in LARP1 KO cells (figure 4 – figure supplement 9).

It should be noted here that we are not ever actually measuring transcript stability but rather steady state abundances. It cannot therefore be ruled out that LARP1 is regulating the stability of our PRRE reporters. Given, though, that their localization was dependent on kinesin activity (figures 7F, 7G), we believe the most likely explanation for the localization effects is active transport.

4) Also Moor et al, 2017 saw that feeding cycles changed the localization of 5'TOP mRNAs. Similarly, does mTOR inhibition or activation or simply active translation alter the localization patterns they observe? Further evidence for dynamic regulation of RNA localization would strengthen this paper

We are very interested in this and have begun exploring it. We have data suggesting that PRREs also mediate the feeding cycle-dependent relocalization of RP mRNAs. As the reviewer says, we think this leads to a very attractive model involving mTOR, and we are currently working to test this model. However, we don’t have the room to include those results in this manuscript and would instead prefer to include them in a later manuscript that focuses on nutrient-induced dynamic relocalization.

5) For smFISH quantification, is every mRNA treated as an independent measurement so that the statistics are calculated on hundreds of mRNAs? Large sample sizes can give significant p-values but have very small differences as observe for Firefly vs. OSBPL3 localization. Since determining the biological interpretation of effect size is not always clear, I would suggest plotting RNA position per cell or only treat biological replicates as independent measurements to determine statistical significance. This should also be done for other smFISH comparisons

This is a good suggestion, and we agree that using individual puncta as independent observations will artificially inflate the statistical power in the experiment. To remedy this in the epithelial cell images, we first reanalyzed the smFISH images using each of the following as a unique observation: the mean location of all smFISH puncta in one cell, the mean location of all puncta in a field of view, and the mean location of all puncta in one coverslip. With each metric, the results we observed were very similar (Reviewer Figure 3) while the statistical power of course decreased. We therefore chose to go with the reviewer-suggested metric of mean transcript position per cell.

Reviewer Figure 3. C2bbe1 monolayer smFISH spot position analysis. RNA localization across the apicobasal axis is measured by smFISH spot position in the Z axis. This can be plotted for each spot, where thousands of spots over-power the statistics. Spot position can be averaged per cell as outlined manually within the FISH-quant software. This reduces sample size and allows for more accurate statistical analysis. When spot position is averaged per field of view, sample size further decreases, statistics are less powered but the localization trends are still robust. Finally, we can average spot position per coverslip, which represents biological replicates. We lose almost all statistical power as sample size is limited to 3 coverslips. Despite this, the localization trends are still recognizable.

When we use this metric, all results remain the same with the exception of the smFISH validation of endogenous OSBPL3 localization. That result loses its statistical significance and has now been omitted from the manuscript. All epithelial smFISH panels have been updated to use this new metric, and the number of cells associated with each observation is indicated for each sample.

For the neuronal images, these were already quantified at the per-cell level as we compare soma and neurite transcript counts from the same cell. In lieu of more imaging of these samples, we chose to perform subcellular fractionation into soma and neurite samples followed by RT-qPCR as an orthogonal technique (figure 3K, figure 3 supplement 14). This technique profiles the population average of approximately 3 million cells.

6) F: How was the segmentation of soma vs. neurites performed? It would be good to have a larger image as a supplemental figure so that it is clear the proximal or distal neurites segments are being compared

All neurite vs. soma segmentations were done manually. An example of this segmentation is included as Reviewer Figure 4. This means that often only proximal neurites segments are included in the analysis as it is often difficult to find an entire soma and an entire neurite in one field of view. However, in our experience, inclusion of more distal neurite segments would likely only strengthen the smFISH results as we often observe many molecules of localized transcripts in the distal tips of these neurites.

Reviewer Figure 4. Manual segmentation of differentiated CAD soma and neurite in FISH-quant software. Neurites that do not overlap adjacent neurites are selected for imaging. Often neurites extend beyond the field of view, limiting this assay to RNA localization in proximal neurites.

Also, it should be noted that the neuronal smFISH results are now supplemented by experiments involving subcellular fractionation and RT-qPCR (figure 3 supplement 14). These subcellular fractionation experiments collect the whole neurite, both the proximal and distal portions.

Text has been added to the methods under the header “smFISH computational analysis” to clarify how the segmentation was done.

Author Response

Reviewer #1 (Public Review):

This is timely and foundational work that links cellular neurophysiology with extracellular single-unit recordings used to study LC function during behavior.

The strengths of this paper include:

1. Providing an updated assessment of LC cell morphology and cell types since much of the prior work was completed in the late 1970s and early to mid-1980s.

2. Connecting LC cell morphology with membrane properties and action potential shape.

3. Showing that neurons of the same type have electrical coupling

Collectively, these findings help to link LC neuron morphology and firing properties with recent work using extracellular recordings that identify different types of LC single units by waveform shape.

Another strength of this work is that it addresses recent findings suggesting the LC neurons may release glutamate by showing that, at least within the LC, there is no local glutamatergic excitatory transmission.

Weaknesses:

The authors also propose to test the role of single LC neuron activity in evoking lateral inhibition, as well as proposing that electrical coupling between LC cell pairs is organized into a train pattern. The former point is based on a weak premise and the latter point has weak support in their data given the analyses performed.

Point 1: lateral inhibition in the LC

The authors write in the abstract that "chemical transmission among LC noradrenergic neurons was not detected" and this was a surprising claim given the wealth of prior evidence supporting this in vitro and in vivo (Ennis & Aston-Jones 1986. Brain Res 374, 299-305; Aghajanian, Cedarbaum & Wang 1977. Brain Res 136, 570-577; Cedarbaum & Aghajanian. 1978 Life Sci 23, 1383-1392).

Huang et al. 2007 (Huang et al. 2007. Proc National Acad Sci 104, 1401-1406) showed that local inhibition in the LC is highly dependent on the frequency of action potentials, such that local release requires multiple APs in short succession and then requires some time for the hyperpolarization to appear (even over 1 sec). This work suggests that it is not a "concentration issue" per se, rather it is just that a single AP will not cause local NE release in the LC. Although the authors did try 5APs at 50Hz this may not be enough to generate local NE release according to this prior work. A longer duration may be needed. Additionally, although the authors incubated the slices with a NET inhibitor, that will not increase volume transmission unless there is actually NE release, which may have not happened under the conditions tested. In sum, there is no reason to expect that a single AP from one neuron would cause an immediate (within the 100 msec shown in Fig 3B) hyperpolarization of a nearby neuron. Therefore, the premise of the experiment that driving one neuron to fire one AP (or even 5AP's at 50Hz in some) is not an actual test of lateral inhibition mediated by NE volume neurotransmission in the LC. Strong claims that "chemical transmission...was not detected" require substantial support and testing of a range of AP frequencies and durations. Given the wealth of evidence supporting lateral inhibition of the LC, this claim seems unwarranted.

We thank the reviewers for their constructive comments and interpretations of the data regarding lateral inhibition. In fact, we were fully aware of the prior wealth of data supporting the existence of lateral inhibition and have discussed possible reasons for the absence of lateral inhibition in our dataset. Now both reviewers provided additional potential explanations for this absence. The most plausible explanation appears to be that α2AR-mediated lateral inhibition is a population phenomenon, which would not be readily detected at the single-cell level in in vitro conditions. As reviewers suggested, we may need to vary spike frequency and timing to identify optimal spiking parameters (or stimulating multiple LC neurons at one time) to detect this phenomenon in slices. Alternatively, we could employ other approaches (optogenetic or chemogenetic approach) to activate a group of neurons at one time to evoke this phenomenon, as a recent preprint paper demonstrated (Line 528-535). All these are excellent suggestions, but it may take more than six months to complete these experiments since we need to train another person from scratch for LC recordings (the first author graduated from the program and has left the lab). We thus chose to remove most of the data (about α2AR-mediated lateral inhibition) from the paper in the revision, as the reviewers suggested. We do plan to further explore this interesting topic in our next study.

Point 2: Train-like connection pattern

Demonstrating that connected cell pairs often share a common member is an important demonstration of a connection motif in the LC. However, a "train" connection implies that you can pass from A to B to C to D (and in reverse). However, the authors do not do an analysis to test whether this occurs. Therefore, "train" is not an appropriate term to describe the interesting connection motif that they observed.

In fact, writing A↔B↔C in the paper implies a train without direct support for that form of electrical transmission. For example, in Fig. 6C, it is clear that cell 6 is coupled to cell 1 and that cell 6 is also coupled to cell 8. In both cases, the connection is bilateral. Using the author's formatting of A↔B↔C , would correspond with Cell 6 being B and cells 1 and 8 being A and C (or vice versa). However, writing A↔B↔C implies a train, whereas one can instead draw this connection pattern where B is a common source:

A C

. .

. .

B

An analysis showing that spikes in A can pass through B and later appear in C is necessary to support the use of "train". The example in Fig. 6C argues against train at least for this one example.

Although the analysis is possible to do with the authors' substantial and unique data set, it should be also noted that prior work on putative electrical coupling in extracellular recordings from rat LC showed that trains among 3 single units occurred at an almost negligible rate because out of 12 rats "Only 1 triplet out of 22,100 possible triplet patterns (0.005%) was found in one rat and 4 triplets out of 1,330 possible triplet patterns (0.301%) were found in the other rat." and moreover patterns beyond 3 units were never observed (Totah et al 2018. Neuron 99, 1055-1068.e6). We thank the reviewer for this astute argument and agree that the word “train-like connection” assumes a physiological, functional relationship A→B→C which the data do not show. Therefore, we now term these connections as “chain-like” to indicate the structural nature of the connection, which we believe leaves no room for the interpretation that there is a functional, physiological connection among the three neurons. In fact, we have discussed this issue as a first-order vs second-order coupling issue in our original manuscript (Line 632-639), and concluded that electrical signals hardly pass through the second-order gap junctions in LC, that is, in those two connections sharing the same partner like A↔B↔C (here A and C are not directly connected, but coupled in the second-order), spikes in A hardly pass-through B and later appear in C (Line 632-639).

Reviewer #2 (Public Review):

McKinney et al set out to better understand local circuit organization within the mouse locus coeruleus (LC). To do so, the authors achieved the technical feat of performing multiple, simultaneous whole-cell recordings (up to 8 LC neurons at once). This approach gives the authors a powerful and relatively high throughput means of assessing LC neuronal activity and potentially its rate of interconnectedness. In addition to recording from these cells, many were also filled with biocytin to recover their morphology. Using traditional reconstruction approaches the authors identified two morphological classes of LC neurons, fusiform(FF) and multipolar (MP). Although the selection of these classes was biased from previous literature, the authors used machine classification to rigorously demonstrate that these classes indeed exist. From there, the electrical properties of these distinct LC neurons were compared and a number of distinct action potential properties were identified between the two groups. Although firing in response to injected current showed that the FF class could maintain a higher firing rate, basal firing was not explicitly compared as the cells were prevented from firing upon entering whole-cell. The authors next explored the extent to which local chemical transmission occurs within the LC. Although there is evidence of glutamatergic transmission from LC neurons, the authors did not directly observe any evidence of local glutamate release from these neurons. This effect might be expected given the severing of axons in the slice preparation. Somewhat less expected is the author's claim that they could not find evidence of local NE release via alpha2 adrenergic receptor activation. This lack of evidence might well arise because this phenomenon does not occur, but it also remains possible that we do not have sufficient understanding of volume transmission to properly detect a change, particularly in whole-cell current clamp. The evidence that alpha2-mediated hyperpolarization is intact is somewhat adjacent to the concept as the concentrations of NE and clonidine used to show this robust suppression of firing is well above what is likely physiologically released by these neurons. One thing the authors do not consider is that the slice orientation (horizontal vs. coronal) greatly alters local glutamatergic input to the point that glutamate-mediated phasic bursts often do not occur in horizontal slices.

A major strength of the multi-patch approach used here is the ability to identify electrical connections between LC neurons. While gap junction-coupling has long been established in these neurons, multiple reports suggest that this coupling is decreased as the animal matures into adulthood. Here the authors provide clear evidence for a stable rate of electrical coupling well into adulthood. This approach also gives the authors the relatively unique ability to look for second-order connections between LC neurons and the amount of coupling was elegantly used to model how the LC might wire together more broadly. Although this approach is very powerful and likely at the edge of what is physically possible for whole-cell recordings in this brain structure it still likely undersamples LC local circuitry and biases investigations to be relatively close to one another spatially. While the authors rightfully consider the intersoma distance (ISD), the longest the gets in these studies is still smaller than most anatomical axes of the LC. This is an important limitation because the electrical coupling between FF-FF and FF-MP both appear to increase as ISD increases, suggesting more coupling could be occurring in distal dendrites. Furthermore, if coupling is occurring in distal dendrites it may be harder to detect as shunting in these distal dendrites could prevent signal detection.

This work is timely and important to the LC field which is on the precipice of having a greater understanding of heterogeneity based on a number of different principles, and this work adds local circuit dynamics as one of these principles. It will be important for the field to see how different efferent anatomical modules align with the cell types and circuit properties identified here.

We appreciate the reviewer’s constructive comments and suggestions.

Author Response:

Reviewer #2 (Public Review):

This study addresses the ways in which bacteriophages antagonize or coopt the DNA restriction or recombination functions of the bacterial RecBCD helicase-nuclease.

The strength of the paper lies in the marriage of biochemistry and structural biology.

A cryo-EM structure of the RecBCD•gp5.9 complex establishes that gp5.9 is a DNA-mimetic dimer composed of an acidic parallel coiled coil that occupies the dsDNA binding site on the RecB and RecC subunits. The structure of gp5.9 is different from that of the RecBCD-inhibiting DNA mimetic protein phage λ Gam.

Cryo-EM structures of Abc2 are solved in complex with RecBCD bound to a forked DNA duplex, revealing that Abc2 interacts with the RecC subunit. A companion structure is solved containing PPI that copurifies with RecBCD•Abc2.

Whereas the gp5.9 structure fully rationalizes the effect of gp5.9 on RecBCD activity, the Abc2 structure - while illuminating the docking site on RecBCD, a clear advance - does not clarify how Abc2 impacts RecBCD function.

The authors speculate that Abc2 binding prevents RecA loading on the unwound DNA 3' strand while favoring the loading of the phage recombinase Erf.

Does the structure provide impetus and clues for further experiments to elaborate on that question and, if so, how?

Regarding the first point (Murphy’s results). We have now included more detail about Murphy’s results and a brief comparative discussion of our own (page 13). An important caveat in interpreting small (<5-fold) effects on RecBCD activity is that the complex is known to possess different levels of specific activity between preparations (from 20% to 100% active based on titration of DNA ends). This is especially problematic when assessing the effect of Abc2 on RecBCD because (unlike gp5.9 for instance) the protein cannot be purified in isolation and titrated into free RecBCD to monitor how activity changes. Instead, one is comparing activity between different preparations either including Abc2 or not. Regarding the second point (how much does the structure tells us about the mechanism of Abc2?). We agree with the general sentiment here: the mechanism of RecBCD hijacking by Abc2 is still a “work in progress”. Nevertheless, the structure is suggestive of effects on Chi recognition and/or RecA loading which is both consistent with biochemical results and suggests new avenues for further investigation.

While the RecBCD-gp5.9 structure “nails” the inhibition mechanism as steric exclusion of substrate, the RecBCD-Abc2 structure is less informative. Previously published biochemical and in vivo analyses of Abc2 show that it modulates rather than completely inhibits the enzyme. The hypothesis is that Abc2 modifies the process of Chi recognition and/or RecA loading (which are themselves coupled processes) in order to facilitate loading of the phage recombinase Erf. Given current structural models for the mechanism of RecBCD, it is not entirely obvious from the structure of RecBCD-Abc2 what exactly this small phage protein is doing, because (a) there is no significant change to the structure of RecBCD induced by Abc2 interaction and (b) no known protein interaction site (eg with RecA) is blocked. Indeed, our original manuscript ended with an acknowledgement that understanding how P22 controls recombination in E. coli was ongoing work. As we see it, in addition to simply revealing the binding site of Abc2, our structure has two significant impacts. Firstly, it is consistent with and extends the existing hypothesis. For example, (a) the interaction of Abc2 with RecC is precisely with the domains of the protein that are responsible for Chi recognition and close to a putative site of RecA loading; (b) the recognition that a conserved proline in Abc2 interacts with the active site of PPI implies that Abc2 function is dependent on proline isomerisation; (c) the possible bridging of RecB and RecC by the C-and N-terminal regions of the protein suggest that Abc2 might hinder intersubunit conformational changes. Secondly, the structure facilitates the testing of this hypothesis. For example, (a) does RecA and/or Erf loading depend on interactions with the surfaces destroyed or created by Abc2 at the interface with RecC (b) does P68A mutation inactivate Abc2?; (c) does failure to recognise and respond to Chi require bridging of RecB and RecC that limits conformational transitions? Crucially, as we explain in the discussion, the future study of the P22 recombination system will require the purification and characterisation of additional factors (Abc2, Arf and Erf) beyond just Abc2. This is something we are working on currently in the lab and consider to be beyond the scope of this work.

Author Response

We thank the reviewers for their comments and helpful suggestions. We are currently preparing a revised version of this manuscript. Notable changes we are making include:

• adding a diagram to the introduction to show the overall workflow of the study,
• quantitatively analyzing the fraction of OCT4+ and DDX4+ cells in our immunofluorescence images over time,
• collecting and analyzing additional bulk RNA-seq data on KGN cells and adult human ovarian tissue,
• performing estradiol assays on additional lines of hiPSC-derived granulosa-like cells,
• presenting images from day 70 ovaroids which clearly show follicle formation,
• changing the colors in the figures to be more accessible to colorblind readers,
• clarifying which TFs are present in which of our clonal lines.

These changes will address the weaknesses identified by the reviewers. Along with our revised manuscript, we will also prepare a more comprehensive author response for these reviewer comments.

Author Response:

Reviewer #1 (Public Review):

This paper reports an analysis of the inhibition of the serotonin transporter, SERT, by a novel compound, ECSI#6. The authors perform a comprehensive analysis of SERT transport inhibition for the new agent and compare its properties to those of other well-characterized agents: cocaine and noribogaine, with the data pointing to an unusual noncompetitive mechanism of inhibition, a model also supported by electrophysiological recordings of transport currents. Based on the results of these experiments the authors conclude that ESCI#6 binds essentially exclusively to the inward-facing state of the transporter. The authors further present experiments suggesting that ESCI#6 can stabilize the folded form of an ER-arrested SERT mutant and recover its trafficking to the plasma membrane, with some in-vivo drosophila experiments perhaps also supporting this conclusion. Finally, kinetic simulations using a transport model with rate constants from previous experiments support the basic conclusions of the first sections of the paper.

Strengths:<br /> The transport experiments and simulations here are thorough, carefully performed, and reasonably interpreted. The authors' arguments for noncompetitive inhibition seem well-thought-out and reasonable, as is the conclusion that ESCI#6 binds to the inward-facing state of the transporter. The simulations are also thorough and support the conclusions. In the discussion, the comparison of enzyme noncompetitive inhibition to the process studied here was thoughtful and interesting. Also, the care and analysis of the uptake data are a strength of the paper, with well-presented evidence of reproducibility and statistics. The electrophysiology data is more limited but does communicate the essential conclusion.

Weaknesses:<br /> The most important concern about the work is the interpretation of the in-vivo drosophila data. Though the SERT fluorescence with WT protein is strong, I cannot see any fluorescence in either drug-treated image from the PG mutant. In this context, shouldn't there be additional intracellular staining for ER-resident SERT? If the cell bodies of these cells are elsewhere this should be clearly pointed out.

We have modified Fig. 6 to include, in all instances, images of the posterior brain, where the neurons (FB6K) reside, from which the serotonergic projections originate. These images visualize expression of membrane-anchored GFP (mCD8GFP; in panel B), immunoreactivity of serotonin (panel B’), wild type SERT (panels C’,D’,E’) and mutant SERT-PG601,602AA (panels F’,G’,H’) in the soma. The description of these panels has been added to the pertinent sentences starting on p. 20, line 6 from bottom to the end of end of the first paragraph p. 21, which read:

“These projections (Fig. 6A-A’’) and the FB6K-type neurons, from which they originate in the posterior brain (Fig. 6B-B’’) can be visualized by expressing membrane-anchored GFP (i.e. GFP fused to the C-terminus of murine CD8; [36]) under the control of TRH-T2A-Gal4. Similarly, when placed under the control of TRH-T2A-Gal4, YFP-tagged wild-type human SERT was expressed in the FB6K-type neurons (Fig. 6C’) and delivered to the fan-shaped body (Fig. 6C). In contrast, in flies harboring human SERT-PG601,602AA, the transporter was visualized in the soma of FB6K-type neurons (Fig. 6F’), but the fan-shaped body was devoid of any specific fluorescence (Fig. 6F). However, if three-day old male flies expressing human SERT- PG601,602AA were fed with food pellets containing 100 μM ECSI#6 or 100 μM noribogaine for 48 h, fluorescence accumulated to a level, which allowed for delineating the fan-shaped body (Fig. 6G and H, respectively). This show that ECSI#6 and noribogaine exerted a pharmacochaperoning action in vivo, which partially restored the delivery of the mutant transporter to the presynaptic territory. As expected, in flies harboring wild-type human SERT, feeding of ECSI#6 and noribogaine did not have any appreciable effect on the level of fluorescence in the fan-shaped body (Fig. 6D and E, respectively). “

Similarly, the single Western blot demonstrating enhanced glycosylation in the presence of Noribogaine or ECSI#6 could be strengthened. I can see the increased band at a high molecular weight that the authors attribute to the fully glycosylated form, but this smear, and the band below, look quite different from those in the blot shown in the El-Kasaby et al reference, raising concerns that the band could be aggregated or dimerized protein rather than a glycosylated form. This concern could easily be addressed by control experiments with appropriate glycosidases, as shown in the reference.

We understand that the appearance of the mature glycosylated species is being criticized, at least in part, because it differs from sharper bands, which can be found in our previously published papers. We stress that the resolution very much depends on the electrophoretic conditions. We addressed the reviewers’ criticism by carrying out the recommended deglycosylation experiments: a representative experiment is shown in (the new) panel F of Fig. 5, with lysates prepared from HEK293 cells expressing wild type SERT, from untransfected HEK293 cells and from HEK293 cells, which had been preincubated with 30 μM cocaine, 100 μM ECSI#6 and 30 μM noribogaine. The experiment confirms the band assignment with the upper band(s) M representing the mature glycostylated species (which are resistant to deglycosylation by endoglycosidase H) and the lower band C corresponding to the core- gylcoylated species (which are susceptible to cleavage that (as expected) the mature band show a representative degylcosylation by endoglycosidase H). We also think that the immunoblot in panel F ought to satisfy the aesthetic criticism: the bands are sharper/less smeared.

The description of panel F can be found on p. 18, starting in line 7 from bottom to end of page, and reads: “We confirmed the band assignment by enzymatic deglycosylation (Fig. 5F): the upper bands (labeled M), which appeared in cells incubated in the presence of ECSI#6 and of norbogaine, were resistant to deglycosylation by endoglycosidase H (which cannot cleave mature glycans). In contrast, the core-glycosylated species (labeled C), was susceptible to cleavage by endoglycosidase H resulting in the appearance of the deglycosylated band D.”

The overall interest in the work is reduced given the quite low affinity of ECSI#6 for the transporter.

We agree that it would be preferable to have a compound, which works in the submicromolar/nanomolar range. However, it is worth pointing out that the EC50 is low enough for allowing in vivo rescue of the folding-deficient SERT-PG: feeding flies restores its trafficking to the cell surface and to the presynaptic specialization. Obviously, there is room for improvement, but ECSI#6 provides a starting point.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided.

We assessed this point by plotting the data in Fig. 2A,B,C as Dixon plots in (the new) panels D,E,F of Fig. 2. We refer the reader to Segel’s textbook on enzyme kinetics (new ref. 18) on using Dixon plots in the presence of two inhibitors. The pertinent description is on p. 9, lines 12-22 and reads as follows: “We transformed the data summarized in Figs. 2A-C by plotting the reciprocal of bound radioligand as a function of inhibitor concentration to yield Dixon plots (Fig. 2D-F): the x-intercept corresponds to -IC50 of the inhibitor [18]. Thus, Dixon plots allow for differentiating mutually exclusive from mutually non-exclusive binding, if one inhibitor (i.e., cocaine, noribogaine or ECSI#6) is examined at a fixed concentration of the second inhibitor (i.e., serotonin) [18]: if binding of the two inhibitors is mutually non-exclusive, a family of lines of progressively increasing slope, which intersect at -IC50, is to be seen. In contrast, if the two inhibitors bind to the same site, the slope of the inhibition curves is not affected and the x- intercept (i.e, -IC50 of the variable inhibitor) is shifted to more negative values. It is evident from Fig. 2D-E that the presence of 1 and 10 μM serotonin progressively shifted the (expected) x-intercept for cocaine (Fig. 2D), noribogaine (Fig. 2E) and ECSI#6 (Fig. 2D). Thus, binding to SERT of serotonin and of these three ligands was mutually exclusive.” Based on the Dixon plots, we feel that our conclusion is justified, i.e., binding of serotonin and ECSI#6 (and of the other ligands) is mutually exclusive.

2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point.

We did the suggested experiments: the data are summarized in panel I of Fig. 4 and described in the first paragraph on p. 15, which also explains why this experiments is possibly inconclusive due to the high diffusivity of ECSI#6:

“Fig. 4I shows representative traces of 5-HT induced currents recorded from SERT expressing cells in the absence (in blue) and presence of 100 μM ECSI#6 (in red) in the electrode solution: when thus applied from the intracellular side, ECSI#6 did not cause an appreciable current block. The right-hand panel summarizes the current amplitude obtained from cells measured in the absence (blue open circles) and presence of intracellular ECSI#6 (open circles in red). These data seem to indicate that ECSI#6 binds to SERT from the extracellular side. Yet this conclusion can be challenged based on the following consideration: in earlier experiments, ibogaine, the parent compound of noribogaine, was found to block HERG channels when applied from the bath solution but failed to do so when added to the electrode solution [27]. However, at a lower intracellular pH (i.e., pH 5.5), ibogaine gained the ability to inhibit HERG from the intracellular side (i.e., via application through the electrode). Conversely, ibogaine was less effective when applied to an acidified bath solution. These observations led to the conclusion that ibogaine blocked HERG from the cytosolic side: because the molecule in its neutral form was so diffusive, a low intracellular pH was required to force its protonation and thus preclude diffusion from the interior of the cell. ECSI#6 is presumed to also be very diffusible given its estimated logP value and polar surface area of 2.48 and 66 Å2, respectively. However, ECSI#6 harbors an amide nitrogen (see Fig. 1A) and thus remains neutral in the experimentally accessible pH range. Hence, it is not possible to verify to which side of SERT it binds.”

Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6.

We did not quite understand this comment, because displacement experiments were done with cocaine (Fig. 2A, new Fig 2G/previous Fig. 2D). However, if the reviewer questions why we do not use cocaine rather than 5-HT, in the three-way competition experiment, it is precisely, because we wanted to compare the action/binding mode of ECSI#6 to that of cocaine.

3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.

These experiments were done and are shown in (the new) panels G and H of Fig. 4; the pertinent description is in the second paragraph of p. 14 and reads:

“The protocol depicted in Fig. 4B can also be used to gauge the apparent affinity of ECSI#6 for SERT in the presence of 5-HT. Plotted in Fig. 4G is the block of the serotonin-induced current as a function of the co-applied ECSI#6 concentration. The current was evoked by a saturating concentration of 5-HT (30μM) and inhibited by 3, 10, 30 and 100 μM co-applied ECSI#6, respectively (the inset in Fig. 4G shows representative current traces). A fit of an inhibition curve to the data points yielded an IC50 value of approx. 5 μM. This value was lower but still in reasonable agreement, with the IC50 obtained in the radioligand uptake assay for the condition where the 5-HT concentration had been saturating (cf. dashed line in Fig.1C; 10 μM 5-HT). In the uptake assay the IC50 value of ECSI#6 dropped to about 0.5 mM, in the presence of a low 5-HT concentration (i.e., 0.1 μM). In contrast to uptake experiments, electrophysiological recordings also allow for assessing the apparent affinity of ECSI#6 for SERT in the absence of the substrate. This can be achieved by employing the protocol depicted in Fig. 4H (see representative current traces on the left-hand side): we first applied 30 μM 5- HT to a cell expressing SERT for 0.5 s to elicit a peak current (i.e., a control pulse). We then reapplied 30 μM 5-HT after a superfusing the cell with 100 μM ECSI#6 for 1 s (second upper trace in panel H). We chose this time period because it had been sufficient to allow for full current block in the other protocol (see Fig. 4G): the amplitude of the peak current following pre-application of 100 μM ECSI#6 was essentially identical to the prior control pulse. When we pre-applied 100 μM ECSI#6 for a longer period (i.e., 3 s) the amplitude of the two peak currents also remained the same (cf. lower traces in panel H). The right-hand panel shows the summary of several experiments. Plotted in the graph is the ratio of the second and first pulse, which was always close to one. We previously used this protocol to assess the binding kinetics of cocaine, methylphenidate and desipramine on SERT and DAT. Pre-application of these inhibitors consistently led to a concentration dependent reduction in the peak current amplitude of the second pulse in comparison to the first [23]. The lack of inhibition, thus, indicates that the affinity of ECSI#6 in the absence of 5-HT is low. To obtain estimates for the affinity of ECSI# for SERT in the absence of 5-HT we would need to apply this compound at much higher concentrations. This, however, is not possible, because ECSI#6 is poorly soluble in aqueous solutions (i.e., max. 0.03 mg/ml).”

Author Response

Reviewer #1 (Public Review):

The authors succeeded in fitting their Jansen-Rit model parameters to accurately reproduce individual TEPs. This is a major success already and the first study of this kind to the best of my knowledge. Then the authors make use of this fitted model to introduce virtual lesions in specific time windows after stimulation to analyze which of the response waveforms are local and which come from recurrent circles inside the network. The methodological steps are nicely explained. The authors use a novel parameter fitting method that proves very successful. They use completely openly available data sets and publish their code in a manner that makes reproduction easy. I really enjoyed reading this paper and suspect its methodology to set a new landmark in the field of brain stimulation simulation. The conclusions of the authors are well supported by their results, however, some analysis steps should be clarified, which are specified in the essential revisions.

We are delighted and flattered by the Reviewer’s positive evaluation of our work, and appreciation of our efforts to maximize its reproducibility. We wish also to thank the Reviewer for their compelling and interesting points, which we have addressed in full, and we believe further enhance the quality of the paper. Thanks again!

Reviewer #2 (Public Review):

Here the authors tackle the problem of identifying which parts of a TMS-evoked response are local to the stimulation site versus driven by reverberant activity from other regions. To do this they use a dataset of EEG recorded simultaneously with TMS pulses, and examine virtual lesions of a network of neural masses fitted to the data. The fitting uses a very recent model inversion method developed by the authors, able to fit time series directly rather than just summary statistics thereof. And it apparently works rather well indeed, at least after the first ~50 ms post-stimulus. I expect many readers will be keen to try this fitting method in their own work.

We are delighted by the Reviewer’s appreciation and consideration of our paper. We have addressed the comments and revisions requested following the flow suggested by the Reviewer’s comments. We would take this opportunity to kindly thank the Reviewer for his/her contribution and for helping us to improve the manuscript.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artifacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

We are most grateful to the Reviewer for their positive evaluation of our manuscript. We also sincerely appreciate all the comments and suggestions raised, and for contributing their evident expertise with TMS-EEG data towards the constructive improvement of this research. We hope the Reviewer will appreciate our efforts made to address their excellent points, and are pleased with the resultant strengthening of the paper.

Author Response

Reviewer #2 (Public Review):

Wen et al. developed a useful tool for causal network inference based on scRNA-seq data. The authors comprehensively benchmarked 9 feature selection and 9 causal discovery algorithms using both synthetic data and real scRNA-seq data. Their conclusions regarding the performance of these algorithms on synthetic data are solid and valuable. I believe this tool or platform has the potential to help biologists discover novel cell type-specific signaling pathways or gene regulatory events since there is no prior knowledge (such as known pathway annotations) as inputs. However, several major concerns below need to be addressed to improve the paper.

1) Current validation of the inferred causal networks using real scRNA-seq datasets seems quite simple and is not sufficient to support the accuracy and reliability of results. Annotations from the STRING database do not contain directions of edges among genes or proteins. However, the edge direction in the inferred network is a crucial aspect to explain the causal relationships. Besides using "spike-in" data, a systematic validation of the inferred network, especially the edge directions, should be provided.

We have used the data of the five lung cancer cell lines and alveolar cells and the genes in several pathways (in which causal interactions are better annotated) in the KEGG and WikiPathway databases to validate network inference systematically. Please see the responses to the Essential Revisions (for the authors).

2) In order to illustrate the novel discovery, CausalCell should be further compared to existing gene network construction methods based on scRNA-seq data such as SCENIC (Aibar et al. Nature Methods, 2017).

(a) We have added a "TF=No/Yes" option to feature selection. If this option is ignored, feature selection is as before. If "TF=Yes" is selected, all feature genes are TFs. If "TF=NO" is selected, all feature genes are non-TFs. With this option, normally, two rounds of feature selection are performed. The first round ("TF=Yes" is selected) selects TFs as feature genes of a response variable (RV), and the second round ("TF=No/Yes" is ignored) selects feature genes as before (feature genes contain both TFs and non-TFs). The user selects genes from the results of two rounds to build input to causal discovery.

(b) The networks inferred by SCENIC are TF-centered: each TF and its potential target genes form a regulon, it searches for genes co-expressed with a TF (through GENIE3/GRNBoost), and the union of all or some of the regulons forms a network. Thus, SCENIC helps uncover the "one TF->all targets" relationships. Different from SCENIC, this "TF=No/Yes" option provides a target-centered transcription regulation analysis and helps uncover the "all TF->one target" relationships (the target is the response variable). Thus, the two approaches are complementary. Feature selection based on the "TF=No/Yes" option also differs from SCENIC in that no predefined regulons (defined upon "cisTarget" databases) are needed.

(c) We used SCENIC in our initial analysis of the young and old mouse CD4 T cells (see Figure 5 in Elyahu et al. 2019). In the re-analysis of tumor-infiltrating exhausted CD8 T cells, we find that the "TF=No/Yes" option helps uncover transcription regulation. For example, the transcription factor TOX is reported to regulate PDCD1 critically in mice. When we perform feature selection to identify feature genes of PDCD1, TOX is in the top 50 feature genes in the colorectal cancer dataset but not in the lung and liver cancer datasets (Supplementary file1:Table 1). To re-examine whether TOX critically regulates PDCD1 in the two latter datasets, we perform feature selection with "TF=Yes", and the results are that TOX is a top TF of PDCD1.

3) The authors should also claim what type of the inferred causal network represent from the biological perspective (e.g. signaling networks or gene regulatory networks?).

(a) Although methods have been developed specifically for inferring signaling and regulatory networks, whether a network is a signaling network or a gene regulatory network depends on the input data. Also, many proteins and noncoding RNAs function as complexes instead of individually in both kinds of networks, and RNA-seq and scRNA-seq data contain only transcripts. Thus, researchers must infer signaling and gene regulation in cells upon inferred networks.

(b) The input to causal discovery can be (a) a target gene and its potential TFs, (b) a TF and its potential targets, (c) genes encoding both TFs and non-TFs. Thus, whether an inferred network is signaling or gene regulatory network depends on the input. We have made this clear in the Discussion.

4) Besides edge direction, an important feature of CausalCell is the determination of edge sign (i.e. activation or inhibition). The authors should describe its related procedures.

In the revised section "2.5 Causal discovery", we wrote, ""In all inferred causal networks, edges have a sign that indicates activation or repression and have a thickness that indicates CI test's statistical significance. The sign of the edge from A to B is determined by computing a Pearson correlation coefficient between A and B, which is ‘repression’ if the coefficient is negative or ‘activation’ if the coefficient is positive. In most cases, ‘A activating B’ and ‘A repressing B’ correspond to up-regulated A in the case dataset compared with down-regulated B in the control dataset."

5) The authors did not provide an example of constructing a causal network between cells or cell types, although they mentioned its importance in the Abstract. Such intercellular network examples can distinguish the utility of CausalCell in single-cell data analysis from bulk data analysis.

Constructing causal networks between cells is a quite different work. We delete this sentence in the manuscript because we are still working on it.

6) If the control dataset is available, it is currently not clear whether batch effects of the query and control datasets will be removed in the data preprocessing step. Differentially expressed genes cannot be selected correctly if batch effects exist.

Please see our responses to the second point of Essential Revisions.

Author Response:

Reviewer #1 (Public Review):

This paper investigates waves in embryonic mouse retinas. These stage 1 waves have been studied less than the post-natal (stage 2) waves. The paper uses calcium imaging in whole retinas to determine the properties of the waves and their dependence on cholinergic and electrical synapses. A strength of the work is the ability to monitor waves over the entire retina at high resolution and weaknesses include reliance on pharmacology and some missing details in analysis.

Reliance on pharmacology

The results in the paper depend largely on pharmacological manipulations. Not enough consideration is given to the possible unintended effects of those manipulations. This is particularly true for the gap junction inhibitors. The Discussion brings up the possibility of such effects, but they need to come up much earlier. Is there anything else that can be done to mitigate concerns about the drugs - e.g. does MFA affect waves in Cx36 KO mice?

We have added additional experiments based on whole cells recordings to address some off target effects of MFA but we do make note of the limitations of these new controls since we observed significant variability of voltage-gated conductances across RGCs at this age as well as the limited ability to maintain stable recordings for the requisite time to have within cell controls for MFA. (see Figure 2 Supplemental Figure 1).

Over the years we have done several experiments assessing different Cx knockouts and retinal waves (e.g. F. Caval-Holme, et al, “The Retinal Basis of Light Avoidance in Neonatal Mice”, Journal of Neuroscience 42:2022; Blankenship A.G., et al “The role of neuronal connexins 36 and 45 in shaping spontaneous firing patterns in the developing retina, Journal of Neuroscience, 3, 2011). It appears that there are multiple connexins in RGCs and which regulate stage 1 retina waves beyond Cx 36 and Cx45 and therefore it is difficult to use these mice as controls for general gap junction antagonists.

In the revision, we are more explicit about the caveats of using MFA both in the results (page 5) and discussion (page 10). Notably, we draw attention to past studies where we have done several controls regarding MFA and RGC activity in older retinas in addition to our more limited controls we were able to carry out in E16-E18 retina.

Comparison of ACh receptor block and knockout mice

The ACh receptor knockout mouse provides a useful alternative to the pharmacological block of ACh receptors. But different features are described in Figures 2 and 3, preventing direct comparison of the two.

Our intention was not to use the knockout mice as an alternative to the pharmacological block since we knew that there are compensatory wave mechanisms in the knockout. Rather we are using the β2-nAChR-KO to establish the effectiveness of this KO as a means of testing the role of Stage 1 waves in developmental processes. We do hope the revised manuscript explains this motivation more clearly.

A related point is the apparent increased role of gap junctions in mediating waves in the absence of ACh receptors. On this point, the description of the effect of MFA (page 8, second paragraph, 3rd sentence) was confusing. It looks to me like MFA almost completely eliminates waves in both WT and KO - so the connection to an altered role of gap junctions was not clear.

We clarified our description of the MFA result (page 5):

Application of the gap junction blocker meclofenamic acid (MFA, 50μM) nearly abolished Stage 1 waves, causing a significant reduction in frequency of waves and cell participation during waves (Fig 2A & 2F).

ipRGC densities

The goal of the measurements of ipRGC densities was not entirely clear. Why are ipRGCs a reasonable way to determine the importance of waves for development? For example, the introduction raises the issue that changes in RGC proliferation depend on RGC type. Is there reason to think ipRGCs are "special" or, alternatively, that they are following the same developmental trajectory as other RGCs? Is it possible to track another RGC type (e.g. using SMI32 staining)? Related to this general point, page 9 (top) sets up the need to identify the mechanism of RGC cell death but then jumps to waves without a clear connection between the two. It would also be good to mention early that the measurements include multiple ipRGC types, so that issue does not come up only as an explanation for why the ipRGCs are not organized spatially (page 10 top).

We have revised text extensively to better motivate our selection of ipRGCs (page 6). Our goal was to use an identified differentiated RGC subtype for which we had genetic access to assess the impact of reduced retinal waves on cell number. We settled on ipRGCs because: 1) ipRGCs undergo a significant amount of cell death during the same period there are retinal waves (Chen et al, Elife 2013) and 2) we show ipRGCs participate in retinal waves.

Analysis

Quantitative analysis of the calcium measurements relies on the discretization of the signals measured in small ROIs. It was not clear how closely the discretized signals represented the original recordings. The traces illustrated in Figures 1 and 2, for example, appear to be measured in larger ROIs. Two things would be helpful here: (1) an illustration of several original recorded traces in the small ROIs plotted with the discretized versions of those traces; (2) a determination of how sensitive the results are to specifics of the discretization process.

We have modified Figure 1 to include example traces of the fractional change in fluorescence computed across the small ROIs used for the analysis of waves on the macroscope. They are at the top of Figure 1B. As can be seen by these traces, the signal-to-noise is fantastic.

Reviewer #2 (Public Review):

The overall goal of this study is to determine the mechanism of early retinal wave initiation and propagation. Despite a number of earlier studies, the precise mechanism of Stage1 waves and how they differ from Stage 2 waves remained controversial. To address this, the authors describe the timing and character of Stage 1 retinal waves using a custom build imaging system allowing for wide-field monitoring of neuronal activity while preserving high spatial resolution. In a set of elegantly designed experiments, they reveal that the initiation and propagation of Stage 1 waves are driven by distinct mechanisms involving complex circuitry of nAChRs and gap junctions. Interestingly, the data also demonstrate that Stage 1 and Stage 2 waves rely on different subtypes of AChRs. The signaling via beta2AChRs appears to be the driver of Stage 2 waves. However, the precise identity of nAChRs and GJs contributing to Stage 1 waves remains a mystery. Next, to determine the impact of early retinal waves on retinal circuit formation, the authors evaluate their impact on the survival of ipRGC. They show that ipRGC cell survival and their distribution mosaics are not influenced by spontaneous activity. While the observation of ipRGC data and their mosaic are interesting, the rationale for these experiments in the context of this study is not well presented.

We thank the reviewer for positive comments. We do hope the revised rationale for ipRGC measurements addresses these comments. It is included here for convenience (page 7)

RGCs undergo a period of dramatic cell death during the first two postnatal weeks of development, the majority occurring during the first postnatal week (Abed et al., 2022; Braunger et al., 2014). Whether this cell death process is regulated by retinal waves is unknown. We looked specifically at intrinsically photosensitive ganglion cells (ipRGCs) for several reasons. First, ipRGCs have completed proliferation (Lucas and Schmidt, 2019; McNeill et al., 2011) and appear to be fully differentiated by E16 (Shekhar et al., 2022; Whitney et al., 2022), the onset of Stage 1 waves. ipRGCs undergo a period of dramatic cell death during the first two postnatal weeks of development, the majority occurring during the first postnatal week, prevention of which profoundly disrupts several important developmental processes in the retina – including spacing of ipRGC somas as well as rod and cone mediated circadian entrainment through the activation of ipRGCs (Chen et al., 2013). However, the exact mechanism regulating ipRGC cell death is unknown. Here we assessed the impact of disrupting Stage 1 and Stage 2 waves on the number and distribution of ipRGCs.

Reviewer #3 (Public Review):

The manuscript by Voufo et al. aims to advance our understanding of the mechanisms responsible for the earliest pattern of spontaneous activity in the mouse retina, stage I retinal waves. These waves occur during embryonic development (E16-18) and are the least known form of activity in the immature retina.

The authors show that stage I waves have broad spatiotemporal features and are mediated by circuitry involving subtypes of nicotinic acetylcholine receptors (nAChRs) and gap junctions. The authors also found that the developmental decrease of intrinsic photoreceptor retinal ganglion cells (ipRGCs) density is preserved between control and ß2-nAChR-KO mice, indicating that processes regulating ipRGC distribution are not influenced by early spontaneous activity.

The quality of the data is excellent, and the conclusions of this paper are mostly well supported by data, but the presentation of the data and the analysis need to be clarified and extended.

Strengths:

The earliest patterns of spontaneous activity are crucial for the correct development of sensory circuits. In the visual system, most studies focus on postnatal activity (stage 2 and 3 retinal waves) overlooking embryonic stages, likely due to challenges related to methods and animal handling. Therefore, in this manuscript, the authors from a laboratory pioneer in studying retinal waves in the mouse, tackle a very relevant subject that has not been explored in detail. The bibliography that encompasses most of the current knowledge about stage 1 retinal waves in mammals is compressed into three fairly dated publications: Galli and Maffei 1988, Bansal et al 2000, and Syed et al 2004. These publications were pioneering attempts to describe early spontaneous activity; however, much work remained to be done regarding the molecular and cellular mechanisms involved. Here, Voufo and colleagues provide additional fundamental details about the properties and components of stage 1 waves. The dataset has excellent quality and plenty of information could be extracted from it. The authors used a macroscope that allows the acquisition of images from the entire retina while preserving a good spatial resolution.

Weakness:

The authors distinguish different subtypes of activity during embryonic stages in the retina of mice. However, they do not provide a detailed characterization that allows a clear definition of these subtypes (and specifically stage 1 waves). Moreover, throughout the manuscript, there are many technical details of the analysis that are missing and preclude a complete understanding of the robustness of the data. The authors have an excellent dataset that needs more analysis and an improvement in the presentation of the results.

We do hope the extensive revisions satisfy reviewer.

Author Response

Reviewer #1 (Public Review):

Ciliary length control is a basic question in cell biology and is fascinating. Regulation of IFT via calcium is a simple model that can explain length control. In this model, ciliary elongation associates with an increase in intraciliary calcium level that leads to calcium increase at the ciliary base. Calcium increase acts to reduce IFT injection and thus ciliary assembly rate. The longer the cilia, the more increase of calcium level and the more reduction of IFT injection and thus the ciliary assembly rate. When the cilia approach the genetic defined length, the gradual reducing assembly rate eventually balances the constitutive disassembly activity. Cilia then stop elongation and a final length is achieved. This work tested this model by manipulating the calcium level in cilia by using an ion channel mutant and treatment of the cells with EGTA. In addition, IFT injection was measured before and after calcium ciliary influx. Based on the outcome of these and other experiments, it was concluded that there is no correlation between changes in calcium level and IFT injection, thus challenging the previous model. This work is well written and the experiments appear to be properly executed. It nicely showed an increase of intraciliary calcium during cilia elongation, and beautifully showed that ciliary calcium influx depends on extracellular calcium. However, I felt the current data are inadequate to support the author's conclusion.

We thank the reviewer for the positive assessment of the interest in our work, and we have performed additional experiments to address the reviewers concerns as discussed below.

The authors showed that ciliary calcium increases along with ciliary elongation, which correlates with reduction of IFT injection. Thus, this result would support that calcium increase reduces IFT injection. To test whether reducing calcium influx would alter the IFT injection, the authors used an ion channel mutant cav2. Indeed, ciliary calcium level in the mutant cilia appears to be lower compared to the control in average. After measuring ciliary calcium level and IFT injection during ciliary elongation with mathematical analysis, it was concluded that reducing ciliary calcium level did not lead to increased IFT injection, which is distinct from the control cells. Thus, the authors concluded that calcium does not act as a negative regulator of IFT injection. However, if one examines the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 micron, one may draw a different conclusion. For the mutant cilia, the calcium influx is higher than that in control cilia and IFT injection is reduced compared to the control. Thus, this analysis is the opposite of the authors' conclusion, and is supporting the previous model. There is a rapid change in ciliary assembly rate at the early stages of ciliary assembly (see Figure 1C), thus, the changes in calcium influx and IFT injection in the earlier assembly stage would be more appropriate to assess the relationship between intraciliary calcium level and IFT injection.

We thank the reviewer for raising this issue, which led us to examine the data more carefully. In looking at the numbers of cells with flagella in each length range, we became concerned that the apparently low calcium influx in shorter flagella in control cells compared to ppr2 or EGTA treatment might actually due to bias from technical issues: it is relatively difficult to image shorter flagella in our TIRF imaging setup, because shorter flagella have less flagellar surface area to attach the coverslip. The more motile the flagella are, the more likely are the cells to detach when their flagella are short, because the bending force of the flagella is strong enough to pull them away from their small area of adhesion. This effect is much stronger in control cells than in either the ppr2 mutants or EGTA treated cells, whose flagella are less motile. This led to a reduced number of cells examined with flagella shorter than 6 um (17 versus 34 for control and ppr2 cells, respectively). To overcome the difficulties and biased result, we observed more flagella in control cells. The new data has now been integrated with our previous data and shown in Figure 3. The new result shows that calcium influx in control cells is in fact higher than in the ppr2 mutant cells. So, our result is remains consistent with our conclusion, and we believe that it is not useful to analyze the shorter flagella separately.

The authors used EGTA treatment to support their conclusion. However, EGTA treatment may induce a global calcium change of the cell, the outcome may not reflect actual regulation of IFT injection by ciliary calcium influx. For example, as reported elsewhere, the change of cAMP level in the cell body and cilia has a different impact on ciliary length and hedgehog regulation. The slower assembly of cilia in EGTA treated cells may be caused by many other factors instead of sole regulation by IFT.

It is certainly possible that EGTA is affecting some process inside the cell that then indirectly affects IFT. Our experiments cannot rule this out. The fact that similar effects are seen with the ppr2 mutant argues against this idea, but again cannot rule it out. We have added the following caveat to the discussion:

"Other calcium dependent processes in the cytoplasm might also potentially address IFT, and our results cannot rule out this possibility. However, we note that the ppr2 mutant also fails to show the effect on IFT or regeneration predicted by the ion current model."

The authors only examined the impact of reducing ciliary calcium influx. To further support the authors' conclusion, it is recommended that the authors should examine IFT injection in a condition where ciliary calcium level is increased. Using calcium ionophore may not be a good choice as it may change the global calcium level. One approach to consider is using mutants of a calcium pump present in cilia.

We thank the reviewers for this suggestion. The calcium current model would predict that if a calcium pump mutant failed to export calcium, the increased calcium building up inside the flagellum should lead to decreased IFT entry and a shorter flagellar length. We found at least two calcium pumps in the published Chlamydomonas flagella proteome (Pazour et al., 2005) and ordered several mutant strains from Chlamydomonas Library Project (CLiP) which are annotated as affecting these pumps. We measured the flagellar length of these potential calcium pump mutant strains, but none showed a statistically significant difference in length relative to control cells. We have now included this data as Figure S4. Because no length change was observed, we did not perform the extremely time consuming process of constructing strains that contain these mutations along with DRC4-GCaMP and KAP-GFP.

As an alternative strategy to get at this reviewer's suggestion, we measured DRC4-GCaMP and KAP-GFP intensity in 1 mM CaCl2 treated flagella and found that CaCl2 treatment increases both the flagellar calcium level (Figure 3, see below) and IFT injection (Figure 4). This increase in IFT injection is the opposite of what the calcium current model predicts.

Based on these results, we think the calcium pump experiment is not necessary because of the following reasons. 1. These calcium pump mutants might not increase the flagellar calcium level. 2. Even if the flagellar calcium was increased in these mutants, it does not affect the flagellar length and thus our conclusions would still hold. 3. These mutant strains might still have functional calcium pumps since the existing data on calcium pumps in flagella is likely to be incomplete. 4. The CaCl2 experiment clearly increased the flagellar calcium level inside flagella, directly addressing the point that the reviewer is getting at.

The conclusion on line 272-273 may need more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this as one of the evidences to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia or revise their conclusion.

We have now performed these measurements and have included the data in Figure 3D.

The authors showed nicely the changes in IFT properties before, during and after ciliary calcium influx and found that the intensity and frequency of IFT do not have a correlation with calcium influx though calcium influx restarts paused IFT trains for retrograde transport as previously reported (Collingride 2013). The authors again concluded that this is supporting their conclusions in that there is no correlation between IFT injection and calcium influx. However, I am not sure whether the short pulses of calcium influx at one time point would change the calcium level in the whole cilia in a significant way that would alter IFT injection at the ciliary base.

We agree that individual pulses might not have an effect on the average level of IFT injection. We were specifically trying to see if, having previously ruled out the predicted correlation at the level of average rates, there might still be a trace of the correlation for individual events.

Reviewer #2 (Public Review):

The authors use a genetically encoded calcium indicator to measure Ca in flagella to establish that Ca influx correlates with flagellar length. (Despite this correlation, there is so much noise that it is dubious that Ca level can regulate the flagella's length.) Then, they show that reduced Ca decreases the rate of IFT trains entering flagella, which ruins the ion-current model of regulating flagella's length. (Ca can still be one of the factors that sets the target length.) Ca does not seem to change the disassembly rate either. There are also no correlations between Ca influx spikes and IFT injection events. Curiously, these spikes broke pauses of retrograde IFT trains, but that still did not affect IFTs entering dynamics.

Some other possibilities like Ca regulating unloading rates are discussed and convincingly rejected.

The study ends with an interesting Discussion, which talks about other possible models, and concludes that the only model not easily rejected so far is the mechanism relying on diffusion time for kinesins from flagella to the cell body being greater in longer flagella.

The paper is well written, very thorough, contains significant results.

We thank the reviewer for this strong positive assessment.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

We thank the reviewer for this positive assessment, including of the relevance of the model. We have attempted to address all suggestions.

Author Response

Evaluation summary

This important study advances our understanding of respiratory complex I. The authors present convincing structural data for the enzyme from Drosophila melanogaster although the interpretation of conformational states is still not conclusively settled. This work will be of interest to researchers studying respiratory enzymes, the evolution of respiration, and mitochondrial diseases.

Thank you for this positive evaluation of our work. Although we have presented a robust and coherent interpretation of the conformational states we observe, we accept that different opinions on this topic still exist in the field.

Reviewer #1 (Public Review):

Agip et al. have resolved the first cryoEM structure of the mitochondrial Complex I from Drosophila melanogaster, an important model organism in biology. The structure revealed a 43-subunit enzyme complex that closely resembles the mammalian Complex I. The authors resolved Complex I in three different conformational states at 3.3-4.0 Å global resolution, with an overall resemblance to the active form of the mammalian Complex I, but also with some characteristic conformational changes near the quinone substrate pocket and surrounding subunits that resemble, at least in part, the deactive form of the mammalian enzyme. The third resolved class was considered 'damaged/broken', and a possible artifact arising from the sample preparation. Biochemical assays showed that the Drosophila Complex I does not undergo an active/deactive transition (as characterized by the N-ethylmaleimide sensitivity), although the structures revealed an exposed ND3 loop that has been linked to transition. The authors could also show that conformational change between an alpha and pi form of transmembrane helix (TM3-ND6) is likely to be involved in catalysis, and distinct from the deactivation mechanism of the mammalian isoform. Due to the 3.3 Å global resolution, water molecules could not be experimentally resolved, and how the observed conformational changes link to the proton pumping activity therefore remains an open question and basis for future studies. Overall I find that this work provides an important basis for understanding mechanistic principles of the mitochondrial Complex I and more specifically a starting point for detailed genetic studies on the fruit fly Complex I.

We thank the reviewer for their positive evaluation of our manuscript.

We would like to note that in all three conformational states of Drosophila complex I observed in our study, we do not observe an exposed ND3 loop (Cys39 in particular), as outlined in Figures 3 & 6 and Figure 6 – Figure Supplement 1 (as well as in Figures 5 and 7). This observation is fully consistent with the lack of N-ethylmaleimide (NEM) sensitivity observed in our Drosophila preparation.

We discuss the relevance of the π-bulge/α-helical nature of ND6-TMH3 to catalysis in the Discussion section in the context of an intercalated phospholipid molecule in the Dm1 structure: “Indeed, if ND6-TMH3 converts between its -bulge and -helical structures during catalysis (Agip et al., 2018; Kampjut and Sazanov, 2020; Kravchuk et al., 2022; Parey et al., 2021; Röpke et al., 2021), then the intercalating phospholipid is very unlikely to be present in the -helical state, moving repeatedly in and out.” While our structures are consistent with this helical change being involved in catalysis, they are resting-state structures and therefore do not provide further evidence in this regard.

Finally, the reviewer is correct in that the resolutions of the structures resolved here are insufficient to model water molecules, and that how the conformational changes observed here contribute to our currently limited knowledge of the coupling mechanism remains to be answered.

Reviewer #2 (Public Review):

• Aim of the study:

Agip et al. studied the structure of respiratory complex I from Drosophila melanogaster, an important model organism with well-developed genetic toolkit and sufficiently close phylogenetic relationship to mammals. They isolated the complex and analyzed its structure by single-particle electron cryo-microscopy (cryo-EM). They also used mass spectrometry to characterize new subunits. So far, the structures of complex I have been reported for several organisms, including mammals, plants, ciliates, fungi and bacteria, but ones from insects have been missing. This study aims to fill this gap and shed light on some of the key questions pertaining complex I biology, such as 1) the conservacy of supernumerary subunits, 2) the mechanisms and physiological relevance of active/deactive transition and 3) the correspondence between the structurally defined closed/open conformations and the biochemically defined active/deactive states.

We thank the reviewer for clearly summarising the key aims of the study relative to the current status of the complex I field.

• Strengths:

The study provides the first structure of complex I from insects, the organisms at an important phylogenetic branch that has diverged from mammals more recently than other eukaryotic species such as plants and fungi. Using purification methods they developed for mammalian enzymes previously, the authors successfully purified the insect enzyme with high quality - a monodisperse peak in gel filtration, the NADH oxidation activity comparable to mammalian enzymes, and the homogenous subunit composition as confirmed by single-particle analyses. It is noteworthy that the authors used state-of-the art tools in model building and validation, such as ISOLDE and MapQ, which makes this model of high standard. In my opinion such careful validation is particularly important for modelling such a gigantic complex, since without cares one can easily misinterpret the density and draw wrong conclusions.

The resolution is 3.3 Angstrom for the best class (Dm1), which allowed modelling side chains and comparing between the observed 3D classes and to the known structures. The model confirms the presence of 43 subunits, akin to mammalinan enzymes, composed of 14 conserved core subunits, 28 supernumerary subunits that have close homologs in mammals, and one supernumerary subunit CG9034 that has not been predicted. They are also structurally similar to mammalian enzymes except for minor local differences. The two supernumerary subunits (NDUFC1 and NDUFA2) that are present in mammals are missing. The authors discuss evidence that NDUFC1 is absent from the Drosophila genome and NDUFA2 is genomically present but its expression is restricted to the male germline. Together, the overall similarity to the mammalian enzyme underlines the use of Drosophila complex I as a model system.

One of the remarkable findings is that common biochemical treatments that are used to deactivate mammalian complex I - heat treatment or NEM treatment - did not reveal deactive state of Drosophila complex I. This is in agreement with their observation that most structural elements are in the active state. The major Dm1 conformation shows all structural features in the active conformation, whereas Dm2 state shows two features in the deactive conformations. Here the author raises an interesting point that the structural elements formerly believed to behave in a consorted manner are actually not coupled, providing new perspective in interpreting complex I structures presented so far and in future. Notably, the authors adopted the same purification procedure for bovine and murine samples. This is a particular strength that they applied a similar procedure for but still observed different behaviors for Drosophila (the absence of the deactive state).

We thank the reviewer for their positive evaluation of the strengths of the paper.

• Weaknesses:

As the authors point out in Discussion, the biochemical statuses of the two described conformations, Dm1 and Dm2, are uncertain. If we assume that Dm1 is a ready-to-go active state, Dm2 could represent several of the possible states; a partially broken state due to delipidation by detergent, a meta-stable state during enzyme turnover, an intermediate towards "full deactiving" structural transition (which the authors argue is unlikely to occur), or a fully reversible state that is in equilibrium to Dm1. Despite these uncertainties, the structure will serve as an excellent starting point to address many open questions in the complex I field in future.

We agree that the biochemical status of Dm2 is uncertain and as the reviewer notes, we made an attempt to address this question in the Discussion section.

In the final 3D classification the number of classes was set to 3 (K = 3). This is an arbitrary human decision and implicitly forces particles to separate into 3 descrete classes. It would have been great to mention if the authors had tried different classification parameters and, if so, whether that had led to similar classification results. There are different methods available to dissect conformational heterogeneity other than simple 3D classification. For example, focused classification can differentiate local structural features. MultiBody refinement and 3D variabitlity can analyze continuous conformational changes. The simple 3D classification with local angular sampling employed here may lead to over-simplification of the more complex structural heterogeneity.

First, the number of classes was set to 5 (K = 5) as written in the Materials and methods section (page 20), which is greater than the number of complex I conformations observed in this study. We apologise if this was not clear and we have amended Figure 1 – Figure Supplement 2 to clarify it.

Second, as the reviewer correctly points out, there are many different methods to dissect conformational heterogeneity, and for this reason we purposefully performed several classification strategies before validating that the Global 3D classification approach used here (with local angular search extending to 0.2º sampling) yielded comparable (or even better) results. These additional classification strategies include:

(i) Focus-revert-classify (a strategy often used for complex I (Kampjut and Sazanov, 2020; Klusch et al., 2021; Kravchuk et al., 2022; Letts et al., 2019)) in RELION, where the membrane arm of complex I is first subtracted to focus-refine on the hydrophilic arm, the subtraction reverted, and then focus-classification performed without alignment on the membrane arm. Here, we used a regularisation parameter, t = 8, and K = 5, and the process yielded three complex I classes matching Dm1, Dm2, and Dm3 with comparable population distribution to the aforementioned Global 3D classification method, plus two junk classes.

(ii) A 3D classification without alignment approach (a strategy also used for complex I (Gu et al., 2022)) in RELION. We used t = 20 and up to K = 12 classes, which resulted in two < 4 Å resolution complex I classes, with the major class matching Dm1 and the minor class a likely mixture of Dm2 and Dm3.

Based on these three classification strategies, we chose to work with the Global 3D classification approach that has previously proven robust for separating complex I heterogeneity in our hands (Agip, 2018; Chung et al., 2022b; Zhu et al., 2016). However, we agree with the reviewer that it would be valuable to provide this extra information. Therefore, we have amended the Materials and methods section on page 20: “The ‘Focus-Revert-Classify’ classification strategy (Letts et al., 2019), applied using the regularisation parameter t = 8 and K = 5, yielded comparable population distributions (three complex I classes matching Dm1, Dm2, and Dm3, plus two junk classes) whilst 3D classification without alignment using t = 20 and K ≤ 12 yielded two < 4 Å complex I classes, with the major class matching Dm1 and the minor class an apparent mixture of Dm2 and Dm3. The 3D classification approach with local angular sampling was therefore employed to give the final set of Dm1, Dm2 and Dm3 particles as described above.” Furthermore, clear cryo-EM densities for Dm2-specific local features, including the ‘flipped’ ND1-TMH4-Tyr149 and the ND6-TMH3 π-bulge, revealed no evidence for Dm1 contamination in the Dm2 population. This is also now noted on page 20.

Although 37 degrees heat treatment and NEM treatment did not reveal any sign of deactivation in Drosophila complex I, it does not rule out the possibility that insect complex I has different ways to deactivate the enzyme, to prevent ROS production. It is probably the limitation of applying existing assays that are originally for mammalian and fungal enzymes to the study of insect enzymes.

The reviewer is correct that Drosophila complex I may have a different way to ‘deactivate’ that does not involve an exposure of ND3-Cys39, and it is also possible that the conditions used for deactivation of mammalian mitochondrial membranes (i.e. 37 ºC heat treatment for 30 min) may not be sufficient to deactivate the Drosophila enzyme. Our approach here was to evaluate if Drosophila complex I undergoes the same active/deactive transition as the mammalian enzyme both structurally and biochemically (and our results suggest that it doesn’t). Moving forward, deactivation mechanisms in different phylogenetic lineages will be an important question to address in the complex I field. We have addressed this question in the first paragraph of the Discussion.

• Whether they achieved the aims and whether the conclusions are supported by the results:

Overall, they successfully isolated the active enzyme and determined its structure at 3.3 A resolution, which meets the current state-of-the-art for single-particle cryo-EM and provided an atomic picture of the enzyme composition. The study confirms that the Drosophila complex I is structurally similar to mammalian complex I, but biochemically different in that it does not show the deactive state. It still does not exclude the possibility that Drosophila complex I can transition into a currently unknown state that prevents reverse electron transfer. This question however can be tackled in future by mutagenesis analyses as Drosophila is a genetically tractable organism.

We agree with the reviewer on his evaluation of the study, and the genetic tractability of the Drosophila enzyme will serve as a crucial tool for future studies.

• Impact to the field and utility of the data to the community:

Complex I is important not only for human health but also for understanding universal principles of biological respiration, because of its universal presence in most organisms on Earth. This study provides a basis for relating mammalian complex I with those from other branches of organisms. The current structures will allow Drosophila researchers to interpret and design any mutations that affect complex I functions, and relate them to behavioral, developmental and metabolical changes at tissues, organs and individuals levels.

We agree with the reviewer on his evaluation of the impact of the study, and thank the reviewer for their comments on the manuscript.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

We thank the reviewer for clearly summarising the key findings of the study. We agree that Drosophila complex I may have limitations for studying the full active/deactive transition so far observed exclusively in mammalian enzymes, but we argue that the lack of a fully deactivated state also provides a good system to study which local elements in complex I may offer protection against RET. Despite these limitations, Drosophila remains a powerful model system to study complex I mechanism, assembly, and regulation in physiological contexts.

Author Response

Reviewer #1 (Public Review):

Neuronal tissues are very complex and are composed of a large number of neuronal types. With the advent of single-cell sequencing, many researchers have used this technology to generate atlases of neuronal structures that would describe in detail the transcriptome profiles of the different cell types. Along these lines, in this manuscript, the authors present single-cell transcriptomic data of the fruitless-expressing neurons in the Drosophila male and female central nervous systems. The authors initially compare cell cluster composition between male and female flies. They then use the expression of known markers (such as Hox genes and KC neuronal markers) to annotate several of their clusters. Then, they look in detail at the expression of different terminal neuronal genes in their transcriptomic data: first, they look into neurotransmitter-related genes and how they are expressed in the fruitless-expressing neurons; they describe in detail these populations that they then verify the expression patterns by looking into genetic intersections of Fru with different neurotransmitter-related genes. Then, they look at Fru-neurons that express circadian clock genes, different neuropeptides and neuropeptide receptors, and different subunits of acetylcholine receptors. Finally, they look into genes that are differentially expressed between male and female neurons that belong to the same clusters. They find a large number of genes; through GO term enrichment analysis, they conclude that many IgSF proteins are differentially expressed, so they look into their expression in Fru-neurons in more detail. Finally, they compare transcription factor expression between male and female neurons of the same cluster and they identify 69 TFs with cluster-specific sex-differential expression.

In general, the authors achieved their goal of generating and presenting a large and very useful dataset that will definitely open a large number of research avenues and has already raised a number of interesting hypotheses. The data seem to be of good quality and the authors present a different aspect of their atlas.

The main drawback is that many of the analyses are very superficial, resulting in the manuscript being handwavy and unsupported. The manuscript would benefit by reducing the number of "analyses" to the ones that are also in vivo validated and by discussing some of the drawbacks that are inherent to their experimental procedure.

scRNA-seq studies generate atlases that are descriptive, by their nature. Therefore, we decided to keep interesting gene-expression analyses in the paper that are based on the scRNA-seq results, especially for the discoveries that point to exciting avenues for future pursuit. We reduced the text as suggested.

1) The authors treat their male, female, and full datasets as three different samples. At the end of the day, these are, for the most part, equivalent neuronal types. The authors should decide to a) either only use the full dataset and present all analyses in this, or b) give a clear correspondence of male and female clusters onto the full ones.

In this paper, all the analyses presented are on the full data set, with some links to the male or female data sets included. We now make clear that the full data set is the focus of the paper (lines 137-141). We provide the male and female data sets for our reader, with the individual Seurat objects uploaded to GEO, to make it easy for the reader to do follow-up analyses using the same criteria we used. We think this is helpful for our research community. We also compare the male and female clusters to the full data set using ClustifyR and report which clusters in the male or female data set analyses correspond to those in the full data set (Source data 2), as suggested by the reviewer, though ClustifyR has some limitations based on our evaluation of this tool for other annotations (see below).

2) Most of their sections are heavily reliant on marker genes. In fact, in almost every section they mention how many of their genes of interest are marker genes. This depends heavily on specific cutoffs, making the conclusions fragile. Similarly, GO terms are used selectively and are, in many cases, vague (such as “signaling”, “neurogenesis”, “translation”).

We evaluated marker genes, as those provide molecular identities to the clusters, given by definition they are significantly more highly expressed in a specific cluster, compared to all clusters. We used a Wilcox rank sum test with the following parameters in Seurat: (min.pct=0.25, logfc.threshold=0.25), which resulted in all called marker genes having p values < 0.05. We did not use a more stringent criteria given that most of the marker gene analyses are descriptive, and it is important to capture a broad range of genes. Our criteria are similar to Ma et al. 2021 (PMID: 33438579) and Corrales at al. 2022 (PMID: 36289550). In the text, we refer to the top 5 marker genes in several analyses, though these marker genes have a much more significant enrichment. We agree with the reviewers’ criticisms regarding the cluster-specific GO-term analyses in the text and those have been removed from the manuscript.

3) A few of the results are not confirmed in vivo. The authors should add a Discussion section where they discuss the inherent issues of their analyses. Are there clusters of low quality? Are there many doublets?

We have added discussion around these topics to the conclusions section of the manuscript and the results, when appropriate.

On the same note, their clusters are obviously non-homogeneous (i.e. they house more than one cell types. This could obviously affect the authors' cluster-specific sex-differential expression, as differences could also be attributed to the differential composition of the male and female subclusters.

We discuss this potential limitation in the discussion of sex differences in gene expression (Lines 959-961).

4) Immunostainings are often unannotated and, in some cases especially in the Supplement, they are blurry. The authors should annotate their images and provide better images whenever possible.

We appreciate this being pointed out and have provided higher resolution figures. The issue was we exceeded the manuscript submission file size on initial submission.

5) I believe that the manuscript would benefit significantly by being heavily reduced in size and being focused on in vivo rigorously confirmed observations.

We have addressed this comment by removing some of the analyses.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

We appreciate this suggestion and have now added results from the doublet detection algorithm, DoubletFinder to our manuscript. Please see above response in editorial comments. We provide a table in Figure 1 – supplement 1 that indicates the number of cells removed by our filtering criteria: We acknowledge that there may be additional doublets in our data set that were not removed in our filtering criteria in the discussion (Lines 1098-1102) and have also provided a new table in Source data 2 indicating the number of potential doublets identified by DoubletFinder that are present in each cluster.

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

We appreciate these comments and address this above in the editorial comments section.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

We appreciate this suggestion and made changes to the conclusions section to address this comment.

Author Response

eLife assessment

The author customises an alpha-fold multimer neural network to predict TCR-pMHC and applies this to the problem of identifying peptides from a limited library, that might engage TCR with a known sequence from a limited list of potential peptides. This is an important structural problem and a useful step that can be further improved through better metrics, comparison to existing approaches, and consideration of the sensitivity of the recognition processes to small changes in structure.

I appreciate the time taken by the editor and reviewers to assess this manuscript. In response to their comments, I've made significant changes and additions to the manuscript, most importantly adding (1) comparisons to TCRpMHCmodels and sequence-similarity based template selection, (2) analysis of peptide modeling accuracy in structure prediction and epitope prediction, (3) analysis and discussion of bias in the ternary structure database, (4) identification of key factors driving structure prediction accuracy, (5) binding predictions for three experimental systems with altered peptide ligand data, and (6) additional discussion of the generalizability of the epitope specificity prediction results to systems without structural characterization.

One minor correction to the wording of the above assessment: the alphafold network used as the basis of our protocol is the original "monomer" network, not the multimer network. We chose to start from the monomer network because it was not trained on complexes, allowing for a more accurate assessment of the expected performance when modeling unseen TCR:pMHC complexes. On the other hand, performance comparisons such as in Fig. 2 are made to the AlphaFold multimer pipeline, since that pipeline can directly build models of complexes.

Reviewer #1 (Public Review):

The author has generated a specific version of alpha-fold deep neural network-based protein folding prediction programme for TCR-pMHC docking. The alpha-fold multimer programme doesn't perform well for TCR-pMHC docking as the TCR uses random amino acids in the CDRs and the docking geometry is flexible. A version of the alpha-fold was developed that provides templates for TCR alpha-beta pairing and docking with class I pMHC. This enables structural predictions that can be used to rank TCR for docking with a set of peptides to identify the best peptide based on the quality of the structural prediction - with the best binders having the smallest residuals. This approach provides a step toward more general prediction and may immediately solve a class of practical problems in which one wants to determine what pMHC a given TCR recognizes from a limited set of possible peptides.

Very minor point: the structure prediction pipeline (Fig. 2) handles both MHC class I and class II complexes. For epitope binding specificity prediction (Figs. 3-6), I only tested MHC class I targets due to limitations in data availability (very few class II epitopes have had their TCR repertoires mapped and also ternary complexes solved).

Reviewer #2 (Public Review):

The application of AlphaFold to the prediction of the peptide TCR recognition process is not without challenge; at heart, this is a multi-protein recognition event. While Alphafold does very well at modelling single protein chains its handling of multi-chain interactions such as those of antibody-antigens pairs have performed substantially lower than for other targets (Ghani et al. 2021). This has led to the development of specialised pipelines that tweak the prediction process to improve the prediction of such key biological interactions. Prediction of individual TCR:pMHC complexes shares many of the challenges apparent within antibody-antigen prediction but also has its own unique possibilities for error.

One of the current limitations of AlphaFold Multimer is that it doesn't support multi-chain templating. As with antibodies, this is a major issue for the prediction of TCR:pMHC complexes as the nearest model for a given pMHC, TRAV, or TRBV sequence may be in entirely different files. Bradley's pipeline creates a diverse set of 12-hybrid AlphaFold templates to circumvent this limitation, this approach constrains inter-chain docking and therefore speeds predictions by removing the time-consuming MSA step of the AlphaFold pipeline. This adapted pipeline produces higher-quality models when benchmarked on 20 targets without a close homolog within the training data.

The challenge to the work is of course not generating predictions but establishing a functional scoring system for the docked poses of the pMHC:TCR and most importantly clearly understanding/communicating when modelling has failed. Thus, importantly Bradley's pipeline shows a strong correlation between its predicted and observed model accuracy. To this end, Bradley uses a receiver operating characteristic curve to discriminate between a TCR's actual antigen and 9 test decoys. This is an interesting testing regime, which appears to function well for the 8 case studies reported. It certainly leaves me wanting to better understand the failure mode for the two outliers - have these correctly modelled the pMHC but failed to dock the TCRs for example or visa versa?

From the analysis in Figure 5 and Figure 5, supplement 2, it looks to me like the pMHC is pretty well modeled in all cases, and the main difference between the working and non-working targets is in the docking of TCR to pMHC. But as the reviewer rightly points out below, binding specificity is likely sensitive to small details of the structure that may not be well captured by these RMSD metrics. With an N of 8, it's hard to make definitive conclusions. As additional systems with ternary structures and TCR repertoires become available, we should be able to provide better answers.

The real test of the current work, or its future iteration, will be the ability to make predictions from large tetramer-sorted datasets which then couple with experimental testing. The pipeline's current iteration may have some utility here but future improvements will make for exciting changes to current experimental methods. Overall the work is a step towards applying structural understanding to the vast amount of next-generation TCR sequence data currently being produced and improves upon current AlphaFold capability.

I completely agree. I am also excited about using this pipeline for design of TCR sequences with altered specificity and/or enhanced affinity. Even an imperfect in silico specificity prediction method can be a useful filter for designed TCRs (in other words, we want TCR designs that are predicted to have specificity for their intended targets). This has been amply demonstrated for protein fold design, where re-prediction of the structure from the designed sequence provides one of the most powerful quality metrics.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1) In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

This is a good point, especially since what constitutes a "displaced peptide" is open to interpretation. I've added an analysis of peptide backbone RMSD (Fig. 2, supplement 2) that should make it possible for readers to assess this more quantitatively using an RMSD threshold (e.g. 10 Å) that makes sense to them.

2) The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

This was poorly explained; I hope I've clarified it now in the methods. The same four templates for each of the individual chains are used in each of the three AlphaFold runs, only the docking geometries vary between the runs. In other words, not all combinations of chain template and docking geometry are provided to AlphaFold.

3) Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

This is an excellent suggestion. The new Figure 2, supplement 2, addresses this.

4) It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

This has been fixed. More details below.

5) The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

This is a great suggestion. We've now added a comparison to TCRpMHCmodels (Fig. 2, supplement 3), which shows that the AlphaFold-based TCR pipeline significantly improves over that baseline method on MHC Class I complexes. Unfortunately, ImmuneScape is not available as a stand-alone software package, and the web interface doesn't allow customization of the template selection process to exclude closely-related templates, which is necessary for benchmarking. Given that ImmuneScape selects a single docking template based on sequence similarity, I compared the AF_TCR dock RMSDs to the dock RMSDs of the closest sequence template (excluding related complexes). This analysis (Fig. 2, supplement 3) shows that AlphaFold modeling produces significantly better docking geometries than simply taking the closest template by sequence similarity.

6) As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

This is an excellent point. It wasn't at all clear in the original submission, but the AlphaFold model that was fine-tuned on TCR complexes was only used for the mouse comparison in Fig. 2D (now Fig. 2F), and for exactly the reasons you mention. There is too much overlap between the epitopes with well-characterized repertoires and the epitopes with solved structures. This is also the reason we used the original AlphaFold monomer network, which was only trained on individual protein chains, rather than the AlphaFold multimer network, as the basis of the AF_TCR pipeline. As noted in the discussion, there is still the possibility that individual TCR chain structures in the benchmark or specificity prediction sets were part of the AlphaFold monomer training set, which could make the docking and specificity prediction results look better than they should (though not in Fig. 2B).

7) The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

This is another great point. For me, the main motivation for the alanine scanning results was to further "stress test" the pipeline to see if it produced plausible results. A particular worry was that the use of pMHC:TCR confidence scores might allow the results to be skewed by peptide-MHC binding strength, rather than the intended TCR - pMHC interaction strength. We've seen in other work that the AlphaFold confidence scores for the peptide are correlated with peptide-MHC affinity. In the AF_TCR specificity predictions, we use the mean binding scores for the "irrelevant" background TCRs to subtract out peptide-intrinsic effects. The fact that we don't see strong signal in Figure 6 at the peptide anchor positions suggests that this is working, at least to some extent. It is also encouraging that the native peptide-MHC has stronger predicted binding than the majority of the alanine variants (excepting the two epitopes with poor performance).

I agree that comparing the repertoire-level mutation sensitivity predictions to real-world experimental data is challenging, given uncertainty about which TCR clones drive selection for escape, and other viral fitness pressures that influence the escape process. The fact that some of the positions predicted to be most sensitive are also the sites of escape mutations (examples now given in the text) is encouraging. But the new peptide-variant results (Fig. 6, supplement 1) highlight the challenges that remain in discriminating between very similar peptides (especially in the single-TCR setting).

Author Response

Reviewer #1 (Public Review):

In this study, Menjivar et al. examine the specific role of the enzyme arginase 1 (Arg1), which is expressed in immunosuppressive macrophages and catabolizes arginine to ornithine, in pancreatic cancer. They use an elegant genetic approach that leverages a dual recombinase-based genetically engineered mouse model of pancreatic cancer, which efficiently deletes Arg1 and recovers extracellular arginine in cultured macrophages. Within the pancreas, macrophage Arg1 deletion increased T cell infiltration and fewer mice developed invasive pancreatic cancer. Interestingly, when tumors did develop, the authors observed that compensatory mechanisms of arginine depletion were induced, including Arg1 overexpression in epithelial cells identified as tuft cells or Arg2 overexpression in macrophages. To overcome these compensatory mechanisms, pharmacological targeting of arginase was tested and found to increase T cell infiltration and sensitize to immune checkpoint blockade, suggesting this is a promising approach for pancreatic cancer.

Strengths:

This is a very rigorous, well-designed study and the findings are broadly interesting for the metabolism, immunometabolism, and pancreatic cancer communities. The methods are comprehensive and the experimental details in the legends are complete.

Weaknesses:

The claim that Arg1 deletion in macrophages delayed the formation of invasive disease is not completely justified by the data presented. Only a small number of mice are analyzed, and no statistics are included.

While in the original submission this claim was based on a relatively small number of animals, we have now increased each cohort. The new graph is included in Figure 2E (Response Figure 1); statistical analysis is also included and show the differences to be significant.

Moreover, the abstract does not comprehensively summarize the findings. Many findings, including compensatory upregulation of ARG1 in tuft cells and ARG2 in myeloid cells, are not mentioned, nor was the rationale for the pharmacological approach. Finally, the claim that their data demonstrate that Arg1 is more than simply a marker of macrophage function. While this is the first time this has been examined in pancreatic cancer, a general role for Arg1 and arginine metabolism by myeloid cells in immunosuppression has already been established by multiple studies, including those cited by the authors, in multiple tumor types. This is an overstatement of the findings.

We apologize for the lack of clarity, in the attempt to meet the word limit for the abstract. We have now amended the abstract to better reflect the total of our findings and the context of our work.

Author Response

Reviewer #3 (Public Review):

Yamada et al utilizes the full strength of Drosophila neural circuit approaches to investigate second-order conditioning. The new insights into the mechanisms of how a learned cue can act as reinforcement are relevant beyond the fly field and have the potential to spark broad interest. The main conclusions of the authors are justified and the experiments, to my understanding, are well done.

Some minor aspects must be addressed. To avoid misunderstandings a clear distinction should be made between those experiments using real world sugar and those using artificial activation of dopamine neurons as reward. For example, the proposed teacher - student model is mostly based on the work established with artificial activation.

We split Figure 1 and made two separate figures. The new Figure 1 displays experiments with only real sugar or optogenetic activation of sugar receptor neurons (new data), whereas the new Figure 2 shows mostly experiments with direct DAN activations. This new figure arrangement makes a clear distinction between experiments with sugar and DAN activation, and allows readers to compare them more easily. We also modified the second paragraph of the discussion to clarify this point.

To emphasize the generality of the model, it might help to provide some further evidence using real world sugar approaches, especially since the only known sugar-reward driven plasticity is reported in the student (g5b`2a) but not the teacher compartments. In this line, it would be useful to extend the functional interference used during the sugar experiments beyond the a1 compartment.

In response to the reviewer’s comment, we added new data in Figure 2D to show that blocking PAM-DANs in γ4, γ5 and β′2a compartments impairs second-order conditioning following odor-sugar first-order conditioning. In contrast to blocking α1 DANs, blocking those non-α1 PAM-DANs did not impair one-day first-order memory (Figure 2D), which further strengthens our model of differential requirement of compartments for first-order and second-order memory formation.

We think transient blocks of individual DAN cell types during second-order conditioning after odor-sugar conditioning will be informative to map second-order memories to specific compartments in naturalistic settings. For the reasons detailed above, however, we will need to develop a new way of transient purturbation for that.

We would also point out that, to our knowledge, sugar-reward-driven plasticity has not been fully demonstrated in MBON-γ5β′2a. Owald et al., 2015 Neuron (10.1016/j.neuron.2015.03.025) showed a reduced CS+ odor response after odor-sugar conditioning in MBON-b′2mp (their Fig 3). However, they could not investigate the plasticity of MBON-γ5β′2a because the magnitude of odor response was too low (their Figure S3).

Further, general statements about the compartments, for example for g5 and a1, might need adjustment since the tools used, the respective driver lines, often don't label all dopamine neurons in one specific compartment. In fact, functional heterogeneity among dopamine neurons innervating the g5 compartment have been recently established (sugar-reward, extinction) and might apply here.

To clarify the point that we are manipulating a subset of DANs in each compartment, we added “cell count” information in Figure 2A. Also, we made Figure 4-figure supplement 2 to show which subtypes of DANs are connected with SMP108.

Lastly, I would like to recommend that the authors discuss alternative feedback pathways that might serve similar or parallel functions.

Despite these minor points, the study is impressive.

Figure 4C shows several candidate interneurons that may have similar functions as SMP108. For instance, CRE011 may acquire enhanced response to reward-predicting odor as an outcome of reduced inhibition from MBON-γ5β′2a, and send excitatory inputs to DANs.

In Figure 4-figure supplement 3, we made additional scatter plots to visualize other outlier cell types in terms of their connectivity with MBONs and DANs.

Author Response:

We thank the three reviewers for their thoughtful comments and constructive critique.

Reviewer #1 (Public Review):

Hu et al. present findings that extend the understanding of the cellular and synaptic basis of fast network oscillations in the sensory cortex. They developed the ex vivo model system to study synaptic mechanisms of ultrafast (>400Hz) network oscillation ("ripplets") elicited in layer 4 (L4) of the barrel cortex in the mouse brain slice by optogenetically activating thalamocortical axon terminals at L4, which mimic the thalamic transmission of somatosensory information to the cortex. This model allowed them to reproduce extracellular ripplet oscillations in the slice preparation and investigate the temporal relationship of cellular and synaptic response in fast-spiking (FS) inhibitory interneurons and regular spiking (RS) with extracellular ripplet oscillations to common excitatory inputs at these cells. FS cells show precisely timed firing of spike bursts at ripplet frequency, and these spikes are highly synchronized with neighboring FS cells. Moreover, the phase-locked temporal relationship between the ripplets and responses of FS and RS cells, although different phases, to thalamocortical activation are found to closely coincide with EPSCs in RS cells, which suggests that common excitatory inputs to FS and RS cells and their synaptic connectivity are essential to generate reverberating network activity as ripplet oscillations. Additionally, they show that spikes of FS cells in layer 5 (L5) reduced in the slice with a cut between L4 and L5, proposing that recurrent excitation from L4 excitatory cells induced by thalamocortical optogenetic stimulation is necessary to drive FS spike bursts in layer 5 (L5).

Overall, this study helps extend our knowledge of the synaptic mechanisms of ultrafast oscillations in the sensory cortex. However, it would have been nice if the authors had utilized various methodologies and systems.

Although the overall findings are interesting, the conclusion of the study could have been strengthened according to the following points:

1. The authors investigate the temporal relationship between ripplets and FS and RS cells' response elicited by optogenetic activation of TC axon terminals, which is mainly supported by phase-locked responses of FS and RS cells with local ripplets oscillations to optogenetic activation. They also show highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4.

We understand the reviewer’s issue with the logical flow of our argument. We will address this concern by textual changes and/or by rearranging the order of the presentation and figures.

2. The authors suggest that the optogenetic activation of TC axon terminal elicits local ripplet oscillations via synchronized spike burst of FS inhibitory interneurons and alternating EPSC-IPSC of RS cells in phase-locked with ripplets in L4 barrel cortex, which would be generated by following common excitatory inputs from the local circuits to these cells at the ripple frequency. Thus they intend to investigate the source of these excitatory inputs at this local network of L4 by suppressing the firing of L4 RS cells. However, they show FS spike bursts in L5B, instead of L4, due to the technical limitations of their experimental setup, as described in the manuscript. Although L5 FS spike bursts decrease after cutting the L4/L5 boundary, supposedly inhibiting excitatory input from L4 as depicted in Fig 6D in the author's manuscript, the interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities which are phase-locked with the ripplets observed in L4.

We have not studied network oscillation in layer 5 at the same level of detail we have studied layer 4; however the oscillations in both layers are phase locked. We will show this as supplemental data in the revised manuscript.

3. Authors suggested a circuit model. It would be recommended that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular-spiking neurons to support their circuit model.

We agree that a computational model of the layer 4 network, demonstrating ripplets in silico, would enhance our understanding of this re-discovered ultrafast oscillation. Moreover, such a model would also help constrain the allowable parameter space of other, existing models of layer 4 or of the complete cortical column, as the ability of these existing models to recreate ripplets in response to strong, synchronous thalamocortical activation could now be used as a stringent test of the assumptions underlying these models. We hope to reproduce ripplets in silico, within an experimentally constrained parameter space, in a near future study.

Reviewer #2 (Public Review):

This manuscript studied potential cellular mechanisms that generate ultrafast oscillations (250-600Hz) in the cortex. These oscillations correlate with sensory stimulation and might be relevant for the perception of relevant sensory inputs. The authors combined ex-vivo whole-cell patch-clamp recordings, local field potential (LFP) recordings, and optogenetic stimulation of thalamocortical afferents. In a technical tour de force, they recorded pairs of fast-spiking (FS)-FS and FS-regular-spiking (RS) neurons in the cortex and correlated their activity with the LFP signal.

Optogenetic activation of thalamic afferents generated ripple-like extracellular waveforms in the cortex, which the authors referred to as ripplets. The timing of the peaks and troughs within these ripplets was consistent across slices and animals. Activation of thalamic inputs induced precisely timed FS spike bursts and RS spikes, which were phase-locked to the ripplet oscillation. The authors described the sequences of RS and FS neuron discharge and how they phase-locked to the ripplet, providing a model for the cellular mechanism generating the ripplet.

The manuscript is well-written and guides the reader step by step into the detailed analysis of the timing of ripplets and cellular discharges. The authors appropriately cite the known literature about ultrafast oscillations and carefully compare the novel ripplets to the well-known hippocampal ripples. The methods used (ex-vivo patch-clamp and LFP) were appropriate to study the cellular mechanisms underlying the ripplets.

Overall, this manuscript develops means for studying the role of cortical ultrafast oscillations and proposes a coherent model for the cellular mechanism underlying these cortical ultrafast oscillations.

We thank the reviewer for his supportive comments.

Reviewer #3 (Public Review):

In this study, Hu et al. aimed to identify the neuronal basis of ultrafast network oscillations in S1 layer 4 and 5 evoked by the optogenetic activation of thalamocortical afferents in vitro. Although earlier in vivo demonstration of this short-lived (~25 ms) oscillation is sparse and its significance in detecting salient stimuli is not known the available publications clearly show that the phenomenon is consistently present in the sensory systems of several species including humans.

In this study using optogenetic activation of thalamocortical (TC) fibers as a proxy for a strong sensory stimulus the in vitro model accurately captures the in vivo phenomenon. The authors measure the features of oscillatory LFP signals together with the intracellular activity of fast-spiking (FS) interneurons in layer 4 and 5 as well as in layer 4 regular spiking (RS) cells. They accurately measure the coherence of intra- and extracellular activity and convincingly demonstrate the synchronous firing of FS cells and antiphase firing of RS and FS cells relative to the field oscillation.

Major points:

1) The authors conclude the FS cell network has a primary role in setting the frequency of the oscillation. While these data are highly plausible and entirely consistent with the literature only correlational not causal results are shown thus direct demonstration of the critical role of GABAergic mechanisms is missing.

We find that blocking fast inhibition (by puffing a gabazine solution locally) converts ripplets into long-duration paroxysmal events with high-frequency firing of both RS and FS cells. While we do not think that this experiment is diagnostic in distinguishing between competing models (in all models fast inhibition is a necessary component), we will add these experiments as supplemental material.

2) The authors put a strong emphasis on the role of RS-RS interactions in maintaining the oscillation once it was launched by a TC activity. Its direct demonstration, however, is not presented. The alternative scenario is that TC excitation provides a tonic excitatory background drive (or envelope) for interacting FS cells which then impose ultrafast, synchronized IPSPs on RS cells. Similar to the RS-RS drive in this scenario RS cells can also only fire in the "windows of opportunity" which explains their antiphase activity relative to FS cells, but RS cells themselves do not participate in the maintenance of oscillation. Distinguishing between these two scenarios is critical to assess the potential impact of ultrafast oscillation in sensory transmission. If TC inputs are critical the magnitude of thalamic activity will set the threshold for the oscillation if RS-RS interactions are important intracortical operation will build up the activity in a graded manner.

Earlier theoretical studies (e.g Brunel and Wang, 2003; Geisler et al., 2005) strongly suggested that even in the case of the much slower hippocampal ripples (below 200 Hz) phasic activation of local excitatory cells cannot operate at these frequencies. Indeed, rise time, propagation, and integration of EPSPs can likely not take place in the millisecond (or submillisecond) range required for efficient RS-RS interactions. The alternative scenario (tonic excitatory background coupled with FS-FS interactions) on the other hand has been clearly demonstrated in the case of the CA3 ripples in the hippocampus (Schlingloff et al., 2014. J.Nsci).

The Schlingloff et al. study is important, and we actually think that their results, and many of their conclusions, are consistent with our own. We agree with these authors that “…PV cells are essential for the initiation and maintenance of sharp waves and the generation of ripple oscillations”, that “…perisomatic inhibition enforces ripple synchrony by phase-locking firing during SWRs”, and also that “…neuronal coupling via gap junctions is not essential in ripple synchronization”. We also agree that “The tonic excitatory ‘envelope’ arising from the buildup of activity of PCs drives the firing of PV cells”, as far as initiation of ripples in CA3 is concerned. In our model system, thalamocortical excitation serves the same role, of initiating the oscillation. However I do not see how the data of Schlingloff et al. support the conclusion that (in the legend to their Fig. 11) “…there is no cycle-by-cycle reciprocal interaction between the PCs and the PV [interneurons]”, or the implication that FS cells function as independent pacemakers “…because of their reciprocal inhibition”, as their FINO model suggests. The Schlingloff et al. data clearly show cycle-by-cycle alternations of EPSCs and IPSCs (their Fig. 1C, D, as well as their Fig. 7B), as we show in our Fig. 5A. These phasic EPSCs, occurring at ripple frequency, by necessity originate from pyramidal cells synchronized (as a population) to the ripple oscillation, as indeed shown in their multi-unit recordings. This precise, phasic (and clearly not “tonic”) excitatory drive cannot be uncoupled from the ripple (or ripplet) oscillation, and therefore cannot be dismissed as a factor driving the oscillation.

The strongest evidence the Schlingloff et al. study provides that FS cells synchronize independently of excitatory cells – and then impose this oscillation on the excitatory cells - is in their demonstration of ripples generated by prolonged direct optogenetic stimulation of PV cells, in the presence of glutamatergic blockers (their Fig. 6). However this manipulation worked only in some of their slices, and the oscillations only lasted as long as the light stimulus and therefore were exogenously driven rather than network driven. They do not show intracellular responses from either inhibitory or excitatory cells, nor multi-unit activity, during this manipulation, so it is difficult to know if excitatory cells were indeed entrained to the same frequency, as the FINO model posits. Nevertheless this is a very interesting experiment which we plan to attempt in our own model system in a future study.

When the properties of the ultrafast oscillation were tested as various stimulation strengths (Figure 2) weaker stimulation resulted in less precise timing. If TC input is indeed required only to launch the oscillation not to maintain it, this is not expected since once a critical number of RS cells were involved to start the activity their rhythmicity should no longer depend on the magnitude of the initial input. On the other hand, if the entire transient oscillation depends on TC excitation weaker input would result in less precise firing.

Our interpretation for the lesser spike precision with a weaker optogenetic stimulation is that fewer FS cells fired upon the initial thalamocortical volley, and therefore a weaker IPSP wavefront was propagated to RS cells allowing for a wider “window of opportunity” for RS firing,  and this loss of synchrony then propagated from cycle to cycle. This interpretation will be added in the revised manuscript.

3) The experiments indicating the spread of phasic activity from L4 RS to L5 FS cells can not be accepted as fully conclusive. The horizontal cut not only severed the L4 RS to L5 FS connections but also many TC inputs to the L5 FS apical dendrites as well as the axons of L4 FS cells to L5 FS cells both of which can be pivotal in the translaminar spread.

FS cells do not have apical dendrites so we assume the reviewer meant to say “L5 RS apical dendrites”; however if the cut reduced the excitability of L5 RS cells, that only strengthens our conclusion that RS firing is required for maintaining the oscillation. While the cut could have also disrupted L4 FS to L5 FS connections, we are not aware of any evidence that such inter-laminar connections exist. On the other hand, the Pluta et al. 2015 study shows very robust excitatory connections between L4 RS and L5 FS cells.  

Having said that, we agree with the reviewer (indeed, with all three reviewers) that the L4/L5 cut experiments are not conclusive, and we will make this clear in our discussion in the revised manuscript. We plan to do a more conclusive test of our model by using a transgenic line to express inhibitory opsins specifically in L4. This will require expressing ChR2 in the thalamus by virus injection and a careful comparison of ripplets between the two models; we therefore reserve these experiments to a future study.

Author Response

Reviewer #2 (Public Review):

"The cellular architecture of memory modules in Drosophila supports stochastic input integration" is a classical biophysical compartmental modelling study. It takes advantage of some simple current injection protocols in a massively complex mushroom body neuron called MBON-a3 and compartmental models that simulate the electrophysiological behaviour given a detailed description of the anatomical extent of its neurites.

This work is interesting in a number of ways:

• The input structure information comes from EM data (Kenyon cells) although this is not discussed much in the paper - The paper predicts a potentially novel normalization of the throughput of KC inputs at the level of the proximal dendrite and soma - It claims a new computational principle in dendrites, this didn’t become very clear to me Problems I see:

• The current injections did not last long enough to reach steady state (e.g. Figure 1FG), and the model current injection traces have two time constants but the data only one (Figure 2DF). This does not make me very confident in the results and conclusions.

These are two important but separate questions that we would like to address in turn.

As for the first, in our new recordings using cytoplasmic GFP to identify MBON-alpha3, we performed both a 200 ms current injection and performed prolonged recordings of 400 ms to reach steady state (for all 4 new cells 1’-4’). For comparison with the original dataset we mainly present the raw traces for 200 ms recordings in Figure 1 Supplement 2. In addition, we now provide a direct comparison of these recordings (200 ms versus 400 ms) and did not observe significant differences in tau between these data (Figure 1 Supplement 2 K). This comparison illustrates that the 200 ms current injection reaches a maximum voltage deflection that is close to the steady state level of the prolonged protocol. Importantly, the critical parameter (tau) did not change between these datasets.

Regarding the second question, the two different time constants, we thank the reviewer for pointing this out. Indeed, while the simulated voltage follows an approximately exponential decay which is, by design, essentially identical to the measured value (τ≈ 16ms, from Table 1; ee Figure 1 Supplement 2 for details), the voltage decays and rises much faster immediately following the onset and offset of the current injections. We believe that this is due to the morphology of this neuron. Current injection, and voltage recordings, are at the soma which is connected to the remainder of the neuron by a long and thin neurite. This ’remainder’ is, of course, in linear size, volume and surface (membrane) area much larger than the soma, see Fig 2A. As a result, a current injection will first quickly charge up the membrane of the soma, resulting in the initial fast voltage changes seen in Fig 2D,F, before the membrane in the remainder of the cell is charged, with the cell’s time constant τ.

We confirmed this intuition by running various simplified simulations in Neuron which indeed show a much more rapid change at step changes in injected current than over the long-term. Indeed, we found that the pattern even appears in the simplest possible two-compartment version of the neuron’s equivalent circuit which we solved in an all-purpose numerical simulator of electrical circuitry (https://www.falstad.com/circuit). The circuit is shown in Figure 1. We chose rather generic values for the circuit components, with the constraints that the cell capacitance, chosen as 15pF, and membrane resistance, chosen as 1GΩ, are in the range of the observed data (as is, consequently, its time constant which is 15ms with these choices); see Table 1 of the manuscript. We chose the capacitance of the soma as 1.5pF, making the time constant of the soma (1.5ms) an order of magnitude shorter than that of the cell.

Figure 1: Simplified circuit of a small soma (left parallel RC circuit) and the much larger remainder of a cell (right parallel RC circuit) connected by a neurite (right 100MΩ resistor). A current source (far left) injects constant current into the soma through the left 100MΩ resistor.

Figure 2 shows the somatic voltage in this circuit (i.e., at the upper terminal of the 1.5pF capacitor) while a -10pA current is injected for about 4.5ms, after which the current is set back to zero. The combination of initial rapid change, followed by a gradual change with a time constant of ≈ 15ms is visible at both onset and offset of the current injection. Figure 3 show the voltage traces plotted for a duration of approximately one time constant, and Fig 4 shows the detailed shape right after current onset.

Figure 2: Somatic voltage in the circuit in Fig. 1 with current injection for about 4.5ms, followed by zero current injection for another ≈ 3.5ms.

Figure 3: Somatic voltage in the circuit, as in Fig. 2 but with current injected for approx. 15msvvvvv

While we did not try to quantitatively assess the deviation from a single-exponential shape of the voltage in Fig. 2E, a more rapid increase at the onset and offset of the current injection is clearly visible in this Figure. This deviation from a single exponential is smaller than what we see in the simulation (both in Fig 2D of the manuscript, and in the results of the simplified circuit here in the rebuttal). We believe that the effect is smaller in Fig. E because it shows the average over many traces. It is much more visible in the ’raw’ (not averaged) traces. Two randomly selected traces from the first of the recorded neurons are shown in Figure 2 Supplement 2 C. While the non-averaged traces are plagued by artifacts and noise, the rapid voltage changes are visible essentially at all onsets and offsets of the current injection.

Figure 4: Somatic voltage in the circuit, as in Fig. 2 but showing only for the time right after current onset, about 2.3ms.

We have added a short discussion of this at the end of Section 2.3 to briefly point out this observation and its explanation. We there also refer to the simplified circuit simulation and comparison with raw voltage traces which is now shown in the new Figure 2 Supplement 2.

• The time constant in Table 1 is much shorter than in Figure 1FG?

No, these values are in agreement. To facilitate the comparison we now include a graphical measurement of tau from our traces in Figure 1 Supplement 2 J.

• Related to this, the capacitance values are very low maybe this can be explained by the model’s wrong assumption of tau?

Indeed, the measured time constants are somewhat lower than what might be expected. We believe that this is because after a step change of the injected current, an initial rapid voltage change occurs in the soma, where the recordings are taken. The measured time constant is a combination of the ’actual’ time constant of the cell and the ’somatic’ (very short) time constant of the soma. Please see our explanations above.

Importantly, the value for tau from Table 1 is not used explicitly in the model as the parameters used in our simulation are determined by optimal fits of the simulated voltage curves to experimentally obtained data.

• That latter in turn could be because of either space clamp issues in this hugely complex cell or bad model predictions due to incomplete reconstructions, bad match between morphology and electrophysiology (both are from different datasets?), or unknown ion channels that produce non-linear behaviour during the current injections.

Please see our detailed discussion above. Furthermore, we now provide additional recordings using cytoplasmic GFP as a marker for the identification of MBON-alpha3 and confirm our findings. We agree that space-clamp issues could interfere with our recordings in such a complex cell. However, our approach using electrophysiological data should still be superior to any other approach (picking text book values). As we injected negative currents for our analysis at least voltage-gated ion channels should not influence our recordings.

• The PRAXIS method in NEURON seems too ad hoc. Passive properties of a neuron should probably rather be explored in parameter scans.

We are a bit at a loss of what is meant by the PRAXIS method being "too ad hoc." The PRAXIS method is essentially a conjugate gradient optimization algorithm (since no explicit derivatives are available, it makes the assumption that the objective function is quadratic). This seems to us a systematic way of doing a parameter scan, and the procedure has been used in other related models, e.g. the cited Gouwens & Wilson (2009) study.

Questions I have:

• Computational aspects were previously addressed by e.g. Larry Abbott and Gilles Laurent (sparse coding), how do the findings here distinguish themselves from this work

In contrast to the work by Abbott and Laurent that addressed the principal relevance and suitability of sparse and random coding for the encoding of sensory information in decision making, here we address the cellular and computational mechanisms that an individual node (KC>MBON) play within the circuitry. As we use functional and morphological relevant data this study builds upon the prior work but significantly extends the general models to a specific case. We think this is essential for the further exploration of the topic.

• What is valence information?

Valence information is the information whether a stimulus is good (positive valence, e.g. sugar in appetitive memory paradigms, or negative valence in aversive olfactory conditioning - the electric shock). Valence information is provided by the dopaminergic system. Dopaminergic neurons are in direct contact with the KC>MBON circuitry and modify its synaptic connectivity when olfactory information is paired with a positive or negative stimulus.

• It seems that Martin Nawrot’s work would be relevant to this work

We are aware of the work by the Nawrot group that provided important insights into the processing of information within the olfactory mushroom body circuitry. We now highlight some of his work. His recent work will certainly be relevant for our future studies when we try to extend our work from an individual cell to networks.

• Compactification and democratization could be related to other work like Otopalik et al 2017 eLife but also passive normalization. The equal efficiency in line 427 reminds me of dendritic/synaptic democracy and dendritic constancy

Many thanks for pointing this out. This is in line with the comments from reviewer 1 and we now highlight these papers in the relevant paragraph in the discussion (line 442ff).

• The morphology does not obviously seem compact, how unusual would it be that such a complex dendrite is so compact?

We should have been more careful in our terminology, making clear that when we write ’compact’ we always mean ’electrotonically compact," in the sense that the physical dimensions of the neuron are small compared to its characteristic electrotonic length (usually called λ). The degree of a dendritic structure being electrotonically compact is determined by the interaction of morphology, size and conductances (across the membrane and along the neurites). We don’t believe that one of these factors alone (e.g. morphology) is sufficient to characterize the electrical properties of a dendritic tree. We have now clarified this in the relevant section.

• What were the advantages of using the EM circuit?

The purpose of our study is to provide a "realistic" model of a KC>MBON node within the memory circuitry. We started our simulations with random synaptic locations but wondered whether such a stochastic model is correct, or whether taking into account the detailed locations and numbers of synaptic connections of individual KCs would make a difference to the computation. Therefore we repeated the simulations using the EM data. We now address the point between random vs realistic synaptic connectivity in Figure 4F. We do not observe a significant difference but this may become more relevant in future studies if we compute the interplay between MBONs activated by overlapping sets of KCs. We simply think that utilizing the EM data gets us one step closer to realistic models.

• Isn’t Fig 4E rather trivial if the cell is compact?

We believe this figure is a visually striking illustration that shows how electrotonically compact the cell is. Such a finding may be trivial in retrospect, once the data is visualized, but we believe it provides a very intuitive description of the cell behavior.

Overall, I am worried that the passive modelling study of the MBON-a3 does not provide enough evidence to explain the electrophysiological behaviour of the cell and to make accurate predictions of the cell’s responses to a variety of stochastic KC inputs.

In our view our model adequately describes the behavior of the MBON with the most minimal (passive) model. Our approach tries to make the least assumptions about the electrophysiological properties of the cell. We think that based on the current knowledge our approach is the best possible approach as thus far no active components within the dendritic or axonal compartments of Drosophila MBONs have been described. As such, our model describes the current status which explains the behavior of the cell very well. We aim to refine this model in the future if experimental evidence requires such adaptations.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model. 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

The study does not provide experimental validation of the results of the computational model.

The goal of our study is to use computational approaches to provide insights into the computation of the MBON as part of the olfactory memory circuitry. Our data is in agreement with the current model of the circuitry. Our study therefore forms the basis for future experimental studies; those would however go beyond the scope of the current work.

The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.

We do, indeed, find that synaptic integration in this neuron is almost completely linear. We demonstrate that this result holds in a variety of different ways. All analyses in the study serve this purpose. These results are in line with the findings by Hige and Turner (2013) who demonstrated that also synaptic integration at PN>KC synapses is highly linear. As such our data points to a feature conservation to the next node of this circuit.

The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

We respectfully disagree. We demonstrate that this linear integration is a combination of the size of the cell and the combination of its biophysical parameters, mainly the conductances across and along the neurites. As to why it holds, our main argument is that results based on the linear model agree with all known (to us) empirical results, and this is the simplest model.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Please see the comment above as to the ’why’ we believe the neuron is electrotonically compact: a model with this assumption agrees well with empirically found results.

We agree that the small variations in Fig 5F-H are likely not biologically meaningful. We state this now more clearly in the figure legends and in the text. This result is important to show, however. It is precisely because these variations are small, compared to the differences between voltage differences between different numbers of activated KCs (Fig 5D) or different levels of activated synapses (Fig 5E) that we can conclude that a 25% change in either synaptic strength or number can represent clearly distinguishable internal states, and that both changes have the same effect. It is important to show these data, to allow the reader to compare the differences that DO matter (Fig 5D,E) and those that DON’T (Fig 5F-H).

The same applies to Fig 3I. The reviewer is entirely correct: the differences in the somatic voltage shown in Figure 3I are minuscule, less than a micro-Volt, and it is very unlikely that these difference have any biological meaning. The point of this figure is exactly to show this!. It is to demonstrate quantitatively the transformation of the large differences between voltages in the dendritic tree and the nearly complete uniform voltage at the soma. We feel that this shows very clearly the extreme "democratization" of the synaptic input!

Author Response

Public Review:

In this article, the authors have taken up the substantial task of combing through thousands of published meta-analyses and systematic reviews, with the goal of identifying the subset that specifically seeks to measure the association between elapsed time ("lag-time") in various milestones of cancer diagnosis or treatment (e.g. time elapse from symptom onset to first seen by primary care physician) and cancer outcomes. Within this subset, they have identified and summarized the findings on how these lag times are related to certain cancer outcomes. For example, how much does a delay in the start of adjuvant chemotherapy after surgery for breast cancer increase the mortality rate for these patients? The overarching goal of this work is to characterize the pre-Covid-19 landscape of these relationships and thereby provide a basis for studying what impact the pandemic had on worsened outcomes for cancer patients due to treatment delays. The authors have done an excellent job in their review of systematic reviews and meta-analyses, both describing their methodology well and interpreting their findings. The immediate connection to the Covid-19 pandemic is somewhat tenuous and primarily left to the reader to determine.

We thank Dr. Boonstra for this positive feedback regarding our detail-oriented systematic search and review process. The main concern of Dr. Boonstra was the need to elaborate on the translation component of our results onto the pandemic. We clarify the utility of contextualizing our findings with the pandemic and corresponding revisions to our manuscript.

Author Response

Reviewer #1 (Public Review):

It appears in the text that "there are key differences between the model and actual bacteria-phage systems, and the model should not be interpreted as one that will directly map onto a biological scenario". I agree with this statement. However, by distancing the model from biological scenarios it makes its predictions hard to validate in a real system, leaving us with no obvious way to infer how to apply its conclusions. Indeed, both explicit examples given in lines 125-130: phase-bacteria and T-cell-antigen are not quite captured by modeling choices. I would have much preferred a specific biological system fixed in mind, then minimally modeled in a way that there is hope to directly link the modeling results to experiments. Especially since there is a wealth of available microbial population data, as well as much being generated.

I do believe that the model can be related to or at least adapted to experimental comparison, specifically once there are sufficiently many datasets measuring binding affinities between proteins that govern the types of interactions described herein. This is starting to happen for TCR-antigen pairs (eg VDJdb), but this database is still far from a large enough to be able to fit a reasonable model, or perform a controlled experiment. I am not sure of an equivalent database for phage binding proteins and their relevant binding rates. As the reviewer notes, the model will need to be tailored to certain particularities of the T cell-pathogen, T cell-tumor, or phage-bacteria dynamics, but these are achievable, and should not impact the qualitative results too much. The current model is instead a minimal model that captures essential aspects of these systems, which have both been modeled as predator-prey populations in the literature.

As stated, "the population fitness distribution is never able to 'settle'..." is indicative of the driven nature (driven by strong noise) of the quasi steady state as opposed to a stability that arises from the system dynamics.

I agree with this. The steady state is a sort of “statistical” one rather than an “explicit” one. I think I have made this fairly clear in the text, but please let me know if there are any specific suggestions wrt clarifying this point.

Reviewer #2 (Public Review):

This work by Martis illustrates, in a predator-prey or parasite-host eco-evolutionary context, the classical idea of bet hedging or biological insurance: where a single population would fluctuate and perhaps risk extinction, summing over multiple sub-populations with asynchronous dynamics (some going up while others go down) allows a stabler total abundance.

Here the sub-populations are various genotypes of one predator and one prey species, fluctuations are due to their ecological interactions, their dynamics are more asynchronous when predation is more specialized (i.e. the various predator genotypes differ more in which prey types they can eat), and mutations allow the regeneration of genotypes that have gone extinct, thus ensuring that the diversity of subpopulations is not lost (corresponding to a "clonal interference" regime with multiple coexisting genotypes).

While the general idea of bet hedging has been explored in many settings, the devil is usually in the details: for instance, sub-populations should be connected enough to allow the rescue of those going extinct, but a too strong connection would simply synchronize their temporal dynamics and lose the benefit of bet hedging. In some cases, connections between sub-populations could even be destabilizing (e.g. Turing instabilities in space).

In a recent surge of physics-inspired many-species theories, where fluctuations arise from ecological dynamics, these details are notably starting to be understood in the case of spatial bet hedging, i.e. genetically identical subpopulations in multiple patches connected by migration (see e.g. Roy et al PLoS Comp Bio 2020 or Pierce et al PNAS 2020).

These spatial models certainly served as inspiration and have been cited. However, there is a key difference in that the spatial models rely on something akin to the “storage effect,” where (loosely speaking) strains persist by transiently living on islands with a somewhat favorable ecological context. In my model there is no such storage effect and the persistence of the whole population relies on the generation of strains that are favorable in the current context by chance mutations. There is an analogy to be made with antigen escape, or more generally “Kill-The-Winner” type dynamics. However, the dynamics in my model are more complex than this – specifically, the dynamics are “high-dimensional” and there can be several prey “Winners” with multiple predators in pursuit. However, I clarify the differences between my model and spatial models in Appendix 6.

In the non-spatial eco-evolutionary setting considered here, the connecting flux is one of mutations rather than migrations, and a predator genotype can in principle interact with all prey genotypes (whereas in usual spatialized models, interactions cannot occur between different patches). Another possibly important detail here is that similar genotypes do not have similar interaction phenotypes, meaning there is no risk of evolution being confined in a neighborhood of similar phenotypes. According to the author and my own cursory exploration of the relevant eco-evo literature (with which I am less familiar than pure ecology), this setting has yet to see many developments in the spirit of the many-species theories mentioned above.

These differences make this new inquiry worthwhile and I applaud the author for undertaking it. From a theoretical perspective, three results emerging from the simulations stand out in this article as potentially very interesting:

• rather sharp transitions in extinction probability and strain diversity as mutation flux and predator specialization increase.

• how mutation rate and interaction strength combine, notably in power-law expressions for total population abundance

• the discussion of susceptibilities, i.e. how predator and prey populations respond to perturbations, as a key ingredient in understanding the previous results, in particular with counter-intuitive negative susceptibilities indicating positive feedback loops.

It is a bit unfortunate that these more novel points are only briefly explored in the main text: while they are more developed in appendices, these arguments are not always as complete, polished and distilled as they might have been in a main text, so an article focusing entirely on explaining them deeply and intuitively would have been far more exciting to me.

Thank you for expressing such interest in the work. And I understand the point about the structure of the manuscript. This was a compromise on my part to make the text readable for a more diverse audience. There are “intuitive” descriptions in the main text, and more extensive intuitive descriptions in the supplement. The technical details are also primarily in the supplement. I have tried my best to make the supplement as readable as possible and cross-reference it with the relevant sections in the main text, but I understand that it is nonetheless particularly long and dense. I certainly understand the difficulty in reading and internalizing it all on a constrained timeframe.

Finally, I will note that I am not convinced by the framing of the current manuscript as a counterpoint to Robert May's idea of destabilizing diversity - in many ways I think this is a less relevant context than that of bet hedging, and it does a worse job at showcasing what is genuinely interesting and original here; I would thus encourage readers to read this paper in the framing I propose above.

As mentioned above, I reduced the emphasis on the May result and have explicitly mentioned the analogy to bet-hedging in the main text. I’ve also made a direct comparison to spatial models with a mainland in the supplement.

Author Response

Reviewer #2 (Public Review):

The authors performed a series of impressive experiments to systematically establish each part of their CRISPRi method. They provided one of the most compact design of CRISPRi dual-guideRNA library, with a genome-wide coverage; they confirmed prior finding on the optimal repressor domain to generate a set of useful vectors for expressing the repressor; they showcased the usage of the system in multiple common cancer cell lines. The authors also took an important step towards providing a detailed and well-annotated protocol (in the supplementary materials) to help users of their methods. The items listed below would be helpful to further improve this work:

First, while the dual guideRNA design is a useful development, the author also noted the significant rate (~30%) recombination between the two sgRNAs. This should be further discussed and evaluated in the manuscript to help readers understand the implication of this high recombination rate. For example, across replicate experiments or across cell types tested, would the recombination be stochastic, or there may be some bias of which guide would be recombined? Are there any cell-type dependencies here in terms of the recombination rate? This would also help future users to decide if they would need to check for this effect during functional screening.

We agree that recombination is an important limitation of dual-sgRNA screens. We included additional analyses and data in the revised manuscript to help readers understand the implications of the observed recombination.

First, we performed growth screens using dual-sgRNA libraries in two additional cell lines (RPE1 and Jurkat) to address the potential cell type specificity of lentiviral recombination. We cloned a dual-sgRNA library targeting DepMap Common Essential genes (n=2291 dual-sgRNA elements). We transduced cells with this library, harvested cells at day 7 post-transduction, amplified sgRNA cassettes from extracted genomic DNA, and sequenced to quantify sgRNA recombination rates. We found similar recombination rates of dual-sgRNA constructs isolated from these three cell types (observed K562 recombination rate 29%; observed RPE1 recombination rate 26%; observed Jurkat recombination rate 24%).

Next, we compared the recombination rates of each dual-sgRNA element. Our expectation was that lentiviral recombination would be largely stochastic per element based on the known mechanism of lentiviral recombination previously discussed in Adamson et al. 2018 (https://www.biorxiv.org/content/10.1101/298349v1.full) given that the constant region between sgRNAs (400bp) far exceeds the length of sgRNA targeting regions (20bp). However, we would also expect apparent recombination rates to be artificially inflated for dual-sgRNAs with strong growth phenotypes, as the stronger growth phenotypes of unrecombined dual-sgRNAs compared to recombined dual-sgRNAs will lead to dropout of unrecombined dual-sgRNAs. To account for this bias, we began by comparing the recombination rate for non-targeting control dual-sgRNAs excluding those with growth phenotypes across replicates of our K562 screens. There was only a weak correlation between the recombination rate for non-targeting control dual-sgRNAs (r = 0.30; Figure 1 – Figure Supplement 1E). We next compared the recombination rates of all dual-sgRNA elements (both targeting and non-targeting) across replicates of our K562 screens. As expected, we observed that the recombination rate of elements was correlated across replicates (r = 0.77; Figure 1 – Figure Supplement 1F), and the recombination rate was strongly anticorrelated with the growth phenotype of dual-sgRNAs in K562 cells (r = -0.84; Figure 1 – Figure Supplement 1G). We have integrated these data into the manuscript.

Second, on the repressor development and evaluation. As the author mentioned in the text, the expression level of the repressor can confound their conclusion on fitness/efficiency comparisons of CRISPR repressor. Thus, it would be helpful to perform protein level validation using the cell lines they generated, such as a WesternBlot comparison to rule out this potential issue.

We agree that differences in expression levels of the effectors can confound comparisons and that Western Blotting for such differences would be valuable. That said, any such analyses would not substantively alter the main claim of our paper, which is that Zim3-dCas9 provides excellent on-target knockdown in the absence of non-specific effects on cell growth or gene expression. This finding is of immediate practical use to the community. By no means are we claiming that we eliminated all possible confounding factors nor do we think that it is possible to do so. To avoid overstating our findings, we had acknowledged in the discussion that expression levels may indeed be a confounding factor, we had noted in the methods section that the dCas9-MeCP2 effector uses a different coding sequence for dCas9, which may contribute to differences in expression, and we had noted that other effectors may prove useful in some settings. We have further emphasized that differences in expression levels may contribute to our results in the revised manuscript.

This work would also benefit from including cell proliferation/viability measurement using the selected Zim3-dCas9 repressor in multiple cell lines, as it seems this was only done initially in K562 cells. As authors noted, the fitness effects of the CRISPR repressor would be a major concern when performing functional genomics screening, so such validation of fitness-neutrality of the repressor can be very helpful for potential users of their method and approach.

To address this point, we assessed the proliferation of HepG2, HuTu-80, and HT29 cells expressing Zim3-dCas9. Expression of Zim3-dCas9 did not have a negative impact on proliferation in any of these cell types, providing further evidence that the Zim3-dCas9 will be broadly useful. We included these data in Figure 4 – Figure Supplement 2 in the revised manuscript. That said, we cannot rule out that expression of Zim3-dCas9 may be detrimental in other cell types. Indeed, we want to emphasize that users should evaluate both on-target knockdown and lack of non-specific effects of effectors in new cell models before proceeding to large-scale experiments. The assays and protocols we describe are ideally suited for this purpose. We have further emphasized this point in the discussion section to guide users.

Third, a major resource from this work, as the authors noted, is a suite of useful Zim3-dCas9 cell lines. The authors have performed a set of experiments to demonstrate the knockdown efficiency with dozens of guideRNAs. While this is a good initial validation, to really ensure the cell lines are performing as expected, a small scale screening in pooled fashion will be more convincing. This would be a setting more relevant for potential readers, given that pooled screening would likely be the most powerful application of these cell lines.

While conducting the work described in this manuscript, we had used the Zim3-dCas9 RPE1 cell line for a large-scale pooled screen with single-cell RNA-seq readout (Perturb-seq, Replogle et al. 2022). Across greater than 2000 target genes, the median knockdown was 91.6%, which provides strong validation that Zim3-dCas9 performs as expected in this cell line. We had noted this point in the discussion section of our manuscript.

Author Response

Reviewer #1 (Public Review):

Oxidation of some KCNQ7 channels enhances channel activity. The manuscript by Nuñez and coauthors concluded that oxidation in the S2S3 linker of these channels disrupted the interaction between S2S3 and CaM EF-hand 3 (EF3). This mechanism is Ca2+-dependent. The apo EF3 no longer interacted with S2S3, and H2O2 no longer activated the channel. Electrophysiological recordings and fluorescence and NMR measurements of CaM with isolated helices A and B (CRD) and S2S3 of the channel were performed. While the results were in general clear with good quality, how the results support the conclusion was not clearly described. The approach using isolated molecular components in the study needs further validation since some of the results seem to show major conflicts with the results and mechanisms proposed in previous studies.

1) Previous studies showed differential responses of Kv7 channels to oxidation; Kv7.2, 4, and 5 are sensitive to oxidation regulation but Kv7.1 and 3 do not change upon H2O2 treatment. These differences were attributed at least partially to the sequence differences in S2S3 among Kv7 channels (ref 10 of this manuscript). The results in this manuscript show some major differences from the previous study. First, in all experiments, no difference was observed among Kv7 channels. Second, in Fig 3-6, S2S3 from KCNQ1 was used. The rationale for using KCNQ1 S2S3 and the interpretation of results is not justified considering that KCNQ1 S2S3 has fewer Cys residues and was least affected by oxidation in the previous study.

We addressed the issue of differential sensitivity of Kv7 channels to H2O2 in the section 3.2 above (and in the discussion, lines 364-380). In brief, Kv7.3 is likely to display diminished redox-sensitivity due to its high tonic Po (as discussed in ref 10). Kv7.1 does have reduced number of Cys residues in the S2S3 linker and is also insensitive to H2O2 but introducing additional cysteine residues into Kv7.1 S2S3 confers only a fairly weak redox sensitivity. Hence, we think that on the structural level, all Kv7 channels have a redoxresponsive element (S2S3 linker) but Kv7.1 and Kv7.3 have other constrains that prevent their activity to be modulated by their redox-responsive domains.

We have performed new experiments with Kv7.2 and Kv7.4 peptides (3 cysteine residues). These new data confirm our conclusions, and are now included in Figure 6.

2) In Fig 6, oxidation of S2S3 leads to a reduction of S2S3-CaM interaction, which leads to an increase of currents (Fig 1C). In Fig 4, Ca2+ loading leads to a reduced S2S3-CaM (EF3) interaction, which should also lead to an increase of currents based on Fig 6 conclusions. However, it is the EF3 mutation (destroying Ca2+ binding) that leads to the current increase (Fig 1B), contradictory to what Fig 6 data suggested.

Figure 6 and supplemental Figure 12 suggest that the effect of the peptides on the CRD is lost or reduced after oxidation. These data suggest that the oxidized S2S3 can no longer affect the CRD-CaM interaction. We propose that when EF3 is able to bind Ca2+ there is a tonic inhibition, and that oxidation relieves this inhibition leading to current increase.

As we explain above (see response 2.1), the effect is complicated due to CaMdependent promotion of surface expression.

Author Response

Reviewer #1 (Public Review):

Major

The observations on the hook lipids are critical and should be documented better. Based on previous work, it had been proposed that the hook lipids are associated with the inner leaflet and that they leave upon (partial) channel opening. In contrast, the present MD simulations indicate these lipids are associated with the outer leaflet and that their association to the channel persists on opening. These critical observations need to be documented better.

i) Do the authors observe hook lipids in the cryoEM structure of the open channel? If yes, data should be shown. If no, then the discrepancy between MD and EM should be explicitly addressed.

The resolution of the original cryo-EM density map of MscS in PC14 nanodiscs was not sufficient to reveal clear densities for the “hook” lipids. However, through further analysis we have now obtained an improved map to 3.1-Å resolution that offers new insights into this question – see Figure 2 – Figure Supplement 1. The new map confirms all the characteristics previously determined for the open conformation: same helical movements resulting in a similar opening of the pore, and the absence of lipids blocking it, all indicating a conducting conformation. In addition, the new map reveals a series of densities consistent with the dimensions of a phospholipid headgroup near the C-terminus of TM2 (facing the outside), filling a small cavity in-between adjacent TM1 helices. This position is precisely that occupied by the hook lipids in the close MscS structure obtained in PC18 nanodiscs. A headgroup residing in this density would also be well positioned to interact directly with Arg88, a key element in the hook-lipid interaction site, whose mutation leads to a strong loss-of-function phenotype (Reddy et al, 2019). These consistencies notwithstanding, we want to be cautious in this interpretation; these densities are of the same intensity as and blend with that of the nanodisc lipid, and so it is not possible to discern the acyl chains, which were more clearly resolved in the closed state. Therefore, while the new densities are consistent with a model in which the hook lipids are a structural feature of both closed and open states, as indicated by the simulation data, additional experimental data (or further improvements in the map) will be needed for an unequivocal assignment.

ii) Please show the comparison of the position and coordination of the hook lipids in MD simulations and in the closed (and/or open) structures.

See new Figure 2 – Figure Supplement 1 in comparison with Figure 5 and new Figure 4 – Figure Supplement 1.

iii) The authors acknowledge that the volume of the cavity where the hook lipids are located decreases on channel opening. How does this not affect the association of the hook lipids with the protein?

There appears to be a misunderstanding. The hydrophobic cavities that explain the membrane protrusions discussed in the manuscript are not where the “hook” lipids are observed – we hope to have fully clarified this in the new Figure 4 – Figure Supplement 1. These hydrophobic cavities are underneath each of the TM1-TM2 hairpins, on the cytoplasmic side of the transmembrane domain of the channel; accordingly the protrusions are formed in and exchange lipids with the inner leaflet of the bilayer. Upon reorientation of the TM1-TM2 hairpin, i.e. in the open state, these cavities indeed become smaller but more importantly, they become embedded in the membrane – and hence the protrusions are largely eliminated – see Figure 8 – Figure Supplement 1. The sites where the “hook” lipids observed are elsewhere in the structure, towards the outer entrance of the pore; these lipids originate in the outer leaflet. As discussed in the manuscript, the geometry of these sites in the experimentally determined structures of closed and open states is largely invariant; consistent with that observation, the occupancy of the “hook” lipid sites is also similar when simulations of closed and open states are compared. At this point, therefore, it is unclear whether the “hook” lipids are involved in tension sensing; it is plausible that their primary role is structural (for both open and closed states).

iv) Past work revealed several lipids in MscS structures near these cavities besides the hook lipids, and their ordered dissociation from the channel was proposed to be important for gating. Do the simulations show lipids in these cavities?

Yes. Previous structural studies identified individual lipid densities under the TM2-TM3 hairpins. Our data show these lipids are not isolated sites but integrated into a larger morphological feature.

v) Does the occupancy of the hook lipids in MD simulations change between the open and closed conformations? This should be analyzed.

Please see our answer to point (iii).

vi) Is the occupancy of other lipids in the nearby cavity altered upon channel opening?

Please see our answer to point (iii).

vii) Is the exchange of lipids near Ile150 affected by the conformational change?

Please see our answer to point (iii).

I am a bit confused by the claim that "The comparison clearly highlights the reduction in the width of the transmembrane span of the channel upon opening, and how this changed is well matched by the thickness of the corresponding lipid nanodiscs (approximately from 38 to 23 Å)."

This statement has been clarified. Our intention was to state is that in the open conformation stabilized by PC14, the increased tilt of the TM1-TM2 hairpins towards the midplane of the bilayer leads to a reduction in the hydrophobic width of the protein parallel to the membrane normal. (This reduction is clearly illustrated by our simulation data – see Figure 8 – Figure Supplement 1.) This change correlates with the reduction in thickness from the PC18 to the PC14 nanodiscs, explaining why the latter stabilizes the open state while the former stabilizes the closed state.

i. How was the nanodisc membrane thickness determined? This should be described.

ii. I do not see a ~15A change in the vertical length of the channel protein or of the nanodisc. While the panels in Fig.2 clearly show a vertical compression of the membrane, it appears that the ~15 A claim might be overstated. Adding a panel with measurements would be helpful to quantify this claim. If this is difficult on the membrane, maybe measurements could be performed on the protein.

The reviewer is correct. The original estimate, based on a cursory measurement of distances between two sets of protein atoms seemingly aligned with the water-lipid interface, turned out to be less accurate than expected. A better and more reproducible estimate has now been derived from the OPM database (https://opm.phar.umich.edu/). Using V3 of the database the closed-state is 32.6 Å and the open is 25.8 Å. The change is 6.8 Å. This is the value we now report.

iii. What happens to the N-terminal cap structure in the open state? What are the rearrangements that allow the extracellular ends of the TM1 to disassemble the cap.

In the open conformation part of the N-terminal cap appears to re-folds into TM1 extending its length as this helix tilts to embed itself at the membrane/water interface. The detailed side-chain structure of this domain is not clearly resolved but the C trace can be approximately inferred.

The data shown in Fig. 6 is cryptic and should be explained better in the main text. As it stands there is a cursory mention in pg. 12 and not much else.

i. It would be helpful if the authors showed the position of Ile150 in the structure.

Please see the revised version of Figure 6 and the corresponding caption.

ii. Does the total number of lipids in proximity of Ile150 change over time? Or the fold change represents ~1:1 exchange of lipids in the pocket?

Please see the revised version of Figure 6. The total number of lipids in proximity of Ile150 in closed MscS, i.e. the number of lipids filling the cavities under the TM1-TM2 hairpins, is approximately constant at any given timepoint; in both the CG and AA representations, we find about 4 lipids for each of the 7 subunits. However, these are not always same lipid molecules. For example, in a period of 20 s of CG simulation, 40 different lipid molecules were observed to transiently reside in each of protrusions. We trust that this new format of the figure will be more intuitive than the original version.

iii. I am confused by the difference in the maximum possible fold-change in unique lipids, does this reflect the difference in total number of lipids in each leaflet in each system? If so, I am a bit confused as to why there is a ~30% difference in the AA simulations whereas the values are nearly identical for the CG one.

Please see the revised version of Figure 6. For clarity we have eliminated the concept of fold-change (and maximum fold-change, relative to the total number of lipids in each leaflet), and now simply quantify the number of lipids in proximity to each site.

iv. Is it possible to quantify the residence time of the lipids in the pocket of each subunit?

Please see the revised version of Figure 6. From the data presented in panels C and D, it can be deduced that a full turnover takes 2-4 microseconds in the CG representation of the system; in the AA representation, we observe a turnover of about 75% in 10 microseconds, on average over all subunits.

The authors state on Pg. 21 "Nevertheless, we question the prevailing view that density signals of this kind are evidence of regulatory lipid binding sites; that is, we do not concur with the assumption that lipids regulate the gating equilibrium of MscS just like an agonist or antagonist would for a ligand-gated receptor-channel." I am a bit confused by this statement. In principle, binding and unbinding of modulatory ligands can happen on relatively fast time scales, so the observation that in MD simulations lipids exchange on a faster time scale than that of channel gating is not sufficient to make this inference. Indeed, there is ample evidence from other channels (i.e. Trp channels, HCN channels etc) where visualization of similar signals led to the identification of modulatory lipid binding sites. Thus, while I do not necessarily disagree with the authors, I would encourage them to tone down the general portion of the statement.

The statement has been rephrased as “Nevertheless, our data puts into question the prevailing view that density signals of this kind necessarily reflect long-lasting lipid immobilization, as one might expect for an agonist or antagonist of a ligand-gated receptor-channel.”

Reviewer #2 (Public Review):

1) Are the structures stable in the membrane also without the weak restraints on the dihedral angles? Continuing at least one of the atomistic simulations without restraints for about 1 microsecond in a tension-free membrane would address a possible concern that the severe membrane distortion could go away by a more extensive relaxation of the channel structure.

Please see our responses to the Editor.

2) Does the observed effect occur also in membranes with physiologically relevant PE lipids? Performing a simulation with a lipid mix closer to that in E. coli (and thus high in PE) would address a possible concern that the observed effect is not physiologically relevant.

Please see our responses to the Editor.

3) Please include a figure showing that the lipid positions in the MD simulations match the lipid densities in the cryo-EM maps.

Rather than re-rendering images already published, or generating new images that might not adequately represent the authors’ interpretation of their own data, we have to opted to specify the specific figures in previous studies where lipid densities under the TM1-TM2 hairpin have been clearly highlighted, for both MscS and MSL1. Specifically, for MscS, see Figure 4 in Zhang et al. [Ref. 16] and Figures 3-5 and Supplementary Figure 11 in Flegler et al [Ref. 15]; for MSL1, see Supplementary Figure 8 in Deng et al [Ref. 18].

4) Is the reported mobility of helices TM2-TM3 of MSL1, as deduced from a comparison of different cryo-EM structures (ref 18), sufficient to impact the lipid organisation?

In the naming convention used in Ref. 18, TM3 in MSL1 corresponds to TM1 in MscS. Different channels in this family feature different N-terminal domains preceding TM1. MscS features a short helix that has been referred as the N-cap, which lies on the membrane surface. MSL1 from Arabidopsis however features two additional TM helices – which confusingly Ref. 18 refers to as TM1 and TM2, while the key hairpin adjacent to the pore domain is referred to as TM3-TM4. Neither TM1 or TM2 in MSL1 are clearly resolved, presumably because they are indeed mobile, but they are in any case peripheral and therefore not likely to be critically influential for the morphological changes in the membrane that we discuss in the manuscript. Indeed, our simulations of MSL1 do not, by design, include those two N-terminal helices – in part because, as mentioned, they are poorly resolved, but also so that the results can be directly contrasted with MscS. Nevertheless, both channels show very similar deformations in the membrane for the closed state, and an elimination of these deformations in the open state.

5) Did the initial lipid configuration in atomistic MD simulations already contain the deformations of the inner leaflet, or did these form spontaneously both in coarse-grained and atomistic simulations?

Please see our responses to the Editor.

6) Did the earlier MD simulations of the closed-state structure 6PWN of MscL give any indications on the membrane deformation?

The simulation reported in Reddy et al alongside the structure of closed MscS in PC18 [Ref. 17] did not reveal the kind of deformations observed in this study, most probably due to insufficient equilibration time. However, that simulation did reveal a translational displacement of the channel relative to what had been previously assumed to be the transmembrane span. In retrospect, it seems clear that the observed translation was driven by the strong hydrophobic mismatch between the protein surface and the flat lipid bilayer; the membrane deformations we now observe represent the adaptation that ultimately minimizes that mismatch.

7) Are there distinct interactions between the headgroups of distorted inner-leaflet lipids with charged amino acids? If so, are these amino acids conserved?

Please see the new Figure 4 – Figure Supplement 1. As discussed in the manuscript, the interior of the cavities formed under the TM1-TM2 hairpins, and flanked by TM3a and TM3b, are lined almost entirely by hydrophobic residues. Charged and polar amino-acids are only observed on the outer face of the TM1-TM2 hairpin and are primarily in contact water.

Author Response

Reviewer #1 (Public Review):

The authors focused on linking physiological data on theta phase precession and spike-timing-dependent plasticity to the more abstract successor representation used in reinforcement learning models of spatial behavior. The model is presented clearly and effectively shows biological mechanisms for learning the successor representation. Thus, it provides an important step toward developing mathematical models that can be used to understand the function of neural circuits for guiding spatial memory behavior.

However, as often happens in the Reinforcement Learning (RL) literature, there is a lack of attention to non-RL models, even though these might be more effective at modeling both hippocampal physiology and its role in behavior. There should be some discussion of the relationship to these other models, without assuming that the successor representation is the only way to model the role of the hippocampus in guiding spatial memory function.

We thank the reviewer for the positive comments about the work, and for the detailed and constructive feedback. We agree with the reviewer that the manuscript will benefit from significantly more discussion of non-RL models, and we’ve detailed below a number of modifications to the manuscript to better incorporate prior work from the hippocampal literature, including the citations the reviewer has listed. Since our goal with this paper is to contextualise hippocampal phenomena in the context of an RL learning rule, this is really important and we appreciate the reviewers recommendations. We have added text (outlined in the point-by-point responses below) to the introduction and to the discussion that we hope better demonstrates the connections between the SR and existing computational models of hippocampus, and communicates clearly that the SR is not unique in capturing phenomena such as factorization of space and reward or capturing sequence statistics, but is rather a model that captures these phenomena while also connecting with downstream RL computations. Existing RL accounts of hippocampal representation often do not connect with known properties of hippocampus (as illustrated by the fact that TD learning was proposed in prior work to be the learning mechanism for SRs, even though this doesn’t have an obvious mechanism in HPC), so the purpose of this work is to explore the extent to which TD learning effectively overlaps with the well-studied properties of STDP and theta oscillations. In that sense, this paper is an effort to connect RL models of hippocampus to more physiologically plausible mechanisms rather than an attempt to model phenomena that the existing computational hippocampus literature could not capture.

1) Page 1- "coincides with the time window of STDP" - This model shows effectively how theta phase precession allows spikes to fall within the window of spike-timing-dependent synaptic plasticity to form successor representations. However, this combination of precession and STDP has been used in many previous models to allow the storage of sequences useful for guiding behavior (e.g. Jensen and Lisman, Learning and Memory, 1996; Koene, Gorchetchnikov, Cannon, Hasselmo, Neural Networks, 2003). These previous models should be cited here as earlier models using STDP and phase precession to store sequences. They should discuss in terms of what is the advantage of an RL successor representation versus the types of associative sequence coding in these previous models.

We agree that the idea of using theta precession to compress sequences onto the timescale of synaptic learning is a long-standing concept in sequence learning, and that we need to be careful to communicate what the advantages are of considering this in the RL context. We have added these citations to the introduction:

“One of the consequences of phase precession is that correlates of behaviour, such as position in space, are compressed onto the timescale of a single theta cycle and thus coincide with the time-window of STDP O(20 − 50 ms) [8, 18, 20, 21]. This combination of theta sweeps and STDP has been applied to model a wide range of sequence learning tasks [22, 23, 24], and as such, potentially provides an efficient mechanism to learn from an animal’s experience – forming associations between cells which are separated by behavioural timescales much larger than that of STDP.” and added a paragraph to the discussion as well that makes this clear:

“That the predictive skew of place fields can be accomplished with a STDP-type learning rule is a long-standing hypothesis; in fact, the authors that originally reported this effect also proposed a STDP-type mechanism for learning these fields [18, 20]. Similarly, the possible accelerating effect of theta phase precession on sequence learning has also been described in a number of previous works [22, 55, 23, 24]. Until recently [40, 41], SR models have largely not connected with this literature: they either remain agnostic to the learning rule or assume temporal difference learning (which has been well-mapped onto striatal mechanisms [37, 56], but it is unclear how this is implemented in hippocampus) [54, 31, 36, 57, 58]. Thus, one contribution of this paper is to quantitatively and qualitatively compare theta-augmented STDP to temporal difference learning, and demonstrate where these functionally overlap. This explicit link permits some insights about the physiology, such as the observation that the biologically observed parameters for phase precession and STDP resemble those that are optimal for learning the SR (Fig 3), and that the topographic organisation of place cell sizes is useful for learning representations over multiple discount timescales (Fig 4). It also permits some insights for RL, such as that the approximate SR learned with theta-augmented STDP, while provably theoretically different from TD (Section 5.8), is sufficient to capture key qualitative phenomena.”

2) On this same point, in the introduction, the successor representation is presented as a model that forms representations of space independent of reward. However, this independence of spatial associations and reward has been a feature of most hippocampal models, that then guide behavior based on interactions between a reward representation and the spatial representation (e.g. Redish and Touretzky, Neural Comp. 1998; Burgess, Donnett, Jeffery, O'Keefe, Phil Trans, 1997; Koene et al. Neural Networks 2003; Hasselmo and Eichenbaum, Neural Networks 2005; Erdem and Hasselmo, Eur. J. Neurosci. 2012). The successor representation should not be presented as if it is the only model that ever separated spatial representations and reward. There should be some discussion of what (if any) advantages the successor representation has over these other modeling frameworks (other than connecting to a large body of RL researchers who never read about non-RL hippocampal models). To my knowledge, the successor representation has not been explicitly tested on all the behaviors addressed in these earlier models.

We agree – a long-standing property of computational models in the hippocampal literature is a factorization of spatial and reward representations, and we have edited the text of the paper to make it clear that this is not a unique contribution of the SR. We have modified our description of the SR to better place it in the context of existing theories about hippocampal contributions to the factorised representations of space and goals, and included all citations mentioned here by adding the following text.

We have added a sentence to the introduction:

“However, the computation of expected reward can be decomposed into two components – the successor representation, a predictive map capturing the expected location of the agent discounted into the future, and the expected reward associated with each state [26]. Such segregation yields several advantages since information about available transitions can be learnt independently of rewards and thus changes in the locations of rewards do not require the value of all states to be re-learnt. This recapitulates a number of long-standing theories of hippocampus which state that hippocampus provides spatial representations that are independent of the animal’s particular goal and support goal-directed spatial navigation[27, 28, 23, 29, 30]”

We have also added a paragraph to the discussion:

“The SR model has a number of connections to other models from the computational hippocampus literature that bear on the interpretation of these results. A long-standing property of computational models in the hippocampal literature is a factorisation of spatial and reward representations [27, 28, 23, 29, 30], which permits spatial navigation to rapidly adapt to changing goal locations. Even in RL, the SR is also not unique in factorising spatial and reward representations, as purely model-based approaches do this too [26, 25, 67]. The SR occupies a much more narrow niche, which is factorising reward from spatial representations while caching long-term occupancy predictions [26, 68]. Thus, it may be possible to retain some of the flexibility of model-based approaches while retaining the rapid computation of model-free learning.”

3) Related to this, successes of the successor representation are presented as showing thebackward expansion of place cells. But this was modeled at the start by Mehta and colleagues using STDP-type mechanisms during sequence encoding, so why was the successor representation necessary for that? I don't want to turn this into a review paper comparing hippocampal models, but the body of previous models of the role of the hippocampus in behavior warrants at least a paragraph in each of the introduction and discussion sections. In particular, it should not be somehow assumed that the successor representation is the best model, but instead, there should be some comparison with other models and discussion about whether the successor representation resembles or differs from those earlier models.

We agree this was not clear. This is a nuanced point that warrants substantial discussion, and we have added a paragraph to the discussion (see the paragraph in the response to point 1 that begins “That the predictive skew of place fields can be accomplished…”).

4) The text seems to interchangeably use the term "successor representation" and "TD trained network" but I think it would be more accurate to contrast the new STDP trained network with a network trained by Temporal Difference learning because one could argue that both of them are creating a successor representation.

We now refer to these as “STDP successor features” and “TD successor features”. We have also replaced all references of “true successor representation/features” to “TD successor representation/feature” and have edited the text at the beginning of the results section to reflect this:

“The STDP synaptic weight matrix Wij (Fig. 1d) can then be directly compared to the temporal difference (TD) successor matrix Mij (Fig. 1e), learnt via TD learning on the CA3 basis features (the full learning rule is derived in Methods and shown in Eqn. 27). Further, the TD successor matrix Mij can also be used to generate the ‘TD successor features’...”

Reviewer #2 (Public Review):

The authors present a set of simulations that show how hippocampal theta sequences may be combined with spike time-dependent plasticity to learn a predictive map - the successor representation - in a biologically plausible manner. This study addresses an important question in the field: how might hippocampal theta sequences be combined with STDP to learn predictive maps? The conclusions are interesting and thought-provoking. However, there were a number of issues that made it hard to judge whether the conclusions of the study are justified. These concerns mainly surround the biological plausibility of the model and parameter settings, the lack of any mathematical analysis of the model, and the lack of direct quantitative comparison of the findings to experimental data.

While the model uses broadly realistic biological elements to learn the successor representation, there remain a number of important concerns with regard to the biological plausibility of the model. For example, the model assumes that each CA3 cell connects to exactly 1 CA1 cell throughout the whole learning process so that each CA1 cell simply inherits the activity of a single CA3 cell. Moreover, neurons in the model interact directly via their firing rate, yet produce spikes that are used only for the weight updates. Certain model parameters also appeared to be unrealistic, for example, the model combined very wide place fields with slow running speeds. This leaves open the question as to whether the proposed learning mechanism would function correctly in more realistic parameter settings. Simulations were performed for a fixed running speed, thereby omitting various potentially important effects of running speed on the phase precession and firing rate of place cells. Indeed, the phase precession of CA1 place cells was not shown or discussed, so it is unclear as to whether CA1 cells produce realistic patterns of phase precession in the model.

The fact that a successor-like representation emerges in the model is an interesting result and is likely to be of substantial interest to those working at the intersection between neuroscience and artificial intelligence. However, because no theoretical analysis of the model was performed, it remains unclear why this interesting correspondence emerges. Was it a coincidence? When will it generalise? These questions are best answered by mathematical analysis of the model (or a reduced form of it).

Several aspects of the model are qualitatively consistent with experimental data. For example, CA1 place fields clustered around doorways and were elongated along walls. While these findings are important and provide some support for the model, considerable work is required to draw a firm correspondence between the model and experimental data. Thus, without a quantitative comparison of the place field maps in experimental data and the model, it is hard to draw strong conclusions from these findings.

Overall, this study promises to make an important contribution to the field, and will likely be read with interest by those working in the fields of both neuroscience and artificial intelligence. However, given the above caveats, further work is required to establish the biological plausibility of the model, develop a theoretical understanding of the proposed learning process, and establish a quantitative comparison of the findings to experimental data.

Thank you for the positive comments about the work, and for the detailed and constructive review. We appreciate the time spent evaluating the model and understanding its features at a deep level. Your comments and suggestions have led to exciting new simulation results and a theoretical analysis which shed light on the connections between TD learning, STDP and phase precession.

We have incorporated a number of new simulations to tackle what we believe are your most pressing concerns surrounding the model’s biological plausibility. As such, we have extended the hyperparameter sweep (Supp. Fig 3) to include the phase precession parameters you recommended, as well as three new multipanel supplementary figures satisfying your recommendations (Supp. Figs. 1, 2 & 4). Collectively, these figures show that the specifics of our results, which as you pointed out might have been produced with biologically implausible values (place cell size, movement speed/statistics, weight initialisation, weight updating schedule and phase precession parameters), do not fundamentally depend on the specific values of these parameters: the mechanism still learns predictive maps close in form to the TD successor features. In the hyperparameter sweep, we do find that results are sensitive to specific parameter values (Supp. Fig 3), but that interestingly, the optimal values of these parameters are remarkably close to those observed experimentally. We have also written an extensive new theory section analysing why theta sequences plus STDP approximates TD learning. In addition the methods section has been added to and reordered to make some of the subtler aspects of our model (i.e. the mapping of rates-to-rates and weight fixing during learning) more clear.

At a high level, regarding our claim of biological plausibility, we like to clarify our intended contribution and give context to some responses below. We have added the following paragraph to the discussion in order to accurately represent the scope of our work:

“While our model is biologically plausible in several respects, there remain a number of aspects of the biology that we do not interface with, such as different cell types, interneurons and membrane dynamics. Further, we do not consider anything beyond the most simple model of phase precession, which directly results in theta sweeps in lieu of them developing and synchronising across place cells over time [60]. Rather, our philosophy is to reconsider the most pressing issues with the standard model of predictive map learning in the context of hippocampus (e.g., the absence of dopaminergic error signals in CA1 and the inadequacy of synaptic plasticity timescales). We believe this minimalism is helpful, both for interpreting the results presented here and providing a foundation for further work to examine these biological intricacies, such as the possible effect of phase offsets in CA3, CA1 [61] and across the dorsoventral axis [62, 63], as well as whether the model’s theta sweeps can alternately represent future routes [64] e.g. by the inclusion of attractor dynamics [65].”

Author Response:

Reviewer #1 (Public Review):

Chakrabarti et al study inner hair cell synapses using electron tomography of tissue rapidly frozen after optogenetic stimulation. Surprisingly, they find a nearly complete absence of docked vesicles at rest and after stimulation, but upon stimulation vesicles rapidly associate with the ribbon. Interestingly, no changes in vesicle size were found along or near the ribbon. This would have indicated a process of compound fusion prior to plasma membrane fusion, as proposed for retinal bipolar cell ribbons. This lack of compound fusion is used to argue against MVR at the IHC synapse. However, that is only one form of MVR. Another form, coordinated and rapid fusion of multiple docked vesicles at the bottom of the ribbon, is not ruled out. Therefore, I agree that the data set provides good evidence for rapid replenishment of the ribbon-associated vesicles, but I do not find the evidence against MVR convincing. The work provides fundamental insight into the mechanisms of sensory synapses.

We thank the reviewer for the appreciation of our work and the constructive comments. As pointed out below, we now included this discussion (from line 679 onwards).

We wrote:

“This might reflect spontaneous univesicular release (UVR) via a dynamic fusion pore (i.e. ‘kiss and run’, (Ceccarelli et al., 1979), which was suggested previously for IHC ribbon synapses (Chapochnikov et al., 2014; Grabner and Moser, 2018; Huang and Moser, 2018; Takago et al., 2019) and/or and rapid undocking of vesicles (e.g. Dinkelacker et al., 2000; He et al., 2017; Nagy et al., 2004; Smith et al., 1998). In the UVR framework, stimulation by ensuing Ca2+ influx triggers the statistically independent release of several SVs. Coordinated multivesicular release (MVR) has been indicated to occur at hair cell synapses (Glowatzki and Fuchs, 2002; Goutman and Glowatzki, 2007; Li et al., 2009) and retinal ribbon synapses (Hays et al., 2020; Mehta et al., 2013; Singer et al., 2004) during both spontaneous and evoked release. We could not observe structures which might hint towards compound or cumulative fusion, neither at the ribbon nor at the AZ membrane under our experimental conditions. Upon short and long stimulation, RA-SVs as well as docked SVs even showed a slightly reduced size compared to controls. However, since some AZs harbored more than one docked SV per AZ in stimulated conditions, we cannot fully exclude the possibility of coordinated release of few SVs upon depolarization.”

Reviewer #2 (Public Review):

Chakrabarti et al. aimed to investigate exocytosis from ribbon synapses of cochlear inner hair cells with high-resolution electron microscopy with tomography. Current methods to capture the ultrastructure of the dynamics of synaptic vesicle release in IHCs rely on the application of potassium for stimulation, which constrains temporal resolution to minutes rather than the millisecond resolution required to analyse synaptic transmission. Here the authors implemented a high-pressure freezing method relying on optogenetics for stimulation (Opto-HPF), granting them both high spatial and temporal resolutions. They provide an extremely well-detailed and rigorously controlled description of the method, falling in line with previously use of such "Opto-HPF" studies. They successfully applied Opto-HPF to IHCs and had several findings at this highly specialised ribbon synapse. They observed a stimulation-dependent accumulation of docked synaptic vesicles at IHC active-zones, and a stimulation-dependent reduction in the distance of non-docked vesicles to the active zone membrane; while the total number of ribbon-associated vesicles remained unchanged. Finally, they did not observe increases in diameter of synaptic vesicles proximal to the active zone, or other potential correlates to compound fusion - a potential mode of multivesicular release. The conclusions of the paper are mostly well supported by data, but some aspects of their findings and pitfalls of the methods should be better discussed.

We thank the reviewer for the appreciation of our work and the constructive comments.

Strengths:

While now a few different groups have used "Opto-HPF" methods (also referred to as "Flash and Freeze) in different ways and synapses, the current study implemented the method with rigorous controls in a novel way to specifically apply to cochlear IHCs - a different sample preparation than neuronal cultures, brain slices or C. elegans, the sample preparations used so far. The analysis of exocytosis dynamics of IHCs with electron microscopy with stimulation has been limited to being done with the application of potassium, which is not physiological. While much has been learned from these methods, they lacked time resolution. With Opto-HPF the authors were successfully able to investigate synaptic transmission with millisecond precision, with electron tomography analysis of active zones. I have no overall questions regarding the methodology as they were very thoroughly described. The authors also employed electrophysiology with optogenetics to characterise the optical simulation parameters and provided a well described analysis of the results with different pulse durations and irradiance - which is crucial for Opto-HPF.

Thank you very much.

Further, the authors did a superb job in providing several tables with data and information across all mouse lines used, experimental conditions, and statistical tests, including source code for the diverse analysis performed. The figures are overall clear and the manuscript was well written. Such a clear representation of data makes it easier to review the manuscript.

Thank you very much.

Weaknesses:

There are two main points that I think need to be better discussed by the authors.

The first refers to the pitfalls of using optogenetics to analyse synaptic transmission. While ChR2 provides better time resolution than potassium application, one cannot discard the possibility that calcium influx through ChR2 alters neurotransmitter release. This important limitation of the technique should be properly acknowledged by the authors and the consequences discussed, specifically in the context in which they applied it: a single sustained pulse of light of ~20ms (ShortStim) and of ~50ms (LongStim). While longer, sustained stimulation is characteristic for IHCs, these are quite long pulses as far as optogenetics and potential consequences to intrinsic or synaptic properties.

We thank the reviewer for pointing this out. We would like to mention that upon 15 min high potassium depolarization, the number of docked SVs only slightly increased as shown in Chakrabarti et al., 2018, EMBO rep and Kroll et al. 2020 JCS, but it was not statistically significant. In the current study, we report a similar phenomenon, but here light induced depolarization resulted in a more robust increase in the number of docked SVs.

To compare the data from the previous studies with the current study, we included an additional table 3 (line 676) now in the discussion with all total counts (and average per AZ) of docked SVs.

Furthermore, in response to the reviewers’ concern, we now discuss the Ca2+ permeability of ChR2 in addition to the above comparison to our previous studies that demonstrated very few docked SVs in the absence of K+ channel blockers and ChR2 expression in IHCs. We are not entirely certain, if the reviewer refers to potential dark currents of ChR2 (e.g. as an explanation for a depletion of docked vesicles under non-stimulated conditions) or to photocurrents, the influx of Ca2+ through ChR2 itself, and their contribution to Ca2+ concentration at the active zone.

However, regardless this, we consider it unlikely that a potential contribution of Ca2+ influx via ChR2 evokes SV fusion at the hair cell active zone.

First of all, we note that the Ca2+ affinity of IHC exocytosis is very low. As first shown in Beutner et al., 2001 and confirmed thereafter (e.g. Pangrsic et al., 2010), there is little if any IHC exocytosis for Ca2+ concentrations at the release sites below 10 µM. Two studies using CatCh (a ChR2 mutant with higher Ca2+ permeability than wildtype ChR2 (Kleinlogel et al., 2011; Mager et al., 2017) estimated a max intracellular Ca2+ increase below 10 µM, even at very negative potentials that promote Ca2+ influx along the electrochemical gradient or at high extracellular Ca2+ concentrations of 90 mM. In our experiments, IHCs were depolarized, instead, to values for which extrapolation of the data of Mager et al., 2017 indicate a submicromolar Ca2+ concentration. In addition, we and others have demonstrated powerful Ca2+ buffering and extrusion in hair cells (e.g. Tucker and Fettiplace, 1995; Issa and Hudspeth., 1996; Frank et al., 2009 Pangrsic et al., 2015). As a result, the hair cells efficiently clear even massive synaptic Ca2+ influx and establish a low bulk cytosolic Ca2+ concentration (Beutner and Moser, 2001; Frank et al., 2009). We reason that these clearance mechanisms efficiently counter any Ca2+ influx through ChR2. This will likely limit potential effects of ChR2 mediated Ca2+ influx on Ca2+ dependent replenishment of synaptic vesicles during ongoing stimulation.

We have now added the following in the discussion (starting in line 620):

“We note that ChR2, in addition to monovalent cations, also permeates Ca2+ ions and poses the question whether optogenetic stimulation of IHCs could trigger release due to direct Ca2+ influx via the ChR2. We do not consider such Ca2+ influx to trigger exocytosis of synaptic vesicles in IHCs. Optogenetic stimulation of HEK293 cells overexpressing ChR2 (wildtype version) only raises the intracellular Ca2+ concentration up to 90 nM even with an extracellular Ca2+ concentration of 90 mM (Kleinlogel et al., 2011). IHC exocytosis shows a low Ca2+ affinity (~70 µM, Beutner et al., 2001) and there is little if any IHC exocytosis for Ca2+ concentrations below 10 µM, which is far beyond what could be achieved even by the highly Ca2+ permeable ChR2 mutant (CatCh: Ca2+ translocating channelrhodopsin, Mager et al., 2017). In addition, we reason that the powerful Ca2+ buffering and extrusion by hair cells (e.g., Frank et al., 2009; Issa and Hudspeth, 1996; Pangršič et al., 2015; Tucker and Fettiplace, 1995) will efficiently counter Ca2+ influx through ChR2 and, thereby limit potential effects on Ca2+ dependent replenishment of synaptic vesicles during ongoing stimulation. “

The second refers to the finding that the authors did not observe evidence of compound fusion (or homotypic fusion) in their data. This is an interesting finding in the context of multivesicular release in general, as well as specifically for IHCs. While the authors discussed the potential for "kiss-and-run" and/or "kiss-and-stay", it would be valuable if they could discuss their findings further in the context of the field for multivesicular release. For example, the evidence in support of the potential of multiple independent release events. Further, as far as such function-structure optical-quick-freezing methods, it is not unusual to not capture fusion events (so-called omega-shapes or vesicles with fusion pores); this is largely because these are very fast events (less than 10 ms), and not easily captured with optical stimulation.

We agree with the reviewer that the discussion on MVR and UVR should be extended. We now added the following paragraph to the discussion from line 679 on:

“This might reflect spontaneous univesicular release (UVR) via a dynamic fusion pore (i.e. ‘kiss and run’, (Ceccarelli et al., 1979), which was suggested previously for IHC ribbon synapses (Chapochnikov et al., 2014; Grabner and Moser, 2018; Huang and Moser, 2018; Takago et al., 2019) and/or and rapid undocking of vesicles (e.g. Dinkelacker et al., 2000; He et al., 2017; Nagy et al., 2004; Smith et al., 1998). In the UVR framework, stimulation by ensuing Ca2+ influx triggers the statistically independent release of several SVs. Coordinated multivesicular release (MVR) has been indicated to occur at hair cell synapses (Glowatzki and Fuchs, 2002; Goutman and Glowatzki, 2007; Li et al., 2009) and retinal ribbon synapses (Hays et al., 2020; Mehta et al., 2013; Singer et al., 2004) during both spontaneous and evoked release. We could not observe structures which might hint towards compound or cumulative fusion, neither at the ribbon nor at the AZ membrane under our experimental conditions. Upon short and long stimulation, RA-SVs as well as docked SVs even showed a slightly reduced size compared to controls. However, since some AZs harbored more than one docked SV per AZ in stimulated conditions, we cannot fully exclude the possibility of coordinated release of few SVs upon depolarization.”

Reviewer #3 (Public Review):

Precise methods were developed to validate the expression of channelrhodopsin in inner hair cells of the Organ of Corti, to quantify the relationship between blue light irradiance and auditory nerve fiber depolarization, to control light stimulation within the chamber of a high-pressure freezing device, and to measure with good precision the delay between stimulation and freezing of the specimen. These methods represent a clear advance over previous experimental designs used to study this synaptic system and are an initial application of rapid high-pressure freezing with freeze substitution, followed by high-resolution electron tomography (ET), to sensory cells that operate via graded potentials.

Short-duration stimuli were used to assess the redistribution of vesicles among pools at hair cell ribbon synapses. The number of vesicles linked to the synaptic ribbon did not change, but vesicles redistributed within the membrane-proximal pool to docked locations. No evidence was found for vesicle-to-vesicle fusion prior to vesicle fusion to the membrane, which is an important, ongoing question for this synapse type. The data for quantifying numbers of vesicles in membrane-tethered, non-tethered, and docked vesicle pools are compelling and important.

We thank the reviewer for the appreciation of our work and the constructive comments.

These quantifications would benefit from additional presentation of raw images so that the reader can better assess their generality and variability across synaptic sites.

The images shown for each of the two control and two experimental (stimulated) preparation classes should be more representative. Variation in synaptic cleft dimensions and numbers of ribbon-associated and membrane-proximal vesicles do not track the averaged data. Since the preparation has novel stimulus features, additional images (as the authors employed in previous publications) exhibiting tethered vesicles, non-tethered vesicles, docked vesicles, several sections through individual ribbons, and the segmentation of these structures, will provide greater confidence that the data reflect the images.

Thank you very much for pointing this out. We now included more details in supplemental figures and in the text.

Precisely, we added:

• More details about the morphological sub-pools (analysis and images):

  -We now show a sequence of images with different tethering states of membrane proximal SVs together with examples for docked and non-tethered SVs as we did in Chakrabarti et al., 2018 for each condition (Fig. 6-figure supplement 2, line 438). Moreover, we included for each condition additional information, we selected further tomograms, one per condition, and depict two additional virtual sections: Fig. 6-figure supplement 2.

  -Moreover, we present a more detailed quantification for the different morphological sub-pools: For the MP-SV pool, we analyzed the SV diameters and the distances to the AZ membrane and PD of different SV sub-pools separately, we now included this information in Fig. 7 For the RA-SVs, we analyzed in addition the morphological sub-pools and the SV diameters in the distal and the proximal ribbon part as done in Chakrabarti et al. 2018. We now added a new supplement figure (Fig. 7-figure supplement 2, line 558 and a supplementary file 2).

• We replaced the virtual section in panel 6D: In the old version, it appeared that the ribbon was contacting the membrane and we realized that this virtual section was not representative: actually, the ribbon was not directly contacting the AZ membrane, a presynaptic density was still visible adjacent to the docked SVs. To avoid potential confusion, we selected a different virtual section of the same tomogram and now indicated the presynaptic density also as graphical aid in Fig. 6.

The introduction raises questions about the length of membrane tethers in relation to vesicle movement toward the active zone, but this topic was not addressed in the manuscript.

We apologize for not stating it sufficiently clear, we now rephrased this sentence. We now wrote:

“…and seem to be organized in sub-pools based on the number of tethers and to which structure these tethers are connected. “

Seemingly quantification of this metric, and the number of tethers especially for vesicles near the membrane, is straightforward. The topic of EPSC amplitude as representing unitary events due to variation in vesicle volume, size of the fusion pore, or vesicle-vesicle fusion was partially addressed. Membrane fusion events were not evident in the few images shown, but these presumably occurred and could be quantified. Likewise, sites of membrane retrieval could also be marked. These analyses will broaden the scope of the presentation, but also contribute to a more complete story.

Regarding the presence/absence of membrane fusion events we agree with the reviewer that this should be clearly addressed in the MS. We would like to point out that we

(i) did not observe any omega shapes at the AZ membrane, which we also mention in the MS. We can also report that we could not see them in data sets from previous publications (Vogl et al., 2015, JCS; Jung et al., 2015, PNAS).

(ii) To be clear on our observations on potential SV-SV fusion events we now point out in the discussion from line 688ff:

“We could not observe structures which might hint towards compound or cumulative fusion, neither at the ribbon nor at the AZ membrane under our experimental conditions. Upon short and long stimulation, RA-SVs as well as docked SVs even showed a slightly reduced size compared to controls. However, since some AZs harbored more than one docked SV per AZ in stimulated conditions, we cannot fully exclude the possibility of coordinated release of few SVs upon depolarization.”

Furthermore, we agree with the reviewer that a complete presentation of endo-exocytosis structural correlates is very important. However, we focused our study on exocytosis events and therefore mainly analyzed membrane proximal SVs at active zones.

Nonetheless, in response to the reviewer’s comment, we now included a quantification of clathrin-coated (CC) structures. We determined the appearance of CC vesicles (V) and CC invaginations within 0-500 nm away from the PD. We measured the diameter of the CCV, and their distance to the membrane and the PD. We only found very few CC structures in our tomograms (now added in a table to the result section (Supplementary file 1). Sites for endocytic membrane retrieval likely are in the peri-active zone area or even beyond. We did not observe obvious bulk endocytosis events that were connected to the AZ membrane. However, we do observe large endosomal like vesicles that we did not quantify in this study. More details were presented in two of our previous studies: Kroll et al., 2019 and 2020, however, under different stimulation conditions.

Overall, the methodology forms the basis for future studies by this group and others to investigate rapid changes in synaptic vesicle distribution at this synapse.

Reviewer #4 (Public Review):

This manuscript investigates the process of neurotransmitter release from hair cell synapses using electron microscopy of tissue rapidly frozen after optogenetic stimulation. The primary finding is that in the absence of a stimulus very few vesicles appear docked at the membrane, but upon stimulation vesicles rapidly associate with the membrane. In contrast, the number of vesicles associated with the ribbon and within 50 nm of the membrane remains unchanged. Additionally, the authors find no changes in vesicle size that might be predicted if vesicles fuse to one-another prior to fusing with the membrane. The paper claims that these findings argue for rapid replenishment and against a mechanism of multi-vesicular release, but neither argument is that convincing. Nonetheless, the work is of high quality, the results are intriguing, and will be of interest to the field.

We thank the reviewer for the appreciation of our work and the constructive comments.

1) The abstract states that their results "argue against synchronized multiquantal release". While I might agree that the lack of larger structures is suggestive that homotypic fusion may not be common, this is far from an argument against any mechanisms of multi-quantal release. At least one definition of synchronized multiquantal release posits that multiple vesicles are fusing at the same time through some coordinated mechanism. Given that they do not report evidence of fusion itself, I fail to see how these results inform us one way or the other.

We agree with the reviewer that the discussion on MVR and UVR should be extended. It is important to point out that we do not claim that the evoked release is mediated by one single SV. As discussed in the paper (line 672), we consider that our optogenetic stimulation of IHCs triggers the release of more than 10 SVs per AZ. This falls in line with the previous reports of several SVs fusing upon stimulation. This type of evoked MVR is probably mediated by the opening of Ca2+ channels in close proximity to each SV Ca2+ sensor. We indeed sometimes observed more than one docked SV per AZ upon long optogenetic stimulation. This could reflect that possibility. However, given the absence of large structures directly at the ribbon or the AZ membrane that could suggest the compound fusion of several SVs prior or during fusion, we argue against compound MVR release at IHCs. As mentioned above, we added to the discussion (from line 679 onwards).

We wrote:

“This might reflect spontaneous univesicular release (UVR) via a dynamic fusion pore (i.e. ‘kiss and run’, (Ceccarelli et al., 1979), which was suggested previously for IHC ribbon synapses (Chapochnikov et al., 2014; Grabner and Moser, 2018; Huang and Moser, 2018; Takago et al., 2019) and/or and rapid undocking of vesicles (e.g. Dinkelacker et al., 2000; He et al., 2017; Nagy et al., 2004; Smith et al., 1998). In the UVR framework, stimulation by ensuing Ca2+ influx triggers the statistically independent release of several SVs. Coordinated multivesicular release (MVR) has been indicated to occur at hair cell synapses (Glowatzki and Fuchs, 2002; Goutman and Glowatzki, 2007; Li et al., 2009) and retinal ribbon synapses (Hays et al., 2020; Mehta et al., 2013; Singer et al., 2004) during both spontaneous and evoked release. We could not observe structures which might hint towards compound or cumulative fusion, neither at the ribbon nor at the AZ membrane under our experimental conditions. Upon short and long stimulation, RA-SVs as well as docked SVs even showed a slightly reduced size compared to controls. However, since some AZs harbored more than one docked SV per AZ in stimulated conditions, we cannot fully exclude the possibility of coordinated release of few SVs upon depolarization.”

2) The complete lack of docked vesicles in the absence of a stimulus followed by their appearance with a stimulus is a fascinating result. However, since there are no docked vesicles prior to a stimulus, it is really unclear what these docked vesicles represent - clearly not the RRP. Are these vesicles that are fusing or recently fused or are they ones preparing to fuse? It is fine that it is unknown, but it complicates their interpretation that the vesicles are "rapidly replenished". How does one replenish a pool of docked vesicles that didn't exist prior to the stimulus?

In response to the reviewers’ comment, we would like to note that we indeed reported very few docked SVs in wild type IHCs at resting conditions without K+ channel blockers in Chakrabarti et al. EMBO Rep 2018 and in Kroll et al., 2020, JCS. In both studies, a solution without TEA and Cs was used for the experiments (resting solution Chakrabarti: 5 mM KCl, 136.5 mM NaCl, 1 mM MgCl2, 1.3 mM CaCl2, 10 mM HEPES, pH 7.2, 290 mOsmol; control solution Kroll: 5.36 mM KCl, 139.7 mM NaCl, 2 mM CaCl2, 1 mM MgCl2, 0.5 mM MgSO4, 10 mM HEPES, 3.4 mM L-glutamine, and 6.9 mM D-glucose, pH 7.4). Similarly, our current study shows very few docked SVs in the resting condition even in the presence of TEA and Cs. Based on the results presented in ‘Response to reviewers Figure 1’, we assume that the scarcity of docked SVs under control conditions is not due to depolarization induced by a solution containing 20 mM TEA and 1 mM Cs but is rather representative for the physiological resting state of IHC ribbon synapses. Upon 15 min high potassium depolarization, the number of docked SVs only slightly increased as shown in Chakrabarti et al., 2018 and Kroll et al. 2020, but it was not statistically significant. In the current study, we report a similar phenomenon, but here depolarization resulted in a more robust increase in the number of docked SVs.

To compare the data from the previous studies with the current study, we included an additional table 3 (line 676) now in the discussion with all total counts (and average per AZ) of docked SVs.

Author Response:

eLife assessment

This paper reports a useful set of results that uses a reduced network model based on a previously published large-scale network model to explain the generation of theta-gamma rhythms in the hippocampus. Combining the detailed and reduced models and comparing their results is a powerful approach. However, the evidence for the main claim that CCK+ basket cells play a key role in theta-gamma coupling in the hippocampus is currently incomplete.

We thank the reviewers for their thorough and thoughtful notes, and we are pleased that there is acknowledgement of the combination of models as a powerful approach.  We agree with many of the comments made and we intend to address them in subsequent revisions. 

In particular, we think that our ‘narrative’ as presented was perhaps not as clear as it could have been, based on the somewhat different comments from the reviewers (R#1 and #3).  That is, we created a reduced population rate model based on the theta/gamma generation hypotheses from the detailed model and then explored the PRM in more detail to predict cellular contributions.  The goal was not to validate the original detailed model per se (R#1) nor to do a fitting of parameters in the PRM directly from the detailed model (R#3).  Rather, it was to obtain a set of parameter values in PRM that would be in accordance with the hypotheses of the detailed model that could be fully explored to derive cellular-based predictions that could help design experiments to understand theta/gamma rhythms.

Responses specific to the Reviewers are given below.

Reviewer #1 (Public Review):

This paper investigates potential mechanisms underlying the generation of hippocampal theta and gamma rhythms using a combination of several modeling approaches. The authors perform new simulation experiments on the existing large-scale biophysical network model previously published by Bezaire et al. Guided by their analysis of this detailed model, they also develop a strongly reduced, rate-based network model, which allows them to run a much larger number of simulations and systematically explore the effects of varying several key parameters. The combined results from these two in silico approaches allow them to predict which cell types and connections in the hippocampus might be involved in the generation and coupling of theta and gamma oscillations.

In my view, several aspects of the general methodology are exemplary. In the current work as well as several earlier papers, the authors are re-using a large-scale network model that was originally developed in a different laboratory (Bezaire et al., 2016) and that still represents the state-of-the-art in detailed hippocampal modeling. Such model reuse is quite rare in computational neuroscience, which is rather unfortunate given the amount of time and effort required to build and share such a complex model. Very often, and also, in this case, the original publication that describes a detailed model provides only limited validation and analysis of model behavior, and the re-use of the same model in later studies represents a great opportunity to further examine and validate the model.

Combining detailed and simplified models can also be a powerful approach, especially when the correspondence between the two is carefully established. Matching results from the two models, in this case, allow strong arguments about key mechanisms of biological phenomena, where the simplified model allows the identification and characterization of necessary and sufficient components, while the detailed model can firmly anchor the models and their predictions to experimental data.

On the other hand, I have several major concerns about the implementation of these approaches and the interpretation of the results in the current study. First of all, the detailed model of Bezaire et al. is considered strictly equivalent, in all of its relevant details, to biological reality, and no attempt is made to verify or even discuss the validity of this assumption, even when particular details of the model are apparently critical for the results presented. I see this as a fundamental limitation of the current work - the fact that the Bezaire et al. model is the best one we have at the moment does not automatically make it correct in all its details, and features of the model that are essential for the new results certainly deserve careful scrutiny (preferably via detailed comparison with experimental data).

An important case in point is the strength of the interactions between specific neuronal populations. This is represented by different quantities in the detailed and simplified model, but the starting point is always the synaptic weight (conductance) values given by Bezaire et al. (2016), also listed in Tables 2 and 3 of the current manuscript. Looking at these parameters, one can identify a handful of connections whose conductance values are much higher than those of the other connections, and also more than an order of magnitude higher (50-100 nS) than commonly estimated values for cortical synapses (normally less than about 5 nS, except for a few very special types of synapse such as the hippocampal mossy fibers). Not surprisingly, several of these connections (such as the pyramidal cell to pyramidal cell connections, and the CCK+BC to PV+BC connections) were found to be critical for the generation and control of theta and gamma oscillations in the model. Given their importance for the conclusions of the paper, it would be essential to double-check the validity of these parameter values. In this context, it is worth noting that, unlike the anatomical parameters (cell numbers and connectivity) that had been carefully calculated and discussed in Bezaire and Soltesz (2013), biophysical parameters (the densities of neuronal membrane conductances and synaptic conductances) in Bezaire et al. (2016) were obtained by relatively simple (partly manual) fitting procedures whose reliability and robustness are mostly unknown. Specifically for synaptic parameters in CA1, a more systematic review and calculation were recently carried out by Ecker et al. (2020); their estimates for the synaptic conductances in question are typically much lower than those of Bezaire et al. (2016) and appear to be more in line with widely accepted values for cortical (hippocampal) synapses.

Furthermore, some key details concerning the construction of the simplified rate model are unclear in the current manuscript. The process of selecting cell types and connections for inclusion in the rate model is described, and the criteria are mostly clear, although the results are likely to be heavily affected by the problems discussed above, and I do not understand why the strength of external input was included among the selection criteria for cell types (especially if the model is meant to capture the internal dynamics of the isolated CA1 region). However, the main issue is that it remains unclear how the parameters of the rate model (the 24 parameters in Table 4) were obtained. The authors simply state that they "found a set of parameters that give rise to theta-gamma rhythms," and no further explanation is provided. Ideally, the parameters of the rate model should be derived systematically from the detailed biophysical model so that the two models are linked as strongly as possible; but even if this was not the case, the methods used to set these parameters should be described in detail.

An important inaccuracy in the presentation of the results concerns the suggested coupling of theta and gamma oscillations in the models. Although the authors show that theta and gamma oscillations can be simultaneously present in the network under certain conditions, actual coupling of the two rhythms (e.g., in the form of phase-amplitude coupling) is not systematically characterized, and it is therefore not clear under what conditions real coupling is present in the two models (although a probable example can be seen in Figure 1C(ii)).

The Discussion of the paper states that gamma oscillations in the model(s) are generated via a pure interneuronal (ING) mechanism. This is an interesting claim; however, I could not find any findings in the Results section that directly support this conclusion.

Finally, although the authors write that they can "envisage designing experiments to directly test predictions" from their modeling work, no such experimental predictions are explicitly identified in the current manuscript.

As noted above, our goal was not to validate the original detailed model but to carry out further analysis of the Bezaire model in its re-use, since as noted by this Reviewer, the original publication was limited in validation and analysis.  Further validation/extensions of Bezaire et al can be carried out given their acknowledged limitations (some as mentioned by the Reviewer).  However, as noted, more detailed models of CA1 microcircuitry now exist (Ecker et al 2020), and it would be interesting to examine whether and how these more detailed models might express theta/gamma rhythms.  In essence, we completely agree that all the details of the Bezaire et al model are not automatically correct.  We were using it as a biological proxy, albeit imperfect.  However, it is able to produce theta/gamma rhythms using parameter values that are experimentally derived in many ways (Bezaire & Soltesz 2013), and with minimal tuning, and thus our assumption is that it captures a potential ‘biological balance’ to generate these rhythms.  Hence, we carried out additional simulations and explorations to derive generation hypotheses that are “applied” in the development of the reduced population rate model (PRM).  The “ING” aspect is due to CCK+BCs and PV+BCs firing coherent gamma rhythms that are imposed onto the PYR cell population as mentioned in the Results.  Without PYR input, they still fire coherent gamma rhythms.  Experiments in which theta/gamma rhythms are characterized (CFC, frequencies)  with and without the presence of CCK+BCs would allow the main prediction of the modeling work to be explored – i.e., whether CCK+BCs are essential for the existence of these coupled rhythms.  We know from Dudok et al that there are alternating sources of perisomatic inhibition, but how they might control theta/gamma rhythms has not been explored to the best of our knowledge.

We will more fully describe our process for PRM parameters in subsequent revisions as well as formally apply CFC metrics.

Reviewer #2 (Public Review):

The goal of this study is to find a minimal model that produces both theta and gamma rhythms in the hippocampus CA1, based on the full-scale model (FSM) of Bezaire et al, 2016. The FSM here is treated as equivalent to biological data. This seems to be a second part of a study that the same authors published in 2021, and is extensively cited here. The study reduces the FSM to a neural rate model with 4 neurons, which is capable of producing both rhythms. This model is then simulated and its parameter dependencies are explored.

The authors succeed in producing a rate model, based on 4 neuron types, that captures the essence of the two rhythms. This model is then analyzed at a descriptive level to claim that the synapse from one interneuron type (CCK) to another (PV+) is more effective than its reciprocal counterpart (PV+ to CCK synapse) to control theta rhythm frequency.

The results fall short on several fronts:<br /> The conclusions rely exclusively on the assumption that the FSM is in fact able to faithfully reflect the biological circuits involved, not just in its output, but in response to a variety of perturbations. Although the authors mention and discuss this assumption, in the end, the reader is left with a (reduced) model of a (complex) model, but no real analysis based on this reduction. In fact, the reduced model is treated in a manner that could have been done with the full one. Thus the significance of the work is greatly reduced not by what the authors do, but by what they fail to do, which is to properly analyze their own reduced model. Consequently, the impact of this study on the field is minimal.<br /> Related to the first point, throughout the manuscript, multiple descriptive findings, based on the authors' observations of the model output, are presented as causal relationships. Even the main finding of the study (that one synapse has a larger effect on theta than another) is not quantified, but just simply left as a judgment call by the authors and reader of comparing slopes on graphs.

We agree with this Reviewer that analysis of the PRM is needed and is currently underway.  It will hopefully help us understand what ‘balances’ are essential for theta/gamma rhythm expression.  However, the overall goal of this work was not to “find” a minimal model per se, but rather to determine how theta/gamma rhythms in the hippocampus are generated (hence building on previous works).  However, it was important to use the detailed model (biological proxy – albeit imperfect – see response to Reviewer#1) to obtain hypotheses on which the PRM is based.  We do not envisage the minimal model as a `replacement’ for the detailed model in general, but rather, to show that using a combination approach (detailed and/or experimental observations with ‘derived’ reduced models) allows us to gain insight into cellular contributions to rhythm generation. Quantification of observations will be applied in subsequent revisions.

Reviewer #3 (Public Review):

While full-scale and minimal models are available for CA1 hippocampus and both exhibiting theta and gamma rhythms, it is not fully clear how inhibitory cells contribute to rhythm generation in the hippocampus. This paper aims to address this question by proposing a middle ground - a reduced model of the full-scale model. The reduced model is derived by selecting neural types for which ablations show that these are essential for theta and gamma rhythms. A study of the reduced model proposes particular inhibitory cell types (CCK+BC cells) that play a key role in inhibitory control mechanisms of theta rhythms and theta-gamma coupling rhythms.

Strengths:<br /> The paper identifies neural types contributing to theta-gamma rhythms, models them, and provides analysis that derives control diagrams and identifies CCK+BC cells as key inhibitory cells in rhythm generation. The paper is clearly written and approaches are well described. Simulation data is well depicted to support the methodology.

Weaknesses:<br /> The derivation methodology of the reduced model is hypotheses based, i.e. it is based on the selection of cell types and showing that these need to be included by ablation simulations. Then the reduced model is fitted. While this approach has merit, it could "miss" cell types or not capture the particular balance between all types. In particular, it is not known what is the "error" by considering the reduced model. As a result, the control plots (Fig. 5 and 6) might be deformed or very different. An additional weakness is that while the study predicts control diagrams and identifies CCK+BC cell types as key controllers, experimental data to validate these predictions is not provided. This weakness is admissible, in my opinion, since these recordings are not easy to obtain and the paper focuses on computational investigation rather than computationally guided experiments.

This Reviewer has provided a succinct description of our work which we will leverage in subsequent revisions as we more fully describe our process – thank you.  We agree with the Reviewer that we could ‘miss’ cell types and not capture particular balances etc., as we based our PRM on hypotheses from the detailed model.  Our PRM and its reference parameter values are ‘designed’ based on hypotheses from our set of explorations of the detailed model, and we were able to determine particular predictions that can be experimentally explored.  Subsequent theoretical analyses will help us understand the required ‘balances’ but as noted above (see response to Reviewer#2), we are not aiming for a minimal model (in general), but rather to use such a combined approach (detailed model and/or experimental observations with ‘derived’ reduced models) to come up with (cellular-based) predictions underlying theta/gamma generation.  As noted by this Reviewer, specific inhibitory cell recordings are not easy to obtain and we hope our work would help with computationally guided experiments – i.e, even though the reduced model may ‘miss’ other aspects, it would hopefully capture some aspects that are biologically salient for consideration in experimental design and future detailed model explorations.

Author Response

Public Evaluation Summary:

Powers and colleagues reveal that commonly used "genetic markers" (selectable cassettes that allow for genome modification) may lead to unintended consequences and unanticipated phenotypes. These consequences arise from cryptic expression directed from within the cassettes into adjacent genomic regions. In this work, they identify a particularly strong example of marker interference with a neighboring gene's expression and develop and test next-generation tools that circumvent the problem. The work will be primarily of interest to yeast biologists using these types of tools and interpreting these types of data.

Thank you for your time and thoughtfulness in assessing our manuscript. We agree the immediate and most direct importance of our findings is to those using cassette-based genome editing in yeast or interpreting data that comes from these experiments. However, the relevance of our findings is not limited to yeast researchers, as yeast deletion phenotypes and synthetic phenotypes are often used to guide studies in other organisms. For example, just one popular synthetic genetic interaction study from yeast (Costanzo et al, Science 2010) has been cited over 1100 times since 2010, and a large subset of these citations are not from studies focused on budding yeast.

The central finding of our work (which we regret was not sufficiently highlighted in the original manuscript), is important to an even broader scientific community: because eukaryotic promoters are inherently bidirectional, divergent promoter activity from genome-inserted expression cassettes can drive off-target gene neighboring gene repression.

Although instances of cassette induced off-target effects have been described previously, the mechanism behind these effects was previously unknown. Our study leveraged a strong case of selection cassette-driven off-target effects to identify the mechanism by which these confounding phenotypes occur. Our finding that cassettes of disparate sequence composition and expression level are competent to drive disruption of neighboring gene expression helped us determine that bidirectional promoter activity, inherent to most eukaryotic promoters, drives this effect. Thus, our data suggests a much wider pool of overlooked mutants are potentially affected by effects like the “neighboring gene effect” (NGE, Ben-shtrit et al. Nature Methods 2012) than previously considered. We find that bidirectional promoter activity from expression cassettes occurs at all cassette-inserted loci analyzed, but the resultant divergent transcripts are often terminated before disrupting neighboring genes, apparently through the mechanisms terminating most endogenous divergent transcripts (eg. CUTs; Xu et al. Nature 2009; Schultz et al. Cell 2013). These data help explain why some loci are sensitive to disruption of neighboring gene expression while others are immune. Based on identification of this mechanism of action, we find that a simply “insulating” the promoter internal to the inserted cassette with transcription termination sequences prevents this type of off-target effect. We share these updated editing tools with the community to decrease confounding off-target effects in future studies.

Because the mechanisms driving these off-target effects are fundamental, they are likely occurring in other eukaryotes. Considering the specific cassette induced LUTI-based mis-regulation reported here, this off-target mis-regulation could be seen, regardless of organism, if the following conditions are met:

1) Insertion of a cassette housing a bidirectional promoter

• Most, if not all, promoters have bidirectional activity (Teodorovic, Walls, and Elmendorf, NAR 2007; Xu et al., Nature 2009, Neil et al, Nature 2009, Trinklein et al. Genome Research 2004, Seila et al., Science 2008, Core and Lis Science 2008; Preker et al Science 2008), including commonly used mammalian promoters (CMV and EF1alpha; Curtin et al. Gene Therapy 2008; SV40: Gidoni et al. Science 1985). Insulator use is rare in construct design and has been primarily used in cases in which the concern is protecting expression of the expression cassette from the local chromatin environment. Although not the dominant mode of gene deletion in mammalian cells, expression cassettes are commonly inserted for knock-in experiments, for example, in the form of antibiotic resistance genes or fluorescent protein-encoding genes.

• It is interesting that in their native context in both yeast and mammals, most promoters do not produce a stable divergent transcript. In yeast, this results from mechanisms including the NNS termination pathway coupled to Rrp6/exosome-mediated RNA degradation (Schultz et al. Cell 2013). The TEF1 promoter is a prime example, with evidence for a divergent transcript that is visible only when RRP6 is deleted (Xu et al., Nature 2009) or when nascent transcripts are analyzed (Churchman and Weissman, Nature 2011). In mammals, the NNS pathway does not serve this role, but rather the production of stable divergent transcripts is limited by early polyA signals that prevent transcriptional interference from naturally occurring more pervasively and the instability of the resultant short transcripts (Ntini et al, NSMB 2013; Almada et al, Nature 2013). Note that persistence of a stable (detectable) transcript is not needed for neighboring gene disruption to occur, but the production of a transcript that extends into the regulatory sequences for a neighboring gene’s transcript is.

2) A neighboring gene within a distance that allows transcription interference without intervening transcription termination

• This is hard to assess systematically, but natural transcription interference and LUTI occur in both human and yeast cells (Chen et al., eLife 2017; Chia et al. eLife 2017; Hollerer et al., G3 2019; Otto and Cheng et al., Cell 2018; Van Dalfsen et al. Dev Cell 2018). Data from our lab suggests this regulation can even be effective up to spans of ~2KB (Vander Wende et al, bioRxiv is an interesting example), so it seems that the artificial regulation described here could have similar range.

• Although yeast genes are more closely spaced than those in human or mice, there are many gene dense regions in these organisms cases and it has been shown that roughly ¼ of head-to-head oriented genes are within 2KB in human (Gherman, Wang, and Avramopolous, Human Genomics, 2009)

3) A neighboring gene in the divergent orientation to the cassette (ie. Head-to-head orientation; should be present in half of cassette insertions)

4) Competitive uORF sequences in the extended 5’ transcript region

• This is, again, hard to systematically assess, but our studies indicate that approximately half of AUG uORFs are effective at competing with main ORF translation. Because almost every intergenic region houses at least one AUG this may not be a major limiting factor. As in yeast, AUG uORF translation has been seen to be pervasive in naturally 5’ extended human transcripts (Floor and Doudna, eLife, 2016 as just one example).

While these conditions must be met to match the exact LUTI-based repression that we report at the DBP1/MRP51 locus, even situations in which only conditions 1 and 2 are met could drive potent transcriptional interference impacting neighboring gene expression.

Our findings offer a new perspective important for designing or interpreting genome engineering experiments in any organism, and identification of a mechanism for neighboring gene effects of expression cassette insertion allow it to be prevented in future studies.

We regret the narrow framing of our study in the initial manuscript, but hope that our revised manuscript better demonstrates how our findings fit into existing literature regarding neighboring gene effects from cassette insertion, and that their broad relevance is now clear.

Reviewer #1 (Public Review):

This manuscript presents information that will be of great interest to yeast geneticists - standard gene deletions can lead to misleading phenotypes due to effects on adjacent genes. The experiments carefully document this in one case, for the DBP1 gene, and present additional evidence that it can occur at additional genes. An improved version of the standard gene replacement cassette is described, with evidence that it functions in an improved fashion, insulated from affecting adjacent genes.

We appreciate the reviewer’s enthusiasm for the data in our study, and their perspective that this will be of great interest to the yeast community. We hope that we have improved the writing in the revised manuscript to emphasize our finding that a conserved feature of eukaryotic gene regulation drives this effect suggests it likely to be occurring in other organisms.

Reviewer #2 (Public Review):

The impact of the work will be for yeast researchers in the clear and careful presentation of a case study wherein phenotypes might be ascribed to the knockout of a particular gene but instead derive from effects on a neighboring gene. In this case, a transcript expressed from within or adjacent to a knockout of DBP1 by a selectable marker towards the adjacent gene MRP51 interferes with the adjacent gene's normal transcription start sites. Furthermore, although neighboring MRP51 ORF is present on the longer mRNA isoform that is generated, it is not efficiently translated. The authors expand on this phenotypic observation to demonstrate that a substantial fraction of selectable marker insertions can generate transcription adjacent to or within and going away from, selectable markers.

The strengths of the work are that the derivation of the observed phenotypes for the dpb1∆ alleles is clearly and carefully elucidated and the creation of new selectable marker cassettes that overcome the potential for cryptic transcript emanation from or near to the selectable markers. This is valuable for the community as a clear demonstration of how only the exact right experiments might detect underlying mechanisms for potentially misattributed phenotypes and that many times these experiments may not be performed.

Thanks very much to the reviewer for their thoughtful assessment of our manuscript. We are thrilled that they find the work to be valuable for yeast researchers, and more broadly, to those interested in avoiding misinterpretations of mutant phenotypes. We propose this to be a mechanism that is likely to be important beyond yeast studies and hope that we have made this clearer in the revised manuscript.

While understandable in terms of how the experiments likely played out, the manuscript seems in between biology and tool development, as the biology in question was related to a gene that is not the focus of this lab. The tool development is likely to be useful but potentially non-optimal.

We agree with the reviewer’s point that this is a good opportunity to improve the standard yeast cassettes further and have now done so. We now include a further improved pair of cassettes that minimize shared sequences (Figure 3H). These and the previously described constructs (Figure 3F) will all be deposited at Addgene and we hope that they will be of value to the yeast community.

The reviewer’s comment also made us realize that our previous presentation of the work was not ideal. We have adjusted the order of data in the revised manuscript, including swapping the data in Figures 3 and 4 and adding a Figure 5 to further emphasize the mechanism that we identify to drive this off-target effect, rooted in bidirectional promoter activity. While we hope the new cassettes are useful to others, they also serve a specific biological role in this manuscript, which is to show that bidirectional transcription driven from existing cassettes is the cause of the off-target effect that we report.

The mechanism for interference identified in this example case (via a long undecoded transcript isoform (LUTI) has already been described for other loci and in a number of species, including in work from the Brar lab. The concept of marker interference with neighboring genes has also been increasingly appreciated by a number of other studies.

Indeed, because of our recent research interests, we were aware that natural LUTI-based regulation was widespread prior to this study, but even we were surprised to see it occurring in this artificial context. The idea that constitutive LUTI-based repression can be easily driven at loci that are not otherwise LUTI-regulated is an interesting point to consider in designing gene editing approaches. We agree with the reviewer that a greater discussion of previously published work regarding marker interference is necessary to understand the novelty of our findings, including the discussion of some work that should have been cited and discussed in the original manuscript (Ben-Shitrit et al. Nature Methods 2012 and Egorov et al. NAR 2021, in particular). In the reframing of our revised manuscript, we aimed to emphasize the novel aspects of our work, and how they relate to previous reports of the “neighboring gene effect” (NGE). Although the phenomenon of the NGE had been reported, it was not previously clear what caused it to occur, which made it impossible to prevent in planning new approaches or to diagnose in existing data. In revealing this unexpected mechanism driven by bidirectional promoter activity that is general to expression cassette-based editing, rather than resulting from any particular cassette sequence, we were able to design constructs to prevent this from occurring in future studies. Moreover, because bidirectional promoter activity is a highly conserved feature of eukaryotic gene expression, this finding suggests that the type of off-target effect that we describe here is likely to occur with expression cassette insertion in more complex eukaryotes, as well. To our knowledge, this has not been widely considered as a possibility.

Author Response

Reviewer #1 (Public Review):

This study analyzes the detailed chemical mechanics of the formation of a physiologically important protein multimer. The primary strengths of the study are careful analyses of two distinct methods, CG-MALS a direct measure of multimerization, and environment-sensitive tryptophan fluorescence, that each indicates that Ca2+ activation of the C-lobe alone can change the physical interaction with an SK2 C-terminal peptide. An intriguing finding is that while either the N- or C-lobes alone can interact with the C-terminal peptide, only with full-length CaM can the SK C-terminal peptide be bound by two CaM molecules simultaneously. This study also clearly demonstrates that Ca2+ activation of the N-lobe triggers binding to the SK2 Cterminal peptide. Methods descriptions are thorough and excellent. Discussion of relevance to structures and function are nuanced and free of presumptions. The weaknesses of this manuscript are that the physiological implications of these findings are not clear: CaM interacts with regions of SK channels besides the C-terminal peptide studied here, and no evidence is provided here that C-lobe calcium binding alters channel opening. Overall, the evidence for conformational changes of the complex due to Ca2+ binding to the C-lobe alone is very strong, and physiological importance seems likely. The interpretation of data in this manuscript is mostly cautious and logically crystalline, with alternative interpretations discussed at many junctures.

We thank Reviewer #1 for very helpful and thoughtful considerations and catching some oversights in our work. Our work was improved by addressing their comments.

Reviewer #2 (Public Review):

Activation of SK channels by calcium through calmodulin (CaM) is physiologically important in tuning membrane excitability. Understanding the molecular mechanism of SK activation has therefore been a high priority in ion channel biophysics and calcium signaling. The prevailing view is that the C-terminal lobe of CaM serves as an immobile Ca2+-independent tether while the N-lobe acts as a sensor whose binding activates the channel. In the present study, the authors undertake extensive biophysical/biochemical analysis of CaM interaction with SK channel peptide and rigorous electrophysiological experiments to show that Ca2+ does bind to the C-lobe of CaM and this potentially evokes conformational changes that may be relevant for channel gating. Beyond SK channels, the approach and findings here may bear important implications for an expanding number of ion channels and membrane proteins that are regulated by CaM.

A strength of the study is that the electrophysiological recordings are innovative and of high quality. Given that CaM is ubiquitous in nearly all eukaryotes, dissecting the effects of mutants particularly on individual lobes is technically challenging, as endogenous CaM can overwhelm low-affinity mutants. The excised patch approach developed here provides a powerful methodology to dissect fundamental mechanisms underlying CaM action. I imagine this could be adaptable for studying other ion channels. Armed with this strategy authors show that both N- and C-lobe of CaM are essential for maximal activation of SK channels. This revises the current model and may have physiological importance.

The major weakness is that nearly all biochemical inferences are made from analysis of isolated peptides that do not necessarily recapitulate their arrangement in an intact channel. While the use of MALS provides new evidence of the potentially complex conformational arrangement of CaM on the C-terminal SK peptide (SKp), it is not fully clear that these complexes correspond to functionally relevant states. Lastly, perhaps as a consequence of these ambiguities, the overarching model or mechanism is not fully clear.

We thank Reviewer #2 for their helpful review and requesting context to alleviate some the ambiguities in channel mechanism arising from our data. Although the ultimate goal of our field is to understand gating mechanism, there are too many parameters to solve with a single study. First off, we agree that there is not a clear model out there and we have only continued to assemble building blocks to make one.

Our report is centered on calmodulin more than it is SK, which is why we studied more CaM mutants and no channel mutants. There are simply too many unanswered questions regarding stoichiometry and state dependencies to make even a basic working model. We invite the greater ion channel field to scrutinize these questions and delve deeper into approaches across disciplines.

We strived to put our work in context with the decades of research on CaM and SK. Our work focuses on the C-terminus of SK and whether the C-lobe of CaM anchored independent of Ca2+. An anchored C-lobe would be fundamental to building any gating model with the proper energetics. Although we used only a piece of the full-length channel, a peptide that we call SKp has Ca2+-dependent associations with a full-length protein, WT-CaM. We do not have nearly enough data to solve the gating mechanism, nor do we make a claim to have solved the mechanism for SK gating, but if a piece of the channel has Ca2+-dependent interactions with another full-length protein, calmodulin, it is highly unlikely that the full-length SK channel is going to inhibit that interaction in all its closed and open states. Structures do not show inhibitory actions related to conformational Ca2+-sensitivity. The C-lobe is simply captured in most populated binding state, not necessarily its functional state. Indeed, we need a lot more data to get a clearer understanding. It was helpful to discuss this and we added more context to our work.

Reviewer #3 (Public Review):

Halling et. al. probe the mechanism whereby calmodulin (CaM) mediates SK channel activity in response to calcium. CaM regulation of SK channels is a critical modulator of membrane excitability yet despite numerous structural and functional studies significant gaps in our understanding of how each lobe participates in this regulation remain. In particular, while Ca2+ binding to the N-lobe of CaM has a clear functional effect on the channel, the C-lobe of CaM does not appear to participate beyond a tethering role, and structural studies have indicated that the C-lobe of CaM may not bind Ca2+ in the context of the SK channel. This study pairs functional and protein binding data to bridge this gap in mechanistic understanding, demonstrating that both lobes of CaM are likely Ca2+ sensitive in the context of SK channels and that both lobes of CaM are required for channel activation by Ca2+.

Strengths:

The molecular underpinnings of CaM-SK regulation are of significant interest and the paper addresses a major gap in knowledge. The pairing of functional data with protein binding provides a platform to bridge the static structural results with channel function. The data is robust, and the experiments are carefully done and appear to be of high quality. The use of multiple mutant CaMs and electrophysiological studies using a rescue effect in pulled patches to enable a more quantified evaluation of the functional impact of each lobe of CaM provides a compelling assessment of the contribution of each lobe of CaM to channel activation. The calibration of the patch data by application of WT CaM is innovative and provides precise internal control, making the conclusions drawn from these experiments clear. This data fully supports the conclusion that both lobes of CaM are required for channel activation.

Weaknesses:

The paper focuses heavily on the results of multi-angle light scattering experiments, which demonstrate that a peptide derived from the C-terminus of the SK channel can bind to CaM in multiple stochiometric configurations. However, it is not clear if these complexes are functionally relevant in the full channel, making interpretation challenging.

We thank Reviewer #3 for their helpful review and for providing their concerns with our interpretation of the MALS experiments. From our previous work (Li et al. 2009 and Halling et al. 2014), we have had suggestions that stoichiometry at different functional states is complicated. Our new data presented here adds to the complexity. We do not claim to have solved whether Ca2+-dependent stoichiometry is important for channel function. That requires further research.

As we stated with reviewer #2, we emphasize our findings convey how CaM interacts with one site on SK. CaM is the Ca2+ sensor, and Ca2+ alters how CaM binds. The channel will have more determinants for interacting with CaM, but just by studying one domain we see extraordinary complexity. We have firm results from our MALS and fluorescent binding assays that challenge the models on the full-channel even with the simplest interpretations, i.e., CaM is not a simple switch. We have shown fundamentally that CaM binding is Ca2+-dependent with a single SK binding site.

There are several major studies that still need to be done to relate binding data to channel function: 1) Calmodulin binding studies to other calmodulin domains need to be completed 2) The dependence of Ca2+ concentration on calmodulin binding need to be determined and 3) Ca2+-dependent Calmodulin binding studies on full-length SK channels need to be completed. We invite more discussion from the ion channel field on developing models that are consistent with all data.

Author Response

Reviewer 1 (Public Review):

Weaknesses: The main conclusion that ablation of the cadherin code decreases synaptic connectivity between the rVRG and phrenic motor neurons is never directly shown. This can only be inferred by the data.

1) Conclusion that the connectivity between rVRG premotor and phrenic nerve motor neurons is "weaker". This conclusion is inferred from several experiments but is never directly demonstrated. Alternative interpretations of the decreased amplitude of the in vitro phrenic nerve burst is that the rootlet contains fewer axons (as predicted by the fewer motor neurons in S3 and innervation of the diaphragm S2). Additionally, the intrinsic electrophysiological properties of the motor neurons might be different. To show this decisively, the authors could use electrophysiological recordings of phrenic motor neurons to directly measure a change in synaptic input (for example, mEPSPs or EPSPs after optogenetic stimulation of rVRG axon terminals). Without a direct measurement, the synaptic connectivity can only be inferred.

We agree with the reviewer that without anatomical evidence, we can only infer the loss of synaptic connectivity. However, we believe that this is the most likely interpretation of our data (see response to the editor summary). Unfortunately, the experiment suggested (optogenetic stimulation of rVRG terminals) is not feasible at the moment, as a) a molecular tool to specifically express channelrhodopsin in rVRG does not currently exist; even if it did, it would require crossing two more alleles in our current mouse model, which contains 5 alleles, making the genetics/breeding cumbersome and b) viral-mediated channelrhodopsin expression in the rVRG is not feasible since the mice die at birth. We will continue to explore alternative approaches to directly demonstrate the loss of rVRG-PMC connectivity in the future.

2) Conclusion that the small phenic nerve burst size in Dbx1 deleted cadherin signaling is due to less synaptic input to the motor neurons. Dbx1 is expressed in multiple compartments of the medullary breathing control circuit, like the breathing rhythm generator (preBötC). The smaller burst size could be due to altered activity between preBötC neurons to create a full burst, the transmission of this burst from the preBötC to the rVRG, etc.

We agree with the reviewer about the alternative interpretations of the data, which we mention in the discussion. At this point, we can only conclude that cadherin signaling is required in Dbx1derived respiratory populations for proper phrenic respiratory output. We are currently developing the tools in our lab to further dissect the exact contributions of cadherins to rVRG development, connectivity, and function. As this will require significant time and effort, we believe it is outside the scope of the current work.

3) In vitro burst size. The authors use 4 bursts from each animal to calculate the average burst size. How were the bursts chosen? Why did the authors use so few bursts? What is the variability of burst size within each animal? What parameters are used to define a burst? This analysis and the level of detail in the figure legend/methods section is inadequate to rigorously establish the conclusion that burst size is altered in the various genotypes.

To address the reviewer’s concern, we have updated the data by analyzing 7 bursts per animal. Some control mice have burst frequencies as low as 0.2 bursts per minute (see fig. 4b), and thus acquiring 7 bursts requires 35 minutes of recording time, a substantial amount when an entire litter is being recorded in a day. All data is from 7 bursts per animal except for 4 out of 11 NMNΔ6910-/- mice, which only had 1-3 bursts total. To analyze the data, either every single burst was analyzed, or for those traces of higher frequency, bursts were selected randomly, spaced throughout the trace. Bursts were defined as activity above baseline that persists for at least 50ms. Some bursts contain pauses in activity in the middle; activity that was spaced less than 1 second apart was defined as a single burst.

Updating the data for more bursts slightly changed some of our findings. We now find that 6910/- mice no longer exhibit significantly increased burst duration and burst activity. This was barely significant in our previous analysis, and is now just barely non-significant (p=0.065 for burst duration, p=0.059 for burst activity).

We have included this more detailed description in the methods section. We have also included an excel sheet as source data for fig. 4 to indicate the variability of burst size within each animal and across animals.

4) The authors state that the in vitro frequency in figure 4 is inaccurate, but then the in vitro frequency is used to claim the preBötC is not impacted in Dbx1 mutants (conclusion section "respiratory motor circuit anatomy and assembly"). To directly assess this conclusion, the bursting frequency of the in vitro preBötC rhythm should be measured.

We have now included the quantitation of respiratory frequency data for control and βγ-catDbx1∆ mice, showing that there are no significant changes in burst frequency in βγ-catDbx1∆ mice. However, we do agree with the reviewer that the loss of excitatory drive could be due to changes either in the rVRG or the preBötC and we have toned down our conclusions to indicate that the preBötC could be impacted in βγ-catDbx1∆ mice.

5) The burst size in picrotoxin/strychnine is used to conclude that the motor neurons intrinsic physiology is not impacted. The bursts are described, and examples are shown, but this is never quantified across many bursts within in a single recording nor in multiple animals of each genotype.

We have now included quantification of this data, using 6-11 bursts/mouse from 3 control and 3 NMNΔ6910-/- mice. We find that both the spinal burst total duration (shown as % of recording time) and the normalized integrated spinal activity over time are not significantly different between control and NMNΔ6910-/- mice.

Reviewer 3 (Public Review):

Major points

1) Page 8: 'In addition, NMNΔ and NMNΔ6910-/- mice showed a similar decrease in phrenic MN numbers, likely from the loss of trophic support due to the decrease in diaphragm innervation (Figure S3c).' This statement should be corrected: phrenic MN number in NMNΔ mice does not differ from controls, in contrast to NMNΔ6910-/- mice (Fig. S3). Similarly, diaphragm innervation is not significantly different from controls in NMNΔ (Fig. S2). Alternatively, these observations could be strengthened by increasing the number of mice analyzed to determine whether there is a significant reduction in PMN number and diaphragm innervation in NMNΔ mice.

Following the reviewer’s suggestion, we increased the number of control mice analyzed for diaphragm innervation (n=7) and MN numbers (n=6). We now find that there is a significant reduction in both parameters in NMNΔ mice. We have modified the results section accordingly.

2) A similar comment relates to the interpretation of the dendritic phenotype in NMNΔ and NMNΔ6910-/- mice (Fig. 3m): the authors conclude 'When directly comparing NMNΔ and NMNΔ6910-/- mice, NMNΔ6910-/- mice had a more severe loss of dorsolateral dendrites and a more significant increase in ventral dendrites (Figure 3l-m).' (page 9). The loss of dorsolateral dendrites in NMNΔ6910-/- mice indeed differs significantly from control mice, and is more severe than in NMNΔ mice, which do not differ significantly from controls. For ventral dendrites however, the increase compared to controls is significant for both NMNΔ and NMNΔ6910-/- mice, and the two genotypes do not appear to differ from each other. This suggests cooperative action of N-cadherin and cadherin 6,9,10 for dorsolateral dendrites, but suggests that N-cad is more important for ventral dendrites. This should be phrased more clearly.

We agree with the reviewer and apologize for the lack of clarity. We have modified our description to highlight the contribution of N-cadherin to dendritic development.

3) Related comment, page 10: 'Furthermore, the fact that phrenic MNs maintain their normal activity pattern in NMNΔ mice suggests that neither cell body position nor phrenic MN numbers significantly contribute to phrenic MN output.' This should be rephrased, phrenic MN number does not differ from control in NMNΔ mice (Fig. S2c).

After analyzing additional control mice, we find that phrenic MN numbers are significantly reduced in NMNΔ mice.

4) The authors conclude that spinal network activity in control and NMNΔ6910-/- mice does not differ (page 10, Fig. 4f). It is difficult to judge this from the example trace in 4f. How is this concluded from the figure and can this be quantified?

We have now included quantification of this data, using 6-11 bursts/mouse from 3 control and 3 NMNΔ6910-/- mice. We find that both the spinal burst total duration (shown as % of recording time) and the normalized integrated spinal activity over time are not significantly different between control and NMNΔ6910-/- mice.

5) RphiGT mice: please explain the genetic strategy better in Results section or Methods, do these mice also express the TVA receptor in a Cre-dependent manner? Crossing with the Cdh9:iCre line will then result in expression of TVA and G protein in phrenic motor neurons and presynaptic rVRG neurons in the brainstem, as well as additional Cdh9-expressing neuronal populations. How can the authors be sure that they are looking at monosynaptically connected neurons?

We have added additional information in the methods to describe the rabies virus genetic strategy. Although the mice do express the TVA receptor, we did not include this in the description as it is not relevant to our strategy. We are using a Rabies∆G virus that is not pseudotyped with EnvA so it does not require TVA to infect cells. The specificity of primary cell (phrenic MN) infection rather comes from diaphragm injections. We only analyze mice in which we can confirm the injection was specific to the diaphragm muscle and did not leak to body wall or hypaxial muscles (about 50% of injections). We have tested different infection times to determine when monosynaptically connected neurons are labeled. We do not see any labeling at the brainstem 5 days post injection and we start to see additional labeling (possible 2nd order neurons) 10 days post injection. Thus we are confident that our analysis at 7 days post injection captures monosynaptically-connected neurons. We have also performed rabies virus tracing in ChAT::Cre mice, where the expression of G-protein is restricted to motor neurons, and we observe a similar distribution of pre-motor neurons in the brainstem, as with Cdh9::iCre, indicating that we are reproducibly labeling 1st order neurons with both genetic strategies.

6) The authors use a Dbx1-cre strategy to inactivate cadherin signaling in multiple brainstem neuronal populations and perform analysis of burst activity in phrenic nerves. Based on the similarity in phenotype with NMNΔ6910-/- mice it is concluded that cadherin function is required in both phrenic MNs and Dbx1-derived interneurons. However, this manipulation can affect many populations including the preBötC, and the impact of this manipulation on rVRG and phrenic motor neurons (neuron number, cell body position, dendrite orientation, diaphragm innervation etc) is not described, although a model is presented in Fig. 7. These parameters should be analyzed to interpret the functional phenotype.

We agree with the reviewer that the Dbx1-Cre mediated manipulation can affect multiple respiratory populations (see response to reviewer 1). However, Dbx1-mediated recombination does not target phrenic MNs. We have now added a figure (Figure 6-figure supplement 1), demonstrating this. Thus, we think that it is unlikely to cause any cell-autonomous changes in MN number, diaphragm innervation etc. It is plausible that there might be secondary changes in phrenic MNs as a result of changes in rVRG properties (for example, the dendritic orientation of phrenic MNs could be altered if rVRG synapses are lost), but the primary impact of this manipulation will be on Dbx1-derived neurons.

Author Response

Reviewer #1 (Public Review):

This paper describes the results of a MEG study where participants listened to classical MIDI music. The authors then use lagged linear regression (with 5-fold cross-validation) to predict the response of the MEG signal using (1) note onsets (2) several additional acoustic features (3) a measure of note surprise computed from one of several models. The authors find that the surprise regressors predict additional variance above and beyond that already predicted by the other note onset and acoustic features (the "baseline" model), which serves as a replication of a recent study by Di Liberto.

They compute note surprisal using four models (1) a hand-crafted Bayesian model designed to reflect some of the dominant statistical properties of Western music (Temperley) (2) an ngram model trained on one musical piece (IDyOM stm) (3) an n-gram model trained on a much larger corpus (IDyOM ltm) (4) a transformer DNN trained on a mix of polyphonic and monophonic music (MT). For each model, they train the model using varying amounts of context.

They find that the transformer model (MT) and long-term n-gram model (IDyOM stm) give the best neural prediction accuracy, both of which give ~3% improvement in predicted correlation values relative to their baseline model. In addition, they find that for all models, the prediction scores are maximal for contexts of ~2-7 notes. These neural results do not appear to reflect the overall accuracy of the models tested since the short-term n-gram model outperforms the long-term n-gram model and the music transformer's accuracy improves substantially with additional context beyond 7 notes. The authors replicate all these findings in a separate EEG experiment from the Di Liberto paper.

Overall, this is a clean, nicely-conducted study. However, the conclusions do not follow from the results for two main reasons:

1) Different features of natural stimuli are almost always correlated with each other to some extent, and as a consequence, a feature (e.g., surprise) can predict the neural response even if it doesn't drive that response. The standard approach to dealing with this problem, taken here, is to test if a feature improves the prediction accuracy of a model above and beyond that of a baseline model (using cross-validation to avoid over-fitting). If the feature improves prediction accuracy, then one can conclude that the feature contributes additional, unique variance. However, there are two key problems: (1) the space of possible features to control for is vast, and there will almost always be uncontrolled-for features (2) the relationship between the relevant control features and the neural response could be nonlinear. As a consequence, if some new feature (here surprise) contributes a little bit of additional variance, this could easily reflect additional un-controlled features or some nonlinear relationship that was not captured by the linear model. This problem becomes more acute the smaller the effect size since even a small inaccuracy in the control model could explain the resulting finding. This problem is not specific to this study but is a problem nonetheless.

We understand the reviewer’s point and agree that it indeed applies not exclusively to the present study, but likely to many studies in this field and beyond. We disagree, however, that it constitutes a problem per se. We maintain that the approach of adding a feature, observing that it increases crossvalidated prediction performance, and concluding that therefore the feature is relevant, is a valid one. Indeed, it is possible and even likely that not all relevant features (or non-linear transformations thereof) will be present in the control/baseline model. If a to-be-tested feature increases predictive performance and therefore explains relevant variance, then that means that part of what drives the neural response is non-trivially related to the to-be-tested feature. The true underlying relationship may not be linear, and later work may uncover more complex relationships that subsume the earlier discovery, but the original conclusion remains justified.

Importantly, we wish to emphasize that the key conclusions of our study primarily rest upon comparisons between regression models that are by design equally complex, such as surpriseaccording-to-MT versus surprise-according-to-IDyOM and comparisons across different context lengths. We maintain that the comparison with the Baseline model is also important, but even taking the reviewer’s worry here into account, the comparison between different equally-complex regression models should not suffer from it to the same extent as a model-versus-baseline comparison.

2) The authors make a distinction between "Gestalt-like principles" and "statistical learning" but they never define was is meant by this distinction. The Temperley model encodes a variety of important statistics of Western music, including statistics such as keys that are unlikely to reflect generic Gestalt principles. The Temperley model builds in some additional structure such as the notion of a key, which the n-gram and transformer models must learn from scratch. In general, the models being compared differ in so many ways that it is hard to conclude much about what is driving the observed differences in prediction accuracy, particularly given the small effect sizes. The context manipulation is more controlled, and the fact that neural prediction accuracy dissociates from the model performance is potentially interesting. However, I am not confident that the authors have a good neural index of surprise for the reasons described above, and this limits the conclusions that can be drawn from this manipulation.

First of all, we would like to apologize for any unclarity regarding the distinction between Gestalt-like and statistical models. We take Gestalt-like models to be those that explain music perception as following a restricted set of rules, such as that adjacent notes tend to be close in pitch. In contrast, as the reviewer correctly points out, statistical learning models have no such a priori principles and must learn similar or other principles from scratch. Importantly, the distinction between these two classes of models is not one we make for the first time in the context of music perception. Gestalt-like models have a long tradition in musicology and the study of music cognition dating back to (Meyer, 1957). The Implication-Realization model developed by Eugene Narmour (Narmour, 1990, 1992; Schellenberg, 1997) is another example for a rule-based theory of music listening, which has influenced the model by David Temperley, which we applied as the most recently influential Gestalt-model of melodic expectations in the present study. Concurrently to the development of Gestalt-like models, a second strand of research framed music listening in light of information theory and statistical learning (Bharucha, 1987; Cohen, 1962; Conklin & Witten, 1995; Pearce & Wiggins, 2012). Previous work has made the same distinction and compared models of music along the same axis (Krumhansl, 2015; Morgan et al., 2019a; Temperley, 2014). We have updated the manuscript to elaborate on this distinction and highlight that it is not uncommon.

Second, we emphasize that we compare the models directly in terms of their predictive performance both of upcoming musical notes and of neural responses. This predictive performance is not dependent on the internal details of any particular model; e.g. in principle it would be possible to include a “human expert” model where we ask professional composers to predict upcoming notes given a previous context. Because of this independence of the relevant comparison metric on model details, we believe comparing the models is justified. Again, this is in line with previously published work in music (Morgan et al., 2019a), language, (Heilbron et al., 2022; Schmitt et al., 2021; Wilcox et al., 2020), and other domains (Planton et al., 2021). Such work compares different models in how well they align with human statistical expectations by assessing how well different models explain predictability/surprise effects in behavioral and/or brain responses.

Third, regarding the doubts on the neural index of surprise used: we respond to this concern below, after reviewer 1’s first point to which the present comment refers (the referred-to comment was not included in the “essential revisions” here).

Reviewer #2 (Public Review):

This manuscript focuses on the basis of musical expectations/predictions, both in terms of the basis of the rules by which these are generated, and the neural signatures of surprise elicited by violation of these predictions.

Expectation generation models directly compared were gestalt-like, n-gram, and a recentlydeveloped Music Transformer model. Both shorter and longer temporal windows of sampling were also compared, with striking differences in performance between models.

Surprise (defined as per convention as negative log prior probability of the current note) responses were assessed in the form of evoked response time series, recorded separately with both MEG and EEG (the latter in a previously recorded freely available dataset). M/EEG data correlated best with surprise derived from musical models that emphasised long-term learned experiences over short-term statistical regularities for rule learning. Conversely, the best performance was obtained when models were applied to only the most recent few notes, rather than longer stimulus histories.

Uncertainty was also computed as an independent variable, defined as entropy, and equivalent to the expected surprise of the upcoming note (sum of the probability of each value times surprise associated with that note value). Uncertainty did not improve predictive performance on M/EEG data, so was judged not to have distinct neural correlates in this study.

The paradigm used was listening to naturalistic musical melodies.

A time-resolved multiple regression analysis was used, incorporating a number of binary and continuous variables to capture note onsets, contextual factors, and outlier events, in addition to the statistical regressors of interest derived from the compared models.

Regression data were subjected to non-parametric spatiotemporal cluster analysis, with weights from significant clusters projected into scalp space as planar gradiometers and into source space as two equivalent current dipoles per cluster

General comments:

The research questions are sound, with a clear precedent of similar positive findings, but numerous unanswered questions and unexplored avenues

I think there are at least two good reasons to study this kind of statistical response with music: firstly that it is relevant to the music itself; secondly, because the statistical rules of music are at least partially separable from lower-level processes such as neural adaptation.

Whilst some of the underlying theory and implementation of the musical theory are beyond my expertise, the choice, implementation, fitting, and comparison of statistical models of music seem robust and meticulous.

The MEG and EEG data processing is also in line with accepted best practice and meticulously performed.

The manuscript is very well-written and free from grammatical or other minor errors.

The discussion strikes a brilliant balance of clearly laying out the interim conclusions and advances, whilst being open about caveats and limitations.

Overall, the manuscript presents a range of highly interesting findings which will appeal to a broad audience, based on rigorous experimental work, meticulous analysis, and fair and clear reporting.

We thank the reviewer for their detailed and positive evaluation of our manuscript.

Reviewer #3 (Public Review):

The authors compare the ability of several models of musical predictions in their accuracy and in their ability to explain neural data from MEG and EEG experiments. The results allow both methodological advancements by introducing models that represent advancements over the current state of the art and theoretical advancements to infer the effects of long and shortterm exposure on prediction. The results are clear and the interpretation is for the most part well reasoned.

At the same time, there are important aspects to consider. First, the authors may overstate the advancement of the Music Transformer with the present stimuli, as its increase in performance requires a considerably longer context than the other models. Secondly, the Baseline model, to which the other models are compared, does not contain any pitch information on which these models operate. As such, it's unclear if the advancements of these models come from being based on new information or the operations it performs on this information as claimed. Lastly, the source analysis yields some surprising results that don't fit with previous literature. For example, the authors show that onsets to notes are encoded in Broca's area, whereas it should be expected more likely in the primary auditory cortex. While this issue is not discussed by the authors, it may put the rest of the source analysis into question.

While these issues are serious ones, the work still makes important advancements for the field and I commend the authors on a remarkably clear and straightforward text advancing the modeling of predictions in continuous sequences.

We thank the reviewer for their compliments.

Author Response

Public Evaluation Summary:

This work would be of interest to global health scientists, particularly in low- and middleincome countries where childhood stunting is an ongoing challenge, and to statisticians interested in building clinical prediction rules. The authors leveraged large, rich datasets from multi-center studies to build and validate predictive models. But by using change in growth, rather than absolute growth, as the only outcome, it may be missing children of concern who are already experiencing growth failure and require intervention but have reached a growth faltering floor.

Thank you for this suggestion. We have added additional models for the following predictions: a) growth faltering in those NOT stunted (HAZ≥-2) at presentation, b) any stunting (HAZ<-2) at follow-up, and c) any stunting at follow-up in those not stunted at presentation. While we agree the addition of these models improves the manuscript, we also want to highlight that these models have distinct outcomes and therefore have separate clinical uses. Our original goal was to identify children whose growth was likely to slow down after diarrhea. As we show, top predictors and predictive performance is similar for growth faltering across baseline stunting status. We present any stunting at follow-up as a comparison, but argue that this is a different clinical outcome that may warrant different intervention. We have edited the manuscript for clarity as follows.

P.22 L339-343: . In sensitivity analyses, we demonstrated our ability to predict any stunting at follow-up with high accuracy (Table 1, Table S5). However, this represents a related but distinct outcome from our original aim, namely a slowing down of growth as opposed to stunting, and may warrant different clinical intervention.

P.23 L.353-357: Current malnutrition recommendations are based on patient presentation – whether a child is underweight when they present to the clinic. Our CPR could be used to identify children not currently stunted and therefore not currently recommended for nutritional interventions, but who are likely to slow down in growth and therefore at higher risk of incident stunting.

P.23 L352-361: Our CPR provides a tool for identifying patients likely to experience additional growth faltering after acute diarrhea. Current malnutrition recommendations are based on patient presentation – is a child underweight when they come to the clinic. Our CPR could be used to identify children not currently stunted and therefore not currently recommended for nutritional interventions, but who are likely to slow down in growth and therefore at higher risk of incident stunting. Identifying these children would allow clinicians to connect patients with community-based nutrition interventions (e.g. maternal support for safe introduction of weening foods, small quantity lipid nutrient supplements (SQ-LNS), etc.(45-48)) to prevent additional effects of chronic malnutrition, namely irreversible stunting.

P.25 L.390-393: Our findings indicate that use of prediction rules, potentially applied as clinical decision support tools, could help to identify additional children at risk of poor outcomes after an episode of diarrheal illness, i.e. not currently stunted but likely to decelerate growth.

Reviewer #1 (Public Review):

In this manuscript, the authors built logistic regression prediction models for linear growth faltering using demographic, socioeconomic, and clinical variables, with the objective of developing a clinical prediction rule that could be applied by healthcare workers to identify and treat high-risk children. A model with 2 variables selected by random forest variable importance performed similarly to a model with 10 variables. Age and HAZ at baseline were selected for the 2-variable model, consistent with existing literature. The authors externally validated the 2variable model and found similar discriminative ability. Based on typical rule-of-thumb cutoffs, model performance was moderate (AUCs of ~0.65-0.75, depending on model specification); models may still be useful in practice, but this should be further discussed by the authors.

We agree that our overall ability to predict growth faltering was moderate. As we present in-depth below, we do not intend for our clinical prediction rule (CPR) to replace existing guidelines. Therefore, we are not proposing that our CPR be used to withhold nutritional treatment. Rather, we intend for our CPR to be used in conjunction with existing clinical practices to identify additional children who may or may not be currently stunted, but at are increased risk of decelerated growth and therefore would also benefit from nutritional interventions.

Strengths:

Linear growth faltering is a pressing issue with broad, negative impacts on the health, development, and well-being of children worldwide. In this work, the authors applied clearly explained, thoughtful approaches to variable selection, model specification, and model validation, with large, multi-country cohorts used for training and external validation. Appropriate datasets for external validation can be challenging to find, but the MAL-ED data used here is well-suited to the task, with similar predictor and outcome measurements to the GEMS training data. The well-characterized studies allowed the authors to explore a wide range of potential predictors for stunting, including socioeconomic factors, antibiotic use, and diarrheal etiology.

Weaknesses:

This work would benefit from additional discussion around the clinical relevance of the results. For example, what is the current standard of care for prevention of stunting, and how much would this model improve the status quo? Is specificity of 0.47 in the context of sensitivity of 0.80 an acceptable tradeoff with regards to the interventions that would be used? More discussion around these points is necessary to support the authors' conclusions that these models could potentially be used to support clinical decisions and target resources.

Current practice focuses on the identification and treatment of malnutrition, with malnutrition classified based on mid-upper arm circumference (MUAC), weight for length or height z-score, or bipedal oedema. None of these measurements compare child size to their age. At the International Centre for Diarrhoeal Research, Bangladesh (ICDDRB), children are only evaluated for stunting if their weight for age z-score is too low. While stunting can be the result of chronic malnutrition, it can also be a contributing factor to future health problems (see first paragraph of Introduction). Therefore, while related to malnutrition, stunting is a distinct health outcome that would benefit from explicit identification strategies. Furthermore, current practice only identifies children who are already stunted when they present to care. A CPR to identify children whose growth is likely to slow down and therefore who are at risk of new or additional stunting could help prevent additional stunting and its downstream health outcome. The Discussion now includes the following:

P.23 L.353-361: Current malnutrition recommendations are based on patient presentation – whether a child is underweight when they present to the clinic. Our CPR could be used to identify children not currently stunted and therefore not currently recommended for nutritional interventions, but who are likely to slow down in growth and therefore at higher risk of incident stunting. Identifying these children would allow clinicians to connect patients with communitybased nutrition interventions (e.g. maternal support for safe introduction of weening foods, small quantity lipid nutrient supplements (SQ-LNS), etc.(46-49)) to prevent additional effects of chronic malnutrition, namely irreversible stunting.

In addition to the external validation, further investigation of model performance in key subpopulations would strengthen the importance and applicability of the work. For example, performance of prediction models may vary widely by setting; it would be valuable to show that the model has similar performance in each country. Another key sensitivity analysis would be to show consistent model performance by HAZ at baseline. The authors note that stunting may be challenging to reverse (p.20), and many of the children are already below the typical cutoff of HAZ<-2 at baseline; it would be valuable to show model performance among the subgroup of children for whom treatment would be most beneficial.

We appreciate this suggestion. We have added additional analysis regarding stunting at baseline as described above. We have added country-specific CPRs in the Supplement. We have also added a sensitivity analysis whereby we fit models to all data from one continent in GEMS, and then validated that model on the other continent in GEMS data. As you can see from Supplementary Table S5, top predictors and discriminative performance were similar across countries and continents

P.10 L.171-173: Finally, we conducted a quasi-external validation within the GEMS data by fitting a model to one continent and validating it on the other.

P.24 L.380-383: The quasi-external validation between continents within GEMS data, as well as the country-specific models within GEMS, all had similar top predictors and discriminative performance, further supporting the overall validity of our CPR. Finally, we explored a range of AFe cutoffs for etiology, with consistent results.

Reviewer #2 (Public Review):

The manuscript documents a thorough and well-validated clinical prediction model for risk of severe child linear growth faltering after diarrheal disease episodes, using data from multiple studies and countries. They identified a parsimonious model of child age and current size with relatively good predictive accuracy. However, I don't believe the prediction rule should be used in it's current form due to the outcome used the danger of missing treating children who require nutritional supplementation.

As described in-depth above, we do not intend for this CPR to replace existing guidelines, but rather to function as a complementary tool to identify additional children not currently stunted but who are at risk of their growth slowing down.

The outcome used for prediction in a binary indicatory for a decrease in height-for-age Z-score >= 0.5. A child who fails to gain height by future measurements is of concern, but this outcome also misses children who are already experiencing growth failure, and is vulnerable to regression to the mean effect. The two most important predictors were age and current size, with current size having a positive association with risk of growth faltering. As mentioned in the discussion, there is "the possibility that children need to have high enough HAZ in order to have the potential to falter." Additionally, there may be children with erroneously high height measurements at the first measurement, so that the HAZ change >= 0.5 associated with high baseline HAZ is from measurement-error regression to the mean. I recommend also predicting absolute HAZ (or stunting status) as a secondary outcome and comparing if the important predictors change.

See above.

In its current form, the results and conclusions from the results have problematic implications for the treatment of child malnutrition. The conclusion states: "In settings with high mortality and morbidity in early childhood, such tools could represent a cost-effective way to target resources towards those who need it most." If the current CPR was used in a resourceconstrained setting, it would recommend that larger children should be prioritized for nutritional supplementation over already stunted children who may have reached their growth faltering floor. In addition, with a sensitivity of 80%, the tool would miss treating a large number of children who would experience growth faltering. The results of the clinical prediction tool need to be presented with care in how it could be used to prioritize treatment without missing treating children who would benefit from nutritional supplementation. Including absolute HAZ as an outcome will help, along with additional discussion of how the CPR fits alongside current treatment recommendations. For example, does this rule indicate treating children who aren't currently treated, or are there children who don't need treatment given current guidelines and the created CPR.

We thank the Reviewers for pointing out this oversight. We have edited the Discussion for clarity as follows.

P.23 L.352-361: Our CPR provides a tool for identifying patients likely to experience additional growth faltering after acute diarrhea. Current malnutrition recommendations are based on patient presentation – is a child underweight when they come to the clinic. Our CPR could be used to identify children not currently stunted and therefore not currently recommended for nutritional interventions, but who are likely to slow down in growth and therefore at higher risk of incident stunting. Identifying these children would allow clinicians to connect patients with community-based nutrition interventions (e.g. maternal support for safe introduction of weening foods, small quantity lipid nutrient supplements (SQ-LNS), etc.(45-48)) to prevent additional effects of chronic malnutrition, namely irreversible stunting.

P.25 L.390-393: Our findings indicate that use of prediction rules, potentially applied as clinical decision support tools, could help to identify additional children at risk of poor outcomes after an episode of diarrheal illness, i.e. not currently stunted but likely to decelerate growth.

In sum, this is a thorough, well done, clearly explained exercise in creating a clinical prediction tool for predicting child risk of future growth faltering. The writing and motivation is clear, and the methods have applicability far beyond the specific use-case.

Author Response

Public Review:

1) Despite I do not find negative arguments for any special section of the study, I have a question regarding Triprismatoolithu stephensis:

As mentioned in the text, Triprismatoolithus is analysed by the authors, and several pictures are provided in Fig.S12 alongside a brief description in de Supplementary Text 4. But it seems that it is not included in any of the phylogenetic analyses or figures. Why?

If the specimen has no implication for any of the main analyses, there is no need to be considered as "studied material".

We added more explanation for the purpose of Triprismatoolithus (Lines 803–806). We presented Triprismatoolithus to show the prismatic shell units of maniraptoran eggshell other than a famous case of Prismatoolithus levis. Thus, Triprismatoolithus was also presented in the Figure S1C along with other eggshells with prismatic shell unit microstructure. Without this ootaxon, there are just three comparative pieces of material in Figure S1, and so we prefer maintaining this ootaxon. Admittedly, this eggshell was not used in our analysis in Figures 13–16 because the specific egg-laying taxon is unknown so its taxon-ootaxon relationship is not as solid as the cases of Elongatoolithus, Macroelongatoolithus, and Prismatoolithus levis. But please note that the role of this ootaxon in Figure S1 is not trivial because it supports the view that even prismatic shell units have rugged grain boundaries in the squamatic zone.

Author Response

Reviewer #1 (Public Review)

Overall the claims in the manuscript are clearly communicated and justified by the data. However, one of the features on NeuronBridge that was mentioned in the manuscript did not work intuitively and could use more description in the manuscript. This was the feature to upload a confocal stack to search for other Gal4 lines or the appropriate neurons in the EM hemibrain. When a known Gal4 was in the database, it was easy and intuitive to go from a driver line to an EM neuron or, alternatively if an EM neuron was known it was easy to go from that neuron to find a driver line. It was, however, difficult to upload a stack and find the neuron names or a driver line. The example on Neuronbridge was somewhat helpful but an accompanying brief 'How-to' for this process in the manuscript would be very welcome. If it's a possibility, I recommend adding this in as a 'box' or Figure in the revised paper. Further, the authors may want to provide a troubleshooting guide on the website for uploading a confocal stack onto Neuronbridge.

We are revising the text on the website for clarity and adding additional troubleshooting information. This, along with other updates to the website, will be available in the next release of NeuronBridge towards the end of 2022.

Reviewer #2 (Public Review):

1) Figure 4 and its two supplements show the distribution of correct hits in the top 100 for a forward search, as well as illustrating the complementary nature of the 2 methods, with some correct hits found by one of the methods but not the other. Figure 5 shows the results for a reverse search. It seems that this does not correlate to neuron morphology. The manuscript does not mention however if any attempts were made to improve the scoring so that correct hits would be more highly ranked. It would be helpful to clarify this.

Development of CDM and PPPM search algorithms and associated pre- and post-processing optimizations has proceeded in parallel with the MCFO data release and NeuronBridge application described in the paper. Mais et al., 2021 describes in detail their work to optimize PPPM. CDM improvements since Otsuna et al., 2018 will be described in Otsuna et al., 2023, which isn't ready yet. While we view the search approach evaluations as showing that neuron matches can be found with CDM and PPPM, the evaluation can't be comprehensive across all neurons, datasets, and algorithm variations.

2) Related to the point above, the examples used for the forward search are all visual projection neurons. In order to illustrate the usefulness and comprehensiveness of the searches, it would be helpful if some examples of central brain neurons, not truncated in Hemibrain, were also used.

We acknowledge the limited set of neurons examined in the evaluation of CDM and PPPM search, and tried to weight the claims accordingly in lines 305 and 309 of the submission. We agree more examples would be useful, but providing them hasn't proven feasible during the revision period. While the example neurons are truncated, it does not appear likely that searches with completely reconstructed neurons would generally produce worse results.

Author Response

Reviewer #1 (Public Review):

The current study uses microbiology, biochemistry, microscopy, and viral vectors to establish a role for prefrontal cortex expression of the immediate early gene NPAS4 in sucrose preference and dendritic spine morphology in the mouse social defeat stress model. The experimental designs are appropriate and the hypotheses addressed are interesting. The paper is generally very well-written and the figures are clear. Most of the statistical analyses are appropriate, and they are reported in clear and useful tables. Thus, the general potential for the studies is quite high. The authors conclusively show that NPAS4 is induced in mPFC in response to social defeat stress and that NPAS4 is important for stress-induced changes in mPFC dendritic spine number. However, some of the key data regarding reward motivation are difficult to properly interpret and do not convincingly demonstrate a behavioral result of NPAS4 knockdown in mPFC. Moreover, the spine morphology and sequencing analyses lack depth. Most importantly, although the authors explore the effects of reducing NPAS4 expression in mPFC, they do not explore the effects of increasing NPAS4 expression or function, and thus the studies seem incomplete and cannot be fully interpreted.

We appreciate the reviewer's overall positive feedback on our study and the constructive comments to improve the manuscript. In the revised document, we have addressed the key concerns about NPAS4’s function on motivated behavior by providing the new data by which NPAS4 limits natural reward motivation in the CSDS-susceptible group (Figure 3C-D). We encountered the major challenge that animals that sustained injuries during CSDS had to be removed from the study resulting in relatively few susceptible mice. Other factors likely contributed to the low proportion of susceptible mice, including the biological sex of the investigator (Georgiou et al., Nature Neuroscience, 2022). For the gene expression analysis, we provided comparative analysis of our RNA-seq data with published NPAS4 ChIP-seq data to demonstrate genome-wide NPAS4 association, suggesting potential direct NPAS4 target genes. Furthermore, to extend the structural synapse data, we now provide new electrophysiology data (Figure 4C-H). These new data demonstrate that NPAS4 is required for the CSDS-induced reduction of mEPSC frequency. Using new single-nuclei RNA-seq data from adult mPFC tissues, we observe that NPAS4 is expressed predominantly (~93%) in excitatory neuron clusters, but is also expressed in multiple interneuron populations (~7%). Since our NPAS4 knockdown strategy is not cell type-specific, we have revised the discussion to reflect the possibility that some of the NPAS4-dependent CSDS effects on structural and functional glutamatergic synapses and anhedonia-like behaviors could be due, at least in part, to NPAS4 function in one or more classes of GABAergic interneurons. We have discussed these limitations of interpretation, and the need for future cell type-specific approaches, in the revised manuscript.

Reviewer #2 (Public Review):

The authors investigate whether neuronal activity-regulated transcription factor 4 (NPAS4) in the medial prefrontal cortex (mPFC) is involved in stress-induced effects on neuronal spine synapse density (as a proxy for synaptic activity) and reward behaviors. A major strength of the manuscript is that NPAS4 is shown to be necessary for stress-induced reward deficits and pyramidal neuron spine density. In addition, whole transcriptome analysis of NPAS4 target genes identify a number of genes previously found to be regulated in the postmortem brain of humans with MDD, providing translational relevance to these studies. A weakness is that studies were only performed in male mice so its unclear how generalizable these effects are to females. Despite this, the work will likely impact the field of neuropsychiatry by providing novel information about the molecular and cellular mechanisms in mPFC responsible for stressinduced effects on spines synapses and reward behaviors.

We would like to thank the reviewer for the positive comments, including comparison of our NPAS4-dependent PFC genes with published data from postmortem brains of human’s diagnosed with MDD. We agree with the reviewer that assessing the role of NPAS4 in CSDS or similar chronic stress paradigm in females will be an important future direction for our work, and we acknowledge this limitation of our study in the revised manuscript.

Reviewer #3 (Public Review):

Hughes et al. report a role for the transcription factor NPAS4 in mediating chronic stressinduced reward-related behavioral changes, but not other depression-like behaviors. The authors find that NPAS4 is transiently upregulated in Camk2a+ PFC neurons following a single bout or repeated social defeat stress, and that knocking down PFC Npas4 prevents anhedonia. Presentation of linked individual data for social interaction/avoidance measures with/without interaction partners (Fig2C, E) is commended - all CSDS papers should show data this way. Npas4 also appears to mediate the known effect of stress on spines in PFC, providing novel mechanistic insight into this phenomenon. Npas4 knockdown altered baseline transcription in PFC, which overlapped with other stress and MDD-associated transcriptional changes and modules. However, stress-induced changes in transcription with knockdown remain unknown. A major drawback is that only male mice were used, although this is discussed to some extent. Results are presented with appropriate context and references to the literature. Conclusions are appropriate.

Additional context: Given NPAS4's role as an immediate early gene, it will be important for future work to elucidate whether IEG knockdown generally dampens transcriptional response to stress/other salient experiences. Nevertheless, the authors do show several pieces of evidence that Npas4 knockdown does not simply make mice less sensitive to stress and/or produce deficits in threat/fear-related learning and memory which is an important piece of this puzzle.

We appreciate the thoughtful and generous comments from the reviewer regarding our display method for social interaction/avoidance data. We agree that a major limitation of our study is the lack of females. Unfortunately, we’ve had limited success with reported adaptations for the use of females in CSDS, and follow-up studies will be critical to assess NPAS4’s mPFC role in chronic stress-induced anhedonia-like behavior. We address this limitation of our current study in the discussion section.

We agree that IEG manipulation might produce profound changes in the stress-dependent transcriptome of the mPFC. Toward this goal, we investigated the gene expression of several candidate NPAS4 target genes at 1-hour after acute social defeat stress, a timepoint of nearpeak protein expression of NPAS4 (Supplemental Figure 4). Although we observed a main effect of Npas4 knockdown, we did not observe an impact of NPAS4 on stress-induced gene expression (Supplemental Figure 4). NPAS4 is a very rapidly and transiently expressed by stress and neural activity, so to determine the impacts of NPAS4 on stress-induced changes in transcription, multiple time points of research will need to be examined. Future studies performing single-cell transcriptomics at various time points following acute or chronic social defeat stress, sucrose SA, and social interaction will be important to address these questions.

Author Response

Reviewer #3 (Public Review):

The manuscript by the Qiu and Lu labs investigates the mechanism of desensitization of the acid-activated Cl- channel, PAC. These trimeric channels reside in the plasma membrane of cells as well as in organelles and play important roles in human physiology. PAC channels, like many other ion channels, undergo a process known as desensitization, where the channel adopts a non-conductive conformation in the presence of a prolonged physiological stimulus. For PAC the mo-lecular mechanisms regulating this process are not well understood. Here the authors use a com-bination of electrophysiological recordings and MD simulations to identify several acidic residues and a conserved histidine side chain as important players in PAC desensitization. The results are overall interesting and clearly indicate a role for these residues in this process. However, there are several weaknesses in the experimental design, inconsistencies between the mutagenesis data and the MD results, as well as in the interpretation of the data. For these reasons I do not think the authors have made a convincing mechanistic case.

We thank the reviewer for the constructive comments and address the concerns point-by-point below.

Major weaknesses:

The underlying assumption in the interpretation of all the data is that the mutations stabilize or destabilize the desensitized conformation of the channel. However, none of the functional meas-urements provide direct evidence supporting this key assumption. Without direct evidence sup-porting the notion that the mutations specifically impact the rate of recovery from desensitiza-tion, I do not think the authors have made a convincing mechanistic case.

We agree with the reviewer that our functional data measure the degree and rate of the PAC channel entering desensitization from the activated state upon prolonged acid treatment. This is a common experimental procedure for research on desensitization/inactivation of ion channels. Fol-lowing the reviewer’s suggestion, we also sought to capture the kinetics from the desensitized state to the activated state by switching from more acidic pH to less acidic pH (for example 4.0 to 5.0) or neutral pH. However, we found that such experiments are not feasible partly because the kinetics of PAC desensitization is much slower compared to other channels, such as ASIC channels (see a recent study we cited: https://elifesciences.org/articles/51111). For the mutants with strong desensitization (E94R and D91R), it’s unclear whether the currents we recorded at pH 5.0 right after pH 4.0 representing the activated state or the desensitized state at pH 5.0. In other words, we don’t know if the PAC channel transitions from the desensitized state from a lower pH back to the activated state or rather directly to the desensitized state at a higher pH. For the mutants with reduced desensitization, the current amplitude at pH 4.0 were often similar to that at pH 5.0, which makes the recovery/transition variable. We also tried to switch the acidic pH to neutral pH. We found that the PAC channels (both WT and mutants) go back to the closed state from the desensitized state in seconds as limited by our perfusion speed. These data suggest that the desensitized state of PAC is no longer maintained after switching buffer from low pH to neutral pH. In summary, it’s technically infeasible, in our opinion, to measure the rate of recovery from desensitization to activation for the PAC channel. However, our data do support the con-clusion that the rates of entering desensitization from the activated state, a standard measurement of desensitization, change for various channel mutants we studied.

Overall, the agreement between the MD simulations, functional data, and interpretation are often weak and some issues should be acknowledged and addressed.

For example:

1) The experimental data suggests that H98, E107, and D109 play analogous roles in PAC desen-sitization. However, the MD simulations suggest that the H98-D109 interaction energy is ~4 times larger than that of H98-E107. This should lead to a much greater effect of the D109 muta-tion. How is this rationalized?

The purpose of quantifying the interaction between H/R98 with E107 and D109 is to better dis-sect the mechanism by which H/R98 interacts with the acidic pocket residues. The result suggests that R98 has a reduced association with E107/D109 when compared to H98. It also suggests that D109 makes a more direct interaction with H/R98 when compared to E107. We acknowledge that this is not clear in our initial manuscript and we have updated the text to better describe this result. However, this doesn’t imply that the desensitization phenotype of E107R should be less pronounced than D109R. Both E107R and D109R are expected to disrupt the integrity of the acidic pocket, thus resulting in diminished channel desensitization. It is worth pointing out that E107 played a more complex role as it was identified in our previous papers as one of the major proton sensors. The E107R mutant could allow the PAC channel to become more sensitive to ac-id-induced activation (Figure 4d-e in Ruan et al, Nature, 2020), further complicating its effect in desensitization. Taken together, we don’t think the E107/D109 and H/R98 interaction strength could have quantitative correlation with the desensitization phenotype of E107R and D109R.

2) The experimental data shows that E94 plays a key role in desensitization and the authors argue that this is due to the interactions of this residue with the β10-11 linker. However, the MD simu-lations show that these interactions happen for a small fraction, ~10%, of the time and with inter-action energies comparable to those of the H98-E107-D109 cluster. It is not clear how these sparse and transient interactions can play such a critical role in desensitization. Also, if the inter-action energies are of the same sign, how come one set of mutants favors desensitization and one does not?

The 10% value is the amount of time when at least a hydrogen bond forms between E94/R94 and the β10–β11 loop. It is NOT the amount of time that they form interactions, as there could be other types of non-bonded interactions such as Van der Waals interaction and Coulombic interaction. In fact, our non-bonded energy calculation clearly suggests that R94 interacts with the β10–β11 loop much more favorably than E94 (Figure 4C). The impact of E94R on β10–β11 loop is also reflected in the root-mean-square-fluctuation analysis, where the β10–β11 loop shows a reduced flexibility when R94 is present (Figure 4B).

Our central hypothesis is that PAC becomes more prone to desensitization when the desensitized conformation is stabilized. Two critical interactions are characteristic of the desensitized structure of PAC, including the association of the E94 with the β10–β11 loop, and H98 with E107/D109. Therefore, we expect mutations that alter these interactions to affect PAC channel desensitization. Based on the MD simulations, we observed the root-mean-square-fluctuation of β10–β11 loop are reduced for E94R when compared to WT (Figure 4B), suggesting that β10–β11 loop is stabilized when E94 is replaced by an arginine. The non-bonded interaction energy between E94 and the β10–β11 loop is also more negative for E94R when compared to WT (Figure 4C), another indicator of conformation stabilization. As a result, the E94R mutant favors desensitization. This is in sharp contract with the H98R data, in which H98R interact less favorably with E107/D109 (Figure 2F, G, H, I) when compared to WT. Although the interaction energies are of the same sign, it is the difference between WT and the mutants that will ultimately determine whether a certain mutation will favor desensitization or not.

The authors' MD analysis critically depends on assumptions on the protonation states of multiple residues, that are often located in close proximity to each other. In the methods, the authors state they use PropKa to estimate the pKa of residues and assigned the protonation states based on this. I have several questions about this procedure:

• What pH was considered in the simulations? I imagine pH 4.0 to match that of the electrophys-iological experiments.

The exact pH environment cannot be explicitly modeled in standard MD as the protonation state of an ionizable group is not allowed to change during the simulation. Therefore, in our simulation, we prepared the MD system by first predicting the pKa of titratable residues of PAC in the de-sensitized state, and then assign the protonation status of these residues based on the pKa values. We acknowledge that the description in this part is not very clear in our original manuscript. We have revised the method to better describe how the protonation status is assigned.

• Was the propKa analysis run considering how choices in the protonation state of neighboring residues affect the pKa of the other residues? This is critical because the interaction energies will greatly depend on the protonation state chosen.

The pKa analysis was done based on the WT structure and the residue protonation status was assigned based on the predicted value. It is possible that mutations on certain residues could change the pKa of neighboring residues. To evaluate this impact, we carried out pKa prediction for all the mutant structures that we used as input for simulation. This is summarized in the table below:

As shown in the table, although mutations will affect the pKa of neighboring residues, the impact is generally within 0.3 units. As our simulation is carried out based on a pH of 4.0, this variability will not affect how we assign the residue protonation status.

• Was the pKa for the mutant constructs re-evaluated? For example, does having a Gln or Arg in place of a His affect the pKa of nearby acidic residues?

We didn’t re-evaluate the pKa for each mutant in our initial manuscript. We have conducted such an analysis as indicated in the above table. The result suggests that arginine substitutions of H98/E94/D91 could have an impact on the pKa value of nearby residues. However, the differ-ence is relatively small and does not alter the predominant protonation status of these residues at pH 4.0.

• H98R and Q have the same functional effect. The MD partially rationalizes the effect of H98R, however, it is not clear how Q would have the same effect as R on the interaction energies.

Our analysis on H98R and H98Q serves two different purposes. H98 is expected to be protonat-ed at pH 4.0. The fact that H98Q mutant reduced PAC desensitization suggests that positive charge at the location is critical for PAC desensitization, which we attribute to the loss of favora-ble interaction between H98 and E107/D109. This is different from H98R mutant as arginine bears the same amount of charge as a protonated histidine. Our data suggest that the exact bio-chemical property, including its charge and side-chain flexibility, of H98 is crucial for PAC de-sensitization.

• Are 600 ns sufficient to evaluate sampling of the different conformations?

Our MD analysis doesn’t intend to sample large conformational transitions between different functional state. Instead, our analysis focused on local dynamics which allowed us to correlate the observation with electrophysiology data. During the revision, we have extended our simula-tion to 1 μs for each mutant. It is worth pointing out that because PAC protein is a trimer, and we performed all the calculations across three subunits. Therefore, the effective sampling time would become 3 μs in total. The new result remains the same as our initial analysis, suggesting that the sampling time is sufficient to evaluate the metrics reported in the study. We also acknowledged this limitation of our study in the discussion.

Author Response

eLife Assessment:

This manuscript follows the still unanswered concept of 'original antigenic sin' and shows the existence of a 24-year periodicity of the immune response against influenza H3N2. The valuable work suggests a long-term periodicity of individual antibody response to influenza A (H3N2) within a city. But, to substantiate their argument, the authors would need to provide additional supporting data.

Thank you for your comments. We have performed additional analyses and included those results in the revision to support our findings.

Specifically, we included a sensitivity analyses that predicting phases by fitting models with 35- and 6-years periodicity, which were found to provide poorer predictions than the 24-year periodicity used in our main results (Figure 4 – figure supplementary 1).

We also generated a antigenic map with the locations of our tested strains shown in the map. We also compared the paired antigenic distance of A(H3N2) strains (including our tested strains). These results (Figure 1 – figure supplementary 3) suggested that the tested strains that we used spanned the circulation of A(H3N2) since its emergence and well covered the antigenic space of the virus.

Reviewer #1 (Public Review):

The authors suggest that there is a long-term periodicity of individual antibody response to influenza A (H3N2). The interesting periodicity may be surely appeared. Though the authors assume that the periodicity is driven by pre-existing antibody responses, the authors could provide more supportive data and discuss some possibilities.

Thank you for your comments and please find our point-to-point responses below.

1) The authors can investigate whether the periodicity reflects an epidemic/invasion record of A(H2N3) within Guangzhou or the surrounding city, e.g., the numbers of flu-infected people yearly can be referred to.

Thank you for your comments. We aimed to investigate the periodicity in individual level antibody responses, so we made several efforts to minimize the impacts of population level A(H3N2) activity in our analyses. In particular, we have removed the average activity at population level (i.e., strain-specific intercepts), to minimize the impact of higher circulation of a certain stain on the periodicity.

In our simulations, we tested models that only incorporated population level activity but not including cross-reactions (Figure 3B, I), which did not recover the observed periodicity. In the models that including both population level activity and cross-reactions, we found that less predictable population level activities (i.e., less regular annual epidemics) would increase the variations in individual-level long-term periodicity (Figure 3G-H). We also found that measured periodicities did not vary substantially when comparing those measured at baseline compared to those measured at follow up (~3-4 years later). These results suggested that the local epidemics may only have limited impacts on the observed periodicity in individual’s antibody responses, while the cross-reactions between previous exposed and currently circulating strains may be the main drivers.

To address this comment, we added a paragraph in discussion (lines 336-342):

“In this study, we did not explore the interactions between individual level antibody responses with population level A(H3N2) activity (e.g., epidemic sizes). We minimized the impacts from population level by performing the Fourier analysis with individual departures from population average and validating the results with data from the Vietnam cohort. Simulation results further suggested that the population level virus activity alone was not able to recover the observed periodicity, though epidemics with less regularity seemed to increase the variability in individual-level periodicity in the presence of broad cross-reactions (Figure 3G-H).”

2) The authors can consider whether the participants are recently/previously vaccinated and/or infected with flu. The remaining antibodies may reflect a long memory but may show a recent activation.

Thank you for your comments. We agree with the reviewer that the observed seroconversion of the circulating strains may reflect responses recent re-exposures. Given the low influenza vaccine coverage in our cohort (1.3%, 10 out of 777) and in China in general (<5% [3, 4]), we believe that our observed periodicity and seroconversion patterns were unlikely to be caused by to recent influenza vaccinations.

We think that the pervasive exposure to A(H3N2) could be a driver to the observed seroconversions to circulating strains between our baseline and follow-up were likely due to the pervasive exposures (or reinfections for those who developed into infections). Using the same data set, we previously reported 98% and 74% of participants experienced 2- and 4-fold rise to any of the 21 tested A(H3N2) strains [5].

As the reviewer and previous studies suggested, the antibody responses could reflect long term memories that were activated after recent exposures [1, 6]. We generated our hypothesis based on this features, and to characterize the periodicity that may arose from the interactions between long term memories and newly generated antibodies.

We incorporate the re-infection mechanism in our simulations, with and without subsequent cross-reactions with previously exposed distant strains (Figure 3I). Results indicate that reinfection alone cannot recover the observed long-term periodicity (Figure 3A), while reinfection plus the resulting cross-reactions can recover such long-term periodicity (Figure 3D). Therefore, we believe that the repeated exposures or re-infections would not affect our reported periodicity, while they may be drivers of continuous formulation of the life-course antibody profiles and the observed periodicity. Of particular note is the consistency of measured periodic behaviour at baseline and follow up (~3-4 years later).

To address this comment, we reported the vaccination status of our participants when introducing the data (lines 127-129) and in the discussions (lines 280-282 and 313-315):

“Only 0.6% (n = 5) of participants self-reported influenza vaccinations between the two visits, therefore, the observed changes in HI titers between the two visits were likely due to natural exposures.”

“Due to the low influenza coverage in our participants and in China in general, the observed seroconversions likely reflected antibody responses after natural exposures during the study period.”

“Particularly, our simulation results suggested that model including repeated exposures or population level A(H3N2) activity alone did not recover the long-term periodicity (Figure 3).”

3) The strains inducing high HI titers may have similar mutations and may be reactive to the same antibodies. What are the mutation frequencies among 21 A(H3N2) strains?

Thank you for your comments. We selected the 21 tested strains to cover the span of the circulation of A(H3N2) strains since 1968 and antigenic diversity. We prioritized with the strains that were included in the vaccine formulation and tested to create the antigenic map by Fonville et al. [1].

We reproduced the antigenic map (up to strains isolated in 2010) by Fonville et al. [1] and compared the antigenic locations of our tested A(H3N2) strains (Figure 1—figure supplement 3). The 21 strains (or their belonging antigenic clusters if the strains were not used for the map) largely tracked the antigenic evolution of A(H3N2) since its emergence in 1968, with a reportedly mutation rate of 0.778-unit changes in antigenic space per year [1, 2].

We further calculated the paired antigenic distance of strains tested in the antigenic map, which was highly correlated with the time intervals between the isolation of the two strains. The figure also suggested our tested strains cover the time spans and antigenic distances that were shown in the original antigenic map. In addition, our observed periodicity was identified in individual time series of residuals, which has removed the shared virus responses or assay measurements (Figure 1). Therefore, we believe that the impact of specific mutations may have limited impacts on our findings.

To address this comment, we included the reproduced antigenic map showing the locations of the tested strains and their pair-wise antigenic distance in Figure 1—figure supplement 3 and referenced in the main text (line 127).

Reviewer #2 (Public Review):

This is a well-thought-out, clearly exposed article. It builds upon the platform of 'original antigenic sin' (OAS), a notion first developed from studying individuals infected with influenza. According to OAS, the initial infection will set the dominant immune response targets (antigens) that immune cells will recognize, such that infection with a related strain will cause a strong response focused mainly against the initially infecting strain, that then goes on to protect against the new-infecting strain. This study builds off this idea, showing that as strains become increasingly antigenically distant as inferred by the time between strain appearance, the cross-protection can drop to a point where it needs to be invigorated with a potentially new response. The potential biological mechanisms behind this aren't discussed, but a model is built that conveys the potential for 'relative risk' of an individual over the course of the life, based essentially on when one was born.

Thank you for your comments. We expanded our introduction hoping to include more biological mechanisms, especially those related with original antigenic sin.

“Antibodies mounted against a specific influenza virus decay (in either absolute magnitude or antigenic relevance) after exposure until re-exposure or infection to an antigenically similar virus occurs, whereupon back-boosting of antibodies acquired from previous infections (e.g., activation of memory B cells) can occur, as well as updating antigen specific antibodies to the newly encountered infection (e.g., activation of naïve B cells.” (lines 80-84)

“Original antigenic sin (OAS) is a widely accepted concept describing the hierarchical and persistent memory of antibodies from the primary exposure to a pathogen in childhood. Recent studies suggested that non-neutralizing antibodies acquired from previous exposures can be boosted and may blunt the immune responses to new influenza infections.” (lines 92-97)

The basic premise was to measure from serum influenza haemagglutinin-inhibition (HI) titers of 21 strains of influenza A (H3N2) - related strains causing disease at various times over a period of some 40 years- from a diverse set of ≈800 participants of various ages, at two time points, spaced 2 yr apart. The authors then calculated the HI titer for the 21 strains for each individual. From this, each participant's age, their age at the time of a strain's development, and when a strain emerged were used to assess whether there was periodicity to immune responses by performing a splined Fourier transform for each individual and then examining the composite pattern across time for HI titers. The authors propose that on average there is a 24-year periodicity to immune responses to influenza strains, such that after the initial infection, cross-reactivity reduces to the point where it may be less meaningful for protection over around 24-year, and suggests activation of a 'new' immune response might be required to control the more distant strain involved in the response at that time. The periodicity was longer than would be predicted if age were not a factor involved in the HI titer patterns across time. Further, variability in the periodicity was shown to involve broad cross-reactivity between strains and narrow cross-reactivity in more highly-related (closer in time) strains, individual HI titer, and periodic population fluctuations. In the literature, viral strains are estimated to mutate to the point of losing 50% cross-reactivity with a T1/2 of approximately 2.5 yr, which would make the inferred lifespan plausible but perhaps surprisingly long, implying there are immune feedback parameters that influence periodicity. The authors also use an independent cohort of approximately 150 individuals from a separate, published, study to validate some findings revealed in the primary data set.

Thank you for your comments and sorry for the confusion. We agree with the reviewer that the onward protection from the cross-protection should be shorter than 24-year periodicity that was identified in the retrospective antibody responses. We hope to clarify that we identified long-term periodicity by retrospectively investigating the individual antibody profiles, which were results of multiple previous exposures and immunity and cross-reactions that arose from these previous exposures. Therefore, the long-term periodicity is a retrospective characterization, and should not be directly interpretated as the duration of onward protection.

As shown in Figure 4A, the 24-year periodicity consists of phases when individuals’ titers are higher (phase I & II) and lower (phase III & IV) than the population average. As such, the duration of onward protection may be shorter than the entire periodicity. Assuming the protection decreasing with lower titer levels, the onward protection is expected to decrease in phase II and take 1-6 years to drop from the furthest to population average. This is consistent with findings that homotypic cross-protection against PCR-confirmed infections up to about five seasons (lines 291-293), but whether such protection is driven by the declining of cross-reactions still need further investigations.

To address this comment, we rephrased our discussion and make the interpretation less confusing. (lines 285-287):

“Of note, the long-term periodicity is a retrospective characterization of individual antibody profiles that arose from multiple exposures and cross-protection, which should not be directly interpreted as the duration of onward protection conferred by the existing antibodies.”

Strengths: Overall, the study is well executed and the patterns that are visually apparent in Figure 1A (the 'raw' data) are built on to inform a model of the potential breadth of cross-reactivity in a given individual at any given time after birth, integrated with the influenza strains to which they are most likely to have been first exposed. It is a complex thing to make sense of data involving many individuals who could be infected or vaccinated at any and variable points in time over the course of their life, but the authors derive a model that probabilistically accounts for possible infection events, so controls for this nicely, or at least to a degree that is practicable.

Thank you for your supportive comments. We hope to clarify that we identified the long-term periodicity using the residuals of individual HI titers after extracting the population activity that is visually noticeable in Figure 1A. By doing this, we hope to minimize the impacts of population level A(H3N2) activity and laboratory measurements on individual antibody responses (Figure 1C; detailed methods in lines 396-412).

Questions related to the main limitation: The level of math in this paper makes it hard for a basic biologist to critique the approach, but the argued points are intriguing. Foremost, in the final part of the paper the authors move from building a model to testing its potential to predict HI titers in the final quarter strains of the study period, placing individuals into one of four phases: I) early increasing to high titer response, II) waning response phase where they are returning back to the average population-level response against a strain, III) sub-par response against a strain and then reinitiation of HI titers in phase IV. Pleasingly this shows a good correlation between individuals' ages and their predicted phase. However, while the fit predicts phase well in Fig 4C and 4D, it looks to perform less adequately in Fig 4B.

1) Why is this?

Thank you for your comments and sorry for the confusion. In Figure 4B, we aimed to characterize and predict the position instead of the amplitude in the individual time series of residuals. Therefore, we fitted the model using only harmonic terms (i.e., sine and cosine functions; Equation 12 on page 26) [7], while we believe there may be other factors that could affect the observations but were not included in the model. The perditions from the model inform the position and velocity of harmonic oscillators rather than the amplitude or extent of the wave, therefore, the predictions did not exactly fit the observations.

To address this comment, we expand the corresponding methods hoping to make it clear (lines 661-663):

“Of note, we fitted the model aiming to estimate the position of the harmonic oscillators and did not consider for other non- harmonic factors, therefore the model may not fully capture the variations of the data.”

2) Another point for consideration is that the time between samplings (2010-2012) is comparatively short, given a 24-yr predicted periodicity. What would happen to the predictions if the periodicity were 35-yr or 6-yr? Would the model fail to call individuals accurately in these cases?

Thank you for your comments. We repeated our predictions in Figure 4F-G by assuming a 35-year and 6-year periodicity respectively as suggested. Results suggested that model predictions with either 35-year or 6-year did not outcompete the model predictions assuming a 24 years old (Figure 4—figure supplement 1). For instance, the observed proportion of seroconversion to circulating strains in each cohort have correlation coefficients of 0.49 (p-value = 0.05), 0.63 (p-value = 0.02) and -0.12 (p-value = 0.69) with the predicted proportion of phase IV when assuming a 35-, 24- and 6-year periodicity, respectively.

We also hope to clarify that we investigated the prediction potentials of long-term periodicity from two perspectives. Except for using the periodicity to predict the seroconversions between baseline and follow-up, we also predict the phase of each individual in the year of 2012 only using HI titers against strains that were isolated before 2002. Our results suggested our 10-years ahead predictions well correlated with observations (Figure 4C).

To address this comment, we also included the results of analyses using alternative 35- and 6-year periodicity as Figure 4—figure supplement 1, and reported in the main text (lines 262-264).

3) Similarly, if the samples were taken further apart, would the model still be effective at predicting phase?

Thank you for your comments. We hope to clarify that we collected two cross-sectional serum samples, while we identified the long-term periodicity and predicted phase with serums collected from each visit, separately. For instance, in our sensitivity analysis that using serum collected in follow-up (Figure 1—figure supplement 1), we revealed similar long-term periodicity (baseline in Figure 1) with that identified using the baseline serums, despite pervasive exposures during this time period (time separating samples varied from 3-4 years). In addition, the Vietnam data collected sera from six consecutive years. These data showed a similar long-term periodicity (Figure 2—figure supplement 5).

For the phase prediction, we used residuals of HI titers against 14 historical strains that were isolated between 1968 and 2002, and predicted the phase of strain that was isolated in the year 2012. This prediction was derived purely by depending on the periodic pattern of the time series and without information for strains isolated 10 years prior to 2012. Therefore, the prediction was 10 years ahead and was well correlated with observations from the complete time series, further supporting that there may be an intrinsic cycling in individual antibody responses and that this cycle is fairly stationary and predictable.

Author Response

Reviewer #1 (Public Review):

While the circuits underlying the computation of directional motion information in the fly brain are very well described, much less is known about the neurons serving the detection of objects. In a previous publication from the same lab, it has been shown that flies perform body saccades to track a moving object during flight. In the current paper, Frighetto and Frye provide evidence that T3 cells, a population of neurons within the optic lobes, are involved in this task. First, they performed 2-photon Calcium imaging from T3 cells to show that these cells respond to moving bars, which they later use in behavioural experiments. They then silenced T3 cells using genetic tools and tested the behavior of these flies in response to a rotating bar using two different setups. In one, the flies are fixed and bilateral changes in wing stroke amplitude are used as a measure for turning, in the other, flies are magnetically tethered such that they can rotate around the vertical body axis. Silencing T3 cells leads to the abolishment of the steering response induced by object position using a bar that is defined by its motion relative to the surround, but leaves the response to object motion intact. In the magnetically tethered flies, it reduces the number of saccades and thus leads to an impairment of bar-tracking behavior. In another set of experiments they optogenetically activated the whole population of T3 neurons (which supposedly impairs their normal function), which leads to an increase in the number of saccades after the activation (when the light stimulus used to activate the cells is turned off). Silencing the neurons necessary for detection of local motion, T4 and T5 cells, in contrast reduces responses elicited by object motion rather than position, but also has an impact on object tracking saccades. The authors provide a simple model, where speed-dependent signals from multiple T3 cells are integrated and trigger a saccade, when a threshold is reached.

The data generally support the conclusion that T3 cells play a role in detecting bar position and in controlling saccades in response to rotating bars. However, there are some inconsistencies in the data that are not sufficiently explored and discussed.

1) In a previous paper from the lab (Keleş et al., 2020), it was shown that T3 cells respond preferentially to small objects, whereas here they robustly respond to elongated bars and even large-field gratings. This discrepancy is not discussed.

The most likely explanation is that Keleş et al. (2020) work used stimuli of half-contrast (or lower) to probe contrast polarity effects, whereas our stimuli here match the behavior experiments using maximum contrast broadband stimuli. Keleş et al. (2020) work also provided visual stimuli over the full display, >200-degrees in azimuth, whereas here we only provide stimuli unilaterally over <100 degrees; perhaps there was some effect of contralateral stimulation. Finally, different Gal4 drivers; here we use a split-Gal4 that is highly specific for T3. Keleş et al. (2020) work used a normal Gal4 driver less clean than the split. We shall discuss these discrepancies in revision.

2) In a previous paper, the authors showed that integrated positional error rather than bar position is used to elicit bar-tracking saccades and that saccade amplitude is relatively stereotyped. However, here they show, that T3 cells respond much more strongly to a slowly moving stimulus (18{degree sign}/s) rather than to the fast moving stimuli used for the behavioral experiments (> 90{degree sign}/s). This response property plays an important role for the model they propose. My general concern here is that the findings might not be generalizable to slower moving bars, where more precise, position-dependent responses could play a larger role, and that these fast moving bar stimuli represent an extreme situation, where the flies cannot accurately track bar position any more.

We agree that flies will not accurately track purely positional cues at higher bar speeds, since responses to positional signals are inherently sluggish. In free-flight, files execute orientation saccades when a stationary post subtends ~30 degrees (bar width used here), at which point the leading edge of the post is moving ~250°/s (van Breugel and Dickinson 2012). Thus, higher bar speeds are the norm for flies, and our behavioral stimuli (90°/s) was chosen to robustly trigger tracking saccades and to compare with previously published behavioral data sets. Bar velocity of 18°/s is far below the range that robustly triggers orientation saccades. We image at 90°/s and 180°/s to show that T3 responses to behaviorally relevant bar speeds could reasonably act as inputs to an integrate-and-fire behavioral controller. These points shall be clarified in revision.

3) The claim that T3 cells are tuned to stimulus velocity is not supported by the data in my view. For the bar stimuli, the authors only tested speeds of 18{degree sign}/s and above 90{degree sign}/s, but nothing in between. For the grating motion there seems to be an influence of temporal frequency for the same stimulus velocity (see e.g. Fig.1_1), but this is not quantified.

We shall add a full spatiotemporal response profile in revision. One note: we presented T3 responses to different grating speeds in Supplemental material because our goal was merely to indicate speed sensitivity by T3, rather than to present a comprehensive speed tuning curve. T3 is distinct from T4 and T5 in that it is not directionally selective, is full-wave rectified for contrast, and shows similar responses to bars of differing temporal frequencies moving at the same speed. These properties are also likely accompanied by a broad spatial frequency sensitivity (which would bestow speed tuning), but in revision shall either demonstrate this or remove claim to it.

4) The results from the optogenetic activation experiments are hard to interpret, as it is unclear how a prolonged activation of all T3 cells would affect the downstream circuitry. It is not clear that this experiment is equivalent to a "loss-of-function perturbation" of T3 cells as the authors claim in the text.

We are making an assumption, which we shall clarify in revision, that downstream circuitry requires a spatiotemporal progression of columnar activity, as would be generated by the projection of a discrete bar-type-object moving across the eye, and that activation of all columnar inputs together, as would occur with CsChrimson stimulation, would disrupt this discrimination. Although it is a supposition, we feel that it is parsimonious. We compared the effect of CsChrimson stimulation under two different LED intensities but found no effect on bar tracking behavior.

Reviewer #2 (Public Review):

In their manuscript titled "Feature detecting columnar neurons mediate object tracking saccades in Drosophila", Frighetto & Frye study the effect manipulating T3 neurons has on tethered flight saccades. The authors first characterize the responses of T3 neurons to simple visual stimuli, and then manipulate T3 cells (with both Kir2.1 and CsCrimson) and study the effects on the fly's tethered flight behavior, focusing on different types of sharp turns (saccades). Finally, the authors suggest an integrate and fire model to explain how an array of T3-like neurons can produce some of the recorded behavior.

The authors study the elementary, yet challenging, computation of object discrimination. They hone in on a cell type that most likely plays an important role in the circuit. However, the authors do not sufficiently clarify the framework in which they conceptualize T3's role in object discrimination, neither when discussing it in the introduction/discussion nor when explaining experimental results. The authors present the work in comparison to T4/T5 cells. However, T4/T5 cells have been shown to be both local motion detectors and the main cell types to compute motion in the fly's eye. Downstream neurons integrate over these local units to detect different patterns of global and local motion (Authors should cite Krapp 1996 Nature). Are the authors suggesting that T3 neurons perform a similar function only as local object detectors? That is a bold claim that will need to be supported with more experimental results and reconciled with previous results. We already know of other Lobula Columnar neurons (LCs) that respond to different sizes, some even smaller than the optimal T3 stimulus (e.g. Klapoetke 2022 Neuron) and we know of LCs that respond to small objects that do not receive major inputs from T3 cells (e.g. Hindmarsh 2021 Nature).

We are attempting to posit a simple and parsimonious framework for T3 action. Are T3 neurons “local object detectors”? T3 is clearly not “selective” for local objects, since we show that they respond to elongated bars and wide-field gratings (at least when projected over the ipsilateral visual hemisphere). T3 is, however, “sensitive” to objects: vertical bars yielded a mean response peak ~1 ΔF/F whereas a small square object elicited a peak of ~4 ΔF/F (Keleş et al., 2020). This amplitude differential likely indicates surround inhibition, but does not preclude a downstream integrating neuron from pooling columnar inputs to assemble a spatial receptive field for either an elongated bar or a small object. Individual T4/T5 neurons show roughly double the response amplitude to a small object than a long vertical bar (Keleş et al., 2020), which is consistent with other reports, but one would not classify T4/T5 as “small object detectors” as they play a fundamental role in detecting wide-field motion stimuli. We intend to posit that (i) columnar T3 neurons are small-field (local) detectors of the features contained within stimuli that flies readily track, (ii) that the integration of these local signals could support the integrated error computations that flies make to track bars, which (iii) explains why T3 blockade compromises bar tracking saccades. We do not mean to claim that T3 are the first, last, or only inputs to object detection circuitry in deeper neuropiles. We shall endeavor to clarify these issues in revision.

These differences between T4/T5 cells and T3s also make interpreting the experimental manipulations more challenging. When hyperpolarizing T4/T5 or 'blinding' them with CsCrimson activation, the visual motion circuit is severely disrupted. However, the same cannot be said about inactivating/blinding T3 neurons and the object detection circuit (if it is indeed a single circuit). The authors are justified in deducing a connection between blocking T3 neurons and a reduction in bar tracking, but generalizing the results to object detection requires more experiments and clarifications.

We consider “bar tracking” to be one form of object detection, but not the only form. A bar is an “object” (albeit a tall object) in the sense that it is optically disparate from the visual surround. Thus, inactivating/blinding T3 indeed severely disrupts the detection of bar-type-objects. We shall clarify the language to remove any confusion between “object” and “bar”. We do not mean to generalize T3 function to all object vision in the same way that T4/T5 function is generalized to all motion vision, and this shall be clarified in revision.

When framing the manuscript in the object detection framework, previous results regarding the definition of an object should also be addressed. Maimon Curr. Biol. 2008 and work from their own lab (Mongeau, 2019) have already shown that tethered flies respond differently to bars and small objects (fixating on the former while anti-fixating on the latter). Previous work has also shown that T3 neurons respond strongly to small objects and suppress responses to long bars (Tanaka Curr. Biol. 2020). Since all the behavioral experiments in the current manuscript and all the visual stimuli are full arena-length bars, it is impossible to tell whether the T3 results generalize to small objects and even how to reconcile the stronger response to small objects with the role ascribed to T3 cells in generating behavioral responses to long bars.

This amplitude differential between small object and elongated bar responses by T3 likely indicates surround inhibition, but does not preclude a downstream integrating neuron from pooling columnar inputs to assemble a spatial receptive field for either an elongated bar or a small object. Consider that T4/T5 neurons show roughly double the response amplitude to a small object than a long vertical bar (Keleş et al., 2020 and consistent with other reports), but one would not classify T4/T5 as “object detectors” as their small-field columnar signals are integrated by downstream wide-field neurons that assemble spatial filters for specific patterns of optic flow that are generated during flight maneuvers (Krapp et al., 1996 Nature). One downstream integrator of T3 inputs, LC11, is more selective for small objects than T3. We shall clarify these points in revision.

Finally, the authors propose a model for a hypothetical neuron downstream of T3 that would integrate over several T3s and generate saccades. However, given the current knowledge level in the fly vision field, the model should either be grounded more in actual circuit connectivity or produce testable predictions that would guide further research.

We are currently working on the putative downstream partners of T3, and testing for the integration of T3 signals. Preliminary data show that silencing a specific LC class postsynaptic to T3 recapitulates the effects of silencing T3 on saccadic bar pursuit. In the revised version of the manuscript we will provide additional discussion.

The authors should decide whether they would like to address these concerns with more specific experiments that would shed light on the role T3 has to play under different conditions and different definitions of a visual object, or whether they would prefer to limit the scope of their claims.

We shall endeavor to do both!

Reviewer #3 (Public Review):

In free flight, flies largely change their course direction through rapid body turns termed saccades. Given how important these turns are in determining their overall behavior and navigation, it is important to understand the neural circuits that drive the timing of triggering these saccades, as well as their amplitude. In this paper the authors leverage the powerful genetic tools available in the fruit fly, Drosophila, to address this question by performing physiology experiments as well as behavioral experiments with inactivation and activation perturbations.

The authors make three primary conclusions based on their experiments: (1) the feature detecting visual pathway (T3) is responsible for triggering saccades in response to moving objects, but not widefield motion, (2) the pathway primarily responsible for wide field motion encoding (T4/T5) is responsible for triggering saccades in response to widefield motion, and (3) the T4/T5 pathways is responsible for controlling the amplitude of both object and widefield motion triggered saccades.

The authors go on to show that using calcium imaging data of T3 activity it is possible to predict under what conditions flies will initiate a saccade when presented with objects moving at different speeds, resulting in a parsimonious model for how saccades are triggered.

Together, the imaging, behavior, and modeling provide compelling evidence for claims 1 and 2, however, the evidence and modeling for point 3 - the amplitude of the saccades - is lacking. The statistical analysis does not go into sufficient detail in comparing across different cases, and in particular, there is little mention of the effect sizes, which appear to be quite small (this is primarily in reference to 3F and 4E). The data suggest that both the T3 and T4/T5 pathways contribute to saccade amplitude, instead of T4/T5 being the only or primary drivers.

We agree that the evidence suggests that both T3 and T4/T5 pathways contribute to saccade amplitude for bar tracking behavior, and shall clarify this conclusion in revision. However, we also note that the effect of silencing T4/T5 is more prominent (e.g., peak angular velocity) and more consistent across visual conditions. We will dig deeper into the data to substantiate this point. The effect sizes might be small because the silencing approach (i.e., inward rectifying Kir2.1 channels) maintains a hyperpolarized state but does not completely block neuron function; consider that the wide-field optomotor responses of T4/T5>Kir2.1 flies is reduced but not eradicated (Fig. 3A_1).

Author Response

Reviewer #1 (Public Review):

Li et al. have designed a study that examines specific mechanisms for how different DNA sequence variants in the common cancer gene p53 (also known as TP53) influence the sensitivity of tumors to a variety of common cancer treatments. Specifically, they examine a handful of p53 variants with respect to glioblastoma and its response to platinum-based chemotherapy and to radiation therapy. The authors begin by mentioning that looking at DNA variants in cancer is useful but also incomplete: methylation, PTMs, and non-DNA sequence variants can also be critical. They then mention that they have created a model showing that nearly all cancers with p53 mutations have loss-of-function variants and that many cancers with "normal" wildtype p53 in fact have variants causing LOF. These p53 LOF tumors lead to worse patient outcomes, but the authors here show that these tumors appear to be more susceptible to radiation and platinum-based chemotherapy, which they say they have validated in glioblastoma xenografts. This potentially opens up a new avenue for precision medicine for many different sources of cancer that share common p53 LOF variants. The authors have taken a modern approach towards cancer diagnosis and shown how this can improve targeted treatments across a large array of cancer types. They have provided a reasonably convincing proof of concept of this approach for n = 35 PDXs in one cancer type. By and large, the approach and results are reasonable, although many of the exact results concerning the genes and pathways identified that covary with the various treatments and p53 variants are unclear. For instance, the feature selection seems to be somewhat ad hoc, e.g. the method used to determine p53 LOF from p53 WT in the TCGA data was not the same method used for determining p53 LOF from p53 WT in the PDX data.

Thanks for the positive comments. In our study, we used the same method for feature selection (i.e., p53 targets identification), and for calculating CES in different cancer types. This is described in Materials and Methods. However, the methods used to identify the LOF of WT TP53 in TCGA and PDX data are different. For TCGA LUNG, BRCA, COAD, ESCA cohorts, we used the SVM models built from the same cancer type to predict TP53 status. For PDX samples derived from the glioblastoma patients, we used the unsupervised clustering approach. This is because:

1) To train an SVM model, we need a large number of “normal” samples (to represent p53 normal status) and “tumor samples with TP53 truncating mutation” (to present p53 LOF status). In this PDX cohort (n = 35), we have no “normal” samples and only one p53-truncating mutation (Fig. 4f, Table S6). Technically, it is impossible to build an SVM model from this PDX cohort.

2) The TCGA GBM cohort also has very limited “normal” samples (n = 5) which prevents us from training an SVM model for glioblastoma prediction.

3) The TCGA pan-cancer SVM model is not a good choice since GBM was not included into the pan-cancer cohort due to its limited training sample size. Although the pan-cancer model achieved a high AUROC, its performances varied significantly across cancer types. This is most likely due to the imbalanced sample size, since the pan-cancer model is biased by cancer types (e.g., lung and breast) with the larger sample sizes.

4) Even we were able to build a new SVM model from the TCGA pan-cancer with GBM samples included, applying this SVM model to predict non-TCGA samples is still very challenging because of batch effects.

Therefore, we first used the unsupervised clustering as an alternative to the SVM model to classify samples, and then we manually annotate the PDX clusters into “p53-pN” and “p53-pLOF” according to the composite expression score.

We agree with the reviewer that the underlying pathways/mechanisms that can potentially explain the different treatment effects and p53 non-mutational LoF are still unclear and warrant further investigation.

The TCGA AUROCs were incredibly good - over 99% - versus more like 75% for the actual proof of concept. While any significant p-value is fine for basic research, it would be nice to know how this could be improved and bring the results in Figure 4 from ~75% to the >99% that would be necessary for use as a medical diagnostic or for treatment selection for precision medicine.

Thanks for your suggestion. Precision cancer medicines that target TP53 mutations are currently being evaluated in clinical trials. Developing a robust model to predict p53 functional status for medical diagnosis or treatment selection is the primary goal of our study. However, there is still a long way to go to bring the model trained from external data into medical practice. To minimize the biological, clinical and technological heterogeneities and bias, the best approach is to train an SVM model from the same cancer type in the same institute; this requires:

1) The sample sizes of both normal and tumors harboring TP53 truncating mutation should be sufficient to train the SVM model. Take the TCGA lung cancer dataset (n_tumor = 1003) as an example, we built an excellent SVM model from 108 normal samples and 254 tumor samples with TP53 truncating mutations. A much larger sample size is needed if the TP53 truncating mutation frequency is low.

2) Matched data including whole-exome or whole-genome sequencing (to determine TP53 mutation status), RNA-seq (for gene expression), and treatment response.

If one plans to use public data such as TCGA to train the model, the major challenge is integrating data from different sources (i.e., remove batch effects arising from different patients’ cohorts, tumor samples storage and processing, library preparation, sequencing, and bioinformatics analyses).

However, there are significant questions regarding the specific findings uncovered: do the gene pathways identified through bioinformatic analysis fit in with the many highly-studied mechanistic roles of p53? Do the cohort selections - which vary by an order of magnitude in sample size, and come from different locations and different tissues - make statistical sense for cross-validation?

According to our analysis, p53 targets shared by four selected cancer types are significantly enriched in “cell cycle control” and “DNA damage response” pathways, which are the canonical functions of p53 (PMID: 9039259, PMID: 36183376).

For the four TCGA cancer cohorts selected in our study, cross-validations were independently performed for each cancer type. For the pan-cancer cohort, we agree with the reviewer that the samples come from different locations and different tissues, and the pan-cancer SVM model could be potentially biased by a few cancer types with larger number of samples. Building a pan-caner SMV model is a compromised strategy when each cancer type alone does not have sufficient samples to train its own SVM model, and more rigorous evaluations (by independent datasets) are needed. This is why we put the pan-cancer results into the supplementary materials. We have revised the manuscript to make this point clear (Page 9).

Author Response

Reviewer #1 (Public Review)

[...] One potential issue is that the high myelination signal is associated with the compartment in V2 (pale stripes) which was not functionally defined itself but by the absence of specific functional activations. No difference was reported between those stripes that were defined functionally. Other explanations for the differential pattern of a qMRI signals, e.g. ROI distribution for presumed pale stripes is not evenly distributed (more foveal), ROIs with low activations due to some other factor show higher myelin-related signals, cannot be excluded based on the analysis presented.

Indeed, it would have been advantageous to directly functionally delineate pale stripes in V2. Since we were not able to achieve this by fMRI, we needed an indirect method to infer pale stripe contributions in the analysis. We also added a statement in the discussion section to emphasize this more (p. 9, lines 286–288).

Furthermore, different myelination between thin and thick stripes was not tested, since we did not have a concrete hypothesis on this. Despite the conflicting findings of stronger myelination in dark or pale CO stripes in the literature, no histological study stated myelination differences between dark CO thin and thick stripes. Therefore, our primary interest and hypothesis was lying in comparing the different myelination of thin/thick and pale stripes using MRI.

Thank you very much for this comment about potential other sources of differential qMRI parameter patterns. Indeed, based on the original analysis we could not exclude that the absence of functional activation around the foveal representation may have biased our analysis. We therefore added a supporting analysis, in which we excluded the region around the foveal representation from the analysis. The excluded cortical region was kept consistent between participants by excluding the same eccentricity range in all maps. We added more details in the results section of the revised manuscript (p. 8, lines 189–202). In Figure 5-Supplement 1 and Figure 5-Supplement 3, results from this supporting analysis are shown which reproduced the primary findings from the main analysis, particularly the relatively higher myelination of pale stripes.

ROI definitions solely based on fMRI activation amplitude have additional limitations. However, we find it unlikely that a small fMRI effect size and low contrast-to-noise ratio (i.e. stochastic cause of low statistical parameter values/”activation”) has impacted the results, since Figure 3 shows that we could achieve a high degree of reproducibility for each participant.

We would note that the fact that we found consistent differences across MPM and MP2RAGE sessions makes some potential artifacts driving the differences unlikely. We also find it unlikely that systematic cerebral blood volume differences between stripes would have driven the results. A higher local blood volume would lead to increased BOLD responses but also to a higher R1 value due to the deoxy-hemoglobin induced relaxation, which is opposite to the observation of higher activity in the thick/thin stripes but lower R1 values.

Further studies using other functional metrics (e.g. VASO, ASL etc.) may help us to even more clearly demonstrate specificity but were out of the scope of this already rather extensive study. Although we have added extensive further analyses in the revised manuscript such as controlling for foveal effects or registration performance, we did not see a possibility to fully exclude a systematic bias that might potentially be caused by unknown factors.

Another theoretical and practical issue is the question of "ground truth" for the non-invasive qMRI measures, as the authors - as their starting point - roundly dismiss direct histological tissue studies as conflicting, rather than take a critical look at the merit of the conflicting study results and provide a best hypothesis. If so, they need to explain better how they calibrate their non-invasive MR measurements of myelin.

We agree and have now further elaborated on the limits of specificity of the R1 and R2* signal as cortical myelin marker (p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260). However, we still think that it is important for the reader to appreciate the conflicting results in histological studies using staining methods for myelin, which adds to the study’s background.

We did not intend to give the impression that MRI provides the missing ground-truth to adjudicate histological controversies, but that it provides an alternative and additional view on the open questions. We changed the introduction to better reflect the aspect that the study offers a unique view by providing myelination proxies and functional measures in the same individual, which allows for direct comparison and investigation of structure-function relationships (see p. 2, lines 68–70; p. 3, lines 93–95), which is not accessible to any other approach. Nevertheless, we would like to note that R1 has been well established as a myelin marker under particular conditions (Kirilina et al., 2020; Mancini et al., 2020; Lazari and Lipp, 2021). It has also been widely used for cortical myelin mapping across a variety of populations, systems and field strengths. We added this statement to the introduction (see p. 2, lines 82-85). We note that we excluded volunteers with pathologies or neurological disorders from the study and their mean age was about 28 years. Thus, we had conditions comparable to previous (validation) studies.

Because of the contradictory findings of histological studies, we could not further finesse the hypothesis beyond our previous a priori hypothesis that we expected differences in the myelin sensitive MRI metrics between the thin/thick versus pale stripes. To improve the contextual understanding, we added a paragraph in the discussion section covering in more depth how the MRI results relate to known histological findings (see pp. 8–9, lines 216–240).

While this paper makes an important contribution to the question of the association of specific myelination patterns defining the columnar architecture in V2, it is not entirely clear whether the authors can fully resolve it with the data presented.

Indeed, we agree that non invasive aggregate measures, such as the R1 metrics, offer limited specificity which precludes a fully conclusive inference about cortical myelination. We have further emphasized this on several occasions in the text (see p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260). Since the correspondence of cortical myelin levels and R1 (and other metrics) is an active area of research, we expect that the understanding, sensitivity and specificity of R1 to cortical myelination will further improve. We note that the use of qMRI is a substantial advance over weighted MRI typically used, which suffers from lack of specificity due to instrumental idiosyncrasies and varying measurement conditions.

Reviewer #2 (Public Review)

[...] Unfortunately, this particular study seems to fall into an unhappy middle ground in terms of the conclusions that can be drawn: the relaxometry measures lack the specificity to be considered "ground truth", while the authors claim that the literature lacks consensus regarding the structures that are being studied. The authors propose that their results resolve whether or not stripes differ in their patterns of myelination, but R1 lacks the specificity to do this. While myelin is a primary driver of relaxation times in cortex, relaxometry cannot be considered to be specific to myelin. It is possible that the small observed changes in R1 are driven by myelin, but they could also reflect other tissue constituents, particularly given the small observed effect sizes. If the literature was clear on the pattern of myelination across stripes, this study could confirm that R1 measurements are sensitive to and consistent with this pattern. But the authors present the work as resolving the question of how myelination differs between stripes, which over-reaches what is possible with this method. As it stands, the measured differences in R1 between functionally-defined cortical regions are interesting, but require further validation (e.g., using invasive myelin staining).

We agree that we have inadvertently overstated the specificity of R1 at several occasions in the text. We therefore toned down the statements concerning the correspondence between R1 and myelin throughout the manuscript (e.g. see p. 2, lines 68–88; p. 6, line 163; p. 8, line 216; p. 9, lines. 257–260).

We also removed the phrase that gave the impression that MRI can conclusively resolve the conflicting results found in histological studies. In the Introduction, we changed the corresponding paragraph by emphasizing the alternative view, which can be obtained from MRI by the possibility to investigate structure-function relationships in the living human brain, which would not be possible by invasive myelin staining (see p. 2, lines 68–70; p. 3, lines 93–95).

We acknowledge that – perhaps aside from electron microscopy – all common markers have shortcomings, which limit their specificity. For example, classic histology is not quantitative and resulted in conflicting results. It even includes the very fundamental issue, that the composition of myelin varies across the brain and within brain areas significantly (e.g., its lipid composition (González de San Román et al., 2018)). Thus, we regard the different invasive/non-invasive measures as complementary. R1 adds to this arsenal of measures and can be acquired non invasively. It has been shown to be a reliable myelin marker under certain circumstances. It follows the known myeloarchitecture patterns of the human brain, which was also checked for the data of the present study (see Figure 4 and Appendix 2). It is responsive to traumatic changes (Freund et al., 2019), development (Whitaker et al., 2016; Carey et al., 2018; Natu et al., 2019) and plasticity (Lazari et al., 2022). Since we studied healthy volunteers with no known pathologies that were sampled randomly from the population, we believe that the previous results generally apply and suggest sufficient specificity of the R1 marker. Of course, we cannot fully exclude bias due to unknown factors that have not been investigated/discovered by validation studies yet. However, in this case we expect that the systematic differences between stripe types would remain an important result most likely pointing to another interesting biological difference between stripes.

While more research is needed to clarify the precise role of R1 for cortical myelin, we think that the meaningful determination of quantitative MR parameter within one cortical area is still interesting for the neuroscientific community.

Moreover, the results make clear that R1 differences are not sufficiently strong to provide an independent measure of this structure (e.g., for segmentation of stripe). As such, one would still require fMRI to localise stripes, making it unclear what role R1 measures would play in future studies.

Indeed, the observed small effect sizes in the present study still requires a functional localization with fMRI. We expected small effect sizes using R1 and R2* due to the known small inter-areal or intra-cortical differences of MRI myelin markers. Therefore, this study aimed at a proof-of-concept investigating whether intra-areal R1 differences at the spatial scale of columnar structures can be detected using non-invasive MRI. Our study shows that these differences can be seen but currently not at the single voxel level. We anticipate that with further improvements in sequence development and scanner hardware, high-resolution R1 estimates with sufficient SNR can be acquired making fMRI redundant (for this kind of investigations). Please see the reply to the next comment concerning the impact of using R1 in future studies.

The Introduction concludes with the statement that "Whereas recent studies have explored cortical myelination ... using non-quantitative, weighted MR images... we showed for the first time myelination differences using MRI on a quantitative basis". As written, this sentence implies that others have demonstrated that simpler non-quantitative imaging can achieve the same aims as qMRI. Simply showing that a given method is able to achieve an aim would not be sufficient: the authors should demonstrate that this constitutes an important advance.

Thank you for this comment. It goes to the heart of the concerns raised about specificity and sensitivity of MRI based myelin metrics. We elaborate here on the main advantage of using qMRI in our current study and why it is more specific than weighted MR imaging. However, we emphasize that a thorough comparison between qMRI and weighted MRI is highly complex and refer to our recent review paper on qMRI for further details (Weiskopf et al., 2021), which are beyond the scope of our paper. The signal in weighted MRI, even when optimally optimized to the tissue of interest, additionally depends on both inhomogeneities in the RF transmit and receive (bias) fields. Other methods like using a ratio image (T1w/T2w) can cancel out the receive field bias entirely (in the case of no subject movements between scans) but not the transmit field bias. This hampers the direct analysis and interpretation of signal differences between distant regions of the brain. For high resolution imaging applications, the usage of high magnetic fields such as 7 T is beneficial or even mandatory due to signal-to-noise (SNR) penalties. With increasing field strength, these inhomogeneities also apply to small regions as V2. For these cases, qMRI is advantageous since it provides metrics which are free from these technical biases, significantly improving the specificity. As high-field MRI has the potential to non invasively study the structure and function of the human brain at the spatial scale of cortical layers and cortical columns, we believe that the results of our current study, which successfully demonstrate the applicability of qMRI to robustly detect small differences at the level of columnar systems, is relevant for future studies in the field of neuroscience.

We emphasized these considerations in the revised manuscript (see. p. 9, lines 273–285).

The study includes a very small number of participants (n=4). The advantage of non-invasive in-vivo measurements, despite the fact that they are indirect measures, should be that one can study a reasonable number of subjects. So this low n seems to undermine that point. I rarely suggest additional data collection, but I do feel that a few more subjects would shore up the study's impact.

The present study was conducted in line with a deep phenotyping study approach. That is, we focused on acquiring highly reliable datasets on individuals. We did not intend to capture the population variance, which is often the goal of other group studies, since low level and basic features such as stripes in V2 are expected to be present in all healthy individuals. Thus we traded off and prioritized test-retest measurements for fMRI sessions and using an alternative MP2RAGE acquisition over a larger number of individuals. This resulted in 6–7 scanning sessions on different days for each individual, summing up to 26 long scanning session in total. We also note that the used sample size is not smaller than in other studies with a similar research question. For example, another fMRI study investigating V2 stripes in humans used the same sample size of n=4 (Dumoulin et al., 2017).

The paper overstates what can be concluded in a number of places. For example, the paper suggests that R1 and R2 are highly-specific to myelin in a number of places. For example, on p7 the text reads" "We tested whether different stripe types are differentially myelinated by comparing R1 and R2..." Relaxation times lack the specificity to definitively attribute these changes purely to myelin. Similarly, on p11: "Our study showed that pale stripes which exhibit lower oxidative metabolic activity according to staining with CO are stronger myelinated than surrounding gray matter in V2." This implies that the study directly links CO staining to myelination. In addition to using non-specific estimates of myelination, the study does not actually measure CO.

We agree that we did not clearly point out the limitations of R1 myelin mapping. Therefore, we toned down the statements about the connection between cortical myelin and R1. The mentioned statements in the reviewer’s comment were changed accordingly (see p. 6, line 163; p. 11, lines 353–354). We also included a small paragraph to clarify the used terminology (color-selective thin stripes, disparity-selective thick stripes) in the manuscript (see p. 4, lines 110–114) to avoid the inadvertent conflation of CO staining and actually measured brain activity.

I'm confused by the analysis in Figure 5. I can appreciate why the authors are keen to present a "tripartite" analysis (thick, thin, and pale stripes). But I find the gray curves confusing. As I understand it, the gray curves as generated include both the stripe of interest (red or blue plots) and the pale stripes. Why not just generate a three-way classification? Generating these plots in effect has already required hard classification of thin and thick stripes, so it is odd to create the gray plots, which mix two types of stripes. Alternatively, could you explicitly model the partial volume for a given cortical location (e.g., under the assumption that partial volume of thick and thin strips is indicated by the z-score) for the corresponding functional contrast? One could then estimate the relaxation times as a simple weighted sum of stripe-wise R1 or R2.

Figure on weighted average of stripe-wise R1 and R2. (a) shows the weighted sum of R1 (de-meaned and de-curved) over all V2 voxels. z-scores from color-selective thin stripe experiments and disparity-selective thick stripes were used as weights in the left and middle group of bars, respectively. An intermediate threshold of zmax=1.96 was used, i.e., final weights were defined as weights=(z-1.96). Weights with z<0 were set to 0. For pale stripes (right group of bars), we used the maximum z-score value from thin and thick stripe measurements. We then set all weights with z≥1.96 to 0 and used the inverse as final weights. i.e., weights = -1 * (max(z)-1.96). (b) shows the same analysis for R2. Error bars indicate 1 standard error of the mean.

(1) Yes, indeed. We agree that modeling the partial volume of each compartment (thin, thick and pale stripes) in each V2 voxel would be the most elegant approach. However, we note that z-scores between thin and thick stripe experiments may not reflect the voxel-wise partial volume effect, since they are a purely statistical measure and not a partial volume model. Having said this, we think that this general approach can give some additional insights and we provide results for a similar analysis here. We calculated the weighted sum of R1 and R2 values over all V2 voxels for each stripe compartment (thin, thick and pale stripes) independently (see above figure). For R1, we see the same pattern of R1 between stripe types as in the manuscript (Figure 5). Additionally, we show the differences here for each subject, which further demonstrates the reproducibility across subjects in our study. For R2, no clear pattern across subjects emerged, confirming the results in our manuscript. Since, this analysis did not add relavant new information to the manuscript, we refrained from adding this figure to the manuscript, in order not to overload it.

(2) In our current study, we were not primarily interested in investigating differences between thin/thick stripes and pale stripes. While histological analysis found differences (though not consistent) between CO dark stripes (more myelinated, (Tootell et al., 1983)) and CO pale stripes (more myelinated, Krubitzer and Kaas, 1989)), no study stated myelin differences between CO dark stripes. This does not fully exclude the possibility of myelination differences but suggests that if myelination differences between CO dark stripes existed, they would presumably be smaller than differences between CO dark and CO pale stripes. Thus, it would be even more difficult to demonstrate than the hypothesis of this manuscript.

Therefore, we decided to directly test two compartments against each other instead of modeling all three compartments within a single model. In our analysis, we thereby loosely followed the analysis methods described in Li et al. (2019), which compared myelin differences between thin/thick and pale stripes in macaques. We note that this demonstrates further consistency, since it is not trivial that both thick and thin stripes show lower R1 values than the pale stripes. For example, there may be no or opposite differences.

(3) Just for clarification, the plots in Figure 5 show the comparison of R1 (or R2*) between two compartments in V2. The red (blue) curve includes the thin (thick) stripe of interest. The gray curve includes everything in V2 minus contributions from thick (thin) stripes of interest. If we take the thin stripe comparison as example (Figure 5a), then red contains the thin stripes of interest while gray contains everything minus the thick stripes. Therefore, assuming a tripartite stripe arrangement, the gray curve contains both thin and pale stripe contributions.

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Author Response

Reviewer #1 (Public Review):

1) In family 2, the variant was detected by routine trio-based WES diagnostics. Sanger confirmation was not performed. IGV images can be added as supplementary material. Furthermore, median coverage was 75× which might not be sufficient for the identification of all heterozygous variants.

We thank reviewer for pointing it out for clarification. Obviously, at the time (2016) of the reporting of this variant this was our laboratory’s thoroughly validated protocol, which shows that median (!) coverage of 75x with the technology at the time is more than sufficient for robust variant calling. This particular variant was actually below 75X in coverage (at 65x), but Sanger confirmation was not necessary (based on thorough validation of the robustness of calling and GATK scores and other quality parameters for de novo calling). In addition, when coverage goes below 30-35X Sanger confirmation is warranted.

2) Proband 2 (P2) was born as the second child of non-consanguineous parents of Caucasian descent after an uneventful pregnancy and delivery. The boy was macrosomic at birth. Since there was macrosomia, how would the pregnancy be uneventful? At the last assessment at 10 years of age, obesity associated with hyperphagia was of concern; the weight of the patient should be clarified. P2 was diagnosed with autism spectrum disorder but a normal cognitive profile. The identified NM_001014809.2(CRMP1_v001):c.1280C>T variant is very rare and reported in GnomAD exomes with allele frequency 0.0000041.

Routine echographia during pregnancy did not result in any concerns. The pregnancy was indeed uneventful. BMI at last evaluation was 26.1. We included the details in the revised manuscript.

3) Proband 3 (P3) is the first of three children of a non-consanguineous family of European descent. There is a familial history of obesity on both parental sides, and the father is macrocephalic (head circumference: 60.5 cm). Macrocephaly can be isolated and benign, such as in benign familial macrocephaly. However, P3 presented with moderate intellectual disability and an autism spectrum disorder. Since P3 has a macrocephaly also, the PTEN gene should be further interrogated by detailed WGS data analysis as well as an additional orthogonal method(s) since it has pseudogenes.

We have not noted any pathogenic variant of the PTEN gene in the genetic analysis.

Reviewer #2 (Public Review):

Weaknesses of the article include:

1) Spelling errors and difficult-to-understand language. The use of "variant" is now preferred over mutation. According to current nomenclature, predicted but not experimentally confirmed protein alterations should be written as p.(Phe351Ser) rather than p.Phe351Ser.

We apologise for the spelling errors and the difficult-to-understand language in the manuscript. We considered the reviewers comments seriously and corrected the errors and rephrased the sentences wherever necessary.

2) Inconsistent use of in silico pathogenicity predictors and conservation metrics. These should be standardized for each case and should include at least phylop, CADD, and REVEL.

We have applied consistency in the description of in silico pathogenicity predictors and conservation metrics for each patient.

3) CRMP1 is under significant constraint against loss-of-function variation in gnomAD - pLI = 0.99, LOEUF 0.28. Genes in the top decile are highly enriched for haploinsufficiency as a disease mechanism. This should be considered in the interpretation of this data and incorporated into the manuscript.

We thank the reviewer for the comment. As per reviewer’s suggestion, we have included a statement in the revised manuscript under ‘Subjects and Methods’ section.

4) I am not convinced the data supports a dominant-negative interpretation. The variants do not oligomerize as well as wild-type CRMP1, and when co-expressed with wild-type CRMP1 there is an increase in monomeric wild-type CRMP1. While this could support a dominant-negative interpretation, an alternative explanation is these are loss-of-function alleles that cannot oligomerize, and at the stoichiometry of this artificial overexpression system, this leads to increased monomeric wild-type CRMP1. The axonal outgrowth studies are more compelling, but without a loss-of-function control allele, it is difficult to interpret.

The experiments in Figure 2 should be replicated, quantitated, and their statistical significance confirmed.

We thank reviewer for raising concern about the experiment and interpretation of the data. We performed size exclusion chromatography experiments and included the data in the revised Figure 2. Unfortunately, we could not reproduce the experiments for Figure 2B. From our current experimental results, we prove that the CRMP1 variants affect the homo-oligomerization process.

Reviewer #3 (Public Review):

1) The major weakness is Figure 2, as it is not performed up to high standards like the rest of the paper. Panel A does not show any loading control and does not confirm. Panel B at 720 kDa band is not convincing. Results should be repeated with size exclusion chromatography and/or another method to determine molecular weight and should be quantified from triplicate experiments. Panel C is also not convincing and should be repeated to more carefully show results, and quantified.

We thank reviewer for this important concern raised on our Figure 2 experimental data. We addressed the comments in the revised manuscript. We performed size exclusion chromatography and presented the results in the revised manuscript and discussed accordingly in page 23-24.

Fig. 2A: This panel shows the recombinant CRMP1 wildtype and the variants from E-coli expressing system. We repeated the expression several times and obtained similar partially cleaved proteins. Fig. 2A is Coomassie Brilliant Blue staining. Protein size marker and loading control (BSA) were applied on the same gel as shown in Fig.2A original.

Fig.2B: Due to limited protein expression of T313M and P475L mutants, we could not repeat the gel-filtration experiments.

Fig. 2C, 2D: It is difficult to adjust the expression level of each construct (CRMP1 wildtype, T313M, or P475L) in HEK293T cells (input). Therefore, we measured the signal intensity of myc-IP band and input ratio of V5 blot in each condition. Fig. 2D shows the ratio from four independent experiments.

Author Response

Reviewer #1 (Public Review):

In this paper, Quiniou and colleagues show via orthogonal methods that human thymopoiesis releases a large population of CD8+ T cells harboring a/b paired TCRs that (i) have high generation probabilities, (ii) have a preferential usage of some V and J genes, (iii) are shared between individuals and (iv) can each recognize and be activated by multiple unrelated viral peptides, notably from EBV, CMV and influenza.

Major strengths of the paper

Quiniou et al. generated single-cell sequencing datasets of the earliest stages of TCR beta chain gene recombination. And then showed that a subset of them is highly clustered also having high generation probability.

They show that these T cells can bind multiple antigens, both via the use of public antigen-specific datasets as well as corroborating experimental TCR expression and binding essays.

Minor weaknesses

To what extent is TCR clustering and high Pgen and cross-individual sharing correlated? What is the Pgen of the sequences clustered with the high Pgen cells? Can you comment on the correlation between these three phenomena?

Indeed, there is a significant positive correlation between the Pgen and the number of connections among the clustered TCRs, as was reported in Fig.1F of the original manuscript. Furthermore, this correlation is true for both private and public TCRs, as was reported in figure 2B of the original manuscript.

To show the link between the three phenomena, we now have added two supplementary figures showing a high positive correlation between Pgen and the number of connections, and between cross-individual sharing and the number of connections, and to a lesser extent between Pgen and cross-individual sharing (Figure 2-figure supplement 4C and D in the manuscript supplementary information).

However, we would like to emphasize that the difference in the mean Pgen of the clustered and dispersed TCRs is of about 20-fold. This is a high difference for a biological process (and highly statistically significant), but a small one compared to the 10-log10 span of the Pgens of the two populations. Factually, what we observed is not that clustered sequences have a high Pgen, but that they have a higher Pgen than the non-clustered sequences. Yet, many CDR3s with high Pgen do not cluster, and vice versa, indicating that a high Pgen is not the only (nor most important) driver of clustering. We have now added these as Figure 1-figure supplement 3E-F of our revised manuscript.

In other words, to what extent is this surprising to see that highly clustered TCRs have higher Pgen and are more shared?

That for a given CDR3 there is a correlation between having a high Pgen and being public is not surprising as both suggest a positive selection during evolution. What is more surprising is that there are CDR3s forming large clusters that occupy over 20% of the repertoire and that co-cluster between individuals with different HLA, “indicating a convergence of specificities between individuals’ clustered repertoires”. This suggests a surprising selection process that could depend less on HLA than the “classical” selection.

These points are now better emphasized in the revised manuscript.

Potential Impact of the paper

This work highlights an intrinsic property of the adaptive immune response: to generate TCRs with high generation probability that can efficiently bind multiple antigens. This finding has, therefore important impact on drug discovery and vaccine design.

We thank the reviewer for his appreciation.

Reviewer #2 (Public Review):

This study analyses the T cell receptor (TCR) repertoire of double positive human thymocytes, and compares this to mature single positive CD8 cells. The first major finding is that the repertoire post-selection is enriched for groups of TCRs with high generation probabilitites, similar sequences, and for TCRs previously annotated for viral specificity. This data is clearly presented and convincing. The extent of analysis of the human thymocyte repertoire is still very limited, and the paper adds significantly to this important question.

We thank the reviewer for his appreciation.

The second major finding is much more controversial. The authors first investigate the publicly available databases and show that there is a substantial proportion of TCRs which have been annotated to multiple viral specificities, a fact which is well-known to the specialists in the field, but not previously addressed.

Indeed, we are not aware of reports disclosing “a substantial proportion of TCRs which have been annotated to multiple viral specificities”. Actually, one could wonder why “a fact which is well-known to the specialists in the field” is not mentioned and discussed in published articles? To us, it reveals that this point has been overlooked by immunologists as recently in Zhang et al, 2021 where authors aiming at identifying highly specific T cell clones with a new modelling approach, excluded all clones binding more than 1 peptide. Thus, it makes it important to report it, as we do. Furthermore, we would also like to emphasize that we do more than just reporting that some TCR have “been annotated to multiple viral specificities”. We show from a manual curation of public databases that (i) some TCR have been reported to bind to tetramers presenting peptides from unrelated viruses; (ii) that such TCRs co-cluster using Levenshtein distance or GLIPH2 based clustering method; and (iii) that some of these TCRs indeed recognize different, unrelated peptides without significant sequence homology upon re-expression in carrier T cells.

The authors acknowledge that this in silico analysis is mostly based on unpaired alpha/beta sequence data, and that the chain pairing may influence specificity. They, therefore, perform a number of functional assays, demonstrating examples of T cells which respond by interferon gamma production to more than one peptide.

We thank the reviewer for pointing to the fact that, beyond tetramer binding, we performed cumbersome functional studies to document polyreactivity.

The paper is mostly very clearly written and presented and provides some fascinating novel perspectives on T cell cross-reactivity.

We thank the reviewer for his appreciation

The findings will surely be of interest to a broad readership - indeed anyone interested in how adaptive immunity works.

The link between the different sections of the paper is the weakest aspect. The relationship between thymic selection and polyspecificity, and also the real relationship between in silico "cross-reactivity" as evidenced by multiple annotations and the functional polyspecific T cells remains unclear.

Our flow of reasoning/analyzing was as follow. As we were studying the thymic selection of TCR repertoires, (1) we discovered a massive clustering within these repertoires. As for thymocytes this cannot be accounted for by a history of immune responses, this triggered our attention and led us to analyze the properties of these TCRs. This led us (2) to discover in these thymic repertoires “TCRs which have been annotated to multiple viral specificities”, that we were not aware of. We were so much intrigued by these observations that we wanted to substantiate them using datasets of paired  TCRs. As (3) we could confirm these observations in such datasets, this led us (4) to investigate these TCRs in functional studies. This is the link for the 1-to-4 sections.

To make this link clearer, we have reworked the titles of the different Results’ sections such as to emphasize the switch from thymocyte bulk sequencing studies to that of single peripheral cell sequencing studies.

The mechanistic molecular details underlying polyspecificity also remain unclear.

Indeed, we believe that solving the structure of polyreactive TCRs interacting with different peptides will be needed for a molecular understanding of polyreactivity, but that it falls beyond the present work.

But overall, lots of interesting new data, and some very intriguing hypotheses for the community to follow up on.

We thank the reviewer for his overall comment

Reviewer #3 (Public Review):

In this manuscript, the authors propose that there is a special, previously unrecognized, high-frequency population of a/b TCRs that are shared between people, have high generation probabilities, and react to many unrelated viral epitopes. Here is the main flow of the results, with comments on the strengths of the conclusions:

"Thymopoiesis selects a large and diverse set of clustered CDR3s with high generation probabilities" -- this seems correct and has been noted in earlier work by Mora and Walczak and others.

So far, Mora and Walczak selection models in humans are based on studying PBMCs (our ref n° 27 in the revised version), not thymic DP and SP sorted cells, even in the mouse derived models for which they used the total thymic cells (our ref n° 27).

Selection leads to a focusing of the CDR3 length which likely increases the degree of clustering and increases Pgen.

To address this question, we compared the CDR3 length distribution between DP CD3+ cells and CD8 SP cells from our thymic dataset. We did not observe major changes. The distribution and the mean of CDR3 length for the two cell populations remained identical. We only observed a small shifting in the CDR3 length distribution towards shorter sequences post-selection. This is now reported in the new Figure 1-figure supplement 3C in the revised manuscript.

"Clustered CDR3s are enriched for publicness " This also seems correct and again it makes sense: publicness is equivalent to having been independently rearranged (and sequenced) in another individual, which is determined by Pgen, and clustering is also determined to a large extent by Pgen (the factors that contribute to Pgen, shorter CDR3s for example, are largely shared between neighbor TCRs).

We agree that theory could have indeed predicted that. In any case, to our knowledge, this is the first report of large clusters of just selected thymocytes’ CDR3s that moreover co-cluster between individuals with different HLA.

"Clustered public CDR3s are enriched in viral specificities" -- This claim is not justified by the data, which comes from sequence matching against literature-derived databases. Rather, what is true is that "Clustered public CDR3s are enriched in public viral specificities".

We changed “CDR3s are enriched in viral specificities” for “clustered public CDR3s are enriched in public viral specificities".

But this might be a simple consequence of the previous observation, that "clustered CDR3s are enriched for publicness". One would need experimental specificity data on the very same datasets to make a conclusion about viral specificities in general.

We based our interpretation on experimental data.

Indeed, we manually curated databases to identify CDR3s that bind specific tetramers/dextramers. This type of “experimental specificity data” is for immunologists a paradigmatic and yet unchallenged mean to define specificity.

We make the observation that there are more CDR3s from a TCR that does bind tetramers/dextramers presenting viral peptides in clustered than in dispersed CDR3s. This is a highly statistically significant fact, that we now report as a fact that we leave open to discussion/challenge by our community.

"Identification of polyspecific TCRs" -- In this section, the authors report that some of the CDR3 clusters contain CDR3 sequences from literature-derived TCRs with multiple specificities. They conclude that these must represent polyspecific TCRs. The problem with this conclusion is that even having the same CDR3beta, let alone similar CDR3beta sequences, does not imply the same specificity. One can see the problem if one imagines a very deeply sequenced dataset, and focuses on a short CDR3 length with high frequency. With sufficient sampling, one will be able to navigate from nearly any single CDR3beta to any other CDR3beta of the same or similar length by jumping between single-mismatch variants. But this doesn't imply that all the TCRs from which these CDR3s were sampled, which likely have many different Vbeta genes and completely different TCRalpha sequences, must all bind the same thing.

We will first point to the fact that we did not analyze “a very deeply sequenced dataset”, but only the 18 000 most abundant sequences per sample. Singletons were excluded. In addition, we did not mean to say that all the connected TCRs have the same specificities, regardless of their position in the cluster. Clustering algorithms, whether LV distance of GLIPH2 for example, are now commonly used to infer specificity of clusters and it is admitted that the closer the TCR sequences are, the more they share their specificities.

That said, it is precisely because we acknowledge the limitation of bulk sequencing for inferring specificities that we turned to also analyze single-cell datasets.

We made this more apparent by the new sections of the results that more clearly indicate the shift from unpaired bulk thymocyte sequencing and paired single peripheral cell sequencing.

"Binding properties of polyspecific TCRs" -- Here the authors look to validate these results with paired TCR sequences. They analyze a public dataset made available by 10X genomics, featuring single-cell gene expression, TCR sequencing, and dextramer UMI counts for ~150,000 T cells. This is an amazing dataset with lots of interesting features, but, like any large high-throughput dataset, it needs to be analyzed with care.

We can assure the reviewer that we were always very careful. Actually, we even started by carefully reviewing the 10X proposed methodology, in which we identified major biases. This led us to explore this dataset cautiously and without preconceived ideas.

The authors claim to see evidence for large-scale cross-reactivity. This comes mainly from a set of dextramers for A03 and A11-restricted peptides. But these dextramers appear to be binding in a uniquely non-specific manner (by comparison with the other dextramers) and non-TCR-dependent manner in this experiment. One can see this, for example, by comparing the consistency of binding within expanded clonotypes: for a specific dextramer like A*02-GIL(Flu), positive binding for one cell in a clonotype greatly increases the likelihood of binding for other cells in the clonotype, suggesting that the binding is mediated by the TCR.

This is not true for the A03 and A11 dextramers (except for a few expanded clonotypes in an A*11 donor). TCR sequence doesn't appear to be the determining factor for binding to these dextramers; rather it may be expression of KIR genes or other surface proteins that can interact with MHC.

These are indeed striking binding patterns that are remarkably similar for a single CDR3 beta associated with more than 40 different CDR3s alpha (and moreover from two donors). The first attitude of immunologists would indeed be of discarding this observation for non-fitting the paradigms. We would like to rather propose an agnostic view at these results.

These results show that a series of five A03 and A11 dextramers loaded with various peptides bind to cells that express a given CDR3 beta associated with a multitude of CDR3alpha. If it would be an MHC to KIR binding, then such dextramers should bind to most cells, independently of their TCRs. We have added two supplementary figures (Figure 4-figure supplement 8B-C) to show that this is not the case, and that further show very different binding patterns.

If it would be a binding to “other surface proteins”, it would likely be the same.

We identified a CDR3 from donor 3 which binds preferentially to A03 and A11 dextramers. However, it binds to only 4 out of 5 of these. If the binding is non-specific and non-TCR-dependent, a binding for the A0301 RIAAWMATY BCL2L1 dextramer should also have been observed. Moreover, we identified this same CDR3beta in two other cells from donor 1 and 4, and that were associated with a different CDR3alpha. Except for only one binding, these TCRs didn’t show binding to the A03 and A11 dextramers.

Moreover, we identify another CDR3 from donor 1 that is associated with a strong binding to one A1101 dextramer presenting an EBV peptide when associated to many different CDR3alpha. The binding to the other A03 and A011 dextramer is weaker and seem to depend more on the CD3alpha.

If the binding of A03 and A011 dextramers is non-specific and non-TCR-dependent, why is there such a difference between the binding of A1101 IVTDFSVIK and A1101 AVFDRSDAK dextramers?

"Polyspecific T cells are activated in vitro by multiple viral peptides" Here the authors explore polyspecificity experimentally. First they report that polyclonal populations of T cells, sorted for binding to one dextramer, can also produce IFN gamma upon stimulation with a distinct peptide, albeit more weakly than for the cognate peptide.

This is indeed true for CMV+ sorted cells that respond better to CMV peptides than to EBV ones, but not true for EBV+ sorted cells that also respond better to CMV peptides than to EBV ones.

But it's not clear that the concentrations of the peptides are appropriate for stringently detecting cross-reactivity.

We wonder what does mean “stringently”? It is possible that stringently mainly means defining the conditions that eliminates what does not fit the current paradigm?

More factually, the peptide concentration used for these experiments, presented in Fig. 5A-B, was 1 µg/mL, i.e. ~1 µM for a 9-10 aa-long peptide. This is clearly a physiological concentration for viral peptides, routinely used in in-vitro recall assays. We can thus rule out that the observed cross-reactivity is simply due to an excess peptide stimulation.

Then the authors actually synthesize and characterize individual TCRs. Here what is seen is consistent with expectation and does not seem to support the idea of substantial fuzzy cross-reactivity: binding to the cognate peptide is 3-4 orders of magnitude stronger than to the alternative peptides.

We respectfully disagree. First, as shown in Fig. 5C TCR#35-13 (cognate peptide HLA-A2-restricted Flu MP 58-66) indeed recognizes the alternative HLA-A2-restricted CMV IE1 184-192 peptide with a 3-4 higher log EC50; yet, the EC50 of this TCR is approx. 10e-6 M, i.e. 1 µM, which remains a physiological concentration. Second, this is not the case for TCR#36-150 (same cognate peptide HLA-A2-restricted Flu MP 58-66), which actually recognizes the alternative HLA-A2-restricted EBV BMLF1 280-288 peptide with a 4-fold lower EC50.

The only exception is the GAD 114-122 TCR, where the different peptides appear to be closer in binding strength. But in this case, the authors state that they "analyzed their response to a set of peptides comprising their cognate peptide and peptides with no significant structural commonalities, selected by testing combinatorial peptide libraries". If the competitor peptides came from peptide library screening then the observation of strong binding to alternative peptides does not seem as surprising as a TCR that binds well to a Flu peptide, say, and also a CMV peptide, selected from a smallish set of possibilities.

As explained above, this TCR does not stand as an exception compared to Flu-reactive TCRs. Moreover, it should be noted that this GAD 114-122 TCR recognizes its cognate peptide in a similar or even lower concentration range compared to the Flu-reactive TCR #36-150. It should also be pointed out that, contrary to the Flu-reactive TCRs, here we did not have any reference dextramer binding data to guide our peptide selection, which is why we resorted to combinatorial peptide libraries. Thus, although different strategies were used, peptide selection was “guided” in both instances.

It is pretty well established that TCRs are cross-reactive, both for nearby peptides and also for sequence-dissimilar peptides.

We agree and had notably quoted the landmark paper by Don Mason estimating that each TCR may respond to over 106 different peptides from an estimated repertoire of > 1010 peptides. Based on the Don Mason estimate of cross reactivity, the chance to find a cross reactive peptide at random would be around 10-4.

Here, we just tested a few peptides from different viruses. If Don Mason’s estimates are correct, for a given TCR, the chance to find even just 1 cross-reactive peptide among these few peptides would be at most of 10-3, the chance to find 2 cross reactive peptides would be of 10-6 and that to find 3 or more cross reactive peptides would have be infinitesimal.

Thus, if the polyreactivity that we described is part of this general cross reactivity, our results are at least highlight a major previously unreported bias in the selection of these cells.

The question is whether widespread, functionally relevant (not just dextramer binding at some concentration) poly-reactivity to diverse viral peptides is a defining feature of a large fraction of the TCR repertoire. The paper does not appear to present sufficiently strong evidence to support this claim.

We agree with the reviewer that more work is needed to “fully” appreciate the role of polyreactive cells!

Author Response

Reviewer #2 (Public Review):

This paper reports a novel measure of biological age derived from machine-learning analysis of retinal imaging data with chronological age as the criterion measure. The resulting algorithm is impressive. Not only can the retinal image data accurately predict chronological age in the training data and record changes over short time intervals, but it also proves accurate in independent test data and appears to contain information related to mortality risk. In addition, the authors report a GWAS of the new measure.

I would like to see a bit more validation data in the UKB - how does EyeAge relate to (a) tests of visual acuity - e.g. does it explain aging-related differences?

We have extended the supplemental tables and figures (Supplementary table 5 and Figure 3- figure supplement 2) to show additional adjustments to the hazard ratios using visual acuity.

(b) measures of morbidity and disability - e.g. how is EyeAge Accel associated with at least some of the counts of chronic diseases, self-reported physical limitations, tests of physical performance, measures of fluid intelligence?

We felt that all-cause mortality is the most clear outcome to test against, as other outcomes were not available for all participants or would require domain-specific knowledge to properly incorporate which we felt was out of scope. Given this, we have added this limitation to the discussion:

“This study has several limitations. First, further work will be needed to assess whether eyeAgeAccel is correlated with other important health outcomes and measures.“

But overall, this is a very strong report of an exciting new biomarker of aging. It was unclear to me whether the algorithm to compute the measure would be publicly available. The authors should clarify.

Code for both training and evaluation of eyeAge from fundus images is available by minimally modifying open-source software we previously released under the permissive BSD 3-clause license. We have added the following “Code availability” section to the paper:

“To develop the eyeAge model we used the TensorFlow deep learning framework, available at https://www.tensorflow.org. Code for both training and evaluation of chronological age from fundus images is open-source and freely available as a minor modification (https://gist.github.com/cmclean/a7e01b916f07955b2693112dcd3edb60) of our previously published repository for fundus model training57.”

Author Response

Reviewer #1 (Public Review):

This is a very interesting paper trying to quantify excess deaths due to the COVID-19 pandemic in the USA. The paper is roughly divided into two main sections. In the first section, the authors estimate age and cause-specific excess mortality. In the second section, using their excess mortality estimates, the authors attempt to disentangle the impact of SARS-CoV-2 infection (direct impact) vs. the impact of NPIs on this excess mortality (indirect impact). I have some concerns, particularly with respect to the second section.

The model used to estimate excess mortality is quite clear. The authors adjust the baseline model to account for low influenza circulation (and deaths) during the COVID-19 pandemic, to avoid underestimating the number of deaths caused by COVID-19. While this makes sense if the authors are trying to estimate the total number of deaths caused by COVID-19, I'm not sure it needs to be accounted for if the authors want to estimate excess/added deaths. A counterfactual scenario would've included influenza. It also raises the question of whether (conceptually) they should be adjusting for other causes of deaths that may have also decreased during the pandemic. The authors briefly acknowledge this in the discussion ("we can't account for changes in baseline respiratory mortality due to depressed circulation of endemic pathogens other than influenza") but my comment goes beyond respiratory diseases. Analyses of excess mortality from other settings have suggested, for example, decreased deaths due to fewer traffic accidents (not in the US) or due to decreased air pollution, and not accounting for these would also lead to an underestimate of the total deaths caused by COVID-19. I understand that it is not feasible to account for all potential factors, so I wonder if they should focus on reporting excess deaths as compared to a counterfactual with influenza.

Thanks. We think it is helpful to “single out” influenza as it causes major fluctuations in mortality from multiple causes in regular years and is a useful reference to contrast the pandemic impact. But the reviewer’s point is well taken. We have clarified our assumptions about the meaning of the baseline in this analysis (methods p 5), discussed the depressed circulation of other pathogens in depth, and mentioned air pollution (p 12-13). We have also slightly reworked our comparison between COVID19 and influenza so that excess mortality estimates are comparable and now cover periods of the same duration (Nov 2017-Mar 2018 for flu and Nov 2020-Mar 2021 for COVID19, see Figure S11).

The second section, trying to estimate direct vs. indirect effects is also very interesting. However, more details are required about the regression model used and, importantly, what the assumptions and limitations of the approach are. Specifically:

• Please provide a bit more information on the regression used for direct vs. indirect effects. I'd like to see explicit discussion of the assumptions and limitations of the approach but also of the stringency index used. Does this model include an intercept? Was the association between stringency index and excess deaths assumed to be linear? Or were different functional forms considered? It is also not clear how well the model fits the data.

Thanks for these comments which helped us improve this section. We have provided more details about the stringency index in methods (it captures the “sum” of interventions), described the model in methods and supplement, and discussed limitations in caveats section, especially regarding effectiveness of these interventions (p13). We had tried different linear models with and without intercepts but elected to use models with intercepts so as not to overly constrain the relationship between interventions, COVID19 activity and excess mortality. These models also incorporate lags in the predictors that are determined by cross-correlation analysis (as detailed in supplement). In the revised version, we now use gam models, where the relationships between excess mortality and predictors do not have to be linear. We can do so since we were able to add several weeks of data (the regression is now based on 96 pandemic weeks from March 1, 2020 to January 1, 2022). The models are described in detail in supplement p 4-5, and we now specify that they have intercepts. We have also provided additional plots of model fits in main text and supplement (Figures 4 and S16-19).

• Related to the above, please provide more details on how the results of the regressions were translated into the results presented. The main text reports percentages, but the methods only briefly explain how numbers of direct deaths were calculated, and the supplementary tables report coefficients. It is not clear if these estimates of direct and indirect deaths were somehow constrained to add up to the total number of excess deaths, but it doesn't seem like it since point estimates cross 100% in some cases.

As discussed in response to one of the editor’s questions, estimates are not constrained to 100%. We have provided more details in the supplement on how we estimate the direct impact of the pandemic. Briefly, we calculate expected deaths in the gam model with all predictors set to their observed values and again with the COVID19 predictor to zero. The direct impact is the difference between the two predictions, divided by the predictions of the full model.

We note that while some of the estimates derived from gam model exceed 100% (and are similar to the linear model estimates presented in the initial analysis, before revision), these estimates echo the findings from a more empirical analysis, in which we compare all-cause excess deaths with official COVID19 deaths tallies. There, in the two oldest age groups, we find more official COVID19 deaths than estimated by the excess mortality models. Hence both analyses point to an underestimation of the direct burden of COVID19 by the excess mortality approach, specific to the oldest age groups. We return to this point in depth in the discussion (p 12-13) and consider the possible effects of harvesting, depressed circulation of non-SARS pathogens, and inaccurate coding of official statistics (as pointed by reviewer #3).

• Please discuss the potential limitations of using the stringency index to quantify NPIs.

Several limitations have been added to caveats (p 13); major issues include aggregation of multiple interventions into a single index, which does not consider the actual implementation nor the effect of interventions. The index is solely based on mandates in place in different locations and time periods. We also assume that the effectiveness of these interventions, for a given level of stringency, does not change over time.

• When estimating direct and indirect effects, the paper assumes that the estimated parameter is time-invariant? Indirect effects might have changed over the course of the epidemic by factors not necessarily captured by the stringency index used, particularly since the index doesn't take into account the implementation of the measures. Have the authors tested this assumption?

This is an interesting point, which we have explored further. The non-linear relationships we find between NPIs and chronic condition excess mortality may suggest that the reviewer is right. We discuss the role of NPIs in the results section much more deeply than we were previously (bottom of p8).

“At lower levels of interventions (Oxford index between 0 and 50), representing the early stages of the lockdown in March 2020, excess mortality rose with interventions. Later in the pandemic, increased interventions were estimated to have a beneficial effect on excess mortality, driven by comparison between the period when interventions were strengthened in response to increasing COVID19 activity in late 2020 (Oxford index above 60) to the period when interventions were relaxed in 2021 (Oxford index between 50 and 60).”

We cannot run an analysis over different time windows because NPI and time are highly conflated (for instance NPI rise from 0-50% in the very early part of the lockdown period, and then stays above 50% for the rest of the study, so we cannot compare the effect of a 25% level in 2020 and 2021). We have added this limitation in the caveat section p.13.

• The authors state "In contrast, the indirect impact of the pandemic measured by the intervention term was highest in youngest age groups, decreased with age, and lost significance in individuals above 65 years" - I'm not entirely sure of where this statement comes from? For example Table S3 suggests that the indirect effect (multivariate or univariate) is higher in 25-64 yo than in <25s? The same table also suggests negative impacts (protective effects?) in >75s in the multivariate model. Please clarify.

There are fewer deaths in the under 25 yo so this is why the coefficients were lower overall in table S3. Yet we find that the proportion of variance explained by interventions is higher in the under 25 yrs than in 25-44 yrs.

We have now changed our modeling strategy to use gam so Table S3 is no longer relevant but the main conclusion that interventions explain a larger relative portion of excess mortality in the under 25 yrs than in the other age groups, and than other covariates, remains valid. The NPI term is now significant is in all groups (although the relative contribution of NPI still declines with age, as in the prior analysis), so we have rephrased this sentence: “In contrast, the relative contribution of indirect effects, via the intervention variable, was highest in youngest age groups and decreased with age”.

• How do the authors interpret "Percents of excess deaths" over 100%? Similarly, I don't fully understand how to interpret "The upper bound of the 95% confidence interval for heart diseases was above 100% (158%), suggesting that for every excess death from heart disease estimated by our model, up to 1.58 death from heart disease could be directly linked to SARS-CoV-2 infection.

We have rephrased this section although the overall conclusions remain unchanged. GAM estimates of the direct COVID 19 impact is statistically significantly above 100% in the 85 yo and over, suggesting that our excess mortality approach is too conservative and does not estimate enough COVID19 excess deaths in this age group. We draw a similar conclusion from a more empirical analysis, in which we compare all-cause excess death estimates with official COVID19 deaths tallies. In this analysis, we find more official COVID19 deaths than estimated by the excess mortality models in the two oldest age groups (point estimates above 100% in the 75-84 and 85+ yrs). Hence both analyses point to an underestimation of the direct burden of COVID19 in the oldest age groups by excess mortality approaches.

Rephrased results section bottom of p.9: “We estimate that the direct contribution of COVID-19 to excess mortality increases with age, from negative and non-statistically significant in individuals under 25 yrs to over 100% in those over 85 years, echoing the gradient seen in official statistics (Table 4). It is also worth noting that our excess mortality estimates may be too conservative (too high) as we did not account for missed circulation of endemic pathogens. This could explain why our estimates of direct COVID-19 contribution exceed 100% in the oldest age group.“

We return to this point in depth in the discussion and consider the possible effects of harvesting and depressed circulation of non SARS pathogens (p 12-13).

• Table 3: The signs of the point estimate vs CI for vehicle accidents are inconsistent.

Thanks, this was a typo. It should have been 4300 (-700, 9300) excess deaths from accidents. This has been updated with more recent data.

Reviewer #3 (Public Review):

Authors examine mortality data in the US and use time-series approaches to estimate excess mortality during the COVID-19 pandemic.

Major comments:

I would encourage authors to discuss the two different concepts of excess mortality:

(#1) what deaths were caused, directly or indirectly, by the pandemic. This is what the authors have aimed to assess, and I have no major concerns with the methodology

(#2) how many additional deaths occurred during the pandemic, compared to what would have been expected in the absence of a pandemic. For such an analysis I think expected annual influenza deaths should be added back to the baseline (or subtracted from the excess)? Some of the discussion seems to relate more to an impression of #2 rather than #1 but I would be interested in the authors' thoughts.

We have added more details about the approach, in particular why we think that #1 is the proper analysis here (see methods p 5). Given the sheer magnitude of COVID19 excess deaths (over 1 million excess deaths at the end of our study), adding back influenza deaths (up to 52,000 deaths in a recent severe season with a mismatched vaccine, as in 2017-18) would not make a large difference. We have also provided a more direct comparison of the impact of influenza and COVID19.

1. Authors estimate fewer excess COVID deaths in the elderly than there were confirmed deaths (Table 3). Could this be an indication of some confirmed deaths being "deaths with COVID" rather than "deaths from COVID"? I'm not sure how to interpret the %s in the final column when they exceed 100%. The authors suggested a harvesting effect but I would suggest "deaths with COVID" might be a more likely explanation? This issue can be a limitation of confirmed-death data.

This is a good point. We have added a comment along these lines in discussion in the middle of p 12. Still, we think harvesting and/or the depressed circulation of endemic pathogens, which would have inflated our baseline, are more likely explanations for these findings. This is because we find similar estimates (exceeding 100%) in gam models that ignore official statistics and rely on COVID19 case data, or COVID19 hospital occupancy data, and this suggests that other mechanisms, beyond coding of official mortality statistics, are at play.

Yet, as more detailed official statistics become available, a tabulation of confirmed deaths by presence of a primary vs secondary COVID (U07) code may be revealing and get more directly at the reviewer’s question.

Author Response*

Reviewer #1 (Public Review):

ARL3 is a small GTPase that localizes to the primary cilium and plays a role in regulating the localization of some specific ciliary membrane proteins, including PDEδ and NPHP3. Mutations in this gene cause Joubert syndrome, a type of ciliopathy characterized by cerebellar malformation, and retinal degeneration. While the majority of the diseases occur in an autosomal recessive manner, two mutations in ARL3 (D67V and Y90C) have been reported to cause autosomal dominant retinal diseases. In the current paper, Travis et al. sought to understand the pathogenesis of the diseases caused by the two autosomal dominant mutations. They found that D67V acts as a constitutive active mutation, whereas Y90C is a fast-cycling mutant, which can be activated in a guanine nucleotide exchange factor (GEF) independent manner. Since the fast-cycle mutant did not bind to the effector proteins in vitro (likely because the guanine nucleotide falls off from the mutant ARL3, which has a lower affinity to GDP/GTP), they developed a method to snapshot the interaction between ARL3 and its effector. Using this method, they showed that the Y90C mutant indeed has increased interaction with the effectors, suggesting that Y90C is an overactive form of ARL3. They then addressed how photoreceptor cells are affected by these two mutations using a mouse model and found that the mutations disrupt the proper migration of the photoreceptor cells.

Strengths:

• The paper is well written, and it was easy to understand what the authors did from the figure legends and the methods section.

• It was easy to find out what is known or unknown, as the paper has accurate references.

• The authors developed a method to analyze a snapshot of the interaction between ARL3 and its interactors.

• The paper has an in vivo model and connects the biochemical characteristics of ARL3 to in vivo cellular phenotypes.

Weaknesses:

(1) I understand that authors focused on nuclear migration defect as the phenotype was first described in ARL3-Q71L transgenic mice. The similar phenotype observed in RP2 knockout mice further supports the idea that the defect is caused by the hyperactivation of ARL3. Indeed, the defect is not reported in the ARL3 knockout mice, however, I feel that it does not necessarily mean that the defect is not caused by loss of function. Although it has not been assessed, ARL3 knockout mice might have the same defect. Therefore, I think analyzing both the migration defect and trafficking defect would be more informative, rather than focusing on the migration defect. The fact that the relationship between nuclear migration defect and the retinal degeneration phenotype is not entirely clear further enhances the importance of analyzing the trafficking defect.

Does the expression of ARL3-Y90C also cause the trafficking defect? If it is the case, you can separate the nuclear migration phenotype from the one caused by the trafficking defect. Would the expression of lipidated cargo(s) rescue the trafficking defect as well?

I think many questions can be addressed by analyzing the localization of the lipidated cargos, such as PDEδ and GRK1.

The effect of Arl3-Y90C expression on trafficking of lipidated cargos is an interesting question. Previous papers showed mislocalization of lipidated outer segment proteins in Arl3-KO rods and down-regulation or subtle mislocalization in Arl3-Q71L overexpressing rods. So, this was one of the first things we investigated; however, we never observed mislocalization of ciliary or outer segment lipidated cargos (i.e. GRK1, transducin, Rab28, and PDE) in wild type mature rods that were overexpressing Arl3 mutants, and many were tested. It was through these experiments that we first identified the pronounced nuclear migration defect. Rod photoreceptor nuclear migration is a developmental process that is completed by P10, so Arl3-Y90C overexpression is causing a developmental defect. When rods are positioning their nuclei in the ONL, they are still “immature” as their primary cilium has not begun to elaborate disc membranes for light capture. All our analysis was performed in mature rods, so it is not surprising that we did not observe any lipidated trafficking defects at this timepoint. Since the developmental timing of the nuclear migration defect is important for our manuscript, we have added this to our introduction. Additionally, we use “immature” photoreceptors for the cartoon diagrams showing how Arl3 activity is altered by different mutation and rescue experiments, since formation of the mature outer segment occurs post-migration.

(2) I am not quite sure if the nuclear migration was assessed properly. Based on the pictures in Fig.1, some of the FLAG-negative cells also seem to be migrating to INL (please see Fig.1C and Fig.1D). Is this biologically normal during development? Could this analysis be affected by the thickness of OPL, the layer between ONL and INL? Also, the picture is cut out in the middle of INL. Could authors include more layers, such as IPL, of the retina in the picture, so that we can evaluate INL and OPL better? Taking this into account, I think it is worth measuring the nuclear position of FLAG-negative cells as a negative control in all the experiments.

Our electroporation technique results in a small population of rods that express our constructs of interest (~5-15% with a patch). All the experiments were performed in wild type retina which develop normal retinal layers, so analysis of the nuclear position of FLAG-positive cells with the retina is cell autonomous. Migration defects are assessed by differences in the skew of FLAG-labeled rods relative to the boundaries of the wild type ONL, which is marked by Hoechst nuclear stain (also a measure of the FLAG-negative rods). Wild type photoreceptors nuclei are not found within the INL, the nuclei in that layer belong to either horizontal cells or bipolar cells both of which are not targeted by our electroporation approach. As a control, we show that when wild type Arl3-FLAG was expressed FLAG-labeled rods were never observed within the INL. We have now included the % of displaced nuclei in Table 1.

(3) The way that the authors showed the Y90C mutant of ARL3 is a fast-cycling mutant is not very compelling. In Figure 2C, the authors showed that ARL3 Y90C can bind to PDEδ, its effector, once it is pre-loaded with GTP. The authors also showed that the mutant can bind to its effector even without EDTA as long as an excess amount of GTP is added. The authors used endogenous ARL3 as a control to compare the effects between wild-type and mutants. I see that this experiment has multiple pitfalls. First, ideally, this type of experiment needs to be done with a purified protein using fluorescent guanine nucleotide/radioactive guanine nucleotide (e.g. nucleotide loading assay or nucleotide exchange assay) to directly access the kinetics of nucleotide exchange. However, I do understand that this is out of the authors' expertise. In the authors' experimental setting, I am not sure loading the protein with GTP in the presence of the EDTA means anything more than confirming that the protein is intact. Theoretically, wild-type and a fast-cycling mutant can load GTP with similar efficiency in the presence of EDTA. Then during immuno-precipitation, GTP falls off from the Y90C mutant faster than wild-type (because a fast-cycling mutant theoretically has a lower affinity to guanine nucleotides), assuming that GTP was not added during immuno-precipitation (GTP addition was not mentioned in the method, but could authors confirm this?). But in this case, the kinetic of GTP dissociation can be affected by many factors, including the presence of GAP in the reaction, the dissociation constant of Y90C, the volume of the buffer used, and the number of washing steps. Thus, it is not very easy to estimate the difference between wild-type and Y90C. Besides, using endogenous ARL3 rather than ARL3-wild type FLAG as a control can be dangerous. I have experienced that a tagged protein is cleaved to a protein that has a similar size to endogenous protein. (I expressed GFP-protein X in knockout cells lacking protein X, and saw the band at the position where the endogenous protein is observed in wild-type cells). So, the endogenous band that the authors showed could come from the cleaved FLAG-Arl3. (Authors can easily confirm this by having wild-type not expressing FLAG-tagged ARL3, though).

An alternative experiment that I would suggest is doing immuno-precipitation in the buffer containing: 1) no guanine nucleotide, 2) 10mM GDP, or 3) 10mM GTP in the cells expressing the following protein: 1) ARL3 wild-type FLAG, 2) ARL3 Y90C FLAG, or 3) ARL3 D129N FLAG. 10mM guanine nucleotide should be added throughout the process including washing. This experiment might also be affected by many factors, but variability should be lower than the experiment presented in Fig 2C. ARL3-wild type FLAG is also a better control here than endogenous protein.

Variability due to the factors you mention is a concern, but we were able to repeatedly obtain the same results using our method—admittedly our method is testing whether the mutated Arl3 can exchange under a certain condition more than exactly how. We know that we are not providing precise kinetics or elucidating the underlying mechanism for how these mutations lead to what we are calling fast cycling. While that information is important, it is outside the scope of this paper.

As you mention, an important conclusion from the PDEδ binding experiments is that we confirm the Arl3-Y90C protein is intact by showing it can indeed bind nucleotide as long as there is an excess of GTP (Fig 2B. The interesting finding from these experiments is that Arl3-Y90C binds GTP even in the presence of magnesium, a behavior not observed for wild type Arl3. We feel that showing that endogenous Arl3 is not activated in the presence magnesium in each of our preparations is a lovely internal control. However, we agree that showing wild type Arl3-FLAG in these assays is an important negative control and have now included this blot as Fig 2-Sup Fig 1.

(4) In Fig.3, the authors attempted to take a snapshot of the interaction between ARL3 and multiple effector proteins. The three bands that were enriched in the Q71L cells were found as RP2, UNC119, and BART by mass spec (Fig.3B). These bands were used as a readout for the subsequent experiments. I am not quite sure why the authors used this approach rather than using the cell line that expresses both FLAG-ARL3 and GFP tagged protein of interest, just like what the authors did in Fig3G. The reasons why I prefer the latter approach are the following: FLAG bands that correspond to the three proteins (RP2, UNC119, and BART) in wild-type cells are very close to the detection limit, 2) authors failed to confirm that the lowest band actually comes from BART, 3) authors cannot access some important effector proteins, such as PDEδ because 293 cells might not express them. All of the problems can be solved by using the approach that was taken in Figur 3G.

If the authors chose the former approach because of some specific reason, I would appreciate it if the authors could explain that in the main text of the paper.

In vitro crosslinking experiments were performed to test whether overexpression of Arl3 mutants resulted in an active cellular Arl3 without artificially changing any components of the GTPase cycle. We feel these experiments are highly elegant as they allow us to take a snapshot of native Arl3 activities without compromising the analysis by artificially altering GAP/GEF/effector interactions through overexpression or during lysis (as we show that the concentration of GTP/Mg could alter interactions in Fig 2). While AD293T cells are not rod photoreceptors, we are able to use this system to better understand how the Arl3 mutants alter the level of activity within the cell. Yes, this experimental assay is novel, but we confirmed the identity of the effectors by Western and mass spec, used positive and negative controls in each experiment, and show that the method is highly reproducible. We agree with Reviewers 2 and 3 that using this method to study the cellular activity of fast cycling Arl3 mutants is a strength of our paper.

(5) ALR3 Y90C causes nuclear migration defect. Given that Y90C is a fast-cycling mutant (hyperactive) and has a high affinity to ARL13B, the nuclear migration defect might come from either the increased activity of ARL3 or sequestration of ARL13B, which can act as a GEF for ARL3 but potentially have other functions. If my understanding is correct, the authors concluded that the defect caused by ARL3-Y90C is likely due to hyper-activation of the protein, as Y90C/T31N mutant, which cannot bind to effectors but still retains the ability to capture ARL13B, did not cause migration defect. But I am a little confused by the fact that Y90C/R149H, which is unable to bind to ARL13B (Fig.2C) but still retains the ability to interact with the effectors (Fig.3F), did not have migration defect (Fig.7B). Wouldn't this mean that the sequestration of ARL13B could contribute to the phenotype?

If my understanding is correct, the authors are trying to say that both hyper-activation of cytosolic ARL3 and the defect in endogenous ARL3 activation in cilium is necessary to cause migration defect. I am not very convinced by this hypothesis, and still think that the defect could be caused by sequestration of ARL13B to the cytoplasm.

Then why Y90C/T31N did not cause the defect even though they can sequester ARL13B? This might be explained by the localization of the ARL13B mutants. If Y90C can localize to the cilium while the double mutant, Y90C/T31N, does not, then only Y90C might be able to inhibit the ARL13B function in the cilium. This could explain the lack of the defect in the cells expressing Y90C/T31N.

It would be helpful to understand how exactly the fast-cycling mutant causes the defect if the authors can provide more information, including localization of ARL3 (wild-type and mutants) as well as key proteins, such as ARL13B and the effector proteins. Assessing ARL13B defect seems to be particularly important to me because ARL13B deficiency has been connected to neuronal migration defect (Higginbotham et al., 2012)

What I am trying to say here is that how the defect is caused is likely very complex. So, providing more information without sticking to one specific hypothesis might be important for readers/authors to accurately interpret the data.

Our data shows that for the fast cycling Arl3-Y90C mutation both features: blocking endogenous Arl3 activation in the cilium (through Arl13B binding) and increasing activity of Arl3-Y90C in the cell body are required to produce a nuclear migration defect. We find that we can rescue migration defects by either restoring activation in the cilium or reducing GTP activity outside the cilium. As long as there is more Arl3-GTP activity in the cilium, then the rod can handle aberrant Arl3-GTP activity in the cell body. The Y90C/R149H was a critical result that led to our hypothesis that there is a gradient between the two compartments that is used for proper migration. One interesting point is that absence of any activity does not produce the migration phenotype, further suggesting that an imbalance in the gradient is important.

We performed new experiments to investigate whether Arl3-Y90C is sequestering Arl13B away from the cilium but found that localization of Arl13B (both endogenous and overexpressed) is not altered by expression of Arl3-Y90C – see Fig 3-SupFig 1-2.

It is an interesting question as to how different Arl3-FLAG constructs are localized within the photoreceptor. Sadly, we did not analyze the data in a way that would allow us to draw any conclusion about the localization of different Arl3-FLAG constructs. In general, we observed FLAG localization throughout the photoreceptor cell and focused our imaging on the FLAG staining around the nucleus so we could further analyze ONL position. Looking back through our images, some of mutants might have a more prominent localization within a specific subcellular compartment (e.g. Arl3-D67V is more prominent in the inner segment than outer segment and Arl3-Y90C appears to have dominant outer segment localization). Likely, these differences represent each mutant binding a particular effector: D67V to RP2 and Y90C to Arl13B, which we show biochemically. Ideally, Arl3 mutant localization would be analyzed during development to provide a more direct link to the nuclear migration defect, a future direction for our lab. We have updated our manuscript to be more transparent about the potential differences in rod localization of Arl3 mutants.

(6) The rescue experiments that the authors presented in Fig.5-6 are striking and would build a base for future therapy of the diseases caused by ARL3 defects. However, I believe more examinations are needed to accurately interpret the data. The authors did this rescue experiment by co-injecting ARL3-FLAG and chaperons/cargos if I understand the method section correctly. But I feel we can interpret this data correctly only when ARL3-FLAG and chaperons/cargos are co-expressed in the same cells. I think a better way to analyze the data might be by comparing the nuclear migration phenotype between ARL3-FLAG only and ARL3-FLAG;chaperons/cargos double-positive cells.

Our lab has found that the initial estimates by the Cepko Lab that co-injection of two plasmids results in above 90% of rods expressing both proteins is accurate (see reference Matsuda and Cepko PNAS 2004). Since we only assess nuclear position of FLAG-labeled rods, it is true that a small percentage of cells in this analysis express the Arl3-FLAG mutant and not the chaperone/cargo; however, inclusion of these cells really only bolsters our findings as complete rescue would likely be even more robust than measured.

Reviewer #2 (Public Review):

The small GTPase Arl3 (Arf-like 3) is a well-characterized component of primary cilia, including the outer segment of photoreceptors, which contain specialized cilia. Arl3 is critical for the import of multiple lipid-modified proteins into cilia that are vital to ciliary function. Human mutations in Arl3 are reported to cause both autosomal recessive and dominant inherited retinal dystrophies, but the mechanisms through which these mutations disrupt photoreceptor development are not known. Here the authors show that two dominant Arl3 mutants, Arl3-D67V and Arl3-Y90C exhibit increased activity, but for different reasons. Arl3-D67V is constitutively active (unable to hydrolyze GTP), whereas Arl3-Y90C is a classic rapid-cycling mutant, able to bind GTP spontaneously (independent of its guanine nucleotide exchange factor Arl13) but still able to complete the GTPase cycle by hydrolyzing GTP. Expression of either mutant in developing murine retinas results in a nuclear migration defect, specifically aberrant localization of rod nuclei to the inner rather than outer nuclear layer. In this sense, they phenocopy another well-characterized constitutively active mutant, Arl3-Q71L. Normal nuclear distribution could be restored by overexpression of Arl3 effectors, suggesting that the active mutants disrupt nuclear migration, at least in part, by sequestering Arl3 effectors.

While the data are reasonably clear and convincing, there are several instances where the conclusions drawn are either confusing or problematic. Specifically:

1) Although retinal rod cells are ciliated in their outer segment, the authors never actually examine ciliation here. Their only morphological readout is nuclear migration. How does nuclear migration failure impact ciliogenesis in the outer segment?

Imaging was performed in mature retinas at P21 after outer segment formation is completed. Electroporation only targets a small population of cells for which we observed normal outer segments structures in all conditions tested — therefore we conclude that ciliogenesis is unaffected. Previous literature has also showed that defects in rod nuclear migration do not affect ciliation of the outer segment.

2) The Arl3-Y90C mutant seems to act physiologically more like a dominant-negative than an activated mutant. A second mutation in Y90C (R149H) that blocks binding to the GEF Arl13 abrogates the nuclear migration defect, suggesting that Y90C is preventing activation of endogenous Arl3 by sequestering the GEF. Yet overexpression of effectors or cargos still rescues nuclear migration in the presence of Y90C, suggesting that it also sequesters effectors. How do the authors explain this?

We agree with this interpretation. We have now included language about Arl3-Y90C’s role as a dominant negative in that it blocks Arl13B activity. The interesting caveat to this black and white usage is that blocking Arl13B would suggest a reduction in endogenous Arl3 activity in rods (which we find to be true, see Fig 5A). However, the migration defect phenotype mimics overly active Arl3 (Arl3-Q71L) and not a loss of function in Arl3 (Arl3-T31N). Using in vivo crosslinking experiments, we show that the fast cycling nature of Arl3-Y90C also causes GEF-independent activation of Arl3 (Fig 4D-E) that leads to the migration defect. Our rescue data shows that only the combination of both effects – reduced Arl3 activity in the cilium and GEF-independent Arl3 activation outside the cilium - is enough to disrupt the ciliary gradient and produce the migration defect.

3) Fig. 1 suggests that an Arl3-T31N mutant has no phenotype. This is a canonical mutation in small GTPases that typically renders them dominant negative. The lack of phenotype is surprising since most dominant-negative mutants act by sequestering their GEFs, thereby preventing activation of the endogenous GTPase. Fig. 2C suggests that this may not be the case for Arl3-T31N, which binds Arl13 only weakly. Some of this confusion may arise from the fact that Arl13 is not a typical GEF. It is very unusual for one GTPase to directly promote nucleotide exchange on another. Does Arl3-T31N affect ciliation in the rod outer segment, or in other ciliated cells? Some discussion of this point is warranted here.

Our paper finds that Arl3 mutants must produce an aberrant activity outside the cilium, whether through constitutive activity (seen for D67V and Q71L) or fast cycling (seen for Y90C and D129N) to cause the migration defect. Since T31N does not cause excess Arl3 activity in cells (see Fig 4) even if it does have some dominant negative activity toward Arl13B, then it is still not enough to cause the migration phenotype. This was directly tested in Fig 5, where we increase T31N binding to Arl13B by introducing Y90C/T31N and still do not see migration defect. Our results are also in line with a previous study showing that despite rapid photoreceptor degeneration in a retina-specific conditional Arl3 knockout mouse the outer segments were initially formed, in contrast the retina-specific conditional Arl13B knockout mouse did disrupt photoreceptor ciliogenesis leading to a more rapid degeneration (Hanke-Gogokhia, JBC 2017). Since complete loss of Arl3 activity did not disrupt ciliogenesis, it is unlikely that expression of Arl3-T31N in wild type retinas would alter outer segment formation, and we observed that outer segments formed in all Arl3 mutants.

4) Oddly, Arl3-Y90C does robustly bind Arl13 (Fig. 2C), while at the same time binding to effectors (Fig. 3D/E), although less strongly than the canonical Q71L constitutively active mutant (Fig. 2A). As noted in point #2, the Y90C/R149H double mutant, which fails to bind Arl13, abrogates the nuclear migration defect observed with Y90C alone. Although the authors refer to Y90C as "rapid cycling" its phenotype is more similar to a dominant-negative than an activated mutant.

We agree with this interpretation. We have now included language about Arl3-Y90C’s role as a dominant negative in that it blocks Arl13B activity. However, the rapid cycling behavior is important to cause the phenotype.

5) The authors also mention that loss of Arl3 has no phenotype in their assay, however, Arl3 knockout mice exhibit severe retinal degeneration. How do they explain this?

Our study finds that not all human Arl3 mutations will target the same cellular process even though they all result in degeneration. Arl3 knockout mice show drastic alterations in lipidated protein trafficking to the rod outer segment in mature retinas, a phenotype that we did not observe by expressing the dominant Arl3 mutants in wild type rods. Since our tools are not designed to study degeneration of rods, the precise mechanisms of degeneration caused by loss of function or dominant mutations remains to be determined. We outline some ideas in the discussion, but more work needs to be done before making any big statements regarding this. We hope that our manuscript will inspire clinicians to take a closer look at human patients to determine if there are subtle differences between disease presentation for dominant and recessive forms Arl3 inherited mutations. This is beyond the scope of our expertise.

Reviewer #3 (Public Review):

This work provides mechanistic insights into two recently described dominant variants of Arl3, a small GTPase, namely mutations D67V and Y90C. The authors identified a phenotype of these dominant variants during the development of rod photoreceptors by in vivo experiments in mice. They specifically observed a defect in rod nuclear migration to their final outer nuclear layer. This phenotype has been previously observed in another constitutively active variant of Arl3, Q71L. The authors performed a series of extensive and thorough biochemical assays to clarify the mode of action of these variants, mostly the Y90C variant, comparing the behavior of these variants to previously described mutants and combining multiple variants by mutagenesis. They also developed a new in vivo crosslinking strategy to be able to identify transient states of protein-protein interactions. They finally performed phenotypic rescue experiments by co-expression of various relevant proteins interacting/involved with Arl3. They finally propose a model based on differential subcellular compartmentalization of Arl3 activation which when disrupted leads to rod nuclei misplacement. These data add to the current understanding of contribution of different Arl3 variants causing human retinal degeneration, which has strong potential translational implications.

Strengths:

Relevance of Arl3 dominant variants to human retinal degeneration. Identification of Y90C variant as a "fast cycling" GTPase, and not as a predicted destabilizer of the protein structure.

New method of crosslinking to enable snapshots of endogenous protein-protein interactions.

Weaknesses:

• The relevance of this study is justified by the fact that newly identified dominant variants of Arl3 have been associated to retinal degeneration. However, the authors never assess a degeneration phenotype.

Electroporation technique allows for rapid expression of constructs, but the sparse expression makes it a poor means to study retinal degeneration. This is important to examine in the future using robust genetic mouse models.

• The authors show new dominant variants of Arl3, namely Y90C and D67V, cause rod nuclear mislocalization. This phenotype is interesting but this was previously observed with other constitutively active mutation of Arl3, Q71L, and therefore is not novel.

Yes, the Q71L paper is well cited in our manuscript and set the basis for many of our experiments.

• The main claim of this paper is that subcellular compartmentalization of Alr3 activation to the cilium (the so called gradient by the authors) is required for proper rod nuclear migration to their final outer nuclear layer destination. The authors provide multiple experiments to support this model, but this is never directly demonstrated.

We are not aware of any methods that could be done to directly show the subcellular localization of active Arl3-GTP within rod photoreceptors. We agree that we have provided many experiments that support our hypothesis that altering the Arl3-GTP gradient between cilium and cell body produces a nuclear migration defect. Some of our favorites include Fig 6, where we find that the migration phenotype is only rescued with expression of ciliary cargos and not rescued by non-ciliary cargos. Also, the new data requested by reviewers showing Arl13B expression in the cilium can restore the Y90C defect further supports that the Arl3 ciliary gradient is necessary for proper nuclear migration.

Author Response

Reviewer #1 (Public Review):

Pan et al. examined the role of oligodendroglial exocytosis, and specifically the role of L-type prostaglandin D synthase (LPGDS), in modulating oligodendrocyte differentiation and myelination. The topic of autocrine and paracrine signaling within the oligodendrocyte lineage is under-studied and the authors use a novel approach for oligodendrocyte precursor-specific inhibition of VAMP-mediated exocytosis using inducible expression of botulinum toxin with the PDGRFa-CreER transgenic mouse line (PD:ibot). Using a combination of in vitro culture systems and immunohistological analysis in vivo, the authors find ibot expression in OPCs leads to reduced oligodendrogenesis and myelination, leading to a behavioral deficit in rotarod performance. Additional transcriptomic analysis in PD:ibot mice revealed Ptgds, the gene encoding LPGDS, was significantly overexpressed in both mature oligodendrocytes and OPCs. Further pharmacological experiments with cultured OPCs showed direct LPGDS inhibition led to a similar inhibition of oligodendrogenesis as PD:ibot mice. Together, this study reveals VAMP-mediated exocytosis in OPCs is required for normal oligodendrogenesis and identifies LPGDS as a new chemical regulator of oligodendrocyte myelination. These findings are strengthened by careful characterization of the PD:ibot mouse line and effective use of culture systems and pharmacology to uncover a cellular mechanism. Quantification is performed at several levels of resolution using immunohistochemistry, electron micrography, and protein/transcriptomic analyses and control experiments were largely carefully considered.

We thank the reviewer for recognizing the strength of our study.

Despite these strengths, there are some points that need to be further addressed. The interpretation of autocrine/paracrine signaling relies on a critical culture experiment in which PD:ibot OPCs were cultured in the presence of PD:ibot or control OPC well inserts. However, these results had a marginal effect size, raising questions as to the extent to which VAMP inhibition specifically had effects through the blockade of exocytosis (resulting in an autocrine/paracrine signaling deficit) or inhibited oligodendrogenesis in a cell-intrinsic mechanism (e.g. VAMP-dependent trafficking of critical myelination components, such as PLP (Feldmann et al., 2011)).

We agree with the reviewer that both cell autonomous and cell non-autonomous effects may contribute to the defect associated with VAMP inhibition. We performed additional experiments to investigate the contribution of cell non-autonomous mechanisms. We took advantage of the fact that all OPCs purified from PD:ibot mice were not botulinum-GFP-expressing (efficiency ~65% Figure 6B, page 24). The GFP- cells in PD:ibot OPC cultures did not express botulinum toxin and were competent in exocytosis. We compared the development of GFP- control cells in cultures generated from PD:ibot mice vs. control cells in cultures generated from control mice. Interestingly, we found that the percentages and sizes of lamellar cells in control cells in PD:ibot cultures were smaller than in control cells in control cultures (Figure 6C, D text page 25). Although both groups of cells were competent in exocytosis, they were surrounded by exocytosis-deficient vs. exocytosis-competent neighbor cells. The differences in the growth capacity of control cells in the presence of different neighbor cells reveal cell non-autonomous contributions of botulinum-expressing cells in oligodendrocyte development.

As described above under Essential Revisions 4), we performed additional experiments on the role of the secreted protein L-PGDS in oligodendrocyte development. We found that adding a protein that inactivates PGD2, HPGD extracellularly to oligodendrocyte cultures inhibited their development (Figure 7F, G, page 33). Adding L-PGDS protein extracellularly to PD:ibot oligodendrocyte cultures rescued their development defect (Figure 9A, B, page 33). Moreover, overexpressing Ptgds in PD:ibot mice partially rescued the myelination defect (Figure 9E-H, page 36). These observations further strengthened our conclusion that cell non-autonomous mechanisms contribute to the effect of botulinum toxin on oligodendrocyte and myelin development.

Nevertheless, these results do not rule out the cell autonomous effect of botulinum on oligodendrocyte development and, therefore, we included the potential contribution of both cell autonomous and cell non-autonomous mechanisms in the text.

Additionally, the authors claim the reduced number of oligodendrocytes in PD:ibot mice in vivo is not due to oligodendrocyte apoptosis and provide evidence by cleaved caspase-3 immunostaining of the cerebral cortex. While statistically not significant, the data is highly variable.

We thank the reviewer for pointing out the variability of the caspase-3 results. We performed a more thorough analysis of activated caspase-3 at multiple developmental stages. Again, we did not find any statistically significant difference in apoptosis between PD:ibot and control oligodendrocytes, OPCs, or cells of other lineages (Figure 3-figure supplement 1, text page 13).

If true, this would suggest oligodendrocyte differentiation is inhibited, which would coincide with a reduction of OPC proliferation. A complementary experiment comparing the rates of OPC proliferation between control and PD:ibot mice in vivo would provide further clarity on how oligodendrocyte density is being reduced.

We analyzed OPC proliferation in vivo by staining and quantifying Ki67+PDGFRa+ cells. Intriguingly, we found a modest increase in OPC proliferation in PD:ibot mice (Figure 3-figure supplement 3, text page 14).

The relevance of these myelination deficits is assessed with a rotarod assay, however, the mice used for these experiments are several times older (2-5 months) than those used for all other histological quantification (P8-P30). The large variance in results could be due to age-related differences in myelination, and it is unclear whether the deficits at early timepoints show a linear progression with age.

We thank the reviewer for the insightful comment. We have separately labeled data points from 2 months old and 5 months old mice (Figure 3Q-S, text page 17). With the data we have so far (n=20-27 per genotype), there isn’t a striking progression of phenotype with age. Future analysis at multiple time points may resolve any age-dependent changes in the phenotype.

Reviewer #3 (Public Review):

The authors pose an important question of whether oligodendrocyte lineage cells have an autocrine/paracrine signaling loop that contributes to their differentiation and myelination. While prior studies have demonstrated oligodendrocyte lineage cells have cell-intrinsic pathways that impact differentiation and myelination, there isn't a strong precedent for oligodendrocytes to promote their own differentiation via autocrine/paracrine mechanisms. The notion that oligodendrocyte lineage cells promote their own differentiation in an autocrine/paracrine manner is an intriguing one that adds a new layer to our understanding of how oligodendrocyte maturation is controlled. I anticipate this paper will prompt a new direction of future investigations to uncover the extent of oligodendrocyte autocrine/paracrine signaling.

To test the possible role of oligodendrocyte-secreted molecules on oligodendrocyte development, Pan et al. utilized a mouse model where the release of a subset of secretory vesicles (specifically VAMP1/2/3-dependent vesicles) is blocked. Blocking this vesicular release prevented or delayed the differentiation of oligodendrocytes in vivo and in vitro. Further, the authors identified changes to the mRNA and secreted protein levels of prostaglandin D2 synthase (L-PGDS). Prior RNA sequencing and snRNA sequencing datasets of the oligodendrocyte lineage have identified Ptgds as a highly abundant mRNA transcript in oligodendrocyte lineage cells, particularly mature oligodendrocytes. Ptgds encodes L-PGDS, which has an unknown role in oligodendrocyte function. L-PGDS has been shown to regulate Schwann cell myelin formation in the peripheral nervous system, prompting the question of whether this protein acts similarly in the central nervous system. The paper has a clear set of well-rounded experiments, with a few remaining points that would strengthen the conclusions:

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

One of the foundational conclusions of the study is that VAMP1/2/3-dependent exocytosis is critical to oligodendrocyte maturation, by using a PDGFRa-CreER mouse line combined with iBot mice that express botulinum toxin in Cre-expressing cells (abbreviated as PD:iBot). Prior work has demonstrated in vitro that oligodendrocyte morphological maturation, myelin gene expression and myelin protein transport can all be impacted by the loss of VAMPs, including VAMP3. This paper establishes the importance of these SNARE proteins in the oligodendrocyte lineage in vivo: the number of mature (CC1+) oligodendrocytes and myelin basic protein staining is substantially reduced in PD:iBot mice.

1) The data in Figure 3M suggests that PD:iBot oligodendrocytes (GFP+) are lacking MBP+ sheaths and that any myelin formed is by the smaller percent of oligodendrocytes that do not express botulinum (GFP- cells). Furthermore, the efficiency of iBot expression (as evaluated by GFP+ cells) shows that 80% of OPCs and just 60% of oligodendrocyte lineage cells express GFP at P8 and supplementary data shows just 30% of oligodendrocyte lineage cells express GFP at P30. This raises the question of whether PD:iBot cells are unable to differentiate and die. While the authors show no change in caspase-dependent apoptosis in PD:iBot cells in vivo and in vitro, the data still suggests that blocking VAMP-dependent exocytosis itself slows or prevents the progression to a fully myelinating oligodendrocyte in vivo rather than the putative autocrine/paracrine signals are required for OPC differentiation. Confirming whether botulinum-expressing cells also contribute to the population of surviving, differentiated oligodendrocytes in vivo to strengthen the conclusions that autocrine/paracrine secreted molecules contribute to the oligodendrocyte maturation in vivo.

We thank the reviewers for raising a key point in characterizing the consequence of botulinum toxin expression in oligodendrocyte-lineage cells. We analyzed the overlap between GFP+ botulinum-expressing cells and the population of differentiated oligodendrocytes (Olig2+PDGFRa-CC1+ cells) and found that botulinum-expressing cells can survive and become differentiated oligodendrocytes (Figure 3-figure supplement 2, text page 14). Additionally, we performed a more thorough analysis of activated caspase-3+ apoptotic cells than was included in first submission and did not detect statistically significant differences between PD:ibot and control mice (Figure 3-figure supplement 1, text page 13).

2) The paper has complementary in vitro data to pinpoint a mechanism that results in hindered oligodendrocyte maturation. The authors conduct a well-designed set of in vitro co-culture experiments in Fig4 K-M that led them to conclude oligodendrocyte morphology is impacted by secreted molecules from other oligodendrocytes.

2a) The key experiment is the transwell co-culture experiment with control and iBot cells, which suggests that blocking secretion itself has the predominant impact on cell morphology: by eye, both group3 and 4 show the largest reduction in lamellar area and the difference between group 3 and 4 is slight. At day 3 of culture (Fig 4E), the authors show the clearest effect as a reduction in cells with lamellar morphology. The quantification of the lamellar cell area is less obvious than the % of cells with arborized vs lamellar shape, as seen in Figures E & F. I would recommend that the authors show representative images of these observations and quantification of morphologies for the transwell experiments. The impact of secreted factors may be clearer with this measure.

We added representative images (Figure 6G). We quantified both the % and size of lamellar cells. The size of lamellar cells is significantly higher in group 4 than in group 3. Although the % of lamellar cells is numerically higher in group 4 than in group 3, the difference is not statistically significant. To further assess whether cell non-autonomous mechanisms contribute to the oligodendrocyte development defect in PD:ibot mice, we performed additional analysis in culture. We took advantage of the fact that all OPCs purified from PD:ibot mice were not botulinum-GFP-expressing (efficiency ~65% Figure 6B). The GFP- cells in PD:ibot OPC cultures did not express botulinum toxin and were competent in exocytosis. We compared the development of GFP- control cells in cultures generated from PD:ibot mice vs. control cells in cultures generated from control mice. Interestingly, we found that the percentages and sizes of lamellar cells in control cells in PD:ibot cultures is smaller than in control cells in control cultures (Figure 6C, D, text page 25). Although both groups of cells were competent in exocytosis, they were surrounded by exocytosis-deficient vs. exocytosis-competent neighbor cells. The differences in the growth capacity of control cells in the presence of different neighbor cells reveal cell non-autonomous contributions of botulinum-expressing cells in oligodendrocyte development.

2b) On a related note, the cell morphology data is dependent on MBP staining. The authors show that MBP protein is reduced in cells from iBot mice. Since MBP+ cell area/arborized or lamellar structure is being quantified, there remains the possibility that the cells could display a more complex morphology (lamellar) that may be missed by only staining for MBP. The authors use a CellMask dye to show cellular morphology, which is a great idea. The authors state that it labels the plasma membrane; however, the methods (and images) indicate that a cytoplasmic CellMask was used (cat.no. H32720 labels nuclei and cytoplasm, not membranes). These conclusions about cell morphology vs simply MBP expression would be strengthened by an alternative membrane label (e.g., a CellMask plasma membrane dye).

We thank the reviewers for the insightful suggestion. We used the membrane version of CellMask and repeated the transwell co-culture experiment. The new results are consistent with the results based on MBP (Figure 6-figure supplement 1, text page 23). In addition, we used the membrane version of CellMask for all the new cell culture experiments (L-PGDS rescue, HPGD etc.)

3) The authors sought to identify what secreted factors may be affected by blocking VAMP1/2/3-dependent exocytosis. Pan et al. opted for a strategy of examining transcriptional changes, asserting that important genes may be upregulated in response to compensate for blocked secretion. While this is an indirect way to identify secreted candidates, the authors found a fortuitous result that Ptgds was substantially increased in the PD:iBot oligodendrocyte cells. To confirm that L-PGDS secretion is reduced from iBot cells, the authors show Western blots. By eye the change in L-PGDS is variable, however, the authors conduct several experiments with an inhibitor and product of L-PGDS that nonetheless indicate L-PGDS activity can contribute to the morphological maturation of oligodendrocytes. A caveat is that the AT-56 inhibitor reduces MBP+ cells, and the quantification of morphology is dependent on MBP staining (again, see my note in 2b about the CellMask dye). A report on differentiation (% MBP+ cells) may be a more accurate reflection of the result.

We repeated the AT-56 experiment using the membrane version of CellMask and again found that AT-56 inhibits oligodendrocyte maturation (Figure 7-figure supplement 2, text page 33).

The key, compelling experiment demonstrating the role of prostaglandin D2 is the authors' rescue experiment in Fig 4G.

As described above under Essential Revisions 4), we performed additional rescue experiments on the role of L-PGDS in oligodendrocyte development. We found that adding L-PGDS protein extracellularly to PD:ibot oligodendrocyte cultures rescued their development defect (Figure 9A, B, page 34). Moreover, overexpressing Ptgds in PD:ibot mice partially rescued the myelination defect (Figure 9E-H, page 36).

4) Although it's not a direct demonstration that L-PDGS secretion from oligodendrocytes is the key factor, the global L-PDGS knockout mice phenocopy many of the observations of the PD:iBot mice. This is a nice set of observations consistent with the author's hypothesis that L-PDGS impacts oligodendrocyte maturation. Future work should pinpoint whether oligodendrocyte-derived L-PDGS is critical.

We agree with the reviewer that pinpointing whether oligodendrocyte-derived L-PGDS promotes oligodendrocyte development and myelination is an interesting direction to pursue in future work. We are breeding L-PGDS conditional knockout mice to address this question and may report the results in a separate paper in the future.

Minor points:

1) The authors demonstrate that PD:iBot expresses botulinum and loses VAMP2 protein levels in oligodendrocyte lineage cells, but there is no demonstration of whether VAMP3 is expressed or similarly affected. Prior work has demonstrated in vitro that oligodendrocytes express both VAMP2 and VAMP3 (VAMP1 not detected). This would more clearly demonstrate which VAMP-mediated vesicular transport is blocked for the effects observed.

We agree with the reviewer and examined VAMP3 levels with Western blot. We found diminished levels of VAMP3 in oligodendrocyte-lineage cells from PD:ibot mice (Figure 1 J, M, text page 10).

2) It is satisfying to observe a behavioral effect in the PD:iBot mice. I would advise caution in interpreting any direct link between oligodendrocytes maturation and the rotarod behavioral difference at this time. Blocking secretion from PDGFRa-Cre expressing cells may have many indirect effects (beyond myelination) in both the CNS and other cell types that can express PDGFRa and VAMPs1/2/3. I was pleased that the authors did not conclude any direct links at this time.

We agree with the reviewer.

Overall, the authors had a well-rounded manuscript with clearly described and thoughtful experiments. The data support the conclusion that VAMP-mediated exocytosis is critical for oligodendrocyte maturation. The evidence that reduced L-PDGS secretion from the oligodendrocytes can explain the effects of the iBot mice is not as clear cut, but their data does demonstrate that L-PDGS is an important molecule for the differentiation of oligodendrocytes. This work will lead a new direction for future studies to investigate autocrine/paracrine signaling in oligodendrocyte maturation.

We thank the reviewer for the positive comments on our manuscript. As detailed in Essential Revisions 4), we now provide additional evidence on the potential contribution of L-PGDS in the oligodendrocyte development defect in PD:ibot mice.

Author Response

Reviewer #3 (Public Review):

Garratt et al. investigated that transient exposure of young mice during their first two months of life with olfactory cues from con-specific adults would have long-lasting effects on their late-life health and lifespan. They find that the olfactory cues have sex-specific effects on lifespan, which only the lifespan of young females can be extended by odours from adult females but no other combinations, neither young females with adult males nor young males with either sex. Interestingly, their data also suggested that depletion of G protein Gαo in the olfactory system played no role in the lifespan extension, indicating it might be another unknown factor(s) mediating this sex-specific effect on longevity in mice. While the conclusions of this study are well supported by the data, there are some issues with parts of the data analysis and presentation that would need to be clarified and extended.

1) The authors suggested that the G protein Gαo played no role in lifespan extension in the case that transient exposure of young females with olfactory cues from female adults, as they showed in Figure 1. However, it is not clear if the depletion of G Gαo (Gαo mutant) itself has effects on lifespan, compared to its wild type. It would be important to show the lifespan curves from wild type and Gαo mutant individually alongside the pooled lifespan curves, as well as regarding data in a table, followed with a proper discussion.

Data for genotypes is now shown individually.

2) Regarding the functional tests, the authors showed that there was only a small fraction of experiments showed differences between treatments, which were all in figure 2. However, it is necessary to also show the data with no differences, particularly since the conclusion of the study suggested the underlying mechanisms are not clear yet. In my opinion, body weight, plasma glucose, and body temperature all deserve to have their figures individually with all data points.

This data is now shown.

3) As the authors mentioned in the Introduction, the age at sexual maturity correlates positively with the median lifespan across mice strains (Yuan et al. 2012, Wang et al. 2018). Also, young female mice that were exposed to male odours during their developmental stage accelerated sexual maturity (Drickamer 1983), and the same happened to young males that were exposed to the odours from the opposite sex (Vandenbergh 1971). It is, therefore, surprising to see in this study, the exposure of young females or young males to the olfactory information from their opposite sex had no effects on lifespan. One of the solutions to solve this disparity is to measure the sexual maturity of the mice in this study. The authors should seek the possibility to check the record of when the first litter of pups was born between treatments (Shindyapina et al. 2022) or examine preputial separation and vaginal opening (Hoffmann 2018), for instance.

The animals used in the lifespan experiment were not allowed to breed so as not to interfere with the lifespan assessment. Similarly, we did not check animals within the lifespan experiment for sexual maturity as we wanted to minimize the handling of animals after weaning, and this requires daily handling and/or vaginal swabbing.

We conducted a preliminary experiment prior to the main lifespan experiment (in UM-Het3 mice) to test whether sexual maturity was modulated in the expected directions with the odour exposure protocol we planned to impose. This experiment showed that the odor manipulation we applied has the expected effects on sexual maturity. We have now outlined this experiment and its results in the methods section of the paper to justify the odor treatment protocol.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the author characterizes the lattice of kinesin-decorated microtubule reconstituted from porcine tubulins in vitro and Xenopus egg extract using cryo-electron tomography and subtomogram averaging. Using the SSTA, they looked at the transition in the lattice of individual microtubules. The authors found that the lattice is not always uniform but contains transitions of different types of lattices. The finding is quite interesting and probably will lead to more investigation of the microtubule lattice inside the cells later on for this kind of lattice transition.

The manuscript is easy to read and well-organized. The supporting data is very well prepared.

Overall, it seems the conclusion of the author is justified. However, the manuscript appears to show a lack of data. Only 4 tomograms are done for the porcine microtubules. Increasing the data number would make the manuscript statistically convincing.

One tomogram can contain one to several tens of microtubules. For example, 64 microtubules were analyzed in the Xenopus-DMSO dataset obtained on 5 tomograms, versus 24 microtubules for the GTP-dataset obtained on 4 tomograms (see Table 1). Hence, taking the number of tomograms to assess the statistical relevance of our work cannot be considered as a valid criterion. Tomograms are taken randomly on the EM-grid sample, solely based on ice quality and the covering of microtubules in the holes as determined at low magnification before tomographic acquisition. No prior knowledge of the structure and lattice-type organization of the microtubules can be obtained before acquisition. It appears to us that a more pertinent criterion is the number of events that we characterized, specifically lattice-type transitions along individual microtubules. In the dataset mentioned by the referee (see Figure 2-figure supplement 3-4 and Table I), 24 microtubules were analyzed and further divided into 195 segments, providing an equivalent number of individual 3D reconstructions. For each 3D reconstruction, almost all lateral interactions could be characterized in terms of lattice-type, i.e., 2091 of the B-type, 460 of the A-type, and 112 not determined (essentially at transition regions). Most importantly, we document in this specific dataset 119 transitions in lattice-type, which we think is sufficient to characterize such molecular events and provide solid statistics for this dataset. Adding the GMPCPP and Xenopus data, we end-up with 938 individual 3D reconstructions (not including the full-length microtubule volumes), 12 463 lateral interactions analyzed (A-, B-, or ND-type), and the observation of 172 lattice-type transitions. Therefore, we respectfully disagree with the referee stating that our work lacks data.

To highlight the quantity of data used in our work, we have modified the following sentences: L124-131: ' Analysis of 24 microtubules taken on 4 tomograms, representing 195 segments of ~160 nm length (i.e., 2664 lateral interactions), allowed us to characterize 119 lattice type transitions with an average frequency of 3.69 µm-1 (Table 1), but with a high heterogeneity' L160-164: ' Analysis of 31 GMPCPP-microtubules taken on 6 tomograms, representing 338 segments of ~150 nm in length (i.e., 3236 lateral interactions), and using the same strategy as in the presence of GTP (Figure 5—figure supplement 1-2) revealed a transition frequency of 1.25 µm-1 (Table 1), i.e., ~3 fold lower than microtubules assembled in the presence of GTP.' L200-203: ' A total of 64 microtubules taken on 5 tomograms were analyzed in the Xenopus-DMSO dataset (i.e., 419 segments from which we characterized 5446 lateral interactions), and 15 microtubules taken on one tomogram for the Xenopus Ran-dataset (i.e., 86 segments from which we characterized 1118 lateral interactions), (Table 1).'

In addition, having the same transition with the missing wedge orientation randomly from different subtomograms will allow a better average of transition without the missing wedge artifact.

In this work, we did not aim at averaging transitions. Transitions in lattice-types are highly heterogeneous in nature, and we wonder what additional information an averaging strategy would have provided. Conversely, each transition is a unique event that we characterized to obtain useful statistics, and the missing data at high angle inherent to electron tomography were not an obstacle to fulfill this task.

Another thing that I found lacking is the mapping of the transition region/alignment in the raw data.

In Figure 4, we clearly show the correspondence between the segmented sub-tomogram averages (SSTA) and the raw filtered images at the transition region. This is also the case in Figure 5 where the SSTA (Figure 5A) are compared with the raw tomogram (Figure 5B), and where we clearly visualize the holes that result from the transitions in lattice types.

However, it is not easy for me or the reader to understand how each segment is oriented relative to each other apart from the simplified seam diagrams in the figures, and also the orientation of the seam corresponding to the missing wedge in the average. With these improvements, I think the conclusion of the manuscript will be better justified.

The segmentation process is explained in Figure 2-figure supplement 2 and in the Materials and Methods section, which shows that each segment is linearly related to the next. Small rotations can happen between individual segments, and it is important to check that the same protofilaments are followed during the initial modeling (see the online tutorial referenced in the manuscript for full-length microtubules). The segment models are derived from that of the full-length microtubule, as explained in the Materials and Methods section, using a new routine (splitIntoNsegments) implemented into the PEET program. In addition, a detailed protocol describing our SSTA strategy will be submitted following publication of our manuscript.

Reviewer #2 (Public Review):

Differences in protofilament and subunit helical-start numbers for in vitro polymerized and cellular microtubules have previously been well characterized. In this work, Guyomar et al. analyze the fine organization of tubulin dimers within the microtubule lattice using cryo-electron tomography and subtomogram averaging. Microtubules were assembled in vitro or within Xenopus egg cytoplasmic extracts and plunge frozen after addition of a kinesin motor domain to mark the position of tubulin dimers. By generating subtomogram averages of consecutive sections of each microtubule and manually annotating their lattice geometry, the authors quantified changes in lattice arrangement in individual microtubules. They found in vitro polymerized microtubules often contained multiple seams and lattice-type changes. In contrast, microtubules polymerized in the cytoplasmic extract more frequently contained a single seam and fewer lattice-type transitions.

Overall, their segmented subtomogram averaging approach is appropriately used to identify regions of lattice-type transition and quantify their abundance. This study provides new data on how often small holes in the lattice occur and suggests that regulators of microtubule growth in cells also control lateral tubulin interactions. However, not all of the claims are well supported by their data and the presentation of their main conclusions could be improved.

1 - We have corrected approximative claims and conclusions where necessary. In particular, we now discuss separately the Xenopus-DMSO and the Xenopus-Ran egg extract samples, and have modified our conclusions accordingly. We also deposited onto the EMPIAR all tomograms and PEET models to reproduce the 938 segmented sub-tomogram averages analyzed in this study (see new Supplementary file 2).

Reviewer #3 (Public Review):

Protofilament number changes have been observed in in vitro assembled microtubules. This study by Guyomar and colleagues uses cryo-ET and subtomogram averaging to investigate the structural plasticity of microtubules assembled in vitro from purified porcine brain tubulin at high concentrations and from Xenopus egg extracts in which polymerization was initiated either by addition of DMSO or by adding a constitutively active Ran. They show that the microtubule lattice is plastic with frequent protofilament changes and contains multiple seams. A model is proposed for microtubule polymerization whereby these lattice discontinuities/defects are introduced due to the addition of tubulin dimers through lateral contacts between alpha and beta tubulin, thus creating gaps in the lattice and shifting the seam. The study clearly shows quantitatively the lattice changes in two separate conditions of assembling microtubules. The high frequency of defects they observe under their microtubule assembly conditions is much higher than what has been observed in vivo in intact cells. Their observations are clear and supported by the data, but it is not at all clear how generalizable they are and whether the defect frequencies they see are not a result of the assembly conditions, dilutions used and presence of kinesin with which the lattice is decorated. The study definitely has implications for mechanistic studies of microtubules in vitro and raises the question of how these defects vary for protocols from different labs and between different tubulin preparations.

1 - High tubulin concentration: It has been documented by many laboratories since the discovery of tubulin and the characterization of its assembly properties that a sufficient concentration of free tubulin is necessary to self-assemble microtubules. This is called the critical concentration for self-assembly (the CC, i.e., the critical concentration to overcome the nucleation barrier), and has been reported to be in the range 14~25 µM in the presence of GTP depending on laboratories. For example, in the seminal work of Mitchison and Kirschner the CC was estimated at 14 µM (Fig. 5 of ref. (Mitchison & Kirschner, 1984b)) and self-assembly was induced at concentrations in the range 32-59 µM (Mitchison & Kirschner, 1984a). Our own estimate of the CC for porcine brain tubulin was 21 µM (Fig 2C of (Weis et al., 2010)), and we routinely use a tubulin concentration slightly above the CC when we aim at robust microtubule self-assembly. Hence, we argue that 40 µM, which is ~twice the CC, cannot be considered as a "very high" tubulin concentration to induce microtubule self-assembly.

2 - Protofilament number and lattice-type transitions in cells: While microtubules with protofilament numbers different than 13 have been observed in different cell types and species (reviewed in (Chaaban & Brouhard, 2017)), we are aware of only one recent study where changes in protofilament numbers along individual microtubules have been reported in cells (Foster et al., 2021), but with no statistics concerning their frequencies. Hence, we cannot compare changes in protofilament number frequencies in Xenopus egg extracts with those that occur in intact cells. Concerning lattice-type transitions, we are not aware of any previous study that documented such features, whether in vitro or in cells.

3 - Generalization of our results, source of tubulin and protocols: Multi-seams in microtubules assembled in vitro have been reported by several groups in the past (see our Introduction, L49-62), starting from (Kikkawa et al., 1994), the Milligan group (Dias & Milligan, 1999; Sosa et al., 1997), and more recently by the Sindelar group (Debs et al., 2020). In Kikkawa et al. (1994), the authors purified tubulin from porcine brain by three cycles of assembly/disassembly followed by phosphocellulose chromatography. Assembly was carried out at 24 µM in the presence of Taxol. In Sosa and Milligan (1996-1997), the authors used a commercial source (Cytoskeleton) and assembled the microtubules at 30 µM in the presence of Taxol. In Debs et al. (2020), the authors used tubulin purified from porcine brain according to (Castoldi & Popov, 2003), as we did, to assemble GMPCPP microtubules, and bovine brain tubulin (Cytoskeleton) to assemble Taxol-stabilized microtubules. Noticeably, they used an initial tubulin concentration of 100 µM to initiate microtubule polymerization and then added Taxol to continue the reaction.

We add to these previous studies that microtubules with different numbers of seams are not unique ones, but that both the number and location of seams can vary within individual microtubules. The reason why this was not observed before is that the analytical tools used in those previous studies were not suited to reveal this structural heterogeneity within individual microtubules. By contrast, the SSTA approach that we designed was specifically developed towards this aim. Even in the recent work by Debs et al. (2020) that provides the most comprehensive characterization of multi-seams in microtubules assembled in vitro and that obtained a seam distribution very similar to ours (compare their Figure 3C with our new Figure 10C for GDP microtubules, dark blue bars), their protofilament-based approach could not reveal changes in the number and location of seams within individual microtubules. Yet, they probably could have done it if they had asked whether segments with different seam numbers had been extracted from the same microtubules.

Here, we designed a specific approach to tackle the structural heterogeneity of individual lattices that permitted this discovery. Not only do we confirm results obtained by others, but we also propose a molecular mechanism that explains how multi-seams form in microtubules assembled in vitro and how they change in location in a cytoplasmic environment. By doing so, we propose a novel molecular event - formation of unique lateral interactions without longitudinal ones - that was not envisioned before, and which to our opinion, must be incorporated in further modelling studies concerning microtubule nucleation and assembly, including the mechanism of dynamic instability (see the Ideas and speculation section).

4 - Dilution: A 50X dilution was used only for Xenopus egg cytoplasmic extracts to decrease their density on the EM grid just before freezing. These conditions were settled by cryo-fluorescence microscopy to ensure that we had the adequate density of microtubules onto the EM-grid (Figure 7 and Figure 2—figure supplement 1D). Of note, the microtubules analyzed by SSTA were assembled in extracts that were not supplemented with fluorescent tubulin. While we could imagine that dilution may induce the removal of dimers from the microtubule lattice, we cannot foresee how this could change the register between tubulin subunits within the microtubule lattice.

5 - Kinesin decoration: Like many other laboratories (see the Table in Figure 3 of (Manka & Moores, 2018)), we use the non-processive motor domain of kinesin 1 to decorate microtubules, with the aim to differentiate the - and -tubulin monomers within the microtubule lattice. In particular, it has been shown that lattice parameters such as the protofilament skew and lattice spacing are unmodified when kinesin motor domains are added to GMPCPP- or GDP-microtubules (Zhang et al., 2015, 2018). In addition, we cannot envisage how this non processive motor added to preformed microtubules could change the registry of the -tubulin heterodimers within the microtubule lattice.

Author Response

.Reviewer #1 (Public Review):

1) It is important to emphasize that the osteoporotic phenotypes were only demonstrated in males, but not in female mice. The observed phenotypes were not hormone-dependent, as no significant differences in examined bone parameters were observed between wild type andPrdx5KO female mice in an ovariectomy-induced osteoporosis model. However, women over 50 have a four times higher rate of osteoporosis compared with men, and the role of testosterone in the development of osteoporosis in Prdx5KO mice should be investigated. It is known that the osteoporosis is increased in men with low level of testosterone.

Thanks for your comments regarding osteoporosis phenotypes in Prdx5 KO males and their relation with testosterone levels. Based on your suggestion, we re-examined testosterone levels in the serum of male mice and tested the expression levels of the androgen receptor (AR) in the differentiated osteoblasts and osteoclasts of the mice. We have updated the data in Figure 3-figure supplement 2 and included the revised information in the Results (Pages 13-14) and Discussion (Page 34) sections.

2) It is misleading for authors to state throughout the manuscript that osteoporotic phenotypes are observed in Prdx5KO mice, while it is only observed in male mice.

We apologize for this oversight. We have modified the text and indicated that all osteoporotic phenotypes were observed in Prdx5 KO male mice.

Reviewer #2 (Public Review):

1) While the abstract emphasizes transcriptomic analysis and mass spectrometry, extensive imaging techniques have also been used and should be highlighted to give an overview of results from the performed techniques.

In addition, make it clear that it is proteomics-based mass spectrometry, since I was only able to confirm that after seeing Figure 5.

Thanks for your helpful suggestions. We have modified the Abstract based on your suggestions.

2) Line 46-53: I would add more details of how balanced bone mass looks on average, how much is too much, when should we be concerned about bone mass, and does some amount of stress benefit bone mass?

Thank you for the suggestion. We have modified the Introduction. We wanted to explain that for bone as a supporting organ, general mechanical stress is required for its remodeling, although we agree that it is not some necessary information related to our study and may confuse the readers.

Author Response

Reviewer #3 (Public Review):

Results of this manuscript provide a new link between oxygen sensing and cholesterol synthesis. In previous studies, this group showed that the cholesterol synthetic enzyme squalene monooxygenase (SM) is subjected to partial proteasomal degradation, which leads to the production of a truncated, constitutively active enzyme. In this study, the authors provide evidence for the physiological significance of SM truncation. In a series of experiments, the authors show that subjecting cells to hypoxia (oxygen deprivation) induces truncation of SM. The synthesis of cholesterol requires 11 molecules of oxygen and SM is the first oxygen-dependent enzyme in the cholesterol-committed branch of the pathway. Evidence is presented that hypoxia causes squalene, the substrate of SM, to accumulate, which results in the enzyme's truncation. In addition, hypoxia stabilizes MARCHF6, the E3 ligase required for sterol-dependent ubiquitination and degradation of SM. Finally, the authors provide an experiment showing that truncation of SM correlates with hypoxia in endometrial cancer tissues.

Overall, the data presented in this manuscript are compelling for the most part. Hypoxia-induced truncation of SM and MARCHF6 is very clear according to the presented results. The specificity of SM-induced truncation is strong; both direct addition and inhibitor studies are presented. The major strength of this manuscript is that it provides the physiological relevance for the authors' previous finding that squalene accumulation leads to truncation of SM. However, there are a few issues that should be addressed to improve the interpretation of the data presented.

We thank the reviewer for their useful comments.

The manner in which quantified immunoblots are presented is very confusing and difficult to interpret. This is evident in experiments in several figures. For example, it is difficult to determine the role of ubiquitination (Figure 2D) and MARCHF6 (Figure 2E) in the generation of truncated SM. The authors should present quantified data of all lanes of the immunoblots to reduce confusion.

The revised manuscript includes quantification of protein levels for all immunoblot lanes, including in Figure 2D and Figure 2E (now Figure 3A). It also contains updates to the text, figure legends, and axis labels to improve clarity about data normalization. For more information, please refer to our response to Essential Revisions comment #1.

The other important finding of this manuscript is that hypoxia stabilizes MARCHF6. This is supported by the results of Fig. 3A; however, the result of Figure 3B is not clear. A new band appears upon inhibition of VCP and MG-132 seems to reduce protein expression. These results could be removed from the manuscript without impacting the conclusions drawn.

As suggested, the revised manuscript contains only the initial observation that hypoxia stabilizes MARCHF6. Other experiments investigating the mechanism have been removed. For more information, please refer to our response to Essential Revisions comment #2.

Finally, the results shown in Figure 5 showing that truncation of SM correlates with hypoxia in endometrial cancer tissues are a little preliminary. Multiple bands are detected in SM immunoblots, which interferes with interpretation. This experiment could be removed and speculated upon in the discussion.

As suggested, this experiment is removed from the revised manuscript.

Author Response

Reviewer #1 (Public Review):

They established a "behavioral transcriptomics" platform as they cultured mouse primary cell explant on an apparatus, imaged the cells over time, and analyzed cells with differential physiological status by scRNA-seq. They showed evidence that the system recapitulated physiological features of airway cells, including chemical-induced damage response. They further utilized the system to isolate cells of different cellular features and analyzed gene expression through scRNA-seq. The study demonstrates an interesting establishment and application of an in vitro system mimicking in vivo.

However, several major concerns need to be resolved.

First, whereas the overall study seems to focus on the establishment of airway epithelial cell explant apparatus and its application, take home messages that are delivered by the authors seem to emphasize the transcriptome analysis part. The authors introduced "spatial transcriptomics" and"behavioral transcriptomics" in the abstract but it is hard to appreciate that the study resolves spatial transcriptomics. This causes unnecessary confusion. Second, probably related to the first question, it is hard to find the novelty of the study. Third, probably the last and most important part of the manuscript is to analyze the cells by Smart-seq. But the analysis was performed on the SO2 injured animal only and lacked experiment on wildtype mice. If the authors tried to prove the feasibility of the technique rather than resolving physiological mechanism here, then I would recommend explaining why wild type experiment was not performed.

The method described in the manuscript consists of two components: a novel tissue imaging platform, and characterization of a cellular behavior. Both steps can be generalized to different tissue contexts and different cellular behaviors, respectively. We have revised the title and abstract to specify the scope of this study and have also revised the text accordingly.

Live imaging allows us to observe cell behaviors in intact tissues but does not provide information on cell type. By profiling cells that are observed by live imaging to share a behavior at single-cell resolution rather than bulk, we can separate out sources of transcriptional variation, like cell type identity, in order to identify the transcriptional signatures that reflect cell behaviors.

Single-cell sequencing (via Smart-seq) has been previously performed in wild-type mouse trachea (Montoro et al., 2018), and identified underlying cellular heterogeneity. However, the steady state tracheal epithelium is largely quiescent, characterized by slow turnover and a lack of visible cell motility. We performed daily imaging of trachea explants from uninjured mice over 4 days and did not observe any significant displacement of epithelial cells. Furthermore, we also imaged an uninjured explanted tracheal epithelium every 40 minutes for over 19 hours with no significant directional movement (new Movie 3). We added the following text to the manuscript: “Imaging of trachea explant controls from uninjured mice over 19 hours revealed no cellular displacement in the airway epithelium (Movie 3).”

In contrast, regeneration activates cell motility followed by cell proliferation. Therefore, we chose tissue regeneration as the more suitable biological context for this study to examine cellular dynamics. We leveraged the gene signatures derived from the previous wild-type study (Montoro et al., 2018) to identify different cell types and make like-for-like comparisons. We used an independent regeneration dataset in the same tissue but with a different injury model (Plasschaert et al., 2018) to test whether the molecular signatures derived in our study that differentiate moving and non-moving cells are generalizable to other contexts.

Reviewer #2 (Public Review):

Kwok et al. devise a method that uses a transgenic mouse line to make the link between cell behaviour in intact living tissue and subsequent dissociation into distinct groups forsingle cell sequencing. Specifically, they set up a mouse airway culture system in which it is possible to maintain live cells for multiple days and then preserve the same tissue. The analysed tissue section can be fixed and known cell types identified via classical staining protocols. In this system they imaged a number of tissue phenotypes such as ciliary beating, mucociliary clearing and airway regeneration. With respect to airway regeneration they observe that there was cellular heterogeneity between cells with the capacity to move and so-called non-movers, which the authors were able to quantitively track.To make the link with single cell sequencing, they use the Kaede transgenic mouse lines,which contains a green fluorescent reporter gene, that can be converted into a red fluorescent reported gene by illuminating a defined tissue section, in this case regions enriched for movers or non-movers. After dissociation of the tissue, cells were FACSsorted using the reporter protein. Subsequent single cell RNAseq revealed distinct gene signatures that were associated with the mover versus the non-mover phenotype. These phenotypes could also be detected in previously published data sets.

The conclusions of the paper are supported by the data that is presented, but the comparison to existing mouse injury data could be improved. A weakness of the paper is the implication that the technique can be used for any of the phenotypes that they have examined. However, in order to be assessed by this method,there need to be a reasonably large number of cells that show similar behaviour in a region that can be photoconverted. If it is indeed possible to do the photoconversion at the single cell level, the authors should demonstrate that such resolution is possible, or otherwise clearly state this limitation of the technique they have developed.

We recognize that the approach in this study does not involve photoconversion at single-cell resolution. While single-cell photoconversion and subsequent intermittent live imaging has been demonstrated in other systems such as zebrafish (Green and Smith, 2018) and mouse skin (Park et al., 2017), the throughput of doing downstream single-cell analysis would be limited, especially in a cell type-specific manner. Having observed a relatively homogeneous behavior of cells within a small region (~200 μm diameter, Movie 1 and Movie 2) of the airway epithelium, we photoconverted a small area with several hundred cells. Subsequent single cell sequencing allowed us to compare differences in gene expression between basal cells of slow/non-moving regions to basal cells of fast/moving regions.

Reviewer #3 (Public Review):

In this manuscript, the authors identify a pressing need to couple visualized in situ cell behaviour with deep molecular profiling of visualized cells, aiming to move beyond inferences made from time-lapse tissue sampling approaches or the analysis of transcriptional kinetics to identify the molecular pathways that drive cellular behaviour in situ. The authors identify live cell imaging combined with deep molecular profiling of theimaged cells as one possible solution. To this end, the authors establish a novel platform for live cell imaging of tracheal epithelial cells using explants of mouse trachea that allows long-term visualization of cell behaviour, and try to couple live-cell imaging to the transcriptional cell states.

Combining single-cell RNA-seq analyses with live cell imaging offers the unique opportunity to link transcriptional and anatomic, morphological or movement phenotypes of individual cells. To be able to do this in intact tissues at baseline and in response to injury would allow a far more detailed and integral analysis of cellular behaviour in their physiological context. As such, the approach of the authors is interesting and clearly focused on achieving this goal. The only data that can support a claim of successfully achieving this ambitious goal are presented in figure 3, where an advanced mouse model(the Kaede-Green mouse) is used that allows labelling individual cells by photo-conversion, followed by isolation of individual cells by flow cytometry and plate-basedsc RNA-seq analysis of sorted cells. By taking this approach, the authors are able to identify transcriptional differences at the group level between tracheal epithelial cell subsets that differ in their movement after injury.

While this in itself is a remarkable accomplishment, and an interesting observation, the relationship between the 'behaviour' of the cells observed with live cell imaging (the movement after injury) versus the transcriptional phenotype remains rather elusive. One explanation could be that active movement of cells depends on a specific transcriptional program, that is lacking from the non-moving cells. Another explanation could be that the tracheal epithelial cells are inherently heterogeneous, and one subset has the capacity to move whereas others do not, and the transcriptional profile merely identifies these heterogeneous populations. The observation that non-mover cell populations contain both basal and club cells, whereas mover regions only have basal cells seems to support this notion to some extent. However, the authors then claim to use basal-cell derived signatures (excluding the club cells) from mover and non-mover regions and compare this to literature data from another injury model to show that these signatures also identify distinct subsets in a mouse model of polidocanol-induced injury. How the distinction basal vs club cells in the non-mover regions is made remains unclear, and would seem challenging from the number of cells analyzed (as presented in figure 3).

The identification of two behavioural phenotypes of basal cells (mover vs non-mover) in this manuscript is based on group-level phenotypes: the cells belong to a region of moversor a region of non-movers. This is relevant for figures 2 (including supplemental) and 3. In figure 2 supplemental 2C, it seems evident that within one region (or focussing only on all moving regions?), the behaviour of all cells within that region/selection is quite uniform:the variation is really very limited, and all cells seem to speed up and slow down in a highly coordinated fashion within the selected regions shown. At the same time, in figure2D, the distribution of regions across speed categories at 26-36 hours pi (the peak of the movement in suppl 2C) seems almost bimodal, with regions belonging either to non-mover(range 0.5 - 2.5 uM/hr) or mover (range 3.0-7.0 uM/hr) phenotypes. However, all regions display an increased movement at 16h pi compared to the pre-injury movements (Figure2C), indicating that all cells will be induced to induce movement to some extent.

My main concern with this analysis is that the behavioural phenotype of the epithelial cells is assumed to be homogeneous within each region, allowing a contrast to be made in figure3 for the transcriptional phenotypes on the basis of moving phenotypes rather than on the basis of the main variation within the dataset.

For instance, from the t-SNE plot (3B) - for what it's worth of course - and the heatmap (3C) there seems to be at least one non-mover cell that transcriptionally has a higher resemblance to the mover cells than to the other non-mover cells. Of course that can just be the variability present in the dataset, but it could also indicate that non-mover regions are not completely homogeneous, and even more so, that the moving vs non-moving associated transcriptional phenotype is a gradual transition rather than 2 clearly separate sub-phenotypes.

All-in-all, this manuscript describes an interesting technical advance and shows some of the applications thereof. However, the approach also has its limitations: The requirement to mark cells with specific behavioural features for follow-up transcriptomic analysis (such as by photoconversion) necessitates the division of the epithelial cells into major categories on the basis of certain cellular phenotypes (such as movement) that can be visualized by live cell imaging. This limits the analysis opportunities to group-based contrasts in cellular behaviour as also used here by the authors.

Also, the use of explanted tissue is of course less ideal than in vivo imaging, but most likely the only technically feasible approach at this moment. At the same time, the capacity to combine image-based features with single-cell transcriptomic data is an important advance, even when initially only possible in explanted tissue from mouse models carrying all kinds of fluorescent reporters. To strengthen the manuscript, it would therefore be important to discuss the limitations of the approach, as well as to provide a more comprehensive overview of the possible applications that the authors foresee.

We thank the reviewer for the feedback. Our data demonstrates that the movement behavior is an injury-induced phenotype. 24 hours after injury (hpi), the “mover” transcriptional program is transiently enriched, while the “non-mover” transcriptional program is also transiently decreased, consistent with a cell state that is induced by injury (see Figure 4A, 24-hpi).

SO2 removes nearly all the luminal cells (Rock et al., 2009) so we removed the club cells to compare injury response in basal cells. Distinguishing basal vs club cells is done by hierarchical clustering and comparison to established cell type signatures (Montoro et al., 2018). We apologize that the initial presentation did not make this clear. In the revised manuscript, we have provided an additional figure supplement demonstrating the hierarchical clustering (Figure 3 - figure supplement 1A), and the disjoint expression of canonical markers Krt5 (basal) and Scgb1a1 (club), which enabled us to assign unambiguous cell-type identities to discovered clusters (Figure 3 - figure supplement 1B).

We agree with the reviewer that all cells, including cells that we classified as “mover” and “nonmover” are induced to move compared to pre-injury as suggested by Figure 2c. However, “mover” and “non-mover” cells differ dramatically in the amplitude and collective directionality of movement. We investigated the movement phenotypes in detail, including high-resolution imaging at shorter time intervals (10 min). We found that the slow “non-movers” had a large circular directionality variance (akin to oscillations), whereas the rapid “movers” moved directionally across the field of view. We quantified this with particle image velocimetry in Figure 2 – figure supplement 3C-D, and we revised the text to provide additional details about this result.

The reviewer also raises concern about whether the movement is homogeneous enough to account for the variation in the datasets. We used our imaging data to determine the time points in which the mover and non-mover phenotypes varied the most (around 40 hrs post injury) between different regions (Figure 2 - figure supplement 2A, C) but we have also demonstrated that the movement within each region is indeed relatively homogeneous (~200 μm diameter, Movie 1 and Movie 2).

We acknowledge that the presented data did not eliminate the possibility of another main variation within the dataset. We now perform PCA on the dataset, which confirmed that while the first principal component (PC) is associated with a solitary pulmonary neuroendocrine cell, the second PC is strongly associated with the difference between moving and non-moving cells (p=0.003, Wald test). When analyzing only the basal cells, we find that PC-1 provides a very clean separation and overlaps perfectly with the moving vs non-moving distinction (p<2 x 10-16, Wald test, Figure 3 - figure supplemental 2a). Taken together, with this additional analysis we can confirm that our focus on this behavioral phenotype reflects the main variation within the dataset.

We appreciate the reviewer’s nuanced question about the single outlier cell. While we do observe a transcriptional phenotype that is clearly distinct, as the reviewer points out, there is a very small degree of overlap between the two cell type clusters visible on the t-SNE plot in Figure 3B. Given that the physical process of movement is a matter of degree, it is possible that this particular cell is simply not moving as much, and thus activating movement-related transcriptional programs to a lower degree. To analyze this question further in response to this question, we analyzed the separability of these groups by training a machine learning (k-nearest neighbor) classifier to distinguish these clusters (new Figure 3 - figure supplement 2b). We found that the groups could be distinguished with a high accuracy of 98.7% (95% CI: 92.7-99.9) using 5 or more of the signature genes that we defined in Figure 3C. This additional analysis we continue to conclude that while the groups have a very small degree of overlap, the moving and non-moving phenotypes are strongly separable.

We acknowledge the limitations of this approach to groups of cells (see response to Reviewer 1) and both the limitations and advantages of using a tissue rather than cells, and we added these points to the discussion section.

Author Response

Reviewer 1 (Public Review):

1) The finding that thalamic activity exhibits a low dimension structure is in my opinion less of a finding, but rather an assumption that motivates the use of dimensionality reduction techniques. When the authors ask (line 101) "whether thalamic task activity exhibits similar low dimensional structure", what is the alternative hypothesis? I think it is a foregone conclusion that with a restricted number of tasks, and the intrinsic smoothness of fMRI activity data, there are always K<<N components that capture 50,75, 90% of the variance. If you had measured the spiking of the entire population of thalamic neurons or increased the threshold to 99%, the structure of activity would be more high dimensional. So I believe you can either frame this as an assumption going in, or you build carefully an alternative hypothesis of what a "high-dimensional" structure would look like. Generating activity data i.i.d would be the simplest case, but given that both signal and measurement noise in fMRI are reasonably smooth, this would be a VERY trivial null hypothesis.

We thank the reviewer for pointing out this inherent assumption in our analysis. We agree that given the smoothed nature of BOLD signal and the restricted task design we likely cannot effectively test an alternative high dimensional organization hypothesis. We have revised our introduction accordingly and clarify that we use a dimensionality reduction technique with the assumption that we will observe a low dimension structure of thalamic task fMRI data, similar to past fMRI studies that focused on cortical ROIs (line 102). Furthermore, we have revised the discussion section to remove discussion highlighting the low-dimension organization as a novel finding (line 404).

2) The measure of "task hub" properties that is central to the paper would need to be much better explained and justified. You motivate the measure to be designed to find voxels that are "more flexibly recruited by multiple thalamic activity components", but it is not clear to me at this point that the measure defined on line 634 does this. First, sum_n w_i^2 is constrained to be the variance of the voxel across tasks, correct? Would sum_n abs(w) be higher when the weights are distributed across components? Given that each w is weighted by the variance (eigenvalue) of the component across the thalamus, would the score not be maximal if the voxel only loaded on the most important eigenvector, rather than being involved in a number of components? Also, the measure is clearly not rotational invariant - so would this result change after some rotation PCA solution? Some toy examples and further demonstrations that show why this measure makes sense (and what it really captures) would be essential. The same holds for the participation index for the resting state analysis.

Please see our response to essential revision point #1.

3) For the activity flow analysis, the null models (which need to be explained better) appear weak (i.e. no differences across tasks?), and it is no small wonder that the thalamus does significantly better. The Pearson correlations are not overwhelmingly impressive either. To give the reader a feel for how good/bad the prediction actually is, it would be essential that the authors would report noise ceilings - i.e. based on the reliability of the cortical activity patterns and thalamic activity patterns, what correlation would the best model achieve (see King et al., 2022, BioRxiv, as an example).

Please see our response to essential revision point #4.

4) Overall it has not been made clear what the RDM analysis adds to the prediction of the actual activity patterns. If you predicted the activity patterns themselves up to the noise ceiling, you would also hit the RDM correctly. The opposite is not the case, you could predict the correct RDM, but not the spatial location of the activity. However, the two prediction performances are never related to each other and it remains unclear what is learned from the latter (less specific) analysis.

We agree that the utility of the RDM analysis is not clear, and we have removed it from the manuscript.

Author Response

Reviewer #1 (Public Review):

This paper details the creation and data behind the website http://pandemics.okstate.edu/covid19/. The authors attempt to explore if there is a cause and effect between the detection of unusually increased mutation activity in the genomic surveillance databases and subsequent near-term surges in SARS-CoV-2 case numbers.

Overall the premise is interesting as other than following case numbers reported to health authorities and observing what is happening in another country, there is no reliable way to predict when a surge is going to occur. Unfortunately, the data demonstrate that there was no reliable metric that could be used to predict surge events. Interestingly, the website has issued a "surge alert" currently for the month of September. It will be interesting to observe whether their model indeed has predictive power or whether the current analysis is merely coincidental with the surges but not necessarily predictive of them.

In this work, we investigated a number of metrics for finding a reliable signal of surge prediction. The commonly used ratio ka/ks or the derivative of ka/ks with respect to time did not provide a reliable metric. However for the same data, ka has provided a fairly robust surveillance signal so far. We believe ka/ks studies provide insights into genome changes, but not as a function of short time periods such as days (at least not in the case of SARS-CoV-2). As the motivation of our work is to provide the community with a genomic surveillance approach in real time, we believe that the current data shows that ka is, at present, a useful and fairly reliable metric.

As the reviewer mentioned, while this manuscript was being reviewed, we issued a warning on September 7th 2022. Several different types of data (including number of new infections, number of hospitalizations, and COVID19 related deaths) has indicated that our warning was accurate since there was a surge in reported number of cases in September and reached a peak in October. For instance, plots shown in Figure S6 indicate that there was a surge in number of cases around Europe at large, and several individual countries including France, United Kingdom, Germany and Italy. Similarly our earlier warning in June also was followed by surges being reported across many countries and collectively across the world (Figure S5). Therefore, we believe the presented methodology has been validated.

Reviewer #2 (Public Review):

In this manuscript, Najer et al., perform a comprehensive bioinformatic analysis of SARS-CoV-2 sequences available from public repositories. Through a comparison with the genome sequence of the original Wuhan 2020 strain, they identify the total accumulation of non-synonymous mutations as a predictor of the evolution of new strains. The manuscript provides data for three structural proteins - spike (S), membrane (M), and envelope (E) proteins, as well as data for the non-structural RNA-dependent RNA polymerase (RDRp) protein that serves as a negative control. However, the predictivity of this approach is most marked only for the Omicron variant, with considerable variation in the predictive power of SARS-CoV-2 proteins for other variants. Focusing on a spike, the method does not detect the alpha variant or delta variant surges, which were mostly driven by changes in spike protein, although the level of sequencing data available for the delta variant might have been less. Notably, although the authors conclude that other parameters such as the ratio of non-synonymous to synonymous mutations or the rate of accumulation of non-synonymous mutations are not predictive, they appear to have similar success in predicting the omicron surge.

We agree with the reviewer, the case of spike protein during the Alpha surge could have been affected by insufficient number of sequences. In case of Gamma/Delta variants, we did notice changes in the spike and the membrane protein. For the case of Omicron and its various sub-variants, the use of ka provides a reliable signal due to changes in the spike, membrane and envelope proteins.

**Author Response""

Reviewer #2 (Public Review):

The work systematically reassesses fungal mi/miRNA-like characteristics and annotation confidence and identifies that many of the loci fail to meet the key points of the methods developed for animal or plant miRNAs. Therefore, the authors establish a set of criteria suitable for the annotation of fungal miRNAs and provide a centralized annotation of identified mi/milRNA hairpin RNAs in fungi based on their established rules.

Here are some comments and suggestions for the manuscript to be improved:

1) The title mentions "ancestral links", however, the main context of this paper does not include the evolution of fungal mi/milRNAs or show the origins of conserved mi/milRNAs in fungi. The authors are suggested to consider a more appropriate title for this work.

Agreed, we have modified our title to include a more fitting description of the outcome of the study:

“Comprehensive re-analysis of hairpin small RNAs in fungi reveals loci with conserved links”

2) The work proposes a fungal mi/milRNAs hairpin precursor recovery pipeline with three minimal criteria to annotate fungal mi/milRNA loci, which allows nearly half of the loci to pass these rules. To highlight the innovation of this annotation, it is strongly suggested that the authors compare their established pipeline and criteria for fungi with those used in animal or plant miRNAs in detail, and emphasize the advantages of the established pipeline. A figure showing the established pipeline and detailed parameters is needed.

We have now included a clear workflow diagram for establishing miRNA annotation records and confidence tiers (Figure 1-supplemental 3). As for the comparison with rules in plants and animals, this is stated in Table S6, where it shows some rules employed by other tools/papers/species. We believe these combined supplementals give a strong overview of our approach and how it differs from rules in other approaches.

3) The established "standard rules" for fungal mi/milRNA annotation still require more evaluation. It would be better if there is experimental validation to improve confidence.

Sequencing evidence is generally regarded as the gold-standard of experimental support for identifying and annotating miRNAs (Axtell and Meyers, 2018) though the rules are not clear yet in fungi. We agree that developing a standard-rule-set is a high-priority for identifying complete annotation standards. We had a statement (~ line 290) affirming this need, and have now modified this sentence to highlight the need for a sufficient standard.

“While this minimal rule-set is useful for filtering the lowest-confidence loci, it is likely not sufficient to form the basis of an annotation and this analysis further confirms the need for a standardized pipeline and set of criteria for miRNA annotation in fungi.”

To address the question of experimental validation, we have included descriptions of loci with strong-functional support in Table S5, including a section discussing top-tier loci in the discussion, described in the response to reviewer 3.

Author Response

Reviewer #1 (Public Review):

By studying the effect of Treg depletion in a CD8+ T cell-dependent diabetes model the group around Ondrej Stepanek described that in the absence of Treg cells antigen-specific CD8+ OT-I T cells show an activated phenotype and accelerate the development of diabetes in mice. These cells - termed KILR cells - express CD8+ effector and NK cell gene signatures and are identified as CD49d- KLRK1+ CD127+ CD8+ T cells. The authors suggest that the generation of these cells is dependent on TCR stimulation and IL-2 signals, either provided due to the absence of Treg cells or by injection of IL-2 complexed to specific antiIL-2 mAbs. In vivo, these cells show improved target cell killing properties, while the authors report improved anti-tumor responses of combination treatments with doxorubicin combined with IL-2/JES6 complexes. Finally, the authors identified a similar human subset in publicly available scRNAseq datasets, supporting the translational potential of their findings.

The conclusions are mostly well supported, except for the following two considerations:

We are happy for the positive overall evaluation of our manuscript by both reviewers and we are thankful for their specific insightful comments, which helped us to improve the manuscript.

1) From Fig. 4A and B it is not conclusively shown, that Tregs limit IL-2 necessary for the expansion of OT-I cells and subsequent induction of diabetes. An IL-2 depletion experiment (e.g. with combined injection of the S4B6 and JES6-1 antibodies) would further strengthen this claim. Along these lines, the authors claim "IL-2Rα expression on T cells can be induced by antigen stimulation or by IL-2 itself in a positive feedback loop [20]. Accordingly, downregulation of IL-2Rα in OT-I T cells in the presence of Tregs might be a consequence of the limited availability of IL-2.". The cited reference 20 did observe CD25 upregulation by IL-2 on T cells but the observed effect might only be caused by upregulation of CD25 on Treg cells, which increases the MFI for the whole T cell population. Did the authors observe significant upregulation of CD25 on effector CD4+ and CD8+ T cells in their experiments with IL-2/S4B6 or IL-2/JES6 treatment?

We added another reference to support our claim (Sereti, I., et al., Clin Immunol, 2000. 97(3): p. 266-76.). Along this line, we also observed that addition of IL-2 in vitro leads to IL-2Rα upregulation on CD8+ T cells (shown in Fig. 4C), which was IL-2Rα level was lower if Tregs were present. We also observed upregulation of IL-2Rα in vivo upon the stimulation of OT-I T cells with OVA and IL-2ic, which is now shown in the Fig. S6C of the revised manuscript.

To further explore if Tregs limit expansion of OT-I and diabetes progression via IL-2 limitations, we performed the proposed experiment using a combined injection of S4B6 and JES6-1 anti-IL-2 antibodies. At the beginning, we were skeptical that we could completely block the IL-2 using this approach for the following reasons. First, IL-2 is produced locally in the spleen and lymph nodes and might not be easily accessible for the antibodies for a complete block. Second, IL-2 has a relatively short turnover and is continuously produced, but the half-life of the injected antibodies is unknown, which questions the duration of such a block. Third, it is possible that some IL-2 molecules would bound only to one of the two antibodies, which will make it a hyper-stimulating immune-complex, instead of neutralizing it.

Anyway, we were curious enough to perform this experiment. We used a condition that based on our experience leads to diabetes manifestation in Tregs depleted, but not in Treg replete mice (10 k OT-I T cells, OVA + LPS immunization). One additional group of Treg-depleted mice received a single dose of S4B6 and JES6-1 anti-IL-2 (200 µg of each antibody per mouse). We observed that this IL-2 blocking delayed, but not prevented the development of diabetes in most animals (Fig. 1 below).

Overall, we believe that this experiment is rather supporting our conclusions concerning the importance of IL-2, although the effect is only partial. However, we decided not to include this experiment in the manuscript, because we do not have the evidence about how efficient the IL-2 blocking was (see above), which makes the interpretation difficult. Because the reviews and the point-by-point response is public in eLife, we believe that showing the data here is appropriate.

Figure 1. Role of IL-2 blocking on the development of experimental diabetes. Two independent experiments were performed. Statistical significance was calculated using Log-rank (Mantel-Cox) test for survival, and Kruskal-Wallis test for blood glucose (p-value is shown in italics).

2) The anti-tumor efficacy of KILR cells is intriguing but currently, it is unclear if it is indeed mediated by KILR cells. Have KILR cells been identified by flow cytometry in the BCL1 and B16F10 models treated with doxorubicin and IL-2/JES6? Were specific KILR cell depletion studies conducted, e.g. with an anti-KLRK1 depleting antibody? Additional experiments addressing these questions would be desirable to further support the authors' claims.

We are thankful to both reviewers for their similar comments concerning the analysis of CD8+ T cells in the tumor model. Addressing these comments lead to very useful data and significantly improved our manuscript.

We performed the analysis of splenic CD8+ T cells in the BCL1 leukemia model (spleen is the major site of the leukemic cells in this model). We observed that KLRK1+ T cells represented almost half of CD8+ T cells in mice treated with DOX+IL-2, which was much higher frequency than in the control and DOX-only treated mice. Although not all KLRK1+ cells were bona fide KILR cells, the frequencies of KLRK1+ IL-7R+ and KLRK1+ CD49d- cells were also strongly elevated in the Dox+IL-2ic treated mice. Overall, the survival of DOX+IL-2ic treated mice correlated with the frequencies of KILR T cells and KLRK1+ T cells. Moreover, GZMB was almost exclusively expressed by KLRK1+ T cells. We are showing these data in Fig. 7C and Fig. S7B in the revised manuscript.

In the B16 melanoma model, we analyzed CD8+ T cells in the spleens and also in the tumors. We observed a huge population of KLRK1+ GZMB+ CD8+ T-cell population in the spleen of DOX+IL-2ic-treated mice, but not in the untreated or DOX-only treated mice (Fig. 7F). Both KLRK1+ CD49d+ and KLRK1+ CD49d- CD8+ T cells were substantially more frequent in the DOX+IL-2ic-treated, but not in the untreated or DOX-only treated mice (Fig. S7F). In the tumor, the KLRK1+ CD49d- CD8+ T cells were found at large numbers only in the DOX+IL-2ic-treated mice (Fig. 7G). Moreover, these KLRK1+ CD49d- CD8+ T cells expressed high levels of IL-7R and GZMB only in DOX+IL-2ic-treated, but not in untreated and DOX-only treated mice (Fig. 7H).

We believe that these new data provide evidence that the combination of immunogenic chemotherapy with IL-2 treatment induced KILR cells in the spleens and in the tumors and that this correlates with the better survival.

Because the majority of non-naïve CD8+ T cells (and vast majority of GZMB+ CD8+ T cells) in the spleens and tumors of the tumor-bearing mice treated with DOX+IL-2ic were KLRK1+ and because we have shown that the protective effect of the DOX+IL-2ic therapy is largely CD8+ T cell-dependent, we did not find it essential to perform the depletion of KLRK1+ T-cells. We believe that it is almost inevitable that the depletion of KLRK1+ T cells would lead to increased tumor growth as it would probably deplete the majority of antigenspecific CD8+ T cells, mimicking the overall CD8+ T cell depletion. Moreover, we do not have this protocol established.

Reviewer #2 (Public Review):

In this study, the authors determine the superior cell killing abilities of KLRK1+ IL7R+ (KILR) CD8+ effector T cells in experimental diabetes and tumor mouse model. They also provide evidence that Tregs suppress the formation of this previously uncharacterized subset of CD8+ effector T cells by limiting IL-2.

Strength and Limitation

This study focuses on the relationship between Tregs and CD8+ T cells. They used different experimental diabetes mouse models to reveal that Tregs suppress the CD8+ effector T cells by limiting IL-2. They also found a unique subset of KLRK1+ IL7R+ (KILR) CD8+ effector T cells with superior cell killing abilities through single-cell sequencing, but killing abilities could be inhibited by Tregs. They also tested their theory in in vivo tumor model. The data, in general, support the conclusions; however, some issues need to be fully addressed, as detailed below.

We are happy for the positive overall evaluation of our manuscript by both reviewers and we are thankful for their specific insightful comments, which helped us to improve the manuscript.

1) This study used the concentration of urine glucose as the standard for diabetes ({greater than or equal to} 1000 mg/dl for two consecutive days). However, multiple reasons may lead to a high level of urine glucose. As a type I diabetes mouse model, authors could use immunohistological analysis of islet to show the proportion of T cells and islet cells in islet, which can display the geographic distribution of immune cells, severity and histology structure of damaged pancreas islet directly. If possible, different subsets of immune cells, especially CD4 vs CD8+ cells should be stained for their location.

We added the histological examination of the pancreas in control, DEREG-, and DEREG+ mice using contrast H&E staining and immuno-fluorescence (Fig. 1D-E in the revised manuscript). We observed that the high glucose and blood levels are preceded by the destruction of the pancreatic islets (morphology and decreased insulin production) as well as by the infiltration of the islets with immune cells including CD4+ and CD8+ T cells.

2) This article shows that KILR effector CD8+ T cells have strong cytotoxic properties. However, they do not describe the potential proliferation ability vs apoptosis of this subset from islets.

We analyzed the proliferation (KI67 expression) and apoptosis (Annexin V, cleaved Caspase 3) in T cells isolated from the pancreas of DEREG- and DEREG+ mice on day 4 after the induction of diabetes using flow cytometry (Figure 2 below). We did not observe any differences between DEREG- and DEREG+ mice or among different subsets of OT-I T cells in the DEREG+ mice. Essentially, all T cells were proliferative (KI67+) and there was a very low percentage of Annexin V or cleaved Caspase 3 positive cells.

Figure 2. Lymphocytes were isolated from the pancreas of DEREG- RIP.OVA and DEREG+ RIP.OVA mice on day 4 after the induction of diabetes, and analyzed using flow cytometry. Two independent experiments were performed. Gated on OT-I T cells. Top: proliferation rate based on Ki-67 staining. Representative histogram and MFI (median is shown). Middle: Apoptosis rate based on Annexin V staining. Representative histogram shows Annexin V staining in three populations of OT-I T cells from DEREG+ mouse (“AE” - CD49d+ KLRK1-, “++” - CD49d+ KLRK1+, KILR - CD49d- KLRK1+), total OT-I T cells from DEREG-, and a positive control: WT CD8+ T cells treated with hydrogen peroxide. Middle right: Percentage of Annexin V+ cells and MFI (median is shown). Bottom: Apoptosis rate based on cleaved Caspase 3 staining. Representative dot plots show cleaved Caspase 3 staining of OT-I T cells from DEREG+, DEREG-, and a positive control: WT CD8+ T cells treated with hydrogen peroxide. Bottom right: percentage of cleaved Caspase 3+ cells (median is shown).

However, we found question concerning proliferation and apoptosis of KILR cells interesting and worth further investigation. For this reason, we assessed the proliferation, survival, and phenotypic stability of naïve, KILR, and effector T cells by their competitive transfer into CD3ε-/- mice. The phenotype of all these three subsets remained stable for 4 days (Fig. 6F), documenting that KILR cells are not just a very transient stage. Moreover, the KILR cells were ~2 fold more abundant then effector cells 3 days after their 1:1 cotransfer into CD3ε-/- mice (Fig. 6G, Fig. 6SE). This was probably caused by their slight advantages in both proliferation and survival (Fig. 6SF-G).

3) Figure 7 shows that the antitumor efficacy of IL-2 depends on CD8+ T cells. But in this part, there is no data to show the change of KLRK1+ IL7R+ CD8+ effector T cells in tumor tissue. Therefore, the article needs to add more data to verify that IL-2 enhances antitumor ability via KLRK1+ IL7R+ CD8+ effector T cells.

We are thankful to both reviewers for their similar comments concerning the analysis of CD8+ T cells in the tumor model. Addressing these comments lead to very useful data and significantly improved our manuscript.

We performed the analysis of splenic CD8+ T cells in the BCL1 leukemia model (spleen is the major site of the leukemic cells in this model). We observed that KLRK1+ T cells represented almost half of CD8+ T cells in mice treated with DOX+IL-2, which was much higher frequency than in the control and DOX-only treated mice. Although not all KLRK1+ cells were bona fide KILR cells, the frequencies of KLRK1+ IL-7R+ and KLRK1+ CD49d- cells were also strongly elevated in the Dox+IL-2ic treated mice. Overall, the survival of DOX+IL-2ic treated mice correlated with the frequencies of KILR T cells and KLRK1+ T cells. Moreover, GZMB was almost exclusively expressed by KLRK1+ T cells. We are showing these data in Fig. 7C and Fig. S7B in the revised manuscript.

In the B16 melanoma model, we analyzed CD8+ T cells in the spleens and also in the tumors. We observed a huge population of KLRK1+ GZMB+ CD8+ T-cell population in the spleen of DOX+IL-2ic-treated mice, but not in the untreated or DOX-only treated mice (Fig. 7F). Both KLRK1+ CD49d+ and KLRK1+ CD49d- CD8+ T cells were substantially more frequent in the DOX+IL-2ic-treated, but not in the untreated or DOX-only treated mice (Fig. S7F). In the tumor, the KLRK1+ CD49d- CD8+ T cells were found at large numbers only in the DOX+IL-2ic-treated mice (Fig. 7G). Moreover, these KLRK1+ CD49d- CD8+ T cells expressed high levels of IL-7R and GZMB only in DOX+IL-2ic-treated, but not in untreated and DOX-only treated mice (Fig. 7H).

We believe that these new data provide evidence that the combination of immunogenic chemotherapy with IL-2 treatment induced KILR cells in the spleens and in the tumors and that this correlates with the better survival.

4) It is unclear why the authors chose Dox to combine with IL-2/JES6. The authors should provide a more rational introduction to bridge such a combination. Authors should also explain the reason why there is no antitumor effect of IL-2/JES6 treatment alone.

The experiments with OT-I mice showed that the formation of KILR cells required both the antigenic stimulation and IL-2 signals. We believe that there is only very week antigenic stimulation by the tumor itself. For this reason, we combined the treatment with the chemotherapy Doxorubicin, which is known to induce immunogenic cell death of the tumor cells (e.g., Casares et al. 2005, PMID: 16365148). We believe that doxorubicin induces the death of (some) tumor cells and the release and presentation of their tumorspecific antigens. Without it, the tumor are simply too “cold” to induce sufficient T-cell response. We emphasized this in the revised version of the manuscript.

Importantly, some of us observed a similar effect of IL-2ic in a combination with check-point blockade therapy (without chemotherapy) in a different tumor model, which documents that the chemotherapy is not essential for this effect (unpublished data).

Author Response

Reviewer #1 (Public Review):

The authors are trying to determine how time is valued by humans relative to energy expenditure during non-steady-state walking - this paper proposes a new cost function in an optimal control framework to predict features of walking bouts that start and stop at rest. This paper's innovation is the addition of a term proportional to the duration of the walking bout in addition to the conventional energetic term. Simulations are used to predict how this additional term affects optimal trajectories, and human subjects experiments are conducted to compare with simulation predictions.

I think the paper's key strengths are its simulation and experimental studies, which I regard as cleverly-conceived and well-executed. I think the paper's key weakness is the connection between these two studies, which I regard as tenuous for reasons I will now discuss in detail.

The Title asserts that "humans dynamically optimize walking speed to save energy and time". Directly substantiating this claim would require independently manipulating the (purported) energy and time cost of walking for human subjects, but these manipulations are not undertaken in the present study. What the Results actually report are two findings:

1. (simulation) minimizing a linear combination of energy and time in an optimal control problem involving an inverted-pendulum model of walking bouts that (i) start and stop at rest and (ii) walk at constant speed yields a gently-rounded speed-vs-time profile (Fig 2A);

2. (experiment) human subject walking bouts that started and stopped at rest had self-similar speed-vs-time profiles at several bout lengths after normalizing by the average duration and peak speed of each subject's bouts (Fig 4B).

If the paper established a strong connection between (1.) and (2.), e.g. if speed-vs-time trajectories from the simulation predicted experimental results significantly better than other plausible models (such as the 'steady min-COT' and 'steady accel' models whose trajectories are shown in Fig 2A), this finding could be regarded as providing indirect evidence in support of the claim in the paper's Title. Personally, I would regard this reasoning as rather weak evidence - it would be more accurate to assert 'brief human walking bouts look like trajectories of an inverted-pendulum model that minimize a linear combination of energy and time' (of course this phrasing is too wordy to serve as a replacement Title -- I am just trying to convey what assertion I think can be directly substantiated by the evidence in the paper). But unfortunately, the connection between (1.) and (2.) is only discussed qualitatively, and the other plausible models introduced in the Results are not revisited in the Discussion. To my naive eye, the representative 'steady min-COT' trace in Fig 2A seems like a real contender with the 'Energy-Time' trace for explaining the experimental results in Fig 4, but this candidate is rejected at the end of the third-to-last paragraph in the 'Model Predictions' subsection of Results based on the vague rationale that is never revisited.

We have addressed most of this comment above, but respond here regarding Fig. 4. The argument against steady min-COT should also point out the peak speed. The Results have been revised thus: “In contrast to the min-COT hypothesis, the human peak speeds increased with distance, many well below the min-COT speed of about 1.25 m/s. The human speed trajectories did not resemble the trapezoidal profiles of the steady min-COT hypothesis for all distances, nor the triangular profiles of steady acceleration.”

An additional limitation of the approach not discussed in the manuscript is that a fixed step length was prescribed in the simulations. The 'Optimal control formulation' subsection in the Methods summarizes the results of a sensitivity analysis conducted by varying the fixed step length, but all results reported here impose a constant-step-length constraint on the optimal control problem. Although this is a reasonable modeling simplification for steady-state walking, it is less well-motivated for the walking bouts considered here that start and stop at rest. For instance, the representative trial from a human subject in Figure 8 clearly shows initiation and termination steps that differ in length from the intermediate steps (visually discernable via the slope of the dashed line interpolating the black dots). Presumably different trajectories would be produced by the model if the constant-step-length constraint were removed. It is unclear whether this change would significantly alter predictions from either the 'Energy-Time' or 'steady min-COT' model candidates, and I imagine that this change would entail substantial work that may be out of scope for the present paper, but I think it is important to discuss this limitation.

This is addressed elsewhere (Essential Revisions 2), but we explain more here. One of the parameter studies included step length increasing with speed according to the human preferred relationship. This is included in Fig. 3, and so we concluded that variable step lengths are not critical to the speed trajectories. A related assumption is that the energetic cost of modulating step length/frequency is small compared to the step-to-step transition cost. We believe that humans expend substantial energy for both costs, but that the overall cost of walking is still dominated by step-to-step transitions.

With my concerns about the paper's framing and through-line noted as above, I want to emphasize that I regard the computational and empirical work reported here to be top-notch and potentially influential. In particular, the experimental study's use of inexpensive wearable sensors (as opposed to more conventional camera-based motion capture) is an excellent demonstration of efficient study design that other researchers may find instructive. To maximize potential impact, I encourage the authors to release their data, simulations, and details about their experimental apparatus (the first two I regard as essential for reproducibility - the third a selfless act of service to the scientific community).

I think the most important point to emphasize is that the bulk of prior work on human walking has focused on steady-state movement - not because of the real-world relevance (since one study reports 50% of walking bouts in daily life are < 16 steps as summarized in Fig 1B), but rather because steady walking is a convenient behavior to study in the laboratory. Significantly, this paper advances both our theoretical and empirical understanding of the characteristics of non-steady-state walking.

It is also significant to note the relationship between this study, where time was incorporated as an additive term in the cost of walking, with previous studies that incorporated time in a multiplicative discount in the cost of eye and arm movements. There is an emerging consensus that time plays a key role in the generation of movement across the body - future studies will discern whether and when additive or multiplicative effects dominate.

We have acknowledged this in a brief sentence: “Indeed, we have found a similar valuation of time to explain how reaching durations and speed trajectories vary with reaching distance (Wong et al., 2021).” As an aside, in that reference we measured metabolic cost of cyclic arm reaching, combined it with a linear time cost, and predicted reaching durations vs. distance and bell-shaped hand speed trajectories. Others (Shadmehr et al. Curr Biol. 2016) have proposed multiplicative (hyperbolic) temporal discounting to explain durations, but the cost formulas are not dynamical, and cannot produce trajectories. We agree with reviewer’s point, but we think the evidence for hyperbolic discounting is not strong. Linear time costs are simpler and work at least as well. This is of great interest to us, but we didn’t discuss beyond the brief mention above, because we fear it is too far afield.

Reviewer #2 (Public Review):

This paper provides a novel approach to quantifying the tradeoff between energetic optimality during walking and the valuation of time to travel a given distance. Specifically, the authors investigated the relationships between walking speed trajectories, distance traveled, and the valuation of (completion) time. Time has been proposed as a potential factor influencing movement speed, but less is understood about how individuals balance energetic optimality and time constraints during walking. The authors used a simple, sagittal-plane walking model to test competing hypotheses about how individuals optimize gait speed from gait initiation to gait termination. Their approach extends literature in the space by identifying optimal gaits for shorter, partially non-steady speed walking bouts.

The authors successfully evaluated three competing walking objectives (constant acceleration, minimum cost of transport at steady speed, and the energy-time objective), showing that the energy-time objective best matched experimental data in able-bodied adults. Although other candidate objectives may exist, the paper's findings provide a likely-generalizable explanation of how able-bodied humans select movement strategies that encompass studies of steady-speed walking.

Overall, this paper provides a foundation for future studies testing the validity of the energy-time hypothesis for human gait speed selection in able-bodied and patient populations. Extensions of this work to patient populations may explain differences in walking speed during clinical assessments and provide insight into how individual differences in time valuation impact performance on assessments. For example, understanding whether physical capacity or time valuation (or something comparable) better explains individual differences in walking speed may suggest distinct approaches for improving walking speed.

Strengths:

The authors presented a compelling rationale for the tradeoffs between energetic optimality and time and their results provide strong support for a majority of their conclusions. In particular, significant reductions in the variance of experimental speed trajectories provides good support for the scaling of speeds across individuals and the plausibility of the energy-time hypothesis. Comparison to theoretical (model-based) reductions across difference time valuation (cT) parameters would further enhance confidence in the practical significance of the variance reductions. Further, while additional work is needed to determine the range of "normal" valuations of time, the authors present experimental ranges that appear reasonable and are well explained. The computational and analytical methods are rigorous and are supported by the literature. Overall, the paper's conclusions are consistent with experimental and computational results.

The introduction of a model-based analytical approach to quantify the effects of time valuation of walking could generalize to test other cost functions, populations, or locomotion modes. Further, models of varying complexity could be implemented to test more individualized estimates of metabolic cost, ranging from 3D dynamic walking models (Faraji et al., Scientific Reports, 2018) or physiologically-detailed models (Falisse et al., Journal of The Royal Society Interface. 2019). The relatively simple set of analyses used in this paper is consistent with prior literature and should generalize across applications and populations.

The authors justified simplifications in the analysis and addressed major limitations of the paper, such as using a fixed step length in model predictions, using a 2D model, and basing energy estimates on the mechanical work of a simple model. It is unlikely that the paper's conclusions would change given additional model complexity. For example, a 3D walking model would need to control frontal plane stability. However, in able-bodied adults, valuation of frontal-plane stability during normal walking would not likely alter the overall shape of the predicted speed profiles.

Weaknesses:

The primary weakness of this work is that alternative objectives may provide similar speed profiles and thus be plausible objectives for human movement. For example, the authors tested an objective minimizing the steady-speed cost of transport. This cost function is consistent with the literature, but (as predicted) unlikely to explain acceleration and deceleration during gait. An objective more comparable to the energy-time hypothesis would be to minimize the net energy cost over the entire bout, including accelerations and decelerations. This may produce results similar to the energy-time hypothesis. However, a more complex model that incorporates non-mechanical costs (e.g., cost of body weight support) may be needed to test such objectives. Therefore, the energy-time hypothesis should be considered in the context of a simple model that may be incapable of testing certain alternative hypotheses.

We have addressed some of this comment in Essential Revisions 4.

We are unsure what is meant by “net energy over the entire bout, including accelerations and decelerations.” Our hypothesis uses total (gross) energy over the entire bout, and already includes accelerations and decelerations. If “net” refers to the customary definition of metabolic energy minus resting, then it differs from our gross cost (Fig. 6A) only in the amount of constant offset, namely resting cost. Removing the offset is equivalent to a decrease in C_T. As shown in Fig. 3, this would reduce peak speeds magnitudes but not change the shape of the speed, peak speed, and duration patterns. There is also another interpretation where the cost of walking includes only net energy, and the cost of time includes the resting metabolic rate (Fig. 6C). This interpretation yields the same predictions, the only difference is whether resting rate is treated as an energy or a time cost. We have not made further changes, because we are unsure what the reviewer meant. The difference between net and total is at most one of degree, not of qualitatively different behavior.

We do not address the proposed “cost of body weight support” because we are unsure of the definition. There is a hypothesis by Kram & Taylor (1990) that defines a metabolic cost rate proportional to body weight divided by ground contact time. It is unclear if this is what reviewer is referring to, so we did not include it in the manuscript. However, IF this is what reviewer means, we do not consider the Kram & Taylor (“K&T”) cost to be a viable hypothesis for computational models. It is a correlation observed from data, which is inadequate as a model, for several reasons. First, in a model optimization, it leads to absurd predictions, because metabolic cost could then be reduced simply by increasing stance (contact) time. A model could do so simply by walking with very long double support phases, or running with a very brief aerial phase, both of which people clearly do not do. In walking, extended double support durations result in much higher metabolic cost (Gordon et al., APMR 2009). Models must operate quite literally on whatever objective they are given, and here, a literal interpretation of K&T makes absurd predictions.

Another issue with the K&T cost is that it is not mechanistic. A mechanistic model is concerned with the forces and work performed by an actuator such as muscle. Muscles experience forces far greater than body weight, not captured by the K&T cost. Of course, overall cost for animal locomotion is roughly proportional to body weight, but what a model needs is a cost associated with its control inputs, e.g. actuator forces.

We have also examined the K&T hypothesis in previous publications. In Schroeder & Kuo (Plos Comp Biol 2021), we used a simple model of running that minimizes an energetic cost dominated by mechanical work. Even though the model has no cost similar to K&T, its predicted metabolic cost is correlated with the K&T cost. Correlation does not imply causation, which is known in this model.

We have also examined the K&T hypothesis in experimental data. In Riddick & Kuo (Sci Rep 2022), we examined human data and found that there are many variables that correlate quite well with metabolic cost, including the K&T correlate. We use human data to show how mechanical work could explain metabolic cost, and even if it does, the K&T cost appears as a correlate. In our interpretation, both model and data that experience an energetic cost proportional to mechanical work may have a number of variables correlated to energy cost. Those correlates need not have any causal influence.

There are, of course, many similar correlates that could be or have been proposed to explain the metabolic cost of running. Most such correlates are not operational enough to work in a model, and it is also difficult to predict what a reader might consider plausible, even if we do not.

We agree with this statement: “the energy-time hypothesis should be considered in the context of a simple model that may be incapable of testing certain alternative hypotheses.” In fact, in Discussion of limitations we listed other potential factors (e.g. forced leg motion, stability, 3D motion), and stated “We did not explore more complex models here, but would expect similar predictions to result if similar, pendulum-like principles of work and energetic cost apply.” We had also cited other models that include such factors and are compatible with the step-to-step transition concept. Finally, we already stated, “the Energy-Time hypothesis should be regarded as a subset of the many factors that should govern human actions, rendered here in a simple but quantitative form.” We believe this is already aligned with reviewer’s comment.

An experimental design involving an intervention to perturb the valuation of time would provide stronger support for time being a critical factor influencing gait speed trajectories. The authors noted this limitation as an area of future work.

While the results are compelling, the limited sample size and description of participants limit the obvious generalizability of the results. Older adults tend to have higher metabolic costs of walking than younger adults, which may alter the predicted time-energy relationships (Mian OS, et al., Acta physiologica. 2006). As noted in the introduction, differences in walking speeds have been observed in different living environments. General information on where participants lived (city, small town, etc...) may provide readers with insight into the generalizability of the paper's conclusions. Additionally, the experimental results figures show group-level trends, but individual-specific trends and the existence of exceptional cases are unclear.

We wish to defend the “limited sample size.” The present sample size was (in our opinion) sufficient to test the hypothesis, and we have reported confidence intervals and other statistics where appropriate. (As always, it is up to the individual reader to decide whether they are convinced or not.) It is true that the data may be insufficient for other purposes, but we cannot anticipate or address all other purposes.

We appreciate the relevant connection to aging. We have added to Discussion, “We do not know whether that family [of trajectories] also applies to older adults, who prefer slower steady speeds and expend more energy to walk the same speed (Malatesta, 2003). Perhaps an age-related valuation of time might explain some of the differences in speed.”

We agree about the participants, and have added “Subjects were recruited from the community surrounding the University of Calgary; the city has a moderately affluent population of about 1.4 M, with a developed Western culture.”

No specific reviewer recommendation was made about individual-specific trends, but there are several indicators already included in the manuscript. First, all trials from all subjects are shown in Fig. 4A. Any truly exceptional cases should be visible as substantial deviations from the group. Second, the normalization by peak speed in Fig. 4B shows how individuals tend to be fairly consistent in their preferred speeds, in that shorter and longer bouts of an individual are consistent with each other, even if some walk faster than others. Third, this observation is analyzed more quantitatively by the reduction in standard deviations with normalization (Results). Fourth, we will provide a data repository with all the data, to allow readers to inspect individuals more carefully (Data availability statement).

The authors' interpretation of clinical utility is vague and should be interpreted with caution. A simple pendulum-based walking model is unlikely to generalize to patient populations, whose gait energetics may involve greater positive and negative mechanical work (Farris et al., 2015; Holt et al., 2000). Additionally, the proposed analytical framework based on mechanical work as a proxy for the metabolic cost may not generalize to patient populations who have heterogeneous musculotendon properties and increased co-contraction (e.g., children with cerebral palsy; Ries et al., 2018). Consequently, the valuation of time for an individual could be incorrectly estimated if the estimates of metabolic cost were inaccurate. Therefore, as the authors noted for their able-bodied participants, more precise measures of metabolic rates will be critical for translating this work into clinical settings.

We agree, and did not intend to say that clinical populations must walk the same way, rather that the Normal patterns could be used as a basis of comparison. To make this clearer, we have amended the Discussion of clinical implications (new text emphasized): “it may be possible to predict the duration and steady speed for another distance, referenced from a universal family of walking trajectories. We have identified one such family that applies to healthy individuals with pendulum-like gait. Of course, some clinical conditions might be manifested by a deviance from that family, perhaps in the acceleration or deceleration phases, or in how the trajectories vary with distance. If quantified, such deviance might prove clinically useful… the characterization of distance-dependent speed trajectories can potentially provide more information than available from steady speed alone.”

We agree that the valuation of time can be inaccurate if the metabolic cost is inaccurate. That is why we did not make a precise estimate of the valuation. We have amended the text to help clarify that our rough estimates are based on previous data. In addition, our general scientific intent is to reveal behavioral sensitivities, for example of walking duration to bout distance, as opposed to absolute numerical quantities.

Author Response

Reviewer #2 (Public Review):

One other major concern I have regards the conclusion that the participants in these studies use an additive rather than a multiplicative rule to integrate the risk information. The additive rule is problematic in general because it fails to predict the reversal in the effect of probability on payoffs when the payoffs change sign. More specifically, increasing the probability of winning increases the probability of choosing an option when the payoff is positive, but the effect reverses when the payoff is negative. One needs to impose some pretty ad hoc assumptions to make the additive model account for this fundamental interaction between probability and payoff. Of course, the experiments reported here did not include negative payoffs, and so didn't run into this problem. In fact, when the payoffs are positive, it is possible to transform the multiplicative model to an additive model by a log transform. This transformation is only possible for the simple type of gamble investigated in this manuscript - a single amount to win with some probability of winning, otherwise win or lose nothing. If the gambles involved more than one outcome, then the theorist needs to deal with a sum of products and the log transform is no longer possible. For these reasons I am very skeptical about the general application of a summation rule for probability and value in risk choice. The authors do address this issue to some extent. They point out the abundance of other research supporting a multiplicative rule, and they speculate that the additive rule may have occurred within the restrictions of this special situation. The latter discussion is a good start, but I suggest that the authors discuss this fundamental issue in more depth.

Thank you for this very insightful comment. We have now included more in-depth discussions about the decision rules (multiplicative vs. additive) in our Discussion, in which we have absorbed and reflected many of the insights offered by Reviewer #2.

Author Response

Reviewer #1 (Public Review):

This paper tests the hypothesis that 1/f exponent of LFP power spectrum reflects E-I balance in a rodent model and Parkinson's patients. The authors suggest that their findings fit with this hypothesis, but there are concerns about confirmation bias (elaborated on below) and potential methodological issues, despite the strength of incorporating data from both animal model and neurological patients.

First, the frequency band used to fit the 1/f exponent varies between experiments and analyses, inviting concerns about potentially cherry-picking the data to fit with the prior hypothesis. The frequency band used for fitting the exponent was 30-100 Hz in Experiment 1 (rodent model), 40-90 Hz in Experiment 2 (PD, levodopa), and 10-50 Hz in Experiment 3 (PD, DBS). Ad-hoc reasons were given to justify these choices, such as " to avoid a spectral plateau starting > 50 Hz" in Experiment 3. However, at least in Experiment 3 (Fig. 3), if the frequency range was shifted to 1-10 Hz, the authors would have uncovered the opposite effect, where the exponent is smaller for DBS-on condition.

We agree that parameter choice is crucial, in particular, choice of the fitting range. In addition to the 40-90 Hz range (Figure 2C), we have performed aperiodic fitting for five other frequency ranges to test to what extent the reported results are sensitive to the selected frequency range (Figure S2A). This analysis showed that the results are robust when a broad frequency range from 30 to 95 Hz was chosen, which is consistent with what has been suggested by Gao et al., 2017 to make inferences on the E/I ratio.

Accordingly, we have now repeated the analyses for the animal data with the same fitting range used for the ON-OFF medication comparison in humans. Along with Figure S2A where different frequency ranges were tested for data used in Figure 2, this shows that the results in Figure 1 and 2 hold up with higher aperiodic exponents when STN spiking is low and vice versa. Therefore, a broad fitting range from 30 to 90 Hz (excluding harmonics of mains interference) generates consistent results for both human and animal data.

We opted against a fitting range from 1-10 Hz because of two restraints highlighted in Gerster et al., 2022. First, a fitting range starting at 1 Hz could have a larger y-intercept due to the presence of low-frequency oscillations. This could lead to a larger aperiodic exponent and could be misinterpreted as stronger neural inhibition. Therefore, the lower fitting bound should be chosen to best avoid known oscillations in the delta/theta range (Gerster et al., 2022). Second, frequencies should be chosen to avoid oscillations crossing fitting range limits. In Figure 3A, oscillations in the theta/alpha band both ON and OFF stimulation would complicate parameterisation and would likely result in spurious fits.

We also tested the effect of changing the peak threshold, peak width limits and the aperiodic fitting mode on FOOOF parameterisation. Increasing and decreasing the peak threshold from its default value (at 2 standard deviations) did not change results (Figure S2B). Similarly, adapting the peak width limits did not affect the exponent difference between medication states (Figure S2C). Finally, choosing the ‘knee’ mode instead of ‘fixed’ resulted in fundamentally different aperiodic fits that did not differ anymore with medication (Figure S2D). This is most likely a consequence of the near linear PSD in log-log space from 40 to 90 Hz (Figure 2B). If there is no bend in the PSD, the FOOOF algorithm will be forced to assign a ‘random’ knee and the aperiodic fit will then mostly reflect the slope of the spectrum above the knee point.

Second, there are important, fine-grained features in the spectra that are ignored in the analyses, which confounds the interpretation.

One salient example of this is Fig. 2, where based on the plots in B, one would expect that the power of beta-band oscillations to be higher in the Med-On condition, as the oscillatory peaks rise higher above the 1/f floor and reach the same amplitude level as the Med-OFF condition (in other words, similar total power is subtracted by a smaller 1/f power in the Med-ON condition). But this impression is opposite to the model-fitting results in C, where beta power is lower in the Med-ON condition.

We agree that PSDs over a broad frequency range (e.g. 5-90 Hz) typically do not have a single 1/f property. Instead, there can be multiple oscillatory peaks and ‘knees/bends’ in the aperiodic component. For these cases, fitting should be performed using the knee mode. To extract periodic beta power, we parameterise the PSD between 5 and 90 Hz and select the largest oscillatory component between 8 and 35 Hz (this range was extended to include the large oscillatory peaks in hemispheres 27 and 28 at ~ 10 Hz, see Figure R1). We now use the knee mode, to model the aperiodic component between 5 and 90 Hz when periodic beta power is calculated (see our previous comments). Figure R1 provides an overview of all PSDs ON and OFF medication, the aperiodic fits (5-90 Hz (knee) and 40-90 Hz (fixed)) and the detected beta peaks. In spite of this modification in our pipeline, periodic beta power is still larger OFF medication (Figure 2C), in keeping with previous studies (Kim et al., 2022; Kühn et al., 2006; Neumann et al., 2017; Ray et al., 2008). We acknowledge the reviewer’s point that the average spectra in Figure 2B are misleading in that respect and for clarity provide here all 30 spectra in both conditions. Note that the calculation of aperiodic exponents between 40 and 90 Hz is not affected by this change in our pipeline. Figures 2B, D+E were revised accordingly.

We have repeated the analysis of our animal data using the ‘knee mode’ with a fitting range from 30 to 100 Hz. However, using the knee mode did not improve the goodness of fit or fitting error and, in fact, made them slightly worse (Figure S5). Based on this, we think the fixed mode would provide a more holistic model for the PSDs used in this analysis. We have now added this comparison in Figure S5 to justify the choice of the fixed mode.

Figure R1. PSDs from all 30 hemispheres ON and OFF medication. Aperiodic fits are shown between 5-90 Hz (knee mode), which was used to calculate the power of beta peaks, and between 40-90 Hz (fixed mode), which was used to estimate the aperiodic exponent of the spectrum.

Another example is Fig. 1C, where the spectra for high and low STN spiking epochs are identical between 10 and 20 Hz, and the difference in higher frequency range could be well-explained by an overall increase of broadband gamma power (e.g. as observed in Manning et al., J Neurosci 2012, Ray & Maunsell PLoS Biol 2011). This increase of broadband gamma power is trivially expected, as broadband gamma power is tightly coupled with population spiking rate, which was used to define the two conditions.

We agree with the reviewer that in Figure 1C, high and low STN spiking states could well be separated by average gamma power (Figure 1E), too. However, the difference of aperiodic exponents is more prominent between both conditions (Figure 1D+E, based on p-values). What is more, in human LFP data recorded from clinical macroelectrodes, medication states can be reasonably well distinguished using the aperiodic exponent between 40-90 Hz (Figure 2C), but average gamma power does not separate both states (Figure S3A). This suggests that the aperiodic exponent reflects more than just power differences in the high gamma regions. In addition, power changes do not inevitably change the aperiodic exponent and vice versa as elaborated in (Donoghue et al., 2020).

Manning et al., 2009 show that the power spectrum is shifted to higher power values at all observed frequencies (2-150 Hz) as firing rates increase. As the reviewer points out, power spectra of our data are almost identical between 10-20 Hz (despite the marked spiking differences) and only drift apart from > 20 Hz (Figure 1C). This is a relevant difference between our study and Manning et al., 2009 and suggests that power differences in the gamma range are not solely explained by differences in spiking. This is confirmed when cortical activity at different spikes/sec is modelled (Miller et al., 2009). The entire spectrum is shifted to higher power values if spiking rates increase.

Ray & Maunsell, 2011 reported low (30-80 Hz) and high (> 80 Hz) gamma activity in the macaque visual cortex, with a positive correlation between spiking activity and high gamma activity. However, activities in the low gamma range (30-80 Hz), which largely overlaps with the frequency range in our study, does not necessarily correlate with firing rates.

In conclusion, the link between gamma power and spiking activity is not as strong as alluded. Even if the change in spiking activities can lead to changes of both gamma power and the aperiodic exponent, the aperiodic exponent would still constitute a measure to separate E/I levels and medication states.

The above consideration also speaks to a major weakness of the general approach of considering the 1/f spectrum a monolithic spectrum that can be captured by a single exponent. As the authors' Fig. 1C shows, there are distinct frequency regions within the 1/f spectrum that have different slopes. Indeed, this tripartite shape of the 1/f spectrum, including a "knee" feature around 40-70 Hz which is well visible here, was described in multiple previous papers (Miller et al., PLoS Comput Biol 2009; He et al., Neuron 2010), and have been successfully modeled with a neural network model using biologically plausible mechanisms (Chaudhuri et al., Cereb Cortex, 2017). The neglect of these fine-grained features confounds the authors' model fitting, because an overall increase in the broadband gamma power - which can be explained straightforwardly by the change in population firing rates - can result in the exponent, fit over a larger spectral frequency region, to decrease. However, this is not due to the exponent actually changing, but the overall increase of power in a specific sub-frequency-region of the broadband 1/f activity.

We have now used the knee mode for aperiodic fits between 5 and 90 Hz when periodic beta power is calculated. We agree that this broad frequency range is unlikely to have a single 1/f component.

We have also repeated the analysis of our animal data using the knee mode for aperiodic fits between 30 and 100 Hz (Figure S5). However, the goodness of fits had barely changed. In fact, the R2 and error become slightly worse. In addition, the knee parameter complicates interpretation of the aperiodic exponent and has to be considered along with the knee frequency. What is more, we do not see this bend around 40-70 Hz in all subjects. We show PSDs of representative LFP channels in Figure R2 and need to assert that the knee around 40-70 Hz is not a robust finding in our data set. Therefore, we chose the fixed mode for parameterisation within this frequency band.

Please see our answer to the previous comment regarding the link between broad gamma power and changes in population firing rates.

Figure R2. PSDs of representative PSD channels for each animal (data used in Figure 1C). The knee around 40-70 Hz is not a robust finding in all PSDs.

Author Response

Reviewer #3 (Public Review):

Argenty et al. investigated the role of Lissencephaly gene 1 (LIS1), a dynein-binding protein, in thymic development and T cell proliferation. They find that LIS1 is essential for the early stages of T and B cell development, and demonstrate that loss of LIS1 has a negative impact on the transition from DN3 to DN4 thymocytes and on the maturation of pre-pro-B cells into pro-B cells in the bone marrow. Using a CD2Cre Lis1fl/fl murine model, they observe that in thymocytes LIS1 is critical for DN3 proliferation and completion of cell division. Then, using a CD4Cre Lisfl/fl model (Cd4 promoter is up-regulated just in later stages of thymic development and, thus, does not impact DN3 thymocytes) they show that LIS1-deficient CD4 T cells have proliferation defects following both TCR-dependent or -independent stimulation, which results in apoptosis. They also confirm previous reports that show that LIS1-deficient CD8 T cells do not have their proliferation impaired upon TCR stimulation, which suggests that these two cell types rely on different mechanisms to regulate the cell cycle. Finally, the authors make efforts to determine how LIS1 regulates proliferation in thymocytes and CD4 T cells. Interestingly, they show that LIS1 is important for chromosome alignment and centrosome integrity and provide data that support a model where LIS1 would facilitate the assembly of active dyneindynactin complexes. These data provide interesting insights into how different cell types use distinct strategies to undergo mitosis and how this can impact on their proliferation and fate decisions. The conclusions of the manuscript are mostly supported by the provided data, although certain aspects can be further investigated and clarified.

Strengths of the paper:

By combining a re-assessment of previous reports with new findings, the data from this manuscript convincingly demonstrates that LIS1 is crucial for cell proliferation in certain development steps/cell types. Furthermore, the manuscript provides clear evidence of how LIS1 loss causes proliferation defects by disrupting centrosome integrity and chromosome alignment both in CD4+ T cells and thymocytes.

Weakness of the paper:

Although authors successfully address the mechanistic role of LIS in thymocyte and CD4+ T cell division, the manuscript would be strengthened by both providing further evidence to support some of their conclusions and a review of some speculations raised in the discussion.

In Figure 1, the authors claim that LIS1 is not required for pre-TCR assembly, but for expansion/proliferation of DN3 thymocytes as a step prior to reaching the DN4 stage. However, authors indeed observe increased expression of CD5 (which is a downstream event of Notch and IL-7R signalling). Thus, from the data provided it is not clear whether signalling through Notch or IL-7R is definitely not affected, which could be clarified by assessing the expression of other downstream targets of these molecules.

CD5 is a downstream target of the pre-TCR signaling but to our knowledge, it is not a downstream target of Notch or IL-7R signaling. The sentence p7 of the initial manuscript was re-formulated since we understand that it could be misleading. However, we fully agree with the reviewer’s comment on Notch and IL-7R signaling and included new data in the revised version of the manuscript to address this point. Notch signaling stimulates metabolic changes which lead to the increase of thymocyte cell-size following the b-selection checkpoint (Ciofani M. et al., Nature Immunology, 2005; Maillard I. et al., The Journal of Experimental Medicine, 2006) and to the up-regulation of the transferrin receptor CD71 (Kelly, A.P. et al., The EMBO journal, 2007). We now show in Figure 1E of the revised manuscript that the loss of LIS1 does not affect the average cell-size of post-b-selection thymocytes and the expression level of CD71 in these cells, suggesting that Notch signaling is preserved in the absence of LIS1. This was confirmed in vitro following stimulation of DN3a thymocytes with OP9-dl1 cells (Figure 2D of the revised manuscript). In this Figure, we also analyzed the expression level of Bcl-2, which is regulated by IL-7R signaling (von Freeden-Jeffry, U. et al., Immunity, 1997). We show that Bcl-2 is comparable in abundance in LIS1 wild-type and LIS1-deficient thymocytes following stimulation with OP-9dl1, suggesting that Il-7R signaling is not affected by the absence of LIS1.

In Figure 3, the authors mostly confirm previous data from Ngoi, Lopez, Chang, Journal of Immunology, 2016 (reference 34), but also provide evidence of a role of LIS1 in CD4+ T cell proliferation in more physiological setups, using OT2-CD4-Cre Lis1flox/flox (or OT2 Lisflox/flox as controls) in adoptive transfer experiments followed by antigen-specific immunization. However, the evidence provided by the authors about proliferation defects in LIS1-deficient cells in this context is limited by the early timepoint chosen: day 3 post-immunization.

We choose to analyze CD4+ T cells at day 2 and 3 after immunization because we sought to catch early cell-division waves through CTV dilution. We also wanted to show that LIS1 deficient CD4+ T cells could normally survive and migrate to lymph nodes before they start to proliferate. Given the dramatic effect of LIS1 on CD4+ T-cell proliferation at day 3, we anticipated that very low numbers of LIS1 deficient cells would survive at later time points after immunization. To address the reviewer’s comment, we transferred OT2+CD45.1+ CD4+ T cells stained with CTV in C57BL/6 mice and analyzed the percentages and numbers of CD45.1+ T cells as well as the dilution of CTV in those cells at day 7 after immunization. As expected, all CD45.1+ cells were negative for CTV at this time of analysis (data not shown). The percentages and numbers of CD45.1+ T cells were strongly decrease in the absence of LIS1 in comparison to wild-type controls (Figure 3 - Figure Supplement 2C), confirming results obtained at day 3 after immunization.

In the discussion, the authors speculate about the differences observed between CD4 and CD8 T cells, as the latter do now show proliferative defects upon TCR-triggered stimulation, and come up with the hypothesis that LIS1 might be important for symmetric cell divisions, but not for asymmetric cell divisions. However, the arguments used by the authors have few caveats, especially because CD4+ T cells can also undergo asymmetric cell division following TCR-triggered stimulation upon the first cognate antigen encounter (Chang et al., Science, 2007, Ref. 8).

We agree that CD4+ T cells can undergo asymmetric division (actually, this is mentioned and referenced p3 and p18 of the manuscript). However, it is unknown whether these divisions occur systematically or whether they occur with variable frequency which could be context-dependent. It is also unclear whether CD4+ and CD8+ T cells have similar rates of asymmetric division. The literature is lacking of comparative studies in which cellular events associated to mitosis would be investigated side-by-side in those two subsets. As mentioned to reviewer-1, only one study to our knowledge performed a comparative analysis of T-bet repartition in daughter cells after a first round of cell division in CD4+ and CD8+ T cells (Chang, J. T. et al., Immunity, 2011). They found that T-bet segregates unequally in daughter cells in both CD4+ and CD8+ T cells. However, the disparity between daughter cells was higher in CD8+ T cells as compared to that in CD4+ T cells (5- versus 3-fold). This suggests that key molecules are either more equally (or less unequally) distributed in daughter cells from the CD4+ lineage or that the rate of symmetric divisions is higher in CD4+ T cells than in the CD8+ T cells. Those results are in accordance with our interpretation and previous findings (Yingling, J. et al., Cell, 2008; Zimdahl, B. et al, Nature Genetics, 2014), suggesting that LIS1 is predominantly involved in mitosis associated to symmetric divisions. Another possibility to explain this difference is that asymmetrical division might occur at different stages in CD4+ and CD8+ T cells. Although some asymmetrical divisions have been detected early after antigen encounter in CD4+ T cells, a more recent study from the same group suggest that asymmetric division might occur mainly later after several rounds of divisions of CD4+ T cells to enable self-renewal to be coupled to production of differentiated effector CD4+ T cells (Nish, S. A., Journal of Experimental Medicine, 2017). It is therefore possible that LIS1 could be critical early in CD4+ T cell expansion, when cells mainly divide through symmetrical process, and less critical later when cells are engaged in asymmetrical division. This is now discussed in greater details p18 of the revised version of the manuscript.

Finally, the authors discuss that mono-allelic LIS1 defects might contribute to malignancies. Certainly not all points raised in the discussion need to be experimentally addressed, but for this particular hypothesis the authors would likely have the tools to achieve that, which would broaden the relevance of understanding LIS1 function.

We have addressed this point experimentally in the revised version of the manuscript. We show that mono-allelic LIS1 deficiency does not have a significant impact on the percentages of thymocyte populations in Cd2-Cre Lis1flox/+ mice (Figure 1 - Figure Supplement 1B) and on the numbers of peripheral T cells in Cd4-Cre Lis1flox/+ (Figure 3 - Figure Supplement 1E), suggesting that LIS1 does not operate in a dose-dependent fashion in the context of T-cell development and T-cell homeostatic maintenance. Additionally, Cd4-Cre Lis1flox/+ CD4+ T cells proliferate effectively following TCR and CD28 stimulation (Figure 3 - Figure Supplement 2A), indicating further that mono-allelic LIS1 dosage is sufficient to support cell division of CD4+ T cells. The part of the discussion related to Lis1 haplo-deficiency has been rephrased according to this new set of data.